  A small subset of explainability techniques developed initially for image
recognition models has recently been applied for interpretability of 3D
Convolutional Neural Network models in activity recognition tasks. Much like
the models themselves, the techniques require little or no modification to be
compatible with 3D inputs. However, these explanation techniques regard spatial
and temporal information jointly. Therefore, using such explanation techniques,
a user cannot explicitly distinguish the role of motion in a 3D model's
decision. In fact, it has been shown that these models do not appropriately
factor motion information into their decision. We propose a selective relevance
method for adapting the 2D explanation techniques to provide motion-specific
explanations, better aligning them with the human understanding of motion as
conceptually separate from static spatial features. We demonstrate the utility
of our method in conjunction with several widely-used 2D explanation methods,
and show that it improves explanation selectivity for motion. Our results show
that the selective relevance method can not only provide insight on the role
played by motion in the model's decision -- in effect, revealing and
quantifying the model's spatial bias -- but the method also simplifies the
resulting explanations for human consumption.


  Joint probability density function (PDF)-based models in turbulent combustion
provide direct closure for turbulence-chemistry interactions. The joint PDFs
capture the turbulent flame dynamics at different spatial locations and hence
it is crucial to represent them accurately. The jointPDFs are parameterized on
the unconditional means of thermo-chemical state variables, which can be high
dimensional. Thus, accurate construction of joint PDFs at various spatial
locations may require an exorbitant amount of data. In a previous work, we
introduced a framework that alleviated data requirements by constructing joint
PDFs in a lower dimensional space using principal component analysis (PCA) in
conjunction with Kernel Density Estimation (KDE). However, constructing the
principal component (PC) joint PDFs is still computationally expensive as they
are required to be calculated at each spatial location in the turbulent flame.
In this work, we propose the concept of a generalized joint PDF model using the
Deep Operator Network (DeepONet). The DeepONet is a machine learning model that
is parameterized on the unconditional means of PCs at a given spatial location
and discrete PC coordinates and predicts the joint probability density value
for the corresponding PC coordinate. We demonstrate the accuracy and
generalizability of the DeepONet on the Sandia flames, D, E and F. The DeepONet
is trained based on the PC joint PDFs observed inflame E and yields excellent
predictions of joint PDFs shapes at different spatial locations of flamesD and
F, which are not seen during training


  Hyperbolic Lagrangian Coherent Structures (LCSs) are locally most repelling
or most attracting material surfaces in a finite-time dynamical system. To
identify both types of hyperbolic LCSs at the same time instance, the standard
practice has been to compute repelling LCSs from future data and attracting
LCSs from past data. This approach tacitly assumes that coherent structures in
the flow are fundamentally recurrent, and hence gives inconsistent results for
temporally aperiodic systems. Here we resolve this inconsistency by showing how
both repelling and attracting LCSs are computable at the same time instance
from a single forward or a single backward run. These LCSs are obtained as
surfaces normal to the weakest and strongest eigenvectors of the Cauchy-Green
strain tensor.


  In this paper we introduce a method for stacking data cubelets extracted from
interferometric surveys of galaxies in the redshifted 21-cm H\,\textsc{i} line.
Unlike the traditional spectral stacking technique, which stacks
one-dimensional spectra extracted from data cubes, we examine a method based on
image domain stacks which makes deconvolution possible. To test the validity of
this assumption, we mock a sample of 3622 equatorial galaxies extracted from
the GAMA survey, recently imaged as part of a DINGO-VLA project. We first
examine the accuracy of the method using a noise-free simulation and note that
the stacked image and flux estimation are dramatically improved compared to
traditional stacking. The extracted H\,\textsc{i} mass from the deconvolved
image agrees with the average input mass to within 3\%. However, with
traditional spectral stacking, the derived H\,\textsc{i} is incorrect by
greater than a factor of 2. For a more realistic case of a stack with finite
S/N, we also produced 20 different noise realisations to closely mimic the
properties of the DINGO-VLA interferometric survey. We recovered the predicted
average H\,\textsc{i} mass to within $\sim$4\%. Compared with traditional
spectral stacking, this technique extends the range of science applications
where stacking can be used, and is especially useful for characterizing the
emission from extended sources with interferometers.


  Coupling dynamical charges to gauge fields can result in highly non-local
interactions with a linear confining potential. As a consequence, individual
particles bind into mesons which, in one dimension, become the new constituents
of emergent Luttinger liquids. Furthermore, at commensurate fillings, different
Mott-insulating states can be stabilized by including nearest-neighbour (NN)
interactions among charges. However, rich phase diagrams expected in such
models have not been fully explored and still lack comprehensive theoretical
explanation. Here, by combining numerical and analytical tools, we study a
simple one-dimensional $\mathbb{Z}_2$ lattice gauge theory at half-filling,
where U$(1)$ matter is coupled to gauge fields and interacts through NN
repulsion. We uncover a rich phase diagram where the local NN interaction
stabilizes a Mott state of individual charges (or partons) on the one hand, and
a Luttinger liquid of confined mesons on the other. Furthermore, at the
interface between these two phases, we uncover a highly frustrated regime
arising due to the competition between the local NN repulsion and the non-local
confining interactions, realizing a pre-formed parton plasma. Our work is
motivated by the recent progress in ultracold atom experiments, where such
simple model could be readily implemented. For this reason we calculate the
static structure factor which we propose as a simple probe to explore the phase
diagram in an experimental setup.


  In this talk, we review recent developments towards the calculation of
multi-loop scattering amplitudes. In particular, we discuss how the
colour-kinematics duality can provide new integral relations at one-loop level
via the Loop-Tree duality formalism. On the other hand, in order to compute
scattering amplitudes at one- and two-loop level, numerically and analytically,
we describe the preliminary automation of the adaptive integrand decomposition
algorithm. We show preliminary results on the analytic reduction of the $\mu
e$-elastic scattering at one- and two-loop level.


  Let Alt_n be the vector space of all alternating n-by-n complex matrices, on
which the complex general linear group GL_n acts by $x \mapsto g x g^t$. The
aim of this paper is to show that Pfaffian of a certain matrix whose entries
are multiplication operators or derivations acting on polynomials on Alt_n
provides a generating function for the GL_n-invariant differential operators
that play a role in the skew Capelli identity, with coefficients the Hermite
polynomials.


  In a previous preprint we defined an energy associated to every embedding of
a surface into $R^n$ or $S^n$. This energy is invariant under Moebius
tranformations and the "round" sphere is its only absolute minimum. Here we
sketch a proof of the compactness property for a variant of it. The details
will appear elsewhere.


  Recognizing the entailment relation showed that its influence to extract the
semantic inferences in wide-ranging natural language processing domains (text
summarization, question answering, etc.) and enhanced the results of their
output. For Arabic language, few attempts concerns with Arabic entailment
problem. This paper aims to increase the entailment accuracy for Arabic texts
by resolving negation of the text-hypothesis pair and determining the polarity
of the text-hypothesis pair whether it is Positive, Negative or Neutral. It is
noticed that the absence of negation detection feature gives inaccurate results
when detecting the entailment relation since the negation revers the truth. The
negation words are considered stop words and removed from the text-hypothesis
pair which may lead wrong entailment decision. Another case not solved
previously, it is impossible that the positive text entails negative text and
vice versa. In this paper, in order to classify the text-hypothesis pair
polarity, a sentiment analysis tool is used. We show that analyzing the
polarity of the text-hypothesis pair increases the entailment accuracy. to
evaluate our approach we used a dataset for Arabic textual entailment (ArbTEDS)
consisted of 618 text-hypothesis pairs and showed that the Arabic entailment
accuracy is increased by resolving negation for entailment relation and
analyzing the polarity of the text-hypothesis pair.


  It is well known that Neural Network (network) performance often degrades
when a network is used in novel operating domains that differ from its training
and testing domains. This is a major limitation, as networks are being
integrated into safety critical, cyber-physical systems that must work in
unconstrained environments, e.g., perception for autonomous vehicles. Training
networks that generalize to novel operating domains and that extract robust
features is an active area of research, but previous work fails to predict what
the network performance will be in novel operating domains. We propose the task
Network Generalization Prediction: predicting the expected network performance
in novel operating domains. We describe the network performance in terms of an
interpretable Context Subspace, and we propose a methodology for selecting the
features of the Context Subspace that provide the most information about the
network performance. We identify the Context Subspace for a pretrained Faster
RCNN network performing pedestrian detection on the Berkeley Deep Drive (BDD)
Dataset, and demonstrate Network Generalization Prediction accuracy within 5%
or less of observed performance. We also demonstrate that the Context Subspace
from the BDD Dataset is informative for completely unseen datasets, JAAD and
Cityscapes, where predictions have a bias of 10% or less.


  We propose a theoretical framework for an adaptive learning rate policy for
the Mean Absolute Error loss function and Quantile loss function and evaluate
its effectiveness for regression tasks. The framework is based on the theory of
Lipschitz continuity, specifically utilizing the relationship between learning
rate and Lipschitz constant of the loss function. Based on experimentation, we
have found that the adaptive learning rate policy enables up to 20x faster
convergence compared to a constant learning rate policy.


  We report a measurement of $e^+e^-$ pairs from semileptonic heavy-flavor
decays in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Exploring the mass
and transverse-momentum dependence of the yield, the bottom decay contribution
can be isolated from charm, and quantified by comparison to {\sc pythia} and
{\sc mc@nlo} simulations. The resulting $b\bar{b}$-production cross section is
$\sigma^{d{\rm Au}}_{b\bar{b}}=1.37{\pm}0.28({\rm stat}){\pm}0.46({\rm
syst})$~mb, which is equivalent to a nucleon-nucleon cross section of
$\sigma^{NN}_{bb}=3.4\pm0.8({\rm stat}){\pm}1.1({\rm syst})\ \mu$b.


  The classical result of Patterson and Sullivan says that for a non-elementary
convex cocompact subgroup $\Gamma<\text{SO}^\circ (n,1)$, $n\ge 2$, the
Hausdorff dimension of the limit set of $\Gamma$ is equal to the critical
exponent of $\Gamma$. In this paper, we generalize this result for
self-joinings of convex cocompact groups in two ways. Let $\Delta$ be a
finitely generated group and $\rho_i:\Delta\to \text{SO}^\circ(n_i,1)$ be a
convex cocompact faithful representation of $\Delta$ for $1\le i\le k$.
Associated to $\rho=(\rho_1, \cdots, \rho_k)$, we consider the following
self-joining subgroup of $\prod_{i=1}^k \text{SO}(n_i,1)$:
$$\Gamma=\left(\prod_{i=1}^k\rho_i\right)(\Delta)=\{(\rho_1(g), \cdots,
\rho_k(g)):g\in \Delta\} .$$ (1). Denoting by $\Lambda\subset \prod_{i=1}^k
\mathbb{S}^{n_i-1}$ the limit set of $\Gamma$, we first prove that
$$\text{dim}_H \Lambda=\max_{1\le i\le k} \delta_{\rho_i}$$ where
$\delta_{\rho_i}$ is the critical exponent of the subgroup $\rho_{i}(\Delta)$.
  (2). Denoting by $\Lambda_u\subset \Lambda$ the $u$-directional limit set for
each $u=(u_1, \cdots, u_k)$ in the interior of the limit cone of $\Gamma$, we
obtain that for $k\le 3$, $$ \frac{\psi_\Gamma(u)}{\max_i u_i }\le \text{dim}_H
\Lambda_u \le \frac{\psi_\Gamma(u)}{\min_i u_i }$$ where
$\psi_\Gamma:\mathbb{R}^k\to \mathbb{R}\cup\{-\infty\}$ is the growth indicator
function of $\Gamma$.


  We study multiple tilings of 3-dimensional Euclidean space by a convex body.
In a multiple tiling, a convex body $P$ is translated with a discrete multiset
$\Lambda$ in such a way that each point of the space gets covered exactly $k$
times, except perhaps the translated copies of the boundary of $P$. It is known
that all possible multiple tilers in 3D are zonotopes. In 2D it was known by
the work of M. Kolountzakis that, unless $P$ is a parallelogram, the multiset
of translation vectors $\Lambda$ must be a finite union of translated lattices
(also known as quasi periodic sets). In that work [Kolountzakis, 2002], the
author asked whether the same quasi-periodic structure on the translation
vectors would be true in 3D. Here we prove that this conclusion is indeed true
for 3D.
  Namely, we show that if $P$ is a convex multiple tiler in 3D, with a discrete
multiset $\Lambda$ of translation vectors, then $\Lambda$ has to be a finite
union of translated lattices, unless $P$ belongs to a special class of
zonotopes. This exceptional class consists of two-flat zonotopes $P$, defined
by the Minkowski sum of $n+m$ line segments that lie in the union of two
different two-dimensional subspaces $H_1$ and $H_2$. Equivalently, a two-flat
zonotope $P$ may be thought of as the Minkowski sum of two 2-dimensional
symmetric polygons one of which may degenerate into a single line segment. It
turns out that rational two-flat zonotopes admit a multiple tiling with an
aperiodic (non-quasi-periodic) set of translation vectors $\Lambda$. We note
that it may be quite difficult to offer a visualization of these 3-dimensional
non-quasi-periodic tilings, and that we discovered them by using Fourier
methods.


  We present generalized supernova (SN) light curve (LC) models for a variety
of power inputs. We provide an expression for the power input that is produced
by self-similar forward and reverse shocks in SN ejecta - circumstellar matter
(CSM) interaction. We find that this ejecta-CSM interaction luminosity is in
agreement with results from multi-dimensional radiation hydrodynamics
simulations in the optically-thin case. We develop a model for the case of an
optically-thick CSM by invoking an approximation for the effects of radiative
diffusion. In the context of this model, we provide predictions for the time of
forward shock break-out from the optically-thick part of the CSM envelope. We
also introduce a hybrid LC model that incorporates ejecta-CSM interaction plus
Ni-56 and Co-56 radioactive decay input. We fit this hybrid model to the LC of
the Super-Luminous Supernova (SLSN) 2006gy. We find that this model provides a
better fit to the LC of this event than previously presented models. We also
address the relation between Type IIL and Type IIn SN with ejecta-CSM
interaction models. Forward and reverse shock power input due to CSM
interaction can produce the LCs of Type IIn SNe in terms of duration, shape and
decline rate. This model can also produce LCs that are symmetric in shape
around peak luminosity. We conclude that the observed LC variety of SNe Type
IIn and of the SLSNe is likely to be a byproduct of the large range of
conditions relevant to significant ejecta-CSM interaction as a power source.


  Autonomous mobile robots are usually faced with challenging situations when
driving in complex environments. Namely, they have to recognize the static and
dynamic obstacles, plan the driving path and execute their motion. For
addressing the issue of perception and path planning, in this paper, we
introduce OctoPath , which is an encoder-decoder deep neural network, trained
in a self-supervised manner to predict the local optimal trajectory for the
ego-vehicle. Using the discretization provided by a 3D octree environment
model, our approach reformulates trajectory prediction as a classification
problem with a configurable resolution. During training, OctoPath minimizes the
error between the predicted and the manually driven trajectories in a given
training dataset. This allows us to avoid the pitfall of regression-based
trajectory estimation, in which there is an infinite state space for the output
trajectory points. Environment sensing is performed using a 40-channel
mechanical LiDAR sensor, fused with an inertial measurement unit and wheels
odometry for state estimation. The experiments are performed both in simulation
and real-life, using our own developed GridSim simulator and RovisLab's
Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath
in different driving scenarios, both indoor and outdoor, while benchmarking our
system against a baseline hybrid A-Star algorithm and a regression-based
supervised learning method, as well as against a CNN learning-based optimal
path planning method.


  We review the design, theory, and applications of two dimensional periodic
lattices hosting conical intersections in their energy-momentum spectrum. The
best known example is the Dirac cone, where propagation is governed by an
effective Dirac equation, with electron spin replaced by a "fermionic"
half-integer pseudospin. However, in many systems such as metamaterials, modal
symmetries result in the formation of higher order conical intersections with
integer or "bosonic" pseudospin. The ability to engineer lattices with these
qualitatively different singular dispersion relations opens up many
applications, including superior slab lasers, generation of orbital angular
momentum, zero-index metamaterials, and quantum simulation of exotic phases of
relativistic matter.


  In six-dimensional supergravity, there is a natural sense in which matter
lying in certain representations of the gauge group is "generic," in the sense
that other "exotic" matter representations require more fine tuning. From
considerations of the dimensionality of the moduli space and anomaly
cancellation conditions, we find that the generic sets of matter
representations are well-defined for 6D supergravity theories with gauge groups
containing arbitrary numbers of nonabelian factors and $\operatorname{U}(1)$
factors. These generic matter representations also match with those that arise
in the most generic F-theory constructions, both in 6D and in 4D, with
non-generic matter representations requiring more exotic singularity types. The
analysis of generic versus exotic matter illuminates long-standing puzzles
regarding F-theory models with multiple $\operatorname{U}(1)$ factors and
provides a useful framework for analyzing the 6D "swampland" of apparently
consistent low-energy theories that cannot be realized through known string
constructions. We note also that the matter content of the standard model is
generic by the criteria used here only if the global structure is
$\operatorname{SU}(3)_\text{c} \times \operatorname{SU}(2)_\text{L} \times
\operatorname{U}(1)_Y / \mathbb{Z}_6$.


  We formulate an optimal transport problem for matrix-valued density
functions. This is pertinent in the spectral analysis of multivariable
time-series. The "mass" represents energy at various frequencies whereas, in
addition to a usual transportation cost across frequencies, a cost of rotation
is also taken into account. We show that it is natural to seek the
transportation plan in the tensor product of the spaces for the two
matrix-valued marginals. In contrast to the classical Monge-Kantorovich
setting, the transportation plan is no longer supported on a thin zero-measure
set.


  This article presents the results of several magnetic phases of doped
La$_{2-x}$Sr$_x$CuO$_4$ using density-functional theory with an added Hubbard
term (DFT+U). Doping factors from $x=0$ to 0.25 were examined. We found that a
bond centered stripe is the magnetic ground state for $x=1/8$ and $x=1/4$. No
stable stripe order was found for $x=1/6$. Analysis of the electron density
revealed that apical oxygen atoms, those located above and below the copper
atoms in the CuO$_2$ planes, hold a non negligible part of the holes at large
doping and present a small spin polarization. Finally, the charge
reorganization caused by the magnetic stripe modulation was studied for bond
centered and atom centered stripes.


  This paper attempts to strengthen the pursued research on social engineering
(SE) threat identification, and control, by means of the author's illustrated
classification, which includes attack types, determining the degree of possible
harm to each types of possible, known attacks, countermeasures by types of
threats that leads to loss of personal or corporate confidential information
(user id, passwords, closed documentation). Further, this analytical study will
become the starting point for deeper, practically oriented and tested research.


  A suspended fluid film with two free surfaces convects when a sufficiently
large voltage is applied across it. We present a linear stability analysis for
this system. The forces driving convection are due to the interaction of the
applied electric field with space charge which develops near the free surfaces.
Our analysis is similar to that for the two-dimensional B\'enard problem, but
with important differences due to coupling between the charge distribution and
the field. We find the neutral stability boundary of a dimensionless control
parameter ${\cal R}$ as a function of the dimensionless wave number ${\kappa}$.
${\cal R}$, which is proportional to the square of the applied voltage, is
analogous to the Rayleigh number. The critical values ${{\cal R}_c}$ and
${\kappa_c}$ are found from the minimum of the stability boundary, and its
curvature at the minimum gives the correlation length ${\xi_0}$. The
characteristic time scale ${\tau_0}$, which depends on a second dimensionless
parameter ${\cal P}$, analogous to the Prandtl number, is determined from the
linear growth rate near onset. ${\xi_0}$ and ${\tau_0}$ are coefficients in the
Ginzburg-Landau amplitude equation which describes the flow pattern near onset
in this system. We compare our results to recent experiments.


  In this paper, we present a discontinuous Galerkin solver based on previous
work by Markert et al. (2021) for magneto-hydrodynamics in form of a new fluid
solver module integrated into the established and well-known multi-physics
simulation code FLASH. Our goal is to enable future research on the
capabilities and potential advantages of discontinuous Galerkin methods for
complex multi-physics simulations in astrophysical settings. We give specific
details and adjustments of our implementation within the FLASH framework and
present extensive validations and test cases, specifically its interaction with
several other physics modules such as (self-)gravity and radiative transfer. We
conclude that the new DG solver module in FLASH is ready for use in
astrophysics simulations and thus ready for assessments and investigations.


  This paper develops a controller synthesis method for distributed LQG control
problems under output-feedback. We consider a system consisting of three
interconnected linear subsystems with a delayed information sharing structure.
While the state-feedback case has previously been solved, the extension to
output-feedback is nontrivial as the classical separation principle fails. To
find the optimal solution, the controller is decomposed into two independent
components: a centralized LQG-optimal controller under delayed state
observations, and a sum of correction terms based on additional local
information available to decision makers. Explicit discrete-time equations are
derived whose solutions are the gains of the optimal controller.


  Large language models (LLMs) find increasing applications in many fields.
Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4 and Bard) are assessed - in
their current form, as publicly available - for their ability to recognize
Alzheimer's Dementia (AD) and Cognitively Normal (CN) individuals using textual
input derived from spontaneous speech recordings. Zero-shot learning approach
is used at two levels of independent queries, with the second query
(chain-of-thought prompting) eliciting more detailed than the first. Each LLM
chatbot's performance is evaluated on the prediction generated in terms of
accuracy, sensitivity, specificity, precision and F1 score. LLM chatbots
generated three-class outcome ("AD", "CN", or "Unsure"). When positively
identifying AD, Bard produced highest true-positives (89% recall) and highest
F1 score (71%), but tended to misidentify CN as AD, with high confidence (low
"Unsure" rates); for positively identifying CN, GPT-4 resulted in the highest
true-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance
(moderate "Unsure" rates). Overall, three LLM chatbots identify AD vs CN
surpassing chance-levels but do not currently satisfy clinical application.


  With their continued increase in coverage and quality, data collected from
personal air quality monitors has become an increasingly valuable tool to
complement existing public health monitoring systems over urban areas. However,
the potential of using such `citizen science data' for automatic early warning
systems is hampered by the lack of models able to capture the high resolution,
nonlinear spatio-temporal features stemming from local emission sources such as
traffic, residential heating and commercial activities. In this work, we
propose a machine learning approach to forecast high frequency spatial fields
which has two distinctive advantages from standard neural network methods in
time: 1) sparsity of the neural network via a spike-and-slab prior, and 2) a
small parametric space. The introduction of stochastic neural networks
generates additional uncertainty, and in this work we propose a fast approach
for ensure that the forecast is correctly assessed (calibration), both
marginally and spatially. We focus on assessing exposure to urban air pollution
in San Francisco, and our results suggest an improvement of over 58% in the
mean squared error over standard time series approach with a calibrated
forecast for up to 5 days.


  Mobile robots are traditionally developed to be reactive and avoid collisions
with surrounding humans, often moving in unnatural ways without following
social protocols, forcing people to behave very differently from human-human
interaction rules. Humans, on the other hand, are seamlessly able to understand
why they may interfere with surrounding humans and change their behavior based
on their reasoning, resulting in smooth, intuitive avoiding behaviors. In this
paper, we propose an approach for a mobile robot to avoid interfering with the
desired paths of surrounding humans. We leverage a library of previously
observed trajectories to design a decision-tree based interpretable monitor
that: i) predicts whether the robot is interfering with surrounding humans, ii)
explains what behaviors are causing either prediction, and iii) plans
corrective behaviors if interference is predicted. We also propose a validation
scheme to improve the predictive model at run-time. The proposed approach is
validated with simulations and experiments involving an unmanned ground vehicle
(UGV) performing go-to-goal operations in the presence of humans, demonstrating
non-interfering behaviors and run-time learning.


  Let $0<a<b<\infty$ be fixed scalars. Assign independently to each edge in the
lattice $\mathbb{Z}^2$ the value $a$ with probability $p$ or the value $b$ with
probability $1-p$. For all $u,v\in\mathbb{Z}^2$, let $T(u,v)$ denote the first
passage time between $u$ and $v$. We show that there are points
$x\in\mathbb{R}^2$ such that the ``time constant'' in the direction of $x$,
namely, $\lim_{n\to\infty}n^{-1}\mathbf{E}_p[T(\mathbf{0},nx)],$ is not a three
times differentiable function of $p$.


  Catalogues and other records of aurora-borealis events were used to study the
long-term spatial and temporal variation of these phenomena in the period from
1700 to 1905 in the Northern Hemisphere. For this purpose, geographic and
geomagnetic coordinates were assigned to approximately 27 000 auroral events
with more than 80 000 observations. They were analysed separately in three
large-scale areas: i) Europe and North Africa, ii) North America, and iii)
Asia. There was a clear need to fill some gaps existing in the records so as to
have a reliable proxy of solar activity, especially during the 18th century. In
order to enhance the long-term variability, an 11-year smoothing window was
applied to the data. Variations in the cumulative numbers of auroral events
with latitude (in both geographic and geomagnetic coordinates) were used to
discriminate between the two main solar sources: coronal mass ejections and
high-speed streams from coronal holes. The characteristics of the associated
aurorae correlate differently with the solar-activity cycle.


  Using high precision Monte Carlo simulations and a mean-field theory, we
explore coarsening phenomena in a simple driven diffusive system. The model is
reminiscent of vehicular traffic on a two-lane ring road. At sufficiently high
density, the system develops jams (clusters) which coarsen with time. A key
parameter is the passing probability, $\gamma$. For small values of $\gamma$,
the growing clusters display dynamic scaling, with a growth exponent of 2/3.
For larger values of $\gamma$, the growth exponent must be adjusted, suggesting
the ordered (jammed) state is not a genuine phase but rather a finite size
effect.


  We derive a macroscopic heat equation for the temperature of a pinned
harmonic chain subject to a periodic force at its right side and in contact
with a heat bath at its left side.
  The microscopic dynamics in the bulk is given by the Hamiltonian equation of
motion plus a reversal of the velocity of a particle occurring independently
for each particle at exponential times, with rate $\gamma$. The latter produces
a finite heat conductivity. Starting with an initial probability distribution
for a chain of $n$ particles we compute the local temperature given by the
expected value of the local energy and current. Scaling space and time
diffusively yields, in the $n\to+\infty$ limit, the heat equation for the
macroscopic temperature profile $T(t,u),$ $t>0$, $u \in [0,1]$. It is to be
solved for initial conditions $T(0,u)$ and specified $T(t,0)=T_-$, the
temperature of the left heat reservoir and a fixed heat flux $J$, entering the
system at $u=1$. $J$ is the work done by the periodic force which is computed
explicitly for each $n$.


  Generative adversarial imitation learning (GAIL) has shown promising results
by taking advantage of generative adversarial nets, especially in the field of
robot learning. However, the requirement of isolated single modal
demonstrations limits the scalability of the approach to real world scenarios
such as autonomous vehicles' demand for a proper understanding of human
drivers' behavior. In this paper, we propose a novel multi-modal GAIL
framework, named Triple-GAIL, that is able to learn skill selection and
imitation jointly from both expert demonstrations and continuously generated
experiences with data augmentation purpose by introducing an auxiliary skill
selector. We provide theoretical guarantees on the convergence to optima for
both of the generator and the selector respectively. Experiments on real driver
trajectories and real-time strategy game datasets demonstrate that Triple-GAIL
can better fit multi-modal behaviors close to the demonstrators and outperforms
state-of-the-art methods.


  Collisions of metastable antiprotonic helium with atoms of medium induce
transitions between hyperfine structure sublevels as well as shifts and
broadenings of the microwave M1 spectral lines. We consider these phenomena in
the framework of a model with scalar and tensor interactions between
antiprotonic and ordinary helium atoms. S-matrix is obtained by solving
coupled-channels equations involving 4 HFS sublevels (F=L\pm 1/2, J=F\pm 1/2)
of the nL level and relative angular momenta up to l=5 at the kinetic energy
E<25 K. The calculated spin-flip cross sections are less than elastic ones by
four orders of value in the case \Delta J=\pm 1, and by seven orders of value
in the case \Delta F=\pm 1, \Delta J=0 or \Delta J=\pm 2. The considered cross
sections reveal a resonance behaviour at very low energy (E about 1 - 4 K
depending on the model parameters). At the density N=3\times 10^{20} cm^{-3}
and T=6 K we obtain the relaxation time =160 ns, the frequency shift \Delta\nu\
= 80 kHz and the frequency broadening \gamma \lesssim 5.9 MHz for M1 spectral
lines of the favored transitions (\Delta F=\pm 1, \Delta J=\pm 1). The results
are compatible with the recent experimental data obtained by a
laser-microwave-laser resonance method. With the temperature rising up to 25 K
the rate of relaxation \lambda=1/\tau as well as shift and broadening of the M1
microwave lines are lowered by a factor 1.5 - 2 reflecting the displacement of
a mean kinetic energy from the region of resonance scattering.


  It has been noted previously that the sizable areal density of midgap states
which must exist on any non-{n0m} surface of a d-wave superconductor can lead
to a giant magnetic moment. Here we show that this effect is observable, and
discuss two precise ways to observe it: (i) by directly measuring magnetic
moment in a system with a large density of internal {110} surfaces, or (ii) by
performing spin-polarized tunneling on a {110} surface. In both cases a
sufficiently large magnetic field should be applied in the [1$\bar 1$0]
direction.


  We exhibit existence of non-trivial solutions of some fractional linear
Schr\"odinger equations which are radial and vanish at the origin. This is in
stark contrast to what happens in the local case. We also prove analogous
results in the presence of a Hardy potential.


  Human de-occlusion, which aims to infer the appearance of invisible human
parts from an occluded image, has great value in many human-related tasks, such
as person re-id, and intention inference. To address this task, this paper
proposes a dynamic mask-aware transformer (DMAT), which dynamically augments
information from human regions and weakens that from occlusion. First, to
enhance token representation, we design an expanded convolution head with
enlarged kernels, which captures more local valid context and mitigates the
influence of surrounding occlusion. To concentrate on the visible human parts,
we propose a novel dynamic multi-head human-mask guided attention mechanism
through integrating multiple masks, which can prevent the de-occluded regions
from assimilating to the background. Besides, a region upsampling strategy is
utilized to alleviate the impact of occlusion on interpolated images. During
model learning, an amodal loss is developed to further emphasize the recovery
effect of human regions, which also refines the model's convergence. Extensive
experiments on the AHP dataset demonstrate its superior performance compared to
recent state-of-the-art methods.


  Invisible plasma content in blazar jets such as protons and/or thermal
electron-positron ($e^{\pm}$) pairs is explored through combined arguments of
dynamical and radiative processes. By comparing physical quantities required by
the internal shock model with those obtained through the observed
radio-to-gamma-ray spectra for Mrk 421, we find the existence of a copious
amount of invisible plasma in the jet. We speculate that the blazar sequence
could arise from variations of total amount and/or blending ratio of $e^{\pm}$
pair and electron-proton plasma.


  We provide a construction for the kromatic symmetric function
$\overline{X}_G$ of a graph introduced by Crew, Pechenik, and Spirkl using
combinatorial (linearly compact) Hopf algebras. As an application, we show that
$\overline{X}_G$ has a positive expansion into multifundamental quasisymmetric
functions. We also study two related quasisymmetric $q$-analogues of
$\overline{X}_G$, which are $K$-theoretic generalizations of the quasisymmetric
chromatic function of Shareshian and Wachs. We classify exactly when one of
these analogues is symmetric. For the other, we derive a positive expansion
into symmetric Grothendieck functions for graphs $G$ that are natural unit
interval orders.


  A core goal of Continuous Integration (CI) is to make small incremental
changes to software projects, which are integrated frequently into a mainline
repository or branch. This paper presents an empirical study that investigates
if developers adjust their commit activity towards the above-mentioned goal
after projects start using CI. We analyzed the commit and merge activity in 93
GitHub projects that introduced the hosted CI system Travis CI, but have
previously been developed for at least one year before introducing CI. In our
analysis, we only found one non-negligible effect, an increased merge ratio,
meaning that there were more merging commits in relation to all commits after
the projects started using Travis CI. This effect has also been reported in
related work. However, we observed the same effect in a random sample of 60
GitHub projects not using CI. Thus, it is unlikely that the effect is caused by
the introduction of CI alone. We conclude that: (1) in our sample of projects,
the introduction of CI did not lead to major changes in developers' commit
activity, and (2) it is important to compare the commit activity to a baseline
before attributing an effect to a treatment that may not be the cause for the
observed effect.


  It is shown that reflective laser refractometery at Brewster angle can be
usefull for precision measurements of refractive indexes (RI) in the
transparency band of various films of nanoscale thickness. The RI measurements
of nanoscale porous film on the basis of gadolinium orthosilicate and quartz
have been carried out as first experience. It is shown that surface light
scattering in such films that is connected with clustering of nanoscale pores
can decrease the accuracy of the RI measurements at Brewster angle. Estimated
physical dependence RI stipulated by the film thickness reduction (3D-2D
transition) in the range of (20-160)nm has not been not detected.


  The identification of preexisting near-surface faults represents a piece of
crucial information needed to correctly assess the seismic hazard of any area.
The mapping of these structures is particularly challenging in densely
populated and heavily urbanized areas. We use ambient seismic noise recorded by
a dense array in Seal Beach, California, to image shallow fault lines via a
reflected surface wave analysis. Our results highlight the presence of
previously unknown shallow faults that correlate remarkably well with shallow
seismicity and active survey images.


  In this paper, we investigate the ruin probabilities of non-homogeneous risk
models. By employing martingale method, the Lundberg-type inequalities of ruin
probabilities of non-homogeneous renewal risk models are obtained under weak
assumptions. In addition, for the periodic and quasi-periodic risk models the
adjustment coefficients of the Lundberg-type inequalities are obtained.
Finally, examples are presented to show that estimations obtained in this paper
are more accurate and the ruin probability in non-homogeneous risk models may
be fast decreasing which is impossible for the case of homogeneity.


  A thermodynamically related model is developed for describing elastic
rubber-like behavior of amorphous and crystallizing polymers and demonstrated
on example of simple extension. Both the “entropic” and
“energetic” motions of polymer chains that contribute in
macroscopic elastic deformation are taken into account. The model displays a
continuous transition from entropy to energetic elasticity, without common
singularity caused by finite extensibility of polymer chains. A multi-scale
molecular approach, based on recent literature concepts has been employed for
evaluations of continuum parameters. For crystallizing polymers, a simple model
is developed for the stress-induced crystallization, which describes the stress
reinforcement caused by the formation of long, needle-like polymer crystals.


  Input-Convex Neural Networks (ICNNs) are networks that guarantee convexity in
their input-output mapping. These networks have been successfully applied for
energy-based modelling, optimal transport problems and learning invariances.
The convexity of ICNNs is achieved by using non-decreasing convex activation
functions and non-negative weights. Because of these peculiarities, previous
initialisation strategies, which implicitly assume centred weights, are not
effective for ICNNs. By studying signal propagation through layers with
non-negative weights, we are able to derive a principled weight initialisation
for ICNNs. Concretely, we generalise signal propagation theory by removing the
assumption that weights are sampled from a centred distribution. In a set of
experiments, we demonstrate that our principled initialisation effectively
accelerates learning in ICNNs and leads to better generalisation. Moreover, we
find that, in contrast to common belief, ICNNs can be trained without
skip-connections when initialised correctly. Finally, we apply ICNNs to a
real-world drug discovery task and show that they allow for more effective
molecular latent space exploration.


  We discuss the production of new matter particles that are predicted by many
extensions of the Standard Model, at future high--energy pp, eP and $e^+e^-$
linear colliders. We focus on the case of exotic, excited and di--fermions.
Talk given at "Beyond the Standard Model IV", Lake Tahoe, Ca., December 1994.


  We present a language independent, unsupervised method for building word
embeddings using morphological expansion of text. Our model handles the problem
of data sparsity and yields improved word embeddings by relying on training
word embeddings on artificially generated sentences. We evaluate our method
using small sized training sets on eleven test sets for the word similarity
task across seven languages. Further, for English, we evaluated the impacts of
our approach using a large training set on three standard test sets. Our method
improved results across all languages.


  In this paper, we propose a pipeline to convert grade school level algebraic
word problem into program of a formal languageA-IMP. Using natural language
processing tools, we break the problem into sentence fragments which can then
be reduced to functions. The functions are categorized by the head verb of the
sentence and its structure, as defined by (Hosseini et al., 2014). We define
the function signature and extract its arguments from the text using dependency
parsing. We have a working implementation of the entire pipeline which can be
found on our github repository.


  Several self-stabilizing time division multiple access (TDMA) algorithms are
proposed for sensor networks. In addition to providing a collision-free
communication service, such algorithms enable the transformation of programs
written in abstract models considered in distributed computing literature into
a model consistent with sensor networks, i.e., write all with collision (WAC)
model. Existing TDMA slot assignment algorithms have one or more of the
following properties: (i) compute slots using a randomized algorithm, (ii)
assume that the topology is known upfront, and/or (iii) assign slots
sequentially. If these algorithms are used to transform abstract programs into
programs in WAC model then the transformed programs are probabilistically
correct, do not allow the addition of new nodes, and/or converge in a
sequential fashion. In this paper, we propose a self-stabilizing deterministic
TDMA algorithm where a sensor is aware of only its neighbors. We show that the
slots are assigned to the sensors in a concurrent fashion and starting from
arbitrary initial states, the algorithm converges to states where
collision-free communication among the sensors is restored. Moreover, this
algorithm facilitates the transformation of abstract programs into programs in
WAC model that are deterministically correct.


  The shifted fractional trapezoidal rule (SFTR) with a special shift is
adopted to construct a finite difference scheme for the time-fractional
Allen-Cahn (tFAC) equation. Some essential key properties of the weights of
SFTR are explored for the first time. Based on these properties, we rigorously
demonstrate the discrete energy decay property and maximum-principle
preservation for the scheme. Numerical investigations show that the scheme can
resolve the intrinsic initial singularity of such nonlinear fractional
equations as tFAC equation on uniform meshes without any correction. Comparison
with the classic fractional BDF2 and L2-1$_\sigma$ method further validates the
superiority of SFTR in solving the tFAC equation. Experiments concerning both
discrete energy decay and discrete maximum-principle also verify the
correctness of the theoretical results.


  We develop a technique for removing stellar variability in the light curves
of $\delta$-Scuti and similar stars. Our technique, which we name the Linear
Algorithm for Significance Reduction (LASR), subtracts oscillations from a time
series by minimizing their statistical significance in frequency space. We
demonstrate that LASR can subtract variable signals of near-arbitrary
complexity and can robustly handle close frequency pairs and overtone
frequencies. We demonstrate that our algorithm performs an equivalent fit as
prewhitening to the straightforward variable signal of KIC 9700322. We also
show that LASR provides a better fit to seismic activity than prewhitening in
the case of the complex $\delta$-Scuti KOI-976.


  Most discussions of chaotic scattering systems are devoted to two-dimensional
systems. It is of considerable interest to extend these studies to the, in
general, more realistic case of three dimensions. In this context, it is
conceptually important to investigate the quality of semiclassical methods as a
function of the dimensionality. As a model system, we choose various three
dimensional generalizations of the famous three disk problem which played a
central role in the study of chaotic scattering in two dimensions. We present a
quantum-mechanical treatment of the hyperbolic scattering of a point particle
off a finite number of non-overlapping and non-touching hard spheres in three
dimensions. We derive expressions for the scattering matrix S and its
determinant. The determinant of S decomposes into two parts, the first one
contains the product of the determinants of the individual one-sphere
S-matrices and the second one is given by a ratio involving the determinants of
a characteristic KKR-type matrix and its conjugate. We justify our approach by
showing that all formal manipulations in these derivations are correct and that
all the determinants involved which are of infinite dimension exist. Moreover,
for all complex wave numbers, we conjecture a direct link between the
quantum-mechanical and semiclassical descriptions: The semiclassical limit of
the cumulant expansion of the KKR-type matrix is given by the Gutzwiller-Voros
zeta function plus diffractional corrections in the curvature expansion. This
connection is direct since it is not based on any kind of subtraction scheme
involving bounded reference systems. We present numerically computed resonances
and compare them with the corresponding data for the similar two-dimensional
N-disk systems and with semiclassical calculations.


  We present a simple and accurate span-based model for semantic role labeling
(SRL). Our model directly takes into account all possible argument spans and
scores them for each label. At decoding time, we greedily select higher scoring
labeled spans. One advantage of our model is to allow us to design and use
span-level features, that are difficult to use in token-based BIO tagging
approaches. Experimental results demonstrate that our ensemble model achieves
the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012
datasets, respectively.


  In software industry, the DevOps is an increasingly adopting software
development paradigm. Towards the sustainable DevOps adoption, there is a need
to transform the organization Culture, Automation, Measurement and Sharing
(CAMS) aspects concerning to core theme of continues development and
operations. The software organizations face several complexities while
implementing the DevOps principles. The sustainable DevOps implementation
assist the software organizations to develop the quality projects with good
return on investment. This evidence-based study aims to explore the guidelines
of sustainable DevOps implementation, reported in literature and industry
practices. Using systematic literature review and questionnaire survey, we
identified the 48 guidelines for sustainable DevOps implementation. We further
develop a decision-making framework aiming to assist the practitioners to
consider the most significant set of guidelines on priority. The results show
that out of CAMS, culture is the most important principle for sustainable
DevOps implementation. Moreover, (i) enterprises should focus on building a
collaborative culture with shared goals, (ii) assess your organization
readiness to utilize a microservices architecture and (iii) educate executives
at your company about the benefits of DevOps to gain resource and budget
support are the highest priority guidelines for sustainable DevOps
implementation. We believe that this in-depth study helps the practitioners to
understand the core principles and guidelines for sustainable DevOps
implementation.


  Based the Alfven wave oscillation model (AWOM) and relativistic precession
model (RPM) for twin kHz QPOs, we estimate the emission positions of most
detected kHz QPOs to be at r=18+-3 km (R/15km) except Cir X-1 at r = 30\+-5 km
(R/15km). For the proposed Keplerian frequency as an upper limit to kHz QPO,
the spin effects in Kerr Spacetime are discussed, which have about a 5% (2%)
modification for that of the Schwarzchild case for the spin frequency of 1000
(400) Hz.The application to the four typical QPO sources, Cir X-1, Sco X-1, SAX
J1808.4-3658 and XTE 1807-294, is mentioned.


  We present maps of the Large and Small Magellanic Clouds from combined South
Pole Telescope (SPT) and Planck data. The Planck satellite observes in nine
bands, while the SPT data used in this work were taken with the three-band
SPT-SZ camera, The SPT-SZ bands correspond closely to three of the nine Planck
bands, namely those centered at 1.4, 2.1, and 3.0 mm. The angular resolution of
the Planck data ranges from 5 to 10 arcmin, while the SPT resolution ranges
from 1.0 to 1.7 arcmin. The combined maps take advantage of the high resolution
of the SPT data and the long-timescale stability of the space-based Planck
observations to deliver robust brightness measurements on scales from the size
of the maps down to ~1 arcmin. In each band, we first calibrate and
color-correct the SPT data to match the Planck data, then we use noise
estimates from each instrument and knowledge of each instrument's beam to make
the inverse-variance-weighted combination of the two instruments' data as a
function of angular scale. We create maps assuming a range of underlying
emission spectra and at a range of final resolutions. We perform several
consistency tests on the combined maps and estimate the expected noise in
measurements of features in the maps. We compare maps from this work to maps
from the Herschel HERITAGE survey, finding general consistency between the
datasets. All data products described in this paper are available for download
from the NASA Legacy Archive for Microwave Background Data Analysis server.


  Circumbinary disks are considered to exist in a wide variety of astrophysical
objects, e.g., young binary stars, protoplanetary systems, and massive binary
black hole systems in active galactic nuclei (AGNs). However, there is no
definite evidence for the circumbinary disk except for some in a few young
binary star systems. In this Letter, we study possible oscillation modes in
circumbinary disks around eccentric and circular binaries. We find that
progarde, nonaxisymmetric waves are induced in the inner part of the
circumbinary disk by the tidal potential of the binary. Such waves would cause
variabilities in emission line profiles from circumbinary disks. Because of
prograde precession of the waves, the distance between each component of the
binary and the inner edge of the circumbinary disk varies with the beat period
between the precession period of the wave and the binary orbital period. As a
result, light curves from the circumbinary disks are also expected to vary with
the same period. The current study thus provides a new method to detect
circumbinary disks in various astrophysical systems.


  In superconducting systems in which inversion and time-reversal symmetry are
simultaneously broken the critical current for positive and negative current
bias can be different. For superconducting systems formed by Josephson
junctions (JJs) this effect is termed Josephson diode effect. In this work, we
study the Josephson diode effect for a superconducting quantum interference
device (SQUID) formed by a topological JJ with a 4$\pi$-periodic current-phase
relationship and a topologically trivial JJ. We show how the fractional
Josephson effect manifests in the Josephson diode effect with the application
of a magnetic field and how tuning properties of the trivial SQUID arm can lead
to diode polarity switching. We then investigate the AC response and show that
the polarity of the diode effect can be tuned by varying the AC power and
discuss differences between the AC diode effect of asymmetric SQUIDs with no
topological JJ and SQUIDs in which one JJ is topological.


  We show that the duality between F-theory and the CHL string in seven
dimensions defines algebraic correspondences between K3 surfaces polarized by
the rank-ten lattices $H \oplus N$ and $H\oplus E_8(-2)$. In the special case
when the F-theory admits an additional anti-symplectic involution or,
equivalently, the CHL string admits a symplectic one, both moduli spaces
coincide. In this case, we derive an explicit parametrization for the F-theory
compactifications dual to the CHL string, using an auxiliary genus-one curve,
based on a construction given by Andr\'e Weil.


  In this paper, we quantify weak protein protein interactions in solution
using Cross-Interaction Chromatography (CIC) and Surface Plasmon Resonance
(SPR) and demonstrate that they can be modulated by the addition of free amino
acids. With CIC, we determined the second osmotic virial cross-interaction
coefficient (B23) as a proxy for the interaction strength between two different
proteins. We perform SPR experiments to establish the binding affinity between
the same proteins. With CIC, we show that the amino acids proline, glutamine,
and arginine render the protein cross-interactions more repulsive or
equivalently less attractive. Specifically, we measured B23 between lysozyme
(Lys) and bovine serum albumin (BSA) and between Lys and protein isolates (whey
and canola). We find that B23 increases when amino acids are added to the
solution even at millimolar concentrations, corresponding to protein ligand
stoichiometric ratios as low as 1 to 1. With SPR, we show that the binding
affinity between proteins can change by one order of magnitude when 10 mM of
glutamine are added. In the case of Lys and one whey protein isolate it changes
from the mM to the M, thus by three orders of magnitude. Interestingly, this
efficient modulation of the protein cross-interactions does not alter the
protein's secondary structure. The capacity of amino acids to modulate protein
cross-interactions at mM concentrations is remarkable and may have an impact
across fields in particular for specific applications in the food or
pharmaceutical industries.


  We describe the cryogenic system for SPIDER, a balloon-borne microwave
polarimeter that will map 8% of the sky with degree-scale angular resolution.
The system consists of a 1284 L liquid helium cryostat and a 16 L
capillary-filled superfluid helium tank, which provide base operating
temperatures of 4 K and 1.5 K, respectively. Closed-cycle helium-3 adsorption
refrigerators supply sub-Kelvin cooling power to multiple focal planes, which
are housed in monochromatic telescope inserts. The main helium tank is
suspended inside the vacuum vessel with thermally insulating fiberglass
flexures, and shielded from thermal radiation by a combination of two vapor
cooled shields and multi-layer insulation. This system allows for an extremely
low instrumental background and a hold time in excess of 25 days. The total
mass of the cryogenic system, including cryogens, is approximately 1000 kg.
This enables conventional long duration balloon flights. We will discuss the
design, thermal analysis, and qualification of the cryogenic system.


  We study the planetary system of $\upsilon$~Andromed{\ae}, considering the
three-body problem formed by the central star and the two largest planets,
$\upsilon$~And~\emph{c} and
  $\upsilon$~And~\emph{d}. We adopt a secular, three-dimensional model and
initial conditions within the range of the observed values. The numerical
integrations highlight that the system is orbiting around a one-dimensional
elliptic torus (i.e., a periodic orbit that is linearly stable). This invariant
object is used as a seed for an algorithm based on a sequence of canonical
transformations. The algorithm determines the normal form related to a KAM
torus, whose shape is in excellent agreement with the orbits of the secular
model. We rigorously prove that the algorithm constructing the final KAM
invariant torus is convergent, by adopting a suitable technique based on a
computer-assisted proof.


  This paper investigates whether the gravity model (GM) can explain the
statistical properties of the International Trade Network (ITN). We fit data on
international-trade flows with a GM specification using alternative fitting
techniques and we employ GM estimates to build a weighted predicted ITN, whose
topological properties are compared to observed ones. Furthermore, we propose
an estimation strategy to predict the binary ITN with a GM. We find that the GM
successfully replicates the weighted-network structure of the ITN, only if one
fixes its binary architecture equal to the observed one. Conversely, the GM
performs very badly when asked to predict the presence of a link, or the level
of the trade flow it carries, whenever the binary structure must be
simultaneously estimated.


  Mirror Symmetry for a large class of three dimensional $\mathcal{N}=4$
supersymmetric gauge theories has a natural explanation in terms of M-theory
compactified on a product of $\text{ALE}$ spaces. A pair of such mirror duals
can be described as two different deformations of the eleven-dimensional
supergravity background $\mathcal{M}=\mathbb{R}^{2,1} \times \text{ALE}_{1}
\times \text{ALE}_{2}$, to which they flow in the deep IR. Using the $A-D-E$
classification of $\text{ALE}$ spaces, we present a neat way to catalogue dual
quiver gauge theories that arise in this fashion. In addition to the well-known
examples studied in \cite{Intriligator:1996ex}, \cite{deBoer:1996mp}, this
procedure leads to new sets of dual theories. For a certain subset of dual
theories which arise from the aforementioned M-theory background with an
$A$-type $\text{ALE}_{1}$ and a $D$-type $\text{ALE}_2$, we verify the duality
explicitly by a computation of partition functions of the theories on $S^3$,
using localization techniques . We derive the relevant mirror map and discuss
its agreement with predictions from the Type IIB brane construction for these
theories.


  Control barrier functions have shown great success in addressing control
problems with safety guarantees. These methods usually find the next safe
control input by solving an online quadratic programming problem. However,
model uncertainty is a big challenge in synthesizing controllers. This may lead
to the generation of unsafe control actions, resulting in severe consequences.
In this paper, we develop a learning framework to deal with system uncertainty.
Our method mainly focuses on learning the dynamics of the control barrier
function, especially for high relative degree with respect to a system. We show
that for each order, the time derivative of the control barrier function can be
separated into the time derivative of the nominal control barrier function and
a remainder. This implies that we can use a neural network to learn the
remainder so that we can approximate the dynamics of the real control barrier
function. We show by simulation that our method can generate safe trajectories
under parametric uncertainty using a differential drive robot model.


  The Angstrom Project is using a global network of 2m-class telescopes to
conduct a high cadence pixel microlensing survey of the bulge of the Andromeda
Galaxy (M31), with the primary aim of constraining its underlying bulge mass
distribution and stellar mass function. Here we investigate the feasibility of
using such a survey to detect planets in M31. We estimate the efficiency of
detecting signals for events induced by planetary systems as a function of
planet/star mass ratio and separation, source type and background M31 surface
brightness. We find that for planets of a Jupiter-mass or above that are within
the lensing zone (~1 -3 AU) detection is possible above 3 $\sigma$, with
detection efficiencies ~3% for events associated with giant stars, which are
the typical source stars of pixel-lensing surveys. A dramatic improvement in
the efficiency of ~40 -- 60% is expected if follow-up observations on an 8m
telescope are made possible by a real-time alert system.


  This paper presents a methodology for ensuring that the composition of
multiple Control Barrier Functions (CBFs) always leads to feasible conditions
on the control input, even in the presence of input constraints. In the case of
a system subject to a single constraint function, there exist many methods to
generate a CBF that ensures constraint satisfaction. However, when there are
multiple constraint functions, the problem of finding and tuning one or more
CBFs becomes more challenging, especially in the presence of input constraints.
This paper addresses this challenge by providing tools to 1) decouple the
design of multiple CBFs, so that a CBF can be designed for each constraint
function independently of other constraints, and 2) ensure that the set
composed from all the CBFs together is a viability domain. Thus, a quadratic
program subject to all the CBFs simultaneously is always feasible. The utility
of this methodology is then demonstrated in simulation for a nonlinear
orientation control system.


  Within the context of supersymmetric hybrid inflation, we study a
multiple-stage inflationary scenario that can generate a primordial spectrum of
adiabatic density perturbations with a break at $k_b \simeq 0.05 h Mpc^{-1}$.
The presence of such a break is supported by the APM galaxy survey data. We
consider a specific model within this scenario and confront it with
observational data. We reproduce the angular power spectrum of CMB
anisotropies, and also account for the break in the power spectrum of galaxy
clustering. In addition, we find a value for $\sigma_8$ in agreement with the
one deduced from observations. A characteristic property of the spectrum is a
drop of the spectral index from $n \simeq 1$ to $n \sim 0.6$ at $k \sim k_b$.


  We discuss numerical solution of Altarelli-Parisi equations in a
Laguerre-polynomial method and in a brute-force method. In the Laguerre method,
we get good accuracy by taking about twenty Laguerre polynomials in the
flavor-nonsinglet case. Excellent evolution results are obtained in the singlet
case by taking only ten Laguerre polynomials. The accuracy becomes slightly
worse in the small and large $x$ regions, especially in the nonsinglet case.
These problems could be implemented by using the brute-force method; however,
running CPU time could be significantly longer than the one in the Laguerre
method.


  JSON Schema is the de-facto standard schema language for JSON data. The
language went through many minor revisions, but the most recent versions of the
language added two novel features, dynamic references and annotation-dependent
validation, that change the evaluation model. Modern JSON Schema is the name
used to indicate all versions from Draft 2019-09, which are characterized by
these new features, while Classical JSON Schema is used to indicate the
previous versions.
  These new "modern" features make the schema language quite difficult to
understand, and have generated many discussions about the correct
interpretation of their official specifications; for this reason we undertook
the task of their formalization. During this process, we also analyzed the
complexity of data validation in Modern JSON Schema, with the idea of
confirming the PTIME complexity of Classical JSON Schema validation, and we
were surprised to discover a completely different truth: data validation, that
is expected to be an extremely efficient process, acquires, with Modern JSON
Schema features, a PSPACE complexity.
  In this paper, we give the first formal description of Modern JSON Schema,
which we consider a central contribution of the work that we present here. We
then prove that its data validation problem is PSPACE-complete. We prove that
the origin of the problem lies in dynamic references, and not in
annotation-dependent validation. We study the schema and data complexities,
showing that the problem is PSPACE-complete with respect to the schema size
even with a fixed instance, but is in PTIME when the schema is fixed and only
the instance size is allowed to vary. Finally, we run experiments that show
that there are families of schemas where the difference in asymptotic
complexity between dynamic and static references is extremely visible, even
with small schemas.


  Joint synthesis of images and segmentation masks with generative adversarial
networks (GANs) is promising to reduce the effort needed for collecting image
data with pixel-wise annotations. However, to learn high-fidelity image-mask
synthesis, existing GAN approaches first need a pre-training phase requiring
large amounts of image data, which limits their utilization in restricted image
domains. In this work, we take a step to reduce this limitation, introducing
the task of one-shot image-mask synthesis. We aim to generate diverse images
and their segmentation masks given only a single labelled example, and
assuming, contrary to previous models, no access to any pre-training data. To
this end, inspired by the recent architectural developments of single-image
GANs, we introduce our OSMIS model which enables the synthesis of segmentation
masks that are precisely aligned to the generated images in the one-shot
regime. Besides achieving the high fidelity of generated masks, OSMIS
outperforms state-of-the-art single-image GAN models in image synthesis quality
and diversity. In addition, despite not using any additional data, OSMIS
demonstrates an impressive ability to serve as a source of useful data
augmentation for one-shot segmentation applications, providing performance
gains that are complementary to standard data augmentation techniques. Code is
available at https://github.com/ boschresearch/one-shot-synthesis


  The generalized second-order partial derivatives of 1/r, where r is the
radial distance in 3D, are obtained using a result of the potential theory of
classical analysis. Some non-spherical regularization alternatives to the
standard spherical-regularization expression for the derivatives are derived.
The utility of a spheroidal-regularization expression is illustrated on an
example from classical electrodynamics.


  The Gamma ray Burst Monitor (GBM) on board Fermi Gamma-ray Space Telescope
has been providing continuous data to the astronomical community since 2008
August 12. We will present the results of the analysis of the first three years
of these continuous data using the Earth occultation technique to monitor a
catalog of 209 sources. Although the occultation technique is in principle
quite simple, in practice there are many complications including the dynamic
instrument response, source confusion, and scattering in the Earth's
atmosphere, which will be described. We detect 99 sources, including 40
low-mass X-ray binary/neutron star systems, 31 high-mass X-ray binary/neutron
star systems, 12 black hole binaries, 12 active galaxies, 2 other sources, plus
the Crab Nebula and the Sun. Nine of these sources are detected in the 100-300
keV band, including seven black-hole binaries, the active galaxy Cen A, and the
Crab. The Crab and Cyg X-1 are also detected in the 300-500 keV band. GBM
provides complementary data to other sky monitors below 100 keV and is the only
all-sky monitor above 100 keV. In our fourth year of monitoring, we have
already increased the number of transient sources detected and expect several
of the weaker persistent sources to cross the detection threshold. I will
briefly discuss these new sources and what to expect from our five year
occultation catalog.


  The general solution for the static, spherical and asymptotically flat
braneworld is derived by solving the bulk Einstein equation and braneworld
dynamics. We show that it involves a large arbitrariness, which reduces the
predictability of the theory. Ways out of the difficulty are discussed.


  This paper presents a structure-preserving Bayesian approach for learning
nonseparable Hamiltonian systems using stochastic dynamic models allowing for
statistically-dependent, vector-valued additive and multiplicative measurement
noise. The approach is comprised of three main facets. First, we derive a
Gaussian filter for a statistically-dependent, vector-valued, additive and
multiplicative noise model that is needed to evaluate the likelihood within the
Bayesian posterior. Second, we develop a novel algorithm for cost-effective
application of Bayesian system identification to high-dimensional systems.
Third, we demonstrate how structure-preserving methods can be incorporated into
the proposed framework, using nonseparable Hamiltonians as an illustrative
system class. We assess the method's performance based on the forecasting
accuracy of a model estimated from single-trajectory data. We compare the
Bayesian method to a state-of-the-art machine learning method on a canonical
nonseparable Hamiltonian model and a chaotic double pendulum model with small,
noisy training datasets. The results show that using the Bayesian posterior as
a training objective can yield upwards of 724 times improvement in Hamiltonian
mean squared error using training data with up to 10% multiplicative noise
compared to a standard training objective. Lastly, we demonstrate the utility
of the novel algorithm for parameter estimation of a 64-dimensional model of
the spatially-discretized nonlinear Schr\"odinger equation with data corrupted
by up to 20% multiplicative noise.


  The Kennedy-like receiver is a quasi-optimal receiver employed in binary
phase-shift-keyed communication schemes with coherent states. It is based on
the interference of the two signals encoding the message with a reference local
oscillator and on/off photodetection. We show both theoretically and
experimentally that it is possible to extract useful information about the
phase reference by Bayesian processing of the very same data sample used to
discriminate the signals shot by shot. We demonstrate that the minimum
uncertainty in phase estimation, given by the inverse of the Fisher information
associated with the statistics of the collected data, can be achieved. We also
numerically and experimentally investigate the performances of our
phase-estimation method in the presence of phase noise, when either on/off or
photon-number resolving detectors are employed.


  ARM recently introduced a security feature called Memory Tagging Extension or
MTE, which is designed to defend against common memory safety vulnerabilities,
such as buffer overflow and use after free. In this paper, we examine three
aspects of MTE. First, we survey how modern software systems, such as Glibc,
Android, Chrome, Linux, and LLVM, use MTE. We identify some common weaknesses
and propose improvements. Second, we develop and experiment with an
architectural improvement to MTE that improves its memory efficiency. Our
design enables longer memory tags, which improves the accuracy of MTE. Finally,
we discuss a number of enhancements to MTE to improve its security against
certain memory safety attacks.


  We study the effect of H I ionizing photons escaping from high-redshift
(high-z) galaxies have on the He II ionizing ultraviolet background (UVB)
radiation. While these photons do not directly interact with He II ions, we
show that they play an important role, through radiative transport, in
modifying the shape of He II ionizing part of UVB spectrum. Within the observed
range of UV escape from galaxies, we show that the rapid increase in He II
Lyman alpha effective optical depth at z~2.7 can naturally be explained by
radiative transport effects. Therefore, the relationship between a well
measured He II Lyman alpha effective optical depth and the redshift in the
post-He II reionization era can be used to place additional constraints on the
redshift evolution of UV escape from high-z galaxies. Our study also suggests
that the escape fraction of H I ionizing photons from galaxies has an important
role in the fluctuations of the He II ionizing UVB.


  We present near-infrared (NIR) time-series spectroscopy, as well as
complementary ultraviolet (UV), optical, and NIR data, of the Type Ia supernova
(SN Ia) iPTF13ebh, which was discovered within two days from the estimated time
of explosion. The first NIR spectrum was taken merely 2.3 days after explosion
and may be the earliest NIR spectrum yet obtained of a SN Ia. The most striking
features in the spectrum are several NIR C I lines, and the C I {\lambda}1.0693
{\mu}m line is the strongest ever observed in a SN Ia. Interestingly, no strong
optical C II counterparts were found, even though the optical spectroscopic
time series began early and is densely-cadenced. Except at the very early
epochs, within a few days from the time of explosion, we show that the strong
NIR C I compared to the weaker optical C II appears to be general in SNe Ia.
iPTF13ebh is a fast decliner with {\Delta}m15(B) = 1.79 $\pm$ 0.01, and its
absolute magnitude obeys the linear part of the width-luminosity relation. It
is therefore categorized as a "transitional" event, on the fast-declining end
of normal SNe Ia as opposed to subluminous/91bg-like objects. iPTF13ebh shows
NIR spectroscopic properties that are distinct from both the normal and
subluminous/91bg-like classes, bridging the observed characteristics of the two
classes. These NIR observations suggest composition and density of the inner
core similar to that of 91bg-like events, and a deep reaching carbon burning
layer not observed in slower declining SNe Ia. There is also a substantial
difference between the explosion times inferred from the early-time light curve
and the velocity evolution of the Si II {\lambda}0.6355 {\mu}m line, implying a
long dark phase of ~ 4 days.


  This paper presents a new multi-agent model for simulating malware
propagation in device-to-device (D2D) 5G networks. This model allows to
understand and analyze mobile malware-spreading dynamics in such highly
dynamical networks. Additionally, we present a theoretical study to validate
and benchmark our proposed approach for some basic scenarios that are less
complicated to model mathematically and also to highlight the key parameters of
the model. Our simulations identify critical thresholds for "no propagation"
and for "maximum malware propagation" and make predictions on the
malware-spread velocity as well as device-infection rates. To the best of our
knowledge, this paper is the first study applying agent-based simulations for
malware propagation in D2D.


  Citation count of a paper is a commonly used proxy for evaluating the
significance of a paper in the scientific community. Yet citation measures are
widely criticized for failing to accurately reflect the true impact of a paper.
Thus, we propose CausalCite, a new way to measure the significance of a paper
by assessing the causal impact of the paper on its follow-up papers. CausalCite
is based on a novel causal inference method, TextMatch, which adapts the
traditional matching framework to high-dimensional text embeddings. TextMatch
encodes each paper using text embeddings from large language models (LLMs),
extracts similar samples by cosine similarity, and synthesizes a counterfactual
sample as the weighted average of similar papers according to their similarity
values. We demonstrate the effectiveness of CausalCite on various criteria,
such as high correlation with paper impact as reported by scientific experts on
a previous dataset of 1K papers, (test-of-time) awards for past papers, and its
stability across various subfields of AI. We also provide a set of findings
that can serve as suggested ways for future researchers to use our metric for a
better understanding of the quality of a paper. Our code is available at
https://github.com/causalNLP/causal-cite.


  CdTe and CdS are emerging as the most promising materials for thin film
photovoltaics in the quest of the achievement of grid parity. The major
challenge for the advancement of grid parity is the achievement of high quality
at the same time as low fabrication cost. The present paper reports the results
of the new deposition technique, Pulsed Plasma Deposition (PPD), for the growth
of the CdTe layers on CdS/ZnO/quartz and quartz substrates. The PPD method
allows to deposit at low temperature. The optical band gap of deposited layers
is 1.50 eV, in perfect accord with the value reported in the literature for the
crystalline cubic phase of the CdTe. The films are highly crystalline with a
predominant cubic phase, a random orientation of the grains of the film and
have an extremely low surface roughness of 4.6\pm0.7 nm r.m.s.. The low
roughness, compared to traditional thermal deposition methods (close space
sublimation and vapour transport) permits the reduction of the active absorber
and n-type semiconductor layers resulting in a dramatic reduction of material
usage and the relative deposition issues like safety, deposition rate and
ultimately cost


  Cheng constructed product expansions from twists of elliptic genera of
symmetric powers of K3 surfaces that are related to M_24 moonshine. We study
which of them are Siegel modular forms. If the predicted level is
non-composite, they are modular, and their powers can be represented as
products of rescaled Borcherds products.


  In this paper, the problem of distributed optimization is studied via a
network of agents. Each agent only has access to a stochastic gradient of its
own objective function in the previous time, and can communicate with its
neighbors via a network. To handle this problem, an online distributed clipped
stochastic gradient descent algorithm is proposed. Dynamic regrets are used to
capture the performance of the algorithm. Particularly, the high probability
bounds of regrets are analyzed when the stochastic gradients satisfy the
heavy-tailed noise condition. For the convex case, the offline benchmark of the
dynamic regret is to seek the minimizer of the objective function each time.
Under mild assumptions on the graph connectivity, we prove that the dynamic
regret grows sublinearly with high probability under a certain clipping
parameter. For the non-convex case, the offline benchmark of the dynamic regret
is to find the stationary point of the objective function each time. We show
that the dynamic regret grows sublinearly with high probability if the
variation of the objective function grows within a certain rate. Finally,
numerical simulations are provided to demonstrate the effectiveness of our
theoretical results.


  We discuss the current mass dependence of the basic quantities of the quark
models -- constituent quark mass M and quark condensate i<psi^dagger psi >. The
framework of the consideration is QCD instanton vacuum model.


  Vulnerability of dedicated hash functions to various attacks has made the
task of designing hash function much more challenging. This provides us a
strong motivation to design a new cryptographic hash function viz. HF-hash.
This is a hash function, whose compression function is designed by using first
32 polynomials of HFE Challenge-1 with 64 variables by forcing remaining 16
variables as zero. HF-hash gives 256 bits message digest and is as efficient as
SHA-256. It is secure against the differential attack proposed by Chabaud and
Joux as well as by Wang et. al. applied to SHA-0 and SHA-1.


  The automorphisms of the symmetrized polydisc $\mathbb G_n$ are well-known
and are given in the coordinates of the polydisc in \cite{E:Z}. We find an
explicit formula for the automorphisms of $\mathbb G_n$ in its own coordinates.
If $\tau$ is an automorphism of $\mathbb G_n$, then $\tau(S_1,\dots,S_{n-1},P)$
is a $\Gamma_n$-contraction, where a $\Gamma_n$-contraction is a commuting
$n$-tuple of Hilbert space operators for which the closed symmetrized polydisc
$\Gamma_n$ is a spectral set. Corresponding to every $\Gamma_n$-contraction
$(S_1,\dots,S_{n-1},P)$, there exist $n-1$ unique operators $A_1,\dots,A_{n-1}$
such that \[ S_i-S_{n-i}^*P=D_PA_iD_P\,, \quad D_P=(I-P^*P)^{1/2}\,, \] for
$i=1,\dots, n-1$. This unique $(n-1)$-tuple $(A_1,\dots,A_{n-1})$, which is
called the fundamental operator tuple or $\mathcal F_O$-tuple of
$(S_1,\dots,S_{n-1},P)$ in literature, plays central role in every section of
operator theory on $\Gamma_n$. We find an explicit form of the $\mathcal
F_O$-tuple of $\tau (S_1,\dots,S_{n-1},P)$ when $n=3$. We show by an example
that a $\Gamma_n$-contraction may not have commuting $\mathcal F_O$-tuple.
Also, we obtain a necessary and sufficient condition under which two
$\Gamma_n$-contractions are unitarily equivalent.


  In this paper, we consider a tree-structured data model used in many
commercial databases like Dremel, F1, JSON stores. We define identity and
referential constraints within each tree-structured record. The query language
is a variant of SQL and flattening is used as an evaluation mechanism. We
investigate querying in the presence of these constraints, and point out the
challenges that arise from taking them into account during query evaluation.


  We use the complete, X-ray flux-limited ROSAT Bright Survey (RBS) to measure
the space density of magnetic cataclysmic variables (mCVs). The survey provides
complete optical identification of all sources with count rate >0.2/s over half
the sky ($|b|>30^\circ$), and detected 6 intermediate polars (IPs) and 24
polars. If we assume that the 30 mCVs included in the RBS are representative of
the intrinsic population, the space density of mCVs is $8^{+4}_{-2} \times
10^{-7}\,{\rmpc^{-3}}$. Considering polars and IPs separately, we find
$\rho_{polar}=5^{+3}_{-2} \times 10^{-7}\,{\rm pc^{-3}}$ and
$\rho_{IP}=3^{+2}_{-1} \times 10^{-7}\,{\rm pc^{-3}}$. Allowing for a 50%
high-state duty cycle for polars (and assuming that these systems are below the
RBS detection limit during their low states) doubles our estimate of
$\rho_{polar}$ and brings the total space density of mCVs to $1.3^{+0.6}_{-0.4}
\times 10^{-6}\,{\rm pc^{-3}}$. We also place upper limits on the sizes of
faint (but persistent) mCV populations that might have escaped detection in the
RBS. Although the large uncertainties in the $\rho$ estimates prevent us from
drawing strong conclusions, we discuss the implications of our results for the
evolutionary relationship between IPs and polars, the fraction of CVs with
strongly magnetic white dwarfs (WDs), and for the contribution of mCVs to
Galactic populations of hard X-ray sources at $L_X \ga 10^{31} {\rm erg/s}$.
Our space density estimates are consistent with the very simple model where
long-period IPs evolve into polars and account for the whole short-period polar
population. We find that the fraction of WDs that are strongly magnetic is not
significantly higher for CV primaries than for isolated WDs. Finally, the space
density of IPs is sufficiently high to explain the bright, hard X-ray source
population in the Galactic Centre.


  Conflicting interpretations of experimental data preclude the understanding
of the quantum magnetic state of spin-orbit coupled $d^2$ double perovskites.
Whether the ground state is a Janh-Teller-distorted order of quadrupoles or the
hitherto elusive octupolar order remains debated. We resolve this uncertainty
through direct calculations of all-rank inter-site exchange interactions and
inelastic neutron scattering cross-section for the $d^2$ double perovskite
series Ba$_2M$OsO$_6$ ($M$= Ca, Mg, Zn). Using advanced many-body first
principles methods we show that the ground state is formed by ferro-ordered
octupoles coupled by superexchange interactions within the ground-state $E_g$
doublet. Computed ordering temperature of the single second-order
phase-transition is consistent with experimentally observed material-dependent
trends. Minuscule distortions of the parent cubic structure are shown to
qualitatively modify the structure of gaped magnetic excitations.


  We present an investigation of stochastic evolution in which a family of
evolution equations in $L^1$ are driven by continuous-time Markov processes.
These are examples of so-called piecewise deterministic Markov processes
(PDMP's) on the space of integrable functions. We derive equations for the
first moment and correlations (of any order) of such processes. We also
introduce the mean of the process at large time and describe its behaviour. The
results are illustrated by some simple, yet generic, biological examples
characterized by different one-parameter types of bifurcations.


  We investigate numerical solution of
Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) Q^2 evolution equations for
longitudinally polarized structure functions. Flavor nonsinglet and singlet
equations with next-to-leading-order $\alpha_s$ corrections are studied. A
brute-force method is employed. Dividing the variables x and Q^2 into small
steps, we simply solve the integrodifferential equations. Numerical results
indicate that accuracy is better than 1% in the region 10^{-5}<x<0.8 if more
than two-hundred Q^2 steps and more than one-thousand x steps are taken. Our
evolution results are compared with polarized experimental data of the spin
asymmetry A_1 by the SLAC-E130, SLAC-E143, EMC, and SMC collaborations. The
comparison indicates that we cannot assume A_1 is independent of Q^2. We
provide a FORTRAN program for the Q^2 evolution and devolution of polarized
nonsinglet-quark, singlet-quark, Delta q_i+ Delta q-bar_i, and gluon
distributions (and corresponding structure functions).


  In this paper, we generalize the improved quasi-particle model proposed in J.
Cao et al., [ Phys. Lett. B {\bf711}, 65 (2012)] from finite temperature and
zero chemical potential to the case of finite chemical potential and zero
temperature, and calculate the equation of state (EOS) for (2+1) flavor Quantum
Chromodynamics (QCD) at zero temperature and high density. We first calculate
the partition function at finite temperature and chemical potential, then go to
the limit $T=0$ and obtain the equation of state (EOS) for cold and dense QCD,
which is important for the study of neutron stars. Furthermore, we use this EOS
to calculate the quark-number density, the energy density, the quark-number
susceptibility and the speed of sound at zero temperature and finite chemical
potential and compare our results with the corresponding ones in the existing
literature.


  Brillouin light scattering spectroscopy was used along with detailed
composition information obtained from electron probe microanalysis to study the
influence of octahedral site chemistry on the elastic properties of biotite
crystals. Elastic wave velocities for a range of directions in the AC and BC
crystallographic planes were obtained for each crystal by application of the
Brillouin equation with refractive indices and phonon frequencies obtained from
the Becke line test and spectral peak positions, respectively. In general,
velocities increase with decreasing Fe content, approach those of muscovite at
low Fe concentrations. 12 of the 13 elastic constants for the full monoclinic
symmetry were obtained for each crystal by fitting analytic expressions for the
velocities as functions of propagation direction and elastic constants to
corresponding experimental data, with the remaining constant estimated using
hexagonal symmetry. Elastic constants C11, C22, and C66 are comparable to those
of muscovite and show little change with Fe concentration due to strong bonding
within layers. In contrast, nearly all remaining constants show a pronounced
dependence on Fe content, likely due to the weak interlayer bonding. Similar
behaviour is shown by elastic stability, which falls greatly as Fe
concentration increases, and elastic anisotropy within the basal cleavage
plane, which decreases with Fe content. This consistent dependency of all
measured elastic constants on Fe content suggests that biotite elasticity is a
function of octahedral site chemistry and gives a method to determine the
elastic constants and elastic stability of most biotite compositions provided,
Fe or Mg concentrations are known. Moreover, the good agreement between elastic
constants of Fe-poor biotite and those of phlogopite obtained from DFT
simulations indicate a method to predict elastic properties of biotites.


  We investigate the phase structure of three-dimensional quantum gravity
coupled to an Ising spin system by means of numerical simulations. The quantum
gravity part is modelled by the summation over random simplicial manifolds, and
the Ising spins are located in the center of the tetrahedra, which constitute
the building blocks of the piecewise linear manifold. We find that the coupling
between spin and geometry is weak away from the critical point of the Ising
model. At the critical point there is clear coupling, which however does not
seem to change the first order transition between the ``hot'' and ``cold''
phase of three dimensional simplicial quantum gravity observed earlier.


  The low-temperature ferroelectric polarization of multiferroic $YMn_2O_5$ is
completely reversed at a critical pressure of 10 kbar and the phase transition
from the incommensurate to the commensurate magnetic phase is induced by
pressures above 14 kbar. The high-pressure data correlate with thermal
expansion measurements indicating a significant lattice strain at the
low-temperature transition into the incommensurate phase. The results support
the exchange striction model for the ferroelectricity in multiferroic
$RMn_2O_5$ compounds and they show the importance of magnetic frustration as
well as the spin-lattice coupling.


  We consider a perimeter defense problem in a planar conical environment
comprising a single turret that has a finite range and non-zero service time.
The turret seeks to defend a concentric perimeter against $N\geq 2$ intruders.
Upon release, each intruder moves radially towards the perimeter with a fixed
speed. To capture an intruder, the turret's angle must be aligned with that of
the intruder's angle and must spend a specified service time at that
orientation. We address offline and online versions of this optimization
problem. Specifically, in the offline version, we establish that in general
parameter regimes, this problem is equivalent to solving a Travelling
Repairperson Problem with Time Windows (TRP-TW). We then identify specific
parameter regimes in which there is a polynomial time algorithm that maximizes
the number of intruders captured. In the online version, we present a
competitive analysis technique in which we establish a fundamental guarantee on
the existence of at best $(N-1)$-competitive algorithms. We also design two
online algorithms that are provably $1$ and $2$-competitive in specific
parameter regimes.


  Large top quark flavor changing through neutral currents is expected by many
extensions of the standard model. Direct and indirect searches for flavor
changing neutral currents (FCNC) in the top quark decays to an up type quark
(up,charm) and a Higgs boson are presented. We probe the observability of the
top-Higgs FCNC couplings through the process e$^-$e$^+ \rightarrow t
(\rightarrow \ell \nu_{\ell} b) \,\, \bar{t} (\rightarrow q H)$, where $\ell$ =
e, $\mu$ and $q$ reflects up and charm quarks. It is shown that the branching
ratio $Br(t\rightarrow qH)$ can be probed down to $1.12\times 10^{-3}$ at
$95\%$ C.L. at the center-of-mass energy of 500 GeV with the integrated
luminosity of 3000 fb$^{-1}$. We also update the constraint on the top-Higgs
FCNC coupling using the electroweak precision observables related to
$Z\rightarrow c\bar{c}$ decay.


  The purpose of this paper is to extend the cohomology and conformal
derivation theories of the classical Lie conformal algebras to Hom-Lie
conformal algebras. In this paper, we develop cohomology theory of Hom-Lie
conformal algebras and discuss some applications to the study of deformations
of regular Hom-Lie conformal algebras. Also, we introduce
$\alpha^k$-derivations of multiplicative Hom-Lie conformal algebras and study
their properties.


  Kolmogorov's first axiom of probability is probability takes values between 0
and 1; however, in Cox's derivation of probability having a maximum value of
unity is arbitrary since he derives probability as a tool to rank degrees of
plausibility. Probability can then be used to make inferences in instances of
incomplete information, which is the foundation of Baysian probability theory.
This article formulates a rule, which if obeyed, allows probability to take
complex values and still be consistent with the interpretation of probability
theory as being a tool to rank plausibility. It is then shown that Kirkwood
distributions and the conditional complex probability distributions proposed by
Hofmann do not obey this rule and therefore cannot rank plausibility. Not only
do these quasiprobability distributions relax Kolmogorov's first axiom of
probability, they also are void of the defining property of a probability
distribution from a Coxian and Baysian perspective - they lack the ability to
rank plausibility.


  In order to describe correctly the interplay of extrinsic and intrinsic
spin-orbit mechanisms to the spin Hall effect, it is necessary to consider
different sources of spin relaxation. We take into account the spin relaxation
time $\tau_{DP}$ due to the Dyakonov-Perel mechanism as well as the
Elliot-Yafet spin-relaxation time $\tau_s$ due to the spin-orbit scattering
from impurities. The total spin Hall conductivity depends crucially on the
ratio $\tau_s /\tau_{DP}$.


  We present 170 optical spectra of 35 low-redshift stripped-envelope
core-collapse supernovae observed by the Carnegie Supernova Project-I between
2004 and 2009. The data extend from as early as -19 days (d) prior to the epoch
of B-band maximum to +322 d, with the vast majority obtained during the
so-called photospheric phase covering the weeks around peak luminosity. In
addition to histogram plots characterizing the red-shift distribution, number
of spectra per object, and the phase distribution of the sample, spectroscopic
classification is also provided following standard criteria. The CSP-I spectra
are electronically available and a detailed analysis of the data set is
presented in a companion paper being the fifth and final paper of the series


  We report low temperature specific heat measurements of
Pr$_{1-x}$Ca$_{x}$MnO$_{3}$ ($0.3\leq x \leq 0.5$) and
La$_{0.5}$Ca$_{0.5}$MnO$_{3}$ with and without applied magnetic field. An
excess specific heat, $C^{\prime}(T)$, of non-magnetic origin associated with
charge ordering is found for all the samples. A magnetic field sufficient to
induce the transition from the charge-ordered state to the ferromagnetic
metallic state does not completely remove the $C^{\prime}$ contribution. This
suggests that the charge ordering is not completely destroyed by a "melting"
magnetic field. In addition, the specific heat of the
Pr$_{1-x}$Ca$_{x}$MnO$_{3}$ compounds exhibit a large contribution linear in
temperature ($\gamma T$) originating from magnetic and charge disorder.


  (Abridged) Context. This is the second paper of a series devoted to the WIde
Field Nearby Galaxy-cluster Survey (WINGS). WINGS is a long term project which
is gathering wide-field, multi-band imaging and spectroscopy of galaxies in a
complete sample of 77 X-ray selected, nearby clusters (0.04<z<0.07) located far
from the galactic plane (|b|>=20deg). The main goal of this project is to
establish a local reference for evolutionary studies of galaxies and galaxy
clusters. Aims. This paper presents the optical (B,V) photometric catalogs of
the WINGS sample and describes the procedures followed to construct them.
Methods. We have constructed photometric catalogs based on wide-field images in
B and V bands using SExtractor. Photometry has been performed on images in
which large galaxies and halos of bright stars were removed after modeling them
with elliptical isophotes. Results. We publish deep optical photometric
catalogs (90% complete at V~21.7, which translates to ~M*(V)+6 at mean
redshift), giving positions, geometrical parameters, and several total and
aperture magnitudes for all the objects detected. For each field we have
produced three catalogs containing galaxies, stars and objects of "unknown"
classification (~16%).