  We extend the scalar-tensor reconstruction techniques for classical cosmology
frameworks, in the context of loop quantum cosmology. After presenting in some
detail how the equations are generalized in the loop quantum cosmology case, we
discuss which new features and limitations does the quantum framework brings
along, and we use various illustrative examples in order to demonstrate how the
method works. As we show the energy density has two different classes of
solutions, and one of these yields the correct classical limit while the second
captures the quantum phenomena. We study in detail the scalar tensor
reconstruction method for both these solutions. Also we discuss some scenarios
for which the Hubble rate becomes unbounded at finite time, which corresponds
for example in a case that a Big Rip occurs. As we show this issue is
non-trivial and we discuss how this case should be treated in a consistent way.
Finally, we investigate how the classical stability conditions for the
scalar-tensor solutions are generalized in the loop quantum framework.


  M 87 is the first extragalactic source detected in the Very High Energy (VHE;
E > 100 GeV) gamma-ray domain that is not a blazar, its large scale jet not
being aligned to the line of sight. Slight modification of standard emission
models of TeV blazars allows to account for the gamma-ray spectra obtained with
H.E.S.S. We present a multi-blob synchrotron self-Compton model taking
explicitly into account large viewing angles and moderate values of the Lorentz
factor as inferred from MHD simulations of jet formation. Predictions of the
VHE emission for the nearby radiogalaxy Cen A and an interpretation of the
broadband radiation of M 87 are presented.


  Many physical systems are described by nonlinear differential equations that
are too complicated to solve in full. A natural way to proceed is to divide the
variables into those that are of direct interest and those that are not,
formulate solvable approximate equations for the variables of greater interest,
and use data and statistical methods to account for the impact of the other
variables. In the present paper the problem is considered in a fully
discrete-time setting, which simplifies both the analysis of the data and the
numerical algorithms. The resulting time series are identified by a NARMAX
(nonlinear autoregression moving average with exogenous input) representation
familiar from engineering practice. The connections with the Mori-Zwanzig
formalism of statistical physics are discussed, as well as an application to
the Lorenz 96 system.


  Despite large-scale diffusion models being highly capable of generating
diverse open-world content, they still struggle to match the photorealism and
fidelity of concept-specific generators. In this work, we present the task of
customizing large-scale diffusion priors for specific concepts as
concept-centric personalization. Our goal is to generate high-quality
concept-centric images while maintaining the versatile controllability inherent
to open-world models, enabling applications in diverse tasks such as
concept-centric stylization and image translation. To tackle these challenges,
we identify catastrophic forgetting of guidance prediction from diffusion
priors as the fundamental issue. Consequently, we develop a guidance-decoupled
personalization framework specifically designed to address this task. We
propose Generalized Classifier-free Guidance (GCFG) as the foundational theory
for our framework. This approach extends Classifier-free Guidance (CFG) to
accommodate an arbitrary number of guidances, sourced from a variety of
conditions and models. Employing GCFG enables us to separate conditional
guidance into two distinct components: concept guidance for fidelity and
control guidance for controllability. This division makes it feasible to train
a specialized model for concept guidance, while ensuring both control and
unconditional guidance remain intact. We then present a null-text
Concept-centric Diffusion Model as a concept-specific generator to learn
concept guidance without the need for text annotations. Code will be available
at https://github.com/PRIV-Creation/Concept-centric-Personalization.


  Coupled nonlinear systems under certain conditions exhibit phase
synchronization, which may change for different frequency bands or with
presence of additive system noise. In both cases, Fourier filtering is
traditionally used to preprocess data. We investigate to what extent the phase
synchronization of two coupled R\"{o}ssler oscillators depends on (1) the
broadness of their power spectrum, (2) the width of the band-pass filter, and
(3) the level of added noise. We find that for identical coupling strengths,
oscillators with broader power spectra exhibit weaker synchronization. Further,
we find that within a broad band width range, band-pass filtering reduces the
effect of noise but can lead to a spurious increase in the degree of
synchronization with narrowing band width, even when the coupling between the
two oscillators remains the same.


  Given the open nature of the Internet, there is a need for authentication
schemes to address inherent trust issues. We present Tortoise, an experimental
nonce-based authenticated encryption scheme modeled on the Synthetic
Counter-in-Tweak. This paper demonstrates a generalizable plug-and-play
framework for converting block cipher into Authenticated Encryption with
Associated Data. As part of this work, we utilized an XOR procedure for
constructing a generic tweakable cipher. Finally, we support two modes:
nonce-respecting and nonce-misuse-resistant. Source code available at
https://github.com/kenluck2001/cipherResearch/tree/main/src/tortoise.


  Collisional relaxation describes the stochastic process with which a
self-gravitating system near equilibrium evolves in phase space due to the
fluctuating gravitational field of the system. The characteristic timescale of
this process is called the relaxation time. In this paper, we highlight the
difference between two measures of the relaxation time in globular clusters:
(i) the diffusion time with which the isolating integrals of motion (i.e.
energy E and angular momentum magnitude L) of individual stars change
stochastically and (ii) the asymptotic timescale required for a family of
orbits to mix in the cluster. More specifically, the former corresponds to the
instantaneous rate of change of a star's E or L, while the latter corresponds
to the timescale for the stars to statistically forget their initial
conditions. We show that the diffusion timescales of E and L vary
systematically around the commonly used half-mass relaxation time in different
regions of the cluster by a factor of ~10 and ~100, respectively, for more than
20% of the stars. We define the mixedness of an orbital family at any given
time as the correlation coefficient between its E or L probability distribution
functions and those of the whole cluster. Using Monte Carlo simulations, we
find that mixedness converges asymptotically exponentially with a decay
timescale that is ~10 times the half-mass relaxation time.


  Using the FiNLIE solution of the AdS/CFT Y-system, we compute the anomalous
dimension of the Konishi operator in planar N=4 SYM up to eight loops, i.e. up
to the leading double wrapping order. At this order a non reducible
Euler-Zagier sum, zeta(1,2,8), appears for the first time. We find that at all
orders in perturbation, every spectral-dependent quantity of the Y-system is
expressed through multiple Hurwitz zeta functions, hence we provide a
Mathematica package to manipulate these functions, including the particular
case of Euler-Zagier sums. Furthermore, we conjecture that only Euler-Zagier
sums can appear in the answer for the anomalous dimension at any order in
perturbation theory.
  We also resum the leading transcendentality terms of the anomalous dimension
at all orders, obtaining a simple result in terms of Bessel functions. Finally,
we demonstrate that exact Bethe equations should be related to an absence of
poles condition that becomes especially nontrivial at double wrapping.


  We perform theoretical calculations to obtain a distribution of local d-wave
superconducting gaps $\Delta_0({r})$ for a high temperature superconducting
(HTSC) series in a disordered superconductor with an average doping level
$<\rho>$. To reproduce the inhomogeneous medium a nonmagnetic random potential
$V^{imp}$, within a Bogoliubov-de Gennes (BdG) formalism, is considered. First
the phase diagram $\Delta_0 x <\rho>$ for the LSCO HTSC series, with V^{imp}=0,
is obtained. Then, we perform calculations considering a fixed value of the
disorder strength $V^{imp}$ and obtain a distribution of local superconducting
gaps $\Delta_0({r})$, and local density of charge carriers $\rho({r})$. It is
shown that the underdoped compounds are more inhomogeneous than the overdoped
ones, which is in accordance with experimental findings. Also, the spatial
variation of $\Delta_0({r})$ indicates that as <\rho> increases, the system
becomes more homogeneous.


  Retrieving evidences from tabular and textual resources is essential for
open-domain question answering (OpenQA), which provides more comprehensive
information. However, training an effective dense table-text retriever is
difficult due to the challenges of table-text discrepancy and data sparsity
problem. To address the above challenges, we introduce an optimized OpenQA
Table-Text Retriever (OTTeR) to jointly retrieve tabular and textual evidences.
Firstly, we propose to enhance mixed-modality representation learning via two
mechanisms: modality-enhanced representation and mixed-modality negative
sampling strategy. Secondly, to alleviate data sparsity problem and enhance the
general retrieval ability, we conduct retrieval-centric mixed-modality
synthetic pre-training. Experimental results demonstrate that OTTeR
substantially improves the performance of table-and-text retrieval on the
OTT-QA dataset. Comprehensive analyses examine the effectiveness of all the
proposed mechanisms. Besides, equipped with OTTeR, our OpenQA system achieves
the state-of-the-art result on the downstream QA task, with 10.1% absolute
improvement in terms of the exact match over the previous best system.
  All the code and data are available at
https://github.com/Jun-jie-Huang/OTTeR.


  We prove new upper and lower bounds on the number of iterations the
$k$-dimensional Weisfeiler-Leman algorithm ($k$-WL) requires until
stabilization. For $k \geq 3$, we show that $k$-WL stabilizes after at most
$O(kn^{k-1}\log n)$ iterations (where $n$ denotes the number of vertices of the
input structures), obtaining the first improvement over the trivial upper bound
of $n^{k}-1$ and extending a previous upper bound of $O(n \log n)$ for $k=2$
[Lichter et al., LICS 2019].
  We complement our upper bounds by constructing $k$-ary relational structures
on which $k$-WL requires at least $n^{\Omega(k)}$ iterations to stabilize. This
improves over a previous lower bound of $n^{\Omega(k / \log k)}$ [Berkholz,
Nordstr\"{o}m, LICS 2016].
  We also investigate tradeoffs between the dimension and the iteration number
of WL, and show that $d$-WL, where $d = \lceil\frac{3(k+1)}{2}\rceil$, can
simulate the $k$-WL algorithm using only $O(k^2 \cdot n^{\lfloor k/2\rfloor +
1} \log n)$ many iterations, but still requires at least $n^{\Omega(k)}$
iterations for any $d$ (that is sufficiently smaller than $n$).
  The number of iterations required by $k$-WL to distinguish two structures
corresponds to the quantifier rank of a sentence distinguishing them in the $(k
+ 1)$-variable fragment $C_{k+1}$ of first-order logic with counting
quantifiers. Hence, our results also imply new upper and lower bounds on the
quantifier rank required in the logic $C_{k+1}$, as well as tradeoffs between
variable number and quantifier rank.


  It is proved that for projective varieties having Du Bois singularities is
equivalent to the condition that the coherent cohomology groups of the
structure sheaf coincide with the appropriate Hodge components of the singular
cohomology groups.


  The Wolf-Rayet nebula M1-67 around WR124 is located above the Galactic plane
in a region mostly empty of interstellar medium, which makes it the perfect
target to study the mass-loss episodes associated with the late stages of
massive star evolution. Archive photometric observations from WISE, Spitzer
(MIPS) and Herschel (PACS and SPIRE) are used to construct the spectral energy
distribution (SED) of the nebula in the wavelength range of 12-500$\mu$m. The
infrared (photometric and spectroscopic) data and nebular optical data from the
literature are modeled simultaneously using the spectral synthesis code Cloudy,
where the free parameters are the gas density distribution and the dust grain
size distribution. The infrared SED can be reproduced by dust grains with two
size distributions: a MRN power-law distribution with grain sizes between 0.005
and 0.05$\mu$m and a population of large grains with representative size 0.9$
\mu$m. The latter points towards an eruptive origin for the formation of M1-67.
The model predicts a nebular ionized gas mass of $M_\mathrm{ion} =
9.2^{+1.6}_{-1.5}~\mathrm{M}_\odot$ and the estimated mass-loss rate during the
dust-formation period is $\dot{M} \approx 6 \times 10^{-4}
\mathrm{M}_\odot$yr$^{-1}$. We discuss the implications of our results in the
context of single and binary stellar evolution and propose that M1-67
represents the best candidate for a post-common envelope scenario in massive
stars.


  We propose an optimization strategy to control the dynamics of a stochastic
system transferred from one thermal equilibrium to another and apply it
experimentally to a Brownian particle in an optical trap under compression.
Based on a variational principle that treats the transfer duration and the
expended work on an equal footing, our strategy leads to a family of protocols
that are either optimally cheap for a given duration or optimally fast for a
given energetic cost. This approach unveils a universal relation $\Delta
t\,\Delta W \ge (\Delta t\,\Delta W)_{\rm opt}$ between the transfer duration
and the expended work. We verify experimentally that the lower bound is reached
only with the optimized protocols.


  In this paper, we consider a multi-stage dynamic assortment optimization
problem with multi-nomial choice modeling (MNL) under resource knapsack
constraints. Given the current resource inventory levels, the retailer makes an
assortment decision at each period, and the goal of the retailer is to maximize
the total profit from purchases. With the exact optimal dynamic assortment
solution being computationally intractable, a practical strategy is to adopt
the re-solving technique that periodically re-optimizes deterministic linear
programs (LP) arising from fluid approximation. However, the fractional
structure of MNL makes the fluid approximation in assortment optimization
highly non-linear, which brings new technical challenges. To address this
challenge, we propose a new epoch-based re-solving algorithm that effectively
transforms the denominator of the objective into the constraint. Theoretically,
we prove that the regret (i.e., the gap between the resolving policy and the
optimal objective of the fluid approximation) scales logarithmically with the
length of time horizon and resource capacities.


  We report on analysis of observations of the bright transient X-ray pulsar
\src obtained during its 2017-2018 giant outburst with Insight-HXMT,
\emph{NuSTAR}, and \textit{Swift} observatories. We focus on the discovery of a
sharp state transition of the timing and spectral properties of the source at
super-Eddington accretion rates, which we associate with the transition of the
accretion disk to a radiation pressure dominated (RPD) state, the first ever
directly observed for magnetized neutron star. This transition occurs at
slightly higher luminosity compared to already reported transition of the
source from sub- to super-critical accretion regime associate with onset of an
accretion column. We argue that this scenario can only be realized for
comparatively weakly magnetized neutron star, not dissimilar to other
ultra-luminous X-ray pulsars (ULPs), which accrete at similar rates. Further
evidence for this conclusion is provided by the non-detection of the transition
to the propeller state in quiescence which strongly implies compact
magnetosphere and thus rules out magnetar-like fields.


  The IR optical absorption (0.1-1.5 eV) in the $La_{0.7}Ca_{0.3}MnO_3$ films
on LAO substrate exhibits the drastic temperature evolution of the spectral
weight evidencing the insulator to metal transition. Single crystal films were
found to reveal strong linear dichroism with anomalous spectral oscillations
and fairly weak temperature dependence. Starting from the concept of phase
separation, we develop the effective medium model to account for these effects.
The optical anisotropy of the films is attributed to the texturization of the
ellipsoidal inclusions of the quasimetal phase caused by a mismatch of the film
and substrate and the twin texture of the latter.


  We show that any pair of classical solutions of open string field theory can
be related by a formal gauge transformation defined by a gauge parameter $U$
without an inverse. We investigate how this observation can be used to
construct new solutions. We find that a choice of gauge parameter consistently
generates a new solution only if the BRST charge maps the image of $U$ into
itself. When this occurs, we argue that $U$ naturally defines a star algebra
projector which describes a surface of string connecting the boundary conformal
field theories of the classical solutions related by $U$. We also note that
singular gauge transformations give the solution space of open string field
theory the structure of a category, and we comment on the physical
interpretation of this observation.


  We suggest a systematic method of extension of quasi-exactly solvable (QES)
systems. We construct finite-dimensional subspaces on the basis of special
functions (hypergeometric, Airy, Bessel ones) invariant with respect to the
action of differential operators of the second order with polynomial
coefficients. As a example of physical applications, we show that the known
two-photon Rabi Hamiltonian becomes quasi-exactly solvable at certain values of
parameters when it can be expressed in terms of corresponding QES operators
related to the hypergeometric function.


  Extracting implied information, like volatility and/or dividend, from
observed option prices is a challenging task when dealing with American
options, because of the computational costs needed to solve the corresponding
mathematical problem many thousands of times. We will employ a data-driven
machine learning approach to estimate the Black-Scholes implied volatility and
the dividend yield for American options in a fast and robust way. To determine
the implied volatility, the inverse function is approximated by an artificial
neural network on the computational domain of interest, which decouples the
offline (training) and online (prediction) phases and thus eliminates the need
for an iterative process. For the implied dividend yield, we formulate the
inverse problem as a calibration problem and determine simultaneously the
implied volatility and dividend yield. For this, a generic and robust
calibration framework, the Calibration Neural Network (CaNN), is introduced to
estimate multiple parameters. It is shown that machine learning can be used as
an efficient numerical technique to extract implied information from American
options.


  We report a variational approach to the nonlinearly screened interaction of
charged particles with a many-electron system. This approach has been developed
by introducing a modification of the Schwinger variational principle of
scattering theory, which allows to obtain nonperturbative scattering
cross-sections of moving projectiles from the knowledge of the linear and
quadratic density-response functions of the target. Our theory is illustrated
with a calculation of the energy loss per unit path length of slow antiprotons
moving in a uniform electron gas, which shows good agreement with a fully
nonlinear self-consistent Hartree calculation. Since available self-consistent
calculations are restricted to low heavy-projectile velocities, we expect our
theory to have novel applications to a variety of processes where nonlinear
screening plays an important role.


  We prove several results of concentration for eigenfunctions in Toeplitz
quantization. With mild assumptions on the regularity, we prove that
eigenfunctions are $O(exp(-cN^{\delta}))$ away from the corresponding level set
of the symbol, where N is the inverse semiclassical parameter and $0 < \delta <
1$ depends on the regularity. As an application, we prove a precise bound for
the free energy of spin systems at high temperatures, sharpening a result of
Lieb.


  Let $U\in U(N)$ be a random unitary matrix of size $N$, distributed with
respect to the Haar measure on $U(N)$. Let $P(z)=P_U(z)$ be the characteristic
polynomial of $U$. We prove that for $z$ close to the unit circle, $
\frac{P'}{P}(z) $ can be approximated using zeros of $P$ very close to $z$,
with a typically controllable error term. This is an analogue of a result of
Selberg for the Riemann zeta-function. We also prove a mesoscopic central limit
theorem for $ \frac{P'}{P}(z) $ away from the unit circle, and this is an
analogue of a result of Lester for zeta.


  Automated augmentation is an emerging and effective technique to search for
data augmentation policies to improve generalizability of deep neural network
training. Most existing work focuses on constructing a unified policy
applicable to all data samples in a given dataset, without considering sample
or class variations. In this paper, we propose a novel two-stage data
augmentation algorithm, named Label-Aware AutoAugment (LA3), which takes
advantage of the label information, and learns augmentation policies separately
for samples of different labels. LA3 consists of two learning stages, where in
the first stage, individual augmentation methods are evaluated and ranked for
each label via Bayesian Optimization aided by a neural predictor, which allows
us to identify effective augmentation techniques for each label under a low
search cost. And in the second stage, a composite augmentation policy is
constructed out of a selection of effective as well as complementary
augmentations, which produces significant performance boost and can be easily
deployed in typical model training. Extensive experiments demonstrate that LA3
achieves excellent performance matching or surpassing existing methods on
CIFAR-10 and CIFAR-100, and achieves a new state-of-the-art ImageNet accuracy
of 79.97% on ResNet-50 among auto-augmentation methods, while maintaining a low
computational cost.


  This paper investigates differentially private analysis of distance-based
outliers. The problem of outlier detection is to find a small number of
instances that are apparently distant from the remaining instances. On the
other hand, the objective of differential privacy is to conceal presence (or
absence) of any particular instance. Outlier detection and privacy protection
are thus intrinsically conflicting tasks. In this paper, instead of reporting
outliers detected, we present two types of differentially private queries that
help to understand behavior of outliers. One is the query to count outliers,
which reports the number of outliers that appear in a given subspace. Our
formal analysis on the exact global sensitivity of outlier counts reveals that
regular global sensitivity based method can make the outputs too noisy,
particularly when the dimensionality of the given subspace is high. Noting that
the counts of outliers are typically expected to be relatively small compared
to the number of data, we introduce a mechanism based on the smooth upper bound
of the local sensitivity. The other is the query to discovery top-$h$ subspaces
containing a large number of outliers. This task can be naively achieved by
issuing count queries to each subspace in turn. However, the variation of
subspaces can grow exponentially in the data dimensionality. This can cause
serious consumption of the privacy budget. For this task, we propose an
exponential mechanism with a customized score function for subspace discovery.
To the best of our knowledge, this study is the first trial to ensure
differential privacy for distance-based outlier analysis. We demonstrated our
methods with synthesized datasets and real datasets. The experimental results
show that out method achieve better utility compared to the global sensitivity
based methods.


  We propose a generalization of the classical point-line Cremona-Richmond
configuration to a configuration of points and more dimensional subspaces of a
projective space, and present them as geometric realizations of some
interesting abstract incidence structures.


  Based on the gauge invariant variables proposed in [K. Nakamura, Prog. Theor.
Phys. vol.110 (2003), 723.], general framework of the second order gauge
invariant perturbation theory on arbitrary background spacetime is considered.
We derived formulae of the perturbative Einstein tensor of each order, which
have the similar form to the definitions of gauge invariant variables for
arbitrary perturbative fields. As a result, each order Einstein equation is
necessarily given in terms of gauge invariant variables.


  The notion of level posets is introduced. This class of infinite posets has
the property that between every two adjacent ranks the same bipartite graph
occurs. When the adjacency matrix is indecomposable, we determine the length of
the longest interval one needs to check to verify Eulerianness. Furthermore, we
show that every level Eulerian poset associated to an indecomposable matrix has
even order. A condition for verifying shellability is introduced and is
automated using the algebra of walks. Applying the Skolem--Mahler--Lech
theorem, the ${\bf ab}$-series of a level poset is shown to be a rational
generating function in the non-commutative variables ${\bf a}$ and ${\bf b}$.
In the case the poset is also Eulerian, the analogous result holds for the
${\bf cd}$-series. Using coalgebraic techniques a method is developed to
recognize the ${\bf cd}$-series matrix of a level Eulerian poset.


  Indirect inference estimators (i.e., simulation-based minimum distance
estimators) in a parametric model that are based on auxiliary non-parametric
maximum likelihood density estimators are shown to be asymptotically normal. If
the parametric model is correctly specified, it is furthermore shown that the
asymptotic variance-covariance matrix equals the inverse of the
Fisher-information matrix. These results are based on uniform-in-parameters
convergence rates and a uniform-in-parameters Donsker-type theorem for
non-parametric maximum likelihood density estimators.


  We present high resolution ARPES data of the bilayer superconductor
Bi2Sr2CaCu2O8 (Bi2212) showing a clear doubling of the near EF bands. This
splitting approaches zero along the (0,0)-(pi,pi) nodal line and is not
observed in single layer Bi2Sr2CuO6 (Bi2201), suggesting that the splitting is
due to the long sought after bilayer splitting effect. The splitting has a
magnitude of approximately 75 meV near the middle of the zone, extrapolating to
about 100 meV near the (pi,0) point


  We obtain wavenumber-robust error bounds for the deep neural network (DNN)
emulation of the solution to the time-harmonic, sound-soft acoustic scattering
problem in the exterior of a smooth, convex obstacle in two physical
dimensions. The error bounds are based on a boundary reduction of the
scattering problem in the unbounded exterior region to its smooth, curved
boundary $\Gamma$ using the so-called combined field integral equation (CFIE),
a well-posed, second-kind boundary integral equation (BIE) for the field's
Neumann datum on $\Gamma$. In this setting, the continuity and stability
constants of this formulation are explicit in terms of the (non-dimensional)
wavenumber $\kappa$. Using wavenumber-explicit asymptotics of the problem's
Neumann datum, we analyze the DNN approximation rate for this problem. We use
fully connected NNs of the feed-forward type with Rectified Linear Unit (ReLU)
activation. Through a constructive argument we prove the existence of DNNs with
an $\epsilon$-error bound in the $L^\infty(\Gamma)$-norm having a small, fixed
width and a depth that increases $\textit{spectrally}$ with the target accuracy
$\epsilon>0$. We show that for fixed $\epsilon>0$, the depth of these NNs
should increase $\textit{poly-logarithmically}$ with respect to the wavenumber
$\kappa$ whereas the width of the NN remains fixed. Unlike current
computational approaches, such as wavenumber-adapted versions of the Galerkin
Boundary Element Method (BEM) with shape- and wavenumber-tailored solution
$\textit{ansatz}$ spaces, our DNN approximations do not require any prior
analytic information about the scatterer's shape.


  If the pomeron is generated by a two gluon exchange there is no a priori
reason for a drastic suppression of three gluon exchange with negative parity
and charge parity. This would lead to an unacceptably large difference between
$p\,p$ and $p\, \bar{p}$ scattering. It is shown that a natural suppression of
the $C$=$P$=--1 contribution to high energy scattering is given by a cluster
structure of the nucleon.


  We consider a model of gravity and matter fields which is invariant only
under unimodular general coordinate transformations (GCT). The determinant of
the metric is treated as a separate field which transforms as a scalar under
unimodular GCT. Furthermore we also demand that the theory is invariant under a
new global symmetry which we call generalized conformal invariance. We study
the cosmological implications of the resulting theory. We show that this theory
gives a fit to the high-z supernova data which is identical to the standard Big
Bang model. Hence we require some other cosmological observations to test the
validity of this model. We also consider some models which do not obey the
generalized conformal invariance. In these models we can fit the supernova data
without introducing the standard cosmological constant term. Furthermore these
models introduce only one dark component and hence solve the coincidence
problem of dark matter and dark energy.


  We study the deformation theory of the Fano scheme
$\mathrm{F}=\mathrm{F}(\mathrm{X})$ of lines on a cubic $\mathrm{X}$ of
dimension $d$ with only finitely many singularities. By taking the relative
Fano scheme, we define a morphism
$\eta:\mathscr{D}_{\mathrm{X}}\rightarrow\mathscr{D}_{\mathrm{F}}$ of the local
moduli functors associated to $\mathrm{X}$ and $\mathrm{F}$, respectively. We
show that for $d\geqslant 5$, $\eta$ yields an isomorphism on first-order
deformations; in particular, $\eta$ is an isomorphism whenever
$\mathrm{H}^{0}(\Theta_{\mathrm{X}})=0$.


  We introduce branching annihilating attracting walk (BAAW) in one dimension.
The attracting walk is implemented by a biased hopping in such a way that a
particle prefers hopping to a nearest neighbor located on the side where the
nearest particle is found within the range of attraction. We study the BAAW
with four offspring by extensive Monte Carlo simulation. At first, we find the
critical exponents of the BAAW with infinite range of attraction, which are
different from those of the directed Ising (DI) universality class. Our results
are consistent with the recent observation [B. Daga and P. Ray, Phys. Rev. E
{\bf 99}, 032104 (2019)]. Then, by studying crossover behaviors, we show that
as far as the range of attraction is finite the BAAW belongs to the DI class.
We conclude that the origin of non-DI critical behavior of the BAAW with
infinite range of attraction is the long-range nature of the attraction.


  In very high energy collisions, many particles are produced and distributed
in the available phase space volume in various ways. With advent of new
accelerator facilities (especially, for nucleus-nucleus collisions), the
problem of pattern recognition in high multiplicity events becomes very actual
in order to classify such events and to separate those of them with some
special features (e.g. ring-like events, jets and other regular patterns). This
paper presents the first attempt to use wavelet technique for pattern
recognition in nucleus-nucleus collisions. After describing the method in
general, we demonstrate its power by applying it to a single event of Pb-Pb
collision at 158A GeV with 1072 charged particles produced and discuss results
obtained for some other events. Further extension of the method is proposed.


  In the last two years, convolutional neural networks (CNNs) have achieved an
impressive suite of results on standard recognition datasets and tasks.
CNN-based features seem poised to quickly replace engineered representations,
such as SIFT and HOG. However, compared to SIFT and HOG, we understand much
less about the nature of the features learned by large CNNs. In this paper, we
experimentally probe several aspects of CNN feature learning in an attempt to
help practitioners gain useful, evidence-backed intuitions about how to apply
CNNs to computer vision problems.


  We investigate guide-field magnetic reconnection and particle acceleration in
relativistic pair plasmas with three-dimensional particle-in-cell (PIC)
simulations of a kinetic-scale current sheet in a periodic geometry at low
magnetizations. The tearing instability is the dominant mode in the current
sheet for all guide field strengths, while the linear kink mode is less
important even without guide field. Oblique modes seem to be suppressed
entirely. In its nonlinear evolution, the reconnection layer develops a network
of interconnected and interacting magnetic flux ropes. As smaller flux ropes
merge into larger ones, the reconnection layer evolves toward a
three-dimensional, disordered state in which the resulting flux rope segments
contain magnetic substructure on plasma skin depth scales. Embedded in the flux
ropes, we detect spatially and temporally intermittent sites of dissipation
reflected in peaks in the parallel electric field. Magnetic dissipation and
particle acceleration persist until the end of the simulations, with
simulations with higher magnetization and lower guide field strength exhibiting
greater and faster energy conversion and particle energization. At the end of
our largest simulation, the particle energy spectrum attains a tail extending
to high Lorentz factors that is best modeled with a combination of two
additional thermal components. We confirm that the primary energization
mechanism is acceleration by the electric field in the X-line region. We
discuss the implications of our results for macroscopic reconnection sites, and
which of our results may be expected to hold in systems with higher
magnetizations.


  The Hamiltonian of the $N$-particle Calogero model can be expressed in terms
of generators of a Lie algebra for a definite class of representations.
Maintaining this Lie algebra, its representations, and the flatness of the
Riemannian metric belonging to the second order differential operator, the set
of all possible quadratic Lie algebra forms is investigated. For N=3 and N=4
such forms are constructed explicitly and shown to correspond to exactly
solvable Sutherland models. The results can be carried over easily to all $N$.


  We discuss the separability of the position-dependent mass Hamiltonian in
cylindrical coordinates in the framework of a radial power-law
position-dependent mass. We consider two particular radial mass settings; a
harmonic oscillator type, and a Coulombic type. We subject the radial harmonic
oscillator type mass to a radial harmonic oscillator potential and the radial
Coulombic mass to a radial Coulombic potential. Azimuthal symmetry is assumed
and spectral signatures of various z-dependent interaction potentials are
reported.


  Differential cross-section measurements of $Z\gamma$ production in
association with hadronic jets are presented, using the full 139 fb$^{-1}$
dataset of $\sqrt{s}=13$ TeV proton-proton collisions collected by the ATLAS
detector during Run 2 of the LHC. Distributions are measured using events in
which the $Z$ boson decays leptonically and the photon is usually radiated from
an initial-state quark. Measurements are made in both one and two observables,
including those sensitive to the hard scattering in the event and others which
probe additional soft and collinear radiation. Different Standard Model
predictions, from both parton-shower Monte Carlo simulation and fixed-order QCD
calculations, are compared with the measurements. In general, good agreement is
observed between data and predictions from MATRIX and MiNNLO$_\text{PS}$, as
well as next-to-leading-order predictions from MadGraph5_aMC@NLO and Sherpa.


  Thirty years after the first observation of the 7Li isotope in the atmosphere
of metal-poor halo stars, the puzzle about its origin persists. Do current
observations still support the existence of a "plateau": a single value of
lithium abundance, constant over several orders of magnitude in the metallicity
of the target star? If this plateau exists, is it universal in terms of
observational loci of target stars? Is it possible to explain such observations
with known astrophysical processes? Can yet poorly explored astrophysical
mechanisms explain the observations or do we need to invoke physics beyond the
standard model of Cosmology and/or the standard model of Particle Physics to
explain them? Is there a 6Li problem, and is it connected to the 7Li one? These
questions have been discussed at the Paris workshop Lithium in the Cosmos, and
I summarize here its contents, providing an overview from the perspective of a
phenomenologist.


  We present a quantitative study of energy flow away from jets by numerically
solving the evolution equation derived by Banfi, Marchesini and Smye (BMS), and
apply the result to two processes at the LHC: Discriminating high-p_t jets
originating from decays of heavy electroweak bosons from the QCD background,
and the survival probability of BFKL-initiated dijet rapidity gaps. As a
byproduct, we find a hidden symmetry of the BMS equation which is a remnant of
conformal symmetry.


  Large transverse single-spin asymmetries, A_{N}, have been observed in
forward hadron production at RHIC. STAR has reported precision measurements of
the x_{F}-p_{T} dependence of A_{N} for forward pi^0 production. Contrary to
expectation, the asymmetry does not fall with p_{T}. However, questions remain
about the trend at the lower end of the studied range, p_{T}~1 GeV/c. Analysis
of data from the 2008 RHIC run has extended the pi^0 data in the x_{F} > 0.4,
low p_{T} region. Preliminary results are reported.


  We present experimental magnetotunneling results and atomistic
pseudopotential calculations of quasiparticle electron and hole wave functions
of self-assembled InAs/GaAs quantum dots. The combination of a predictive
theory along with the experimental results allows us to gain direct insight
into the quantum states. We monitor the effects of (i) correlations, (ii)
atomistic symmetry and (iii) piezoelectricity on the confined carriers and (iv)
observe a peculiar charging sequence of holes that violates the Aufbau
principle.


  We apply the critical geometry approach for bounded critical phenomena [1] to
$3d$ percolation. The functional shape of the order parameter profile $\phi$ is
related via the fractional Yamabe equation to its scaling dimension
$\Delta_{\phi}$. We obtain $\Delta_{\phi}= 0.4785(7)$ from which the anomalous
dimension $\eta$ is found to be $\eta=-0.0431(14)$, a value compatible with,
and more precise than, its previous direct measurements. A test of hyperscaling
is also performed.


  Many recommender systems suffer from popularity bias: popular items are
recommended frequently while less popular, niche products, are recommended
rarely or not at all. However, recommending the ignored products in the `long
tail' is critical for businesses as they are less likely to be discovered. In
this paper, we introduce a personalized diversification re-ranking approach to
increase the representation of less popular items in recommendations while
maintaining acceptable recommendation accuracy. Our approach is a
post-processing step that can be applied to the output of any recommender
system. We show that our approach is capable of managing popularity bias more
effectively, compared with an existing method based on regularization. We also
examine both new and existing metrics to measure the coverage of long-tail
items in the recommendation.


  It is shown that Nichols algebras over alternating groups A_m, m>4, are
infinite dimensional. This proves that any complex finite dimensional pointed
Hopf algebra with group of group-likes isomorphic to A_m is isomorphic to the
group algebra. In a similar fashion, it is shown that the Nichols algebras over
the symmetric groups S_m are all infinite-dimensional, except maybe those
related to the transpositions considered in [FK], and the class of type (2,3)
in S_5. We also show that any simple rack X arising from a symmetric group,
with the exception of a small list, collapse, in the sense that the Nichols
algebra of (X,q) is infinite dimensional, for q an arbitrary cocycle.
arXiv:0904.3978 is included here.


  This paper presents a visual-inertial odometry-enhanced geometrically stable
Iterative Closest Point (ICP) algorithm for accurate mapping using aerial
robots. The proposed method employs a visual-inertial odometry framework in
order to provide robust priors to the ICP step and calculate the overlap among
point clouds derived from an onboard time-of-flight depth sensor. Within the
overlapping parts of the point clouds, the method samples points such that the
distribution of normals among them is as large as possible. As different
geometries and sensor trajectories will influence the performance of the
alignment process, evaluation of the expected geometric stability of the ICP
step is conducted. It is only when this test is successful that the matching,
outlier rejection, and minimization of the error metric ICP steps are conducted
and the new relative translation and rotational components are estimated,
otherwise the system relies on the visual-inertial odometry transformation
estimates. The proposed strategy was evaluated within handheld, automated and
fully autonomous exploration and mapping missions using a small aerial robot
and was shown to provide robust results of superior quality at an affordable
increase of the computational load.


  In this paper, we consider the triangulation of 2-loop MHV amplituhedron from
"sign flip" definition. Using the isomorphism between $m=2, k=2$ tree
amplituhedron and 1-loop MHV physical amplituhedron, we found the direct
triangulation of 2-loop MHV amplituhedron from sign flip. This triangulation is
different from the BCFW triangulation because of the structure of the cells.
And we also found a formula of the canonical form of $n$-point 2-loop MHV
amplituhedron. This formula looks like a 2-loop version of the Kermit
representation of 1-loop MHV amplitude. We checked that the sum of these cells
of direct triangulation and BCFW or double pentagon diagram, these are
consistent up to at least 22-pt amplitude numerically.


  We present a catalogue of 17 filamentary X-ray features located within a
68\times34 arcmin^2 view centred on the Galactic Centre region from images
taken by Chandra. These features are described by their morphological and
spectral properties. Many of the X-ray features have non-thermal spectra that
are well fitted by an absorbed power law. Of the 17 features, we find six that
have not been previously detected, four of which are outside the immediate
20\times20 arcmin^2 area centred on the Galactic Centre. Seven of the 17
identified filaments have morphological and spectral properties expected for
pulsar wind nebulae (PWNe) with X-ray luminosities of 5\times10^32 to 10^34 erg
s^-1 in the 2.0-10.0 keV band and photon indices in the range of \Gamma = 1.1
to 1.9. In one feature, we suggest the strong neutral Fe K\alpha emission line
to be a possible indicator for past activity of Sgr A*. For G359.942-0.03, a
particular filament of interest, we propose the model of a ram pressure
confined stellar wind bubble from a massive star to account for the morphology,
spectral shape and 6.7 keV He-like Fe emission detected. We also present a
piecewise spectral analysis on two features of interest, G0.13-0.11 and
G359.89-0.08, to further examine their physical interpretations. This analysis
favours the PWN scenario for these features.


  Threat modeling is a popular method to securely develop systems by achieving
awareness of potential areas of future damage caused by adversaries. However,
threat modeling for systems relying on Artificial Intelligence is still not
well explored. While conventional threat modeling methods and tools did not
address AI-related threats, research on this amalgamation still lacks solutions
capable of guiding and automating the process, as well as providing evidence
that the methods hold up in practice. Consequently, this paper presents
ThreatFinderAI, an approach and tool providing guidance and automation to model
AI-related assets, threats, countermeasures, and quantify residual risks. To
evaluate the practicality of the approach, participants were tasked to recreate
a threat model developed by cybersecurity experts of an AI-based healthcare
platform. Secondly, the approach was used to identify and discuss strategic
risks in an LLM-based application through a case study. Overall, the solution's
usability was well-perceived and effectively supports threat identification and
risk discussion.


  We construct a lattice action for ${\cal N}=4$ super Yang-Mills theory in
four dimensions which is local, gauge invariant, free of spectrum doubling and
possesses a single exact supersymmetry. Our construction starts from the
observation that the fermions of the continuum theory can be mapped into the
component fields of a single real anticommuting Kahler-Dirac field. The
original supersymmetry algebra then implies the existence of a nilpotent scalar
supercharge $Q$ and a corresponding set of bosonic superpartners. Using this
field content we write down a $Q$-exact action and show that, with an
appropriate change of variables, it reduces to a well-known twist of ${\cal
N}=4$ super Yang-Mills theory due to Marcus. Using the discretization
prescription developed in an earlier paper on the ${\cal N}=2$ theory in two
dimensions we are able to translate this geometrical action to the lattice.


  This proposal relates to the design, analysis and application of a novel
numerical scheme for the solution of axisymmetric scattering problems. To this
end, a procedure is introduced to iteratively evaluate the solution of the
Lippmann-Schwinger integral equation in $O(N\log^2 N)$ operations, where $N$ is
the number of the discretization points. The method achieves its efficiency
through the use of the addition theorem and Fast Legendre Transforms (FLT).
  For globally smooth sound velocities/refractive indexes the method is
spectrally accurate. More generally the order of convergence is tied to and in
fact, limited by, the smoothness of the solution.


  Convolutional networks have become state-of-the-art techniques for automatic
medical image analysis, with the U-net architecture being the most popular at
this moment. In this article we report the application of a 3D version of U-net
to the automatic segmentation of prostate peripheral and transition zones in 3D
MRI images. Our results are slightly better than recent studies that used 2D
U-net and handcrafted feature approaches.
  In addition, we test ideas for improving the 3D U-net setup, by 1) letting
the network segment surrounding tissues, making use of the fixed anatomy, and
2) adjusting the network architecture to reflect the anisotropy in the
dimensions of the MRI image volumes. While the latter adjustment gave a
marginal improvement, the former adjustment showed a significant deterioration
of the network performance. We were able to explain this deterioration by
inspecting feature map activations in all layers of the network. We show that
to segment more tissues the network replaces feature maps that were dedicated
to detecting prostate peripheral zones, by feature maps detecting the
surrounding tissues.


  Soft robotics holds tremendous potential for various applications, especially
in unstructured environments such as search and rescue operations. However, the
lack of autonomy and teleoperability, limited capabilities, absence of gait
diversity and real-time control, and onboard sensors to sense the surroundings
are some of the common issues with soft-limbed robots. To overcome these
limitations, we propose a spatially symmetric, topologically-stable,
soft-limbed tetrahedral robot that can perform multiple locomotion gaits. We
introduce a kinematic model, derive locomotion trajectories for different
gaits, and design a teleoperation mechanism to enable real-time human-robot
collaboration. We use the kinematic model to map teleoperation inputs and
ensure smooth transitions between gaits. Additionally, we leverage the passive
compliance and natural stability of the robot for toppling and obstacle
navigation. Through experimental tests, we demonstrate the robot's ability to
tackle various locomotion challenges, adapt to different situations, and
navigate obstructed environments via teleoperation.


  We prove local boundedness, Harnack's inequality and local regularity for
weak solutions of quasilinear degenerate elliptic equations in divergence form
with Rough coefficients. Degeneracy is encoded by a non-negative, symmetric,
measurable matrix valued function Q(x) and two suitable non-negative weight
functions. We setup an axiomatic approach in terms of suitable geometric
conditions and local Sobolev-Poincar\'e inequalities. Data integrability is
close to L1 and is exploited in terms of a suitable Stummel-Kato class that in
some cases is necessary for local regularity.


  The results of a search for pair production of light top squarks are
presented, using 4.7 fb^-1 of sqrt(s) = 7 TeV proton-proton collisions
collected with the ATLAS detector at the Large Hadron Collider. This search
targets top squarks with masses similar to, or lighter than, the top quark
mass. Final states containing exclusively one or two leptons (e, mu), large
missing transverse momentum, light-jets and b-jets are used to reconstruct the
top squark pair system. Global mass scale variables are used to separate the
signal from a large ttbar background. No excess over the Standard Model
expectations is found. The results are interpreted in the framework of the
Minimal Supersymmetric Standard Model, assuming the top squark decays
exclusively to a chargino and a b-quark. Light top squarks with masses between
123-167 GeV are excluded for neutralino masses around 55 GeV.


  If the physics behind dark energy and/or dark matter violates the parity
symmetry assumed in the standard cosmological paradigm, the linear polarization
of the cosmic microwave background (CMB) photons can rotate due to their
coupling to the dark sector. Recent 3$\sigma$ hints of this ``cosmic
birefringence" in the EB spectrum of the CMB polarization motivates us to
pursue new directions to independently validate and characterize the signal.
Here, we explore the prospects to probe cosmic birefringence from small-scale
fluctuations in the CMB using polarized Sunyaev-Zel'dovich (pSZ) tomography. We
find that pSZ can be used to infer the redshift dependence of cosmic
birefringence and also help calibrate the instrumental polarization
orientation. To illustrate the prospects, we show that pSZ tomography may probe
an axion-like dark energy model with masses $m_\phi \lesssim 10^{-32}$eV with
$\mathcal{O}(0.1)$ degrees of rotation between reionization and recombination.


  Imagine when you line up in a store, the person in front of you can make you
pay her bill by using a passive wearable device that forces a scan of your
credit card without your awareness. An important assumption of today's
Near-field Communication (NFC) enabled cards is the limited communication range
between the commercial reader and the NFC cards -- a distance below 5~cm.
Previous approaches to attacking this assumption effectively use mobile phones
and active relays to enlarge the communication range, in order to attack the
NFC cards. However, these approaches require a power supply at the adversary
side, and can be easily localized when mobile phones or active relays transmit
NFC signals.
  We propose ReCoil, a system that uses wearable passive relays to attack NFC
cards by expanding the communication range to 49.6 centimeters, a ten-fold
improvement over its intended commercial distance. ReCoil is a magnetically
coupled resonant wireless power transfer system, which optimizes the energy
transfer by searching the optimal geometry parameters. Specifically, we first
narrow down the feasible area reasonably and design the ReCoil-Ant Colony
Algorithm such that the relays absorb the maximum energy from the reader. In
order to reroute the signal to pass over the surface of human body, we then
design a half waist band by carefully analyzing the impact of the distance and
orientation between two coils on the mutual inductance. Then, three more coils
are added to the system to keep enlarging the communication range. Finally,
extensive experiment results validate our analysis, showing that our passive
relays composed of common copper wires and tunable capacitors expand the range
of NFC attacks to 49.6 centimeters.


  Within the NaCo-ISPY exoplanet imaging program, we aim at detecting and
characterizing the population of low-mass companions at wide separations
($\gtrsim$10AU), focusing in particular on young stars either hosting a known
protoplanetary disk or a debris disk. R CrA is one of the youngest (1-3 Myr)
and most promising objects in our sample because of two previous studies that
suggested the presence of a close companion. Our aim is to directly image and
characterize the companion for the first time. We observed R CrA twice with the
NaCo instrument at VLT in the $L'$ filter with a one year time baseline in
between. The high-contrast imaging data were reduced and analyzed, and in both
datasets the companion candidate was detected. The companion is detected at a
separation of $196.8\pm4.5$/$196.6\pm5.9$ mas ($18.7\pm1.3$/$18.7\pm1.4$ AU)
and position angle of $134.7\pm0.5^\circ/133.7\pm0.7^\circ$ in the first/second
epoch observation. We measure a contrast of $7.29\pm0.18$/$6.70\pm0.15$ mag
with respect to the primary. Stellar proper motion study rejects the hypothesis
of the signal being a background object. The companion candidate orbits in the
clockwise direction and, if on a face-on circular orbit, its period is
$\sim43-47$ yr. This value disagrees with the estimated orbital motion and
therefore a face-on circular orbit may be excluded. Depending on the assumed
age, extinction and brightness of the primary, the stellar companion has a mass
between $0.10\pm0.02\, M_\odot$ and $1.03^{+0.20}_{-0.18}\,M_\odot$ range, if
no contribution from circumsecondary material is taken into account. The
presence of the companion needs to be taken into account when analyzing the
complex circumstellar environment of R CrA.


  We present a model of generalised parton distributions based on a forward
ansatz in the DGLAP region. We discuss some aspects of the parametrisations, as
the dependence in t, with factorised and non-factorised approaches, where t is
the square of the four-momentum exchanged at the hadron vertex. The predictions
of this model are then compared with DVCS cross sections from H1 and ZEUS, and
a related observable, the skewing factor, defined as the following ratio
imaginary amplitudes : $R \equiv {Im {\cal A} (\gamma^*+p \to \gamma
+p)\lfrestriction{t=0}} / {Im {\cal A} (\gamma^*+p \to \gamma^*
+p)\lfrestriction{t=0}}$. It is an interesting quantity including both the
non-forward kinematics and the non-diagonal effects. Finally, we discuss the
beam charge asymmetry, which is certainly the most sensitive observable to the
different hypothesis needed in the GPDs parametrisations. We show that the
approximations done for the t dependence lead to significant differences for
the predictions in the HERMES kinematic domain and prospects are given for
COMPASS.


  Misalignment between model predictions and intended usage can be detrimental
for the deployment of computer vision models. The issue is exacerbated when the
task involves complex structured outputs, as it becomes harder to design
procedures which address this misalignment. In natural language processing,
this is often addressed using reinforcement learning techniques that align
models with a task reward. We adopt this approach and show its surprising
effectiveness across multiple computer vision tasks, such as object detection,
panoptic segmentation, colorization and image captioning. We believe this
approach has the potential to be widely useful for better aligning models with
a diverse range of computer vision tasks.


  We define the concept of a partial translation structure T on a metric space
X and we show that there is a natural C*-algebra C*(T) associated with it which
is a subalgebra of the uniform Roe algebra C*_u(X). We introduce a coarse
invariant of the metric which provides an obstruction to embedding the space in
a group. When the space is sufficiently group-like, as determined by our
invariant, properties of the Roe algebra can be deduced from those of C*(T). We
also give a proof of the fact that the uniform Roe algebra of a metric space is
a coarse invariant up to Morita equivalence.


  This paper presents a novel approach for approximate integration over the
uncertainty of noise and signal variances in Gaussian process (GP) regression.
Our efficient and straightforward approach can also be applied to integration
over input dependent noise variance (heteroscedasticity) and input dependent
signal variance (nonstationarity) by setting independent GP priors for the
noise and signal variances. We use expectation propagation (EP) for inference
and compare results to Markov chain Monte Carlo in two simulated data sets and
three empirical examples. The results show that EP produces comparable results
with less computational burden.


  We study a generalisation of the bipartite Ramsey numbers to blowups of
graphs. For a graph $G$, denote the $t$-blowup of $G$ by $G[t]$. We say that
$G$ is $r$-Ramsey for $H$, and write $G \stackrel{r}{\rightarrow} H$, if every
$r$-colouring of the edges of $G$ has a monochromatic copy of $H$. We show that
if $G \stackrel{r}{\rightarrow} H$, then for all $t$, there exists $n$ such
that $G[n] \stackrel{r}{\rightarrow} H[t]$. In fact, we provide exponential
lower and upper bounds for the minimum $n$ with $G[n] \stackrel{r}{\rightarrow}
H[t]$, and conjecture an upper bound of the form $c^t$, where $c$ depends on
$H$ and $r$, but not on $G$. We also show that this conjecture holds for
$G(n,p)$ with high probability, above the threshold for the event $G(n,p)
\stackrel{r}{\rightarrow} H$.


  We study nonparametric estimation of density functions for undirected dyadic
random variables (i.e., random variables defined for all
n\overset{def}{\equiv}\tbinom{N}{2} unordered pairs of agents/nodes in a
weighted network of order N). These random variables satisfy a local dependence
property: any random variables in the network that share one or two indices may
be dependent, while those sharing no indices in common are independent. In this
setting, we show that density functions may be estimated by an application of
the kernel estimation method of Rosenblatt (1956) and Parzen (1962). We suggest
an estimate of their asymptotic variances inspired by a combination of (i)
Newey's (1994) method of variance estimation for kernel estimators in the
"monadic" setting and (ii) a variance estimator for the (estimated) density of
a simple network first suggested by Holland and Leinhardt (1976). More unusual
are the rates of convergence and asymptotic (normal) distributions of our
dyadic density estimates. Specifically, we show that they converge at the same
rate as the (unconditional) dyadic sample mean: the square root of the number,
N, of nodes. This differs from the results for nonparametric estimation of
densities and regression functions for monadic data, which generally have a
slower rate of convergence than their corresponding sample mean.


  Strategies for Higgs boson searches require the knowledge of the total
production cross section and the transverse momentum spectrum. The large
transverse momentum spectrum of the Higgs boson produced in gluon fusion can be
quite different in the Standard Model and the Minimal Supersymmetric Standard
Model. In this paper we present a comparison of the Higgs transverse momentum
spectrum obtained using the PYTHIA event generator and the HIGLU program as
well as the program HQT, which includes NLO corrections and a soft gluon
resummation for the region of small transverse momenta. While the shapes of the
spectra are similar for the Standard Model, significant differences are
observed in the spectra of Minimal Supersymmetric Standard Model benchmark
scenarios with large tan(beta).


  The present paper is a sequel to our paper "Metric characterization of
isometries and of unital operator spaces and systems". We characterize certain
common objects in the theory of operator spaces (unitaries, unital operator
spaces, operator systems, operator algebras, and so on), in terms which are
purely linear-metric, by which we mean that they only use the vector space
structure of the space and its matrix norms. In the last part we give some
characterizations of operator algebras (which are not linear-metric in our
strict sense described in the paper).


  Polycrystalline samples of LaNi2(Ge{1-x}P{x})2 (x = 0, 0.25, 0.50, 0.75, 1)
were synthesized and their properties investigated by x-ray diffraction (XRD),
heat capacity Cp, magnetic susceptibility chi, and electrical resistivity rho
measurements versus temperature T. These compounds all crystallize in the
body-centered-tetragonal ThCr2Si2-type structure. The rho(T) measurements
indicate that all compositions in this system are metallic. The low-T Cp
measurements yield a rather large Sommerfeld coefficient gamma = 12.4(2) mJ/mol
K^2 for x = 0 reflecting a large density of states at the Fermi energy that is
comparable with the largest values found for the AFe2As2 class of materials
with the same crystal structure. The gamma decreases approximately linearly
with x to 7.4(1) mJ/mol K^2 for x = 1. The chi measurements show nearly
temperature-independent paramagnetic behavior across the entire range of
compositions except for LaNi2Ge2, where a broad peak is observed at 300 K from
chi(T) measurements up to 1000 K that may arise from short-range
antiferromagnetic correlations in a quasi-two-dimensional magnetic system.
High-accuracy Pade approximants representing the Debye lattice heat capacity
and Bloch-Gruneisen electron-phonon resistivity functions versus T are
presented and are used to analyze our experimental Cp(T) and rho(T) data,
respectively. The T-dependences of rho for all samples are well-described by
the Bloch-Gruneisen model, although the observed rho(300 K) values are larger
than calculated from this model. A significant T-dependence of the Debye
temperature determined from the Cp(T) data was observed for each composition.
No clear evidence for bulk superconductivity or any other long-range phase
transition was found for any of the compositions studied.


  As the computing landscape evolves towards distributed architectures such as
Internet of Things (IoT),enterprises are moving away from traditional perimeter
based security models toward so called zero trust networking (ZTN) models that
treat both the intranet and Internet as equally untrustworthy. Such security
models incorporate risk arising from dynamic and situational factors, such as
device location and security risk level risk, into the access control decision.
Researchers have developed a number of risk models such as RAdAC (Risk
Adaptable Access Control) to handle dynamic contexts and these have been
applied to medical and other scenarios. In this position paper we describe our
ongoing work to apply RAdAC to ZTN. We develop a policy management framework,
FURZE, to facilitate fuzzy risk evaluation that also defines how to adapt to
dynamically changing contexts. We also consider how enterprise security
situational awareness (SSA) - which describes the potential impact to an
organisations mission based on the current threats and the relative importance
of the information asset under threat - can be incorporated into a RAdAC scheme


  We propose an experiment using a three-gate quantum Hall device to probe the
dependence of the integral quantum Hall effect (IQHE) on the shape of the
lateral confining potential in edge regions. This shape can, in a certain
configuration determine whether or not the IQHE occurs.


  The fuzzy commitment scheme is a cryptographic primitive that can be used to
store biometric templates being encoded as fixed-length feature vectors
protected. If multiple related records generated from the same biometric
instance can be intercepted, their correspondence can be determined using the
decodability attack. In 2011, Kelkboom et al. proposed to pass the feature
vectors through a record-specific but public permutation process in order to
prevent this attack. In this paper, it is shown that this countermeasure
enables another attack also analyzed by Simoens et al. in 2009 which can even
ease an adversary to fully break two related records. The attack may only be
feasible if the protected feature vectors have a reasonably small Hamming
distance; yet, implementations and security analyses must account for this
risk. This paper furthermore discusses that by means of a public
transformation, the attack cannot be prevented in a binary fuzzy commitment
scheme based on linear codes. Fortunately, such transformations can be
generated for the non-binary case. In order to still be able to protect binary
feature vectors, one may consider to use the improved fuzzy vault scheme by
Dodis et al. which may be secured against linkability attacks using
observations made by Merkle and Tams.


  Understanding how the collective physical processes drive robust
morphological transitions in animal development requires the characterization
of the relevant fields underlying morphogenesis. Calcium (Ca2+) is known to be
such a field. Here we show that the Ca2+ spatial fluctuations, in whole-body
Hydra regeneration, exhibit universal properties captured by a field-theoretic
model describing fluctuations in a tilted double-well potential. We utilize an
external electric field and Heptanol, a drug blocking gap junctions, as two
separate controls affecting the Ca2+ activity and pausing the regeneration
process in a reversible way. Subjecting the Hydra tissue to an electric field
increases the calcium activity and its spatial correlations, while applying
Heptanol inhibits the activity and weakens the spatial correlations. The
statistical characteristics of the Ca2+ spatial fluctuations, i.e., the
coefficient of variation and the skewness - exhibit universal shape
distributions across tissue samples and conditions, demonstrating the existence
of global constraints over this field. Our analysis shows that the Hydra's
tissue resides near the onset of bistability; the local Ca2+ activity in
different regions fluctuates between low and high excited states. The controls
modulate the dynamics near that onset, preserving the universal characteristics
of the Ca2+ fluctuations and, by that, maintaining the tissue's ability to
regenerate.


  We investigate the nonequilibrium quantum dynamics of a single and two heavy
fermionic impurities being harmonically trapped and repulsively interacting
with a finite ensemble of majority fermions. A quench of the potential of the
majority species from a double-well to a harmonic trap is applied, enforcing
its counterflow which in turn perturbs the impurities. For weak repulsions it
is shown that the mixture undergoes a periodic mixing and demixing dynamics,
while stronger interactions lead to a more pronounced dynamical spatial
separation. In the presence of correlations the impurity exhibits an expansion
dynamics which is absent in the Hartree-Fock case resulting in an enhanced
degree of miscibility. We generalize our results to different impurity masses
and demonstrate that the expansion amplitude of the impurity reduces for a
larger mass. Furthermore, we showcase that the majority species is strongly
correlated and a phase separation occurs on the two-body level. Most
importantly, signatures of attractive impurity-impurity induced interactions
mediated by the majority species are identified in the time-evolution of the
two-body correlations of the impurities, a result that is supported by
inspecting their spatial size.


  We present a classical analysis of the transient response of Josephson
junctions perturbed by microwaves and thermal fluctuations. The results include
a specific low frequency modulation in phase and amplitude behavior of a
junction in its zero-voltage state. This transient modulation frequency is
linked directly to an observed variation in the probability for the system to
switch to its non-zero voltage state. Complementing previous work on linking
classical analysis to the experimental observations of Rabi-oscillations, this
expanded perturbation method also provides closed form analytical results for
attenuation of the modulations and the Rabi-type oscillation frequency. Results
of perturbation analysis are compared directly (and quantitatively) to
numerical simulations of the classical model as well as published experimental
data, suggesting that transients to phase-locking are closely related to the
observed oscillations.


  We introduce autoboot, a Mathematica program which automatically generates
mixed-correlator bootstrap equations of an arbitrary number of scalar external
operators, given the global symmetry group and the representations of the
operators. The output is a Python program which uses Ohtsuki's cboot which in
turn uses Simmons-Duffin's sdpb. In an appendix we also discuss a simple
technique to significantly reduce the time to run sdpb, which we call
hot-starting.


  Given an action of a reductive group on a normal variety, we construct all
invariant open subsets admitting a good quotient with a quasiprojective or a
divisorial quotient space. Our approach extends known constructions like
Mumford's Geometric Invariant Theory. We obtain several new Hilbert-Mumford
type theorems, and we extend a projectivity criterion of Bialynicki-Birula and
Swiecicka for varieties with semisimple group action from the smooth to the
singular case.


  Extracting energy from fluctuations has been an everlasting endeavor. An
important strategy is to use information-based operation, which allows energy
extraction even from thermal fluctuations. However, it remains unclear whether
a blind external operation, ignorant of such fluctuations, will also work.
Assuming such a small operation, we find that energy extraction requires a
negative response spectrum in a certain frequency region, which inevitably
requires an internal power source to maintain. Energy is then extracted by
operating the system at these frequencies. Negative response is intrinsic to
any system that adapts to a slow external operation, which could be realized
through integral feedback control. These are demonstrated through a solvable
model.


  We give a simple proof of the well-known fact: any group of n elements is
cyclic if and only if n and \phi(n) are coprime. This note is accessible for
students familiar with permutations and basic number theory. No knowledge of
abstract group theory is required; a few necessary notions are introduced in
the course of the proof. The note could also be an interesting easy reading for
mature mathematicians.


  Graph contrastive learning (GCL), standing as the dominant paradigm in the
realm of graph pre-training, has yielded considerable progress. Nonetheless,
its capacity for out-of-distribution (OOD) generalization has been relatively
underexplored. In this work, we point out that the traditional optimization of
InfoNCE in GCL restricts the cross-domain pairs only to be negative samples,
which inevitably enlarges the distribution gap between different domains. This
violates the requirement of domain invariance under OOD scenario and
consequently impairs the model's OOD generalization performance. To address
this issue, we propose a novel strategy "Negative as Positive", where the most
semantically similar cross-domain negative pairs are treated as positive during
GCL. Our experimental results, spanning a wide array of datasets, confirm that
this method substantially improves the OOD generalization performance of GCL.


  In this paper we show how weak joint measurement and local feedback can be
used to control entanglement generation between two qubits. To do this, we make
use of a decoherence free subspace (DFS). Weak measurement and feedback can be
used to drive the system into this subspace rapidly. Once within the subspace,
feedback can generate entanglement rapidly, or turn off entanglement generation
dynamically. We also consider, in the context of weak measurement, some of
differences between purification and generating entanglement.


  The present paper concerns the derivation of phase-integral quantization
conditions for the two-centre Coulomb problem under the assumption that the two
Coulomb centres are fixed. With this restriction we treat the general
two-centre Coulomb problem according to the phase-integral method, in which one
uses an {\it a priori} unspecified {\it base function}. We consider base
functions containing three unspecified parameters $C, \tilde C$ and $\Lambda$.
When the absolute value of the magnetic quantum number $m$ is not too small, it
is most appropriate to choose $\Lambda=|m|\ne 0$. When, on the other hand,
$|m|$ is sufficiently small, it is most appropriate to choose $\Lambda = 0$.
Arbitrary-order phase-integral quantization conditions are obtained for these
choices of $\Lambda$. The parameters $C$ and $\tilde C$ are determined from the
requirement that the results of the first and the third order of the
phase-integral approximation coincide, which makes the first-order
approximation as good as possible.
  In order to make the paper to some extent self-contained, a short review of
the phase-integral method is given in the Appendix.


  We report the synthesis and characterization of the novel silicide LaFeSiH
displaying superconductivity with onset at 11 K. We find that this
pnictogen-free compound is isostructural to LaFeAsO, with a similar
low-temperature tetragonal to orthorhombic distortion. Using density functional
theory we show that this system is also a multiband metal in which the
orthorhombic distortion is likely related to single-stripe antiferromagnetic
order. Electrical resistivity and magnetic susceptibility measurements reveal
that these features occur side-by-side with superconductivity, which is
suppressed by external pressure.


  I investigate energy transfer in a donor-acceptor pair beyond weak
system-bath coupling. I identify a transition from coherent to incoherent
dynamics with increasing temperature, due to multi-phonon effects not captured
by a standard weak-coupling treatment. The crossover temperature has a marked
dependence on the degree of spatial correlation between fluctuations
experienced at the two system sites. For strong correlations, this leads to the
possibility of coherence surviving into a high temperature regime.


  The Mallows-Binomial distribution is the first joint statistical model for
rankings and ratings (Pearce and Erosheva, 2022). Because frequentist
estimation of the model parameters and their uncertainty is challenging, it is
natural to consider the nonparametric bootstrap. However, it is not clear that
the nonparametric bootstrap is asymptotically valid in this setting. This is
because the Mallows-Binomial model is parameterized by continuous quantities
whose discrete order affects the likelihood. In this note, we demonstrate that
bootstrap uncertainty of the maximum likelihood estimates in the
Mallows-Binomial model are asymptotically valid.


  We use computer simulations to study the glass transition of dense fluids
made of polydisperse, repulsive spheres. For hard particles, we vary the volume
fraction, phi, and use compressible particles to explore finite temperatures,
T>0. In the hard sphere limit, our dynamic data show evidence of an avoided
mode-coupling singularity near phi_{MCT} ~ 0.592, they are consistent with a
divergence of equilibrium relaxation times occurring at phi_0 ~ 0.635, but they
leave open the existence of a finite temperature singularity for compressible
spheres at volume fraction phi > phi_0. Using direct measurements and a new
scaling procedure, we estimate the equilibrium equation of state for the hard
sphere metastable fluid up to phi_0, where pressure remains finite, suggesting
that phi_0 corresponds to an ideal glass transition. We use non-equilibrium
protocols to explore glassy states above phi_0 and establish the existence of
multiple equations of state for the unequilibrated glass of hard spheres, all
diverging at different densities in the range phi \in [0.642, 0.664].
Glassiness thus results in the existence of a continuum of densities where
jamming transitions can occur.


  Blazars can be divided into two sub-classes namely high energy and low energy
peaked blazars. In spectral energy distribution, the first synchrotron hump of
the former class peaks in UV/X-rays and in IR/optical bands for the latter
class. The peak of the spectral energy distribution seems to be responsible for
variability properties of these classes of blazars in X-ray and optical bands.
Since, in low energy peaked blazars, the X-ray bands lies well below the
synchrotron hump, one expects that the highest energy electrons available for
the synchrotron emission would have slower effect of variability on X-ray
intra-day timescale. In this paper, by taking the advantage of a sample of 12
low energy peaked blazars with total 50 observations from XMM$-$Newton since
its launch, we confirm that this class is less variable in X-ray bands. We
found that out of 50 observational light curves, genuine intra-day variability
is present in only two of light curves i.e 4%. Similar results we obtained from
our earlier optical intra-day variability studies of high energy peaked blazars
where out of 144 light curves, only genuine intra-day variability was detected
in 6 light curves i.e ~ 4%. Since, X-ray bands lie below the peak of the
spectral energy distribution of LSPs where inverse Compton mechanism is
dominating rather than synchrotron radiation at the peak of the optical band,
leads to slower variability in the X-ray bands. Hence, reducing their intra-day
variability in X-ray bands as compared to the variability in optical bands.


  We study the contrarian voter model for opinion formation in a society under
the influence of an external oscillating propaganda and stochastic noise. Each
agent of the population can hold one of two possible opinions on a given issue
--against or in favor, and interacts with its neighbors following either an
imitation dynamics (voter behavior) or an anti-alignment dynamics (contrarian
behavior): each agent adopts the opinion of a random neighbor with a
time-dependent probability $p(t)$, or takes the opposite opinion with
probability $1-p(t)$. The imitation probability $p(t)$ is controlled by the
social temperature $T$, and varies in time according to a periodic field that
mimics the influence of an external propaganda, so that a voter is more prone
to adopt an opinion aligned with the field. We simulate the model in complete
graph and in lattices, and find that the system exhibits a rich variety of
behaviors as $T$ is varied: opinion consensus for $T=0$, a bimodal behavior for
$T<T_c$, an oscillatory behavior where the mean opinion oscillates in time with
the field for $T>T_c$, and full disorder for $T \gg 1$. The transition
temperature $T_c$ vanishes with the population size $N$ as $T_c \simeq 2/\ln N$
in complete graph. Besides, the distribution of residence times $t_r$ in the
bimodal phase decays approximately as $t_r^{-3/2}$. Within the oscillatory
regime, we find a stochastic resonance-like phenomenon at a given temperature
$T^*$. Also, mean-field analytical results show that the opinion oscillations
reach a maximum amplitude at an intermediate temperature, and that exhibit a
lag respect to the field that decreases with $T$.


  We consider spline estimates which preserve prescribed piecewise convex
properties of the unknown function. A robust version of the penalized
likelihood is given and shown to correspond to a variable halfwidth kernel
smoother where the halfwidth adaptively decreases in regions of rapid change of
the unknown function. When the convexity change points are prescribed, we
derive representation results and smoothness properties of the estimates. A
dual formulation is given which reduces the estimate is reduced to a finite
dimensional convex optimization in the dual space.


  The X-ray observation of AM Her in a very low state was performed with {\it
Suzaku} in October 2008. One flare event with a time scale of $\sim$ 3700 sec
was detected at the X-ray luminosity of $6.0 \times 10^{29} {\rm ~erg
~sec}^{-1}$ in the 0.5 -- 10 keV band assuming at a distance of 91 pc. The
X-ray spectrum is represented by a thermal plasma emission model with a
temperature of $8.67_{-1.14}^{+1.31}$ keV. During the quiescence out of the
flare interval, {\it Suzaku} also detected significant X-rays at a luminosity
of $1.7 \times 10^{29} {\rm ~erg ~sec}^{-1}$ in the 0.5 -- 10 keV band, showing
a clear spin modulation at a period of 0.1289273(2) days at BJD 2454771.581.
The X-ray spectra in the quiescence were represented by a MEKAL + Power Law
(PL) model or a single CEMEKL model, which are also supported by phase-resolved
analyses. A correlation between the temperature and the volume emission measure
was found together with historical X-ray measurements of AM Her in various
states. In order to account for a possible non-thermal emission from AM Her,
particle acceleration mechanisms in the AM Her system are also discussed,
including a new proposal of a shock acceleration process on the top of the
accretion column.


  In this paper, a consistent model of the multifrequency emission of the
starburst galaxy M82, from radio to gamma-rays is presented and discussed.
Predictions for observations with Fermi, MAGIC II/VERITAS and CTA telescopes
are made. The model is also used to self-consistenty compute the (all flavors)
emission of neutrinos resulting from this starburst galaxy, what can be used in
considerations of the diffuse contributions of such objects.


  Data augmentation is conventionally used to inject robustness in Speaker
Verification systems. Several recently organized challenges focus on handling
novel acoustic environments. Deep learning based speech enhancement is a modern
solution for this. Recently, a study proposed to optimize the enhancement
network in the activation space of a pre-trained auxiliary network. This
methodology, called deep feature loss, greatly improved over the
state-of-the-art conventional x-vector based system on a children speech
dataset called BabyTrain. This work analyzes various facets of that approach
and asks few novel questions in that context. We first search for optimal
number of auxiliary network activations, training data, and enhancement feature
dimension. Experiments reveal the importance of Signal-to-Noise Ratio filtering
that we employ to create a large, clean, and naturalistic corpus for
enhancement network training. To counter the "mismatch" problem in enhancement,
we find enhancing front-end (x-vector network) data helpful while harmful for
the back-end (Probabilistic Linear Discriminant Analysis (PLDA)). Importantly,
we find enhanced signals contain complementary information to original.
Established by combining them in front-end, this gives ~40% relative
improvement over the baseline. We also do an ablation study to remove a noise
class from x-vector data augmentation and, for such systems, we establish the
utility of enhancement regardless of whether it has seen that noise class
itself during training. Finally, we design several dereverberation schemes to
conclude ineffectiveness of deep feature loss enhancement scheme for this task.


  We consider renewal stochastic processes generated by non-independent events
from the perspective that their basic distribution and associated generating
functions obey the statistical-mechanical structure of systems with interacting
degrees of freedom. Based on this fact we look briefly into the less known case
of processes that display phase transitions along time. When the density
distribution \psi_{n}(t) for the occurrence of the n-th event at time t is
considered to be a partition function, of a 'microcanonical' type for n
'degrees of freedom' at fixed 'energy' t, one obtains a set of four partition
functions of which that for the generating function variable z and Laplace
transform variable \epsilon, conjugate to n and t, respectively, plays a
central role. These partition functions relate to each other in the customary
way and in accordance to the precepts of large deviations theory, while the
entropy, or Massieu potential, derived from \psi_{n}(t) satisfies an Euler
relation. We illustrate this scheme first for an ordinary renewal process of
events generated by a simple exponential waiting time distribution \psi (t).
Then we examine a process modelled after the so-called Hamiltonian Mean Field
(HMF) model that is representative of agents that perform a repeated task with
an associated outcome, such as an opinion poll. When a sequence of (many)
events takes place in a sufficiently short time the process exhibits clustering
of the outcome, but for larger times the process resembles that of independent
events. The two regimes are separated by a sharp transition, technically of the
second order. Finally we point out the existence of a similar scheme for random
walk processes.


  Classical symmetric pairs consist of a symmetrizable Kac-Moody algebra
$\mathfrak{g}$, together with its subalgebra of fixed points under an
involutive automorphism of the second kind. Quantum group analogs of this
construction, known as quantum symmetric pairs, replace the fixed point Lie
subalgebras by one-sided coideal subalgebras of the quantized enveloping
algebra $U_q(\mathfrak{g})$. We provide a complete presentation by generators
and relations for these quantum symmetric pair coideal subalgebras. These
relations are of inhomogeneous $q$-Serre type and are valid without
restrictions on the generalized Cartan matrix. We draw special attention to the
split case, where the quantum symmetric pair coideal subalgebras are
generalized $q$-Onsager algebras.


  In this paper we study the problem of content placement in a cache network.
We consider a network where routing of requests is based on random walks.
Content placement is done using a novel mechanism referred to as reinforced
counters. To each content we associate a counter, which is incremented every
time the content is requested, and which is decremented at a fixed rate. We
model and analyze this mechanism, tuning its parameters so as to achieve
desired performance goals for a single cache or for a cache network. We also
show that the optimal static content placement, without reinforced counters, is
NP hard under different design goals.


  We use a galaxy sample derived from the DECaLS DR9 to measure the Baryonic
Acoustic Oscillations (BAO). The magnitude-limited sample consists of 10.6
million galaxies in an area of 4974 deg$^2$ over the redshift range of [0.6,
1]. A key novelty of this work is that the true redshift distribution of the
photo-$z$ sample is derived from the self calibration method, which determines
the true redshift distribution using the clustering information of the
photometric data alone. Through the angular correlation function in four
tomographic bins, we constrain the BAO scale dilation parameter $\alpha$ to be
$1.025\pm 0.033 $, consistent with the fiducial Planck cosmology.
Alternatively, the ratio between the comoving angular diameter distance and the
sound horizon, $D_{\rm M} / r_{\rm s}$ is constrained to be $18.94 \pm 0.61 $
at the effective redshift of 0.749. We corroborate our results with the true
redshift distribution obtained from a weighted spectroscopic sample, finding
very good agreement. We have conducted a series of tests to demonstrate the
robustness of the measurement. Our work demonstrates that the self calibration
method can effectively constrain the true redshift distribution in cosmological
applications, especially in the context of photometric BAO measurement.


  We present constraints on Wilson coefficients from a combined fit to data
from top-quark and beauty-physics measurements within the Standard Model
effective field theory. We consider present data on top-quark pair production
and decay, $b\to s$ flavor changing neutral currents, and $Z\to b\bar b$ decay,
as well as different future scenarios. These comprise projections for HL-LHC,
Belle II, and a lepton collider using the example of CLIC. We investigate
opportunities for detecting deviations from the Standard Model hinted at by
present data on $b\to s$ transitions. In the fit, we find strong synergies
between top-quark and beauty physics that allow to tighten constraints on
various coefficients.


  In this paper, we propose a new multiphysics finite element method for a Biot
model with secondary consolidation in soil dynamics. To better describe the
processes of deformation and diffusion underlying in the original model, we
reformulate Biot model by a new multiphysics approach, which transforms the
fluid-solid coupled problem to a fluid coupled problem--a generalized Stokes
problem and a diffusion problem. Then, we give the energy law and prior error
estimate of the weak solution. And we design a fully discrete time-stepping
scheme to use mixed finite element method for $P_2-P_1-P_1$ element pairs to
approximate the space variables and backward Euler method for the time
variable, and we prove the discrete energy laws and the optimal convergence
order error estimates. Also, we show some numerical examples to verify the
theoretical results. Finally, we draw a conclusion to summarize the main
results of this paper.


  Due to imprecision and uncertainties in predicting real world problems,
artificial neural network (ANN) techniques have become increasingly useful for
modeling and optimization. This paper presents an artificial neural network
approach for forecasting electric energy consumption. For effective planning
and operation of power systems, optimal forecasting tools are needed for energy
operators to maximize profit and also to provide maximum satisfaction to energy
consumers. Monthly data for electric energy consumed in the Gaza strip was
collected from year 1994 to 2013. Data was trained and the proposed model was
validated using 2-Fold and K-Fold cross validation techniques. The model has
been tested with actual energy consumption data and yields satisfactory
performance.


  In this paper, we propose an effective point cloud generation method, which
can generate multi-resolution point clouds of the same shape from a latent
vector. Specifically, we develop a novel progressive deconvolution network with
the learning-based bilateral interpolation. The learning-based bilateral
interpolation is performed in the spatial and feature spaces of point clouds so
that local geometric structure information of point clouds can be exploited.
Starting from the low-resolution point clouds, with the bilateral interpolation
and max-pooling operations, the deconvolution network can progressively output
high-resolution local and global feature maps. By concatenating different
resolutions of local and global feature maps, we employ the multi-layer
perceptron as the generation network to generate multi-resolution point clouds.
In order to keep the shapes of different resolutions of point clouds
consistent, we propose a shape-preserving adversarial loss to train the point
cloud deconvolution generation network. Experimental results demonstrate the
effectiveness of our proposed method.


  We consider a generalization of the third degree price discrimination problem
studied in Bergemann et al. (2015), where an intermediary between the buyer and
the seller can design market segments to maximize any linear combination of
consumer surplus and seller revenue. Unlike in Bergemann et al. (2015), we
assume that the intermediary only has partial information about the buyer's
value. We consider three different models of information, with increasing order
of difficulty. In the first model, we assume that the intermediary's
information allows him to construct a probability distribution of the buyer's
value. Next we consider the sample complexity model, where we assume that the
intermediary only sees samples from this distribution. Finally, we consider a
bandit online learning model, where the intermediary can only observe past
purchasing decisions of the buyer, rather than her exact value. For each of
these models, we present algorithms to compute optimal or near optimal market
segmentation.


  We consider the interaction between a matter system and soft gravitons. We
use a functional eikonal expansion to deal with the infrared divergences, and
introduce a "composite generating functional" which allows us to calculate a
decoherence functional for the time evolution of the system. These techniques
allow us to formulate scattering problems in a way which deals consistently
with infrared effects, as well as being manifestly diffeomorphism invariant. We
show how the asymptotic form of the decoherence functional can be written in
terms of the infinitely many conserved charges associated with asymptotic BMS
symmetries, and allow us to address the question of how much information is
lost during the scattering.


  We investigate the phase diagram of color superconducting quark matter with
strange quarks (2SC+s quark matter) in beta equliibrium at zero as well as
finite temperatures within a Nambu-Jona-Lasinio model. The variational method
as used here allows us to investigate simultaneous formation of condensates in
quark--antiquark as well as in diquark channels. Color and electric charge
neutrality conditions are imposed in the calculation of the thermodynamic
potential. Medium dependance of strange quark mass plays a sensitve role in
maintaining charge neutrality conditions. At zero temperature the system goes
from gapless phase to usual BCS phase through an intermediate normal phase as
density is increased. The gapless modes show a smooth behaviour with respect to
temperature vanishing above a critical temperature which is larger than the BCS
transition temperature. We observe a sharp transition from gapless
superconducting phase to the BCS phase as density is increased for the color
neutral matter at zero temperature. As temperature is increased this however
becomes a smooth transition.


  We implement two-cover descent for plane quartics over Q with all 28
bitangents rational and show that on a significant collection of test cases, it
resolves the existence of rational points. We also review a classical
description of the relevant moduli space and use it to generate examples. We
observe that local obstructions are quite rare for such curves, and only seem
to occur in practice at primes of good reduction. In particular, having good
reduction at 11 implies having no rational points. We also gather numerical
data on two-Selmer ranks of Jacobians of these curves, which suggests that
these often have non-trivial Tate-Shafarevich groups.
  We implement two-cover descent for plane quartics over Q with all 28
bitangents rational and show that on a significant collection of test cases, it
resolves the existence of rational points. We also review a classical
description of the relevant moduli space and use it to generate examples. We
observe that local obstructions are quite rare for such curves and only seem to
occur in practice at primes of good reduction. In particular, having good
reduction at 11 implies having no rational points. We also gather numerical
data on two-Selmer ranks of Jacobians of these curves, providing evidence these
behave differently from those of general abelian varieties due to the frequent
presence of an everywhere locally trivial torsor.


  This is a research announcement concerning a series of constructions obtained
by applying the "doubling method" from the theory of automorphic forms to
covering groups. Using these constructions, we obtain partial tensor product
L-functions attached to generalized Shimura lifts, which may be defined in a
natural way since at almost all places the representations are unramified
principal series.


  We present a simple model that enables us to analytically characterize a
floppy to rigid transition and an associated self-adaptive intermediate phase
in a random bond network. In this intermediate phase, the network adapts itself
to lower the stress due to constraints. Our simulations verify this picture. We
use these insights to identify applications of these ideas in computational
problems such as vertex cover and K-SAT.


  The Gamow-Teller strength in nuclei can be strongly affected by the internal
degrees of freedom of the nucleon. It is demonstrated that this feature is
unique to the Gamow-Teller. Excitation modes that involve spatial degrees of
freedom are much less influenced by the internal excitation of nucleons. The
fact that the observed Gamow-Teller strength is quenched by 30-40 percent in
all nuclei suggests that indeed this is due to the nucleon excitation in
nuclei.