  Context. Magnetic fields are key to understand galaxy evolution, regulating
stellar feedback and star formation in galaxies. Aims. We probe the origin of
magnetic fields in late-type galaxies, measuring magnetic field strengths,
exploring whether magnetic fields are only passive constituents of the
interstellar medium, or whether they are active constituents being part of the
local energy equilibrium. Methods. We measure equipartition magnetic field
strengths in 39 galaxies from LoTSS-DR2 using LOFAR observations at 144 MHz
with 6 arcsec angular resolution which (0.1-0.7 kpc). For a subset of 9
galaxies, we obtain atomic and molecular mass surface densities using HI and
CO(2-1) data, from the THINGS and HERACLES surveys, respectively. These data
are at 13 arcsec angular resolution, which corresponds to 0.3-1.2 kpc at the
distances of our galaxies. We measure kinetic energy densities using HI and CO
velocity dispersions. Results. We found a mean magnetic field strength of
3.6-12.5 $\mu$G with a mean of $7.9 \pm 2.0$ $\mu$G across the full sample. The
magnetic field strength has the tightest and steepest relation with the total
gas surface density with $B\propto \Sigma_{\rm HI+H2}^{0.309\pm0.006}$. The
relation with the star-formation rate surface density and molecular gas surface
density has significantly flatter slopes. After accounting for the influence of
cosmic-ray transport, we found an even steeper relation of $B\propto
\Sigma_{\rm HI+H2}^{0.393\pm0.009}$. Conclusions. These results suggest that
the magnetic field is regulated by a $B$-$\rho$ relation, which has its origin
in the saturation of the small-scale dynamo. This is borne out by an agreement
of kinetic and magnetic energy densities although local deviations do exist in
particular in areas of high kinetic energy densities where the magnetic field
is sub-dominant.


  Structural controllability challenges arise from imprecise system modeling
and system interconnections in large scale systems. In this paper, we study
structural control of bilinear systems on the special Euclidean group. We
employ graph theoretic methods to analyze the structural controllability
problem for driftless bilinear systems and structural accessibility for
bilinear systems with drift. This facilitates the identification of a sparsest
pattern necessary for achieving structural controllability and discerning
redundant connections. To obtain a graph theoretic characterization of
structural controllability and accessibility on the special Euclidean group, we
introduce a novel idea of solid and broken edges on graphs; subsequently, we
use the notion of transitive closure of graphs.


  If axions exist they are efficiently produced in the hot and dense interior
of stars, providing a novel energy-loss mechanism. In order to avoid a conflict
with the observed properties of stars, one can derive a lower limit on the
Peccei-Quinn scale (an upper limit on the axion mass). In the early universe,
axions are produced by the ``misalignment mechanism'' and the emission from
global strings as well as the relaxation of the string-domain wall system
formed at the QCD phase transition. In order to avoid an ``overclosed
universe'' the Peccei-Quinn scale must obey an upper limit (a lower limit on
the axion mass). The current values of these bounds are reviewed. There remains
a ``window of opportunity'' $10^{-5}\,\eV\lapprox m_a\lapprox 10^{-2}\,\eV$,
with large uncertainties on either side, where axions could still exist.
[Contribution to the XVth Moriond workshop, 21-28 Jan. 1995, Villars-sur-Ollon,
Switzerland.]


  Monocular depth estimation is an especially important task in robotics and
autonomous driving, where 3D structural information is essential. However,
extreme lighting conditions and complex surface objects make it difficult to
predict depth in a single image. Therefore, to generate accurate depth maps, it
is important for the model to learn structural information about the scene. We
propose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module
(EAM) to solve the difficulty of monocular depth estimation. The proposed
modules extract structural information by learning the relationship between
image patches close to each other in space using edge convolution. Our method
is evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen
split, achieving state-of-the-art performance. We prove that the proposed model
predicts depth robustly in challenging scenes through various comparative
experiments.


  For $s\in (0,1)$ we introduce a notion of fractional $s$-mass on
$(n-2)$-dimensional closed, orientable surfaces in $\R^n$. Moreover, we prove
its $\Gamma$-convergence, with respect to the flat topology, and pointwise
convergence to the $(n-2)$-dimensional area.


  Low-dose Computed Tomography (LDCT) reconstruction is an important task in
medical image analysis. Recent years have seen many deep learning based
methods, proved to be effective in this area. However, these methods mostly
follow a supervised architecture, which needs paired CT image of full dose and
quarter dose, and the solution is highly dependent on specific measurements. In
this work, we introduce Denoising Diffusion LDCT Model, dubbed as DDLM,
generating noise-free CT image using conditioned sampling. DDLM uses pretrained
model, and need no training nor tuning process, thus our proposal is in
unsupervised manner. Experiments on LDCT images have shown comparable
performance of DDLM using less inference time, surpassing other
state-of-the-art methods, proving both accurate and efficient. Implementation
code will be set to public soon.


  The presence of chiral fermions in the physical Hilbert space implies
consistency conditions on the spectral action. These conditions are equivalent
to the absence of gauge and gravitational anomalies. Suggestions for the
fermionic part of the spectral action are made based on the supersymmetrisation
of the bosonic part.


  We report C, Si, and S isotope measurements on 34 presolar silicon carbide
grains of Type AB, characterized by 12C/13C < 10. Nitrogen, Mg-Al-, and
Ca-Ti-isotopic compositions were measured on a subset of these grains. Three
grains show large 32S excesses, a signature that has been previously observed
for grains from supernovae (SNe). Enrichments in 32S may be due to
contributions from the Si/S zone and the result of S molecule chemistry in
still unmixed SN ejecta or due to incorporation of radioactive 32Si from C-rich
explosive He shell ejecta. However, a SN origin remains unlikely for the three
AB grains considered here, because of missing evidence for 44Ti, relatively low
26Al/27Al ratios (a few times 10-3), and radiogenic 32S along with low 12C/13C
ratios. Instead, we show that born-again asymptotic giant branch (AGB) stars
that have undergone a very-late thermal pulse (VLTP), known to have low 12C/13C
ratios and enhanced abundances of the light s-process elements, can produce
32Si, which makes such stars attractive sources for AB grains with 32S
excesses. This lends support to the proposal that at least some AB grains
originate from born-again AGB stars, although uncertainties in the born-again
AGB star models and possible variations of initial S-isotopic compositions in
the parent stars of AB grains make it difficult to draw a definitive
conclusion.


  Given a vector $F=(F_1,\dots,F_m)$ of Poisson functionals $F_1,\dots,F_m$, we
investigate the proximity between $F$ and an $m$-dimensional centered Gaussian
random vector $N_\Sigma$ with covariance matrix $\Sigma\in\mathbb{R}^{m\times
m}$. Apart from finding proximity bounds for the $d_2$- and $d_3$-distances,
based on classes of smooth test functions, we obtain proximity bounds for the
$d_{convex}$-distance, based on the less tractable test functions comprised of
indicators of convex sets. The bounds for all three distances are shown to be
of the same order, which is presumably optimal. The bounds are multivariate
counterparts of the univariate second order Poincar\'e inequalities and, as
such, are expressed in terms of integrated moments of first and second order
difference operators. The derived second order Poincar\'e inequalities for
indicators of convex sets are made possible by a new bound on the second
derivatives of the solution to the Stein equation for the multivariate normal
distribution. We present applications to the multivariate normal approximation
of first order Poisson integrals and of statistics of Boolean models.


  Knowledge is attributed to human whose problem-solving behavior is subjective
and complex. In today's knowledge economy, the need to manage knowledge
produced by a community of actors cannot be overemphasized. This is due to the
fact that actors possess some level of tacit knowledge which is generally
difficult to articulate. Problem-solving requires searching and sharing of
knowledge among a group of actors in a particular context. Knowledge expressed
within the context of a problem resolution must be capitalized for future
reuse. In this paper, an approach that permits dynamic capitalization of
relevant and reliable actors' knowledge in solving decision problem following
Economic Intelligence process is proposed. Knowledge annotation method and
temporal attributes are used for handling the complexity in the communication
among actors and in contextualizing expressed knowledge. A prototype is built
to demonstrate the functionalities of a collaborative Knowledge Management
system based on this approach. It is tested with sample cases and the result
showed that dynamic capitalization leads to knowledge validation hence
increasing reliability of captured knowledge for reuse. The system can be
adapted to various domains


  The CMS experiment obtained a large number of groundbreaking results from the
analysis of 7- and 8-TeV proton-proton collisions produced so far by the Large
Hadron Collider at CERN. In this brief summary only a sample of those results
will be discussed. A new particle with mass m(H) = 125.3 +- 0.4(stat.) +-
0.5(syst.) GeV and characteristics compatible with those expected for a
standard model Higgs boson has been observed in its decays to photon pairs, WW
pairs, and ZZ pairs. Searches for the rare decays B_d -> mu mu and B_s -> mu mu
have allowed to set limits on the branching fractions which are close to
standard model predictions, strongly constraining new physics models. The top
quark has been studied with great detail, obtaining among other results the
world's best measurement of its mass as m(top) = 173.49 +- 0.43(stat. + JES) +-
0.98(syst.) GeV. New physics models have been strongly constrained with the
available data.


  Variational-Quantum-Eigensolver(VQE) method on a quantum computer is a
well-known hybrid algorithm to solve the eigenstates and eigenvalues that uses
both quantum and classical computers. This method has the potential to solve
quantum chemical simulation including polymer and complex optimization problems
that are never able to be solved in a realistic time. Though they are many
papers on VQE, there are many hurdles before practical application. Therefore,
we tried to evaluate VQE methods with Genetic Algorithms(GA). In this paper, we
propose the VQE method with GA. We selected ground and excited-state energy on
hydrogen molecules as the target because there are many local minimum values on
excited states though the molecular structure is extremely simple. Therefore it
is not easy to find the energy of states. We compared the GA method with other
methods from the viewpoint of log error of the ground, triplet, singlet, and
doubly excited state energy value. As a result, we denoted that the BFGS method
has the highest accuracy. We thought that rcGA used as an optimization for the
VQE method was proved disappointing. The rcGA does not show an advantage
compared to other methods. we suggest that the cause is due to initial
convergence. In the future, we want to try to introduce Genetic Algorithms then
local search.


  ODE Test Problems (OTP) is an object-oriented MATLAB package offering a broad
range of initial value problems which can be used to test numerical methods
such as time integration methods and data assimilation (DA) methods. It
includes problems that are linear and nonlinear, homogeneous and
nonhomogeneous, autonomous and nonautonomous, scalar and high-dimensional,
stiff and nonstiff, and chaotic and nonchaotic. Many are real-world problems
from fields such as chemistry, astrophysics, meteorology, and electrical
engineering. OTP also supports partitioned ODEs for testing IMEX methods,
multirate methods, and other multimethods. Functions for plotting solutions and
creating movies are available for all problems, and exact solutions are
provided when available. OTP is desgined for ease of use-meaning that working
with and modifying problems is simple and intuitive.


  We introduce intrinsic Sobolev-Slobodeckij spaces for a class of
ultra-parabolic Kolmogorov type operators satisfying the weak H\"ormander
condition. We prove continuous embeddings into Lorentz and intrinsic H\"older
spaces. We also prove approximation and interpolation inequalities by means of
an intrinsic Taylor expansion, extending analogous results for H\"older spaces.
The embedding at first order is proved by adapting a method by Luc Tartar which
only exploits scaling properties of the intrinsic quasi-norm, while for higher
orders we use uniform kernel estimates.


  This paper concerns the algebraic structure of finite-dimensional complex
Leibniz algebras. In particular, we introduce left central and symmetric
Leibniz algebras, and study the poset of Lie subalgebras using an associative
bilinear pairing taking values in the Leibniz kernel.


  This article presents an efficient hierarchical clustering algorithm that
solves the problem of core community detection. It is a variant of the standard
community detection problem in which we are particularly interested in the
connected core of communities. To provide a solution to this problem, we
question standard definitions on communities and provide alternatives. We also
propose a function called compactness, designed to assess the quality of a
solution to this problem. Our algorithm is based on a graph traversal
algorithm, the LexDFS. The time complexity of our method is in $O(n\times
log(n))$. Experiments show that our algorithm creates highly compact clusters.


  Due to massive amounts of data distributed across multiple locations,
distributed machine learning has attracted a lot of research interests.
Alternating Direction Method of Multipliers (ADMM) is a powerful method of
designing distributed machine learning algorithm, whereby each agent computes
over local datasets and exchanges computation results with its neighbor agents
in an iterative procedure. There exists significant privacy leakage during this
iterative process if the local data is sensitive. In this paper, we propose a
differentially private ADMM algorithm (P-ADMM) to provide dynamic
zero-concentrated differential privacy (dynamic zCDP), by inserting Gaussian
noise with linearly decaying variance. We prove that P-ADMM has the same
convergence rate compared to the non-private counterpart, i.e.,
$\mathcal{O}(1/K)$ with $K$ being the number of iterations and linear
convergence for general convex and strongly convex problems while providing
differentially private guarantee. Moreover, through our experiments performed
on real-world datasets, we empirically show that P-ADMM has the best-known
performance among the existing differentially private ADMM based algorithms.


  Electroabsorption (EA) measurements can be used to identify the type of
excitons contributing to the absorption spectra of semiconductors which have
applications in optoelectronics. However, the inferences from the EA
measurement greatly depend on the method of fitting and extraction of
parameters from the measured spectra. We deconstruct the absorption spectrum by
fitting multiple Gaussians and obtain the relative contribution of first and
second derivative of each absorption band in EA spectrum, which gives
indication of the Frenkel, charge transfer or mixed nature of the excitons
involved. We check the applicability of the method for pentacene which is
widely used and well studied organic semiconductor. We report EA measurements
of
poly[(2,5-bis(2-hexyldecyloxy)-phenylene)-alt-(5,6-difluoro-4,7-di(thiophen-2-yl)benzo[c]-[1,2,5]-thiadiazole)]
(PPDT2FBT). Our analysis shows that besides the feature around 3.07 eV, which
is strongly Frenkel-like, most of the absorption bands for PPDT2FBT are mixed
states, having relatively high charge transfer contributions. Since charge
transfer excitons have higher dissociation efficiencies, we infer PPDT2FBT to
be a promising candidate for photovoltaic applications.


  Few-shot learning aims to generalize the recognizer from seen categories to
an entirely novel scenario. With only a few support samples, several advanced
methods initially introduce class names as prior knowledge for identifying
novel classes. However, obstacles still impede achieving a comprehensive
understanding of how to harness the mutual advantages of visual and textual
knowledge. In this paper, we propose a coherent Bidirectional Knowledge
Permeation strategy called BiKop, which is grounded in a human intuition: A
class name description offers a general representation, whereas an image
captures the specificity of individuals. BiKop primarily establishes a
hierarchical joint general-specific representation through bidirectional
knowledge permeation. On the other hand, considering the bias of joint
representation towards the base set, we disentangle base-class-relevant
semantics during training, thereby alleviating the suppression of potential
novel-class-relevant information. Experiments on four challenging benchmarks
demonstrate the remarkable superiority of BiKop. Our code will be publicly
available.


  Sharp threshold-like transitions between two stable nuclear spin
polarizations are observed in optically pumped individual InGaAs self-assembled
quantum dots embedded in a Schottky diode when the bias applied to the diode is
tuned. The abrupt transitions lead to the switching of the Overhauser field in
the dot by up to 3 Tesla. The bias-dependent photoluminescence measurements
reveal the importance of the electron-tunneling-assisted nuclear spin pumping.
We also find evidence for the resonant LO-phonon-mediated electron
co-tunneling, the effect controlled by the applied bias and leading to the
reduction of the nuclear spin pumping rate.


  In this work, we propose a library that enables on a cloud the creation and
management of tree data structures from a cloud client. As a proof of concept,
we implement a new cloud service CloudTree. With CloudTree, users are able to
organize big data into tree data structures of their choice that are physically
stored in a cloud. We use caching, prefetching, and aggregation techniques in
the design and implementation of CloudTree to enhance performance. We have
implemented the services of Binary Search Trees (BST) and Prefix Trees as
current members in CloudTree and have benchmarked their performance using the
Amazon Cloud. The idea and techniques in the design and implementation of a BST
and prefix tree is generic and thus can also be used for other types of trees
such as B-tree, and other link-based data structures such as linked lists and
graphs. Preliminary experimental results show that CloudTree is useful and
efficient for various big data applications.


  In this work we present a framework which may transform research and praxis
in epidemic planning. Introduced in the context of the ongoing COVID-19
pandemic, we provide a concrete demonstration of the way algorithms may learn
from epidemiological models to scale their value for epidemic preparedness. Our
contributions in this work are two fold: 1) a novel platform which makes it
easy for decision making stakeholders to interact with epidemiological models
and algorithms developed within the Machine learning community, and 2) the
release of this work under the Apache-2.0 License. The objective of this paper
is not to look closely at any particular models or algorithms, but instead to
highlight how they can be coupled and shared to empower evidence-based decision
making.


  Conjecture $\mathbb{Z}$ is a knot theoretical equivalent form of the Kervaire
Conjecture. We say that a knot have property $\mathbb{Z}$ if it satisfies
Conjecture $\mathbb{Z}$ for that specific knot. In this work, we show that
alternating Montesinos knots with three tangles have property $\mathbb{Z}$. We
also show that all the pretzel knots of the form $P(p,q,r)$ (not necessarily
alternating) have property $\mathbb{Z}$.


  In this paper, we extend the bilinear generalized approximate message passing
(BiG-AMP) approach, originally proposed for high-dimensional generalized
bilinear regression, to the multi-layer case for the handling of cascaded
problem such as matrix-factorization problem arising in relay communication
among others. Assuming statistically independent matrix entries with known
priors, the new algorithm called ML-BiGAMP could approximate the general
sum-product loopy belief propagation (LBP) in the high-dimensional limit
enjoying a substantial reduction in computational complexity. We demonstrate
that, in large system limit, the asymptotic MSE performance of ML-BiGAMP could
be fully characterized via a set of simple one-dimensional equations termed
state evolution (SE). We establish that the asymptotic MSE predicted by
ML-BiGAMP' SE matches perfectly the exact MMSE predicted by the replica method,
which is well known to be Bayes-optimal but infeasible in practice. This
consistency indicates that the ML-BiGAMP may still retain the same
Bayes-optimal performance as the MMSE estimator in high-dimensional
applications, although ML-BiGAMP's computational burden is far lower. As an
illustrative example of the general ML-BiGAMP, we provide a detector design
that could estimate the channel fading and the data symbols jointly with high
precision for the two-hop amplify-and-forward relay communication systems.


  We construct a self-similar solution of the heat equation for the fractional
Laplacian with Dirichlet boundary conditions in every fat cone. As
applications, we give the Yaglom limit and entrance law for the corresponding
killed isotropic stable L\'{e}vy process and precise large-time asymptotics for
solutions of the Cauchy problem in the cone.


  This paper elaborates the problem of energy-momentum in the framework of
teleparallel equivalent of General Relativity. For this purpose, we consider
energy-momentum prescription derived from the integral form of the constraint
equations developed in the Hamiltonian formulation of the teleparallel
equivalent of General Relativity. We use this technique to investigate
energy-momentum of stationary axisymmetric Einstein-Maxwell solutions and
cosmic string spacetimes. The angular momentum, gravitational and matter
energy-momentum fluxes of these spacetimes are also evaluated. It is concluded
that the results of teleparallel theory are relatively analogous to the results
of General Relativity.


  The eligibility of various advanced quantum algorithms will be questioned if
they can not guarantee privacy. To fill this knowledge gap, here we devise an
efficient quantum differentially private (QDP) Lasso estimator to solve sparse
regression tasks. Concretely, given $N$ $d$-dimensional data points with $N\ll
d$, we first prove that the optimal classical and quantum non-private Lasso
requires $\Omega(N+d)$ and $\Omega(\sqrt{N}+\sqrt{d})$ runtime, respectively.
We next prove that the runtime cost of QDP Lasso is \textit{dimension
independent}, i.e., $O(N^{5/2})$, which implies that the QDP Lasso can be
faster than both the optimal classical and quantum non-private Lasso. Last, we
exhibit that the QDP Lasso attains a near-optimal utility bound
$\tilde{O}(N^{-2/3})$ with privacy guarantees and discuss the chance to realize
it on near-term quantum chips with advantages.


  In this paper, we present microring resonator (MRR) based polymorphic E-O
circuits and architectures that can be employed for high-speed and
energy-efficient non-binary reconfigurable computing. Our polymorphic E-O
circuits can be dynamically programmed to implement different logic and
arithmetic functions at different times. They can provide compactness and
polymorphism to consequently improve operand handling, reduce idle time, and
increase amortization of area and static power overheads. When combined with
flexible photodetectors with the innate ability to accumulate a high number of
optical pulses in situ, our circuits can support energy-efficient processing of
data in non-binary formats such as stochastic/unary and high-dimensional
reservoir formats. Furthermore, our polymorphic E-O circuits enable
configurable E-O computing accelerator architectures for processing binarized
and integer quantized convolutional neural networks (CNNs). We compare our
designed polymorphic E-O circuits and architectures to several circuits and
architectures from prior works in terms of area, latency, and energy
consumption.


  We extend the Altmann--Mavlyutov construction of homogeneous deformations of
affine toric varieties to the case of toric pairs $(X, \partial X)$, where $X$
is an affine or projective toric variety and $\partial X$ is its toric
boundary. As an application, we generalise a result due to Ilten to the case of
Fano toric pairs.


  Recently a stable monolayer of antimony in buckled honeycomb structure called
antimonene was successfully grown on 3D topological insulator Bi$_2$Te$_3$ and
Sb$_2$Te$_3$, which displays semiconducting properties. By first principle
calculations, we systematically investigate the phononic, electronic and
optical properties of $\alpha-$ and $\beta-$ allotropes of monolayer
arsenene/antimonene. We investigate the dynamical stabilities of these four
materials by considering the phonon dispersions. The obtained electronic
structures reveal the direct band gap of monolayer $\alpha-$As/Sb and indirect
band gap of $\beta-$As/Sb. Significant absorption is observed in $\alpha-$Sb,
which can be used as a broad saturable absorber.


  In a previous report we have evaluated analytically the mutual information
between the firing rates of N independent units and a set of
continuous+discrete stimuli, for finite N and in the limit of large noise.
Here, we extend the analysis to the case of two interconnected populations,
where input units are linked to output ones via gaussian weights and a
threshold linear transfer function. We evaluate the information carried by M
output units about continuous +discrete correlates by means of saddle point
equations under the assumption of replica symmetry. Within this limitation, we
analyze the dependence of the information on the ratio M/N, on the selectivity
of the input units and on the level of the output noise. We show analytically,
and confirm numerically, that in the limit of a linear transfer function and of
a small ratio between output and input noise, the output information approaches
asymptotically the information carried in input. Finally, we show that the
information loss in output does not depend much on the structure of the
stimulus, but mainly on the position of the threshold nonlinearity, and on the
ratio between input and output noise.


  We investigate the creation of entanglement between two quantum emitters
interacting with a realistic common stationary electromagnetic field out of
thermal equilibrium. In the case of two qubits we show that the absence of
equilibrium allows the generation of steady entangled states, which is
inaccessible at thermal equilibrium and is realized without any further
external action on the two qubits. We first give a simple physical
interpretation of the phenomenon in a specific case and then we report a
detailed investigation on the dependence of the entanglement dynamics on the
various physical parameters involved. Sub- and super-radiant effects are
discussed, and qualitative differences in the dynamics concerning both creation
and protection of entanglement according to the initial two-qubit state are
pointed out.


  This paper presents an approach to automatically annotate automotive radar
data with AI-segmented aerial camera images. For this, the images of an
unmanned aerial vehicle (UAV) above a radar vehicle are panoptically segmented
and mapped in the ground plane onto the radar images. The detected instances
and segments in the camera image can then be applied directly as labels for the
radar data. Owing to the advantageous bird's eye position, the UAV camera does
not suffer from optical occlusion and is capable of creating annotations within
the complete field of view of the radar. The effectiveness and scalability are
demonstrated in measurements, where 589 pedestrians in the radar data were
automatically labeled within 2 minutes.


  We present cyber-security problems of high importance. We show that in order
to solve these cyber-security problems, one must cope with certain machine
learning challenges. We provide novel data sets representing the problems in
order to enable the academic community to investigate the problems and suggest
methods to cope with the challenges. We also present a method to generate
labels via pivoting, providing a solution to common problems of lack of labels
in cyber-security.


  Contact angle goniometry is conducted for epitaxial graphene on SiC. Although
only a single layer of epitaxial graphene exists on SiC, the contact angle
drastically changes from 69{\deg} on SiC substrates to 92{\deg} with graphene.
It is found that there is no thickness dependence of the contact angle from the
measurements of single, bi, and multi layer graphene and highly ordered
pyrolytic graphite (HOPG). After graphene is treated with oxygen plasma, the
level of damage is investigated by Raman spectroscopy and correlation between
the level of disorder and wettability is reported. By using low power oxygen
plasma treatment, the wettability of graphene is improved without additional
damage, which can solve the adhesion issues involved in the fabrication of
graphene devices.


  We demonstrate the formation of composite fermions in two-dimensional quantum
dots under high magnetic fields. The composite fermion interpretation provides
a simple way to understand several qualitative and quantitative features of the
numerical results obtained earlier in exact diagonalization studies. In
particular, the ground states are recognized as compactly filled quasi-Landau
levels of composite fermions.


  We analyze the reorganization of free field theory correlators to closed
string amplitudes investigated in hep-th/0308184 hep-th/0402063 hep-th/0409233
hep-th/0504229 in the case of Euclidean thermal field theory and study how the
dual bulk geometry is encoded on them. The expectation value of Polyakov loop,
which is an order parameter for confinement-deconfinement transition, is
directly reflected on the dual bulk geometry. The dual geometry of confined
phase is found to be AdS space periodically identified in Euclidean time
direction. The gluing of Schwinger parameters, which is a key step for the
reorganization of field theory correlators, works in the same way as in the
non-thermal case. In deconfined phase the gluing is made possible only by
taking the dual geometry correctly. The dual geometry for deconfined phase does
not have a non-contractible circle in the Euclidean time direction.


  This paper presents a taxonomy for analytical spreadsheet models. It
considers both the use case that a spreadsheet is meant to serve, and the
engineering resources devoted to its development. We extend a previous
three-type taxonomy, to identify nine types of spreadsheet models, that
encompass the many analytical spreadsheet models seen in the literature. We
connect disparate research literature to distinguish between an "analytical
solution" and an "industrial-quality analytical spreadsheet model". We explore
the nature of each of the nine types, propose definitions for some, relate them
to the literature, and hypothesize on how they might arise. The taxonomy aids
in identifying where various spreadsheet development guidelines are most
useful, provides a lens for viewing spreadsheet errors and risk, and offers a
structure for understanding how spreadsheets change over time. This taxonomy
opens the door to many interesting research questions, including refinements to
itself.


  In the Schroedinger equation, time plays a special role as an external
parameter. We show that in an enlarged system where the time variable denotes
an additional degree of freedom, solutions of the Schroedinger equation give
rise to weights on the enlarged algebra of observables. States in the
associated GNS representation correspond to states on the original algebra
composed with a completely positive unit preserving map. Application of this
map to the functions of the time operator on the large system delivers the
positive operator valued maps which were previously proposed by two of us as
time observables. As an example we discuss the application of this formalism to
the Wheeler-DeWitt theory of a scalar field on a Robertson-Walker spacetime.


  A rotating star with a monopole (or split monopole) magnetic field gives the
simplest, prototype model of a rotationally driven stellar wind. Winds from
compact objects, in particular neutron stars, carry strong magnetic fields with
modest plasma loading, and develop ultra-relativistic speeds. We investigate
the relativistic wind launched from a dense, gravitationally bound, atmosphere
on the stellar surface. We first examine the problem analytically and then
perform global kinetic plasma simulations. Our results show how the wind
acceleration mechanism changes from centrifugal (magnetohydrodynamic) to
electrostatic (charge-separated) depending on the parameters of the problem.
The two regimes give winds with different angular distributions and different
scalings with the magnetization parameter.


  Social media is abundant in visual and textual information presented together
or in isolation. Memes are the most popular form, belonging to the former
class. In this paper, we present our approaches for the Memotion Analysis
problem as posed in SemEval-2020 Task 8. The goal of this task is to classify
memes based on their emotional content and sentiment. We leverage techniques
from Natural Language Processing (NLP) and Computer Vision (CV) towards the
sentiment classification of internet memes (Subtask A). We consider Bimodal
(text and image) as well as Unimodal (text-only) techniques in our study
ranging from the Na\"ive Bayes classifier to Transformer-based approaches. Our
results show that a text-only approach, a simple Feed Forward Neural Network
(FFNN) with Word2vec embeddings as input, performs superior to all the others.
We stand first in the Sentiment analysis task with a relative improvement of
63% over the baseline macro-F1 score. Our work is relevant to any task
concerned with the combination of different modalities.


  It is important to choose the geographical distributions of public resources
in a fair and equitable manner. However, it is complicated to quantify the
equity of such a distribution; important factors include distances to resource
sites, availability of transportation, and ease of travel. We use persistent
homology, which is a tool from topological data analysis, to study the
effective availability and coverage of polling sites. The information from
persistent homology allows us to infer holes in the distribution of polling
sites. We analyze and compare the coverage of polling sites in Los Angeles
County and five cities (Atlanta, Chicago, Jacksonville, New York City, and Salt
Lake City), and we conclude that computation of persistent homology appears to
be a reasonable approach to analyzing resource coverage.


  We prove the bounded packing property for any abelian subgroup of a group
acting properly and cocompactly on a CAT(0) cube complex. A main ingredient of
the proof is a cubical flat torus theorem. This ingredient is also used to show
that central HNN extensions of maximal free-abelian subgroups of compact
special groups are virtually special, and to produce various examples of groups
that are not cocompactly cubulated.


  The phenomenon of superradiance in the context of asymptotically global AdS
spacetimes is investigated with particular accent on its effect on the
stability of the systems under consideration. To this end, the concept of an
asymptotically AdS spacetime is explained, together with its implications on
the boundary conditions at $\mathcal{I}$, as well as the
Newman-Penrose-Teukolsky formalism, whereby the Teukolsky master equation in a
most general form for Kerr-AdS is given. Furthermore, work done in the cases of
RN-AdS and Kerr-AdS is laid out in a concise manner, putting emphasis on the
important steps taken in determining the endpoint of the superradiant
instability in the two configurations. For the former this turns out to be a
black hole with reduced charge and a static charged scalar condensate around
it, whereas for the latter two of the more probable outcomes are presented,
both of which imply a violation of one of the cosmic censorships.


  In this work, we investigate the IVP for a time-fractional fourth-order
equation with nonlinear source terms. More specifically, we consider the
time-fractional biharmonic with exponential nonlinearity and the
time-fractional Cahn-Hilliard equation. By using the Fourier transform concept,
the generalized formula for the mild solution as well as the smoothing effects
of resolvent operators are proved. For the IVP associated with the first one,
by using the Orlicz space with the function $ \Xi(z)=e^{|z|^p}-1$ and some
embeddings between it and the usual Lebesgue spaces, we prove that the solution
is a global-in-time solution or it shall blow up in a finite time if the
initial value is regular. In the case of singular initial data, the
local-in-time/global-in-time existence and uniqueness are derived. Also, the
regularity of the mild solution is investigated. For the IVP associated with
the second one, some modifications to the generalized formula are made to deal
with the nonlinear term. We also establish some important estimates for the
derivatives of resolvent operators, they are the basis for using the Picard
sequence to prove the local-in-time existence of the solution.


  We discuss the technique of Wide-field imaging as it applies to Very Long
Baseline Interferometry (VLBI). In the past VLBI data sets were usually
averaged so severely that the field-of-view was typically restricted to regions
extending a few hundred milliarcseconds from the phase centre of the field.
Recent advances in data analysis techniques, together with increasing data
storage capabilities, and enhanced computer processing power, now permit VLBI
images to be made whose angular size represents a significant fraction of an
individual antenna's primary beam. This technique has recently been
successfully applied to several large separation gravitational lens systems,
compact Supernova Remnants in the starburst galaxy M82, and two faint radio
sources located within the same VLA FIRST field. It seems likely that other
VLBI observing programmes might benefit from this wide-field approach to VLBI
data analysis. With the raw sensitivity of global VLBI set to improve by a
factor 4-5 over the coming few years, the number of sources that can be
detected in a given field will rise considerably. In addition, a continued
progression in VLBI's ability to image relatively faint and extended low
brightness temperature features (such as hot-spots in large-scale astrophysical
jets) is also to be expected. As VLBI sensitivity approaches the $\mu$Jy level,
a wide-field approach to data analysis becomes inevitable.


  Most current action recognition methods heavily rely on appearance
information by taking an RGB sequence of entire image regions as input. While
being effective in exploiting contextual information around humans, e.g., human
appearance and scene category, they are easily fooled by out-of-context action
videos where the contexts do not exactly match with target actions. In
contrast, pose-based methods, which take a sequence of human skeletons only as
input, suffer from inaccurate pose estimation or ambiguity of human pose per
se. Integrating these two approaches has turned out to be non-trivial; training
a model with both appearance and pose ends up with a strong bias towards
appearance and does not generalize well to unseen videos. To address this
problem, we propose to learn pose-driven feature integration that dynamically
combines appearance and pose streams by observing pose features on the fly. The
main idea is to let the pose stream decide how much and which appearance
information is used in integration based on whether the given pose information
is reliable or not. We show that the proposed IntegralAction achieves highly
robust performance across in-context and out-of-context action video datasets.
The codes are available in https://github.com/mks0601/IntegralAction_RELEASE.


  We study the force balance between orthogonally positioned $p$-brane and
$(8-p)$-brane. The force due to graviton and dilaton exchange is repulsive in
this case. We identify the attractive force that balances this repulsion as due
to one-half of a fundamental string stretched between the branes. As the
$p$-brane passes through the $(8-p)$-brane, the connecting string changes
direction, which may be interpreted as creation of one fundamental string. We
show this directly from the structure of the Chern-Simons terms in the D-brane
effective actions. We also discuss the effect of string creation on the 0-brane
quantum mechanics in the type I' theory. The creation of a fundamental string
is related by U-duality to the creation of a 3-brane discussed by Hanany and
Witten. Both processes have a common origin in M-theory: as two M5-branes with
one common direction cross, a M2-brane stretched between them is created.


  In this paper, we consider the dynamics of a predator-prey system of Gause
type with cooperative hunting among predators and Holling III functional
response. The known work numerically shows that the system exhibits saddle-node
and Hopf bifurcations except homoclinic bifurcation for some special parameter
values. Our results show that there are a weak focus of multiplicity three and
a degenerate equilibrium with double zero eigenvalues (i.e., a cusp of
codimension two) for general parameter conditions and the system can exhibit
various bifurcations as perturbing the bifurcation parameters appropriately,
such as the transcritical and the pitchfork bifurcations at the degenerate
boundary equilibrium, the saddle-node and the Bogdanov-Takens bifurcations at
the degenerate positive equilibrium and the Hopf bifurcation around the weak
focus. The comparative study demonstrates that the dynamics are far richer and
more complex than that of the system without cooperative hunting among
predators. The analysis results reveal that appropriate intensity of
cooperative hunting among predators is beneficial for the persistence of
predators and the diversity of ecosystem.


  In this paper we present AIDA, which is an active inference-based agent that
iteratively designs a personalized audio processing algorithm through situated
interactions with a human client. The target application of AIDA is to propose
on-the-spot the most interesting alternative values for the tuning parameters
of a hearing aid (HA) algorithm, whenever a HA client is not satisfied with
their HA performance. AIDA interprets searching for the "most interesting
alternative" as an issue of optimal (acoustic) context-aware Bayesian trial
design. In computational terms, AIDA is realized as an active inference-based
agent with an Expected Free Energy criterion for trial design. This type of
architecture is inspired by neuro-economic models on efficient (Bayesian) trial
design in brains and implies that AIDA comprises generative probabilistic
models for acoustic signals and user responses. We propose a novel generative
model for acoustic signals as a sum of time-varying auto-regressive filters and
a user response model based on a Gaussian Process Classifier. The full AIDA
agent has been implemented in a factor graph for the generative model and all
tasks (parameter learning, acoustic context classification, trial design, etc.)
are realized by variational message passing on the factor graph. All
verification and validation experiments and demonstrations are freely
accessible at our GitHub repository.


  Conventional spoken language understanding systems consist of two main
components: an automatic speech recognition module that converts audio to a
transcript, and a natural language understanding module that transforms the
resulting text (or top N hypotheses) into a set of domains, intents, and
arguments. These modules are typically optimized independently. In this paper,
we formulate audio to semantic understanding as a sequence-to-sequence problem
[1]. We propose and compare various encoder-decoder based approaches that
optimize both modules jointly, in an end-to-end manner. Evaluations on a
real-world task show that 1) having an intermediate text representation is
crucial for the quality of the predicted semantics, especially the intent
arguments and 2) jointly optimizing the full system improves overall accuracy
of prediction. Compared to independently trained models, our best jointly
trained model achieves similar domain and intent prediction F1 scores, but
improves argument word error rate by 18% relative.


  Chaotic dynamics essentially defines the global properties of gravitating
systems, including, probably, the basics of morphology of galaxies. We use the
Ricci curvature criterion to study the degree of relative chaos (exponential
instability) in core-halo gravitating configurations. We show the existence of
a critical core radius when the system is least chaotic, while systems with
both smaller and larger core radius will typically possess stronger chaotic
properties.


  We construct new families of smooth Fano fourfolds with Picard rank $1$ which
contain open $\Bbb A^1$-cylinders, that is, Zariski open subsets of the form $Z
\times \Bbb A^1$, where $Z$ is a quasiprojective variety. In particular, we
show that every Mukai fourfold of genus $8$ is cylindrical and there exists a
family of cylindrical Gushel-Mukai fourfolds.


  The combination of transformers and masked image modeling (MIM) pre-training
framework has shown great potential in various vision tasks. However, the
pre-training computational budget is too heavy and withholds the MIM from
becoming a practical training paradigm. This paper presents FastMIM, a simple
and generic framework for expediting masked image modeling with the following
two steps: (i) pre-training vision backbones with low-resolution input images;
and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead
of original RGB values of the input images. In addition, we propose FastMIM-P
to progressively enlarge the input resolution during pre-training stage to
further enhance the transfer results of models with high capacity. We point out
that: (i) a wide range of input resolutions in pre-training phase can lead to
similar performances in fine-tuning phase and downstream tasks such as
detection and segmentation; (ii) the shallow layers of encoder are more
important during pre-training and discarding last several layers can speed up
the training stage with no harm to fine-tuning performance; (iii) the decoder
should match the size of selected network; and (iv) HOG is more stable than RGB
values when resolution transfers;. Equipped with FastMIM, all kinds of vision
backbones can be pre-trained in an efficient way. For example, we can achieve
83.8%/84.1% top-1 accuracy on ImageNet-1K with ViT-B/Swin-B as backbones.
Compared to previous relevant approaches, we can achieve comparable or better
top-1 accuracy while accelerate the training procedure by $\sim$5$\times$. Code
can be found in https://github.com/ggjy/FastMIM.pytorch.


  The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructure
of the city and is crucial for resuming normal economic activity. To reduce
disease transmission, policymakers can enforce reduced crowding and mandate
wearing of masks. \emph{Cohorting} -- forming groups of travelers that always
travel together, is an additional policy to reduce disease transmission on
\textit{locals} without severe restrictions. Cohorting allows us to: ($i$) form
traveler bubbles, thereby decreasing the number of distinct interactions over
time; ($ii$) potentially quarantine an entire cohort if a single case is
detected, making contact tracing more efficient, and ($iii$) target cohorts for
testing and early detection of symptomatic as well as asymptomatic cases.
Studying impact of cohorts using compartmental models is challenging because of
the ensuing representational complexity. Agent-based models provide a natural
way to represent cohorts along with the representation of the cohort members
with the larger social network. This paper describes a novel multi-scale
agent-based model to study the impact of cohorting strategies on COVID-19
dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a
detailed agent-based model comprising of 12.4 million agents. Individual
cohorts and their inter-cohort interactions as they travel on locals are
modeled using local mean field approximations. The resulting multi-scale model
in conjunction with a detailed disease transmission and intervention simulator
is used to assess various cohorting strategies. The results provide a
quantitative trade-off between cohort size and its impact on disease dynamics
and well being. The results show that cohorts can provide significant benefit
in terms of reduced transmission without significantly impacting ridership and
or economic \& social activity.


  In this paper, we study the value of using synthetically produced videos as
training data for neural networks used for action categorization. Motivated by
the fact that texture and background of a video play little to no significant
roles in optical flow, we generated simplified texture-less and background-less
videos and utilized the synthetic data to train a Temporal Segment Network
(TSN). The results demonstrated that augmenting TSN with simplified synthetic
data improved the original network accuracy (68.5%), achieving 71.8% on HMDB-51
when adding 4,000 videos and 72.4% when adding 8,000 videos. Also, training
using simplified synthetic videos alone on 25 classes of UCF-101 achieved
30.71% when trained on 2500 videos and 52.7% when trained on 5000 videos.
Finally, results showed that when reducing the number of real videos of UCF-25
to 10% and combining them with synthetic videos, the accuracy drops to only
85.41%, compared to a drop to 77.4% when no synthetic data is added.


  The thermal evolution of young neutron stars (NSs) reflects the neutrino
emission properties of their cores. Heinke et al. (2010) measured a 3.6+/-0.6%
decay in the surface temperature of the Cassiopeia A (Cas A) NS between 2000
and 2009, using archival data from the Chandra X-ray Observatory ACIS-S
detector in Graded mode. Page et al. (2011) and Shternin et al. (2011)
attributed this decay to enhanced neutrino emission from a superfluid neutron
transition in the core. Here we test this decline, combining analysis of the
Cas A NS using all Chandra X-ray detectors and modes (HRC-S, HRC-I, ACIS-I,
ACIS-S in Faint mode, and ACIS-S in Graded mode) and adding a 2012 May ACIS-S
Graded mode observation, using the most current calibrations (CALDB 4.5.5.1).
We measure the temperature changes from each detector separately and test for
systematic effects due to the nearby filaments of the supernova remnant. We
find a 0.92%-2.0% decay over 10 years in the effective temperature, inferred
from HRC-S data, depending on the choice of source and background extraction
regions, with a best-fit decay of 1.0+/-0.7%. In comparison, the ACIS-S Graded
data indicate a temperature decay of 3.1%-5.0% over 10 years, with a best-fit
decay of 3.5+/-0.4%. Shallower observations using the other detectors yield
temperature decays of 2.6+/-1.9% (ACIS-I), 2.1+/-1.0% (HRC-I), and 2.1+/-1.9%
(ACIS-S Faint mode) over 10 years. Our best estimate indicates a decline of
2.9+/-0.9 (stat) +1.6/-0.3 (sys) % over 10 years. The complexity of the bright
and varying supernova remnant background makes a definitive interpretation of
archival Cas A Chandra observations difficult. A temperature decline of 1-3.5%
over 10 years would indicate extraordinarily fast cooling of the NS that can be
regulated by superfluidity of nucleons in the stellar core.


  We consider the fundamental problem of maximizing a general quadratic
function over an ellipsoidal domain, also known as the trust region problem. We
give the first provable linear-time (in the number of non-zero entries of the
input) algorithm for approximately solving this problem. Specifically, our
algorithm returns an $\epsilon$-approximate solution in time
$\tilde{O}(N/\sqrt{\epsilon})$, where $N$ is the number of non-zero entries in
the input. This matches the runtime of Nesterov's accelerated gradient descent,
suitable for the special case in which the quadratic function is concave, and
the runtime of the Lanczos method which is applicable when the problem is
purely quadratic.


  Let $\mathbb N_0$ be the set of non-negative integers, and let $P(n,l)$
denote the set of all weak compositions of $n$ with $l$ parts, i.e., $P(n,l)=\{
(x_1,x_2,\dots, x_l)\in\mathbb N_0^l\ :\ x_1+x_2+\cdots+x_l=n\}$. For any
element $\mathbf u=(u_1,u_2,\dots, u_l)\in P(n,l)$, denote its $i$th-coordinate
by $\mathbf u(i)$, i.e., $\mathbf u(i)=u_i$. Let $l=\min(l_1,l_2,\dots, l_r)$.
Families $\mathcal A_j\subseteq P(n_j,l_j)$ ($j=1,2,\dots, r$) are said to be
$r$-cross $t$-intersecting if $\vert \{ i\in [l] \ :\ \mathbf u_1(i)=\mathbf
u_2(i)=\cdots=\mathbf u_r(i)\} \vert\geq t$ for all $\mathbf u_j\in \mathcal
A_j$. Suppose that $l\geq t+2$. We prove that there exists a constant
$n_0=n_0(l_1,l_2,\dots,l_r,t)$ depending only on $l_j$'s and $t$, such that for
all $n_j\geq n_0$, if the families $\mathcal A_j\subseteq P(n_j,l_j)$
($j=1,2,\dots, r$) are $r$-cross $t$-intersecting, then \begin{equation}
\prod_{j=1}^r \vert \mathcal{A}_j \vert\leq \prod_{j=1}^r {n_j+l_j-t-1 \choose
l_j-t-1}.\notag \end{equation} Moreover, equality holds if and only if there is
a $t$-set $T$ of $\{1,2,\dots,l\}$ such that $\mathcal{A}_j=\{\mathbf u\in
P(n_j,l_j)\ :\ \mathbf u(i)=0\ {\rm for\ all}\ i\in T\}$ for $j=1,2,\dots, r$.


  Electrostatic charge dissipation is one of the major concerns for satellites
operating in the Earth's orbits. Under energetic plasma conditions, they may
acquire very high negative potential (up to 10's of kV) due to the collection
of energetic plasma constituents - resulting in temporary outages and permanent
damages to onboard equipment. This study proposes and discusses a couple of
physics-based schemes capable of mitigating/ minimizing the excessive charging
effects over satellites under extreme plasma conditions in LEO/ GEO. An
estimate of charge build-up on the space objects based on the charging dynamics
as a function of ambient plasma parameters has been made. Our calculations
illustrate that in the absence of a significant charge dissipation mechanism, a
severe charging (10's kV) in the dark/ shadowed at GEO and high latitude LEO
regions. We propose that installing a suitable UV lamp and
micro/nano-structuring of the surface fabric can induce an efficient
dissipation mechanism and effectively prevent the surface from deleterious
charging effects during satellite operation. We demonstrate that the UV
illumination may maintain the satellite surface at quite a small positive
potential (~ 2 V) while the surface nanofabrication sustains it at a
sufficiently low negative potential (~ 10 V). Both concepts are shown to work
efficiently in mitigating the potential threat of massive charging and safely
performing the satellite operation.


  We introduce a persistent Hochschild homology framework for directed graphs.
Hochschild homology groups of (path algebras of) directed graphs vanish in
degree $i\geq 2$. To extend them to higher degrees, we introduce the notion of
connectivity digraphs and analyse two main examples; the first, arising from
Atkin's $q$-connectivity, and the second, here called $n$-path digraphs,
generalising the classical notion of line graphs. Based on a categorical
setting for persistent homology, we propose a stable pipeline for computing
persistent Hochschild homology groups. This pipeline is also amenable to other
homology theories; for this reason, we complement our work with a survey on
homology theories of digraphs.


  A method for improving the performance of sparse-matrix based parity check
codes is proposed, based on insight gained from methods of statistical physics.
The advantages of the new approach are demonstrated on an existing
encoding/decoding paradigm suggested by Sourlas. We also discuss the
application of the same method to more advanced codes of a similar type.


  The NP-hard graphical traveling salesman problem (GTSP) is to find a closed
walk of total minimum weight that visits each vertex in an undirected
edge-weighted and not necessarily complete graph. We present a problem kernel
with $\tau^2+\tau$ vertices for GTSP, where $\tau$ is the vertex cover number
of the input graph. Any $\alpha$-approximate solution for the problem kernel
also gives an $\alpha$-approximate solution for the original instance, for any
$\alpha\geq1$.


  We report on the XMM-Newton observation of the HMXRB X Persei, the prototype
of the persistent and low-luminosity Be/neutron star pulsars, which was
performed on February 2003. The source was detected at a luminosity level of ~
1.4x10^35 erg/s, which is the highest level of the latest three decades. The
pulsation period has increased up to 839.3 s, thus confirming the overall
spin-down of the NS detected in the previous observations. The folded
light-curve has a complex structure, with features not observed at lower
luminosities, and shows a significant energy dependence. The spectral analysis
reveals the presence of a significant excess at low energies over the main
power-law spectral component, which can be described by a black-body spectrum
of high temperature (kT_BB ~ 1.5 keV) and small emitting region (R_BB ~ 340 m);
its properties are consistent with a polar-cap origin. Phase-resolved
spectroscopy shows that the emission spectrum varies along the pulse period,
but it is not possible to prove whether the thermal component is pulsed or not.


  Recently, there is a growing interest in applying Transfer Entropy (TE) in
quantifying the effective connectivity between artificial neurons. In a
feedforward network, the TE can be used to quantify the relationships between
neuron output pairs located in different layers. Our focus is on how to include
the TE in the learning mechanisms of a Convolutional Neural Network (CNN)
architecture. We introduce a novel training mechanism for CNN architectures
which integrates the TE feedback connections. Adding the TE feedback parameter
accelerates the training process, as fewer epochs are needed. On the flip side,
it adds computational overhead to each epoch. According to our experiments on
CNN classifiers, to achieve a reasonable computational overhead--accuracy
trade-off, it is efficient to consider only the inter-neural information
transfer of a random subset of the neuron pairs from the last two fully
connected layers. The TE acts as a smoothing factor, generating stability and
becoming active only periodically, not after processing each input sample.
Therefore, we can consider the TE is in our model a slowly changing
meta-parameter.


  Application of interpretable machine learning techniques on medical datasets
facilitate early and fast diagnoses, along with getting deeper insight into the
data. Furthermore, the transparency of these models increase trust among
application domain experts. Medical datasets face common issues such as
heterogeneous measurements, imbalanced classes with limited sample size, and
missing data, which hinder the straightforward application of machine learning
techniques. In this paper we present a family of prototype-based (PB)
interpretable models which are capable of handling these issues. The models
introduced in this contribution show comparable or superior performance to
alternative techniques applicable in such situations. However, unlike ensemble
based models, which have to compromise on easy interpretation, the PB models
here do not. Moreover we propose a strategy of harnessing the power of
ensembles while maintaining the intrinsic interpretability of the PB models, by
averaging the model parameter manifolds. All the models were evaluated on a
synthetic (publicly available dataset) in addition to detailed analyses of two
real-world medical datasets (one publicly available). Results indicated that
the models and strategies we introduced addressed the challenges of real-world
medical data, while remaining computationally inexpensive and transparent, as
well as similar or superior in performance compared to their alternatives.


  We study a generalized double Jaynes-Cummings (JC) model where two entangled
pairs of two-level atoms interact indirectly.
  We focus on the case where the cavities and the entangled pairs are
uncorrelated. We show that there exist initial states of the qubit system so
that two entangled pairs are available at all times. In particular, the minimum
entanglement in the pairs as a function of the initial state is studied.
Finally, we extend our findings to a model consisting of multi-mode atom-cavity
interactions. We use a non-Markovian quantum state diffusion (QSD) equation to
obtain the steady-state density matrix for the qubits. We show that the
multi-mode model also displays dynamical preservation of entanglement.


  Let $W$ be an irreducible finite complex reflection group acting on a complex
vector space $V$. For a positive integer $k$, we consider a class function
$\varphi_k$ given by $\varphi_k(w) = k^{\dim V^w}$ for $w \in W$, where $V^w$
is the fixed-point subspace of $w$. If $W$ is the symmetric group of $n$
letters and $k=n+1$, then $\varphi_{n+1}$ is the permutation character on
(classical) parking functions. In this paper, we give a complete answer to the
question when $\varphi_k$ (resp. its $q$-analogue) is the character of a
representation (resp. the graded character of a graded representation) of $W$.
As a key to the proof in the symmetric group case, we find the greatest common
divisors of specialized Schur functions. And we propose a unimodality
conjecture of the coefficients of certain quotients of principally specialized
Schur functions.


  This Letter proposes a new search for confining dark sectors at the Large
Hadron Collider. As a result of the strong dynamics in the hidden sector, dark
matter could manifest in proton-proton collisions at the Large Hadron Collider
in form of hadronic jets containing stable invisible bound states. These
semi-visible jets have been studied theoretically and experimentally in the
fully hadronic signature where the unstable composite dark matter can only
decay promptly back to Standard Model quarks. We present a simplified model
based on two messenger fields separated by a large mass gap allowing dark bound
states to decay into pairs of oppositely charged leptons. The resulting
experimental signature is characterized by non-isolated lepton pairs inside
semi-visible jets. We propose a search strategy independent from the underlying
model assumptions targeting this new signature, and discuss the orthogonality
with respect to the existing searches. Remaining agnostic on the shape of the
di-lepton spectrum, we determine the sensitivity of a dedicated analysis to the
target signal. The proposed search can claim the 3{\sigma} evidence (exclusion)
of the heavier mediator up to masses of 3.5 TeV (4.5 TeV) with the full Run 2
data of the LHC. Exploiting the resonant feature of the lepton pairs can
enhance the sensitivity reach on a specific model. We estimate that an analysis
using the di-lepton invariant mass information can reach 5{\sigma} discovery up
to masses of 3.5 TeV and improve the exclusion up to more than 5 TeV.


  Deviations from isotropy have been a key tool to identify the sources and the
primary type of cosmic rays (CRs) at low energies. We argue that anisotropies
due to blind regions induced by the Galactic magnetic field, the cosmological
Compton-Getting effect, medium-scale anisotropies reflecting the large-scale
distribution of CR sources and the small-scale clustering of the CR arrival
directions at the highest energies may play the same role for extragalactic
CRs.


  We compare the one-loop functional renormalization group results for the
cuprates and the iron pnictides. Interestingly a coherent picture suggesting
that antiferromagnetic correlation causes pairing for both materials emerges.


  We investigate the group gradings on the algebra of upper triangular matrices
over an arbitrary field, viewed as a Lie algebra. These results were obtained a
few years early by the same authors. We provide streamlined proofs, and present
a complete classification of isomorphism classes of the gradings. We also
provide a classification of the practical isomorphism classes of the gradings,
which is a better alternative way to consider these gradings up to being
essentially the same object. Finally, we investigate in details the case where
the characteristic of the base field is $2$, a topic that was neglected in
previous works.


  We revisit the Markov approximation necessary to derive ordinary Brownian
motion from a model widely adopted in literature for this specific purpose. We
show that this leads to internal inconsistencies, thereby implying that further
search for a more satisfactory model is required.


  A complex projective $t$-design is a configuration of vectors which is
``evenly distributed'' on a sphere in the sense that sampling uniformly from it
reproduces the moments of Haar measure up to order $2t$. We show that the set
of all $n$-qubit stabilizer states forms a complex projective $3$-design in
dimension $2^n$. Stabilizer states had previously only been known to constitute
$2$-designs. The main technical ingredient is a general recursion formula for
the so-called frame potential of stabilizer states. To establish it, we need to
compute the number of stabilizer states with pre-described inner product with
respect to a reference state. This, in turn, reduces to a counting problem in
discrete symplectic vector spaces for which we find a simple formula. We sketch
applications in quantum information and signal analysis.


  We propose a method for measuring the cosmological density parameter $\Omega$
from the statistics of the divergence field, $\theta \equiv H^{-1} \div v$, the
divergence of peculiar velocity, expressed in units of the Hubble constant, $H
\equiv 100 h km/s/Mpc$. The velocity field is spatially smoothed over $\sim 10
h^{-1} Mpc$ to remove strongly nonlinear effects. Assuming weakly-nonlinear
gravitational evolution from Gaussian initial fluctuations, and using
second-order perturbative analysis, we show that $ <\theta^3> \propto
-\Omega^{-0.6} <\theta^2>^2$. The constant of proportionality depends on the
smoothing window. For a top-hat of radius R and volume-weighted smoothing, this
constant is $26/7-\gamma$, where $\gamma=-d\log <\theta^2> / d\log R$. If the
power spectrum is a power law, $P(k)\propto k^n$, then $\gamma=3+n$. A Gaussian
window yields similar results. The resulting method for measuring $\Omega$ is
independent of any assumed biasing relation between galaxies and mass.
  The method has been successfully tested with numerical simulations. A
preliminary application to real data, provided by the POTENT recovery procedure
from observed velocities favors $\Omega \sim 1$. However, because of an
uncertain sampling error, this result should be treated as an assessment of the
feasibility of our method rather than a definitive measurement of $\Omega$.


  We define Baryon Acoustic Oscillation (BAO) distances $\hat{d}_\alpha(z,
z_c)$, $\hat{d}_z(z, z_c)$, and $\hat{d}_/(z, z_c)$ that do not depend on
cosmological parameters. These BAO distances are measured as a function of
redshift $z$ with the Sloan Digital Sky Survey (SDSS) data release DR12. From
these BAO distances alone, or together with the correlation angle
$\theta_\textrm{MC}$ of the Cosmic Microwave Background (CMB), we constrain the
cosmological parameters in several scenarios. We find $4.3 \sigma$ tension
between the BAO plus $\theta_\textrm{MC}$ data and a cosmology with flat space
and constant dark energy density $\Omega_\textrm{DE}(a)$. Releasing one and/or
the other of these constraints obtains agreement with the data. We measure
$\Omega_\textrm{DE}(a)$ as a function of $a$.


  The Braess paradox is a counter-intuitive phenomenon whereby adding roads to
a network results in higher travel time at equilibrium. In this paper we
present an algorithm to detect the occurrence of this paradox in real-world
networks with the help of an improved graph representation accounting for
queues. The addition of queues to the network representation enables a closer
match with real data. Moreover, we search for routes causing this phenomenon
("Braess routes") rather than links, and advocate removing such routes
virtually from navigation systems so that the associated links can continue to
serve other routes. Our algorithm relies on a convex optimization problem
utilizing Beckmann potentials for road links as well as queues, and results in
a route reconfiguration with reduced delay. We assume the availability of
historical data to build the optimization model. We also assume the existence
of a centralized navigation system to manage the routing options and remove the
Braess routes. The theoretical solution demonstrates up to 12% delay reduction
in a network from Montgomery County, Maryland. We validate the improvement with
simulations.


  We present a review of some of the current major challenges in stellar
cluster research, including young clusters, globular clusters, and galactic
nuclei. Topics considered include: primordial mass segregation and runaway
mergers, expulsion of gas from clusters, the production of stellar exotica seen
in some clusters (eg blue stragglers and extreme horizontal--branch stars),
binary populations within clusters, the black--hole population within stellar
clusters, the final parsec problem, stellar dynamics around a massive black
hole, and stellar collisions. The Modest Questions posed here are the outcome
of discussions which took place at the Modest-6A workshop held in Lund, Sweden,
in December, 2005. Modest-6A was organised as part of the activities of the
Modest Collaboration (see www.manybody.org for further details)


  It appears feasible with nanostructures to perform calorimetry at the level
of individual thermal phonons. Here I outline an approach employing
monocrystalline mesoscopic insulators, which can now be patterned from
semiconductor heterostructures into complex geometries with full, three-
dimensional relief. Successive application of these techniques also enables
definition of integrated nanoscale thermal transducers; coupling these to a dc
SQUID readout yields the requisite energy sensitivity and temporal resolution
with minimal back action. The prospect of phonon counting opens intriguing
experimental possibilities with analogies in quantum optics. These include
fluctuation-based phonon spectroscopy, phonon shot noise in the energy
relaxation of nanoscale systems, and quantum statistical phenomena such as
phonon bunching and anticorrelated electron-phonon exchange.


  Learning models of artificial intelligence can nowadays perform very well on
a large variety of tasks. However, in practice different task environments are
best handled by different learning models, rather than a single, universal,
approach. Most non-trivial models thus require the adjustment of several to
many learning parameters, which is often done on a case-by-case basis by an
external party. Meta-learning refers to the ability of an agent to autonomously
and dynamically adjust its own learning parameters, or meta-parameters. In this
work we show how projective simulation, a recently developed model of
artificial intelligence, can naturally be extended to account for meta-learning
in reinforcement learning settings. The projective simulation approach is based
on a random walk process over a network of clips. The suggested meta-learning
scheme builds upon the same design and employs clip networks to monitor the
agent's performance and to adjust its meta-parameters "on the fly". We
distinguish between "reflexive adaptation" and "adaptation through learning",
and show the utility of both approaches. In addition, a trade-off between
flexibility and learning-time is addressed. The extended model is examined on
three different kinds of reinforcement learning tasks, in which the agent has
different optimal values of the meta-parameters, and is shown to perform well,
reaching near-optimal to optimal success rates in all of them, without ever
needing to manually adjust any meta-parameter.


  Calibrating a trading rule using a historical simulation (also called
backtest) contributes to backtest overfitting, which in turn leads to
underperformance. In this paper we propose a procedure for determining the
optimal trading rule (OTR) without running alternative model configurations
through a backtest engine. We present empirical evidence of the existence of
such optimal solutions for the case of prices following a discrete
Ornstein-Uhlenbeck process, and show how they can be computed numerically.
Although we do not derive a closed-form solution for the calculation of OTRs,
we conjecture its existence on the basis of the empirical evidence presented.


  From the geocentric, closed world model of Antiquity to the wraparound
universe models of relativistic cosmology, the parallel history of space
representations in science and art illustrates the fundamental role of
geometric imagination in innovative findings. Through the analysis of works of
various artists and scientists like Plato, Durer, Kepler, Escher, Grisey or the
present author, it is shown how the process of creation in science and in the
arts rests on aesthetical principles such as symmetry, regular polyhedra, laws
of harmonic proportion, tessellations, group theory, etc., as well as beauty,
conciseness and emotional approach of the world.


  Whole brain segmentation from structural magnetic resonance imaging (MRI) is
a prerequisite for most morphological analyses, but is computationally intense
and can therefore delay the availability of image markers after scan
acquisition. We introduce QuickNAT, a fully convolutional, densely connected
neural network that segments a \revision{MRI brain scan} in 20 seconds. To
enable training of the complex network with millions of learnable parameters
using limited annotated data, we propose to first pre-train on auxiliary labels
created from existing segmentation software. Subsequently, the pre-trained
model is fine-tuned on manual labels to rectify errors in auxiliary labels.
With this learning strategy, we are able to use large neuroimaging repositories
without manual annotations for training. In an extensive set of evaluations on
eight datasets that cover a wide age range, pathology, and different scanners,
we demonstrate that QuickNAT achieves superior segmentation accuracy and
reliability in comparison to state-of-the-art methods, while being orders of
magnitude faster. The speed up facilitates processing of large data
repositories and supports translation of imaging biomarkers by making them
available within seconds for fast clinical decision making.


  In this paper, we carry out a new technique named projection ED and indicate
that it is applicable to the frustrated models, e.g., XXZ spin-1/2 Heisenberg
and spinless fermion t-V models, in the strong coupling limit with highly
degenerate ground states in a general way. As an example, we deal with a
spinless fermion t-V model on a triangular lattice and show the usefulness of
the method.


  PSR J0751+1807 is a millisecond pulsar in a circular 6 hr binary system with
a helium white dwarf secondary. Through high precision pulse timing
measurements with the Arecibo and Effelsberg radio telescopes, we have detected
the decay of its orbit due to emission of gravitational radiation. This is the
first detection of the relativistic orbital decay of a low-mass, circular
binary pulsar system. The measured rate of change in orbital period, corrected
for acceleration biases, is dP_b/dt=(-6.4+-0.9)x10^-14. Interpreted in the
context of general relativity, and combined with measurement of Shapiro delay,
it implies a pulsar mass of 2.1+-0.2 solar masses, the most massive pulsar
measured. This adds to the emerging trend toward relatively high neutron star
masses in neutron star--white dwarf binaries. Additionally, there is some
evidence for an inverse correlation between pulsar mass and orbital period in
these systems. We consider alternatives to the general relativistic analysis of
the data, and we use the pulsar timing data to place limits on violations of
the strong equivalence principle.


  For a particular class of pseudo manifolds, we show that the intersection
cohomology groups for any perversity may be naturally represented by extended
weighted $L^2$ harmonic forms for a complete metric on the regular stratum with
respect to some weight determined by the perversity. Extended weighted $L^2$
harmonic forms are harmonic forms that are almost in the given weighted $L^2$
space for the metric in question, but not quite. This result is akin to the
representation of absolute and relative cohomology groups for a manifold with
boundary by extended harmonic forms on the associated manifold with cylindrical
ends. As in that setting, in the unweighted $L^2$ case, the boundary values of
the extended harmonic forms define a Lagrangian splitting of the boundary space
in the long exact sequence relating upper and lower middle perversity
intersection cohomology groups.


  We give a new proof of a theorem of Mints that the positive fragment of
minimal predicate logic is decidable. The idea of the proof is to replace the
eigenvariable condition of sequent calculus by an appropriate scoping
mechanism. The algorithm given by this proof seems to be more practical than
that given by the original proof. A naive implementation is given at the end of
the paper. Another contribution is to show that this result extends to a large
class of theories, including simple type theory (higherorder logic) and
second-order propositional logic. We obtain this way a new proof of the
decidability of the inhabitation problem for positive types in system F.


  We consider the geometry of the space of Borel measures endowed with a
distance that is defined by generalizing the dynamical formulation of the
Wasserstein distance to concave, nonlinear mobilities. We investigate the
energy landscape of internal, potential, and interaction energies. For the
internal energy, we give an explicit sufficient condition for geodesic
convexity which generalizes the condition of McCann. We take an eulerian
approach that does not require global information on the geodesics. As
by-product, we obtain existence, stability, and contraction results for the
semigroup obtained by solving the homogeneous Neumann boundary value problem
for a nonlinear diffusion equation in a convex bounded domain. For the
potential energy and the interaction energy, we present a non-rigorous argument
indicating that they are not displacement semiconvex.


  Multiple avalanches, initiated by simultaneously toppling neighbouring sites,
are studied in three different directed sandpile models. It is argued that,
while the single avalanche exponents are different for the three models, a
suitably defined two-avalanche distribution has identical exponents. The origin
of this universality is traced to particle conservation.


  Here, we consider a gravity theory involving a spontaneous Lorentz symmetry
breaking called the bumblebee model. We show that, at certain values of the
bumblebee field, the G\"odel metric is consistent within this theory.


  We discuss the unexpected enhancement of the $N^*$ electroproduction on
nuclei recently observed in the $(\gamma^*,p\pi^-)$ measurement. A mechanism
which is able to explain this result is proposed. To clarify the situation, we
suggest to perform a new kind of experiment within well specified kinematic
conditions.


  We present the first linear-polarization mosaicked observations performed by
the Atacama Large Millimeter/submillimeter Array (ALMA). We mapped the
Orion-KLeinmann-Low (Orion-KL) nebula using super-sampled mosaics at 3.1 and
1.3 mm as part of the ALMA Extension and Optimization of Capabilities (EOC)
program. We derive the magnetic field morphology in the plane of the sky by
assuming that dust grains are aligned with respect to the ambient magnetic
field. At the center of the nebula, we find a quasi-radial magnetic field
pattern that is aligned with the explosive CO outflow up to a radius of
approximately 12 arc-seconds (~ 5000 au), beyond which the pattern smoothly
transitions into a quasi-hourglass shape resembling the morphology seen in
larger-scale observations by the James-Clerk-Maxwell Telescope (JCMT). We
estimate an average magnetic field strength $\langle B\rangle = 9.4$ mG and a
total magnetic energy of 2 x 10^45 ergs, which is three orders of magnitude
less than the energy in the explosive CO outflow. We conclude that the field
has been overwhelmed by the outflow and that a shock is propagating from the
center of the nebula, where the shock front is seen in the magnetic field lines
at a distance of ~ 5000 au from the explosion center.


  We obtain the regularity of solutions in Sobolev spaces for the mixed
Dirichlet-conormal problem for parabolic operators in cylindrical domains with
time-dependent separations, which is the first of its kind. Assuming the
boundary of the domain to be Reifenberg-flat and the separation to be locally
sufficiently close to a Lipschitz function of $m$ variables, where
$m=0,\ldots,d-2$, with respect to the Hausdorff distance, we prove the unique
solvability for $p\in (2(m+2/(m+3),2(m+2)/(m+1)))$. In the case when $m=0$, the
range $p\in(4/3,4)$ is optimal in view of the known results for Laplace
equations.


  A magnetic field applied to type-II superconductors introduces quantized
vortices that locally quench superconductivity, providing a unique opportunity
to investigate electronic orders that may compete with superconductivity. This
is especially true in cuprate superconductors in which mutual relationships
among superconductivity, pseudogap, and broken-spatial-symmetry states have
attracted much attention. Here we observe energy and momentum dependent
bipartite electronic superstructures in the vortex core of
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ using spectroscopic-imaging scanning
tunneling microscopy (SI-STM). In the low-energy range where the nodal
Bogoliubov quasiparticles are well-defined, we show that the quasiparticle
scattering off vortices generates the electronic superstructure known as
"vortex checkerboard". In the high-energy region where the pseudogap develops,
vortices amplify the broken-spatial-symmetry patterns that preexist in zero
field. These data reveal canonical d-wave superconductivity near the node, yet
competition between superconductivity and broken-spatial-symmetry states near
the antinode.


  What is the optimal way to approximate a high-dimensional diffusion process
by one in which the coordinates are independent? This paper presents a
construction, called the \emph{independent projection}, which is optimal for
two natural criteria. First, when the original diffusion is reversible with
invariant measure $\rho_*$, the independent projection serves as the
Wasserstein gradient flow for the relative entropy $H(\cdot\,|\,\rho_*)$
constrained to the space of product measures. This is related to recent
Langevin-based sampling schemes proposed in the statistical literature on mean
field variational inference. In addition, we provide both qualitative and
quantitative results on the long-time convergence of the independent
projection, with quantitative results in the log-concave case derived via a new
variant of the logarithmic Sobolev inequality. Second, among all processes with
independent coordinates, the independent projection is shown to exhibit the
slowest growth rate of path-space entropy relative to the original diffusion.
This sheds new light on the classical McKean-Vlasov equation and recent
variants proposed for non-exchangeable systems, which can be viewed as special
cases of the independent projection.


  Finding and selecting the most relevant scientific papers from a large number
of papers written in a research community is one of the key challenges for
researchers these days. As we know, much information around research interest
for scholars and academicians belongs to papers they read. Analysis and
extracting contextual features from these papers could help us to suggest the
most related paper to them. In this paper, we present a multi-task
recommendation system (RS) that predicts a paper recommendation and generates
its meta-data such as keywords. The system is implemented as a three-stage deep
neural network encoder that tries to maps longer sequences of text to an
embedding vector and learns simultaneously to predict the recommendation rate
for a particular user and the paper's keywords. The motivation behind this
approach is that the paper's topics expressed as keywords are a useful
predictor of preferences of researchers. To achieve this goal, we use a system
combination of RNNs, Highway and Convolutional Neural Networks to train
end-to-end a context-aware collaborative matrix. Our application uses Highway
networks to train the system very deep, combine the benefits of RNN and CNN to
find the most important factor and make latent representation. Highway Networks
allow us to enhance the traditional RNN and CNN pipeline by learning more
sophisticated semantic structural representations. Using this method we can
also overcome the cold start problem and learn latent features over large
sequences of text.


  A general class of Bayesian lower bounds when the underlying loss function is
a Bregman divergence is demonstrated. This class can be considered as an
extension of the Weinstein--Weiss family of bounds for the mean squared error
and relies on finding a variational characterization of Bayesian risk. The
approach allows for the derivation of a version of the Cram\'er--Rao bound that
is specific to a given Bregman divergence. The new generalization of the
Cram\'er--Rao bound reduces to the classical one when the loss function is
taken to be the Euclidean norm. The effectiveness of the new bound is evaluated
in the Poisson noise setting and the Binomial noise setting.


  We analyze the resolvent $R(k)=(P+k^2)^{-1}$ of Schr\"odinger operators
$P=\Delta+V$ with short range potential $V$ on asymptotically conic manifolds
$(M,g)$ (this setting includes asymptotically Euclidean manifolds) near $k=0$.
We make the assumption that the dimension is greater or equal to 3 and that $P$
has no $L^2$ null space and no resonance at 0. In particular, we show that the
Schwartz kernel of $R(k)$ is a conormal polyhomogeneous distribution on a
desingularized version of $M\times M\times [0,1]$. Using this, we show that the
Riesz transform of $P$ is bounded on $L^p$ for $1<p<n$ and that this range is
optimal if $V$ is not identically zero or if $M$ has more than one end. We also
analyze the case V=0 with one end. In a follow-up paper, we shall deal with the
same problem in the presence of zero modes and zero-resonances.


  With the potential to find global solutions, significant research interest
has focused on convex relaxations of the non-convex OPF problem. Recently,
"moment-based" relaxations from the Lasserre hierarchy for polynomial
optimization have been shown capable of globally solving a broad class of OPF
problems. Global solution of many large-scale test cases is accomplished by
exploiting sparsity and selectively applying the computationally intensive
higher-order relaxation constraints. Previous work describes an iterative
algorithm that indicates the buses for which the higher-order constraints
should be enforced. In order to speed computation of the moment relaxations,
this paper provides a study of the key parameter in this algorithm as applied
to relaxations from both the original Lasserre hierarchy and a recent complex
extension of the Lasserre hierarchy.


  We analyze the BOOMERanG 2003 (B03) 145 GHz temperature map to constrain the
amplitude of a non Gaussian, primordial contribution to CMB fluctuations. We
perform a pixel space analysis restricted to a portion of the map chosen in
view of high sensitivity, very low foreground contamination and tight control
of systematic effects. We set up an estimator based on the three Minkowski
functionals which relies on high quality simulated data, including non Gaussian
CMB maps. We find good agreement with the Gaussian hypothesis and derive the
first limits based on BOOMERanG data for the non linear coupling parameter f_NL
as -300<f_NL<650 at 68% CL and -800<f_NL<1050 at 95% CL.


  A real-space renormalization transformation is constructed for lattices of
non-identical oscillators with dynamics of the general form
$d\phi_{k}/dt=\omega_{k}+g\sum_{l}f_{lk}(\phi_{l},\phi_{k})$. The
transformation acts on ensembles of such lattices. Critical properties
corresponding to a second order phase transition towards macroscopic
synchronization are deduced. The analysis is potentially exact, but relies in
part on unproven assumptions. Numerically, second order phase transitions with
the predicted properties are observed as $g$ increases in two structurally
different, two-dimensional oscillator models. One model has smooth coupling
$f_{lk}(\phi_{l},\phi_{k})=\phi(\phi_{l}-\phi_{k})$, where $\phi(x)$ is
non-odd. The other model is pulse-coupled, with
$f_{lk}(\phi_{l},\phi_{k})=\delta(\phi_{l})\phi(\phi_{k})$. Lower bounds for
the critical dimensions for different types of coupling are obtained. For
non-odd coupling, macroscopic synchronization cannot be ruled out for any
dimension $D\geq 1$, whereas in the case of odd coupling, the well-known result
that it can be ruled out for $D< 3$ is regained.


  The diagnosis and segmentation of tumors using any medical diagnostic tool
can be challenging due to the varying nature of this pathology. Magnetic Reso-
nance Imaging (MRI) is an established diagnostic tool for various diseases and
disorders and plays a major role in clinical neuro-diagnosis. Supplementing
this technique with automated classification and segmentation tools is gaining
importance, to reduce errors and time needed to make a conclusive diagnosis. In
this paper a simple three-step algorithm is proposed; (1) identification of
patients that present with tumors, (2) automatic selection of abnormal slices
of the patients, and (3) segmentation and detection of the tumor. Features were
extracted by using discrete wavelet transform on the normalized images and
classified by support vector machine (for step (1)) and random forest (for step
(2)). The 400 subjects were divided in a 3:1 ratio between training and test
with no overlap. This study is novel in terms of use of data, as it employed
the entire T2 weighted slices as a single image for classification and a unique
combination of contralateral approach with patch thresholding for segmentation,
which does not require a training set or a template as is used by most
segmentation studies. Using the proposed method, the tumors were segmented
accurately with a classification accuracy of 95% with 100% specificity and 90%
sensitivity.


  Ground-state and finite-temperature properties of a special class of exactly
solvable Ising-Heisenberg planar models are examined using the generalized
decoration-iteration and star-triangle mapping transformations. The
investigated spin systems exhibit an interesting quantum behaviour manifested
in a remarkable geometric spin frustration, which appears notwithstanding the
purely ferromagnetic interactions of the considered model systems. This kind of
spin frustration originates from an easy-plane anisotropy in the XXZ Heisenberg
interaction between nearest-neighbouring spins that favours ferromagnetic
ordering of their transverse components, whereas their longitudinal components
are aligned antiferomagnetically.


  We give a brief review on the formation and the calculation of black hole
shadow. Firstly, we introduce the conception of black hole shadow and the
current works on a variety of black hole shadows. Secondly, we present main
methods of calculating photon sphere radius and shadow radius, and then explain
how the photon sphere affect the boundary of black hole shadow. We review the
analytical calculation for black hole shadows which have analytic expressions
for shadow boundary due to the integrable photon motion system. And we
introduce the fundamental photon orbits which can explain the patterns of black
hole shadow shape. Finally, we review the numerical calculation of black hole
shadows with the backward ray-tracing method, and introduce some chaotic black
hole shadows with self-similar fractal structures. Since the gravitational
waves from the merger of binary black holes have been detected, we introduce a
couple of shadows of binary black holes, which all have the eyebrowlike shadows
around the main shadows with the fractal structures. We discuss the invariant
phase space structures of photon motion system in black hole space-time, and
explain the formation of black hole shadow is dominated by the invariant
manifolds of certain Lyapunov orbits near the fixed points.


  We derive two new sum rules for the unpolarized doubly virtual Compton
scattering process on a nucleon, which establish novel low-$Q^2$ relations
involving the nucleon's generalized polarizabilities and moments of the
nucleon's unpolarized structure functions $F_1(x,Q^2)$ and $F_2(x,Q^2)$. These
relations facilitate the determination of some structure constants which can
only be accessed in off-forward doubly virtual Compton scattering, not
experimentally accessible at present. We perform an empirical determination for
the proton and compare our results with a next-to-leading-order chiral
perturbation theory prediction. We also show how these relations may be useful
for a model-independent determination of the low-$Q^2$ subtraction function in
the Compton amplitude, which enters the two-photon-exchange contribution to the
Lamb shift of (muonic) hydrogen. An explicit calculation of the
$\Delta(1232)$-resonance contribution to the muonic-hydrogen $2P-2S$ Lamb shift
yields $-1 \pm 1$ $\mathsf\mu$eV, confirming the previously conjectured
smallness of this effect.


  This paper focuses on additional inspections concerning the fermionic sector
of the Standard Model Extension (SME). In this context, our main effort in this
contribution is to investigate effects of Lorentz-symmetry violation (LSV) on
the Klein Paradox, the Zitterbewegung and its phenomenology in connection to
Condensed Matter Physics, Atomic Physics, and Astrophysics. Finally, we discuss
a particular realization of LSV in the Dirac equation, considering an asymmetry
between space and time due to a scale factor present in the linear momentum of
the fermion, but which does not touch its time derivative. We go further and
extend the implications of this asymmetry in the situation the scale factor
becomes space-time dependent to compute its influence on the kinematics of the
Compton effect with the extended dispersion relation for the fermion that
scatters the photon.


  The Lorentzian metric structure used in any field theory allows one to
implement the relativistic notion of causality and to define a notion of time
dimension. This article investigates the possibility that at the microscopic
level the metric is Riemannian, i.e. locally Euclidean, and that the Lorentzian
structure, that we usually consider as fundamental, is in fact an effective
property that emerges in some regions of a 4-dimensional space with a positive
definite metric. In such a model, there is no dynamics nor signature flip
across some hypersurface; instead, all the fields develop a Lorentzian dynamics
in these regions because they propagate in an effective metric. It is shown
that one can construct a decent classical field theory for scalars, vectors and
(Dirac) spinors in flat spacetime. It is then shown that gravity can be
included but that the theory for the effective Lorentzian metric is not general
relativity but of the covariant Galileon type. The constraints arising from
stability, the equivalence principle and the constancy of fundamental constants
are detailed and a phenomenological picture of the emergence of the Lorentzian
metric is also given. The construction, while restricted to classical fields in
this article, offers a new view on the notion of time.


  We investigate the pion scalar form factor in the Meiman-Okubo framework,
implementing the phase below the inelastic $K\bar K$ threshold, where it is
known from the $\pi\pi$ scalar isoscalar phase shift $\delta_0^0$ by Watson
theorem. State-of-the-art knowledge of the perturbative QCD expansion of the
scalar correlator and the phase shift $\delta_0^0$ is used as input. No
assumptions about the phase above the inelastic threshold or the possible zeros
of the form factor in the complex plane are necessary. We obtain a
model-independent constraint relating the sum of the light quark masses to the
slope and the curvature of the pion scalar form factor at the origin. The
recent lattice results for the light quark masses and the pion scalar radius
are found to satisfy this constraint. We obtain also a strong correlation
between the pion scalar radius and the curvature of the form factor, with
rather high values predicted for the curvature.