  This paper presents a new strategy for simultaneously reducing energy
consumption, transmission delays, and bit error rate in Unmanned Aerial Vehicle
UAV networks. A UAV is fitted with a wireless Bidirectional Relay BR to enable
coverage network extension and increase transmission throughput. The downside
of the BR advantages is the delay in data transmission caused by the UAV
movement. A consequence of this delay is increased total energy consumption,
causing further degradation in bit error rate performance, especially at high
SNR levels. In wireless communication, the trade-off between delay and energy
consumption is, fortunately, possible to improve performance. Therefore, this
study aims to enhance UAV network performance by reducing energy consumption,
data transmission delay, and bit error rate. A multi-objective algorithm is
employed to generate an adaptive optimal energy allocation strategy based on
the balance between energy consumption and transmission delays. The results of
theoretical analysis are illustrated with several examples. As herein
demonstrated, the proposed solution effectively balances delay and energy
efficiency in a customised system design and improves the bit error rate in UAV
networks


  With real-time certificate validation checking, a public-key-using system
that needs to validate a certificate executes a transaction with a specialized
validation party. At the end of the transaction the validation party returns an
indication about the validity status of the certificate. This paper analysis
the public key (PbK) certificate validation service from a practical point of
view by describing the implementation of a system that makes use of the Data
Validation and Certification Server (DVCS) protocols to provide certificate
validation service to the Relying Parties (RPs). However the system is not
restricted to use only the specified protocol and allows the integration of
other validation protocols or mechanisms. Our implementation efforts emphasize
the possibility to pursue a specific RP tradeoff between timeliness, security
and computational resource usage via dynamic selection of several configurable
options.


  The tracking system of the CMS experiment, currently under construction at
the Large Hadron Collider (LHC) at CERN (Geneva, Switzerland), will include a
silicon pixel detector providing three spacial measurements in its final
configuration for tracks produced in high energy pp collisions. In this paper
we present the results of test beam measurements performed at CERN on
irradiated silicon pixel sensors. Lorentz angle and charge collection
efficiency were measured for two sensor designs and at various bias voltages.


  We report on experimental studies of superconducting coupling through a thin
Ni76Al24 film. A new patterning process has been developed, which allows in
combination with the wedge shaped deposition technique the in situ deposition
of 20 single Nb/Al/Al2O3/Ni3Al/Nb multilayers, each with its own well defined
Ni3Al thickness. Every single multilayer consists of 10 different sized
Josephson junctions, showing a high reproducibility and scaling with its
junction area. Up to six damped oscillations of the critical current density
against F-layer thickness were observed, revealing three single 0-pi
transitions in the ground state of Josephson junctions. Contrary to former
experimental studies, the exponential decay length is one magnitude larger than
the oscillation period defining decay length. The theoretical predictions based
on linearized Eilenberger equations result in excellent agreement of theory and
experimental results.


  Previously, Sarkar and Sun have shown that for supercritical oriented
percolation in dimension $1+1$, the set of rightmost infinite open paths
converges to the Brownian web after proper centering and scaling. In this note,
we show that a pair of sticky Brownian webs arise naturally if one considers
the set of right-most infinite open paths for two coupled percolation
configurations with distinct (but close) percolation parameters. This leads to
a natural conjecture on the convergence of the dynamical supercritical oriented
percolation model to the so-called dynamical Brownian web.


  We study solutions of the Bethe Ansatz equation related to the trigonometric
Gaudin model associated to a simple Lie algebra g and a tensor product of
irreducible finite-dimensional representations. Having one solution, we
describe a construction of new solutions. The collection of all solutions
obtained from a given one is called a population. We show that the Weyl group
of g acts on the points of a population freely and transitively (under certain
conditions).
  To a solution of the Bethe Ansatz equation, one assigns a common eigenvector
(called the Bethe vector) of the trigonometric Gaudin operators. The dynamical
Weyl group projectively acts on the common eigenvectors of the trigonometric
Gaudin operators. We conjecture that this action preserves the set of Bethe
vectors and coincides with the action induced by the action on points of
populations. We prove the conjecture for sl_2.


  WR 125 is considered as a Colliding Wind Wolf-rayet Binary (CWWB), from which
the most recent infrared flux increase was reported between 1990 and 1993. We
observed the object four times from November 2016 to May 2017 with Swift and
XMM-Newton, and carried out a precise X-ray spectral study for the first time.
There were hardly any changes of the fluxes and spectral shapes for half a
year, and the absorption-corrected luminosity was 3.0e+33 erg/s in the 0.5 -
10.0 keV range at a distance of 4.1 kpc. The hydrogen column density was higher
than that expected from the interstellar absorption, thus the X-ray spectra
were probably absorbed by the WR wind. The energy spectrum was successfully
modeled by a collisional equilibrium plasma emission, where both the plasma and
the absorbing wind have unusual elemental abundances particular to the WR
stars. In 1981, the Einstein satellite clearly detected X-rays from WR 125,
whereas the ROSAT satellite hardly detected X-rays in 1991, when the binary was
probably around the periastron passage. We discuss possible causes for the
unexpectedly low soft X-ray flux near the periastron.


  HERMES, a fibre-fed high-resolution (R=85000) echelle spectrograph with good
stability and excellent throughput, is the work-horse instrument of the 1.2-m
Mercator telescope on La Palma. HERMES targets building up time series of
high-quality data of variable stellar phenomena, mainly for asteroseismology
and binary-evolution research. In this paper we present the HERMES project and
discuss the instrument design, performance, and a future upgrade. We also
present some results of the first four years of HERMES observations. We
illustrate the value of small telescopes, equipped with efficient
instrumentation, for high-resolution spectroscopy.


  Geometric fluctuations of the density mode in a fractional quantum Hall (FQH)
state can give rise to a nematic FQH phase, a topological state with a
spontaneously broken rotational symmetry. While experiments on FQH states in
the second Landau level have reported signatures of putative FQH nematics in
anisotropic transport, a realistic model for this state has been lacking. We
show that the standard model of particles in the lowest Landau level
interacting via the Coulomb potential realizes the FQH nematic transition,
which is reached by a progressive reduction of the strength of the
shortest-range Haldane pseudopotential. Using exact diagonalization and
variational wave functions, we demonstrate that the FQH nematic transition
occurs when the system's neutral gap closes in the long-wavelength limit while
the charge gap remains open. We confirm the symmetry-breaking nature of the
transition by demonstrating the existence of a "circular moat" potential in the
manifold of states with broken rotational symmetry, while its geometric
character is revealed through the strong fluctuations of the nematic
susceptibility and Hall viscosity.


  There is a family of vector bundles over the moduli space of stable curves
that, while first appearing in theoretical physics, has been an active topic of
study for algebraic geometers since the 1990s. By computing the rank of the
exceptional group $G_2$ case of these bundles in three different ways, we
derive a family of summation formulas for Fibonacci numbers in terms of the
golden ratio.


  We consider the importance of the second-order dissipative self force for
gravitational wave dephasing for an extreme or intermediate mass ratio system
moving along a quasi-circular Schwarzschild orbit. For the first-order self
force we use the fully relativistic force in the Lorenz gauge for eternally
circular geodesics. The second-order self force is modeled by its 3.5 post
Newtonian counterpart. We evolve the system using the osculating orbits method,
and obtain the gravitational waveforms, whose phase includes all the terms -
within our approximation (and using the self force along circular geodesics) -
that are independent of the system's mass ratio. The partial dephasing due to
the second-order dissipative self force is substantially smaller than that of
the first-order conservative self force, although they are both at the same
order in the mass ratio.


  The efficient classification of different types of supernova is one of the
most important problems for observational cosmology. However, spectroscopic
confirmation of most objects in upcoming photometric surveys, such as the The
Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible.
The development of automated classification processes based on photometry has
thus become crucial. In this paper we investigate the performance of machine
learning (ML) classification on the final cosmological constraints using
simulated lightcurves from The Supernova Photometric Classification Challenge,
released in 2010. We study the use of different feature sets for the
lightcurves and many different ML pipelines based on either decision tree
ensembles or automated search processes. To construct the final catalogs we
propose a threshold selection method, by employing a \emph{Bias-Variance
tradeoff}. This is a very robust and efficient way to minimize the Mean Squared
Error. With this method we were able to get very strong cosmological
constraints, which allowed us to keep $\sim 75\%$ of the total information in
the type Ia SNe when using the SALT2 feature set and $\sim 33\%$ for the other
cases (based on either the Newling model or on standard wavelet decomposition).


  Many scientific areas are faced with the challenge of extracting information
from large, complex, and highly structured data sets. A great deal of modern
statistical work focuses on developing tools for handling such data. This paper
presents a new subfield of functional data analysis, FDA, which we call
Manifold Data Analysis, or MDA. MDA is concerned with the statistical analysis
of samples where one or more variables measured on each unit is a manifold,
thus resulting in as many manifolds as we have units. We propose a framework
that converts manifolds into functional objects, an efficient 2-step functional
principal component method, and a manifold-on-scalar regression model. This
work is motivated by an anthropological application involving 3D facial imaging
data, which is discussed extensively throughout the paper. The proposed
framework is used to understand how individual characteristics, such as age and
genetic ancestry, influence the shape of the human face.


  Let $\Sigma$ be Laurent phenomenon (LP) seed of rank $n$,
$\mathcal{A}(\Sigma)$, $\mathcal{U}(\Sigma)$ and $\mathcal{L}(\Sigma)$ be its
corresponding Laurent phenomenon algebra, upper bound and lower bound
respectively. We prove that each seed of $\mathcal{A}(\Sigma)$ is uniquely
defined by its cluster, and any two seeds of $\mathcal{A}(\Sigma)$ with $n-1$
common cluster variables are connected with each other by one step of mutation.
The method in this paper also works for (totally sign-skew-symmetric) cluster
algebras. Moreover, we show that $\mathcal{U}(\Sigma)$ is invariant under seed
mutations when each exchange polynomials coincides with its exchange Laurent
polynomials of $\Sigma$. Besides, we obtain the standard monomial bases of
$\mathcal{L}(\Sigma)$. We also prove that $\mathcal{U}(\Sigma)$ coincides with
$\mathcal{L}(\Sigma)$ under certain conditions.


  This paper critically examines the leakage current reduction techniques for
improving the performance of poly-Si TFTs used in active matrix liquid crystal
displays. This is a first comprehensive study in literature on this topic. The
review assesses important proposals to circumvent the leakage current problem
in poly-Si TFTs and a short evaluation of strengths and weaknesses specific to
each method is presented. Also, a new device structure called the Triple Gate
poly-Si TFT (TG-TFT) is discussed. The key idea in the operation of this device
is to make the dominant conduction mechanism in the channel to be controlled by
the accumulation charge density modulation by the gate (ACMG) and not by the
gate-induced grain barrier lowering (GIGBL). Using twodimensional and
two-carrier device simulation, it is demonstrated that the TG-TFT exhibits a
significantly diminished pseudo-subthreshold conduction leading to several
orders of magnitude reduction in the OFF state leakage current when compared to
a conventional poly-Si TFT. The reasons for the improved performance are
explained.


  Emphasizing first the utility of the generalized Fresnel coefficients in the
theory of the Casimir effect in planar cavities, we complement our previous
discussion of the ordinary Casimir force on and the Casimir stress in a metal
(plasma) slab in a planar cavity. We demonstrate strong dependence of the
Casimir stress in a thin slab on properties of the bounding medium in the
symmetric Lifshitz configuration. Contrary to this, the stress in a thick slab
gradually becomes insensitive on external boundary conditions. We also consider
the position dependence of the Casimir force on and stress in a thin metal slab
in a planar cavity. Whereas the force per unit area on the slab strongly
increases when it approaches a mirror the stress in the slab decreases and
eventually changes the sign. Generally, the stress decreases with the cavity
width and decreasing reflectivity of the mirrors.


  The theory of finite term algebras provides a natural framework to describe
the semantics of functional languages. The ability to efficiently reason about
term algebras is essential to automate program analysis and verification for
functional or imperative programs over algebraic data types such as lists and
trees. However, as the theory of finite term algebras is not finitely
axiomatizable, reasoning about quantified properties over term algebras is
challenging.
  In this paper we address full first-order reasoning about properties of
programs manipulating term algebras, and describe two approaches for doing so
by using first-order theorem proving. Our first method is a conservative
extension of the theory of term algebras using a finite number of statements,
while our second method relies on extending the superposition calculus of
first-order theorem provers with additional inference rules.
  We implemented our work in the first-order theorem prover Vampire and
evaluated it on a large number of algebraic data type benchmarks, as well as
game theory constraints. Our experimental results show that our methods are
able to find proofs for many hard problems previously unsolved by
state-of-the-art methods. We also show that Vampire implementing our methods
outperforms existing SMT solvers able to deal with algebraic data types.


  We propose a simple yet effective method to learn to segment new indoor
scenes from video frames: State-of-the-art methods trained on one dataset, even
as large as the SUNRGB-D dataset, can perform poorly when applied to images
that are not part of the dataset, because of the dataset bias, a common
phenomenon in computer vision. To make semantic segmentation more useful in
practice, one can exploit geometric constraints. Our main contribution is to
show that these constraints can be cast conveniently as semi-supervised terms,
which enforce the fact that the same class should be predicted for the
projections of the same 3D location in different images. This is interesting as
we can exploit general existing techniques developed for semi-supervised
learning to efficiently incorporate the constraints. We show that this approach
can efficiently and accurately learn to segment target sequences of ScanNet and
our own target sequences using only annotations from SUNRGB-D, and geometric
relations between the video frames of target sequences.


  The paper deals with the problem of penalized empirical risk minimization
over a convex set of linear functionals on the space of Hermitian matrices with
convex loss and nuclear norm penalty. Such penalization is often used in low
rank matrix recovery in the cases when the target function can be well
approximated by a linear functional generated by a Hermitian matrix of
relatively small rank (comparing with the size of the matrix). Our goal is to
prove sharp low rank oracle inequalities that involve the excess risk (the
approximation error) with constant equal to one and the random error term with
correct dependence on the rank of the oracle.


  Diabetic retinopathy (DR) grading from fundus images has attracted increasing
interest in both academic and industrial communities. Most convolutional neural
network (CNN) based algorithms treat DR grading as a classification task via
image-level annotations. However, these algorithms have not fully explored the
valuable information in the DR-related lesions. In this paper, we present a
robust framework, which collaboratively utilizes patch-level and image-level
annotations, for DR severity grading. By an end-to-end optimization, this
framework can bi-directionally exchange the fine-grained lesion and image-level
grade information. As a result, it exploits more discriminative features for DR
grading. The proposed framework shows better performance than the recent
state-of-the-art algorithms and three clinical ophthalmologists with over nine
years of experience. By testing on datasets of different distributions (such as
label and camera), we prove that our algorithm is robust when facing image
quality and distribution variations that commonly exist in real-world practice.
We inspect the proposed framework through extensive ablation studies to
indicate the effectiveness and necessity of each motivation. The code and some
valuable annotations are now publicly available.


  We utilized microwave spectroscopy to study the magnetization oscillations
locally induced in a Permalloy film by a pure spin current, which is generated
due to the spin Hall effect in an adjacent Pt layer. The oscillation frequency
is lower than the ferromagnetic resonance of Permalloy, indicating that the
oscillation forms a self-localized nonpropagating spin-wave soliton. At
cryogenic temperatures, the spectral characteristics are remarkably similar to
the traditional spin-torque nano-oscillators driven by spin-polarized currents.
However, the linewidth of the oscillation increases exponentially with
temperature and an additional peak appears in the spectrum below the
ferromagnetic resonance, suggesting that the spectral characteristics are
determined by interplay between two localized dynamical states.


  We study a system of heteronuclear molecules on a triangular lattice and
analyze the potential of this system for the experimental realization of a
supersolid phase. The ground state phase diagram contains superfluid, solid and
supersolid phases. At finite temperatures and strong interactions there is an
additional emulsion region, in contrast to similar models with short-range
interactions. We derive the maximal critical temperature $T_c$ and the
corresponding entropy $S/N = 0.04(1)$ for supersolidity and find feasible
experimental conditions for its realization.


  The Portfolio Optimization task has long been studied in the Financial
Services literature as a procedure to identify the basket of assets that
satisfy desired conditions on the expected return and the associated risk. A
well-known approach to tackle this task is the maximization of the Sharpe
Ratio, achievable with a problem reformulation as Quadratic Programming. While
the sole Sharpe Ratio could be efficiently optimized via classical solvers, in
business scenarios it is common that multiple additional needs arise, which
have to be integrated in the optimization model as either new constraints or
objective function terms. Then, in general, the problem may become non-convex
and hence could potentially be not efficiently solvable via classical
techniques anymore. One example of such additional objective function term
consists of maximizing a diversification measure penalizing portfolios holding
significant portions of investments on assets belonging to the same sector,
while favouring solutions that diversify over multiple sectors. The problem of
optimizing both the Sharpe Ratio and a diversification term can be mapped to a
QUBO and be solved via quantum annealing devices or Hybrid Computing
approaches, which are expected to find high quality solutions. We propose a new
QUBO formulation for the task described and provide the mathematical details
and required assumptions, showing the ease of modeling the optimization as QUBO
against the effort that would be required by classical strategies. We derive
results via the available QUBO solvers, as well as discussing the behaviour of
Hybrid approaches to tackle large scale problems in the near term. We finally
elaborate on the results showing the trade-off between the observed values of
the portfolio's Sharpe Ratio and diversification, as a natural consequence of
solving a multi-objective optimization problem.


  We consider the crystallization problem for a finite one-dimensional
collection of identical hard spheres in a periodic energy landscape. This issue
arises in connection with the investigation of crystalline states of ionic
dimers, as well as in epitaxial growth on a crystalline substrate in presence
of lattice mismatch. Depending on the commensurability of the radius of the
sphere and the period of the landscape, we discuss the possible emergence of
crystallized states. In particular, we prove that crystallization in
arbitrarily long chains is generically not to be expected.


  Path-dependent stochastic processes are often non-ergodic and observables can
no longer be computed within the ensemble picture. The resulting mathematical
difficulties pose severe limits to the analytical understanding of
path-dependent processes. Their statistics is typically non-multinomial in the
sense that the multiplicities of the occurrence of states is not a multinomial
factor. The maximum entropy principle is tightly related to multinomial
processes, non-interacting systems, and to the ensemble picture; It loses its
meaning for path-dependent processes. Here we show that an equivalent to the
ensemble picture exists for path-dependent processes, such that the
non-multinomial statistics of the underlying dynamical process, by
construction, is captured correctly in a functional that plays the role of a
relative entropy. We demonstrate this for self-reinforcing P\'olya urn
processes, which explicitly generalise multinomial statistics. We demonstrate
the adequacy of this constructive approach towards non-multinomial pendants of
entropy by computing frequency and rank distributions of P\'olya urn processes.
We show how microscopic update rules of a path-dependent process allow us to
explicitly construct a non-multinomial entropy functional, that, when
maximized, predicts the time-dependent distribution function.


  I propose novel partial identification bounds on infection prevalence from
information on test rate and test yield. The approach utilizes user-specified
bounds on (i) test accuracy and (ii) the extent to which tests are targeted,
formalized as restriction on the effect of true infection status on the odds
ratio of getting tested and thereby embeddable in logit specifications. The
motivating application is to the COVID-19 pandemic but the strategy may also be
useful elsewhere.
  Evaluated on data from the pandemic's early stage, even the weakest of the
novel bounds are reasonably informative. Notably, and in contrast to
speculations that were widely reported at the time, they place the infection
fatality rate for Italy well above the one of influenza by mid-April.


  Object detection is an important and challenging problem in computer vision.
Although the past decade has witnessed major advances in object detection in
natural scenes, such successes have been slow to aerial imagery, not only
because of the huge variation in the scale, orientation and shape of the object
instances on the earth's surface, but also due to the scarcity of
well-annotated datasets of objects in aerial scenes. To advance object
detection research in Earth Vision, also known as Earth Observation and Remote
Sensing, we introduce a large-scale Dataset for Object deTection in Aerial
images (DOTA). To this end, we collect $2806$ aerial images from different
sensors and platforms. Each image is of the size about 4000-by-4000 pixels and
contains objects exhibiting a wide variety of scales, orientations, and shapes.
These DOTA images are then annotated by experts in aerial image interpretation
using $15$ common object categories. The fully annotated DOTA images contains
$188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.)
quadrilateral To build a baseline for object detection in Earth Vision, we
evaluate state-of-the-art object detection algorithms on DOTA. Experiments
demonstrate that DOTA well represents real Earth Vision applications and are
quite challenging.


  Digital Twins represent a new and disruptive technology, where digital
replicas of (cyber)-physical systems operate for long periods of time alongside
their (cyber)-physical counterparts, with enabled bi-directional communication
between them. However promising, the development of digital twins is a
non-trivial problem, since what can initially be adequate models may become
obsolete in time due to wear and tear of the physical components, accumulated
errors, or the evolving interaction with the environment. As such, there is a
clear need for mechanisms that support swapping in new models, as well changing
model structures as a whole when necessary. To address this challenge, we
propose in this paper a novel artefact, fmiSwap, that is FMI compliant and
allows for run-time swapping in standalone co-simulations, where different
strategies can be tested easily, as well in fully deployed DT settings with
hardware in the loop. We adopt a water-tank case-study consisting of a tank and
its controller to demonstrate how fmiSwap works and how it can support swaps in
a safe manner.


  A linear polymer grafted to a hard wall and underneath an AFM tip can be
modelled in a lattice as a grafted lattice polymer (or self-avoiding walk)
compressed underneath a piston approaching the wall. As the piston approaches
the wall the increasingly confined polymer escapes from the confined region to
explore conformations beside the piston. This conformational change is believed
to be a phase transition in the thermodynamic limit, and has been argued to be
first order, based on numerical results in reference [12]. In this paper a
lattice self-avoiding walk model of the escape transition is constructed. It is
proven that this model has a critical point in the thermodynamic limit
corresponding to the escape transition of compressed grafted linear polymers.
This result relies on the analysis of ballistic self-avoiding walks in slits
and slabs in the square and cubic lattices. Additionally, numerical estimates
of the location of the escape transition critical point is reported based on
Monte Carlo simulations of self-avoiding walks in slits and in slabs.


  This paper has been withdrawn by the author, due to a crucial error in the
proof of Thm.1


  The phenomenon of implicit regularization has attracted interest in recent
years as a fundamental aspect of the remarkable generalizing ability of neural
networks. In a nutshell, it entails that gradient descent dynamics in many
neural nets, even without any explicit regularizer in the loss function,
converges to the solution of a regularized learning problem. However, known
results attempting to theoretically explain this phenomenon focus
overwhelmingly on the setting of linear neural nets, and the simplicity of the
linear structure is particularly crucial to existing arguments. In this paper,
we explore this problem in the context of more realistic neural networks with a
general class of non-linear activation functions, and rigorously demonstrate
the implicit regularization phenomenon for such networks in the setting of
matrix sensing problems, together with rigorous rate guarantees that ensure
exponentially fast convergence of gradient descent.In this vein, we contribute
a network architecture called Spectral Neural Networks (abbrv. SNN) that is
particularly suitable for matrix learning problems. Conceptually, this entails
coordinatizing the space of matrices by their singular values and singular
vectors, as opposed to by their entries, a potentially fruitful perspective for
matrix learning. We demonstrate that the SNN architecture is inherently much
more amenable to theoretical analysis than vanilla neural nets and confirm its
effectiveness in the context of matrix sensing, via both mathematical
guarantees and empirical investigations. We believe that the SNN architecture
has the potential to be of wide applicability in a broad class of matrix
learning scenarios.


  The Fourier transform is approximated over a finite domain using a Riemann
sum. This Riemann sum is then expressed in terms of the discrete Fourier
transform, which allows the sum to be computed with a fast Fourier transform
algorithm more rapidly than via a direct matrix multiplication. Advantages and
limitations of using this method to approximate the Fourier transform are
discussed, and prototypical MATLAB codes implementing the method are presented.


  The Qth-power algorithm produces a useful canonical P-module presentation for
the integral closures of certain integral extensions of
$P:=\mathbf{F}[x_n,...,x_1]$, a polyonomial ring over the finite field
$\mathbf{F}:=\mathbf{Z}_q$ of $q$ elements. Here it is shown how to use this
for several small primes $q$ to reconstruct similar integral closures over the
rationals $\mathbf{Q}$ using the Chinese remainder theorem to piece together
presentations in different positive characteristics, and the extended Euclidean
algorithm to reconstruct rational fractions to lift these to presentations over
$\mathbf{Q}$.


  The James Webb Space Telescope (JWST) has recently uncovered a new
record-breaking quasar, UHZ1, at a redshift of $z\sim10$. This discovery
continues JWST's trend of confronting the expectations from the standard
$\Lambda$CDM model of cosmology with challenges. Namely, too many very massive
galaxies and quasars have been observed at very high redshifts, when the
universe was only a few hundred million years old. We have previously shown
that Supermassive Dark Stars (SMDSs) may offer a solution to this puzzle. These
fascinating objects would be the first stars in the universe, growing to be
$\sim 10^5-10^7 M_{\odot}$ and shining as bright as $10^9$ suns. Unlike
Population III stars (the major alternative proposed model for the first stars
in the universe, which would also have zero metallicity and would be powered by
nuclear fusion), SMDSs would be powered by dark matter heating (e.g. dark
matter annihilation) and would be comparatively cooler. At the ends of their
lives (when they run out of dark matter fuel), SMDSs would directly collapse
into black holes, thus providing possible seeds for the first quasars. Previous
papers have shown that to form at $z\sim10$, UHZ1 would require an incredibly
massive seed ($\sim 10^4 -10^5 M_{\odot}$), which was assumed to be a Direct
Collapse Black Hole (DCBH). In this paper, we demonstrate that Supermassive
Dark Stars (SMDSs) offer an equally valid solution to the mystery of the first
quasars, by examining the four most distant known quasars: UHZ1, J0313-1806,
J1342+0928, and J1007+2115, with particular emphasis on UHZ1.


  The analysis using the partial-wave series expansion (PWSE) method in
spherical coordinates is extended to evaluate the acoustic radiation force
experienced by rigid oblate and prolate spheroids centered on the axis of wave
propagation of high-order Bessel vortex beams composed of progressive, standing
and quasi-standing waves, respectively. A coupled system of linear equations is
derived after applying the Neumann boundary condition for an immovable surface
in a non-viscous fluid, and solved numerically by matrix inversion after
performing a single numerical integration procedure. The system of linear
equations depends on the partial-wave index n and the order of the Bessel
vortex beam m using truncated but converging PWSEs in the least-squares sense.
Numerical results for the radiation force function, which is the radiation
force per unit energy density and unit cross-sectional surface, are computed
with particular emphasis on the amplitude ratio describing the transition from
the progressive to the pure standing waves cases, the aspect ratio (i.e., the
ratio of the major axis over the minor axis of the spheroid), the half-cone
angle and order of the Bessel vortex beam, as well as the dimensionless size
parameter. A generalized expression for the radiation force function is derived
for cases encompassing the progressive, standing and quasi-standing waves of
Bessel vortex beams. This expression can be reduced to other types of
beams/waves such as the zeroth-order Bessel non-vortex beam or the infinite
plane wave case by appropriate selection of the beam parameters. The results
for progressive waves reveal a tractor beam behavior, characterized by the
emergence of an attractive pulling force acting in opposite direction of wave
propagation. Moreover, the transition to the quasi-standing and pure standing
wave cases shows the acoustical tweezers behavior in dual-beam Bessel vortex
beams.


  In a random key graph (RKG) of $n$ nodes each node is randomly assigned a key
ring of $K_n$ cryptographic keys from a pool of $P_n$ keys. Two nodes can
communicate directly if they have at least one common key in their key rings.
We assume that the $n$ nodes are distributed uniformly in $[0,1]^2.$ In
addition to the common key requirement, we require two nodes to also be within
$r_n$ of each other to be able to have a direct edge. Thus we have a random
graph in which the RKG is superposed on the familiar random geometric graph
(RGG). For such a random graph, we obtain tight bounds on the relation between
$K_n,$ $P_n$ and $r_n$ for the graph to be asymptotically almost surely
connected.


  Motivation: To assess the quality of a protein model, i.e. to estimate how
close it is to its native structure, using no other information than the
structure of the model has been shown to be useful for structure prediction.
The state of the art method, ProQ2, is based on a machine learning approach
that uses a number of features calculated from a protein model. Here, we
examine if these features can be exchanged with energy terms calculated from
Rosetta and if a combination of these terms can improve the quality assessment.
Results: When using the full atom energy function from Rosetta in ProQRosFA the
QA is on par with our previous state-of-the-art method, ProQ2. The method based
on the low-resolution centroid scoring function, ProQRosCen, performs almost as
well and the combination of all the three methods, ProQ2, ProQRosFA and
ProQCenFA into ProQ3 show superior performance over ProQ2. Availability: ProQ3
is freely available on BitBucket at https://bitbucket.org/ElofssonLab/proq3


  Resonant graphene dopants, such as hydrogen adatoms, experience long-range
effective interaction mediated by conduction electrons. As a result of this
interaction, when several adatoms are present in the sample, hopping of adatoms
between sites belonging to different sublattices involves significant energy
changes. Different inelastic mechanisms facilitating such hopping -- coupling
to phonons and conduction electrons -- are considered. It is estimated that the
diffusion of hydrogen adatoms is rather slow, amounting to roughly one hop to a
nearest neighbor per millisecond.


  We present a new method for evaluating and training unnormalized density
models. Our approach only requires access to the gradient of the unnormalized
model's log-density. We estimate the Stein discrepancy between the data density
$p(x)$ and the model density $q(x)$ defined by a vector function of the data.
We parameterize this function with a neural network and fit its parameters to
maximize the discrepancy. This yields a novel goodness-of-fit test which
outperforms existing methods on high dimensional data. Furthermore, optimizing
$q(x)$ to minimize this discrepancy produces a novel method for training
unnormalized models which scales more gracefully than existing methods. The
ability to both learn and compare models is a unique feature of the proposed
method.


  Neural style transfer is a powerful computer vision technique that can
incorporate the artistic "style" of one image to the "content" of another. The
underlying theory behind the approach relies on the assumption that the style
of an image is represented by the Gram matrix of its features, which is
typically extracted from pre-trained convolutional neural networks (e.g.,
VGG-19). This idea does not straightforwardly extend to time series stylization
since notions of style for two-dimensional images are not analogous to notions
of style for one-dimensional time series. In this work, a novel formulation of
time series style transfer is proposed for the purpose of synthetic data
generation and enhancement. We introduce the concept of stylized features for
time series, which is directly related to the time series realism properties,
and propose a novel stylization algorithm, called StyleTime, that uses explicit
feature extraction techniques to combine the underlying content (trend) of one
time series with the style (distributional properties) of another. Further, we
discuss evaluation metrics, and compare our work to existing state-of-the-art
time series generation and augmentation schemes. To validate the effectiveness
of our methods, we use stylized synthetic data as a means for data augmentation
to improve the performance of recurrent neural network models on several
forecasting tasks.


  The ATLAS detector has been used to search for high-mass states decaying into
a single high momentum lepton and missing transverse energy, such as new heavy
charged gauge bosons. The latest search results for a W Prime boson decaying to
lepton plus neutrino in 1.04 fb^-1 of proton-proton collisions at a center of
mass energy of 7 TeV produced at the Large Hadron Collider are presented.


  This article examines mean-field games for marriage. The results support the
argument that optimizing the long-term well-being through effort and social
feeling state distribution (mean-field) will help to stabilize marriage.
However, if the cost of effort is very high, the couple fluctuates in a bad
feeling state or the marriage breaks down. We then examine the influence of
society on a couple using mean field sentimental games. We show that, in
mean-field equilibrium, the optimal effort is always higher than the one-shot
optimal effort. We illustrate numerically the influence of the couple's network
on their feeling states and their well-being.


  We consider five different peer-to-peer file sharing systems with two chunks,
with the aim of finding chunk selection algorithms that have provably stable
performance with any input rate and assuming non-altruistic peers who leave the
system immediately after downloading the second chunk. We show that many
algorithms that first looked promising lead to unstable or oscillating
behavior. However, we end up with a system with desirable properties. Most of
our rigorous results concern the corresponding deterministic large system
limits, but in two simplest cases we provide proofs for the stochastic systems
also.


  We show that an infinite sequence of homotopy 4-spheres constructed by
Cappell-Shaneson are all diffeomorphic to S^4. This generalizes previous
results of Akbulut-Kirby and Gompf.


  We have numerically and analytically studied ac+dc driven Josephson-junction
arrays with a single vortex or with a single vortex-antivortex pair present. We
find single-vortex steps in the voltage versus current characteristics (I-V) of
the array. They correspond microscopically to a single vortex phase-locked to
move a fixed number of plaquettes per period of the ac driving current. In
underdamped arrays we find vortex motion period doubling on the steps. We
observe subharmonic steps in both underdamped and overdamped arrays. We
successfully compare these results with a phenomenological model of vortex
motion with a nonlinear viscosity. The I-V of an array with a vortex-antivortex
pair displays fractional voltage steps. A possible connection of these results
to present day experiments is also discussed.


  We consider the problem of covert communication over wireless adhoc networks
in which (roughly) $n$ legitimate nodes (LNs) and $n^{\kappa}$ for $0<\kappa<1$
non-communicating warden nodes (WNs) are randomly distributed in a square of
unit area. Each legitimate source wants to communicate with its intended
destination node while ensuring that every WN is unable to detect the presence
of the communication. In this scenario, we study the throughput scaling law.
Due the covert communication constraint, the transmit powers are necessarily
limited. Under this condition, we introduce a preservation region around each
WN. This region serves to prevent transmission from the LNs and to increase the
transmit power of the LNs outside the preservation regions. For the
achievability results, multi-hop (MH), hierarchical cooperation (HC), and
hybrid HC-MH schemes are utilized with some appropriate modifications. In the
proposed MH and hybrid schemes, because the preservation regions may impede
communication along direct data paths, the data paths are suitably modified by
taking a detour around each preservation region. To avoid the concentration of
detours resulting extra relaying burdens, we distribute the detours evenly over
a wide region. In the proposed HC scheme, we control the symbol power and the
scheduling of distributed multiple-input multiple-output transmission. We also
present matching upper bounds on the throughput scaling under the assumption
that every active LN consumes the same average transmit power over the time
period in which the WNs observe the channel outputs.


  The sequence of the zeroth Landau levels (LLs) between filling factors
$\nu$=-6 to 6 in ABA-stacked trilayer graphene (TLG) is unknown because it
depends sensitively on the non-uniform charge distribution on the three layers
of ABA-stacked TLG. Using the sensitivity of quantum Hall data on the electric
field and magnetic field, in an ultraclean ABA-stacked TLG sample, we
quantitatively estimate the non-uniformity of the electric field and determine
the sequence of the zeroth LLs. We also observe anticrossings between some LLs
differing by 3 in LL index, which result from the breaking of the continuous
rotational to \textit{C}$_3$ symmetry by the trigonal warping.


  Galaxy evolution reveals itself not only through the evolving properties of
galaxies themselves but also through its impact on the surrounding environment.
The intergalactic medium in particular holds a fossil record of past galaxy
activity, imprinted on its thermodynamic and chemical properties. This is most
easily discerned in small galaxy groups, where the gravitational heating of
this gas renders it observable by X-ray telescopes while still leaving its
properties highly susceptible to the effects of galactic feedback. X-ray
observations of the hot gas in groups can therefore provide a view of galactic
feedback history that can complement dedicated studies of AGN and star
formation activity at low and high redshift. Based on high-quality X-ray data
of a sample of nearby groups, we present initial results of such a study and
discuss some implications for the AGN and star formation histories of the group
members.


  Sensing small magnetic fields is relevant for many applications ranging from
geology to medical diagnosis. We present a fiber-coupled diamond magnetometer
with a sensitivity of (310 $\pm$ 20) pT$/\sqrt{\text{Hz}}$ in the frequency
range of 10-150 Hz. This is based on optically detected magnetic resonance of
an ensemble of nitrogen vacancy centers in diamond at room temperature. Fiber
coupling means the sensor can be conveniently brought within 2 mm of the object
under study.


  We propose two fast neural combinatory models for constituency parsing:
binary and multi-branching. Our models decompose the bottom-up parsing process
into 1) classification of tags, labels, and binary orientations or chunks and
2) vector composition based on the computed orientations or chunks. These
models have theoretical sub-quadratic complexity and empirical linear
complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank,
speeding at 1327.2 sents/sec. Both the models with XLNet provide near
state-of-the-art accuracies for English. Syntactic branching tendency and
headedness of a language are observed during the training and inference
processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).


  Many countries have embraced national electronic identification (NeID)
systems, recognising their potential to foster a fair, transparent, and
well-governed society by ensuring the secure verification of citizens'
identities. The inclusive nature of NeID empowers people to exercise their
rights while holding them accountable for fulfilling their obligations.
Nevertheless, the development and implementation of these complex
identity-verification systems have raised concerns regarding security, privacy,
and exclusion. In this study, we discuss the different categories of NeID risk
and explore the successful deployment of these systems, while examining how the
specific risks and other challenges posed by this technology are addressed.
Based on the review of the different NeID systems and the efforts made to
mitigate the unique risks and challenges presented within each deployment, we
highlighted the best practices for mitigating risk, including implementing
strong security measures, conducting regular risk assessments, and involving
stakeholders in the design and implementation of the system.


  Polyhomogeneous symbols, defined by Kohn-Nirenberg and H\"ormander in the
60's, play a central role in the symbolic calculus of most pseudodifferential
calculi. We prove a simple characterisation of polyhomogeneous functions which
avoids the use of asymptotic expansions. Specifically, if $U$ is open subset of
$\mathbb{R}^d$, then a polyhomogeneous symbol on $U \times \mathbb{R}^d$ is
precisely the restriction to $t=1$ of a function on $U \times \mathbb{R}^{d+1}$
which is homogeneous for the dilations of $\mathbb{R}^{d+1}$ modulo Schwartz
class functions. This result holds for arbitrary graded dilations on the vector
space $\mathbb{R}^d$. As an application, using the generalisation of A.~Connes'
tangent groupoid for a filtered manifold, we show that the Heisenberg calculus
of Beals and Greiner on a contact manifold or a codimension 1 foliation
coincides with the groupoid calculus of Van Erp and the second author.


  A result of Belyi can be stated as follows. Every curve defined over a number
field can be expressed as a cover of the projective line with branch locus
contained in a rigid divisor. We define the notion of geometrically rigid
divisors in surfaces and then show that every surface defined over a number
field can be expressed as a cover of the projective plane with branch locus
contained in a geometrically rigid divisor in the plane. The main result is the
characterisation of arithmetically defined divisors in the plane as
geometrically rigid divisors in the plane.


  We present a simple model for biological aging. We studied it through
computer simulations and we have found this model to reflect some features of
real populations.


  Ratios of multiplicity moments, H_q (cumulant over factorial moments
K_q/F_q), have been observed to show an oscillatory behaviour with respect to
order, q. Recent studies of e^+e^- annihilations at LEP have shown, moreover,
that the amplitude and oscillation length vary strongly with the jet resolution
parameter y_{cut}. We study the predictions of the perturbative QCD parton
cascade assuming low non-perturbative cut-off (Q_0\sim \Lambda_{QCD}\sim few
100 MeV) and derive the expectations as a function of the cms energy and jet
resolution from threshold to very high energies. We consider numerical
solutions of the evolution equations of gluodynamics in Double Logarithmic and
Modified Leading Logarithmic Approximations (DLA,MLLA), as well as results from
a parton MC with readjusted parameters. The main characteristics are obtained
in MLLA, while a more numerically accurate description is obtained by the MC
model. A unified description of correlations between hadrons and correlations
between jets emerges, in particular for the transition region of small y_{cut}.


  We develop new adaptive algorithms for variational inequalities with monotone
operators, which capture many problems of interest, notably convex optimization
and convex-concave saddle point problems. Our algorithms automatically adapt to
unknown problem parameters such as the smoothness and the norm of the operator,
and the variance of the stochastic evaluation oracle. We show that our
algorithms are universal and simultaneously achieve the optimal convergence
rates in the non-smooth, smooth, and stochastic settings. The convergence
guarantees of our algorithms improve over existing adaptive methods by a
$\Omega(\sqrt{\ln T})$ factor, matching the optimal non-adaptive algorithms.
Additionally, prior works require that the optimization domain is bounded. In
this work, we remove this restriction and give algorithms for unbounded domains
that are adaptive and universal. Our general proof techniques can be used for
many variants of the algorithm using one or two operator evaluations per
iteration. The classical methods based on the ExtraGradient/MirrorProx
algorithm require two operator evaluations per iteration, which is the dominant
factor in the running time in many settings.


  Copying, or cloning, is a basic operation used in the specification of many
applications in computer science. However, when dealing with complex
structures, like graphs, cloning is not a straightforward operation since a
copy of a single vertex may involve (implicitly)copying many edges. Therefore,
most graph transformation approaches forbid the possibility of cloning. We
tackle this problem by providing a framework for graph transformations with
cloning. We use attributed graphs and allow rules to change attributes. These
two features (cloning/changing attributes) together give rise to a powerful
formal specification approach. In order to handle different kinds of graphs and
attributes, we first define the notion of attributed structures in an abstract
way. Then we generalise the sesqui-pushout approach of graph transformation in
the proposed general framework and give appropriate conditions under which
attributed structures can be transformed. Finally, we instantiate our general
framework with different examples, showing that many structures can be handled
and that the proposed framework allows one to specify complex operations in a
natural way.


  We report a high-pressure high-temperature synthesis of several
millimeter-sized samples of bulk graphanes with the composition C-H(D) from
benzene and graphene-derivative C-H-N$_{0.2}$ from pyridine. X-ray diffraction,
transmission electron microscopy, and infrared spectroscopy of new materials
reveal relatively large (several nanometers in size) crystalline grains of an
sp3-bonded graphane lattice (3-cycle-4-step, the orthorhombic structure with
Pbca space group and parameters a = 9.5-9.8, b = 8.9-9.1, c = 17.1-17.3 $\AA$).
The main hydrogen groups in samples are C-H groups connected by aliphatic
bonds. The synthesized graphanes at atmospheric pressure are stable up to 500
C. The macroscopic density of CH samples is 1.5-1.57 g cm$^{-3} $ and the
refractive index is 1.78-1.80. The absorption spectra of samples with a high
degree of crystallization exhibits a weak absorption maximum at 2.8 eV, which
is responsible for the yellow-orange color, large absorption maximum at 4 eV
and an absorption edge associated with the width of the optical gap at 5.2 eV.
The bulk modulus (30-37 GPa) and shear modulus (15-18 GPa) of the fabricated
samples, as well as their hardness (1-1.5 GPa), are about twice as high as the
respective values for polycrystalline graphite. The solution of metalorganic
complexes in benzene and pyridine makes it possible to obtain doped graphanes,
which can have extraordinary electron transport and magnetic properties.


  In this note, we give a precise construction of one of the families of
$2$-designs arose from studying flag-transitive $2$-designs with parameters
$(v,k,\lambda)$ whose replication numbers $r$ are coprime to $\lambda$. We show
that for a given positive integer $q=2^{2n+1}\geq 8$, there exists a $2$-design
with parameters $(q^{2}+1,q,q-1)$ and the replication number $q^{2}$ admitting
the Suzuki group $\textsf{Sz(q)}$ as its automorphism group. We also construct
a family of $2$-designs with parameters $(q^{2}+1,q(q-1),(q-1)(q^{2}-q-1))$ and
the replication number $q^{2}(q-1)$ admitting the Suzuki groups
$\textsf{Sz(q)}$ as their automorphism groups.


  We study the problem of Arnold's diffusion in an example of isochronous
system by using a geometrical method known as Windows Method. Despite the
simple features of this example, we show that the absence of an anisochrony
term leads to several substantial difficulties in the application of the
method, requiring some additional devices as non-equally spaced transition
chains and variable windows. In this way we are able to obtain a set of fast
orbits whose drifting time matches, up to a constant, the time obtained via
variational methods.


  We analyzed data obtained by the SPI telescope onboard the INTEGRAL
observatory to search for short transient events with a duration from 1 ms to a
few tens of seconds. An algorithm for identifying gamma-ray events against the
background of a large number of charged particle interactions with the detector
has been developed. The classification of events was made. Apart from the
events associated with cosmic gamma-ray bursts (GRBs) confirmed by other space
experiments and the activity of known soft gamma repeaters (for example, SGR
1806-20), previously unreported GRBs have been found. GRB candidates and short
gamma-ray events probably associated with the activity of known SGRs and AXPs
have been selected. The spectral evolution of 28 bright GRBs from the catalog
has been studied extensively. A new method for investigating the spectral
evolution is proposed. The energy dependence of the spectral lag for bursts
with a simple structure of their light curves and for individual pulses of
multipulse events is shown to be described by a logarithmic function, lag ~
A*log(E). It has been established that the parameter A depends on the pulse
duration, with the dependence being universal for all of the investigated GRBs.
No negative spectral lags have been detected for bursts with a simple structure
of their light curves.


  Modern dialog managers face the challenge of having to fulfill human-level
conversational skills as part of common user expectations, including but not
limited to discourse with no clear objective. Along with these requirements,
agents are expected to extrapolate intent from the user's dialogue even when
subjected to non-canonical forms of speech. This depends on the agent's
comprehension of paraphrased forms of such utterances. Especially in
low-resource languages, the lack of data is a bottleneck that prevents
advancements of the comprehension performance for these types of agents. In
this regard, here we demonstrate the necessity of extracting the intent
argument of non-canonical directives in a natural language format, which may
yield more accurate parsing, and suggest guidelines for building a parallel
corpus for this purpose. Following the guidelines, we construct a Korean corpus
of 50K instances of question/command-intent pairs, including the labels for
classification of the utterance type. We also propose a method for mitigating
class imbalance, demonstrating the potential applications of the corpus
generation method and its multilingual extensibility.


  Large language models (LLMs) are being increasingly incorporated into
scientific workflows. However, we have yet to fully grasp the implications of
this integration. How should the advent of large language models affect the
practice of science? For this opinion piece, we have invited four diverse
groups of scientists to reflect on this query, sharing their perspectives and
engaging in debate. Schulz et al. make the argument that working with LLMs is
not fundamentally different from working with human collaborators, while Bender
et al. argue that LLMs are often misused and over-hyped, and that their
limitations warrant a focus on more specialized, easily interpretable tools.
Marelli et al. emphasize the importance of transparent attribution and
responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans
should retain responsibility for determining the scientific roadmap. To
facilitate the discussion, the four perspectives are complemented with a
response from each group. By putting these different perspectives in
conversation, we aim to bring attention to important considerations within the
academic community regarding the adoption of LLMs and their impact on both
current and future scientific practices.


  In this work, we propose a novel scenario to probe the interactions between
dark matter (DM) particles and electrons, via hydrogen-atmosphere pulsating
white dwarfs (DAVs) in globular clusters. In this special configuration, the DM
particles, which are predominantly captured by multiple scattering with the
electrons in a DAV, would annihilate by pairs and provide extra energy source
to the DAV. This mechanism slows down the natural cooling evolution which can
be presented by the period variation rates of pulsation modes. The differences
between the secular rates predicted by the precise asteroseismology and the
secular rates obtained from observation can reveal the DM-electron
interactions. An important observable has been proposed and corresponding
estimations have been made. According to the estimation, if this scenario could
be implemented in the near future, the potential sensitivity on $m_{\chi}$ (DM
particle's mass) and $\sigma_{\chi,e}$ (elastic scattering cross section
between DM and electron) could be hopefully extended to a region $5
\mathrm{GeV} \lesssim m_{\chi} \lesssim 10^{4} \mathrm{GeV}$ and
$\sigma_{\chi,e} \gtrsim 10^{-40} \mathrm{cm}^{2}$. Combining with indirect DM
detection results, this could give us a cross check on the existence of such
leptonphilic DM particles to some extent.


  Heusler compounds offer potential as spintronic devices due to their
spin-polarization and half-metallicity properties, where electron spin-majority
(minority) manifold exhibits states (band gap) at the electronic chemical
potential, yielding full spin-polarization in a single manifold. Yet, Heuslers
often exhibit intrinsic disorder that degrades its half-metallicity and
spin-polarization. Using density-functional theory, we analyze the electronic
and magnetic properties of equiatomic Heusler ($L$2$_{1}$) CoMnCrAl and
CoFeCrGe alloys for effects of hydrostatic pressure and intrinsic disorder
(thermal antisites, binary swaps, and vacancies). Under pressure, CoMnCrAl
undergoes a metallic transition, while half-metallicity in CoFeCrGe is retained
for a limited range. Antisite disorder between Co-Al pairs in CoMnCrAl and
Co-Ge pairs in CoFeCrGe is energetically the most favored, and retain
half-metallic character in Co-excess samples. However, Co-deficient samples
undergo a transition from half-metallic to metallic, with a discontinuity in
the saturation magnetization. For binary swaps, configurations that compete
with the ground state are identified and show no loss of half-metallicity;
however, the minority-spin bandgap and magnetic moments vary depending on the
atoms swapped. For single binary swaps, there is a significant energy cost in
CoMnCrAl but with no loss of half metallicity. Although a few configurations in
CoFeCrGe energetically compete with the ground statei, however the
minority-spin bandgap and magnetic moments vary depending on the atoms swapped.
These informations should help in controlling these potential spintronic
materials.


  The field of explainable AI (XAI) has quickly become a thriving and prolific
community. However, a silent, recurrent and acknowledged issue in this area is
the lack of consensus regarding its terminology. In particular, each new
contribution seems to rely on its own (and often intuitive) version of terms
like "explanation" and "interpretation". Such disarray encumbers the
consolidation of advances in the field towards the fulfillment of scientific
and regulatory demands e.g., when comparing methods or establishing their
compliance with respect to biases and fairness constraints. We propose a
theoretical framework that not only provides concrete definitions for these
terms, but it also outlines all steps necessary to produce explanations and
interpretations. The framework also allows for existing contributions to be
re-contextualized such that their scope can be measured, thus making them
comparable to other methods. We show that this framework is compliant with
desiderata on explanations, on interpretability and on evaluation metrics. We
present a use-case showing how the framework can be used to compare LIME, SHAP
and MDNet, establishing their advantages and shortcomings. Finally, we discuss
relevant trends in XAI as well as recommendations for future work, all from the
standpoint of our framework.


  This short paper provides a description of an architecture to acquisition and
use of knowledge by intelligent agents over a restricted domain of the Internet
Infrastructure. The proposed architecture is added to an intelligent agent
deployment model over a very useful server for Internet Autonomous System
administrators. Such servers, which are heavily dependent on arbitrary and
eventual updates of human beings, become unreliable. This is a position paper
that proposes three research questions that are still in progress.


  In this paper we continue our work on adiabatic time of time-inhomogeneous
Markov chains first introduced in Kovchegov (2010) and Bradford and Kovchegov
(2011). Our study is an analog to the well-known Quantum Adiabatic (QA) theorem
which characterizes the quantum adiabatic time for the evolution of a quantum
system as a result of applying of a series of Hamilton operators, each is a
linear combination of two given initial and final Hamilton operators, i.e.
$\mathbf{H}(s) = (1-s)\mathbf{H_0} + s\mathbf{H_1}$. Informally, the quantum
adiabatic time of a quantum system specifies the speed at which the Hamiltonian
operators changes so that the ground state of the system at any time $s$ will
always remain $\epsilon$-close to that induced by the Hamilton operator
$\mathbf{H}(s)$ at time $s$. Analogously, we derive a sufficient condition for
the stable adiabatic time of a time-inhomogeneous Markov evolution specified by
applying a series of transition probability matrices, each is a linear
combination of two given irreducible and aperiodic transition probability
matrices, i.e., $\mathbf{P_{t}} = (1-t)\mathbf{P_{0}} + t\mathbf{P_{1}}$. In
particular we show that the stable adiabatic time $t_{sad}(\mathbf{P_{0}},
\mathbf{P_{1}}, \epsilon) = O (t_{mix}^{4}(\epsilon \slash 2) \slash
\epsilon^{3}), $ where $t_{mix}$ denotes the maximum mixing time over all
$\mathbf{P_{t}}$ for $0 \leq t \leq 1$.


  During its year-long orbital mission, the Dawn spacecraft has mapped the
surface of main-belt asteroid Vesta multiple times at different spatial
resolutions and illumination and viewing angles. The onboard Framing Camera has
acquired thousands of clear filter and narrow band images, which, with the
availability of high-resolution global shape models, allows for a photometric
characterization of the surface in unprecedented detail. We analyze clear
filter images to retrieve the photometric properties of the regolith. In the
first part of the paper we evaluate different photometric models for the global
average. In the second part we use these results to study variations in albedo
and steepness of the phase curve over the surface. Maps of these two
photometric parameters show large scale albedo variations, which appear to be
associated with compositional differences. They also reveal the location of
photometrically extreme terrains, where the phase curve is unusually shallow or
steep. We find that shallow phase curves are associated with steep slopes on
crater walls and faults, as calculated from a shape model. On the other hand,
the phase curve of ejecta associated with young impact craters is steep. We
interpret these variations in phase curve slope in terms of physical roughness
of the regolith. The lack of rough ejecta around older craters suggests that
initially rough ejecta associated with impact craters on Vesta are smoothed
over a relatively short time of several tens of Myr. We propose that this
process is the result of impact gardening, and as such represents a previously
unrecognized aspect of Vesta space weathering (Pieters et al., 2012). If this
type of space weathering is common, we may expect to encounter this photometric
phenomenon on other main belt asteroids.


  Purpose: Functional imaging is emerging as an important tool for lung cancer
treatment planning and evaluation. Compared with traditional methods such as
nuclear medicine ventilation-perfusion (VQ), positron emission tomography
(PET), single photon emission computer tomography (SPECT), or magnetic
resonance imaging (MRI), which use contrast agents to form 2D or 3D functional
images, ventilation imaging obtained from 4DCT lung images is convenient and
cost-effective because of its availability during radiation treatment planning.
Current methods of obtaining ventilation images from 4DCT lung images involve
deformable image registration (DIR) and a density (HU) change-based algorithm
(DIR/HU); therefore the resulting ventilation images are sensitive to the
selection of DIR algorithms. Methods: We propose a deep convolutional neural
network (CNN)-based method to derive the ventilation images from 4DCT directly
without explicit DIR, thereby improving consistency and accuracy of ventilation
images. A total of 82 sets of 4DCT and ventilation images from patients with
lung cancer were studied using this method. Results: The predicted images were
comparable to the label images of the test data. The similarity index and
correlation coefficient averaged over the ten-fold cross validation were
0.883+-0.034 and 0.878+-0.028, respectively. Conclusions: The results
demonstrate that deep CNN can generate ventilation imaging from 4DCT without
explicit deformable image registration, reducing the associated uncertainty.


  The influence of various matter fields on the confining and
finite-temperature properties of the (2+1)d Georgi-Glashow model is explored.
At zero temperature, these fields are W-bosons, which play the role of heavy
nodes, through which the quark-antiquark string passes. This fact is shown to
increase by a factor $4\sqrt{2}$ the absolute value of the coefficient at the
1/R-term in the large-distance potential with respect to that of the Nambu-Goto
string in 3d. The string tension also acquires a positive correction, which is,
however, exponentially small. At finite temperature, the matter fields of
interest are massless fundamental quarks, which diminish the deconfinement
critical temperature by way of an additional attraction of a monopole and an
antimonopole inside their molecules through the quark zero modes. It is
demonstrated that, outside the BPS-limit, when the number of massless flavors
is 4 or larger, the deconfinement phase transition occurs already at the
temperatures of the order of the temperature of dimensional reduction. In the
BPS limit, this critical number of flavors is 3. Since the temperature of
dimensional reduction is exponenetially small and since monopoles are
instantons in (2+1)d, these numbers can be compared with the one in the
instanton-liquid model of 4d QCD, at which the chiral phase transition occurs
at a vanishingly small temperature. The latter number is known to be of the
order of 5, so that the results of the two models are quite close to each
other.


  This paper investigates the role of quadrature exactness in the approximation
scheme of hyperinterpolation. Constructing a hyperinterpolant of degree $n$
requires a positive-weight quadrature rule with exactness degree $2n$. We
examine the behavior of such approximation when the required exactness degree
$2n$ is relaxed to $n+k$ with $0<k\leq n$. Aided by the Marcinkiewicz--Zygmund
inequality, we affirm that the $L^2$ norm of the exactness-relaxing
hyperinterpolation operator is bounded by a constant independent of $n$, and
this approximation scheme is convergent as $n\rightarrow\infty$ if $k$ is
positively correlated to $n$. Thus, the family of candidate quadrature rules
for constructing hyperinterpolants can be significantly enriched, and the
number of quadrature points can be considerably reduced. As a potential cost,
this relaxation may slow the convergence rate of hyperinterpolation in terms of
the reduced degrees of quadrature exactness. Our theoretical results are
asserted by numerical experiments on three of the best-known quadrature rules:
the Gauss quadrature, the Clenshaw--Curtis quadrature, and the spherical
$t$-designs.


  In this work we address the issue of validating the monodomain equation used
in combination with the Bueno-Orovio ionic model for the prediction of the
activation times in cardiac electro-physiology of the left ventricle. To this
aim, we consider our patients who suffered from Left Bundle Branch Block
(LBBB). We use activation maps performed at the septum as input data for the
model and maps at the epicardial veins for the validation. In particular, a
first set (half) of the latter are used to estimate the conductivities of the
patient and a second set (the remaining half) to compute the errors of the
numerical simulations. We find an excellent agreement between measures and
numerical results. Our validated computational tool could be used to accurately
predict activation times at the epicardial veins with a short mapping, i.e. by
using only a part (the most proximal) of the standard acquisition points, thus
reducing the invasive procedure and exposure to radiation.


  We investigate the chemical abundances of NGC3603 in the Milky Way, of
30Doradus in the Large Magellanic Cloud, and of N66 in the Small Magellanic
Cloud. Mid-infrared observations with the Infrared Spectrograph onboard the
Spitzer Space Telescope allow us to probe the properties of distinct physical
regions within each object: the central ionizing cluster, the surrounding
ionized gas, photodissociation regions, and buried stellar clusters. We detect
[SIII], [SIV], [ArIII], [NeII], [NeIII], [FeII], and [FeIII] lines and derive
the ionic abundances. Based on the ionic abundance ratio (NeIII/H)/(SIII/H), we
find that the gas observed in the MIR is characterized by a higher degree of
ionization than the gas observed in the optical spectra. We compute the
elemental abundances of Ne, S, Ar, and Fe. We find that the alpha-elements Ne,
S, and Ar scale with each other. Our determinations agree well with the
abundances derived from the optical. The Ne/S ratio is higher than the solar
value in the three giant HII regions and points toward a moderate depletion of
sulfur on dust grains. We find that the neon and sulfur abundances display a
remarkably small dispersion (0.11dex in 15 positions in 30Doradus), suggesting
a relatively homogeneous ISM, even though small-scale mixing cannot be ruled
out.


  Deep Convolutional Neural Networks have made an incredible progress in many
Computer Vision tasks. This progress, however, often relies on the availability
of large amounts of the training data, required to prevent over-fitting, which
in many domains entails significant cost of manual data labeling. An
alternative approach is application of data augmentation (DA) techniques that
aim at model regularization by creating additional observations from the
available ones. This survey focuses on two DA research streams: image mixing
and automated selection of augmentation strategies. First, the presented
methods are briefly described, and then qualitatively compared with respect to
their key characteristics. Various quantitative comparisons are also included
based on the results reported in recent DA literature. This review mainly
covers the methods published in the materials of top-tier conferences and in
leading journals in the years 2017-2021.


  Recently, there has been immense interest in using unmanned aerial vehicles
(UAVs) for civilian operations such as package delivery, firefighting, and fast
disaster response. As a result, UAV traffic management systems are needed to
support potentially thousands of UAVs flying simultaneously in the airspace, in
order to ensure their liveness and safety requirements are met. Hamilton-Jacobi
(HJ) reachability is a powerful framework for providing conditions under which
these requirements can be met, and for synthesizing the optimal controller for
meeting them. However, due to the curse of dimensionality, HJ reachability is
only tractable for a small number of vehicles if their set of maneuvers is
unrestricted. In this paper, we define a platoon to be a group of UAVs in a
single-file formation. We model each vehicle as a hybrid system with modes
corresponding to its role in the platoon, and specify the set of allowed
maneuvers in each mode to make the analysis tractable. We propose several
liveness controllers based on HJ reachability, and wrap a safety controller,
also based on HJ reachability, around the liveness controllers. For a single
altitude range, our approach guarantees safety for one safety breach; in the
unlikely event of multiple safety breaches, safety can be guaranteed over
multiple altitude ranges. We demonstrate the satisfaction of liveness and
safety requirements through simulations of three common scenarios.


  We discuss the extension of the maximal-unitarity method to two loops,
focusing on the example of the planar double box. Maximal cuts are
reinterpreted as contour integrals, with the choice of contour fixed by the
requirement that integrals of total derivatives vanish on it. The resulting
formulae, like their one-loop counterparts, can be applied either analytically
or numerically.


  Proto-neutron stars forming a few seconds after core-collapse supernovae are
hot and dense environments where hyperons can be efficiently produced by weak
processes. By making use of various state-of-the-art supernova simulations
combined with the proper extensions of the equations of state including
$\Lambda$ hyperons, we calculate the cooling of the star induced by the
emission of dark bosons $X^0$ through the decay $\Lambda\to n X^0$. Comparing
this novel energy-loss process to the neutrino cooling of SN 1987A allows us to
set stringent constraints on massless dark photons and axions with
flavor-violating couplings to quarks. We find that this new supernova bound can
be orders of magnitude stronger than other limits in dark-sector models.


  We investigate the motion of a test particle in higher dimensions due to the
presence of extended sources like D$p$-branes by studying the motion in the
transverse space of the brane. This is contrasted with the motion of a point
particle in the Schwarzschild background in higher dimensions. Since
D$p$-branes are specific to 10-dimensional space time and exact solutions of
geodesic equations for this particular space time has not been possible so far
for the Schwarzschild background, we focus here to find the leading order
solution of the geodesic equation (for motion of light rays). This enables us
to compute the bending of light in both the backgrounds. We show that contrary
to the well known result of no noncircular bound orbits for a massive particle,
in Schwarzschild background, for $d\geq 5$, the $Dp$-brane background does
allow bound elliptic motion only for $p = 6$ and the perihelion of the ellipse
regresses instead of advancement. We also find that circular orbits for photon
are allowed only for $p\le 3$.


  In this paper we place new constraints on a f(R) modified gravity model
recently proposed by Hu and Sawicki. After checking that the Hu and Sawicki
model produces a viable cosmology, i.e. a matter dominated epoch followed by a
late-time acceleration, we constrain some of its parameters by using recent
observations from the UNION compilation of luminosity distances of Supernovae
type Ia, including complementary information from Baryonic Acoustic
Oscillations, Hubble expansion, and age data. We found that the data considered
is unable to place significant constraints on the model parameters and we
discuss the impact of a different assumption of the background model in cosmic
parameters inference.


  Accurate prediction of NMR chemical shifts can in principle help refine
aqueous solution structure of proteins to the quality of X-ray structures. We
report a new machine learning algorithm for protein chemical shift prediction
that outperforms existing chemical shift calculators on realistic NMR solution
data. Our UCBShift predictor implements two modules: a transfer prediction
module that employs both sequence and structural alignment to select reference
candidates for experimental chemical shift replication, and a redesigned
machine learning module based on random forest regression which utilizes more,
and more carefully curated, feature extracted data. When combined together,
this new predictor achieves state of the art accuracy for predicting chemical
shifts on a "real-world" dataset, with root-mean-square errors of 0.31 ppm for
amide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01
ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy of
popular chemical shift predictors such as SPARTA+ and SHIFTX2.


  Directional emission of photons with designed polarizations and orbital
angular momenta is crucial for exploiting full potential of quantum emitters
(QEs) within quantum information technologies. Capitalizing on the concept of
hybrid plasmon-QE coupled metasurfaces, we develop a holography-based design
approach allowing one to construct surface nanostructures for outcoupling
QE-excited circularly diverging surface plasmon polaritons (SPPs) into
well-collimated beams of photons with desirable polarization characteristics
propagating along given directions. Using the well-established simulation
framework, we demonstrate the efficiency and versatility of the developed
approach by analyzing different hybrid SPP-QE coupled metasurfaces designed for
generation of linearly, radially and circularly polarized photons propagating
in various off-axis directions. Our work enables the design of single-photon
sources with radiation channels that have distinct directional and polarization
characteristics, extending thereby possibilities for designing complex photonic
systems for quantum information processing. Moreover, we believe that the
developed scattering holography approach is generally useful when employing
surface electromagnetic excitations, including SPP modes, as reference waves
for signal wave reconstruction.


  We perform in this work an analysis of the background dynamics for
$\alpha$-attractor models in the context of loop quantum cosmology. Particular
attention is given to the determination of the duration of the inflationary
phase that is preceded by the quantum bounce in these models. From an analysis
of the general predictions for these models, it is shown that we can be able to
put constraints in the parameter $\alpha$ of the potentials and also on the
quantum model itself, especially the Barbero-Immirzi parameter. In particular,
the constraints on the tensor-to-scalar ratio and spectral tilt of the
cosmological perturbations limit the $\alpha$ parameter of the potentials to
values such that $\alpha_{n=0} \lesssim 10$, $\alpha_{n=1} \lesssim 17$ and
$\alpha_{n=2} \lesssim 67$, for the $\alpha$ attractors T, E, and $n=2$ models,
respectively. Using the constraints on the minimal amount of e-folds of
expansion from the quantum bounce up to the end of inflation leads to the upper
bounds for the Barbero-Immirzi parameter for the $\alpha$-attractor models
studied in this work: $\gamma_{n=0} \lesssim 51.2$, $\gamma_{n=1}\lesssim 63.4$
and $\gamma_{n=2} \lesssim 64.2$, which are obtained when fixing the parameter
$\alpha$ in the potential at the values saturating the upper bounds given above
for each model.


  Deuterated molecules have been detected and studied toward Orion BN/KL in the
past decades, mostly with single-dish telescopes. However, high angular
resolution data are critical not only for interpreting the spatial distribution
of the deuteration ratio but also for understanding this complex region in
terms of cloud evolution involving star-forming activities and stellar
feedbacks. We present here the first high angular resolution (1.8 arcsec \times
0.8 arcsec) images of deuterated methanol CH2DOH in Orion BN/KL observed with
the IRAM Plateau de Bure Interferometer from 1999 to 2007 in the 1 to 3 mm
range. Six CH2DOH lines were detected around 105.8, 223.5, and 225.9 GHz. In
addition, three E-type methanol lines around 101-102 GHz were detected and were
used to derive the corresponding CH3OH rotational temperatures and column
densities toward different regions across Orion BN/KL. The strongest CH2DOH and
CH3OH emissions come from the Hot Core southwest region with an LSR velocity of
about 8 km/s. We derive [CH2DOH]/[CH3OH] abundance ratios of 0.8-1.3\times10^-3
toward three CH2DOH emission peaks. A new transition of CH3OD was detected at
226.2 GHz for the first time in the interstellar medium. Its distribution is
similar to that of CH2DOH. Besides, we find that the [CH2DOH]/[CH3OD] abundance
ratios are lower than unity in the central part of BN/KL. Furthermore, the HDO
3(1,2)-2(2,1) line at 225.9 GHz was detected and its emission distribution
shows a shift of a few arcseconds with respect to the deuterated methanol
emission that likely results from different excitation effects. The deuteration
ratios derived along Orion BN/KL are not markedly different from one clump to
another. However, various processes such as slow heating due to ongoing star
formation, heating by luminous infrared sources, or heating by shocks could be
competing to explain some local differences observed for these ratios.


  Granular simulations are used to probe the particle scale dynamics at short,
intermediate, and long time scales for gravity driven, dense granular flows
down an inclined plane. On approach to the angle of repose, where motion
ceases, the dynamics become intermittent over intermediate times, with strong
temporal correlations between particle motions -- temporally heterogeneous
dynamics. This intermittency is characterised through large scale structural
events whereby the contact network periodically spans the system. A
characteristic time scale associated with these processes increases as the
stopped state is approached. These features are discussed in the context of the
dynamics of supercooled liquids near the glass transition.


  Fish feeding intensity assessment (FFIA) aims to evaluate fish appetite
changes during feeding, which is crucial in industrial aquaculture
applications. Existing FFIA methods are limited by their robustness to noise,
computational complexity, and the lack of public datasets for developing the
models. To address these issues, we first introduce AV-FFIA, a new dataset
containing 27,000 labeled audio and video clips that capture different levels
of fish feeding intensity. Then, we introduce multi-modal approaches for FFIA
by leveraging the models pre-trained on individual modalities and fused with
data fusion methods. We perform benchmark studies of these methods on AV-FFIA,
and demonstrate the advantages of the multi-modal approach over the
single-modality based approach, especially in noisy environments. However,
compared to the methods developed for individual modalities, the multimodal
approaches may involve higher computational costs due to the need for
independent encoders for each modality. To overcome this issue, we further
present a novel unified mixed-modality based method for FFIA, termed as U-FFIA.
U-FFIA is a single model capable of processing audio, visual, or audio-visual
modalities, by leveraging modality dropout during training and knowledge
distillation using the models pre-trained with data from single modality. We
demonstrate that U-FFIA can achieve performance better than or on par with the
state-of-the-art modality-specific FFIA models, with significantly lower
computational overhead, enabling robust and efficient FFIA for improved
aquaculture management.


  The use of High Performance Computing (HPC) to compliment urgent decision
making in the event of disasters is an important future potential use of
supercomputers. However, the usage modes involved are rather different from how
HPC has been used traditionally. As such, there are many obstacles that need to
be overcome, not least the unbounded wait times in the batch system queues, to
make the use of HPC in disaster response practical. In this paper, we present
how the VESTEC project plans to overcome these issues and develop a working
prototype of an urgent computing control system. We describe the requirements
for such a system and analyse the different technologies available that can be
leveraged to successfully build such a system. We finally explore the design of
the VESTEC system and discuss ongoing challenges that need to be addressed to
realise a production level system.


  Inspired by Lifshitz theory for quantum critical phenomena in condensed
matter, Horava proposed a theory for quantum gravity with an anisotropic
scaling in ultraviolet. In Horava-Lifshitz gravity (HLG), we have studied the
impacts of six types of equations of state on the evolution of various
cosmological parameters such as Hubble parameters and scale factor. From the
comparison of the general relativity gravity with the HLG with detailed and
without with non-detailed balance conditions, remarkable differences are found.
Also, a noticeable dependence of singular and non-singular Big Bang on the
equations of state is observed. We conclude that HLG explains various epochs in
the early universe and might be able to reproduce the entire cosmic history
with and without singular Big Bang.


  In antiferromagnets, the Dzyaloshinskii-Moriya interaction lifts the
degeneracy of left- and right-circularly polarized spin waves. This
relativistic coupling increases the efficiency of spin-wave-induced domain wall
motion and leads to higher drift velocities. We show that in biaxial
antiferromagnets, the spin-wave helicity controls both the direction and
magnitude of the magnonic force on chiral domain walls. By contrast, in
uniaxial antiferromagnets, the magnonic force is propulsive with a helicity
dependent strength.


  Adjoint methods enable the accurate calculation of the sensitivities of a
quantity of interest. The sensitivity is obtained by solving the adjoint
system, which can be derived by continuous or discrete adjoint strategies. In
acoustic wave propagation, continuous and discrete adjoint methods have been
developed to compute the eigenvalue sensitivity to design parameters and
passive devices (Aguilar, J. G. et al, 2017, J. Computational Physics, vol.
341, 163-181). In this short communication, it is shown that the continuous and
discrete adjoint characteristic decompositions, and Riemann invariants, are
connected by a similarity transformation. The results are shown in the Laplace
domain. The adjoint characteristic decomposition is applied to a
one-dimensional acoustic resonator, which contains a monopole source of sound.
The proposed framework provides the foundation to tackle larger acoustic
networks with a discrete adjoint approach, opening up new possibilities for
adjoint-based design of problems that can be solved by the method of
characteristics.


  We study the electronic properties of the
$\textrm{Fe}\textrm{Se}_{1-x}\textrm{Te}_x$ system ($x=0$, 0.25, 0.5, 0.75, and
1) from the perspective of X-ray spectroscopy and density functional theory
(DFT). The analysis performed on the density of states reveals marked
differences in the distribution of the $5p$ states of Te for $x>0$. We think
that this finding can be associated with the fact that superconductivity is
suppressed in FeTe. Moreover, using resonant inelastic X-ray scattering, we
estimate the spin state of our system which can be correlated to the magnetic
order. We find that the spin state of the
$\textrm{Fe}\textrm{Se}_{1-x}\textrm{Te}_x$ system fluctuates, as a function of
$x$, between $S=0$ and $S=2$ with Fe in FeSe in the highest spin state.
Finally, our DFT calculations nicely reproduce the X-ray emission spectra
performed at the Fe $L$-edge (which probe the occupied states) and suggest that
the $\textrm{Fe}\textrm{Se}_{1-x}\textrm{Te}_x$ system can be considered at
most as a moderately correlated system.


  We formulate the problem of finding the low-energy limit of spin foam models
as a coarse-graining problem in the sense of statistical physics. This suggests
that renormalization group methods may be used to find that limit. However,
since spin foams are models of spacetime at Planck scale, novel issues arise:
these microscopic models are sums over irregular, background-independent
lattices. We show that all of these issues can be addressed by the recent
application of the Kreimer Hopf algebra for quantum field theory
renormalization to non-perturbative statistical physics. The main difference
from standard renormalization group is that the Hopf algebra executes block
transformations in parts of the lattice only but in a controlled manner so that
the end result is a fully block-transformed lattice.


  De-identification is the task of detecting protected health information (PHI)
in medical text. It is a critical step in sanitizing electronic health records
(EHRs) to be shared for research. Automatic de-identification classifierscan
significantly speed up the sanitization process. However, obtaining a large and
diverse dataset to train such a classifier that works wellacross many types of
medical text poses a challenge as privacy laws prohibit the sharing of raw
medical records. We introduce a method to create privacy-preserving shareable
representations of medical text (i.e. they contain no PHI) that does not
require expensive manual pseudonymization. These representations can be shared
between organizations to create unified datasets for training de-identification
models. Our representation allows training a simple LSTM-CRF de-identification
model to an F1 score of 97.4%, which is comparable to a strong baseline that
exposes private information in its representation. A robust, widely available
de-identification classifier based on our representation could potentially
enable studies for which de-identification would otherwise be too costly.


  We present a search for the baryon number $B$ and lepton number $L$ violating
decays $\tau^- \rightarrow \Lambda \pi^-$ and $\tau^- \rightarrow \bar{\Lambda}
\pi^-$ produced from the $e^+e^-\to \tau^+\tau^-$ process, using a 364
fb$^{-1}$ data sample collected by the Belle~II experiment at the SuperKEKB
collider. No evidence of signal is found in either decay mode, which have
$|\Delta(B-L)|$ equal to $2$ and $0$, respectively. Upper limits at 90\%
credibility level on the branching fractions of $\tau^- \rightarrow
\Lambda\pi^-$ and $\tau^- \rightarrow \bar{\Lambda}\pi^-$ are determined to be
$4.7 \times 10^{-8}$ and $4.3 \times 10^{-8}$, respectively.


  The analysis of spatial point patterns that occur in the network domain have
recently gained much attraction and various intensity functions and measures
have been proposed. However, the linkage of spatial network statistics to
regression models has not been approached so far. This paper presents a new
regression approach which treats a generic intensity function of a planar point
pattern that occurred on a network as the outcome of a set of different
covariates and various graph statistics. Different to all alternative
approaches, our model is the first which permits the statistical analysis of
complex regression data in the context of network intensity functions for
spatial point patterns. The potential of our new technique to model the
structural dependencies of network intensity functions on various covariates
and graph statistics is illustrated using call-in data on neighbour and
community disturbances in an urban context.


  A random forest regression based supervised machine learning method to
predict experimental critical temperature of superconductivity from the
electronic band structure, as obtained from Density Functional Theory, is
demonstrated. This complementarity between experiment and theory draws
inspiration from the merging of Kohn-Sham and Bogoliubov-De Gennes equations
[W. Kohn, W, EKU Gross, and LN Oliveira, Int. J. of Quant. Chem., 36(23),
611-615 (1989)]. Features in the Kohn-Sham Density Functional Theory band
structure away from EF becoming decisive for the superconducting gap
demonstrates this divide-and-conquer physical understanding. Not committing to
any microscopic mechanism for the SC at this stage, it implies that in
different classes of materials, different electronic features are responsible
for the superconductivity. However, training on known members of a class, the
performance of new members may be predicted. The method is validated for the
A15 materials, including both binary A3X and ternary A6XY intermetallics, A=V,
Nb, demonstrating that the two do indeed belong to the same class of
superconductors.


  Cellular function is widely believed to be organized in a modular fashion. On
all scales and at all levels of complexity, relatively independent sub-units
perform relatively independent sub-tasks of biological function. This
functional modularity must be reflected in the topology of molecular networks.
But how a functional module should be represented in an interaction network is
an open question. In protein-interaction networks (PIN), one can identify a
protein-complex as a module on a small scale, i.e. modules are understood as
densely linked, resp. interacting, groups of proteins, that are only sparsely
interacting with the rest of the network.
  In this contribution, we show that extrapolating this concept of cohesively
linked clusters of proteins as modules to the scale of the entire PIN
inevitable misses important and functionally relevant structure inherent in the
network. As an alternative, we introduce a novel way of decomposing a network
into functional roles and show that this represents network structure and
function more efficiently. This finding should have a profound impact on all
module assisted methods of protein function prediction and should shed new
light on how functional modules can be represented in molecular interaction
networks in general.


  Recently Yu and Sun [Int. J. Theor. Phys. DOI 10.1007/s10773-014-2360-x] have
presented probabilistic teleportation of 3-qubit cat state via 6-qubit cluster
state. The success probability depends on absolute value of only two of the
coefficients of cluster state i.e. |c|^2 + |d|^2. We have demonstrated the
feasibility to teleport 3-qubit cat state via 2-qubit non maximally entangled
Bell state instead of a complex state like 6-qubit non maximally entangled
cluster state. In this comment we have prescribed an optimal protocol for
teleportation of n-qubit state of the form (\alpha|x> + \beta|\bar{x}>)_n via
non maximally entangled Bell state a|00> + b|11> henceforth allowing
teleportation of n-qubit state via 2-qubit Bell state. The success probability
of the prescribed protocol is |b|^2.


  The zero gap electronic bands in the monolayer graphene are shown to be
unstable relative to the dynamic symmetry violation due to the electron-phonon
interaction.


  We emphasize the role that the associated production of a Higgs boson with a
pair of top-antitop quarks can play at present and future hadron colliders.
Results of recent calculations of the NLO total cross section for the
associated production of a Standard Model like Higgs boson with a pair of
top-antitop quarks at the Tevatron (sqrt(s)=2 TeV) are presented.


  Using the canonical Arnowitt-Deser-Misner Hamiltonian formalism, a "first law
of mechanics" is established for binary systems of point masses moving along
generic stable bound (eccentric) orbits. This relationship is checked to hold
within the post-Newtonian approximation to general relativity, up to third
(3PN) order. Several applications are discussed, including the use of
gravitational self-force results to inform post-Newtonian theory and the
effective one-body model for eccentric-orbit compact binaries.


  We present the discussion of the energy-momentum tensor of the scalar
$\phi^4$- theory on a noncommutative space. The Noether procedure is performed
at the operator level. Additionally, the broken dilatation symmetry will be
considered in a Moyal-Weyl deformed scalar field theory at the classical level.