  The spacetimes obtained by Ernst's procedure for appending an external
magnetic field $B$ to a seed Kerr-Newman black hole are commonly believed to be
asymptotic to the static Melvin solution. We show that this is not in general
true. Unless the electric charge of the black hole satisfies $Q= jB(1+ 1/4 j^2
B^4)$, where $j$ is the angular momentum of the original seed solution, an
ergoregion extends all the way from the black hole horizon to infinity. We give
a self-contained account of the solution-generating procedure, including
including explicit formulae for the metric and the vector potential. In the
case when $Q= jB(1+ 1/4 j^2 B^4)$, we show that there is an arbitrariness in
the choice of asymptotically timelike Killing field $K_\Omega=
\partial/\partial t+ \Omega \partial/\partial \phi$, because there is no
canonical choice of $\Omega$. For one choice, $\Omega=\Omega_s$, the metric is
asymptotically static, and there is an ergoregion confined to the neighbourhood
of the horizon. On the other hand, by choosing $\Omega=\Omega_H$, so that
$K_{\Omega_H}$ is co-rotating with the horizon, then for sufficiently large $B$
numerical studies indicate there is no ergoregion at all. For smaller values,
in a range $B_-<B<B_+$, there is a toroidal ergoregion outside and disjoint
from the horizon. If $B\le B_-$ this ergoregion expands all the way to infinity
in a cylindrical region near to the rotation axis. For black holes whose size
is small compared to the Melvin radius 2/B, we recover Wald's result that it is
energetically favourable for the hole to acquire a charge $2jB$.


  Random functional-linked types of neural networks (RFLNNs), e.g., the extreme
learning machine (ELM) and broad learning system (BLS), which avoid suffering
from a time-consuming training process, offer an alternative way of learning in
deep structure. The RFLNNs have achieved excellent performance in various
classification and regression tasks, however, the properties and explanations
of these networks are ignored in previous research. This paper gives some
insights into the properties of RFLNNs from the viewpoints of frequency domain,
and discovers the presence of frequency principle in these networks, that is,
they preferentially capture low-frequencies quickly and then fit the high
frequency components during the training process. These findings are valuable
for understanding the RFLNNs and expanding their applications. Guided by the
frequency principle, we propose a method to generate a BLS network with better
performance, and design an efficient algorithm for solving Poison's equation in
view of the different frequency principle presenting in the Jacobi iterative
method and BLS network.


  Experts across diverse disciplines are often interested in making sense of
large text collections. Traditionally, this challenge is approached either by
noisy unsupervised techniques such as topic models, or by following a manual
theme discovery process. In this paper, we expand the definition of a theme to
account for more than just a word distribution, and include generalized
concepts deemed relevant by domain experts. Then, we propose an interactive
framework that receives and encodes expert feedback at different levels of
abstraction. Our framework strikes a balance between automation and manual
coding, allowing experts to maintain control of their study while reducing the
manual effort required.


  We extend the usual projective Abel-Radon transform to the larger context of
a smooth complete toric variety X. We define and study toric concavity attached
to an algebraic splitting vector bundle on X and we prove a toric version of
the Abel-inverse theorem.


  We show that the strongly symmetric spectral convex compact sets are
precisely the normalized state spaces of finite-dimensional simple Euclidean
Jordan algebras and the simplices. Spectrality is the property that every state
has a convex decomposition into perfectly distinguishable pure states; strong
symmetry is transitivity, for each integer N, of the affine automorphism group
of the state space on lists of N perfectly distinguishable pure states.
Additional assumptions combine with this theorem to give simple
characterizations of finite-dimensional complex quantum state space.
  Important aspects of quantum and classical thermodynamics and of query
complexity have been generalized to classes of general probabilistic theories
(GPTs) satisfying natural postulates including or implying spectrality and
strong symmetry; our result shows that these apply to a narrower class of
theories than might have been hoped. Sorkin's notion of irreducibly k-th order
interference has been studied in the GPT framework and looked for in
experiments. Our result shows that the assumption of no higher-order (k > 2)
interference, used along with spectrality and strong symmetry to characterize
the same class of Jordan-algebraic convex sets by Barnum, Mueller, and Ududec
in arXiv:1403.4147, was superfluous. It also implies that Lee and Selby's
extension, on the assumption that interference has fixed maximal degree k, of
the important order square root of N lower bound on the quantum black-box query
complexity of searching N possibilities for one having a desired property
(which is achieved by Grover's quantum algorithm), to a class of theories
satisfying certain postulates allowing the formulation of a generalized notion
of query algorithm, actually applies in the Jordan-algebraic setting where
higher-order interference is not possible.


  This article reviews the progress of atomic force microscopy (AFM) in
ultra-high vacuum, starting with its invention and covering most of the recent
developments. Today, dynamic force microscopy allows to image surfaces of
conductors \emph{and} insulators in vacuum with atomic resolution. The mostly
used technique for atomic resolution AFM in vacuum is frequency modulation AFM
(FM-AFM). This technique, as well as other dynamic AFM methods, are explained
in detail in this article. In the last few years many groups have expanded the
empirical knowledge and deepened the theoretical understanding of FM-AFM.
Consequently, the spatial resolution and ease of use have been increased
dramatically. Vacuum AFM opens up new classes of experiments, ranging from
imaging of insulators with true atomic resolution to the measurement of forces
between individual atoms.


  The far-zone flux of energy contains hereditary (tail) contributions that
depend on the entire past history of the source. Using the multipolar
post-Minkowskian wave generation formalism, we propose and implement a
semi-analytical method in the frequency domain to compute these contributions
from the inspiral phase of a binary system of compact objects moving in
quasi-elliptical orbits up to 3PN order. The method explicitly uses the
quasi-Keplerian representation of elliptical orbits at 1PN order and exploits
the doubly periodic nature of the motion to average the 3PN fluxes over the
binary's orbit. Together with the instantaneous (non-tail) contributions
evaluated in a companion paper, it provides crucial inputs for the construction
of ready-to-use templates for compact binaries moving on quasi-elliptic orbits,
an interesting class of sources for the ground based gravitational wave
detectors such as LIGO and Virgo as well as space based detectors like LISA.


  Which quantum states can be reached by controlling open Markovian $n$-level
quantum systems? Here, we address reachable sets of coherently controllable
quantum systems with switchable coupling to a thermal bath of temperature $T$.
The core problem reduces to a toy model of studying points in the standard
simplex allowing for two types of controls: (i) permutations within the
simplex, (ii) contractions by a dissipative semigroup. By illustration, we put
the problem into context and show how toy-model solutions pertain to the
reachable set of the original controlled Markovian quantum system. Beyond the
case $T=0$ (amplitude damping) we present new results for $0 <T < \infty$ using
methods of $d$-majorisation.


  In this paper, we consider the existence of nontrivial weak solutions to a
double critical problem involving fractional Laplacian with a Hardy term:
\begin{equation} \label{eq0.1} (-\Delta)^{s}u-{\gamma} {\frac{u}{|x|^{2s}}}=
{\frac{{|u|}^{ {2^{*}_{s}}(\beta)-2}u}{|x|^{\beta}}}+ \big [ I_{\mu}*
F_{\alpha}(\cdot,u) \big](x)f_{\alpha}(x,u),
  \ \ u \in {\dot{H}}^s(\R^{n}) \end{equation} where $s \in(0,1)$, $0\leq
\alpha,\beta<2s<n$, $\mu \in (0,n)$, $\gamma<\gamma_{H}$,
$I_{\mu}(x)=|x|^{-\mu}$, $F_{\alpha}(x,u)=\frac{ {|u(x)|}^{ {2^{\#}_{\mu}
}(\alpha)} }{ {|x|}^{ {\delta_{\mu} (\alpha)} } }$, $f_{\alpha}(x,u)=\frac{
{|u(x)|}^{{ 2^{\#}_{\mu} }(\alpha)-2}u(x) }{ {|x|}^{ {\delta_{\mu} (\alpha)} }
}$, $2^{\#}_{\mu} (\alpha)=(1-\frac{\mu}{2n})\cdot 2^{*}_{s} (\alpha)$,
$\delta_{\mu} (\alpha)=(1-\frac{\mu}{2n})\alpha$,
${2^{*}_{s}}(\alpha)=\frac{2(n-\alpha)}{n-2s}$ and
$\gamma_{H}=4^s\frac{\Gamma^2(\frac{n+2s}{4})} {\Gamma^2(\frac{n-2s}{4})}$. We
show that problem (\ref{eq0.1}) admits at least a weak solution under some
conditions.
  To prove the main result, we develop some useful tools based on a weighted
Morrey space. To be precise, we discover the embeddings \begin{equation}
\label{eq0.2} {\dot{H}}^s(\R^{n}) \hookrightarrow
{L}^{2^*_{s}(\alpha)}(\R^{n},|y|^{-\alpha}) \hookrightarrow
L^{p,\frac{n-2s}{2}p+pr}(\R^{n},|y|^{-pr}) \end{equation} where $s \in (0,1)$,
$0<\alpha<2s<n$, $p\in[1,2^*_{s}(\alpha))$, $r=\frac{\alpha}{ 2^*_{s}(\alpha)
}$; We also establish an improved Sobolev inequality.
  By using mountain pass lemma along with an improved Sobolev inequality, we
obtain a nontrivial weak solution to problem (\ref{eq0.1}) in a direct way. It
is worth while to point out that the improved Sobolev inequality could be
applied to simplify the proof of the main results in \cite{NGSS} and
\cite{RFPP}.


  Motivated by the possible di-gamma resonance at 750 GeV, we present a basis
of effective operators for the Standard Model plus a scalar singlet at
dimensions 5, 6, and 7. We point out that an earlier list at dimensions 5 and 6
contains two redundant operators at dimension 5.


  The study deals with the parallelization of finite element based
Navier-Stokes codes using domain decomposition and state-ofart sparse direct
solvers. There has been significant improvement in the performance of sparse
direct solvers. Parallel sparse direct solvers are not found to exhibit good
scalability. Hence, the parallelization of sparse direct solvers is done using
domain decomposition techniques. A highly efficient sparse direct solver
PARDISO is used in this study. The scalability of both Newton and modified
Newton algorithms are tested.


  L-BFGS is the state-of-the-art optimization method for many large scale
inverse problems. It has a small memory footprint and achieves superlinear
convergence. The method approximates Hessian based on an initial approximation
and an update rule that models current local curvature information. The initial
approximation greatly affects the scaling of a search direction and the overall
convergence of the method.
  We propose a novel, simple, and effective way to initialize the Hessian.
Typically, the objective function is a sum of a data-fidelity term and a
regularizer. Often, the Hessian of the data-fidelity is computationally
challenging, but the regularizer's Hessian is easy to compute.
  We replace the Hessian of the data-fidelity with a scalar and keep the
Hessian of the regularizer to initialize the Hessian approximation at every
iteration. The scalar satisfies the secant equation in the sense of ordinary
and total least squares and geometric mean regression. Our new strategy not
only leads to faster convergence, but the quality of the numerical solutions is
generally superior to simple scaling based strategies. Specifically, the
proposed schemes based on ordinary least squares formulation and geometric mean
regression outperform the state-of-the-art schemes.
  The implementation of our strategy requires only a small change of a standard
L-BFGS code. Our experiments on convex quadratic problems and non-convex image
registration problems confirm the effectiveness of the proposed approach.


  Spatio-temporal information is very important to capture the discriminative
cues between genuine and fake faces from video sequences. To explore such a
temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements
and head swing) across video frames are very critical. In this paper, we
propose a joint CNN-LSTM network for face anti-spoofing, focusing on the motion
cues across video frames. We first extract the high discriminative features of
video frames using the conventional Convolutional Neural Network (CNN). Then we
leverage Long Short-Term Memory (LSTM) with the extracted features as inputs to
capture the temporal dynamics in videos. To ensure the fine-grained motions
more easily to be perceived in the training process, the eulerian motion
magnification is used as the preprocessing to enhance the facial expressions
exhibited by individuals, and the attention mechanism is embedded in LSTM to
ensure the model learn to focus selectively on the dynamic frames across the
video clips. Experiments on Replay Attack and MSU-MFSD databases show that the
proposed method yields state-of-the-art performance with better generalization
ability compared with several other popular algorithms.


  In this paper, we prove some one level density results for low-lying zeros of
families of $L$-functions. More specifically, the families under consideration
are that of $L$-functions of holomorphic Hecke eigenforms of level 1 and weight
$k$ twisted with quadratic Dirichlet characters and that of cubic and quartic
Dirichlet $L$-functions.


  Galaxy clusters are an established and powerful test-bed for theories of both
galaxy evolution and cosmology. Accurate interpretation of cluster observations
often requires robust identification of the location of the centre. Using a
statistical sample of clusters drawn from a suite of cosmological simulations
in which we have explored a range of galaxy formation models, we investigate
how the location of this centre is affected by the choice of observable -
stars, hot gas, or the full mass distribution as can be probed by the
gravitational potential. We explore several measures of cluster centre: the
minimum of the gravitational potential, which would expect to define the centre
if the cluster is in dynamical equilibrium; the peak of the density; the centre
of BCG; and the peak and centroid of X-ray luminosity. We find that the centre
of BCG correlates more strongly with the minimum of the gravitational potential
than the X-ray defined centres, while AGN feedback acts to significantly
enhance the offset between the peak X-ray luminosity and minimum gravitational
potential. These results highlight the importance of centre identification when
interpreting clusters observations, in particular when comparing theoretical
predictions and observational data.


  A directed network connecting a set A to a set B is a digraph containing an
a-b path for each a in A and b in B. Vertices in the directed network not in A
or B are called Steiner points. We show that in a finitely compact metric space
in which geodesics exist, any two finite sets A and B are connected by a
shortest directed network. We also bound the number of Steiner points by a
function of the sizes of A and B. Previously, such an existence result was
known only for the Euclidean plane [M. Alfaro, Pacific J. Math. 167 (1995)
201-214]. The main difficulty is that, unlike the undirected case (Steiner
minimal trees), the underlying graphs need not be acyclic.
  Existence in the undirected case was first shown by E. J. Cockayne [Canad.
Math. Bull. 10 (1967) 431-450].


  We theoretically investigate the combined effects of the chirality and the
finite total thickness of nematic cholesteric liquid crystals on the
Casimir-Lifshitz torque. We find that, the larger the thickness, the more
sinusoidal the angular dependence of the torque becomes. We use a Fourier
decomposition to quantify this result. The general direction of the torque
depends on whether the configuration of two cholesterics is heterochiral or
homochiral.


  In this paper, we first provide an explicit procedure to glue together
hereditary exact model structures for the recollement of exact categories. To
that end, we use the notion of cotorsion pairs and we investigate the gluing of
complete hereditary cotorsion pairs along the recollement of exact categories.
Moreover, we study liftings of recollements of hereditary exact model
structures to recollements of their associated homotopy categories. This leads
to a new method to produce recollements of triangulated categories.
Applications are given to contraderived categories, projective stable derived
categories and stable categories of Gorenstein injective modules over an upper
triangular matrix ring.


  We study the role of short-range correlations, as well as pion and rho loops
governing long-range RPA correlations, in nuclear matter properties and
response functions. We use an adapted formulation of the Brueckner G-matrix
approach to generate a pair correlation function satisfying the
Beg--Agassi--Gal theorem, providing a natural cutoff to the loop integrals. We
present results for the case of a relativistic chiral theory, including the
effects of quark confinement and of the chirally broken vacuum in a version
where parameters are directly connected to QCD observables or constrained by
well-established hadron phenomenology. This provides a unified and coherent
view of the nuclear matter equation of state and the effect of correlations on
neutrino--nucleus scattering.


  We study the persistent homology of random \v{C}ech complexes. Generalizing a
method of Penrose for studying random geometric graphs, we first describe an
appropriate theoretical framework in which we can state and address our main
questions. Then we define the kth persistent Betti number of a random \v{C}ech
complex and determine its asymptotic order in the subcritical regime. This
extends a result of Kahle on the asymptotic order of the ordinary kth Betti
number of such complexes to the persistent setting.


  We study the problem of out-of-sample risk estimation in the high dimensional
regime where both the sample size $n$ and number of features $p$ are large, and
$n/p$ can be less than one. Extensive empirical evidence confirms the accuracy
of leave-one-out cross validation (LO) for out-of-sample risk estimation. Yet,
a unifying theoretical evaluation of the accuracy of LO in high-dimensional
problems has remained an open problem. This paper aims to fill this gap for
penalized regression in the generalized linear family. With minor assumptions
about the data generating process, and without any sparsity assumptions on the
regression coefficients, our theoretical analysis obtains finite sample upper
bounds on the expected squared error of LO in estimating the out-of-sample
error. Our bounds show that the error goes to zero as $n,p \rightarrow \infty$,
even when the dimension $p$ of the feature vectors is comparable with or
greater than the sample size $n$. One technical advantage of the theory is that
it can be used to clarify and connect some results from the recent literature
on scalable approximate LO.


  A few-body properties of spinless Bose particles interacting via the contact
three-body potential in geometries with fractional dimensions $1<d<2$ are
considered. In the four-body sector at three-body resonance we predict the
existence of infinite tower of the Efimov bound states, and a similar behavior
is found in the five-body problem. It is shown that a ratio of the high-energy
levels in these two sectors is a universal constant. The consequences of
emergence of the Efimov physics on the many-body behavior are briefly
discussed.


  A twisted cocycle taking values on a Lie Group $G$ is a cocycle that, in each
step, is twisted by an automorphism of $G$. In the case when
$G=GL(d,\mathbb{R})$, we prove that if two H\"older continuous twisted cocycles
satisfying the so called fiber-bunching condition have the same periodic data
then they are cohomologous.


  We present a modern C++17-compatible thread pool implementation, built from
scratch with high-performance scientific computing in mind. The thread pool is
implemented as a single lightweight and self-contained class, and does not have
any dependencies other than the C++17 standard library, thus allowing a great
degree of portability. In particular, our implementation does not utilize
OpenMP or any other high-level multithreading APIs, and thus gives the
programmer precise low-level control over the details of the parallelization,
which permits more robust optimizations. The thread pool was extensively tested
on both AMD and Intel CPUs with up to 40 cores and 80 threads. This paper
provides motivation, detailed usage instructions, and performance tests.


  We investigate the phenomenological impact of different sources of lepton
flavour violation arising from realistic models based on supergravity mediated
supersymmetry breaking with Yukawa operators. We discuss four distinct sources
of lepton flavour violation in such models: minimum flavour violation, arising
from neutrino masses and the see-saw mechanism with RG running; supergravity
flavour violation due to the non-universal structure of the supergravity model;
flavour violation due to Froggatt-Nielsen (FN) fields appearing in Yukawa
operators developing supersymmetry breaking F-terms and contributing in a
non-universal way to soft trilinear terms; and finally heavy Higgs flavour
violation arising from the heavy Higgs fields used to break the unified gauge
symmetry which also appear in Yukawa operators and behave analagously to the FN
fields. In order to quantify the relative effects, we study a particular type I
string inspired model based on a supersymmetric Pati-Salam model arising from
intersecting D-branes, supplemented by a U(1) family symmetry


  Up to now, modern Machine Learning is mainly based on fitting high
dimensional functions to enormous data sets, taking advantage of huge hardware
resources. We show that biologically inspired neuron models such as the
Leaky-Integrate-and-Fire (LIF) neurons provide novel and efficient ways of
information encoding. They can be integrated in Machine Learning models, and
are a potential target to improve Machine Learning performance.
  Thus, we derived simple update-rules for the LIF units from the differential
equations, which are easy to numerically integrate. We apply a novel approach
to train the LIF units supervisedly via backpropagation, by assigning a
constant value to the derivative of the neuron activation function exclusively
for the backpropagation step. This simple mathematical trick helps to
distribute the error between the neurons of the pre-connected layer. We apply
our method to the IRIS blossoms image data set and show that the training
technique can be used to train LIF neurons on image classification tasks.
Furthermore, we show how to integrate our method in the KERAS (tensorflow)
framework and efficiently run it on GPUs. To generate a deeper understanding of
the mechanisms during training we developed interactive illustrations, which we
provide online.
  With this study we want to contribute to the current efforts to enhance
Machine Intelligence by integrating principles from biology.


  Disfluent speech has been previously addressed from two main perspectives:
the clinical perspective focusing on diagnostic, and the Natural Language
Processing (NLP) perspective aiming at modeling these events and detect them
for downstream tasks. In addition, previous works often used different metrics
depending on whether the input features are text or speech, making it difficult
to compare the different contributions. Here, we introduce a new evaluation
framework for disfluency detection inspired by the clinical and NLP perspective
together with the theory of performance from \cite{clark1996using} which
distinguishes between primary and collateral tracks. We introduce a novel
forced-aligned disfluency dataset from a corpus of semi-directed interviews,
and present baseline results directly comparing the performance of text-based
features (word and span information) and speech-based (acoustic-prosodic
information). Finally, we introduce new audio features inspired by the
word-based span features. We show experimentally that using these features
outperformed the baselines for speech-based predictions on the present dataset.


  We consider the sedimentation of $N$ spherical particles with identical radii
$R$ in a Stokes flow in $\mathbb R^3$. The particles satisfy a no-slip boundary
condition and are subject to constant gravity. The dynamics of the particles is
modeled by Newton's law but with very small particle inertia as $N$ tends to
infinity and $R$ to $0$. In a mean-field scaling, we show that the particle
evolution is well approximated by the transport-Stokes system which has been
derived previously as the mean-field limit of inertialess particles. In
particular this justifies to neglect the particle inertia in the microscopic
system, which is a typical modelling assumption in this and related contexts.
The proof is based on a relative energy argument that exploits the coercivity
of the particle forces with respect to the particle velocities in a Stokes
flow. We combine this with an adaptation of Hauray's method for mean-field
limits to $2$-Wasserstein distances.Moreover, in order to control the minimal
distance between particles, we prove a representation of the particle forces.
This representation makes the heuristic \enquote{Stokes law} rigorous that the
force on each particle is proportional to the difference of the velocity of the
individual particle and the mean-field fluid velocity generated by the other
particles.


  This paper explored the temporal characteristics of clinical citations of
biomedical papers, including how long it takes to receive its first clinical
citation (the initial stage) and how long it takes to receive two or more
clinical citations after its first clinical citation (the build-up stage). Over
23 million biomedical papers in PubMed between 1940 and 2013 and their clinical
citations are used as the research data. We divide these biomedical papers into
three groups and four categories from clinical citation level and translational
science perspectives. We compare the temporal characteristics of biomedical
papers of different groups or categories. From the perspective of clinical
citation level, the results show that highly clinically cited papers had
obvious advantages of receiving clinical citations over medium and lowly
clinically cited papers in both the initial and build-up stages. Meanwhile, as
the number of clinical citations increased in the build-up stage, the
difference in the length of time to receive the corresponding number of
clinical citations among the three groups of biomedical papers significantly
increased. From the perspective of translational science, the results reveal
that biomedical papers closer to clinical science more easily receive clinical
citations than papers closer to basic science in both the initial and build-up
stages. Moreover, we found that highly clinically cited papers had the
desperate advantage of receiving clinical citations over even the clinical
guidelines or clinical trials. The robustness analysis of the two aspects
demonstrates the reliability of our results.


  In this manuscript we explore the onset of cooperative traits in the Public
Goods game. This well-known game involves N-agent interactions and thus
reproduces a large number of social scenarios in which cooperation appears to
be essential. Many studies have recently addressed how the structure of the
interaction patterns influences the emergence of cooperation. Here we study how
information about the payoffs collected by each individual in the different
groups it participates in, influences the decisions made by its group partners.
Our results point out that cross-information plays a fundamental and positive
role in the evolution of cooperation for different versions of the Public Goods
game and different interaction structures.


  Different from deep neural networks for non-graph data classification, graph
neural networks (GNNs) leverage the information exchange between nodes (or
samples) when representing nodes. The category distribution shows an imbalance
or even a highly-skewed trend on nearly all existing benchmark GNN data sets.
The imbalanced distribution will cause misclassification of nodes in the
minority classes, and even cause the classification performance on the entire
data set to decrease. This study explores the effects of the imbalance problem
on the performances of GNNs and proposes new methodologies to solve it. First,
a node-level index, namely, the label difference index ($LDI$), is defined to
quantitatively analyze the relationship between imbalance and
misclassification. The less samples in a class, the higher the value of its
average $LDI$; the higher the $LDI$ of a sample, the more likely the sample
will be misclassified. We define a new loss and propose four new methods based
on $LDI$. Experimental results indicate that the classification accuracies of
the three among our proposed four new methods are better in both transductive
and inductive settings. The $LDI$ can be applied to other GNNs.


  Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) offers valuable
sensitivity to physics beyond the Standard Model. The Ricochet experiment will
use cryogenic solid-state detectors to perform a precision measurement of the
CE$\nu$NS spectrum induced by the high neutrino flux from the Institut
Laue-Langevin nuclear reactor. The experiment will employ an array of
detectors, each with a mass of $\sim$30 g and a targeted energy threshold of 50
eV. Nine of these detectors (the "Q-Array") will be based on a novel
Transition-Edge Sensor (TES) readout style, in which the TES devices are
thermally coupled to the absorber using a gold wire bond. We present initial
characterization of a Q-Array-style detector using a 1 gram silicon absorber,
obtaining a baseline root-mean-square resolution of less than 40 eV.


  We show that the ring of translation invariant symmetric polynomials in n
variables is isomorphic to the full polynomial ring in n-1 variables, in
characteristic 0. We disprove a conjecture of Haldane regarding the structure
of such polynomials. Our motivation is the fractional quantum Hall effect,
where translation invariant (anti)symmetric complex n-variate polynomials
characterize n-electron wavefunctions.


  We propose a probabilistic definition of solutions of semilinear elliptic
equations with (possibly nonlocal) operators associated with regular Dirichlet
forms and with measure data. Using the theory of backward stochastic
differential equations we prove the existence and uniqueness of solutions in
the case where the right-hand side of the equation is monotone and satisfies
mild integrability assumption, and the measure is smooth. We also study
regularity of solutions under the assumption that the measure is smooth and has
finite total variation. Some applications of our general results are given.


  The nearest-neighbour XY spin glass on a hypercubic lattice in four
dimensions is studied by Monte Carlo simulations. A finite- size scaling
analysis of the data leads to a finite temperature spin glass transition at
$T_c=0.95\pm 0.15$. The critical exponents are estimated to be
$\nu_{sg}=0.70\pm 0.10$ and \eta_{sg}=-0.28\pm 0.38$. The results imply that
the lower critical dimensionality for the XY spin glass is less than four.


  Flowsheet data presents unique challenges and opportunities for integration
into standardized Common Data Models (CDMs) such as the Observational Medical
Outcomes Partnership (OMOP) CDM from the Observational Health Data Sciences and
Informatics (OHDSI) program. These data are a potentially rich source of
detailed curated health outcomes data such as pain scores, vital signs, lines
drains and airways (LDA) and other measurements that can be invaluable in
building a robust model of patient health journey during an inpatient stay. We
present two approaches to integration of flowsheet measures into the OMOP CDM.
One approach was computationally straightforward but of potentially limited
research utility. The second approach was far more computationally and labor
intensive and involved mapping to standardized terms in controlled clinical
vocabularies such as Logical Observation Identifiers Names and Codes (LOINC),
resulting in a research data set of higher utility to population health
studies.


  Recent advances in power system State Estimation (SE) have included
equivalent circuit models for representing measurement data that allows
incorporation of both PMU and RTU measurements within the state estimator. In
this paper, we introduce a probabilistic framework with a new RTU model that
renders the complete SE problem linear while not affecting its accuracy. It is
demonstrated that the probabilistic state of a system can be efficiently and
accurately estimated not only with the uncertainties from the measurement data,
but also while including variations from transmission network models. To
demonstrate accuracy and scalability we present probabilistic state estimation
results for the 82k test case that represents the transmission level grid of
the entire USA. It is shown that the estimated state distributions include the
true grid state, while their mean exactly corresponds to the estimated
deterministic state obtained from the nonlinear state estimator.


  Mini-Proceedings ECT*: Speakable in quantum mechanics: atomic, nuclear and
subnuclear physics tests, ECT*-Trento, 29 August - 2 September, 2011


  The L\'evy, jumping process, defined in terms of the jumping size
distribution and the waiting time distribution, is considered. The jumping rate
depends on the process value. The fractional diffusion equation, which contains
the variable diffusion coefficient, is solved in the diffusion limit. That
solution resolves itself to the stretched Gaussian when the order parameter
$\mu\to2$. The truncation of the L\'evy flights, in the exponential and
power-law form, is introduced and the corresponding random walk process is
simulated by the Monte Carlo method. The stretched Gaussian tails are found in
both cases. The time which is needed to reach the limiting distribution
strongly depends on the jumping rate parameter. When the cutoff function falls
slowly, the tail of the distribution appears to be algebraic.


  Random Forests (RF) is a popular machine learning method for classification
and regression problems. It involves a bagging application to decision tree
models. One of the primary advantages of the Random Forests model is the
reduction in the variance of the forecast. In large scale applications of the
model with millions of data points and hundreds of features, the size of the
fitted objects can get very large and reach the limits on the available space
in production setups, depending on the number and depth of the trees. This
could be especially challenging when trained models need to be downloaded
on-demand to small devices with limited memory. There is a need to approximate
the trained RF models to significantly reduce the model size without losing too
much of prediction accuracy. In this project we study methods that approximate
each fitted tree in the Random Forests model using the multinomial allocation
of the data points to the leafs. Specifically, we begin by studying whether
fitting a multinomial logistic regression (and subsequently, a generalized
additive model (GAM) extension) to the output of each tree helps reduce the
size while preserving the prediction quality.


  In stochastic volatility models based on time-homogeneous diffusions, we
provide a simple necessary and sufficient condition for the discretely sampled
fair strike of a variance swap to converge to the continuously sampled fair
strike. It extends Theorem 3.8 of Jarrow, Kchia, Larsson and Protter (2013) and
gives an affirmative answer to a problem posed in this paper in the case of 3/2
stochastic volatility model. We also give precise conditions (not based on
asymptotics) when the discrete fair strike of the variance swap is higher than
the continuous one and discuss the convex order conjecture proposed by
Keller-Ressel and Griessler (2012) in this context.


  In the modern medical care, venipuncture is an indispensable procedure for
both diagnosis and treatment. In this paper, unlike existing solutions that
fully or partially rely on professional assistance, we propose VeniBot -- a
compact robotic system solution integrating both novel hardware and software
developments. For the hardware, we design a set of units to facilitate the
supporting, positioning, puncturing and imaging functionalities. For the
software, to move towards a full automation, we propose a novel deep learning
framework -- semi-ResNeXt-Unet for semi-supervised vein segmentation from
ultrasound images. From which, the depth information of vein is calculated and
used to enable automated navigation for the puncturing unit. VeniBot is
validated on 40 volunteers, where ultrasound images can be collected
successfully. For the vein segmentation validation, the proposed
semi-ResNeXt-Unet improves the dice similarity coefficient (DSC) by 5.36%,
decreases the centroid error by 1.38 pixels and decreases the failure rate by
5.60%, compared to fully-supervised ResNeXt-Unet.


  Convolutional neural networks have been proven effective in a variety of
image restoration tasks. Most state-of-the-art solutions, however, are trained
using images with a single particular degradation level, and their performance
deteriorates drastically when applied to other degradation settings. In this
paper, we propose deep likelihood network (DL-Net), aiming at generalizing
off-the-shelf image restoration networks to succeed over a spectrum of
degradation levels. We slightly modify an off-the-shelf network by appending a
simple recursive module, which is derived from a fidelity term, for
disentangling the computation for multiple degradation levels. Extensive
experimental results on image inpainting, interpolation, and super-resolution
show the effectiveness of our DL-Net.


  The interior of cells is crowded thus making it important to assess the
effects of macromolecules on the folding of proteins. Using the Self-Organized
Polymer (SOP) model, which is a coarse-grained representation of polypeptide
chains, we probe the mechanical stability of Ubiquitin (Ub) monomers and
trimers ((Ub)$_3$) in the presence of monodisperse spherical crowding agents.
Crowding increases the volume fraction ($\Phi_c$)-dependent average force
($<f_u(\Phi_c)>$), relative to the value at $\Phi_c = 0$, needed to unfold Ub
and the polyprotein. For a given $\Phi_c$, the values of $<f_u(\Phi_c)>$
increase as the diameter ($\sigma_c$) of the crowding particles decreases. The
average unfolding force $<f_u(\Phi_c)>$ depends on the ratio $\frac{D}{R_g}$,
where $D \approx \sigma_c (\frac{\pi}{6 \Phi_c})^{{1/3}}$ with $R_g$ being the
radius of gyration of Ub (or (Ub)$_3$) in the unfolded state. Examination of
the unfolding pathways shows that, relative to $\Phi_c = 0$, crowding promotes
reassociation of ruptured secondary structural elements. Both the nature of the
unfolding pathways and $<f_u(\Phi_c)>$ for (Ub)$_3$ are altered in the presence
of crowding particles with the effect being most dramatic for the subunit that
unfolds last. We predict, based on SOP simulations and theoretical arguments,
that $<f_u(\Phi_c) > \sim \Phi_c^{\frac{1}{3\nu}}$, where $\nu$ is the Flory
exponent that describes the unfolded (random coil) state of the protein.


  We present imaging and spectroscopic observations with HST (WFPC2, ACS/HRC
and STIS), VLT (FORS2) and Keck (HIRES) of the dM8 ultra-cool dwarf DENIS-P
J185950.9-370632, located in the R-CrA region. The presence of lithium
absorption at 670.8 nm and the strong Halpha emission indicate a young age and
a sub-stellar mass. Our diffraction-limited images resolve a companion at the
separation limit of HST/ACS (~0.06"). The 2.1 mJy flux in the LW2 filter
(5.0-8.5 microns) of the Infrared Space Observatory (see 1999A&A...350..883)
likely corresponds to an infrared excess, suggesting the presence of
circumstellar material. Proper motion and photometric measurements, as well as
the Halpha activity, confirm membership in the R-CrA star forming region. If
confirmed by further observations, DENIS-P J185950.9-370632 would be the first
accreting sub-stellar multiple system observed to date.


  The challenge of this paper is to underline the capability of an Event-Cloud
Platform to support efficiently an emergency situation. We chose to focus on a
nuclear crisis use case. The proposed approach consists in modeling the
business processes of crisis response on the one hand, and in supporting the
orchestration and execution of these processes by using an Event-Cloud Platform
on the other hand. This paper shows how the use of Event-Cloud techniques can
support crisis management stakeholders by automatizing non-value added tasks
and by directing decision- makers on what really requires their capabilities of
choice. If Event-Cloud technology is a very interesting and topical subject,
very few research works have considered this to improve emergency management.
This paper tries to fill this gap by considering and applying these
technologies on a nuclear crisis use-case.


  This chapter gives an introduction to low-energy beam transport systems, and
discusses the typically used magnetostatic elements (solenoid, dipoles and
quadrupoles) and electrostatic elements (einzel lens, dipoles and quadrupoles).
The ion beam emittance, beam space-charge effects and the physics of ion source
extraction are introduced. Typical computer codes for analysing and designing
ion optical systems are mentioned, and the trajectory tracking method most
often used for extraction simulations is described in more detail.


  Weak equivalence principle (WEP) is one of the cornerstones of the modern
theories of gravity, stating that the trajectory of a freely falling test body
is independent of its internal structure and composition. Even though WEP is
known to be valid for the normal matter with a high precision, it has never
been experimentally confirmed for relativistic matter and antimatter. We make
an attempt to constrain possible deviations from WEP utilizing the modern
accelerator technologies. We analyze the (absence of) vacuum Cherenkov
radiation, photon decay, anomalous synchrotron losses and the Compton spectra
to put limits on the isotropic Lorentz violation and further convert them to
the constraints on the difference between the gravitational and inertial masses
of the relativistic electrons/positrons. Our main result is the 0.1% limit on
the mentioned difference.


  Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by
legitimate monitoring UAVs (or monitors) can be crucial to public safety and
security. It is non-trivial to optimize the trajectory of a monitor while
conceiving its monitoring intention, due to typically non-convex propulsion and
thrust power functions. This paper presents a novel framework to jointly
optimize the propulsion and thrust powers, as well as the 3D trajectory of a
solar-powered monitor which conducts covert, video-based, UAV-on-UAV tracking
and surveillance. A multi-objective problem is formulated to minimize the
energy consumption of the monitor and maximize a weighted sum of distance
keeping and altitude changing, which measures the disguising of the monitor.
Based on the practical power models of the UAV propulsion, thrust and hovering,
and the model of the harvested solar power, the problem is non-convex and
intangible for existing solvers. We convexify the propulsion power by variable
substitution, and linearize the solar power. With successive convex
approximation, the resultant problem is then transformed with tightened
constraints and efficiently solved by the proximal difference-of-convex
algorithm with extrapolation in polynomial time. The proposed scheme can be
also applied online. Extensive simulations corroborate the merits of the
scheme, as compared to baseline schemes with partial or no disguising.


  This contribution illustrates a new search for the flavor-changing
neutral-current decay ${B^+ \to K^+ \nu \bar \nu}$ performed by the Belle II
experiment at the SuperKEKB asymmetric-energy electron-positron collider. In
this study, a sample corresponding to an integrated luminosity of $63\,
\rm{fb}^{-1}$ collected at the $\rm \Upsilon(4S)$ resonance and an additional
sample of $9\, \rm{fb}^{-1}$ collected at an energy $60\, \rm MeV$ below the
resonance are used. A novel technique, based on an inclusive tagging method and
exploiting the topological features of the ${B^+ \to K^+ \nu \bar \nu}$ decay,
is employed and it provides a higher signal efficiency with respect to the
methods used in the previous searches. No significant signal is observed. An
upper limit of $4.1 \times 10^{-5}$ is set on the ${B^+ \to K^+ \nu \bar \nu}$
branching fraction at the $90\, \%$ confidence level.


  The Planetary Camera and Spectrograph (PCS) for the Extremely Large Telescope
(ELT) will be dedicated to detecting and characterising nearby exoplanets with
sizes from sub-Neptune to Earth-size in the neighbourhood of the Sun. This goal
is achieved by a combination of eXtreme Adaptive Optics (XAO), coronagraphy and
spectroscopy. PCS will allow us not only to take images, but also to look for
biosignatures such as molecular oxygen in the exoplanets' atmospheres. This
article describes the PCS primary science goals, the instrument concept and the
research and development activities that will be carried out over the coming
years.


  The recent development of online static map element (a.k.a. HD Map)
construction algorithms has raised a vast demand for data with ground truth
annotations. However, available public datasets currently cannot provide
high-quality training data regarding consistency and accuracy. To this end, we
present CAMA: a vision-centric approach for Consistent and Accurate Map
Annotation. Without LiDAR inputs, our proposed framework can still generate
high-quality 3D annotations of static map elements. Specifically, the
annotation can achieve high reprojection accuracy across all surrounding
cameras and is spatial-temporal consistent across the whole sequence. We apply
our proposed framework to the popular nuScenes dataset to provide efficient and
highly accurate annotations. Compared with the original nuScenes static map
element, models trained with annotations from CAMA achieve lower reprojection
errors (e.g., 4.73 vs. 8.03 pixels).


  With compact footprint, low energy consumption, high scalability, and mass
producibility, chip-scale integrated devices are an indispensable part of
modern technological change and development. Recent advances in two-dimensional
(2D) layered materials with their unique structures and distinctive properties
have motivated their on-chip integration, yielding a variety of functional
devices with superior performance and new features. To realize integrated
devices incorporating 2D materials, it requires a diverse range of device
fabrication techniques, which are of fundamental importance to achieve good
performance and high reproducibility. This paper reviews the state-of-art
fabrication techniques for the on-chip integration of 2D materials. First, an
overview of the material properties and on-chip applications of 2D materials is
provided. Second, different approaches used for integrating 2D materials on
chips are comprehensively reviewed, which are categorized into material
synthesis, on-chip transfer, film patterning, and property tuning /
modification. Third, the methods for integrating 2D van der Waals
heterostructures are also discussed and summarized. Finally, the current
challenges and future perspectives are highlighted.


  It is well known that if $X$ is a CW-complex, then for every weak homotopy
equivalence $f:A\to B$, the map $f_*:[X,A]\to [X,B]$ induced in homotopy
classes is a bijection. For which spaces $X$ is $f^*:[B,X]\to [A,X]$ a
bijection for every weak equivalence $f$? This question was considered by J.
Strom and T. Goodwillie. In this note we prove that a non-empty space inverts
weak equivalences if and only if it is contractible.


  Mapping class groups are known to admit geometric (proper, cobounded) actions
on injective spaces. Starting with such an action, and relying only on
geometric arguments, we show that all finite generating sets resulting from
taking large enough balls (as in the \v{S}varc-Milnor lemma) in the respective
injective space yield word metrics where pseudo-Anosov maps are exponentially
generic. We also show that growth tightness holds true for the Cayley graphs
corresponding to these finite generating sets, providing a positive answer to a
question by Arzhantseva, Cashen and Tao, who asked if any of the mapping class
group's Cayley graphs is growth tight. To achieve the above results, we
consider the space of all geometric actions of a mapping class group, and for
any injective metric space in this collection, we construct a sequence of word
metrics that converges to it and show that all large enough terms of the
converging sequence inherit many desirable properties from the limiting
injective space.


  Motivated by the discovery of the ultra-strong emission line starburst
galaxies (EELGs) known as "green pea galaxies", we consider here their
contribution to the intergalactic flux of ionizing UV at high redshifts. Most
galaxies that have been observed show a precipitous drop in their flux blueward
of the Lyman limit. However, recent observations of EELGs have discovered that
many more Lyman continuum photons escape from them into intergalactic space
than was previously suspected. We calculate their contribution to the
extragalactic background light (EBL). We also calculate the effect of these
photons on the absorption of high energy $\gamma$-rays. For the more distant
$\gamma$-ray sources, particularly at $z \ge 3$, the intergalactic opacity
above a few GeV is significantly higher than previous estimates which ignored
the Lyman continuum photons. We calculate the results of this increased opacity
on observed $\gamma$-ray spectra, which produces a high-energy turnover
starting at lower energies than previously thought, and a gradual spectral
steepening that may also be observable.


  We propose a method to experimentally measure the internal energy of a system
of ultracold atoms trapped in optical lattices by coupling them to the fields
of two optical cavities. We show that the tunnelling and self-interaction terms
of the one-dimensional Bose-Hubbard Hamiltonian can be mapped to the field and
photon number of each cavity, respectively. We compare the energy estimated
using this method with numerical results obtained using the density matrix
renormalisation group algorithm. Our method can be employed for the assessment
of power and efficiency of thermal machines whose working substance is a
strongly correlated many-body system.


  The multivariate Tutte polynomial $\hat Z_M$ of a matroid $M$ is a
generalization of the standard two-variable version, obtained by assigning a
separate variable $v_e$ to each element $e$ of the ground set $E$. It encodes
the full structure of $M$. Let $\bv = \{v_e\}_{e\in E}$, let $K$ be an
arbitrary field, and suppose $M$ is connected. We show that $\hat Z_M$ is
irreducible over $K(\bv)$, and give three self-contained proofs that the Galois
group of $\hat Z_M$ over $K(\bv)$ is the symmetric group of degree $n$, where
$n$ is the rank of $M$. An immediate consequence of this result is that the
Galois group of the multivariate Tutte polynomial of any matroid is a direct
product of symmetric groups. Finally, we conjecture a similar result for the
standard Tutte polynomial of a connected matroid.


  We have formulated a twist operator argument for the geometrically frustrated
quantum spin systems on the kagome and triangular lattices, thereby extending
the application of the Lieb-Schultz-Mattis (LSM) and Oshikawa-Yamanaka-Affleck
(OYA) theorems to these systems. The equivalent large gauge transformation for
the geometrically frustrated lattice differs from that for non-frustrated
systems due to the existence of multiple sublattices in the unit cell and
non-orthogonal basis vectors. Our study for the $S=1/2$ kagome Heisenberg
antiferromagnet at zero external magnetic field gives a criterion for the
existence of a two-fold degenerate ground state with a finite excitation gap
and fractionalized excitations. At finite field, we predict various plateaux at
fractional magnetisation, in analogy with integer and fractional quantum Hall
states of the primary sequence. These plateaux correspond to gapped quantum
liquid ground states with a fixed number of singlets and spinons in the unit
cell. A similar analysis for the triangular lattice predicts a single
fractional magnetization plateau at $1/3$. Our results are in broad agreement
with numerical and experimental studies.


  Recently Maleknejad and Sheikh-Jabbari have proposed a new model for
inflation with non-Abelian gauge fields (Gauge-flation), and they have studied
the model by numerical methods\cite{Sheikh}. In this model, the isotropy of
space-time is recovered by suitable combination of gauge configurations, and a
scalar field is constructed by gauge field and the scale factor, which produces
inflation period. In this work, exact solutions for the scalar field and the
Hubble parameter are presented and we provide analytic solutions for the
numerical results. We explicitly present Hubble parameter and fields as
functions of time and it is also demonstrated that in some conditions they are
damped oscillator. Moreover, reheating period in the model is discussed.


  Variable selection in high-dimensional scenarios is of great interested in
statistics. One application involves identifying differentially expressed genes
in genomic analysis. Existing methods for addressing this problem have some
limits or disadvantages. In this paper, we propose distance based variable
importance measures to deal with these problems, which is inspired by the
Multi-Response Permutation Procedure (MRPP). The proposed variable importance
assessments can effectively measure the importance of an individual dimension
by quantifying its influence on the differences between multivariate
distributions. A backward selection algorithm is developed that can be used in
high-dimensional variable selection to discover important variables. Both
simulations and real data applications demonstrate that our proposed method
enjoys good properties and has advantages over other methods.


  We discuss the problem of counting vertices in Gelfand-Zetlin polytopes.
Namely, we deduce a partial differential equation with constant coefficients on
the exponential generating function for these numbers. For some particular
classes of Gelfand-Zetlin polytopes, the number of vertices can be given by
explicit formulas.


  We suggest an ansatz for representation of affine Yangian $Y(\hat{
\mathfrak{gl}}_1)$ by differential operators in the triangular set of
time-variables ${\bf P}_{a,i}$ with $1\leqslant i\leqslant a$, which saturates
the MacMahon formula for the number of $3d$ Young diagrams/plane partitions. In
this approach the 3-Schur polynomials are defined as the common eigenfunctions
of an infinite set of commuting "cut-and-join" generators $\psi_n$ of the
Yangian. We manage to push this tedious program through to the 3-Schur
polynomials of level 5, and this provides a rather big sample set, which can be
now investigated by other methods.


  Although effective for two dimensional (2D) systems, some approximations may
fail in describing the properties of one-dimensional (1D) models, which belong
to a different universality class. In this paper, we analyze the adequacy of
the Composite Operator Method (COM), which provides a good description of many
features of 2D strongly correlated systems, in grasping the physics of 1D
models. To this purpose, the 1D Hubbard model is studied within the framework
of the COM by considering a two-pole approximation and a paramagnetic ground
state. The local, thermodynamic and single-particle properties, the correlation
functions and susceptibilities are calculated in the case of half filling and
arbitrary filling. The results are compared with those obtained by the Bethe
Ansatz (BA) as well as by other numerical and analytical techniques. The
advantages and limitations of the method are analyzed in detail.


  Traditional quantum hydrodynamics of Bose-Einstein condensates (BECs) is
restricted by the continuity and Euler equations. It corresponds to the
well-known Gross-Pitaevskii equation. However, the quantum Bohm potential,
which is a part of the momentum flux, has a nontrivial part with can evolve
under the quantum fluctuations. To cover this phenomenon in terms of
hydrodynamic methods we need to derive equations for the momentum flux, and the
third rank tensor. In all equations we consider the main contribution of the
short-range interaction (SRI) in the first order by the interaction radius.
Derived hydrodynamics consists of four hydrodynamic equations. The third moment
evolution equation contains interaction leading to the quantum fluctuations. It
includes new interaction constant. The Gross-Pitaevskii interaction constant is
the integral of potential, but the second interaction constant is the integral
of second derivative of potential. If we have dipolar BECs we deal with a
long-range interaction. Its contribution is proportional to the potential of
dipole-dipole interaction (DDI). The Euler equation contains the derivative of
the potential. The third rank tensor evolution equation contains the third
derivative of the potential. The quantum fluctuations lead to existence of the
second wave solution. Moreover, the quantum fluctuations introduce the
instability of BECs. If the DDI is attractive, but being smaller then the
repulsive SRI presented by the first interaction constant, there is the
long-wavelength instability. For the repulsive DDI these is more complex
picture. There is the small area with the long-wavelength instability which
transits into stability interval, where two waves exist. There is the
short-wavelength instability as well. These results are found for the DDI
strength comparable with the Gross-Pitaevskii SRI.


  The practical application of a new class of coprime arrays based on the
Chinese remainder theorem (CRT) over quadratic fields is presented in this
paper. The proposed CRT arrays are constructed by ideal lattices embedded from
coprime quadratic integers. The geometrical constructions and theoretical
foundations were discussed in the accompanying paper in great detail, while
this paper focuses on aspects of the application of the proposed arrays in
two-dimensional (2D) remote sensing. A generalization of CRT arrays based on
two or more pairwise coprime ideal lattices is proposed with closed-form
expressions on sensor locations, the total number of sensors and the achievable
DOF. The issues pertaining to the coprimality of any two quadratic integers are
also addressed to explore all possible ideal lattices. Exploiting the symmetry
of lattices, sensor reduction methods are discussed with the coarray remaining
intact for economic maximization. In order to extend conventional angle
estimation techniques based on uniformly distributed arrays to the method that
can exploit any coarray configurations based on lattices, this paper introduces
a hexagon-to-rectangular transformation to 2D spatial smoothing, providing the
possibility of finding more compact sensor arrays. Examples are provided to
verify the feasibility of the proposed methods.


  The effect of Co-Ga paired substitution on the superconducting properties of
YBa2Cu3O7-d (Y-123) has been investigated by X-ray diffraction, ac
susceptibility,dc resistivity and oxygen content measurements. We report in
this paper the results of our studies on the paired substitution of a magnetic
and non-magnetic ion at Cu site in Y-123, while keeping the total dopant
concentration fixed. The simultaneous substitution of Co and Ga at Cu site
shows variation in the transition temperature (Tc), oxygen content and hole
concentration as a function of change in the balance of magnetic (Co) and
non-magnetic (Ga) concentration. Orthorhombicity (D), given as (b-a)/a, also
varies as a function of increasing dopant concentration. The variation in Tc
due to Co-Ga substitution is discussed in the light of dopant valency and hole
filling mechanism.


  Low-energy tests of fundamental symmetries are extremely sensitive probes of
physics beyond the Standard Model, reaching scales that are comparable, if not
higher, than directly accessible at the energy frontier. The interpretation of
low-energy precision experiments and their connection with models of physics
beyond the Standard Model relies on controlling the theoretical uncertainties
induced by the nonperturbative nature of QCD at low energy and of the nuclear
interactions. In these proceedings, I will discuss how the interplay of Lattice
QCD and nuclear Effective Field Theories can lead to improved predictions for
low-energy experiments, with controlled uncertainties. I will describe the
framework of chiral Effective Field Theory, and then discuss a few examples,
including non-standard $\beta$ decays, neutrinoless double beta decay and
searches for electric dipole moments, to highlight the progress achieved in
recent years, and the role that Lattice QCD will play in addressing the
remaining open problems.


  Police agencies across the globe are increasingly using Online Social Media
(OSM) to acquire intelligence and connect with citizens. Developed nations have
well thought of strategies to use OSM for policing. However, developing nations
like India are exploring and evolving OSM as a policing solution. India, in
recent years, experienced many events where rumors and fake content on OSM
instigated communal violence. In contrast to traditional media (e.g. television
and print media) used by Indian police departments, OSM offers velocity,
variety, veracity and large volume of information. These introduce new
challenges for police like platforms selection, secure usage strategy,
developing trust, handling offensive comments, and security / privacy
implication of information shared through OSM. Success of police initiatives on
OSM to maintain law and order depends both on their understanding of OSM and
citizen's acceptance / participation on these platforms.
  This study provides multidimensional understanding of behavior, perceptions,
interactions, and expectation regarding policing through OSM. First, we
examined recent updates from four different police pages- Delhi, Bangalore,
Uttar Pradesh and Chennai to comprehend various dimensions of police
interaction with citizens on OSM. Second, we conducted 20 interviews with IPS
officers (Indian Police Service) and 17 interviews with citizens to understand
decision rationales and expectation gaps between two stakeholders (police and
citizens); this was followed up with 445 policemen surveys and 204 citizen
surveys. We also present differences between police expectations of Indian and
police departments in developed countries.


  Vector autoregressive (VAR) models assume linearity between the endogenous
variables and their lags. This assumption might be overly restrictive and could
have a deleterious impact on forecasting accuracy. As a solution, we propose
combining VAR with Bayesian additive regression tree (BART) models. The
resulting Bayesian additive vector autoregressive tree (BAVART) model is
capable of capturing arbitrary non-linear relations between the endogenous
variables and the covariates without much input from the researcher. Since
controlling for heteroscedasticity is key for producing precise density
forecasts, our model allows for stochastic volatility in the errors. We apply
our model to two datasets. The first application shows that the BAVART model
yields highly competitive forecasts of the US term structure of interest rates.
In a second application, we estimate our model using a moderately sized
Eurozone dataset to investigate the dynamic effects of uncertainty on the
economy.


  The bbob-largescale test suite, containing 24 single-objective functions in
continuous domain, extends the well-known single-objective noiseless bbob test
suite, which has been used since 2009 in the BBOB workshop series, to large
dimension. The core idea is to make the rotational transformations R, Q in
search space that appear in the bbob test suite computationally cheaper while
retaining some desired properties. This documentation presents an approach that
replaces a full rotational transformation with a combination of a
block-diagonal matrix and two permutation matrices in order to construct test
functions whose computational and memory costs scale linearly in the dimension
of the problem.


  In order to examine the far-IR/radio correlation at high redshift we have
studied the Spitzer 70um/far-infrared (far-IR) properties of sub-mJy radio
sources from the 13^H XMM-Newton/Chandra Deep Field by redshift and galaxy
type: active galactic nucleus (AGN) or star forming galaxy (SFG). We directly
detect 70um counterparts (at >3sigma significance) for 22.5% (92/408) of the
radio sources, while for the rest we perform stacking analysis by redshift and
galaxy type. For the sources detected at 70um we find that the median and
scatter of the observed flux density ratio, q70, are similar to previous
results in the literature, but with a slight decrease in q70 towards higher
redshifts. Of the radio sources detected at 70um 8/92 were already classified
as AGN, but two of which maybe SFGs. For the stacked sources we obtain a
detection for the SFGs at every redshift bin which implies they have mean flux
densities a factor ~5 below the original 70um detection limit. For the stacked
AGN we obtain a detection only in our highest redshift bin (1<~z<~5) where we
may be sampling hot dust associated with the AGN at rest-frame 12-35um. The
combined observed mean value of q70 for the SFGs (detected and non-detected at
70um) decreases gradually with redshift, consistent with tracks derived from
empirical spectral energy distributions (SEDs) of local SFGs. Upon closer
inspection and when comparing with tracks of appropriate luminosity, the values
of q70 broadly agree at low redshift. However, at z~1, the observed q70 (for
ULIRGs) is 2sigma below the value seen for local ULIRGs tracks, implying a
difference in the SED between local and z~1 ULIRGs. At higher redshifts, the
convergence of the tracks and the higher uncertainties in q70 do not allow us
to determine if such a difference persists.


  We consider an ensemble of mass collisionless particles, which interact
mutually either by an attraction of Newton's law of gravitation or by an
electrostatic repulsion of Coulomb's law, under a background downward gravity
in a horizontally-periodic 3D half-space, whose inflow distribution at the
boundary is prescribed. We investigate a nonlinear asymptotic stability of its
generic steady states in the dynamical kinetic PDE theory of the Vlasov-Poisson
equations. We construct Lipschitz continuous space-inhomogeneous steady states
and establish exponentially fast asymptotic stability of these steady states
with respect to small perturbation in a weighted Sobolev topology. In this
proof, we crucially use the Lipschitz continuity in the velocity of the steady
states. Moreover, we establish well-posedness and regularity estimates for both
steady and dynamic problems.


  Unprojection is a theory due to Reid which constructs more complicated rings
starting from simpler data. The idea of unprojection is intended for serial
use. Papadakis and Neves developed a theory of parallel unprojection. In the
present work we develop a new method of unprojection. Starting from a
codimension 3 ideal defined by the pfaffians of a 5x5 skewsymmetric matrix, we
use parallel unprojection of Kustin-Miller type in order to construct
Gorenstein rings of codimension 6. We give two applications. These are two
families of codimension 6 Fano 3-folds, in weighted projective space.


  One of the presuppositions of science since the times of Galileo, Newton,
Laplace, and Descartes has been the predictability of the world. This idea has
strongly influenced scientific and technological models. However, in recent
decades, chaos and complexity have shown that not every phenomenon is
predictable, even if it is deterministic. If a problem space is predictable, in
theory we can find a solution via optimization. Nevertheless, if a problem
space is not predictable, or it changes too fast, very probably optimization
will offer obsolete solutions. This occurs often when the immediate solution
affects the problem itself. An alternative is found in adaptation. An adaptive
system will be able to find by itself new solutions for unforeseen situations.


  Norm overlap kernels of the cranked Hartree-Fock-Bogoliubov states are
studied in the context of angular momentum projection. In particular, the
geometrical distribution of nodal lines, i.e., one dimensional structures where
the overlap kernels possess null value, is investigated in the three
dimensional space defined by the Euler angles. It is important to know the
distribution of these nodal lines when one attempts to determine the phase of
norm overlap kernels.


  We characterize the Carleson measures for the Drury-Arveson Hardy space and
other Hilbert spaces of analytic functions of several complex variables. This
provides sharp estimates for Drury's generalization of Von Neumann's
inequality. The characterization is in terms of a geometric condition, the
"split tree condition", which reflects the nonisotropic geometry underlying the
Drury-Arveson Hardy space.


  This talk summarizes some of the theoretical issues currently most debated in
the field of hot and dense QCD matter and ultra-relativistic heavy ion
collisions.


  We translate Akin's notion of {\it good} (and related concepts) from measures
on Cantor sets to traces on dimension groups, and particularly for invariant
measures of minimal homeomorphisms (and their corresponding simple dimension
groups), this yields characterizations and examples, which translate back to
the original context. Good traces on a simple dimension group are characterized
by their kernel having dense image in their annihilating set of affine
functions on the trace space; this makes it possible to construct many examples
with seemingly paradoxical properties. In order to study the related property
of {\it refinability,} we consider goodness for sets of measures (traces on
dimension groups), and obtain partial characterizations in terms of (special)
convex subsets of Choquet simplices. These notions also very closely related to
unperforation of quotients of dimension groups by convex subgroups (that are
not order ideals), and we give partial characterizations. Numerous examples
illustrate the results.


  The mutual compatibility of the dynamical equations and constraints
describing a massive particle of arbitrary spin, though essential for
consistency, is generically lost in the presence of interactions. The
conventional Lagrangian approach avoids this difficulty, but fails to ensure
light-cone propagation and becomes very cumbersome. In this paper, we take an
alternative route--the involutive form of the equations and constraints--to
guarantee their algebraic consistency. This approach enormously simplifies the
search for consistent interactions, now seen as deformations of the involutive
system, by keeping manifest the causal propagation of the correct number of
degrees of freedom. We consider massive particles of arbitrary integer spin in
electromagnetic and gravitational backgrounds to find their possible
non-minimal local couplings. Apart from easily reproducing some well-known
results, we find restrictions on the backgrounds for consistent propagation of
such a particle in isolation. The results can be altered by non-local
interactions that may arise from additional massive states in the interacting
theory.


  In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the amount of boilerplate code to be written. nmtpy has been used for
LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News
Translation tasks in 2016 and 2017.


  We develop a tensorial constitutive model for dense, shear-thickening
particle suspensions subjected to time-dependent flow. Our model combines a
recently proposed evolution equation for the suspension microstructure in
rate-independent materials with ideas developed previously to explain the
steady flow of shear-thickening ones, whereby friction proliferates among
compressive contacts at large particle stresses. We apply our model to shear
reversal, and find good qualitative agreement with particle-level,
discrete-element simulations whose results we also present.


  Weak values and measurements have been proposed as means to achieve dramatic
enhancements in metrology based on the greatly increased range of possible
measurement outcomes. Unfortunately, the very large values of measurement
outcomes occur with highly suppressed probabilities. This raises three vital
questions in weak-measurement-based metrology, namely, (Q1) Does post-selection
enhance the measurement precision? (Q2) Does weak measurement offer better
precision than strong measurement? (Q3) Is it possible to beat the standard
quantum limit or to achieve the Heisenberg limit with weak measurement using
only classical resources? We analyse these questions for two prototypical, and
generic, measurement protocols and show that while the answers to the first two
questions are negative for both protocols, the answer to the last is
affirmative for measurements with phase-space interactions, and negative for
configuration space interactions. Our results, particularly the ability of weak
measurements to perform at par with strong measurements in some cases, are
instructive for the design of weak-measurement-based protocols for quantum
metrology.


  We establish the dominant effect of anisotropic weak localization (WL) in
three dimensions associated with a propagative Fermi surface, on the
conductivity correction in heavily nitrogen doped ultrananocrystalline diamond
(UNCD) films based on magneto-resistance studies at low temperatures. Also, low
temperature electrical conductivity can show weakly localized transport in 3D
combined with the effect of electron-electron interactions in these materials,
which is remarkably different from the conductivity in 2DWL or strong
localization regime. The corresponding dephasing time of electronic
wavefunctions in these systems described as ~ T^-p with p < 1, follows a
relatively weak temperature dependence compared to the generally expected
nature for bulk dirty metals having $p \geq 1$. The temperature dependence of
Hall (electron) mobility together with an enhanced electron density has been
used to interpret the unusual magneto-transport features and show delocalized
electronic transport in these n-type UNCD films, which can be described as
low-dimensional superlattice structures.


  Current research in biology heavily depends on the availability and efficient
use of information. In order to build new knowledge, various sources of
biological data must often be combined. Semantic Web technologies, which
provide a common framework allowing data to be shared and reused between
applications, can be applied to the management of disseminated biological data.
However, due to some specificities of biological data, the application of these
technologies to life science constitutes a real challenge. Through a use case
of biological data integration, we show in this paper that current Semantic Web
technologies start to become mature and can be applied for the development of
large applications. However, in order to get the best from these technologies,
improvements are needed both at the level of tool performance and knowledge
modeling.


  We solve the fully-connected Ising model in the presence of dissipation and
time-periodic field, with the corresponding Lindblad equation having a
time-periodic Liouvillian. The dynamics of the magnetizations is studied by
using both the semiclassical approach and the numerical simulation with the
help of permutation symmetry. The semiclassical approach shows a transition
from the periodic response for small field amplitude to the chaotic dynamics
for large amplitude. The trajectory of the magnetizations and the Lyapunov
exponents are calculated, which support the existence of a chaotic phase. But
in the exact numerical simulation, the response is periodic for both small and
large amplitude. The scaling analysis of Floquet Liouvillian spectrum confirms
the periodic response in the thermodynamic limit. The semiclassical
approximation is found to fail as the field amplitude is large.


  In this paper we introduce, and characterize, a class of graph parameters
obtained from tensor invariants of the symplectic group. These parameters are
similar to partition functions of vertex models, as introduced by de la Harpe
and Jones, [P. de la Harpe, V.F.R. Jones, Graph invariants related to
statistical mechanical models: examples and problems, Journal of Combinatorial
Theory, Series B 57 (1993) 207-227]. Yet they give a completely different class
of graph invariants. We moreover show that certain evaluations of the cycle
partition polynomial, as defined by Martin [P. Martin, Enum\'erations
eul\'eriennes dans les multigraphes et invariants de Tutte-Grothendieck, Diss.
Institut National Polytechnique de Grenoble-INPG; Universit\'e
Joseph-Fourier-Grenoble I, 1977], give examples of graph parameters that can be
obtained this way.


  We prove that every cusped hyperbolic 3-manifold has a finite cover admitting
infinitely many geometric ideal triangulations. Furthermore, every long Dehn
filling of one cusp in this cover admits infinitely many geometric ideal
triangulations. This cover is constructed in several stages, using results
about separability of peripheral subgroups and their double cosets, in addition
to a new conjugacy separability theorem that may be of independent interest.
The infinite sequence of geometric triangulations is supported in a geometric
submanifold associated to one cusp, and can be organized into an infinite
trivalent tree of Pachner moves.


  We provide a methodology for generating interatomic potentials for use in
classical molecular dynamics simulations of atomistic phenomena occurring at
energy scales ranging from lattice vibrations to crystal defects to high energy
collisions. A rigorous method to objectively determine the shape of an
interatomic potential over all length scales is introduced by building upon a
charged-ion generalization of the well-known Ziegler-Biersack-Littmark
universal potential that provides the short- and long-range limiting behavior
of the potential. At intermediate ranges the potential is smoothly adjusted by
fitting to ab initio data. Our formalism provides a complete description of the
interatomic potentials that can be used at any energy scale, and thus,
eliminates the inherent ambiguity of splining different potentials generated to
study different kinds of atomic materials behavior. We exemplify the method by
developing rigid-ion potentials for uranium dioxide interactions under
conditions ranging from thermodynamic equilibrium to very high atomic energy
collisions relevant for fission events.


  We study rotation $r$-graphs and show that for every $r$-graph $G$ of odd
regularity there is a simple rotation $r$-graph $G'$ such that $G$ can be
obtained form $G'$ by a finite number of $2$-cut reductions. As a consequence,
some hard conjectures as the (generalized) Berge-Fulkerson Conjecture and
Tutte's 3- and 5-flow conjecture can be reduced to rotation $r$-graphs.


  The ensemble Kalman filter is widely used in applications because, for high
dimensional filtering problems, it has a robustness that is not shared for
example by the particle filter; in particular it does not suffer from weight
collapse. However, there is no theory which quantifies its accuracy as an
approximation of the true filtering distribution, except in the Gaussian
setting. To address this issue we provide the first analysis of the accuracy of
the ensemble Kalman filter beyond the Gaussian setting. We prove two types of
results: the first type comprise a stability estimate controlling the error
made by the ensemble Kalman filter in terms of the difference between the true
filtering distribution and a nearby Gaussian; and the second type use this
stability result to show that, in a neighbourhood of Gaussian problems, the
ensemble Kalman filter makes a small error, in comparison with the true
filtering distribution. Our analysis is developed for the mean field ensemble
Kalman filter. We rewrite the update equations for this filter, and for the
true filtering distribution, in terms of maps on probability measures. We
introduce a weighted total variation metric to estimate the distance between
the two filters and we prove various stability estimates for the maps defining
the evolution of the two filters, in this metric. Using these stability
estimates we prove results of the first and second types, in the weighted total
variation metric. We also provide a generalization of these results to the
Gaussian projected filter, which can be viewed as a mean field description of
the unscented Kalman filter.


  Band surgery is an operation relating pairs of knots or links in the
three-sphere. We prove that if two quasi-alternating knots $K$ and $K'$ of the
same square-free determinant are related by a band surgery, then the absolute
value of the difference in their signatures is either 0 or 8. This obstruction
follows from a more general theorem about the difference in the Heegaard Floer
$d$-invariants for pairs of L-spaces that are related by distance one Dehn
fillings and satisfy a certain condition in first homology. These results imply
that $T(2, 5)$ is the only torus knot $T(2, m)$ with $m$ square-free that
admits a chirally cosmetic banding, i.e. a band surgery operation to its mirror
image. We conclude with a discussion on the scarcity of chirally cosmetic
bandings.


  We define a notion of a rotund quasi-uniform space and describe a new direct
construction of a (right-continuous) quasi-pseudometric on a (rotund)
quasi-uniform space. This new construction allows to give alternative proofs of
several classical metrizability theorems for (quasi-)uniform spaces and also
obtain some new metrizability results. Applying this construction to
topological monoids with open shifts, we prove that the topology of any
(semiregular) topological monoid with open shifts is generated by a family of
(right-continuous) left-subinvariant quasi-pseudometrics, which resolves an
open problem posed by Ravsky in 2001. This implies that a topological monoid
with open shifts is completely regular if and only if it is semiregular. Since
each paratopological group is a topological monoid with open shifts these
results apply also to paratopological groups.


  There are two halves to RL systems: experience collection time and policy
learning time. For a large number of samples in rollouts, experience collection
time is the major bottleneck. Thus, it is necessary to speed up the rollout
generation time with multi-process architecture support. Our work, dubbed
WALL-E, utilizes multiple rollout samplers running in parallel to rapidly
generate experience. Due to our parallel samplers, we experience not only
faster convergence times, but also higher average reward thresholds. For
example, on the MuJoCo HalfCheetah-v2 task, with $N = 10$ parallel sampler
processes, we are able to achieve much higher average return than those from
using only a single process architecture.


  An increasing number of applications is concerned with recovering a sparse
matrix from noisy observations. In this paper, we consider the setting where
each row of the unknown matrix is sparse. We establish minimax optimal rates of
convergence for estimating matrices with row sparsity. A major focus in the
present paper is on the derivation of lower bounds.


  The quality of the incoming experimental data has a significant importance
for both analysis and running the experiment. The main point of the Baikal-GVD
DQM system is to monitor the status of the detector and obtained data on the
run-by-run based analysis. It should be fast enough to be able to provide
analysis results to detector shifter and for participation in the global
multi-messaging system.


  Electric vehicle (EV) supply equipment location and allocation (EVSELCA)
problems for freight vehicles are becoming more important because of the
trending electrification shift. Some previous works address EV charger location
and vehicle routing problems simultaneously by generating vehicle routes from
scratch. Although such routes can be efficient, introducing new routes may
violate practical constraints, such as drive schedules, and satisfying
electrification requirements can require dramatically altering existing routes.
To address the challenges in the prevailing adoption scheme, we approach the
problem from a fixed-route perspective. We develop a mixed-integer linear
program, a clustering approach, and a metaheuristic solution method using a
genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach
simplifies the problem by grouping customers into clusters, while the GA
generates solutions that are shown to be nearly optimal for small problem
cases. A case study examines how charger costs, energy costs, the value of time
(VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment
costs were found to be the most significant component in the objective
function, leading to a substantial reduction in cost when decreased. VOT costs
exhibited a significant decrease with rising energy costs. An increase in VOT
resulted in a notable rise in the number of fast chargers. Longer EV ranges
decrease total costs up to a certain point, beyond which the decrease in total
costs is negligible.


  In this note we correct two oversights in [Mapping cones in the bounded
derived category of a gentle algebra, J. Algebra 530 (2019), 163--194, also
arXiv:1609.09688] which only occur when a band complex is involved. As a
consequence we see that the mapping cone of a morphism between two band
complexes can decompose into arbitrarily many indecomposable direct summands.


  Using air to displace a viscous fluid contained in Hele-Shaw cell can create
a fingering pattern at the interface between the fluids, if the capillary
number exceeds a critical value. This Saffman-Taylor instability is revisited
for the inverse case of a viscous fluid displacing air, when partially wettable
hydrophilic particles are lying on the walls. Though the inverse case is
otherwise stable, the presence of the particles results in a fingering
instability at low capillary number. This capillary-driven instability is
driven-by the integration of particles into the interface which results from
the minimization of the interfacial energy. Both axisymmetric and rectangular
geometries are considered in order to quantify this phenomenon.


  We find that characteristics of quantum tunneling in the presence of chaos
can be regarded as a manifestation of the Julia set of the complex dynamical
system. Several numerical evidences for the standard map together with a
rigorous statement for the H\'enon map are presented demonstrating that the
complex classical paths which contribute to the semiclassical propagator are
dense in the Julia set. Chaotic tunneling can thus be characterized by the
transitivity of the dynamics and high density of the trajectories on the Julia
set.


  We derive a sharp lower bound for the scalar curvature of non-flat and
non-compact expanding gradient Ricci soliton provided that the scalar curvature
is non-negative and the potential function is proper. We also give an upper
bound for the scalar curvature of noncompact expander when the Ricci curvature
is nonpositive and the potential function is proper. We then provide a
sufficient condition for the scalar curvature of expanding soliton being
nonnegative. We also estimate the curvature of expanding soliton in dimensions
three and four. As an application, we prove a gap theorem on three dimensional
gradient expander.


The exposition has been significantly altered, hopefully improved.


  In this note a new high performance least squares parameter estimator is
proposed. The main features of the estimator are: (i) global exponential
convergence is guaranteed for all identifiable linear regression equations;
(ii) it incorporates a forgetting factor allowing it to preserve alertness to
time-varying parameters; (iii) thanks to the addition of a mixing step it
relies on a set of scalar regression equations ensuring a superior transient
performance; (iv) it is applicable to nonlinearly parameterized regressions
verifying a monotonicity condition and to a class of systems with switched
time-varying parameters; (v) it is shown that it is bounded-input-bounded-state
stable with respect to additive disturbances; (vi) continuous and discrete-time
versions of the estimator are given. The superior performance of the proposed
estimator is illustrated with a series of examples reported in the literature.


  Force-based multiphysics coupling methods have become popular since they
provide a simple and efficient coupling mechanism, avoiding the difficulties in
formulating and implementing a consistent coupling energy. They are also the
only known pointwise consistent methods for coupling a general atomistic model
to a finite element continuum model. However, the development of efficient and
reliable iterative solution methods for the force-based approximation presents
a challenge due to the non-symmetric and indefinite structure of the linearized
force-based quasicontinuum approximation, as well as to its unusual stability
properties. In this paper, we present rigorous numerical analysis and
computational experiments to systematically study the stability and convergence
rate for a variety of linear stationary iterative methods.


  We aim to take a census of molecular hydrogen emission line objects (MHOs) in
the Rho Ophiuchi molecular cloud and to make the first systematic proper motion
measurements of these objects in this region. Deep H2 near-infrared imaging is
performed to search for molecular hydrogen emission line objects. Multi-epoch
data are used to derive the proper motions of the features of these objects,
and the lengths and opening angles of the molecular hydrogen outflows. Our
imaging covers an area of about 0.11 deg2 toward the L1688 core in the Rho
Ophiuchi molecular cloud. In total, six new MHOs are discovered, 32 previously
known MHOs are detected, and the proper motions for 86 features of the MHOs are
measured. The proper motions lie in the range of 14 to 247 mas/yr,
corresponding to transversal velocities of 8 to 140 km s-1 with a median
velocity of about 35 km s-1. Based on morphology and proper motion
measurements, 27 MHOs are ascribed to 21 driving sources. The molecular
hydrogen outflows have a median length of about 0.04 pc and random
orientations. We find no obvious correlation between H2 jet length, jet opening
angle, and the evolutionary stage of the driving sources as defined by their
spectral indices. We find that the fraction of protostars (23%) that drive
molecular hydrogen outflows is similar to the one for Class II sources (15%).
For most molecular hydrogen outflows, no obvious velocity variation along the
outflow has been found. In Ophiuchus the frequency of occurrence of molecular
hydrogen outflows has no strong dependency on the evolutionary stage of the
driving source during the evolution from the protostellar stage to the
classical T Tauri stage.


  In this work, we performed temperature-dependent Raman spectroscopy studies
on three layered metal thiophosphates: CuInP2S6, CuCrP2S6, and
CuInP2S6-In4/3P2S6 heterostructures. These materials are emerging
multiferroics, with CuInP2S6 and CuInP2S6-In4/3P2S6 exhibiting room temperature
ferrielectric polarization while CuCrP2S6 is both anti-ferroelectric and
antiferromagnetic at low temperatures (<145 and <30 K, respectively). We
studied two prominent peaks in the Raman spectra of these compounds, namely
their P-P and P-S stretching modes. First order linear fits to the mode
softening yielded coefficients similar to other layered materials. Furthermore,
analysis of the phonon decay revealed the dominant role of anharmonic effects
rather than thermal expansion. In particular, the decay of the optical phonons
was found to be governed by three-phonon processes in CuInP2S6 and CuCrP2S6 and
by the four-phonon process in the CuInP2S6-In4/3P2S6 heterostructures. These
phonon lifetimes were found to be less than 1 ps, indicating high scattering
rates and showing the potential for these materials to be used in
thermoelectric applications.


  Importance: Non-contrast head CT scan is the current standard for initial
imaging of patients with head trauma or stroke symptoms.
  Objective: To develop and validate a set of deep learning algorithms for
automated detection of following key findings from non-contrast head CT scans:
intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH),
intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH)
hemorrhages, calvarial fractures, midline shift and mass effect.
  Design and Settings: We retrospectively collected a dataset containing
313,318 head CT scans along with their clinical reports from various centers. A
part of this dataset (Qure25k dataset) was used to validate and the rest to
develop algorithms. Additionally, a dataset (CQ500 dataset) was collected from
different centers in two batches B1 & B2 to clinically validate the algorithms.
  Main Outcomes and Measures: Original clinical radiology report and consensus
of three independent radiologists were considered as gold standard for Qure25k
and CQ500 datasets respectively. Area under receiver operating characteristics
curve (AUC) for each finding was primarily used to evaluate the algorithms.
  Results: Qure25k dataset contained 21,095 scans (mean age 43.31; 42.87%
female) while batches B1 and B2 of CQ500 dataset consisted of 214 (mean age
43.40; 43.92% female) and 277 (mean age 51.70; 30.31% female) scans
respectively. On Qure25k dataset, the algorithms achieved AUCs of 0.9194,
0.8977, 0.9559, 0.9161, 0.9288 and 0.9044 for detecting ICH, IPH, IVH, SDH, EDH
and SAH respectively. AUCs for the same on CQ500 dataset were 0.9419, 0.9544,
0.9310, 0.9521, 0.9731 and 0.9574 respectively. For detecting calvarial
fractures, midline shift and mass effect, AUCs on Qure25k dataset were 0.9244,
0.9276 and 0.8583 respectively, while AUCs on CQ500 dataset were 0.9624, 0.9697
and 0.9216 respectively.


  We introduce a practical method for modeling the small-signal behavior of
dispersive and inhomogeneous helix-type traveling-wave tube (TWT) amplifiers
based on a generalization of the one-dimensional Pierce model. Our model is
applicable to both single-stage and multi-stage TWTs. Like the Pierce model, we
assume that electrons flow linearly in one direction, parallel and in proximity
to a slow-wave structure (SWS) which guides a single dominant electromagnetic
mode. Realistic helix TWTs are modeled with position dependent and
frequency-dependent SWS characteristics, such as loss, phase velocity, plasma
frequency reduction factor, and interaction impedance. For the multi-stage
helix TWT, we provide a simple lumped element circuit model for combining the
stages separated by a sever, or gap, which attenuates the guided circuit mode
while allowing the space-charge wave on the beam to pass freely to the next
stage. The dispersive SWS characteristics are accounted for using full-wave
eigenmode simulations for a realistic helix SWS supported by dielectric rods in
a metal barrel, all of which contribute to the distributed circuit loss. We
compare our computed gain vs frequency, computed using transfer matrices, to
results found through particle-in-cell (PIC) simulations and the 1D TWT code
LATTE to demonstrate the accuracy of our model. Furthermore, we demonstrate the
ability of our model to reproduce gain ripple due to mismatches at the input
and output ports of the TWT.


  In recent years end-to-end verifiable voting (E2EVV) has emerged as a
promising new paradigm to conduct evidence-based elections. However, E2EVV
systems thus far have primarily been designed for the developed world and the
fundamental assumptions underlying the design of these systems do not readily
translate to the developing world, and may even act as potential barriers to
adoption of these systems. This is unfortunate because developing countries
account for 80\% of the global population, and given their economic and
socio-political dilemmas and their track record of contentious elections, these
countries arguably stand to benefit most from this exciting new paradigm. In
this paper, we highlight various limitations and challenges in adapting E2EVV
systems to these environments, broadly classed across social, political,
technical, operational, and human dimensions. We articulate corresponding
research questions and identify significant literature gaps in these
categories. We also suggest relevant strategies to aid researchers,
practitioners, and policymakers in visualizing and exploring solutions that
align with the context and unique ground realities in these environments. Our
goal is to outline a broader research agenda for the community to successfully
adapt E2EVV voting systems to developing countries.