  Machine learning is nowadays the methodology of choice for flare forecasting
and supervised techniques, in both their traditional and deep versions, are
becoming the most frequently used ones for prediction in this area of space
weather. Yet, machine learning has not been able so far to realize an operating
warning system for flaring storms and the scientific literature of the last
decade suggests that its performances in the prediction of intense solar flares
are not optimal.
  The main difficulties related to forecasting solar flaring storms are
probably two. First, most methods are conceived to provide probabilistic
predictions and not to send binary yes/no indications on the consecutive
occurrence of flares along an extended time range. Second, flaring storms are
typically characterized by the explosion of high energy events, which are
seldom recorded in the databases of space missions; as a consequence,
supervised methods are trained on very imbalanced historical sets, which makes
them particularly ineffective for the forecasting of intense flares.
  Yet, in this study we show that supervised machine learning could be utilized
in a way to send timely warnings about the most violent and most unexpected
flaring event of the last decade, and even to predict with some accuracy the
energy budget daily released by magnetic reconnection during the whole time
course of the storm. Further, we show that the combination of
sparsity-enhancing machine learning and feature ranking could allow the
identification of the prominent role that energy played as an Active Region
property in the forecasting process.


  Few-shot meta-learning has been recently reviving with expectations to mimic
humanity's fast adaption to new concepts based on prior knowledge. In this
short communication, we give a concise review on recent representative methods
in few-shot meta-learning, which are categorized into four branches according
to their technical characteristics. We conclude this review with some vital
current challenges and future prospects in few-shot meta-learning.


  We report the first observation of off-axis neutrino interactions in the
MiniBooNE detector from the NuMI beamline at Fermilab. The MiniBooNE detector
is located 745 m from the NuMI production target, at 110 mrad angle
($6.3^{\circ}$) with respect to the NuMI beam axis. Samples of charged current
quasi-elastic $\nu_{\mu}$ and $\nu_e$ interactions are analyzed and found to be
in agreement with expectation. This provides a direct verification of the
expected pion and kaon contributions to the neutrino flux and validates the
modeling of the NuMI off-axis beam.


  Compression and sparsification algorithms are frequently applied in a
preprocessing step before analyzing or optimizing large networks/graphs. In
this paper we propose and study a new framework contracting edges of a graph
(merging vertices into super-vertices) with the goal of preserving pairwise
distances as accurately as possible. Formally, given an edge-weighted graph,
the contraction should guarantee that for any two vertices at distance $d$, the
corresponding super-vertices remain at distance at least $\varphi(d)$ in the
contracted graph, where $\varphi$ is a tolerance function bounding the
permitted distance distortion. We present a comprehensive picture of the
algorithmic complexity of the contraction problem for affine tolerance
functions $\varphi(x)=x/\alpha-\beta$, where $\alpha\geq 1$ and $\beta\geq 0$
are arbitrary real-valued parameters. Specifically, we present polynomial-time
algorithms for trees as well as hardness and inapproximability results for
different graph classes, precisely separating easy and hard cases. Further we
analyze the asymptotic behavior of contractions, and find efficient algorithms
to compute (non-optimal) contractions despite our hardness results.


  We present a computational study using Molecular Dynamics of the development
of an accumulation of grains in one side of a two dimensional U-tube under
vertical vibrations. Studying the evolution of the height difference between
the centers of mass of the branches of the tube, we found that it reaches a
saturation value after vibrating for some time. We obtain that this saturation
value is the same if the simulation start with the arms leveled or with a large
initial height difference. We explore the effect of the width of the tube, the
density of the grains and the coefficient of restitution between the grains and
the wall on the value of the saturation. We obtain a value of the width of the
tube for which the saturation value reaches a maximum, and show that the
transport of grains between arms is favored for low grain densities and high
grain-wall restitution coefficient.


  Our team has obtained observations of the photosphere of the two closest red
supergiant stars Betelgeuse ($\alpha$ Ori) and Antares ($\alpha$ Sco) using
near infrared interferometry. We have been monitoring the photosphere of
Betelgeuse with the VLTI/PIONIER instrument for three years. On Antares, we
obtained an unprecedented sampling of the visibility function. These data allow
us to probe the convective photosphere of massive evolved stars.


  We evaluate the quantum backreaction due to a gauge field coupled to a pseudo
scalar field driving a slow-roll inflationary stage, the so-called axion
inflation. The backreaction is evaluated for the first time using a gauge
invariant approach, going to second order in perturbation theory and taking in
consideration inflaton fluctuations as well as scalar perturbations of the
metric. Within our gauge invariant, but observer-dependent approach, we
naturally consider as physical observer the one comoving with the inflaton
field. Considering the effective expansion rate consequent to the gauge fields
bacreaction, we observe that the backreaction effect becomes significant quite
rapidly, moving the system out of the perturbative regime and into what is
often referred to the strong backreaction regime. This behavior also applies to
the parameter which dictates the production of the gauge fields. The space-time
dynamic is initially mainly influenced by the helicity contribution, with a
significant response to the energy density which occurs much later with respect
to the number of e-folds. As a final result, we see that the evaluated
backreaction prolongs the inflationary period much more compared to the
scenarios previously studied neglecting scalar metric perturbations.


  We consider the defocusing nonlinear Schr{\"o}dinger equation in several
space dimensions, in the presence of an external potential depending on only
one space vari-able. This potential is bounded from below, and may grow
arbitrarily fast at infinity. We prove existence and uniqueness in the
associated Cauchy problem, in a suitable functional framework, as well as the
existence of wave operators when the power of the nonlinearity is sufficiently
large. Asymptotic completeness then stems from at least two approaches, which
are briefly recalled.


  We study the notion of recurrence and some of its variations for linear
operators acting on Banach spaces. We characterize recurrence for several
classes of linear operators such as weighted shifts, composition operators and
multiplication operators on classical Banach spaces. We show that on separable
complex Hilbert spaces the study of recurrent operators reduces, in many cases,
to the study of unitary operators. Finally, we study the notion of product
recurrence and state some relevant open questions.


  Lung cancer classification in screening computed tomography (CT) scans is one
of the most crucial tasks for early detection of this disease. Many lives can
be saved if we are able to accurately classify malignant/cancerous lung
nodules. Consequently, several deep learning based models have been proposed
recently to classify lung nodules as malignant or benign. Nevertheless, the
large variation in the size and heterogeneous appearance of the nodules makes
this task an extremely challenging one. We propose a new Progressive Growing
Channel Attentive Non-Local (ProCAN) network for lung nodule classification.
The proposed method addresses this challenge from three different aspects.
First, we enrich the Non-Local network by adding channel-wise attention
capability to it. Second, we apply Curriculum Learning principles, whereby we
first train our model on easy examples before hard ones. Third, as the
classification task gets harder during the Curriculum learning, our model is
progressively grown to increase its capability of handling the task at hand. We
examined our proposed method on two different public datasets and compared its
performance with state-of-the-art methods in the literature. The results show
that the ProCAN model outperforms state-of-the-art methods and achieves an AUC
of 98.05% and an accuracy of 95.28% on the LIDC-IDRI dataset. Moreover, we
conducted extensive ablation studies to analyze the contribution and effects of
each new component of our proposed method.


  An analytic solution for Bragg grating with linear chirp in the form of
confluent hypergeometric functions is analyzed in the asymptotic limit of long
grating. Simple formulas for reflection coefficient and group delay are
derived. The simplification makes it possible to analyze irregularities of the
curves and suggest the ways of their suppression. It is shown that the increase
in chirp at fixed other parameters decreases the oscillations in the group
delay, but gains the oscillations in the reflection spectrum. The conclusions
are in agreement with numerical calculations.


  We study the interaction of upstream ultra-low frequency (ULF) waves with
collisionless shocks by analyzing the outputs of eleven 2D local hybrid
simulation runs. Our simulated shocks have Alfv\'enic Mach numbers between
4.29-7.42 and their $\theta_{BN}$ angles are 15$^\circ$, 30$^\circ$, 45$^\circ$
and 50$^\circ$. The ULF wave foreshocks develop upstream of all of them. The
wavelength and the amplitude of the upstream waves exhibit a complex dependence
on the shock's M$_A$ and $\theta_{BN}$. The wavelength positively correlates
with both parameters, with the dependence on $\theta_{BN}$ being much stronger.
The amplitude of the ULF waves is proportional to the product of the reflected
beam velocity and density, which also depend on M$_A$ and $\theta_{BN}$. The
interaction of the ULF waves with the shock causes large-scale (several tens of
upstream ion inertial lengths) shock rippling. The properties of the shock
ripples are related to the ULF wave properties, namely thier wavelength and
amplitude. In turn, the ripples have a large impact on the ULF wave
transmission across the shock because they change local shock properties
($\theta_{BN}$, strength), so that different sections of the same ULF wave
front encounter shock with different characteristics. Downstream fluctuations
do not resemble the upstream waves in terms the wavefront extension,
orientation or their wavelength. However some features are conserved in the
Fourier spectra of downstream compressive waves that present a bump or
flattening at wavelengths approximately corresponding to those of the upstream
ULF waves. In the transverse downstream spectra these features are weaker.


  In this paper we study almost complex manifolds admitting a quasi-K\"ahler
Chern-flat metric (Chern-flat means that the holonomy of the Chern connection
is trivial). We prove that in the compact case such manifolds are all
nilmanifolds. Some partial classification results are established and we prove
that a quasi-K\"ahler Chern-flat structure can be tamed by a symplectic form if
and only if the ambient space is isomorphic to a flat torus.


  Halo merger trees are constructed from ELUCID, a constrained $N$-body
simulation in the Sloan Digital Sky Survey (SDSS) volume. These merger trees
are used to populate dark matter halos with galaxies according to an empirical
model of galaxy formation. Mock catalogs in the SDSS sky coverage are
constructed, which can be used to study the spatial distribution of galaxies in
the low-$z$ Universe. These mock catalogs are used to quantify the cosmic
variance in the galaxy stellar mass function (GSMF) measured from the SDSS
survey. The GSMF estimated from the SDSS magnitude-limited sample can be
affected significantly by the presence of the under-dense region at $z<0.03$,
so that the low-mass end of the function can be underestimated significantly.
Several existing methods designed to deal with the effects of the cosmic
variance in the estimate of GSMF are tested, and none is found to be able to
fully account for the cosmic variance. We propose a method based on the
conditional stellar mass functions in dark matter halos, which can provide an
unbiased estimate of the global GSMF. The application of the method to the SDSS
data shows that the GSMF has a significant upturn at $M_*< 10^{9.5} h^{-1}{\rm
M}_\odot$, which has been missed in many earlier measurements of the local
GSMF.


  This paper presents an empirical exploration of the use of capsule networks
for text classification. While it has been shown that capsule networks are
effective for image classification, their validity in the domain of text has
not been explored. In this paper, we show that capsule networks indeed have the
potential for text classification and that they have several advantages over
convolutional neural networks. We further suggest a simple routing method that
effectively reduces the computational complexity of dynamic routing. We
utilized seven benchmark datasets to demonstrate that capsule networks, along
with the proposed routing method provide comparable results.


  We perform systematic first-principles calculations of the proximity exchange
coupling, induced by cobalt (Co) and nickel (Ni) in graphene, via a few (up to
three) layers of hexagonal boron nitride (hBN). We find that the induced spin
splitting of the graphene bands is of the order of 10 meV for a monolayer of
hBN, decreasing in magnitude but alternating in sign by adding each new
insulating layer. We find that the proximity exchange can be giant if there is
a resonant $d$ level of the transition metal close to the Dirac point. Our
calculations suggest that this effect could be present in Co heterostructures,
in which a $d$ level strongly hybridizes with the valence-band orbitals of
graphene. Since this hybridization is spin dependent, the proximity spin
splitting is unusually large, about 10 meV even for two layers of hBN. An
external electric field can change the offset of the graphene and
transition-metal orbitals and can lead to a reversal of the sign of the
exchange parameter. This we predict to happen for the case of two monolayers of
hBN, enabling electrical control of proximity spin polarization (but also spin
injection) in graphene/hBN/Co structures. Nickel-based heterostructures show
weaker proximity effects than cobalt heterostructures. We introduce two
phenomenological models to describe the first-principles data. The minimal
model comprises the graphene (effective) $p_z$ orbitals and can be used to
study transport in graphene with proximity exchange, while the $p_z$-$d$ model
also includes hybridization with $d$ orbitals, which is important to capture
the giant proximity exchange. Crucial to both models is the
pseudospin-dependent exchange coupling, needed to describe the different spin
splittings of the valence and conduction bands.


  The consumption history of online media content such as music and video
offers a rich source of data from which to mine information. Trends in this
data are of particular interest because they reflect user preferences as well
as associated cultural contexts that can be exploited in systems such as
recommendation or search. This paper classifies songs as seasonal using a
large, real-world dataset of user listening data. Results show strong
performance of classification of Christmas music with Gaussian Mixture Models.


  In the coming years, quantum networks will allow quantum applications to
thrive thanks to the new opportunities offered by end-to-end entanglement of
qubits on remote hosts via quantum repeaters. On a geographical scale, this
will lead to the dawn of the Quantum Internet. While a full-blown deployment is
yet to come, the research community is already working on a variety of
individual enabling technologies and solutions. In this paper, with the
guidance of extensive simulations, we take a broader view and investigate the
problems of Quality of Service (QoS) and provisioning in the context of quantum
networks, which are very different from their counterparts in classical data
networks due to some of their fundamental properties. Our work leads the way
towards a new class of studies that will allow the research community to better
understand the challenges of quantum networks and their potential commercial
exploitation.


  We demonstrate numerically the existence of a nontrivial topological Haldane
phase for the one-dimensional extended ($U$-$V$) Hubbard model with a mean
density of one particle per site, not only for bosons but also for anyons,
despite a broken reflection parity symmetry. The Haldane insulator, surrounded
by superfluid, Mott insulator and density-wave phases in the $V$-$U$ parameter
plane, is protected by combined (modified) spatial-inversion and time-reversal
symmetries, which is verified within our matrix-product-state based infinite
density-matrix renormalization group scheme by analyzing generalized transfer
matrices. With regard to an experimental verification of the anyonic Haldane
insulator state the calculated asymmetry of the dynamical density structure
factor should be of particular importance.


  The Polya number of a classical random walk on a regular lattice is known to
depend solely on the dimension of the lattice. For one and two dimensions it
equals one, meaning unit probability to return to the origin. This result is
extremely sensitive to the directional symmetry, any deviation from the equal
probability to travel in each direction results in a change of the character of
the walk from recurrent to transient. Applying our definition of the Polya
number to quantum walks on a line we show that the recurrence character of
quantum walks is more stable against bias. We determine the range of parameters
for which biased quantum walks remain recurrent. We find that there exist
genuine biased quantum walks which are recurrent.


  In this paper new complexity and approximation results on the robust versions
of the representatives selection problem, under the scenario uncertainty
representation, are provided, which extend the results obtained in the recent
papers by Dolgui and Kovalev (2012), and Deineko and Woeginger (2013). Namely,
it is shown that if the number of scenarios is a part of input, then the
min-max (regret) representatives selection problem is not approximable within a
ratio of $O(\log^{1-\epsilon}K)$ for any $\epsilon>0$, where $K$ is the number
of scenarios, unless the problems in NP have quasi-polynomial time algorithms.
An approximation algorithm with an approximation ratio of $O(\log K/ \log \log
K)$ for the min-max version of the problem is also provided.


  Normalization techniques are important in different advanced neural networks
and different tasks. This work investigates a novel dynamic
learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN),
which is able to learn different normalization methods for different
convolutional layers and image samples of a deep network. EN significantly
improves flexibility of the recently proposed switchable normalization (SN),
which solves a static L2N problem by linearly combining several normalizers in
each normalization layer (the combination is the same for all samples). Instead
of directly employing a multi-layer perceptron (MLP) to learn data-dependent
parameters as conditional batch normalization (cBN) did, the internal
architecture of EN is carefully designed to stabilize its optimization, leading
to many appealing benefits. (1) EN enables different convolutional layers,
image samples, categories, benchmarks, and tasks to use different normalization
methods, shedding light on analyzing them in a holistic view. (2) EN is
effective for various network architectures and tasks. (3) It could replace any
normalization layers in a deep network and still produce stable model training.
Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of
tasks including image recognition, noisy label learning, and semantic
segmentation. For example, by replacing BN in the ordinary ResNet50,
improvement produced by EN is 300% more than that of SN on both ImageNet and
the noisy WebVision dataset.


  Despite their performance and widespread use, little is known about the
theory of Random Forests. A major unanswered question is whether, or when, the
Random Forest algorithm is consistent. The literature explores various variants
of the classic Random Forest algorithm to address this question and known
short-comings of the method. This paper is a contribution to this literature.
Specifically, the suitability of grafting consistent estimators onto a shallow
CART is explored. It is shown that this approach has a consistency guarantee
and performs well in empirical settings.


  The paper discusses empirical Bayes methodology for repeated quality
comparisons of health care institutions using data from the Dutch VOKS study
that annually monitors the relative performance and quality of nearly all Dutch
gynecological centres with respect to different aspects of the childbirths
taking place in these centres. This paper can be seen as an extension of the
pioneering work of Thomas, Longford and Rolph and Goldstein and Spiegelhalter.
First of all, this paper introduces a new simple crude estimate of the centre
effect in a logistic regression setting. Next, a simple estimate of the
expected percentile of a centre given all data and a measure of rankability of
the centres based on the expected percentiles are presented. Finally, the
temporal dimension is explored and methods are discussed to predict next years
performance.


  Fast Radio Bursts (FRBs) are recently discovered extra-galactic radio
transients which are now used as novel cosmological probes. We show how the
Bursts' Dispersion Measure can model-independently probe the history of
Hydrogen reionization. Using a FlexKnot free-form parameterization to
reconstruct the reionization history we predict an 11% accuracy constraint on
the CMB optical depth, and 4% accuracy on the midpoint of reionization, to be
achieved with 100 FRBs originating from redshifts z>5.


  The concept of chirality is extended to the Minimal Supersymmetric Standard
Model (MSSM) and the mu term is forbidden by a gauged U(1)' symmetry. R-parity
automatically emerges after symmetry breaking, suppressing proton decay and
protecting the LSP. Exotics charged under the SM pose a challenge to
traditional SU(5) unification, but unification is still implemented in
deconstructed GUTs. Because of the multitude of additional states to the MSSM,
the Z' has a large width, and the SM background, neglected in previous
theoretical studies, becomes important for Z' discovery. As a result, the LHC
reach is reduced from 3.2 TeV, for a Z' with SM decays, to 1.5 TeV, when
additional decay channels are included. This model also predicts possibly
long-lived colored and electroweak exotics.


  Linearized four-derivative gravity with a general gauge fixing term is
considered. By a Legendre transform and a suitable diagonalization procedure it
is cast into a second-order equivalent form where the nature of the physical
degrees of freedom, the gauge ghosts, the Weyl ghosts, and the intriguing
"third ghosts", characteristic to higher-derivative theories, is made explicit.
The symmetries of the theory and the structure of the compensating
Faddeev-Popov ghost sector exhibit non-trivial peculiarities.


  In this paper, we fully investigate the cosmological effects of the moduli
dependent one-loop corrections to the gravitational couplings of the string
effective action to explain the cosmic acceleration problem in early (and/or
late) universe. These corrections comprise a Gauss-Bonnet (GB) invariant
multiplied by universal non-trivial functions of the common modulus $\sigma$
and the dilaton $\phi$. The model exhibits several features of cosmological
interest, including the transition between deceleration and acceleration
phases. By considering some phenomenologically motivated ansatzs for one of the
scalars and/or the scale factor (of the universe), we also construct a number
of interesting inflationary potentials. In all examples under consideration, we
find that the model leads only to a standard inflation ($w \geq -1$) when the
numerical coefficient $\delta$ associated with modulus-GB coupling is positive,
while the model can lead also to a non-standard inflation ($w<-1$), if $\delta$
is negative. In the absence of (or trivial) coupling between the GB term and
the scalars, there is no crossing between the $w< -1$ and $w> -1$ phases, while
this is possible with non-trivial GB couplings, even for constant dilaton phase
of the standard picture. Within our model, after a sufficient amount of e-folds
of expansion, the rolling of both fields $\phi$ and $\sigma$ can be small. In
turn, any possible violation of equivalence principle or deviations from the
standard general relativity may be small enough to easily satisfy all
astrophysical and cosmological constraints.


  Sequential recommendation aims to provide users with personalized suggestions
based on their historical interactions. When training sequential models,
padding is a widely adopted technique for two main reasons: 1) The vast
majority of models can only handle fixed-length sequences; 2) Batching-based
training needs to ensure that the sequences in each batch have the same length.
The special value \emph{0} is usually used as the padding content, which does
not contain the actual information and is ignored in the model calculations.
This common-sense padding strategy leads us to a problem that has never been
explored before: \emph{Can we fully utilize this idle input space by padding
other content to further improve model performance and training efficiency?}
  In this paper, we propose a simple yet effective padding method called
\textbf{Rep}eated \textbf{Pad}ding (\textbf{RepPad}). Specifically, we use the
original interaction sequences as the padding content and fill it to the
padding positions during model training. This operation can be performed a
finite number of times or repeated until the input sequences' length reaches
the maximum limit. Our RepPad can be viewed as a sequence-level data
augmentation strategy. Unlike most existing works, our method contains no
trainable parameters or hyperparameters and is a plug-and-play data
augmentation operation. Extensive experiments on various categories of
sequential models and five real-world datasets demonstrate the effectiveness
and efficiency of our approach. The average recommendation performance
improvement is up to 60.3\% on GRU4Rec and 24.3\% on SASRec. We also provide
in-depth analysis and explanation of what makes RepPad effective from multiple
perspectives. Our datasets and codes are available at
\url{https://github.com/KingGugu/RepPad}.


  The general d-dimensional twisted group lattice is solved. The irreducible
representations of the corresponding group are constructed by an explicit
procedure. It is proven that they are complete. All matrix representation
solutions to the quantum hyperplane equations are obtained.


  We consider Benders decomposition for solving two-stage stochastic programs
with complete recourse based on finite samples of the uncertain parameters. We
define the Benders cuts binding at the final optimal solution or the ones
significantly improving bounds over iterations as valuable cuts. We propose a
learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut
classification step in each iteration to selectively generate cuts that are
more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i)
sampling cuts and collecting information from training problems and (ii)
solving testing problems with a support vector machine (SVM) cut classifier. We
run the LearnBD algorithm on instances of capacitated facility location and
multi-commodity network design under uncertain demand. Our results show that
SVM cut classifier works effectively for identifying valuable cuts, and the
LearnBD algorithm reduces the total solving time of all instances for different
problems with various sizes and complexities.


  A spin-wave theory that includes the antisymmetric Dzyaloshinskii-Moriya
exchange interactions and long-range dipole-dipole interactions is presented
for finite-length ferromagnetic spin chains. It is found that three different
physical situations arise, depending on the direction chosen in this geometry
for the axial vector of the Dzyaloshinskii-Moriya interactions. In some cases
this leads to a tilting of the equilibrium orientations near the ends of the
chain due to interfacial effects and with consequential effects on the spectrum
of discrete dipole-exchange spin waves. When variations are introduced for the
dominant bilinear exchange interactions at the ends of the spin chain, it is
shown that localized spin waves with spatial decay characteristics may occur.


  Extending deterministic compartments pharmacokinetic models as diffusions
seems not realistic on biological side because paths of these stochastic
processes are not smooth enough. In order to extend one compartment
intra-veinous bolus models, this paper suggests to modelize the concentration
process $C$ by a class of stochastic differential equations driven by a
fractional Brownian motion of Hurst parameter belonging to $]1/2,1[$.
  The first part of the paper provides probabilistic and statistical results on
the concentration process $C$ : the distribution of $C$, a control of the
uniform distance between $C$ and the solution of the associated ordinary
differential equation, an ergodic theorem for the concentration process and its
application to the estimation of the elimination constant, and consistent
estimators of the driving signal's Hurst parameter and of the volatility
constant.
  The second part of the paper provides applications of these theoretical
results on simulated concentration datas : a qualitative procedure for choosing
parameters on small sets of observations, and simulations of the estimators of
the elimination constant and of the driving signal's Hurst parameter. The
relationship between the estimations quality and the size/length of the sample
is discussed.


  SuperNEMO is a next generation neutrinoless double beta decay experiment with
a design capability to reach a half-life sensitivity of $10^{26}$ years
corresponding to an effective Majorana neutrino mass of $\langle m_{\beta\beta}
\rangle$ $<$ 50 - 100 meV. To achieve this sensitivity, stringent radio-purity
requirements are imposed resulting in an equally stringent screening programme.
Dedicated facilities have been established in the UK for screening and
selection of detector construction materials.
  Gamma ray spectroscopy using high-purity germanium (HPGe) detectors has been
the standard method for the measurement of material contamination. A
low-background facility has been established at Boulby Underground Laboratory.
The first results from the 2 current HPGe detector are shown.
  Radon is one of the most critical backgrounds for SuperNEMO and most other
low background experiments. It can enter the detector either through diffusion,
contamination during construction or emanation from the detector materials
resulting in radioactive daughter isotopes. To reach the target sensitivity,
the $^{222}$Rn concentration inside the SuperNEMO tracker volume must be less
than 150 $\mu$Bq/m3.
  A "Radon Concentration Line" (RnCL) was developed to be used in conjunction
with a state-of-the-art radon detector to make a more sensitive measurement of
large gas volumes. This apparatus has now been commissioned and is capable of
measuring radon levels in large samples down to 10 $\mu$Bq/m3. The results from
first measurements of radon content using the RnCL are presented. These
measurements include gas bottles, boil-off nitrogen and a SuperNEMO sub-module
during the early stages of construction.


  Finding the minimum and the minimizers of convex functions has been of
primary concern in convex analysis since its conception. It is well-known that
if a convex function has a minimum, then that minimum is global. The
minimizers, however, may not be unique. There are certain subclasses, such as
strictly convex functions, that do have unique minimizers when the minimum
exists, but other subclasses, such as constant functions, that do not. This
paper addresses the question of how many convex functions have unique
minimizers. We show, using Baire category theory, that the set of proximal
mappings of convex functions that have a unique fixed point is generic.
Consequently, the set of classes of convex functions that have unique
minimizers is generic.


  In this article we will discuss a Lorentzian sector calculation of the
entropy of a minimally coupled scalar field in the Kerr black hole background.
We will use the brick wall model of G.'t Hooft. In the Kerr black hole,
complications arise due to the absence of a global timelike Killing field and
the presence of the ergosphere. Nevertheless, it is possible to calculate the
entropy of a thin shell of matter field in the near-horizon region using the
brick wall model. The corresponding leading order entropy of the
nonsuperradiant modes is found to be proportional to the area of the horizon
and is logarithmically divergent. Thus, to the leading order, the entropy of a
three-dimensional system in the near-horizon region is proportional to the
boundary surface. This aspect is also valid in the Schwarzschild black hole.
The corresponding internal energy remains finite if the entropy is chosen to be
of the order of the black hole entropy itself. For a fixed value of the brick
wall cut-off, the leading order entropy in the Kerr black hole is found to be
half of the corresponding term in the Schwarzschild black hole. This is due to
rotation and is consistent with the preferential emission of particles in the
Kerr black hole with azimuthal angular momentum in the same direction as that
of the black hole itself. However, we can obtain the Schwarzschild case
expression by including a subleading term and taking the appropriate slow
rotation limit. In this version, we will discuss a sub-leading term for the
scalar field entropy in Schwarzschild black hole. This term reduces to an
expression similar to the corresponding flat spacetime entropy in the limit
when radius of event horizon is vanishing.


  We calculate, to 3 loops in perturbation theory, the bare $\beta$-function of
QCD, formulated on the lattice with the clover fermionic action. The dependence
of our result on the number of colors $N$, the number of fermionic flavors
$N_f$, as well as the clover parameter $c_{SW}$, is shown explicitly.
  A direct outcome of our calculation is the two-loop relation between the bare
coupling constant $g_0$ and the one renormalized in the MS-bar scheme.
  Further, we can immediately derive the three-loop correction to the relation
between the lattice $\Lambda$-parameter and $g_0$, which is important in checks
of asymptotic scaling. For typical values of $c_{SW}$, this correction is found
to be very pronounced.


  We compare the end-to-end training approach to a modular approach in which a
system is decomposed into semantically meaningful components. We focus on the
sample complexity aspect, in the regime where an extremely high accuracy is
necessary, as is the case in autonomous driving applications. We demonstrate
cases in which the number of training examples required by the end-to-end
approach is exponentially larger than the number of examples required by the
semantic abstraction approach.


  In oncology, phase II or multiple expansion cohort trials are crucial for
clinical development plans. This is because they aid in identifying potent
agents with sufficient activity to continue development and confirm the proof
of concept. Typically, these clinical trials are single-arm trials, with the
primary endpoint being short-term treatment efficacy. Despite the development
of several well-designed methodologies, there may be a practical impediment in
that the endpoints may be observed within a sufficient time such that adaptive
go/no-go decisions can be made in a timely manner at each interim monitoring.
Specifically, Response Evaluation Criteria in Solid Tumors guideline defines a
confirmed response and necessitates it in non-randomized trials, where the
response is the primary endpoint. However, obtaining the confirmed outcome from
all participants entered at interim monitoring may be time-consuming as
non-responders should be followed up until the disease progresses. Thus, this
study proposed an approach to accelerate the decision-making process that
incorporated the outcome without confirmation by discounting its contribution
to the decision-making framework using the generalized Bayes' theorem. Further,
the behavior of the proposed approach was evaluated through a simple simulation
study. The results demonstrated that the proposed approach made appropriate
interim go/no-go decisions.


  Fast and accurate protein structure prediction is one of the major challenges
in structural biology, biotechnology and molecular biomedicine. These fields
require 3D protein structures for rational design of proteins with improved or
novel properties. X-ray crystallography is the most common approach even with
its low success rate, but lately NMR based approaches have gained popularity.
The general approach involves a set of distance restraints used to guide a
structure prediction, but simple NMR triple-resonance experiments often provide
enough structural information to predict the structure of small proteins.
Previous protein folding simulations that have utilised experimental data have
weighted the experimental data and physical force field terms more or less
arbitrarily, and the method is thus not generally applicable to new proteins.
Furthermore a complete and near error-free assignment of chemical shifts
obtained by the NMR experiments is needed, due to the static, or deterministic,
assignment. In this thesis I present Chemshift, a module for handling chemical
shift assignments, implemented in the protein structure determination program
Phaistos. This module treats both the assignment of experimental data, as well
as the weighing compared to physical terms, in a probabilistic framework where
no data is discarded. Provided a partial assignment of NMR peaks, the module is
able to improve the assignment with the intension to utilise this in the
protein folding with little bias.


  This paper describes the ATLAS production tool AtCom (for ATLAS Commander).
The purpose of the tool is to automate as much as possible the task of a
production manager: defining and submitting jobs in large quantities, following
up upon their execution, scanning log files for known and unknown errors,
updating the various ATLAS bookkeeping databases on successful completion of a
job whilst cleaning up and resubmitting otherwise. The design of AtCom is
modular, separating the generic basic job management functionality from the
interactions with the various databases on the one hand, and the computing
systems on the other hand. Given the near future reality of different flavors
of computing systems (legacy and GRID) deployed concurrently at the various, or
even a single ATLAS site, AtCom allows several of them to be used at the same
time transparently.


  This work investigates the macroscopic thermomechanical behavior of lunar
boulders by modeling their response to diurnal thermal forcing. Our results
reveal a bimodal, spatiotemporally-complex stress response. During sunrise,
stresses occur in the boulders' interiors that are associated with large-scale
temperature gradients developed due to overnight cooling. During sunset,
stresses occur at the boulders' exteriors due to the cooling and contraction of
the surface. Both kinds of stresses are on the order of 10 MPa in 1 m boulders
and decrease for smaller diameters, suggesting that larger boulders break down
more quickly. Boulders <30 cm exhibit a weak response to thermal forcing,
suggesting a threshold below which crack propagation may not occur. Boulders of
any size buried by regolith are shielded from thermal breakdown. As boulders
increase in size (>1 m), stresses increase to several 10s of MPa as the
behavior of their surfaces approaches that of an infinite halfspace. As the
thermal wave loses contact with the boulder interior, stresses become limited
to the near-surface. This suggests that the survival time of a boulder is not
only controlled by the amplitude of induced stress, but also by its diameter as
compared to the diurnal skin depth. While stresses on the order of 10 MPa are
enough to drive crack propagation in terrestrial environments, crack
propagation rates in vacuum are not well constrained. We explore the
relationship between boulder size, stress, and the direction of crack
propagation, and discuss the implications for the relative breakdown rates and
estimated lifetimes of boulders on airless body surfaces.


  The next-generation astronomy archives will cover most of the universe at
fine resolution in many wavelengths. One of the first of these projects, the
Sloan Digital Sky Survey (SDSS) will create a 5-wavelength catalog over 10,000
square degrees of the sky. The 200 million objects in the multi-terabyte
database will have mostly numerical attributes, defining a space of 100+
dimensions. Points in this space have highly correlated distributions. The
archive will enable astronomers to explore the data interactively. Data access
will be aided by multidimensional spatial indices. The data will be partitioned
in many ways. Small tag objects consisting of the most popular attributes speed
up frequent searches. Splitting the data among multiple servers enables
parallel, scalable I/O. Hashing techniques allow efficient clustering and
pairwise comparison algorithms. Randomly sampled subsets allow debugging
otherwise large queries at the desktop. Central servers will operate a data
pump that supports sweeping searches that touch most of the data.


  Predicting structured outputs such as semantic segmentation relies on
expensive per-pixel annotations to learn supervised models like convolutional
neural networks. However, models trained on one data domain may not generalize
well to other domains without annotations for model finetuning. To avoid the
labor-intensive process of annotation, we develop a domain adaptation method to
adapt the source data to the unlabeled target domain. We propose to learn
discriminative feature representations of patches in the source domain by
discovering multiple modes of patch-wise output distribution through the
construction of a clustered space. With such representations as guidance, we
use an adversarial learning scheme to push the feature representations of
target patches in the clustered space closer to the distributions of source
patches. In addition, we show that our framework is complementary to existing
domain adaptation techniques and achieves consistent improvements on semantic
segmentation. Extensive ablations and results are demonstrated on numerous
benchmark datasets with various settings, such as synthetic-to-real and
cross-city scenarios.


  The present paper is devoted to power series of SP type, i.e. with
coefficients that are syntactically sum-product combinations. Apart from their
applications to analytic knot theory and the so-called "Volume Conjecture",
SP-series are interesting in their own right, on at least four counts: (i) they
generate quite distinctive resurgence algebras (ii) they are one of those
relatively rare instances when the resurgence properties have to be derived
directly from the Taylor coefficients (iii) some of them produce singularities
that unexpectedly verify finite-order differential equations (iv) all of them
are best handled with the help of two remarkable, infinite-order
integral-differential transforms, "mir" and "nir".


  Contents:
  1 Frustrated quantum spin systems
  1.1 The Pyrochlore checkerboard
  1.2 Singlets in reflection symmetric spin systems
  2 Wehrl entropy of Bloch coherent states
  2.1 Conjectures of Wehrl and Lieb
  2.2 Proof of Lieb's conjecture for low spin
  3 Symmetries of the Hubbard model
  4 Quantum integrable systems
  4.1 Twisted quantum Lax equations
  4.2 Face algebras and Ruijsenaars models
  5 Noncommutative gauge theory
  5.1 Gauge theory on noncommutative spaces
  5.2 Seiberg-Witten map and Kontsevich formality, Born-Infeld action in the
intermediate picture
  5.3 Nonabelian noncommutative gauge fields


  While bars are common in disk galaxies, their formation conditions are not
well understood. We use $N$-body simulations to study bar formation and
evolution in isolated galaxies consisting of a stellar disk, a classical bulge,
and a dark halo. We consider 24 galaxy models that are similar to the Milky Way
but differ in the mass and compactness of the classical bulge and halo
concentration. We find that the bar formation requires
$(Q_{T,\text{min}}/1.2)^2 + (\text{CMC}/0.05)^2\lesssim 1$, where
$Q_{T,\text{min}}$ and CMC refers to the minimum value of the Toomre stability
parameter and the central mass concentration, respectively. Bars tend to be
stronger, longer, and rotate slower in galaxies with a less massive and less
compact bulge and halo. All bars formed in our models correspond to slow bars.
A model with the bulge mass of $\sim10$--$20$\% of the disk under a
concentrated halo produces a bar similar to the Milky-Way bar. We discuss our
findings in relation to other bar formation criteria suggested by previous
studies.


  Bilateral trade is one of the most natural and important forms of economic
interaction: A seller has a single, indivisible item for sale, and a buyer is
potentially interested. The two parties typically have different, privately
known valuations for the item, and ideally, they would like to trade if the
buyer values the item more than the seller. The celebrated impossibility result
by Myerson and Satterthwaite shows that any mechanism for this setting must
violate at least one important desideratum. In this paper, we investigate a
richer paradigm of bilateral trade, with many self-interested buyers and
sellers on both sides of a single trade who cannot be excluded from the trade.
We show that this allows for more positive results. In fact, we establish a
dichotomy in the possibility of trading efficiently. If in expectation, the
buyers value the item more, we can achieve efficiency in the limit. If this is
not the case, then efficiency cannot be achieved in general. En route, we
characterize trading mechanisms that encourage truth-telling, which may be of
independent interest. We also evaluate our trading mechanisms experimentally,
and the experiments align with our theoretical results.


  In this paper, we provide a correct proof for the lower bounds of the local
antimagic chromatic number of the corona product of friendship and fan graphs
with null graph respectively as in [On local antimagic vertex coloring of
corona products related to friendship and fan graph, {\it Indon. J. Combin.},
5(2) (2021) 110--121]. Consequently, we obtained a sharp lower bound that gives
the exact local antimagic chromatic number of the corona product of friendship
and null graph.


  The mutant allele frequencies in oncogenes peak around 0.40 and rapidly
decrease. In this article, we explain why this is the case. Invoking a key
result from mathematical analysis in our model, namely, the inverse function
theorem, we estimate the selection pressures of the mutations as a function of
germline allele frequencies. Under complete dominance of oncogenic mutations,
this selection function is expected to be linearly correlated with the
distribution of the mutant alleles. We demonstrate that this is the case by
investigating the allele frequencies of mutations in oncogenes across various
cancer types, validating our model for mean effective selection. Consistent
with the population genetics model of fitness, the selection function fits a
gamma distribution curve that accurately describes the trend of the mutant
allele frequencies. While existing equations for selection explain evolution at
low allele frequencies, our equations are general formulas for natural
selection under complete dominance operating at all frequencies. We show that
selection exhibits linear behavior at all times, favoring dominant alleles with
respect to the change in recessive allele frequency. Also, these equations
show, selection behaves like power-law against the recessive alleles at low
dominant allele frequency.


  In this paper we propose a distributed model predictive control architecture
to coordinate the longitudinal motion of a vehicle platoon at a signalized
intersection. Our control approach is cooperative; we use vehicle-to-vehicle
(V2V) communication in order to maintain small inter-vehicle distances and
correspondingly achieve large vehicle throughput at an intersection. We study
the trade-off between safety and road throughput for this problem. In
particular, we present the link between traffic efficiency gains in terms of
throughput, and safety of the connected platoon measured as trust on the
predicted speed profile of other vehicles in the network.


  A vanishing theorem is proved for Ext groups over non-commutative graded
algebras. Along the way, an "infinite" version is proved of the non-commutative
Auslander-Buchsbaum theorem.


  We construct and analyze a microscopic model for insulating rock salt ordered
double perovskites, with the chemical formula A$_2$BB'O$_6$, where the magnetic
ion B' has a 4d$^2$ or 5d$^2$ electronic configuration and forms a face
centered cubic (fcc) lattice. For these B' ions, the combination of the
triply-degenerate antisymmetric two-electron orbital states and strong
spin-orbit coupling forms local quintuplets with an effective spin moment
$j=2$. Moreover, due to strongly orbital-dependent exchange, the effective
spins have substantial biquadratic and bicubic interactions (fourth and sixth
order in the spins, respectively). This leads, at the mean field level, to a
rich ground state phase diagram which includes seven different phases: a
uniform ferromagnetic phase with an ordering wavevector ${\bf p} = {\bf 0}$ and
uniform magnetization along $[111]$ direction, four two-sublattice phases with
an ordering wavevector ${\bf p} = 2\pi(001)$ and two four-sublattice
antiferromagnetic phases. Amongst the two-sublattice phases there is a
quadrupolar ordered phase which preserves time reversal symmetry. Extending the
mean field theory to finite temperatures, we find ten different magnetization
processes with different magnetic thermal transitions. In particular, we find
that thermal fluctuations stabilize the two-sublattice quadrupolar ordered
phase in a large portion of phase diagram. Existing and possible future
experiments are discussed in light of these theoretical predictions.


  It is known that the conductance of a quantum point contact is quantized in
units of $2e^2/h$ and this quantization is destroyed by a non-adiabatic
scatterer in the point contact, due to backscattering. Recently, it was shown
[Phys. Rev. Lett. 71, 137 (1993)] that taking many non-adiabatic scatterers
periodically in a quantum channel, the quantization can be recovered. We study
this conductance quantization of a periodic system in the presence of a strong
defect. A periodic arrangement of double-stubs give remarkable quantization of
conductance. A periodic arrangement of double-constrictions also gives a very
good quantization only when the separation between the constrictions is small.
We conclude that conductance quantization of a periodically modulated channel
is robust.


  We present magnetization measurements on oriented powder of
ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$ along and perpendicular to the orienting field. We
find a dramatic difference in the magnetization between the two directions. It
is biggest at low measurement fields $H$ or high temperatures. We show that the
difference at high temperatures must emerge from Ising-like exchange
anisotropy. This allows us to explain muon spin rotation data at $T\to 0$ in
terms of an exotic ferromagnetic ground state.


  Cuprous oxide (Cu2O) nanowires were grown by a simple, catalyst-free thermal
oxidation method starting from a copper foil substrate. Wire growth was
performed at different conditions by varying oxygen partial pressure and
heating temperature, and produced structural defects such as stacking faults
and twin structures of grown nanowires were characterized by transmission
electron microscopy. A moderate oxygen partial pressure ~5% with a relatively
high temperature at 500 oC is found to be an optimal condition for growing
high-quality Cu2O nanowires, while either lowering temperature or enhancing O2
partial pressure can yield higher density of planar defects. In order to
further investigate optical properties of grown nanowires, photoluminescence
measurements were also performed and correlated with structural
characterization results, indicating that increase of structural defects such
as stacking faults and twins tended to enhance broad defect-related emissions
and suppress the exciton emissions near Cu2O band-edge.


  We develop the notion of a Kleinian Sphere Packing, a generalization of
"crystallographic" (Apollonian-like) sphere packings defined by
Kontorovich-Nakamura [KN19]. Unlike crystallographic packings, Kleinian
packings exist in all dimensions, as do "superintegral" such. We extend the
Arithmeticity Theorem to Kleinian packings, that is, the superintegral ones
come from Q-arithmetic lattices of simplest type. The same holds for more
general objects we call Kleinian Bugs, in which the spheres need not be
disjoint but can meet with dihedral angles pi/m for finitely many m. We settle
two questions from [KN19]: (i) that the Arithmeticity Theorem is in general
false over number fields, and (ii) that integral packings only arise from
non-uniform lattices.


  For $k \geq 2$, we consider the number $A_k(Z)$ of positive integers $n \leq
Z$ such that both $n$ and $n+1$ are $k$-free. We prove an asymptotic formula
$A_k(Z) = c_k Z + O(Z^{14/(9k)+\epsilon})$, where the error term improves upon
previously known estimates. The main tool used is the approximative determinant
method of Heath-Brown.


  We establish the first connection between $2d$ Liouville quantum gravity and
natural dynamics of random matrices. In particular, we show that if $(U_t)$ is
a Brownian motion on the unitary group at equilibrium, then the measures $$
|\det(U_t - e^{i \theta})|^{\gamma} dt d\theta $$ converge in the limit of
large dimension to the $2d$ LQG measure, a properly normalized exponential of
the $2d$ Gaussian free field. Gaussian free field type fluctuations associated
with these dynamics were first established by Spohn (1998) and convergence to
the LQG measure in $2d$ settings was conjectured since the work of Webb (2014),
who proved the convergence of related one dimensional measures by using inputs
from Riemann-Hilbert theory.
  The convergence follows from the first multi-time extension of the result by
Widom (1973) on Fisher-Hartwig asymptotics of Toeplitz determinants with real
symbols. To prove these, we develop a general surgery argument and combine
determinantal point processes estimates with stochastic analysis on Lie group,
providing in passing a probabilistic proof of Webb's $1d$ result. We believe
the techniques will be more broadly applicable to matrix dynamics out of
equilibrium, joint moments of determinants for classes of correlated random
matrices, and the characteristic polynomial of non-Hermitian random matrices.


  We theoretically explore the mechanism of the colossal magnetoresistance in
manganese oxides by explicitly taking into account the phase competition
between the double-exchange ferromagnetism and the charge-ordered insulator. We
find that quenched disorder causes a drastic change of the multicritical phase
diagram by destroying the charge-ordered state selectively. As a result, there
appears a nontrivial phenomenon of the disorder-induced insulator-to-metal
transition in the multicritical regime. On the contrary, the disorder induces a
highly-insulating state above the transition temperature where charge-ordering
fluctuations are much enhanced. The contrasting effects provide an
understanding of the mechanism of the colossal magnetoresistance. The obtained
scenario is discussed in comparison with other theoretical proposals such as
the polaron theory, the Anderson localization, the multicritical-fluctuation
scenario, and the percolation scenario.


  We study a refined large spin limit for twist operators in the sl(2) sector
of AdS/CFT. We derive a novel non-perturbative equation for the generalized
two-parameter scaling function associated to this limit, and analyze it at weak
coupling. It is expected to smoothly interpolate between weakly coupled gauge
theory and string theory at strong coupling.


  We examine coupling interactions used to synchronize macroscopic Josephson
oscillations induced in intrinsic Josephson junction (IJJ) mesa stacks made of
a Bi2212 single crystal. Synchronized radiations of terahertz electromagnetic
(EM) waves are detected under common voltage and current bias operations of two
connected mesas with close individual radiation frequencies, while uncoupled
and bimodal radiations are frequently observed in two mesas with different
individual radiation frequencies. Detailed observations of the polarizations of
the EM waves emitted when two mesas are biased in parallel or series allow us
to reveal the coupling matrix components, which include ratios of synchronized
IJJs in the mesas and phase delay between the macroscopic Josephson
oscillations. A frequency evolution of the phase delay implies that the
coupling between the Josephson oscillations is mediated by the small amplitude
Josephson plasmon inside the superconducting substrate. This finding stimulates
systematic survey on polarization of EM wave emitted from synchronized multiple
mesa devices in order to realize powerful terahertz emissions from
superconductors.


  Index theory revealed its outstanding role in the study of periodic orbits of
Hamiltonian systems and the dynamical consequences of this theory are enormous.
Although the index theory in the periodic case is well-established, very few
results are known in the case of homoclinic orbits of Hamiltonian systems.
Moreover, to the authors' knowledge, no results have been yet proved in the
case of heteroclinic and halfclinic (i.e. parametrised by a half-line) orbits.
  Motivated by the importance played by these motions in understanding several
challenging problems in Classical Mechanics, we develop a new index theory and
we prove at once a general spectral flow formula for heteroclinic, homoclinic
and halfclinic trajectories. Finally we show how this index theory can be used
to recover all the (classical) existing results on orbits parametrised by
bounded intervals.


  We introduce a notion of fractional convexity that extends naturally the
usual notion of convexity in the Euclidean space to a fractional setting. With
this notion of fractional convexity, we study the fractional convex envelope
inside a domain of an exterior datum (the largest possible fractional convex
function inside the domain that is below the datum outside) and show that the
fractional convex envelope is characterized as a viscosity solution to a
non-local equation that is given by the infimum among all possible directions
of the $1-$dimensional fractional Laplacian. For this equation we prove
existence, uniqueness and a comparison principle (in the framework of viscosity
solutions). In addition, we find that solutions to the equation for the convex
envelope are related to solutions to the fractional Monge-Ampere equation.


  We consider mapping properties of the iterated Stieltjes transform,
establishing its new relations with the iterated Hilbert transform (a singular
integral) on the half-axis and proving the corresponding convolution and
Titchmarsh's type theorems. Moreover, the obtained convolution method is
applied to solve a new class of singular integral equations.


  A microstructure-based model is presented to predict the fatigue life of
polycrystalline metallic alloys which present a bilinear Coffin-Manson
relationship. The model is based in the determination of the maximum value of a
fatigue indicator parameter obtained from the plastic energy dissipated by
cycle in the microstructure. The fatigue indicator parameter was obtained by
means of the computational homogenization of a representative volume element of
the microstructure using a crystal-plasticity finite element model. The
microstructure-based model was applied to predict the low cyclic fatigue
behavior of IN718 at 400$^\circ$C which exhibits a bilinear Coffin-Manson
relationship under the assumption that this behavior is triggered by a
transition from highly localized plasticity at low cyclic strain amplitudes to
more homogeneous deformation at high cyclic strain amplitudes. The model
predictions were in very good agreement with the experimental results for a
wide range of cyclic strain amplitudes and two strain ratios ($R_\epsilon$ = 0
and -1) and corroborated the initial hypothesis. Moreover, they provided a
micromechanical explanation for the influence of the strain ratio on the
fatigue life at low cyclic strain amplitudes.


  The study of the dynamic properties of bubbles in the interstellar medium is
important for understanding the feedback mechanisms from star-formation
processes in galaxies. The ongoing integral field spectroscopy of nearby
star-forming galaxies reveals many expanding bubbles and superbubbles
identified by the local increase in gas velocity dispersion. The limited
angular resolution often prevents bona fide measures of the expansion
velocities in galaxies outside the Local Group, even despite sufficient
spectroscopic resolution. We present a method that makes it possible to measure
the expansion velocity of bubbles surrounding massive stars and clusters based
on the data about local variations in gas velocity dispersion. We adapt the
method for the Fabry-Perot interferometers used with the 6-m telescope of the
Special Astrophysical Observatory of the Russian Academy of Sciences, as well
as for any integral field spectrograph with a Gaussian line spread function. We
apply the method described to analyze the kinematics of ionized superbubbles
gas and the only known supernova remnant in the IC 1613 galaxy. The estimate of
the kinematic age of the supernova remnant (on the order of 3100 years) agrees
well with the previously obtained independent estimate based on X-ray data.


  An approximation to General Relativity is presented that agrees with the
Einstein field equations up to and including the fourth post-Newtonian (PN)
order. This approximation is formulated in a fully constrained scheme: all
involved equations are explicitly elliptic except the wave equation that
describes the two independent degrees of freedom of the gravitational field.
The formalism covers naturally the conformal-flat-condition (CFC) approach by
Isenberg, Wilson, and Mathews and the improved second PN-order exact approach
CFC+. For stationary configurations, like Kerr black holes, agreement with
General Relativity is achieved even through 5PN order. In addition, a
particularly interesting 2PN-exact waveless approximation is analyzed in
detail, which results from imposing more restrictive conditions. The proposed
scheme can be considered as a further development on the waveless approach
suggested by Schaefer and Gopakumar [Phys. Rev. D {\bf 69}, 021501 (2004)].


  Predictions for our ability to distinguish quark and gluon jets vary by more
than a factor of two between different parton showers. We study this problem
using analytic resummed predictions for the thrust event shape up to NNLL'
using $e^+e^- \to Z \to q \bar q$ and $e^+e^- \to H \to gg$ as proxies for
quark and gluon jets. We account for hadronization effects through a
nonperturbative shape function, and include an estimate of both perturbative
and hadronization uncertainties. In contrast to previous studies, we find
reasonable agreement between our results and predictions from both Pythia and
Herwig parton showers. We find that this is due to a noticeable improvement in
the description of gluon jets in the newest Herwig 7.1 compared to previous
versions.


  The geometry of the moduli space of stable spin curves is studied, with
emphasis on its combinatorial properties. In this context, the standard graph
theoretic framework is not just a book-keeping device: some purely
combinatorial results are proved, having moduli theoretic applications. In
particular, certain strata of the moduli space of stable curves are
characterized by a (finite) set of integers, measuring the non-reducedness of
the scheme of spin curves, and definable in purely graph-theoretical terms.


  We give necessary and sufficient conditions for nuclearity of
Cuntz-Nica-Pimsner algebras for a variety of quasi-lattice ordered groups.
First we deal with the free abelian lattice case. We use this as a stepping
stone to tackle product systems over quasi-lattices that are controlled by the
free abelian lattice and satisfy a minimality property. Our setting
accommodates examples like the Baumslag-Solitar lattice for $n=m>0$ and the
right-angled Artin groups. More generally the class of quasi-lattices for which
our results apply is closed under taking semi-direct and graph products.
  In the process we accomplish more. Our arguments tackle Nica-Pimsner algebras
that admit a faithful conditional expectation on a small fixed point algebra
and a faithful copy of the co-efficient algebra. This is the case for
CNP-relative quotients in-between the Toeplitz-Nica-Pimsner algebra and the
Cuntz-Nica-Pimsner algebra. We complete this study with the relevant results on
exactness.


  A subset of machine learning research intersects with societal issues,
including fairness, accountability and transparency, as well as the use of
machine learning for social good. In this work, we analyze the scholars
contributing to this research at the intersection of machine learning and
society through the lens of the sociology of science. By analyzing the
authorship of all machine learning papers posted to arXiv, we show that
compared to researchers from overrepresented backgrounds (defined by gender and
race/ethnicity), researchers from underrepresented backgrounds are more likely
to conduct research at this intersection than other kinds of machine learning
research. This state of affairs leads to contention between two perspectives on
insiders and outsiders in the scientific enterprise: outsiders being those
outside the group being studied, and outsiders being those who have not
participated as researchers in an area historically. This contention manifests
as an epistemic question on the validity of knowledge derived from lived
experience in machine learning research, and predicts boundary work that we see
in a real-world example.


  Inferring the presence of critical dynamics from continuous measure- ments is
a challenging problem. We solve this problem by showing that continuous
narrowband dynamics from a critical system exhibit qualita- tively differing
behaviors which depend on the universality class; we term each region of
critical avalanche parameters which generates qualitatively constant behavior a
meta-universality class. This theoretical observation allows us to infer
membership of a given meta-universality class and thus yields a robust test for
criticality. We validate these theoretical predic- tions in simulations and
provide unequivocal evidence for criticality in the human brain on the basis
electrophysiological recordings.


  Tomographic imaging has benefited from advances in X-ray sources, detectors
and optics to enable novel observations in science, engineering and medicine.
These advances have come with a dramatic increase of input data in the form of
faster frame rates, larger fields of view or higher resolution, so high
performance solutions are currently widely used for analysis. Tomographic
instruments can vary significantly from one to another, including the hardware
employed for reconstruction: from single CPU workstations to large scale hybrid
CPU/GPU supercomputers. Flexibility on the software interfaces and
reconstruction engines are also highly valued to allow for easy development and
prototyping. This paper presents a novel software framework for tomographic
analysis that tackles all aforementioned requirements. The proposed solution
capitalizes on the increased performance of sparse matrix-vector multiplication
and exploits multi-CPU and GPU reconstruction over MPI. The solution is
implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel
integration, and on SciPy for CPU sparse matrix computation. As opposed to
previous tomography solutions that are tailor-made for specific use cases or
hardware, the proposed software is designed to provide flexible, portable and
high-performance operators that can be used for continuous integration at
different production environments, but also for prototyping new experimental
settings or for algorithmic development. The experimental results demonstrate
how our implementation can even outperform state-of-the-art software packages
used at advanced X-ray sources worldwide.


  We present the results of a numerical study focusing on the propagation of a
hypersonic bullet subject to radiative cooling. Our goal is to explore the
feasibility of such a model for the formation of ``strings'' observed in the
Eta Carinae Homunculus nebula. Our simulations were performed in cylindrical
symmetry with the adaptive mesh refinement code AstroBEAR. The radiative
cooling of the system was followed using the cooling curve by Dalgarno & McCray
(1972). In this letter we discuss the evolution and overall morphology of the
system as well as key kinematic properties. We find that radiative bullets can
produce structures with properties similar to those of the Eta Carinae strings,
i.e. high length-to-width ratios and Hubble-type flows in the form of a linear
velocity increase from the base of the wake to the bullet head. These features,
along with the appearance of periodic ``ring-like'' structures, may also make
this model applicable to other astrophysical systems such as planetary nebulae,
e.g. CRL 618 and NGC 6543, young stellar objects, etc.


  Spin dependent transport in a multi-terminal mesoscopic ring is investigated
in presence of Rashba and Dresselhaus spin-orbit interactions. Within a
tight-binding framework we use a general spin density matrix formalism to
evaluate all three components ($P_x$, $P_y$ and $P_z$) of the polarization
vector associated with the charge current through the outgoing leads. It
explores the dynamics of the spin polarization vector of current propagating
through the system subjected to the Rashba and/or the Dresselhaus spin-orbit
couplings. The sensitivity of the polarization components on the electrode-ring
interface geometry is discussed in detail. Our present analysis provides an
understanding of the coupled spin and electron transport in mesoscopic bridge
systems.


  Representations learnt through deep neural networks tend to be highly
informative, but opaque in terms of what information they learn to encode. We
introduce an approach to probabilistic modelling that learns to represent data
with two separate deep representations: an invariant representation that
encodes the information of the class from which the data belongs, and an
equivariant representation that encodes the symmetry transformation defining
the particular data point within the class manifold (equivariant in the sense
that the representation varies naturally with symmetry transformations). This
approach is based primarily on the strategic routing of data through the two
latent variables, and thus is conceptually transparent, easy to implement, and
in-principle generally applicable to any data comprised of discrete classes of
continuous distributions (e.g. objects in images, topics in language,
individuals in behavioural data). We demonstrate qualitatively compelling
representation learning and competitive quantitative performance, in both
supervised and semi-supervised settings, versus comparable modelling approaches
in the literature with little fine tuning.


  We evaluate the N to Delta axial transition form factors in lattice QCD in
the quenched theory, with two degenerate flavors of dynamical Wilson fermions
and using domain wall valence fermions with staggered sea quark configurations.
We predict the ratio $C_5^A(q^2)/C_3^V(q^2)$ relevant to the parity violating
asymmetry and check the validity of the off-diagonal Goldberger-Treiman
relation.


  A tree search algorithm called successive cancellation ordered search (SCOS)
is proposed for $\boldsymbol{G}_N$-coset codes that implements
maximum-likelihood (ML) decoding with adaptive complexity for transmission over
binary-input AWGN channels. Unlike bit-flip decoders, no outer code is needed
to terminate decoding; therefore, SCOS also applies to $\boldsymbol{G}_N$-coset
codes modified with dynamic frozen bits. The average complexity is close to
that of successive cancellation (SC) decoding at practical frame error rates
(FERs) for codes with wide ranges of rate and lengths up to $512$ bits, which
perform within $0.25$ dB or less from the random coding union bound and
outperform Reed--Muller codes under ML decoding by up to $0.5$ dB. Simulations
illustrate simultaneous gains for SCOS over SC-Fano, SC stack (SCS) and SC list
(SCL) decoding in FER and the average complexity at various SNR regimes. SCOS
is further extended by forcing it to look for candidates satisfying a
threshold, thereby outperforming basic SCOS under complexity constraints. The
modified SCOS enables strong error-detection capability without the need for an
outer code. In particular, the $(128, 64)$ polarization-adjusted convolutional
code under modified SCOS provides gains in overall and undetected FER compared
to CRC-aided polar codes under SCL/dynamic SC flip decoding at high SNR.


  An internal polarized hydrogen storage cell gas target is proposed for the
AD--ring to determine for the first time the two total spin--dependent cross
sections $\sigma_1$ and $\sigma_2$ at antiproton beam energies in the range
from 50 to 200 MeV. The data will allow the definition of the optimum working
parameters of a dedicated Antiproton Polarizer Ring (APR), which has recently
been proposed by the PAX collaboration for the new Facility for Antiproton and
Ion Research (FAIR) at GSI in Darmstadt, Germany. The availability of an
intense beam of polarized antiprotons will provide access to a wealth of
single-- and double--spin observables, thereby opening a new window to QCD
transverse spin physics. The physics program proposed by the PAX collaboration
includes a first measurement of the transversity distribution of the valence
quarks in the proton, a test of the predicted opposite sign of the
Sivers--function, related to the quark distribution inside a transversely
polarized nucleon, in Drell--Yan (DY) as compared to semi--inclusive Deep
Inelastic Scattering, and a first measurement of the moduli and the relative
phase of the time--like electric and magnetic form factors $G_{E,M}$ of the
proton.


  A dark energy model (DE) is proposed based on Ginzburg-Landau theory of phase
transition (GLT). This model, GLTofDE, surprisingly provides a framework to
study not only temporal tensions in cosmology e.g. $H_0$ tension but also
spatial anomalies of CMB e.g. the hemispherical power asymmetry and
quadrupole-octopole alignment. In the mean field (or Landau) approximation of
GLTofDE, there is a spontaneously symmetry breaking exactly like the Higgs
potential. We modeled this transition, phenomenologically, and showed that
GLTofDE can resolve both the $H_0$ tension and Lyman-$\alpha$ anomaly in a
non-trivial way. According to $\chi^2$-analysis the transition happens at
$z_t=0.738\pm0.028$ while $H_0=71.89\pm0.93$ km/s/Mpc and
$\Omega^{like}_{k}=-0.225\pm0.049$ which are consistent with the latest $H(z)$
reconstructions. In addition, the GLTofDE proposes a framework to address the
CMB anomalies when it is considered beyond the mean field approximation. In
this regime existence of a long wavelength mode is a typical consequence which
is named the Goldstone mode in the case of continuous symmetries. This mode,
which is an automatic byproduct in GLTofDE, makes cosmological constant,
direction dependent. This means one side of the sky should be colder than the
other side in agreement with what has been already observed in CMB. In addition
between initial stochastic pattern and the final state with one long wavelength
mode, we can observe smaller patches or protrusions of the biggest remaining
patch in the simulation. Our simulations show these protrusions are few in
numbers and will be evolved according to Alan-Cahn mechanism. These protrusions
can give an additional effect on CMB which is the existence of aligned
quadrupole-octopole mode and its direction should be orthogonal to the dipole
direction. We conclude that GLTofDE is a fertile framework both theoretically
and phenomenologically.


  We look at two contrasting spin-down models for isolated radio pulsars and,
accounting for selection effects, synthesize observable populations. While our
goal is to reproduce all of the observable characteristics, in this paper we
pay particular attention to the form of the spin period vs. period derivative
(P-Pdot) diagram and its dependence on various pulsar properties. We analyse
the initial spin period, the braking index, the magnetic field, various beaming
models, as well as the pulsar's luminosity. In addition to considering the
standard magnetic dipole model for pulsar spin-down, we also consider the
recent hybrid model proposed by Contopoulos & Spitkovsky. The magnetic dipole
model, however, does a better job of reproducing the observed pulsar
population. We conclude that random alignment angles and period dependent
luminosity distributions are essential to reproduce the observed P-Pdot
diagram. We also consider the time decay of alignment angles, and attempt to
reconcile various models currently being studied. We conclude that, in order to
account for recent evidence for the alignment found by Weltevrede & Johnston,
the braking torque on a neutron star should not depend strongly on the
inclination. Our simulation code is publically available and includes a
web-based interface to examine the results and make predictions for yields of
current and future surveys.


  We consider a problem of optimal investment with intermediate consumption and
random endowment in an incomplete semimartingale model of a financial market.
We establish the key assertions of the utility maximization theory assuming
that both primal and dual value functions are finite in the interiors of their
domains as well as that random endowment at maturity can be dominated by the
terminal value of a self-financing wealth process. In order to facilitate
verification of these conditions, we present alternative, but equivalent
conditions, under which the conclusions of the theory hold.


  Applications based on Deep Neural Networks (DNNs) have grown exponentially in
the past decade. To match their increasing computational needs, several
Non-Volatile Memory (NVM) crossbar based accelerators have been proposed.
Recently, researchers have shown that apart from improved energy efficiency and
performance, such approximate hardware also possess intrinsic robustness for
defense against adversarial attacks. Prior works quantified this intrinsic
robustness for vanilla DNNs trained on unperturbed inputs. However, adversarial
training of DNNs is the benchmark technique for robustness, and sole reliance
on intrinsic robustness of the hardware may not be sufficient. In this work, we
explore the design of robust DNNs through the amalgamation of adversarial
training and intrinsic robustness of NVM crossbar-based analog hardware. First,
we study the noise stability of such networks on unperturbed inputs and observe
that internal activations of adversarially trained networks have lower
Signal-to-Noise Ratio (SNR), and are sensitive to noise compared to vanilla
networks. As a result, they suffer on average 2x performance degradation due to
the approximate computations on analog hardware. Noise stability analyses show
the instability of adversarially trained DNNs. On the other hand, for
adversarial images generated using Square Black Box attacks, ResNet-10/20
adversarially trained on CIFAR-10/100 display a robustness gain of 20-30%. For
adversarial images generated using Projected-Gradient-Descent (PGD) White-Box
attacks, adversarially trained DNNs present a 5-10% gain in robust accuracy due
to underlying NVM crossbar when $\epsilon_{attack}$ is greater than
$\epsilon_{train}$. Our results indicate that implementing adversarially
trained networks on analog hardware requires careful calibration between
hardware non-idealities and $\epsilon_{train}$ for optimum robustness and
performance.


  In this paper we present improved bounds for approximating maximum matchings
in bipartite graphs in the streaming model. First, we consider the question of
how well maximum matching can be approximated in a single pass over the input
using $\tilde O(n)$ space, where $n$ is the number of vertices in the input
graph. Two natural variants of this problem have been considered in the
literature: (1) the edge arrival setting, where edges arrive in the stream and
(2) the vertex arrival setting, where vertices on one side of the graph arrive
in the stream together with all their incident edges. The latter setting has
also been studied extensively in the context of online algorithms, where each
arriving vertex has to either be matched irrevocably or discarded upon arrival.
In the online setting, the celebrated algorithm of Karp-Vazirani-Vazirani
achieves a $1-1/e$ approximation. Despite the fact that the streaming model is
less restrictive in that the algorithm is not constrained to match vertices
irrevocably upon arrival, the best known approximation in the streaming model
with vertex arrivals and $\tilde O(n)$ space is the same factor of $1-1/e$.
  We show that no single pass streaming algorithm that uses $\tilde O(n)$ space
can achieve a better than $1-1/e$ approximation to maximum matching, even in
the vertex arrival setting. This leads to the striking conclusion that no
single pass streaming algorithm can do better than online algorithms unless it
uses significantly more than $\tilde O(n)$ space. Additionally, our bound
yields the best known impossibility result for approximating matchings in the
edge arrival model.
  We also give a simple algorithm that achieves approximation ratio
$1-e^{-k}k^{k-1}/(k-1)!=1-\frac1{\sqrt{2\pi k}}+o(1/k)$ in $k$ passes in the
vertex arrival model using linear space, improving upon previously best known
convergence.


  An edge-colored graph $F$ is {\it rainbow} if each edge of $F$ has a unique
color. The {\it rainbow Tur\'an number} $\mathrm{ex}^*(n,F)$ of a graph $F$ is
the maximum possible number of edges in a properly edge-colored $n$-vertex
graph with no rainbow copy of $F$. The study of rainbow Tur\'an numbers was
introduced by Keevash, Mubayi, Sudakov, and Verstra\"ete. Johnson and Rombach
introduced the following rainbow-version of generalized Tur\'an problems: for
fixed graphs $H$ and $F$, let $\mathrm{ex}^*(n,H,F)$ denote the maximum number
of rainbow copies of $H$ in an $n$-vertex properly edge-colored graph with no
rainbow copy of $F$.
  In this paper we investigate the case $\mathrm{ex}^*(n,C_\ell,P_\ell)$ and
give a general upper bound as well as exact results for $\ell = 3,4,5$. Along
the way we establish a new best upper bound on $\mathrm{ex}^*(n,P_5)$. Our main
motivation comes from an attempt to improve bounds on
$\mathrm{ex}^*(n,P_\ell)$, which has been the subject of several recent
manuscripts.


  Recent years have witnessed a thriving growth of computing facilities
connected at the network edge, cultivating edge computing networks as a
fundamental infrastructure for supporting miscellaneous intelligent services.
Meanwhile, Artificial Intelligence frontiers have extrapolated Machine Learning
to the graph domain and promoted Graph Intelligence (GI), which unlocks
unprecedented ability in learning from massive data in graph structures. Given
the inherent relation between graphs and networks, the interdiscipline of graph
representation learning and edge networks, i.e., Edge GI or EGI, has revealed a
novel interplay between them -- GI models principally open a new door for
modeling, understanding, and optimizing edge networks, and conversely, edge
networks serve as physical support for training, deploying, and accelerating GI
models. Driven by this delicate closed-loop, EGI can be widely recognized as a
promising solution to fully unleash the potential of edge computing power and
is garnering significant attention. Nevertheless, research on EGI yet remains
nascent, and there is a soaring demand within both the communications and AI
communities for a dedicated venue to share recent advancements. To this end,
this paper promotes the concept of EGI, explores its scope and core principles,
and conducts a comprehensive survey concerning recent research efforts on this
emerging field and specifically, introduces and discusses: 1) fundamentals of
edge computing and graph representation learning, 2) emerging techniques
centering on the closed loop between graph intelligence and edge networks, and
3) open challenges and research opportunities of future EGI. By bridging the
gap across communication, networking, and graph learning areas, we believe that
this survey can garner increased attention, foster meaningful discussions, and
inspire further research ideas in EGI.


  A protein structure is represented as a network of residues whereby edges are
determined by intra-molecular contacts. We introduce inhomogeneity into these
networks by assigning each edge a weight that is determined by amino-acid pair
potentials. Two methodologies are utilized to calculate the average path
lengths (APLs) between pairs: To minimize (i) the maximum weight in the strong
APL, and (ii) the total weight in the weak APL. We systematically screen edges
that have higher than a cutoff potential and calculate the shortest APLs in
these reduced networks, while keeping chain connectivity. Therefore,
perturbations introduced at a selected region of the residue network propagate
to remote regions only along the non-screened edges that retain their ability
to disseminate the perturbation. The shortest APLs computed from the reduced
homogeneous networks with only the strongest few non-bonded pairs closely
reproduce the strong APLs from the weighted networks. The rate of change in the
APL in the reduced residue network as compared to its randomly connected
counterpart remains constant until a lower bound. Upon further link removal,
this property shows an abrupt increase, towards a random coil behavior. Under
different perturbation scenarios, diverse optimal paths emerge for robust
residue communication.


  The $f_0(1500)$ is identified in three glueball favoured production
mechanisms: $p\bar{p}$ annihilation, $\psi \to \gamma f_0$ and Central
Production. The production rate for glueballs in $\psi \to \gamma G$ has been
quantified and the $f_0(1500)$ is found to be consistent with a glueball -
$q\bar{q}$ mixture. We illustrate a remarkable property of central production
where kinematic cuts appear to make a systematic separation between glueballs
and $q\bar{q}$ of the same $J^{PC}$. When the cut favouring glueballs is
applied, the $f_0(1500)$ and other enigmatic states appear prominently while
confirmed $q\bar{q}$ states are empirically suppressed.


  Protoplanetary disks are known to posses a stunning variety of substructure
in the distribution of their mm~sized grains, predominantly seen as rings and
gaps (Andrews et al. 2018), which are frequently interpreted as due to the
shepherding of large grains by either hidden, still-forming planets within the
disk (Zhang et al. 2018) or (magneto-)hydrodynamic instabilities (Flock et al.
2015). The velocity structure of the gas offers a unique probe of both the
underlying mechanisms driving the evolution of the disk, the presence of
embedded planets and characterising the transportation of material within the
disk, such as following planet-building material from volatile-rich regions to
the chemically-inert midplane, or detailing the required removal of angular
momentum. Here we present the radial profiles of the three velocity components
of gas in upper disk layers in the disk of HD 163296 as traced by 12CO
molecular emission. These velocities reveal significant flows from the disk
surface towards the midplane of disk at the radial locations of gaps argued to
be opened by embedded planets (Isella et al. 2016, 2018, Teague et al. 2018,
Pinte et al. 2018), bearing striking resemblance to meridional flows, long
predicted to occur during the early stages of planet formation (Szulagyi et al.
2014, Morbidelli et al. 2014, Fung & Chiang 2016, Dong et al. 2019). In
addition, a persistent radial outflow is seen at the outer edge of the disk,
potentially the base of a wind associated with previously detected extended
emission (Klaassen et al. 2013).


  We construct electronic oscillator from acellular slime mould Physarum
polycephalum. The slime mould oscillator is made of two electrodes connected by
a protoplasmic tube of the living slime mould. A protoplasmic tube has an
average resistance of 3~MOhm. The tube's resistance is changing over time due
to peristaltic contractile activity of the tube. The resistance of the
protoplasmic tube oscillates with average period of 73~sec and average
amplitude of 0.6~MOhm. We present experimental laboratory results on dynamics
of Physarum oscillator under direct current voltage up to 15~V and speculate
that slime mould P. polycephalum can be employed as a living electrical
oscillator in biological and hybrid circuits.


  Searches for topologies predicted by gauge-mediated SUSY breaking models were
performed using data collected with the L3 detector at LEP. All possible
lifetimes of the next-to-lightest SUSY particle (NLSP), neutralino or scalar
tau, were considered. No evidence for these new phenomena was found and limits
on the production cross sections and sparticle masses were derived. A scan over
the parameters of the minimal GMSB model was performed, leading to lower limits
of 62.2 GeV, 11 TeV, and 0.07 eV on the NLSP mass, the mass scale parameter
Lambda, and the gravitino mass, respectively. The status of the LEP combined
searches is also discussed.


  We consider global 2-SLE$_{\kappa}$ $(\eta_1, \eta_2)$ in a topological
rectangle with $\kappa\in (4,8)$. We derive the law of a random hitting point
of the curves and show that, conditional on this random hitting point, the pair
of two curves has the same law as Gaussian free field flow lines with proper
boundary data. Using a similar idea, we derive the asymptotic of the
probability for $\eta_1\cap\eta_2=\emptyset$. As an application, we derive the
asymptotic of the probability for the existence of two disjoint open paths in
critical FK-Ising model.


  In 2004, Enriquez-Etingof-Marshall suggested a new approach to the
Ginzburg-Weinstein linearization theorem for a quasitriangular Lie bialgebra
$(\g,r)$. This approach is based on solving a system of PDEs for a gauge
transformation between the classical r-matrix $r$ and the Alekseev-Meinrenken
dynamical r-matrix. They proved that the semiclassical limit of an admissible
Drinfeld twist gives rise to a solution of the PDEs.
  In this paper, we explain that preferred gauge transformations can be
constructed as connection maps for a certain irregular Riemann-Hilbert problem
(provided $r$ is the standard classical r-matrix). Along the way, we give a
symplectic geometric interpretation of their PDEs, as a symplectic neighborhood
version of the Ginzburg-Weinstein linearization theorem. Our construction is
based on earlier works by Boalch. We then prove that for semisimple Lie algebra
$\g$, any solution of the PDEs for the gauge transformation is the
semiclassical limit of an admissible Drinfeld twist. As byproducts, we find a
surprising relation between the connection maps and Drinfeld twists as well as
a new description of the Lu-Weinstein symplectic double.


  We study a novel cubic nonlinear effect, the tails-of-memory, which consist
of a combination of the tail effect (backscattering of linear gravitational
waves against the curvature of spacetime generated by the source) and the
memory effect (due to reradiation of gravitational waves by linear
gravitational waves themselves). Our final result is consistent with a
straightforward direct computation of the memory effect, but also involves many
non-trivial tail-like terms.


  The shell of the classical nova V5668 Sgr was resolved by ALMA at the
frequency of 230 GHz 927 days after eruption, showing that most of the
continuum bremsstrahlung emission originates in clumps with diameter smaller
than $10^{15}$ cm. Using VLA radio observations, obtained between days 2 and
1744 after eruption, at frequencies between 1 and 35 GHz, we modeled the nova
spectra, assuming first that the shell is formed by a fixed number of identical
clumps, and afterwards with the clumps having a power law distribution of
sizes, and were able to obtain the clump's physical parameters (radius, density
and temperature). We found that the density of the clumps decreases linearly
with the increase of the shell's volume, which is compatible with the existence
of a second media, hotter and thinner, in pressure equilibrium with the clumps.
We show that this thinner media could be responsible for the emission of the
hard X-rays observed at the early times of the nova eruption, and that the
clump's temperature evolution follows that of the super-soft X-ray luminosity.
We propose that the clumps were formed in the radiative shock produced by the
collision of the fast wind of the white dwarf after eruption, with the slower
velocity of the thermonuclear ejecta. From the total mass of the clumps, the
observed expansion velocity and thermonuclear explosion models, we obtained an
approximate value of 1.25 M$_{\odot}$ for the mass of the white dwarf, a
central temperature of $10^7$ K and an accretion rate from the secondary star
of $10^{-9}-10^{-8}$ M$_{\odot}$ y$^{-1}$.


  We aim to characterize the physical and chemical properties of fragmented
cores during the earliest evolutionary stages in the very young star-forming
regions ISOSS J22478+6357 and ISOSS J23053+5953. NOEMA 1.3 mm data are used in
combination with archival mid- and far-infrared observations to construct and
fit the SEDs of individual fragmented cores. The radial density profiles are
inferred from the 1.3 mm continuum visibility profiles and the radial
temperature profiles are estimated from H2CO rotation temperature maps.
Molecular column densities are derived with the line fitting tool XCLASS. The
physical and chemical properties are combined by applying the physical-chemical
model MUSCLE in order to constrain the chemical timescales of a few line-rich
cores. The morphology and spatial correlations of the molecular emission are
analyzed using the HOG method. The mid-infrared data show that both regions
contain a cluster of young stellar objects. Bipolar molecular outflows are
observed in the CO 2-1 transition toward the strong mm cores indicating
protostellar activity. We find strong molecular emission of SO, SiO, H2CO, and
CH3OH in locations which are not associated with the mm cores. These shocked
knots can be either associated with the bipolar outflows or, in the case of
ISOSS J23053+5953, with a colliding flow that creates a large shocked region
between the mm cores. The mean chemical timescale of the cores is lower (20 000
yr) compared to that of the sources of the more evolved CORE sample (60 000
yr). With the HOG method, we find that the spatial emission of species tracing
the extended emission and of shock-tracing molecules are well correlated within
transitions of these groups.


  Text-to-3D generation has achieved remarkable success via large-scale
text-to-image diffusion models. Nevertheless, there is no paradigm for scaling
up the methodology to urban scale. Urban scenes, characterized by numerous
elements, intricate arrangement relationships, and vast scale, present a
formidable barrier to the interpretability of ambiguous textual descriptions
for effective model optimization. In this work, we surmount the limitations by
introducing a compositional 3D layout representation into text-to-3D paradigm,
serving as an additional prior. It comprises a set of semantic primitives with
simple geometric structures and explicit arrangement relationships,
complementing textual descriptions and enabling steerable generation. Upon
this, we propose two modifications -- (1) We introduce Layout-Guided
Variational Score Distillation to address model optimization inadequacies. It
conditions the score distillation sampling process with geometric and semantic
constraints of 3D layouts. (2) To handle the unbounded nature of urban scenes,
we represent 3D scene with a Scalable Hash Grid structure, incrementally
adapting to the growing scale of urban scenes. Extensive experiments
substantiate the capability of our framework to scale text-to-3D generation to
large-scale urban scenes that cover over 1000m driving distance for the first
time. We also present various scene editing demonstrations, showing the powers
of steerable urban scene generation. Website: https://urbanarchitect.github.io.


  A model of the identification of information tonality, based on Bayesian
approach and neural networks was described. In the context of this paper
tonality means positive or negative tone of both the whole information and its
parts which are related to particular concepts. The method, its application is
presented in the paper, is based on statistic regularities connected with the
presence of definite lexemes in the texts. A distinctive feature of the method
is its simplicity and versatility. At present ideologically similar approaches
are widely used to control spam.