  During the course of this conference, much evidence was presented that points
to an intimate connection between the energetic outflows driven by AGN and the
energy budget and quite possibly also the evolution of their gaseous
environments. However, it is still not clear if and how the AGN activity is
triggered by the cooling gas, how long the activity lasts for and how these
effects give rise to the observed distribution of morphologies of the outflows.
In this contribution we concentrate on the high radio luminosity end of the AGN
population. While most of the heating of the environmental gas may be due to
less luminous and energetic outflows, these more powerful objects have a very
profound influence on their surroundings. We will describe a simple model for
powerful radio galaxies and radio-loud quasars that explains the dichotomy of
their large-scale radio morphologies as well as their radio luminosity
function.


  Representations of braid group $B_n$ on $n \geq 2$ strands by automorphisms
of a free group of rank $n$ go back to Artin (1925). In 1991 Kauffman
introduced a theory of virtual braids and virtual knots and links. The virtual
braid group $VB_n$ on $n \geq 2$ strands is an extension of the classical braid
group $B_n$ by the symmetric group $S_n$. In this paper we consider flat
virtual braid groups $FVB_n$ on $n\geq 2$ strands and construct a family of
representations of $FVB_n$ by automorphisms of free groups of rank $2n$. It has
been established that these representations do not preserve the forbidden
relations between classical and virtual generators. We investigated some
algebraic properties of the constructed representations. In particular, we
established conditions of faithfulness in case $n=2$ and proved that the kernel
contains a free group of rank two for $n\ge3$.


  In this paper, the \lambda\phi^4 scalar feld effective action, in the
one-loop approximation, is calculated by using the Krein space quantization. We
show that the effective action is naturally fnite and the singularity does not
appear in the theory. The physical interaction mass, the running coupling
constant and \beta-function are then calculated. The effective potential which
is calculated in the Krein space quantization is different from the usual
Hilbert space calculation, however we show that \beta-function is the same in
the two different methods.


  In periodic media gap solitons with frequencies inside a spectral gap but
close to a spectral band can be formally approximated by a slowly varying
envelope ansatz. The ansatz is based on the linear Bloch waves at the edge of
the band and on effective coupled mode equations (CMEs) for the envelopes. We
provide a rigorous justification of such CME asymptotics in two-dimensional
photonic crystals described by the Kerr nonlinear Maxwell system. We use a
Lyapunov-Schmidt reduction procedure and a nested fixed point argument in the
Bloch variables. The theorem provides an error estimate in $H^2(\mathbb R^2)$
between the exact solution and the envelope approximation. The results justify
the formal and numerical CME-approximation in [Dohnal and D\"orfler, Multiscale
Model. Simul., p. 162-191, 11 (2013)].


  The question of whether information is lost in black holes is investigated
using Euclidean path integrals. The formation and evaporation of black holes is
regarded as a scattering problem with all measurements being made at infinity.
This seems to be well formulated only in asymptotically AdS spacetimes. The
path integral over metrics with trivial topology is unitary and information
preserving. On the other hand, the path integral over metrics with non-trivial
topologies leads to correlation functions that decay to zero. Thus at late
times only the unitary information preserving path integrals over trivial
topologies will contribute. Elementary quantum gravity interactions do not lose
information or quantum coherence.


  A methodology is presented here for deriving true experimental axial
stress-strain curves in both tension and compression for monolayer graphene
through the shift of the 2D Raman peak that is present in all graphitic
materials. The principle behind this approach is the observation that the shift
of the 2D wavenumber as a function of strain for different types of PAN based
fibres is a linear function of their Young's moduli and, hence, the
corresponding value of the shift of the 2D Raman peak over axial stress is, in
fact, a constant. By moving across the length scales we show that this value is
also valid at the nanoscale as it corresponds to the in plane breathing mode of
graphene that is present in both PAN based fibres and monolayer graphene.
Hence, the values of the shift of the 2D Raman peak can be easily converted to
values of stress in the linear elastic region without the aid of modelling or
the need to resort to cumbersome experimental procedures for obtaining the
axial force transmitted to the material and the cross sectional area of the two
dimensional membrane.


  A major goal in optomechanics is to observe and control quantum behavior in a
system consisting of a mechanical resonator coupled to an optical cavity. Work
towards this goal has focused on increasing the strength of the coupling
between the mechanical and optical degrees of freedom; however, the form of
this coupling is crucial in determining which phenomena can be observed in such
a system. Here we demonstrate that avoided crossings in the spectrum of an
optical cavity containing a flexible dielectric membrane allow us to realize
several different forms of the optomechanical coupling. These include cavity
detunings that are (to lowest order) linear, quadratic, or quartic in the
membrane's displacement, and a cavity finesse that is linear in (or independent
of) the membrane's displacement. All these couplings are realized in a single
device with extremely low optical loss and can be tuned over a wide range in
situ; in particular, we find that the quadratic coupling can be increased three
orders of magnitude beyond previous devices. As a result of these advances, the
device presented here should be capable of demonstrating the quantization of
the membrane's mechanical energy.


  Since the advent of bitcoin in 2008, the concept of a blockchain has widely
spread. Besides crypto currencies and trading activities, there is a wide range
of potential application areas where blockchains are providing the main
building block for secure solutions. From a technical point of view, a
blockchain involves a set of cryptographic primitives to provide a data
structure with security and trust properties. However, a blockchain is not a
golden bullet. It may be well suited for some problems, but often an
inappropriate data structure for many applications. In this paper, we review
the high-level concept of a blockchain and present possible applications in the
military field. Our review is targeted to readers with little prior domain
knowledge as a support to decide where it makes sense to use a blockchain and
where a blockchain might not be the right tool at hand.


  We consider a $p2$-brane model as a theory of maps from the vertices of the
Bruhat-Tits tree times $\mathbb{Z}$ into $\mathbb{R}^d$. We show that in order
for the worldsheet time evolution to be unitary, a certain spectral parameter
of the model must localize at special loci in the complex plane, which include
the (0,1) interval and the critical line of real part $1/2$. The excitations of
the model are supported at these loci, with commutation relations closely
resembling those of the usual bosonic string. We show that the usual
Hamiltonian, momentum, and angular momentum are conserved quantities, and the
Poincar\'e algebra is obeyed. Assuming an Euler product relation for the
spectrum, the Riemann zeta zeros on the critical line have spectral
interpretation as the massless photon and graviton in the Archimedean theory.


  The rise in data has led to the need for dimension reduction techniques,
especially in the area of non-scalar variables, including time series, natural
language processing, and computer vision. In this paper, we specifically
investigate dimension reduction for time series through functional data
analysis. Current methods for dimension reduction in functional data are
functional principal component analysis and functional autoencoders, which are
limited to linear mappings or scalar representations for the time series, which
is inefficient. In real data applications, the nature of the data is much more
complex. We propose a non-linear function-on-function approach, which consists
of a functional encoder and a functional decoder, that uses continuous hidden
layers consisting of continuous neurons to learn the structure inherent in
functional data, which addresses the aforementioned concerns in the existing
approaches. Our approach gives a low dimension latent representation by
reducing the number of functional features as well as the timepoints at which
the functions are observed. The effectiveness of the proposed model is
demonstrated through multiple simulations and real data examples.


  Learning from demonstration (LfD) and imitation learning offer new paradigms
for transferring task behavior to robots. A class of methods that enable such
online learning require the robot to observe the task being performed and
decompose the sensed streaming data into sequences of state-action pairs, which
are then input to the methods. Thus, recognizing the state-action pairs
correctly and quickly in sensed data is a crucial prerequisite for these
methods. We present SA-Net a deep neural network architecture that recognizes
state-action pairs from RGB-D data streams. SA-Net performed well in two
diverse robotic applications of LfD -- one involving mobile ground robots and
another involving a robotic manipulator -- which demonstrates that the
architecture generalizes well to differing contexts. Comprehensive evaluations
including deployment on a physical robot show that \sanet{} significantly
improves on the accuracy of the previous method that utilizes traditional image
processing and segmentation.


  The Drell-Yan process is an invaluable tool for probing the structure of
hadrons, and the pion-induced Drell-Yan process is unique in its sensitivity to
several subtleties of the partonic structure. This is a brief review of the
pionic Drell-Yan process with particular emphasis on the dilepton angular
distributions and nuclear modification of the parton distributions (the EMC
effect).


  We construct height functions defined stochastically on projective varieties
equipped with endomorphisms, and we prove that these functions satisfy analogs
of the usual properties of canonical heights. Moreover, we give a dynamical
interpretation of the kernel of these stochastic height functions, and in the
case of the projective line, we relate the size of this kernel to the Julia
sets of the original maps. Finally, as an application, we establish the
finiteness of some generalized Zsigmondy sets over global fields.


  Inspired by the LHCb's newest observation of two new excited $\Xi_b^0$
states, we systematically study the strong decays of the low-lying $\lambda$-
and $\rho$-modes $1D$-wave $\Xi_{b}$ and $\Xi^{'}_{b}$ baryons using the chiral
quark model within the $j$-$j$ coupling scheme. Based on the measured masses
and strong decay properties of $\Xi_{b}(6327)^{0}$ and $\Xi_{b}(6333)^{0}$, we
explain the two states as the $\lambda$-mode $1D$ $\Xi_{b}$ states with $
J^{P}=3/2^{+} $ and $ J^{P}=5/2^{+} $, respectively. Moreover, under this
assignment, another dominant decay channel of $\Xi_{b}(6327)^{0}$ is
$\Xi'_b\pi$ and that of $\Xi_{b}(6333)^{0}$ is $\Xi_b^*\pi$. Hence, the decay
modes $\Xi'_b\pi$ and $\Xi_b^*\pi$ may be another ideal channels as well to
decode the inner structure of $\Xi_{b}(6327)^{0}$ and $\Xi_{b}(6333)^{0}$,
respectively. For other unseen $1D$ $\Xi_b$ and $\Xi'_b$ states, our results
indicate: (i) $\Xi_b|J^P=\frac{3}{2}^+,2\rangle_{\rho}$ and
$\Xi_b|J^P=\frac{5}{2}^+,2\rangle_{\rho}$ are most likely to be narrow states
with a width of $\Gamma\simeq(12-30)$ MeV, and dominantly decay into
$\Sigma_bK$ and $\Sigma^*_bK$, respectively; (ii) The $1D$ $\Xi'_b$ baryons are
not broad states, and the widths vary in the range of $\Gamma\simeq(14-46)$
MeV. These states have a good potential to be observed in their dominant decay
processes.


  We have carried out an intensive study of the AGN heating-ICM cooling network
by comparing various cluster parameters of the HIFLUGCS sample to the
integrated radio luminosity of the central AGN, L_R, defined as the total
synchrotron power between 10 MHz and 15 GHz. We adopt the central cooling time,
t_cool, as the diagnostic to ascertain cooling properties of the clusters and
classify clusters with t_cool < 1 Gyr as strong cooling core (SCC) clusters,
with 1 Gyr < t_cool <7.7 Gyr as weak cooling core (WCC) clusters and with
t_cool > 7.7 Gyr as non-cooling core (NCC) clusters. We find 48 out of 64
clusters (75%) contain cluster center radio sources (CCRS) cospatial with or
within 50 h^{-1}_{71} kpc of the X-ray peak emission. Further, we find that the
probability of finding a CCRS increases from 45% to 67% to 100% for NCC, WCC
and SCC clusters, respectively, suggesting an AGN-feedback machinery in SCC
clusters which regulates the cooling in the central regions.
  We find L_R in SCC clusters depends strongly on the cluster scale such that
more massive clusters harbor more powerful radio AGN. The same trend is
observed between L_R and the classical mass deposition rate, MDR, albeit much
stronger, in SCC and partly also in WCC clusters. We also perform correlations
of the 2MASS K-band luminosity of the brightest cluster galaxy, L_BCG, with L_R
and cluster parameters. We invoke the relation between L_BCG and the black hole
mass, M_BH, and find a surprisingly tight correlation between M_BH and L_R for
SCC clusters. We find also an excellent correlation of L_BCG with M500 and L_X
for the entire sample; however, SCC clusters show a tighter trend in both the
cases. We discuss the plausible reasons behind these scaling relations in the
context of cooling flows and AGN feedback. [Abridged]


  We develop a self-contained approach to bosonization and refermionization
using the Keldysh functional integral. Starting from fermionic particles, we
bosonize the system and obtain a description in terms of the Tomonaga-Luttinger
liquid, with, in addition, an infinite series of interaction terms arising from
the curvature of the fermionic particle spectrum. We explicitly calculate the
leading interaction term and check its consistency with a different approach
based on the Matsubara framework, within which we calculate the second leading
interaction term, as well. Moreover, we bosonize weakly and strongly
interacting bosonic particles, and, finally, refermionize interacting phonons
into non-interacting fermionic quasiparticles. The work culminates in a map
between bosonic and fermionic particles and effective bosonic and fermionic
excitations, representing phonons and fermionic quasiparticles.


  FU Orionis-type objects (FUors) are low-mass pre-main-sequence objects which
go through a short-lived phase (~100 years) of increased mass accretion rate
(from 10^-8 to 10^-4 M_sun yr^-1). These eruptive young stars are in the early
stages of stellar evolution and, thus, still deeply embedded in a massive
envelope that feeds material to the circumstellar disk that is then accreted
onto the star. Some FUors drive molecular outflows, i.e. low-velocity
wide-angle magneto-hydrodynamical winds, that inject energy and momentum back
to the surrounding envelopes, and help clear the material surrounding the young
star. Here we present a 12CO (3--2), 13CO (3--2) and 12CO (4--3) survey of 20
FUor-type eruptive young stars observed with APEX. We use our 13CO (3--2)
observations to measure the masses of the envelopes surrounding each FUor and
find an agreement with the FUor evolutionary trend found from the 10um silicate
feature. We find outflows in 11 FUors, calculate their masses and other
kinematic properties, and compare these with those of outflows found around
quiescent young stellar objects gathered from the literature. This comparison
indicates that outflows in FUors are more massive than outflows in quiescent
sources, and that FUor outflows have a higher ratio outflow mass with respect
to the envelope than the quiescent sample, indicating that the eruptive young
stars have lower star-forming efficiencies. Finally, we found that the outflow
forces in FUors are similar to those of quiescent young stellar objects,
indicating that their accretion histories are similar or that the FUor outflows
have lower velocities.


  The use of artificial intelligence in clinical care to improve decision
support systems is increasing. This is not surprising since, by its very
nature, the practice of medicine consists of making decisions based on
observations from different systems both inside and outside the human body. In
this paper, we combine three general systems (ICU, diabetes, and comorbidities)
and use them to make patient clinical predictions. We use an artificial
intelligence approach to show that we can improve mortality prediction of
hospitalized diabetic patients. We do this by utilizing a machine learning
approach to select clinical input features that are more likely to predict
mortality. We then use these features to create a hybrid mortality prediction
model and compare our results to non-artificial intelligence models. For
simplicity, we limit our input features to patient comorbidities and features
derived from a well-known mortality measure, the Sequential Organ Failure
Assessment (SOFA).


  The purpose of this article is to deal with the multiple values and
uniqueness problem of meromorphic mappings from $\mathbb{C}^{m}$ into the
complex projective space $\mathbb{P}^{n}(\mathbb{C})$ sharing fixed and moving
hypersurfaces. We obtain several uniqueness theorems which improve and extend
some known results.


  It is known that most of the craters on the surface of the Moon were created
by the collision of minor bodies of the Solar System. Main Belt Asteroids,
which can approach the terrestrial planets as a consequence of different types
of resonance, are actually the main responsible for this phenomenon. Our aim is
to investigate the impact distributions on the lunar surface that low-energy
dynamics can provide. As a first approximation, we exploit the hyberbolic
invariant manifolds associated with the central invariant manifold around the
equilibrium point L_2 of the Earth - Moon system within the framework of the
Circular Restricted Three - Body Problem. Taking transit trajectories at
several energy levels, we look for orbits intersecting the surface of the Moon
and we attempt to define a relationship between longitude and latitude of
arrival and lunar craters density. Then, we add the gravitational effect of the
Sun by considering the Bicircular Restricted Four - Body Problem. As further
exploration, we assume an uniform density of impact on the lunar surface,
looking for the regions in the Earth - Moon neighbourhood these colliding
trajectories have to come from. It turns out that low-energy ejecta originated
from high-energy impacts are also responsible of the phenomenon we are
considering.


  Most real-world complex systems can be modelled by coupled networks with
multiple layers. How and to what extent the pattern of couplings between
network layers may influence the interlaced structure and function of coupled
networks are not clearly understood. Here we study the impact of correlated
inter-layer couplings on the network robustness of coupled networks using
percolation concept. We found that the positive correlated inter-layer coupling
enhaces network robustness in the sense that it lowers the percolation
threshold of the interlaced network than the negative correlated coupling case.
At the same time, however, positive inter-layer correlation leads to smaller
giant component size in the well-connected region, suggesting potential
disadvantage for network connectivity, as demonstrated also with some
real-world coupled network structures.


  The minimum mass ratio for tidal stability of a contact binary containing two
unevolved main-sequence stars is calculated to be $\qmin\simeq0.09$ in the case
of a mostly radiative primary, and is higher if an appreciable fraction of the
mass lies in a convective envelope. At least one observed system, AW UMa, has a
mass ratio just below this value ($q=0.075$), implying, if the system is
stable, that the primary must be slightly evolved and must have a very shallow
convective envelope. Contact binaries with mass ratios significantly below that
of AW UMa should not be observed, since they are tidally unstable and quickly
merge into a single, rapidly rotating object, on a timescale
$\sim10^3$--$10^4\,$yr.


  We consider two core algorithmic problems for probabilistic verification: the
maximal end-component decomposition and the almost-sure reachability set
computation for Markov decision processes (MDPs). For MDPs with treewidth $k$,
we present two improved static algorithms for both the problems that run in
time $O(n \cdot k^{2.38} \cdot 2^k)$ and $O(m \cdot \log n \cdot k)$,
respectively, where $n$ is the number of states and $m$ is the number of edges,
significantly improving the previous known $O(n\cdot k \cdot \sqrt{n\cdot k})$
bound for low treewidth. We also present decremental algorithms for both
problems for MDPs with constant treewidth that run in amortized logarithmic
time, which is a huge improvement over the previously known algorithms that
require amortized linear time.


  We study the problem of decision-theoretic online learning (DTOL). Motivated
by practical applications, we focus on DTOL when the number of actions is very
large. Previous algorithms for learning in this framework have a tunable
learning rate parameter, and a barrier to using online-learning in practical
applications is that it is not understood how to set this parameter optimally,
particularly when the number of actions is large.
  In this paper, we offer a clean solution by proposing a novel and completely
parameter-free algorithm for DTOL. We introduce a new notion of regret, which
is more natural for applications with a large number of actions. We show that
our algorithm achieves good performance with respect to this new notion of
regret; in addition, it also achieves performance close to that of the best
bounds achieved by previous algorithms with optimally-tuned parameters,
according to previous notions of regret.


  Intent-based networking (IBN) facilitates the representation of consumer
expectations in a declarative and domain-independent form. However, mapping
intents to service and resource models remains an open challenge. IBN requires
handling existing system data in a structured yet flexible structure way.
Knowledge graphs provide an efficient conceptual framework for constructing
contexts and organizing known information. We utilize knowledge graphs to
construct a knowledge-based for modeling of intents in the networking domain.
In addition, this work also proposes a knowledge-based intent modeling and
processing methodology, extending the standardized intent common model proposed
by TM Forum for next-generation cellular networks and services. The proposed
knowledge-based IBN approach is demonstrated for next-generation cellular
services, validating its potential.


  Transmission electron microscopy observations reveal the presence of complex
superstructures and remarkable phase separation in association with Na-ordering
phenomenon in y-Na0.5CoO2. Resistivity and magnetization measurements indicate
that three phase transitions at the temperatures of 25 K, 53 K and 90 K
respectively appear commonly in y-Na0.5CoO2 samples. Under a high pressure up
to 10 Kbar, the low-temperature transport properties show certain changes below
the charge order transition; under an applied magnetic field of 7 Tesla, phase
transitions at around 25 K and 53 K, proposed fundamentally in connection with
alternations of magnetic structure and charge ordering maintain almost
unchanged.


  The main goal of this document is to help the research community to
understand the basic concepts of software distribution: Free software, Open
source software, licenses. This document also includes a procedure for research
software and data dissemination.


  We study the problem of sensor placement in environments in which
localization is a necessity, such as ad-hoc wireless sensor networks that allow
the placement of a few anchors that know their location or sensor arrays that
are tracking a target. In most of these situations, the quality of localization
depends on the relative angle between the target and the pair of sensors
observing it. In this paper, we consider placing a small number of sensors
which ensure good angular $\alpha$-coverage: given $\alpha$ in $[0,\pi/2]$, for
each target location $t$, there must be at least two sensors $s_1$ and $s_2$
such that the $\angle(s_1 t s_2)$ is in the interval $[\alpha, \pi-\alpha]$.
One of the main difficulties encountered in such problems is that since the
constraints depend on at least two sensors, building a solution must account
for the inherent dependency between selected sensors, a feature that generic
Set Cover techniques do not account for. We introduce a general framework that
guarantees an angular coverage that is arbitrarily close to $\alpha$ for any
$\alpha <= \pi/3$ and apply it to a variety of problems to get bi-criteria
approximations. When the angular coverage is required to be at least a constant
fraction of $\alpha$, we obtain results that are strictly better than what
standard geometric Set Cover methods give. When the angular coverage is
required to be at least $(1-1/\delta)\cdot\alpha$, we obtain a
$\mathcal{O}(\log \delta)$- approximation for sensor placement with
$\alpha$-coverage on the plane. In the presence of additional distance or
visibility constraints, the framework gives a $\mathcal{O}(\log\delta\cdot\log
k_{OPT})$-approximation, where $k_{OPT}$ is the size of the optimal solution.
We also use our framework to give a $\mathcal{O}(\log \delta)$-approximation
that ensures $(1-1/\delta)\cdot \alpha$-coverage and covers every target within
distance $3R$.


  We prove that phase transition occurs in the dilute ferromagnetic
nearest-neighbour $q$-state clock model in $\mathbb{Z}^d$, for every $q\geq 2$
and $d\geq 2$. This follows from the fact that the Edwards-Sokal random-cluster
representation of the clock model stochastically dominates a supercritical
Bernoulli bond percolation probability, a technique that has been applied to
show phase transition for the low-temperature Potts model. The domination
involves a combinatorial lemma which is one of the main points of this article.


  The presence of source code that is no longer needed is a handicap to project
maintainability. The larger and longer-lived the project, the higher the
chances of accumulating dead code in its different forms.
  Manually detecting unused code is time-consuming, tedious, error-prone, and
requires a great level of deep knowledge about the codebase. In this paper, we
examine the kinds of dead code (specifically, oxbow code) that can appear in
Erlang projects, and formulate rules to identify them with high accuracy.
  We also present an open-source static analyzer that implements these rules,
allowing for the automatic detection and confident removal of oxbow code in
Erlang codebases, actively contributing to increasing their quality and
maintainability.


  We give criteria for the existence of bifurcations of symmetric periodic
orbits in reversible Hamiltonian systems in terms of local equivariant
Lagrangian Rabinowitz Floer homology. As an example, we consider the family of
the direct circular orbits in the rotating Kepler problem and observe
bifurcations of torus-type orbits. Our setup is motivated by numerical work of
H\'enon on Hill's lunar problem.


  Shariat et al previously investigated the possibility of predicting from
clinical data (including Gleason grade and stage) and preoperative biomarkers,
which of any pair of patients would suffer recurrence of prostate cancer first.
We wished to establish the extent to which predictions of time of relapse from
such a model could be improved upon using Bayesian methods.
  The same dataset was reanalysed with a Bayesian skew-Student mixture model.
Predictions were made of which of any pair of patients would relapse first and
of the time of relapse. The benefit of using these biomarkers relative to
predictions made without them was measured by the apparent Shannon information,
using as prior an exponential attrition model of relapse time independent of
input variables.
  Using half the dataset for training and the other half for testing,
predictions of relapse time from the strict Cox model gave $-\infty$ nepers of
apparent Shannon information (it predicts that relapse can only occur at times
when patients in the training set relapsed). Deliberately smoothed predictions
from the Cox model gave -0.001 (-0.131 to +0.120) nepers, while the Bayesian
model gave +0.109 (+0.021 to +0.192) nepers (mean, 2.5 to 97.5 centiles), being
positive with posterior probability 0.993 and beating the blurred Cox model
with posterior probability 0.927.
  These predictions from the Bayesian model thus outperform those of the Cox
model, but the overall yield of predictive information leaves scope for
improvement of the range of biomarkers in use. The Bayesian model presented
here is the first such model for prostate cancer to consider the variation of
relapse hazard with biomarker concentrations to be smooth, as is intuitive. It
is also the first to be shown to provide more apparent Shannon information than
the Cox model or to be shown to provide positive apparent information relative
to an exponential prior.


  We study the gravitational collapse in modified gravitational theories. In
particular, we analyze a general $f(R)$ model with uniformly collapsing cloud
of self-gravitating dust particles. This analysis shares analogies with the
formation of large-scale structures in the early Universe and with the
formation of stars in a molecular cloud experiencing gravitational collapse. In
the same way, this investigation can be used as a first approximation to the
modification that stellar objects can suffer in these modified theories of
gravity. We study concrete examples, and find that the analysis of
gravitational collapse is an important tool to constrain models that present
late-time cosmological acceleration.


  In many Hamiltonian systems, propagation of steadily travelling solitons or
kinks is prohibited because of resonances with linear excitations. We show that
Hamiltonian systems with resonances may admit an infinite number of travelling
solitons or kinks if the closest to the real axis singularities in the complex
upper half-plane of limiting asymptotic solution are of the form
$z_\pm=\pm\alpha+i\beta$, $\alpha\ne 0$. This quite a general statement is
illustrated by examples of the fifth-order Korteweg--de Vries-type equation,
the discrete cubic-quintic Klein--Gordon equation, and the nonlocal double
sine--Gordon equations.


  The temperature and frequency dependences of the optical conductivity of Co
and Ni-doped BaFe2As2 are analyzed and the electron-boson spectral density
a2F(w) extracted using Eliashberg's formalism. The characteristic energy of a
large peak in the spectrum around 10 meV coincide with the resonance peak in
the spin excitation spectra, giving compelling evidence that in iron-based
superconductors spin fluctuations strongly couple to the charge carriers and
mediate superconductivity. In addition the spectrum is found to evolve with
temperature towards a less structured background at higher energies as in the
spin susceptibility.


  Federated continual learning (FCL) has received increasing attention due to
its potential in handling real-world streaming data, characterized by evolving
data distributions and varying client classes over time. The constraints of
storage limitations and privacy concerns confine local models to exclusively
access the present data within each learning cycle. Consequently, this
restriction induces performance degradation in model training on previous data,
termed "catastrophic forgetting". However, existing FCL approaches need to
identify or know changes in data distribution, which is difficult in the real
world. To release these limitations, this paper directs attention to a broader
continuous framework. Within this framework, we introduce Federated Bayesian
Neural Network (FedBNN), a versatile and efficacious framework employing a
variational Bayesian neural network across all clients. Our method continually
integrates knowledge from local and historical data distributions into a single
model, adeptly learning from new data distributions while retaining performance
on historical distributions. We rigorously evaluate FedBNN's performance
against prevalent methods in federated learning and continual learning using
various metrics. Experimental analyses across diverse datasets demonstrate that
FedBNN achieves state-of-the-art results in mitigating forgetting.


  A simple procedure is developed to simultaneously eliminate multiple
scattering contributions from two reflection electron energy loss spectra
(REELS) measured at different energies or for different experimental
geometrical configurations. The procedure provides the differential inverse
inelastic mean free path (DIIMFP) and the differential surface excitation
probability (DSEP). The only required input parameters are the differential
cross section for elastic scattering and a reasonable estimate for the
inelastic mean free path (IMFP). No prior information on surface excitations is
required for the deconvolution. The retrieved DIIMFP and DSEP can be used to
determine the dielectric function of a solid by fitting the DSEP and DIIMFP to
theory. Eventually, the optical data can be used to calculate the (differential
and total) inelastic mean free path and the surface excitation probability. The
procedure is applied to Fe, Co and Ni and the retrieved optical data as well as
the inelastic mean free paths and surface excitation parameters derived from it
are compared to values reported earlier in the literature. In all cases,
reasonable agreement is found between the present data and the earlier results,
supporting the validity of the procedure.


  In mathematics, many notations have been invented for the concise
representation of mathematical formulae. Tensor index notation is one of such
notations and has been playing a crucial role in describing formulae in
mathematical physics. This paper shows a programming language that can deal
with symbolical tensor indices by introducing a set of tensor index rules that
is compatible with two types of parameters, i.e., scalar and tensor parameters.
When a tensor parameter obtains a tensor as an argument, the function treats
the tensor argument as a whole. In contrast, when a scalar parameter obtains a
tensor as an argument, the function is applied to each component of the tensor.
On a language with scalar and tensor parameters, we can design a set of index
reduction rules that allows users to use tensor index notation for arbitrary
user-defined functions without requiring additional description. Furthermore,
we can also design index completion rules that allow users to define the
operators concisely for differential forms such as the wedge product, exterior
derivative, and Hodge star operator. In our proposal, all these tensor
operators are user-defined functions and can be passed as arguments of
high-order functions.


  A theory of the dynamical conductance of mesoscopic conductors is presented.
It is applied to mesoscopic capacitors, resonant double barriers, ballistic
wires, metallic diffusive wires, and to the Corbino disk and the Hall bar in
quantizing magnetic fields. Central to this approach is a discussion of the
charge and potential distribution in mesoscopic conductors. It is necessary to
take into account the implications of the long-range Coulomb interaction in
order to obtain a charge and current conserving theory. We emphasize the
low-frequency response. This has the advantage that the approach is of
considerable generality. The theory can be used to discuss the self-consistency
of the dc-conductance formula. The theory can also be applied to discuss the
rectifying (nonlinear) behavior of mesoscopic conductors.


  The Nancy Grace Roman Space Telescope (RST), with its field of view and high
sensitivity will make surveys of cosmological large-scale structure possible at
high redshifts. We investigate the possibility of detecting Baryon Acoustic
Oscillations (BAO) at redshifts $z>7.6$ for use as a standard ruler. We use
data from the hydrodynamic simulation \textsc{BlueTides} in conjunction with
the gigaparsec-scale Outer Rim simulation and a model for patchy reionization
to create mock RST High Latitude Survey grism data for Lyman-alpha emission
line selected galaxies at redshifts $z=7.4$ to $z=10$, covering 2280 square
degrees. We measure the monopoles of galaxies in the mock catalogues and fit
the BAO features. We find that for a line flux of $L = 7\times 10^{-17} \ {\rm
erg/s/cm}^{2}$, the $5 \sigma$ detection limit for the current design, the BAO
feature is partially detectable (measured in three out of four survey quadrants
analysed independently). The resulting root mean square error on the angular
diameter distance to $z=7.7$ is 7.9$\%$. If we improve the detection
sensitivity by a factor of two (i.e. $L = 3.5\times 10^{-17} \ {\rm
erg/s/cm}^{2}$), the distance error reduces to $1.4\%$. We caution that many
more factors are yet to be modelled, including dust obscuration, the damping
wing due to the intergalactic medium, and low redshift interlopers. If these
issues do not strongly affect the results, or different observational
techniques (such as use of multiple lines) can mitigate them, RST or similar
instruments may be able to constrain the angular diameter distance to the high
redshift Universe.


  Object recognition in the video sequence or images is one of the sub-field of
computer vision. Moving object recognition from a video sequence is an
appealing topic with applications in various areas such as airport safety,
intrusion surveillance, video monitoring, intelligent highway, etc. Moving
object recognition is the most challenging task in intelligent video
surveillance system. In this regard, many techniques have been proposed based
on different methods. Despite of its importance, moving object recognition in
complex environments is still far from being completely solved for low
resolution videos, foggy videos, and also dim video sequences. All in all,
these make it necessary to develop exceedingly robust techniques. This paper
introduces multiple moving object recognition in the video sequence based on
LoG Gabor-PCA approach and Angle based distance Similarity measures techniques
used to recognize the object as a human, vehicle etc. Number of experiments are
conducted for indoor and outdoor video sequences of standard datasets and also
our own collection of video sequences comprising of partial night vision video
sequences. Experimental results show that our proposed approach achieves an
excellent recognition rate. Results obtained are satisfactory and competent.


  We present insightful results of a survey on the adoption of generative
artificial intelligence (GenAI) by university teachers in their teaching
activities. The transformation of education by GenAI, particularly large
language models (LLMs), has been presenting both opportunities and challenges,
including cheating by students. We prepared the online survey according to best
practices and the questions were created by the authors, who have pedagogy
experience. The survey contained 12 questions and a pilot study was first
conducted. The survey was then sent to all teachers in multiple departments
across different campuses of the university of interest in Sweden: Lule{\aa}
University of Technology. The survey was available in both Swedish and English.
The results show that 35 teachers (more than half) use GenAI out of 67
respondents. Preparation is the teaching activity with the most frequency that
GenAI is used for and ChatGPT is the most commonly used GenAI. 59% say it has
impacted their teaching, however, 55% say there should be legislation around
the use of GenAI, especially as inaccuracies and cheating are the biggest
concerns.


  We show that every $\alpha$-approximate minimum cut in a connected graph is
the unique minimum $(S,T)$-terminal cut for some subsets $S$ and $T$ of
vertices each of size at most $\lfloor2\alpha\rfloor+1$. This leads to an
alternative proof that the number of $\alpha$-approximate minimum cuts in a
$n$-vertex connected graph is $n^{O(\alpha)}$ and they can all be enumerated in
deterministic polynomial time for constant $\alpha$.


  We present light curves for 149 sources monitored with the Green Bank
Interferometer. The light curves are at two radio frequencies (approximately
2.5 and 8.2 GHz) and range from 3 to 15 yrs in length, covering the interval
1979--1996, and have a typical sampling of one flux density measurement every 2
days. We have used these light curves to conduct various variability analysis
(rms flux density variations and autoregressive, integrated, moving average
modeling) of these sources. We find suggestive, though not unambiguous
evidence, that these sources have a common, broadband mechanism for intrinsic
variations, in agreement with previous studies of a subset of these source. We
also find that the sources generally display a short-term variability (~ 10 d)
that arises from radio-wave scattering in an extended medium. These conclusions
extend those of Fiedler et al. (1987) who used a sub-sample of these data. The
primary motivation for this monitoring program was the identification of
extreme scattering events. In an effort to identify ESEs in a systematic
manner, we have taken the wavelet transform of the light curves. We find 15
events in the light curves of 12 sources that we classify as probable ESEs.
However, we also find that five ESEs previously identified from these data do
not survive our wavelet selection criteria. Future identification of ESEs will
probably continue to rely on both visual and systematic methods. Instructions
for obtaining the data are also presented.


  This paper investigates GRB 050802, one of the best examples of a it Swift
gamma-ray burst afterglow that shows a break in the X-ray lightcurve, while the
optical counterpart decays as a single power-law. This burst has an optically
bright afterglow of 16.5 magnitude, detected throughout the 170-650nm spectral
range of the UVOT on-board Swift. Observations began with the XRT and UVOT
telescopes 286s after the initial trigger and continued for 1.2 x 10^6s. The
X-ray lightcurve consists of three power-law segments: a rise until 420s,
followed by a slow decay with alpha_2 = 0.63 +/- 0.03 until 5000s, after which,
the lightcurve decays faster with a slope of alpha_3 = 1.59 +/- 0.03. The
optical lightcurve decays as a single power-law with alpha_O = 0.82 +/- 0.03
throughout the observation. The X-ray data on their own are consistent with the
break at 5000s being due to the end of energy injection. Modelling the optical
to X-ray spectral energy distribution, we find that the optical afterglow can
not be produced by the same component as the X-ray emission at late times,
ruling out a single component afterglow. We therefore considered two-component
jet models and find that the X-ray and optical emission is best reproduced by a
model in which both components are energy injected for the duration of the
observed afterglow and the X-ray break at 5000s is due to a jet break in the
narrow component. This bright, well-observed burst is likely a guide for
interpreting the surprising finding of Swift that bursts seldom display
achromatic jet breaks.


  This contribution presents the optical spectral properties of FIR-luminous
galaxies, whose distinctive feature is often the simultaneous presence in the
spectra of a strong $\rm H\delta$ line in absorption and of emission lines
(e(a) spectra). A discrepancy between the star formation rate estimated from
the FIR luminosity and that derived from the $\rm H\alpha$ luminosity persists
even after having corrected the $\rm H\alpha$ flux for dust according to the
observed Balmer decrement. It is shown that the e(a) spectrum can be reproduced
assuming a current starburst and dust extinction affecting the youngest stellar
populations much more than the older stars.


  Much of text-to-speech research relies on human evaluation, which incurs
heavy costs and slows down the development process. The problem is particularly
acute in heavily multilingual applications, where recruiting and polling judges
can take weeks. We introduce SQuId (Speech Quality Identification), a
multilingual naturalness prediction model trained on over a million ratings and
tested in 65 locales-the largest effort of this type to date. The main insight
is that training one model on many locales consistently outperforms mono-locale
baselines. We present our task, the model, and show that it outperforms a
competitive baseline based on w2v-BERT and VoiceMOS by 50.0%. We then
demonstrate the effectiveness of cross-locale transfer during fine-tuning and
highlight its effect on zero-shot locales, i.e., locales for which there is no
fine-tuning data. Through a series of analyses, we highlight the role of
non-linguistic effects such as sound artifacts in cross-locale transfer.
Finally, we present the effect of our design decision, e.g., model size,
pre-training diversity, and language rebalancing with several ablation
experiments.


  We provide an algorithm for computing semi-Fourier sequences for expressions
constructed from arithmetic operations, exponentiations and integrations. The
semi-Fourier sequence is a relaxed version of Fourier sequence for polynomials
(expressions made of additions and multiplications).


  We have calculated the single-particle density of states (DOS) for a model of
spinfull Tomonaga-Luttinger liquid with frequency dependent parameter $K_c$ of
the charge sector (and $K_s=1$ of spin sector).Such frequency dependence may
originate from interactions withoptical phonons.DOS exhibits a power-low
suppressed asymptotic behaviour near $\omega=0$ with exponent largerthan one,
in agreement with previous results. For larger frequencies, but stillnot far
away from the origin,DOS exhibits a peak, the position of which scales
as$[K(0)+K^{-1}(0)]\omega_0$, where $\omega_0$ is the characteristic
phononfrequency. The position of the peak decreases as the phonon'sfrequency
becomes smaller. It is interesting that the peak of DOS does not coincide with
the characteristic phonon frequency.


  In the present paper a numerical method is developed to approximate the
solution of two-dimensional NLS equation in the presence of a singular
potential. The method leads to Lyapunov-Syslvester algebraic operators that are
shown to be invertible using original topological and differential calculus
issued methods. The numerical scheme is proved to be consistent, convergent and
stable using the based on Lyapunov criterion, lax equivalence theorem and the
properties of the Lyapunov-Syslvester operators.


  The Relativistic Heavy Ion Collider (RHIC) has brought the study of spin
effects in hadronic collisions to a new energy regime. In conjunction with
other experiments at facilities around the world, much can be learned from the
high-energy polarized proton collisions RHIC provides, allowing the collider to
serve as a powerful tool to continue to understand the rich subtleties and
surprises of spin effects in QCD, some of which were originally discovered more
than three decades ago.


  We investigate the $o\tau$-continuous/bounded/compact and Lebesgue operators
from vector lattices to topological vector spaces; the KB operators between
locally solid lattices and topological vector spaces; and the Levi operators
from locally solid lattices to vector lattices. The main idea of operator
versions of notions related to vector lattices lies in redistributing
topological and order properties of a topological vector lattice between the
domain and range of an operator under investigation. Domination properties for
these classes of operators are studied.


  We study the efficiency of quantum tomographic reconstruction where the
system under investigation (quantum target) is indirectly monitored by looking
at the state of a quantum probe that has been scattered off the target. In
particular we focus on the state tomography of a qubit through a
one-dimensional scattering of a probe qubit, with a Heisenberg-type
interaction. Via direct evaluation of the associated quantum Cram\'{e}r-Rao
bounds, we compare the accuracy efficiency that one can get by adopting
entanglement-assisted strategies with that achievable when entanglement
resources are not available. Even though sub-shot noise accuracy levels are not
attainable, we show that quantum correlations play a significant role in the
estimation. A comparison with the accuracy levels obtainable by direct
estimation (not through a probe) of the quantum target is also performed.


  In this article we investigate the behavior of multi-matrix unitary invariant
models under a potential $V_\beta=\beta U+W$ when the inverse temperature
$\beta$ becomes very large. We first prove, under mild hypothesis on the
functionals $U,W$ that as soon at these potentials are "confining" at infinity,
the sequence of spectral distribution of the matrices are tight when the
dimension goes to infinity. Their limit points are solutions of
Dyson-Schwinger's equations. Next we investigate a few specific models, most
importantly the "strong single variable model" where $U$ is a sum of potentials
in a single matrix and the "strong commutator model" where $U = -[X,Y]^2$.


  Using a five-dimensional warped model including a scalar potential with an
exponential behavior in the infrared, and strong back-reaction over the metric,
we study the electroweak phase transition, and explore parameter regions that
were previously inaccessible. The model exhibits gravitational waves and
predicts a stochastic gravitational wave background observable, both at the
Laser Interferometer Space Antenna and at the Einstein Telescope. Moreover,
concerning the collider signatures predictions, the radion evades current
constraints but may show up in future LHC runs.


  What is quantum geometry? This question is becoming a popular leitmotiv in
theoretical physics and in mathematics. Conformal field theory may catch a
glimpse of the right answer. We review global aspects of the geometry of
conformal fields, such as duality and mirror symmetry, and interpret them
within Connes' non-commutative geometry. Extended version of lectures given by
the 2nd author at the Mathematical Quantum Theory Conference, Vancouver,
Canada, August 4 to 8, 1993


  Finding appropriate experts is essential in Community Question Answering
(CQA) platforms as it enables the effective routing of questions to potential
users who can provide relevant answers. The key is to personalized learning
expert representations based on their historical answered questions, and
accurately matching them with target questions. There have been some
preliminary works exploring the usability of PLMs in expert finding, such as
pre-training expert or question representations. However, these models usually
learn pure text representations of experts from histories, disregarding
personalized and fine-grained expert modeling. For alleviating this, we present
a personalized pre-training and fine-tuning paradigm, which could effectively
learn expert interest and expertise simultaneously. Specifically, in our
pre-training framework, we integrate historical answered questions of one
expert with one target question, and regard it as a candidate aware
expert-level input unit. Then, we fuse expert IDs into the pre-training for
guiding the model to model personalized expert representations, which can help
capture the unique characteristics and expertise of each individual expert.
Additionally, in our pre-training task, we design: 1) a question-level masked
language model task to learn the relatedness between histories, enabling the
modeling of question-level expert interest; 2) a vote-oriented task to capture
question-level expert expertise by predicting the vote score the expert would
receive. Through our pre-training framework and tasks, our approach could
holistically learn expert representations including interests and expertise.
Our method has been extensively evaluated on six real-world CQA datasets, and
the experimental results consistently demonstrate the superiority of our
approach over competitive baseline methods.


  Deep neural networks are a promising solution for applications that solve
problems based on learning data sets. DNN accelerators solve the processing
bottleneck as a domain-specific processor. Like other hardware solutions, there
must be exact compatibility between the accelerator and other software
components, especially the compiler. This paper presents a LOCAL (Low
Complexity mapping Algorithm) that is favorable to use at the compiler level to
perform mapping operations in one pass with low computation time and energy
consumption. We first introduce a formal definition of the design space in
order to define the problem's scope, and then we describe the concept of the
LOCAL algorithm. The simulation results show 2x to 38x improvements in
execution time with lower energy consumption compared to previous proposed
dataflow mechanisms.


  We study the linear large $n$ behavior of the average number of distinct
sites $S(n)$ visited by a random walker after $n$ steps on a large random
graph. An expression for the graph topology dependent prefactor $B$ in $S(n) =
Bn$ is proposed. We use generating function techniques to relate this prefactor
to the graph adjacency matrix and then devise message-passing equations to
calculate its value. Numerical simulations are performed to evaluate the
agreement between the message passing predictions and random walk simulations
on random graphs. Scaling with system size and average graph connectivity are
also analysed.


  We study the cross-stitch flat band lattice with a $\mathcal{PT}$-symmetric
on-site potential and uncover mobility edges with exact solutions. Furthermore,
we study the relationship between the $\mathcal{PT}$ symmetry broken point and
the localization-delocalization transition point, and verify that mobility
edges in this non-Hermitian model is available to signal the $\mathcal{PT}$
symmetry breaking.


  Let $G$ be a graph, and let $\lambda(G)$ denote the smallest eigenvalue of
$G$. First, we provide an upper bound for $\lambda(G)$ based on induced
bipartite subgraphs of $G$. Consequently, we extract two other upper bounds,
one relying on the average degrees of induced bipartite subgraphs and a more
explicit one in terms of the chromatic number and the independence number of
$G$. In particular, motivated by our bounds, we introduce two graph invariants
that are of interest on their own. Finally, special attention goes to the
investigation of the sharpness of our bounds in various classes of graphs as
well as the comparison with an existing well-known upper bound.


  For a class of idealized chaotic systems (hyperbolic systems) correlations
decay exponentially in time. This result is asymptotic and rigorous. The decay
rate is related to the Ruelle-Pollicott resonances. Nearly all chaotic model
systems, that are studied by physicists, are not hyperbolic. For many such
systems it is known that exponential decay takes place for a long time. It may
not be asymptotic, but it may persist for a very long time, longer than any
time of experimental relevance. In this review a heuristic method for
calculation of this exponential decay of correlations in time is presented. It
can be applied to model systems, where there are no rigorous results concerning
this exponential decay. It was tested for several realistic systems (kicked
rotor and kicked top) in addition to idealized systems (baker map and perturbed
cat map). The method consists of truncation of the evolution operator
(Frobenius-Perron operator), and performing all calculations with the resulting
finite dimensional matrix. This finite dimensional approximation can be
considered as coarse graining, and is equivalent to the effect of noise. The
exponential decay rate of the chaotic system is obtained when the
dimensionality of the approximate evolution operator is taken to infinity,
resulting in infinitely fine resolution, that is equivalent to vanishing noise.
The corresponding Ruelle-Pollicott resonances can be calculated for many
systems that are beyond the validity of the Ruelle-Pollicott theorem.


  Code comment generation techniques aim to generate natural language
descriptions for source code. There are two orthogonal approaches for this
task, i.e., information retrieval (IR) based and neural-based methods. Recent
studies have focused on combining their strengths by feeding the input code and
its similar code snippets retrieved by the IR-based approach to the
neural-based approach, which can enhance the neural-based approach's ability to
output low-frequency words and further improve the performance. However,
despite the tremendous progress, our pilot study reveals that the current
combination is not generalizable and can lead to performance degradation. In
this paper, we propose a straightforward but effective approach to tackle the
issue of existing combinations of these two comment generation approaches.
Instead of binding IR- and neural-based approaches statically, we combine them
in a dynamic manner. Specifically, given an input code snippet, we first use an
IR-based technique to retrieve a similar code snippet from the corpus. Then we
use a Cross-Encoder based classifier to decide the comment generation method to
be used dynamically, i.e., if the retrieved similar code snippet is a true
positive (i.e., is semantically similar to the input), we directly use the
IR-based technique. Otherwise, we pass the input to the neural-based model to
generate the comment. We evaluate our approach on a large-scale dataset of Java
projects. Experiment results show that our approach can achieve 25.45 BLEU
score, which improves the state-of-the-art IR-based approach, neural-based
approach, and their combination by 41%, 26%, and 7%, respectively. We propose a
straightforward but effective dynamic combination of IR-based and neural-based
comment generation, which outperforms state-of-the-art approaches by a
substantial margin.


  The ratios of the baryon electric ($G_E$) to magnetic ($G_M$) form factors,
$G_E/G_M$, can provide us with important information on the structure of
baryons in vacuum as demonstrated in recent studies for the proton and neutron
systems. It has been argued that the corresponding in-medium ratios,
$G_E^\ast/G_M^\ast$, can also provide useful information on the electromagnetic
structure of baryons in a nuclear medium. Although the ratios may not be
measured directly in experiments except for the proton at present, the double
ratios $(G_E^\ast/G_M^\ast)/(G_E/G_M)$ can be directly related to the
polarization-transfer measurements of bound baryons. In the present work we
estimate, for the first time, the double ratios $(G_E^\ast/G_M^\ast)/(G_E/G_M)$
for all the members of octet baryons in symmetric nuclear matter in the range
$Q^2=0$--3 GeV$^2$, where $Q^2=- q^2$ with $q$ being the four-momentum
transfer.


  We present an overview of the GW SAID group effort to analyze on new pion
photoproduction on both proton- and neutron-targets. The main database
contribution came from the recent CLAS and MAMI unpolarized and polarized
measurements. The differential cross section for the processes gamma n --> pi-
p was extracted from new measurements accounting for Fermi motion effects in
the impulse approximation (IA) as well as NN and piN effects beyond the IA. The
EM coupling results are compared to other recent studies.


  We present the results obtained from timing and spectral studies of Be/X-ray
binary pulsar EXO 2030+375 using observations with the Large Area Xenon
Proportional Counters and Soft X-ray Telescope of AstroSat, at various phases
of its Type-I outbursts in 2016, 2018, and 2020. The pulsar was faint during
these observations as compared to earlier observations with other
observatories. At the lowest luminosity of 2.5$\times$10$^{35}$ erg s$^{-1}$ in
0.5--30 keV energy range, $\approx$41.3 s pulsations were clearly detected in
the X-ray light curves. This finding establishes the first firm detection of
pulsations in EXO 2030+375 at an extremely low mass accretion rate to date. The
shape of the pulse profiles is complex due to the presence of several narrow
dips. Though pulsations were detected up to $\sim$80 keV when the source was
brighter, pulsations were limited up to $\sim$25 keV during the third AstroSat
observation at lowest source luminosity. A search for quasi-periodic
oscillations in 2$\times$10$^{-4}$ Hz to 10 Hz yielded a negative result.
Spectral analysis of the AstroSat data showed that the spectrum of the pulsar
was steep with a power-law index of $\sim$2. The values of photon-indices at
observed low luminosities follow the known pattern in sub-critical regime of
the pulsar.


  This work is devoted to the study of Einstein equations with a special shape
of the energy-momentum tensor. Our results continue Stepanov's classification
of Riemannian manifolds according to special properties of the energy-momentum
tensor to K\"ahler manifolds. We show that in this case the number of classes
reduces.


  In this comment on "How many principles does it take to change a light bulb
... into a laser?" by Howard M. Wiseman, Physica Scripta 91 (3), 033001 (2016)
I am analyzing this extremely nicely formulated question in the framework of
the Hanbury-Brown & Twiss experiment thus celebrating also its 60th
anniversary. I am giving a straightforward, concise and unique answer on this
question, emphasizing that the intensity correlations or the photon statistics
make the difference between a light bulb and a laser.


  Taking into account the effect of electron screening on the electron energy
and electron capture threshold energy, by using the method of Shell-Model Monte
Carlo and Random Phase Approximation theory, we investigate the strong electron
screening capture rates of chromium isotopes according to the linear response
theory screening model. The strong screening rates can decrease by about
40.43\% (e.g., for $^{60}$Cr at $T_9=3.44,Y_e=0.43$). Our conclusions may be
helpful to the researches of supernova explosion and numerical simulation.


  We show that the five-dimensional Maxwell theory with the Chern-Simons term
is tachyonic in the presence of a constant electric field. When coupled to
gravity, a sufficiently large Chern-Simons coupling causes instability of the
Reissner-Nordstrom black holes in anti-de Sitter space. The instability happens
only at non-vanishing momenta, suggesting a spatially modulated phase in the
holographically dual quantum field theory in 3+1 dimensions, with spontaneous
current generation in a helical configuration. The three-charge extremal black
hole in the type IIB superstring theory on AdS_5 x S^5 barely satisfies the
stability condition.


  We investigate a model, inspired by (Johnston et al., Sci. Rep., 7:42134,
2017), to describe the movement of a biological population which consists of
isolated and grouped organisms. We introduce biases in the movements and then
obtain a scalar reaction-diffusion equation which includes a convective term as
a consequence of the biases. We focus on the case the diffusivity makes the
parabolic equation of forward-backward-forward type and the reaction term
models a strong Allee effect, with the Allee parameter lying between the two
internal zeros of the diffusion. In such a case, the unbiased equation (i.e.,
without convection) possesses no smooth traveling-wave solutions; on the
contrary, in the presence of convection, we show that traveling-wave solutions
do exist for some significant choices of the parameters. We also study the sign
of their speeds, which provides information on the long term behavior of the
population, namely, its survival or extinction.


  Strain-induced pseudo magnetic fields offer the possibility of realizing zero
magnetic field Quantum Hall effect in graphene, possibly up to room
temperature, representing a promising avenue for lossless charge transport
applications. Strain engineering on graphene has been achieved via random
nanobubbles or artificial nanostructures on the substrate, but the highly
localized and non-uniform pseudomagnetic fields can make spectroscopic probes
of electronic structure difficult. Heterostructure engineering offers an
alternative approach: By stacking graphene on top of another van der Waals
material with large lattice mismatch at a desired twist angle, it is possible
to generate large strain-induced pseudo magnetic fields uniformly over the
entire heterostructure. Here, we report using nano-angle resolved photoemission
spectroscopy (nano-ARPES) to probe the electronic bandstructure of a
graphene/black phosphorus heterostructure (G/BP). By directly measuring the
iso-energy contours of graphene and black phosphorus we determine a twist angle
of 20-degrees in our heterostructure. High-resolution nano-ARPES of the
graphene bands near the Fermi level reveals the emergence of flat bands located
within the Dirac cone. The spacing of the flat bands is consistent with Landau
level formation in graphene, and corresponds to a pseudo-field of 11.36 T. Our
work provides a new way to study quantum Hall phases induced by strain in 2D
materials and heterostructures.


  In the Internet of Things (IoT) devices are exposed to various kinds of
attacks when connected to the Internet. An attack detection mechanism that
understands the limitations of these severely resource-constrained devices is
necessary. This is important since current approaches are either customized for
wireless networks or for the conventional Internet with heavy data
transmission. Also, the detection mechanism need not always be as
sophisticated. Simply signaling that an attack is taking place may be enough in
some situations, for example in NIDS using anomaly detection. In graph
networks, central nodes are the nodes that bear the most influence in the
network. The purpose of this research is to explore experimentally the
relationship between the behavior of central nodes and anomaly detection when
an attack spreads through a network. As a result, we propose a novel anomaly
detection approach using this unique methodology which has been unexplored so
far in communication networks. Also, in the experiment, we identify presence of
an attack originating and propagating throughout a network of IoT using our
methodology.


  We derive a power series formula for the $p$-adic regulator on the higher
dimensional algebraic K-groups of number fields. This formula is designed to be
well suited to computer calculations and to reduction modulo powers of $p$. In
addition we describe a series of regulator questions concerning higher
dimensional K-theoretic analogues of conjectures of Gross and Serre.


  It is shown that measured heat capacity depends on the rate of the
temperature variation and on the initial state of a system. It is calculated
here in the framework of two-level model. Fenomenological approach gives the
same results.


  Bulk samples of $(Sn_{1-x}Sb_{x}O_{2-\delta})$ with x = 0.00, 0.10, 0.20,
0.30 are synthesized by solid-state reaction route. Samples were characterized
by X-ray powder diffraction (XRD), scanning electron microscopy (SEM),
transmission electron microscopy (TEM) and UV-Vis spectroscopy. The x-ray
diffraction patterns indicate that the gross structure/phase of $(Sn_{1-x}$
$Sb_{x}O_{2-\delta})$ do not change with the substitution of antimony (Sb) up
to x = 0.30. The surface morphological examination with SEM revealed the fact
that the grain size in the antimony doped sample is larger than that of undoped
one and hence pores/voids between the grains increase with Sb concentration up
to 0.30. TEM image of undoped sample indicates that the $SnO_{2}$ grains have
diameters ranging from 25 to 120 nm and most grains are in cubic or spherical
shape. As antimony content increases, the nanocubes/spheres are converted into
microcubes/spheres. The reflectance of $Sn_{1-x}Sb_{x}O_{2-\delta}$ samples
increases whereas absorbance of these samples decreases with the increased
concentration of antimony (Sb) for the wavelength range 360 - 800 nm. The
energy bandgap of Sb doped - $SnO_{2}$ samples were obtained from optical
absorption spectra by UV-Vis absorption spectroscopy. Upon increasing the Sb
concentration the bandgap of the samples was found to increase from 3.367 eV to
3.558 eV.


  Thompson sampling has become a ubiquitous approach to online decision
problems with bandit feedback. The key algorithmic task for Thompson sampling
is drawing a sample from the posterior of the optimal action. We propose an
alternative arm selection rule we dub TS-UCB, that requires negligible
additional computational effort but provides significant performance
improvements relative to Thompson sampling. At each step, TS-UCB computes a
score for each arm using two ingredients: posterior sample(s) and upper
confidence bounds. TS-UCB can be used in any setting where these two quantities
are available, and it is flexible in the number of posterior samples it takes
as input. TS-UCB achieves materially lower regret on a comprehensive suite of
synthetic and real-world datasets, including a personalized article
recommendation dataset from Yahoo! and a suite of benchmark datasets from a
deep bandit suite proposed in Riquelme et al. (2018). Finally, from a
theoretical perspective, we establish optimal regret guarantees for TS-UCB for
both the K-armed and linear bandit models.


  Despite the widespread use of zeolites in chemical industry, their formation
process is not fully understood due to the complex and heterogeneous structure
of traditional synthesis media. Hydrated silicate ionic liquids (HSILs) have
been proposed as an alternative. They are truly homogeneous and transparent
mixtures with low viscosity, facilitating experimental characterization.
Interestingly, their homogeneous nature and simple speciation brings realistic
molecular models of a zeolite growth liquid within reach for the first time. In
this work, a simple molecular model is developed that gives insight into the
crucial role of the alkali cations (sodium, potassium, rubidium and cesium).
Thereby molecular dynamics simulations are combined with experimental
measurements to demonstrate that the HSIL liquid structure strongly depends on
the charge density and concentration of the alkali cation. As the water content
increases, it transitions from a glassy network with fast ion exchange to an
aqueous solution containing long-lasting, solvated ion pairs. Furthermore,
simulations reveal that the cation is capable of bringing several silicate
monomers together in the glassy network, displaying perfect orientations for
condensation reactions that underlie zeolite formation. This work is an
important step towards the development of molecular models that can fully
describe the early nucleation process of zeolites in combination with
experiments.


  From the first stages of star and galaxy formation, non-gravitational
processes such as ram pressure stripping, SNs, galactic winds, AGNs,
galaxy-galaxy mergers, etc... lead to the enrichment of the IGM in stars,
metals as well as dust, via the ejection of galactic material into the IGM. We
know now that these processes shape, side by side with gravitation, the
formation and the evolution of structures. We present here hydrodynamic
simulations of structure formation implementing the effect of the cooling by
dust on large scale structure formation. We focus on the scale of galaxy
clusters and study the statistical properties of clusters. Here we present our
results on the $T_X-M$ and the $L_X-M$ scaling relations which exhibit changes
on both the slope and normalization when adding cooling by dust to the standard
radiative cooling model. For example, the normalization of the $T_X-M$ relation
changes only by a maximum of 2% at $M=10^{14}$ M$_\odot$ whereas the
normalization of the $L_X-T_X$ changes by as much as 10% at $T_X=1$ keV for
models that including dust cooling. Our study shows that the dust is an added
non-gravitational process that contributes shaping the thermodynamical state of
the hot ICM gas.


  Let $G=(V,E)$ be a simple graph with maximum degree $d$. For an integer
$k\in\mathbb{N}$, the $k$-disc of a vertex $v\in V$ is defined as the rooted
subgraph of $G$ that is induced by all vertices whose distance to $v$ is at
most $k$. The $k$-disc frequency distribution vector of $G$, denoted by
$\text{freq}_{k}(G)$, is a vector indexed by all isomorphism types of rooted
$k$-discs. For each such isomorphism type $\Gamma$, the corresponding entry in
$\text{freq}_{k}(G)$ counts the fraction of vertices in $V$ that have a
$k$-disc isomorphic to $\Gamma$. In a sense, $\text{freq}_{k}(G)$ is one way to
represent the "local structure" of $G$. The graph $G$ can be arbitrarily large,
and so a natural question is whether given $\text{freq}_{k}(G)$ it is possible
to construct a small graph $H$, whose size is independent of $|V|$, such that
$H$ has a similar local structure. N. Alon proved that for any $\epsilon>0$
there always exists a graph $H$ whose size is independent of $|V|$ and whose
frequency vector satisfies
$||\text{freq}_{k}(G)-\text{freq}_{k}(H)||_{1}\le\epsilon$. However, his proof
is only existential and does not imply that there is a deterministic algorithm
to construct such a graph $H$. He gave the open problem of finding an explicit
deterministic algorithm that finds $H$, or proving that no such algorithm
exists. Our main result is that Alon's problem is undecidable if and only if a
much more general problem (involving directed edges and edge colors) is
undecidable. We also prove that both problems are decidable for the special
case when $G$ is a path. We show that the local structure of any directed
edge-colored path $G$ can be approximated by a suitable fixed-size directed
edge-colored path $H$ and we give explicit bound on the size of $H$.


  In this paper we proceed with investigation of connections between Fokker -
Planck equation and continuum mechanics. We base upon expressions from our work
[2], based upon the spectral decomposition of Fokker - Planck equation
solution. In this decomposition we preserve only terms with the smallest
degrees of damping. We find, that macroscopic parameters of Fokker-Planck
flows, obtained in this way, satisfy the set of conservation laws of classic
hydrodynamics. The expression for stresses (30) contains additional term - this
term is negligible in big times limit. We proved also, that the velocities
field alone satisfy Burgers equation without mass forces - but with some
additional term. This term is also negligible in big times limit. For the zero
degree theory, considered in [1], there are no additional terms. But this
theory is valid only for the potential velocities field, fully deductible from
density - the potential is proportional to density logarithm. In this theory we
can not specify initial conditions for velocities independently from density.
Taking in account of the next degree terms could partly solve this problem, but
result in some loss of exactness.


  We prove the existence of quadratic polynomials having a Julia set with
positive Lebesgue measure in three cases: the presence of a Cremer fixed point,
the presence of a Siegel disk, the presence of infinitely many (satellite)
renormalizations.


  GRBs are expected to occur at redshifts far higher than the highest quasar
redshifts so far detected. And unlike quasars, GRB afterglows may provide
"clean" probes of the epoch of reionization because no complications (such as
the presence of a strong Ly-alpha emission line) or "proximity effects" (such
as the Stromgren sphere produced by ionizing photons from the quasar) are
expected. Thus NIR and optical observations of GRB afterglows may provide
unique information about the epoch of reionization. In particular, the flux at
wavelengths shortward of Ly-alpha provides a direct measure of the density
fluctuations of the IGM at the GRB redshift, while the flux at wavelengths
longward of Ly-alpha provides an integrated measure of the number of ionizing
photons produced by stars in the host galaxy of the GRB up until the burst
occurs. A comparison of the sizes of the Stromgren spheres produced by stars in
the host galaxies of GRBs and by quasars then provides an estimate of the
relative contributions of star formation and quasars to reionization. We use
detailed calculations of the expected shape of the GRB afterglow spectrum in
the vicinity of Ly-alpha for GRBs at a variety of redshifts to illustrate these
points.


  In this paper we propose that recommendation systems (RSs) for multimedia
services should be "QoS-aware", i.e., take into account the expected QoS with
which a content can be delivered, to increase the user satisfaction.
Network-aware recommendations have been very recently proposed as a promising
solution to improve network performance. However, the idea of QoS-aware RSs has
been studied from the network perspective. Its feasibility and performance
performance advantages for the content-provider or user perspective have only
been speculated. Hence, in this paper we aim to provide initial answers for the
feasibility of the concept of QoS-aware RS, by investigating its impact on real
user experience. To this end, we conduct experiments with real users on a
testbed, and present initial experimental results. Our analysis demonstrates
the potential of the idea: QoS-aware RSs could be beneficial for both the users
(better experience) and content providers (higher user engagement). Moreover,
based on the collected dataset, we build statistical models to (i) predict the
user experience as a function of QoS, relevance of recommendations (QoR) and
user interest, and (ii) provide useful insights for the design of QoS-aware
RSs. We believe that our study is an important first step towards QoS-aware
recommendations, by providing experimental evidence for their feasibility and
benefits, and can help open a future research direction.


  In this work we address the problem of the online robust control of nonlinear
dynamical systems perturbed by disturbance. We study the problem of attenuation
of the total cost over a duration $T$ in response to the disturbances. We
consider the setting where the cost function (at a particular time) is a
general continuous function and adversarial, the disturbance is adversarial and
bounded at any point of time. Our goal is to design a controller that can learn
and adapt to achieve a certain level of attenuation. We analyse two cases (i)
when the system is known and (ii) when the system is unknown. We measure the
performance of the controller by the deviation of the controller's cost for a
sequence of cost functions with respect to an attenuation $\gamma$, $R^p_t$. We
propose an online controller and present guarantees for the metric $R^p_t$ when
the maximum possible attenuation is given by $\overline{\gamma}$, which is a
system constant. We show that when the controller has preview of the cost
functions and the disturbances for a short duration of time and the system is
known $R^p_T(\gamma) = O(1)$ when $\gamma \geq \gamma_c$, where $\gamma_c =
\mathcal{O}(\overline{\gamma})$. We then show that when the system is unknown
the proposed controller with a preview of the cost functions and the
disturbances for a short horizon achieves $R^p_T(\gamma) = \mathcal{O}(N) +
\mathcal{O}(1) + \mathcal{O}((T-N)g(N))$, when $\gamma \geq \gamma_c$, where
$g(N)$ is the accuracy of a given nonlinear estimator and $N$ is the duration
of the initial estimation period. We also characterize the lower bound on the
required prediction horizon for these guarantees to hold in terms of the system
constants.


  We study a six-species Lotka-Volterra type system on different
two-dimensional lattices when each species has two superior and two inferior
partners. The invasion rates from predator sites to a randomly chosen
neighboring prey's site depend on the predator-prey pair, whereby cyclic
symmetries within the two three-species defensive alliances are conserved.
Monte Carlo simulations reveal an unexpected non-monotonous dependence of
alliance survival on the difference of alliance-specific invasion rates. This
behavior is qualitatively reproduced by a four-point mean-field approximation.
The study addresses fundamental problems of stability for the competition of
two defensive alliances and thus has important implications in natural and
social sciences.


  We discuss various mechanisms that can lead to interband sign reversal of the
order parameter in a multiband superconductor. In particular, we generalize
Abrikosov-Gor'kov solution of the problem of weakly coupled superconductor with
magnetic and nonmagnetic impurities on the case of arbitary order parameter
anisotropy, including extreme cases as $d-$pairing or interband sign reversal
of the order parameter, and show that interband scattering by magnetic
impurities can stabilize an interband sign-reversal state. We discuss a
possibility of such state in YBa$_2$Cu$_3$O$_7$ in the context of various
experiments: Josephson tunneling, neutron scattering, isotope effect
measurements.


  An IP-space is a pseudomanifold whose defining local properties imply that
its middle perversity global intersection homology groups satisfy Poincar\'e
duality integrally. We show that the symmetric signature induces a map of Quinn
spectra from IP bordism to the symmetric $L$-spectrum of $\Z$, which is, up to
weak equivalence, an $E_\infty$ ring map. Using this map, we construct a
fundamental $L$-homology class for IP-spaces, and as a consequence we prove the
stratified Novikov conjecture for IP-spaces.


  In this paper, we study the interaction between the two-level atom and a
bimodal cavity field, namely, two-mode Jaynes-Cummings model when the atom and
the modes are initially in the atomic superposition state and two-mode squeezed
vacuum state, respectively. For this system we investigate the atomic
inversion, linear entropy and atomic Wehrl entropy. We show that there is a
connection between all these quantities. Also we prove that the atomic Wehrl
entropy exhibits behaviors similar to those of the linear entropy and the von
Neumann entropy. Moreover, we show that the bipartite exhibits periodical
disentanglement and derive the explicit forms of the states of the atom and the
modes at these values of the interaction times.


  We give relative error coresets for training linear classifiers with a broad
class of loss functions, including the logistic loss and hinge loss. Our
construction achieves $(1\pm \epsilon)$ relative error with $\tilde O(d \cdot
\mu_y(X)^2/\epsilon^2)$ points, where $\mu_y(X)$ is a natural complexity
measure of the data matrix $X \in \mathbb{R}^{n \times d}$ and label vector $y
\in \{-1,1\}^n$, introduced in by Munteanu et al. 2018. Our result is based on
subsampling data points with probabilities proportional to their $\ell_1$
$Lewis$ $weights$. It significantly improves on existing theoretical bounds and
performs well in practice, outperforming uniform subsampling along with other
importance sampling methods. Our sampling distribution does not depend on the
labels, so can be used for active learning. It also does not depend on the
specific loss function, so a single coreset can be used in multiple training
scenarios.


  Character of the low-energy $E1$ excitations is investigated by analyzing
transition densities obtained from the RPA calculations in the doubly-magic
nuclei. We propose a decomposition method of the $E1$ excitations into the $pn$
mode (i.e. oscillation between protons and neutrons) and the skin mode
(oscillation of the neutron skin against the inner core) via the transition
densities, by which their mixing is handled in a straightforward manner.
Crossover behavior of the $E1$ excitations is found, from the skin mode at low
energy to the $pn$ mode at higher energy. The ratio of the skin-mode strength
to the full strength turns out to be insensitive to nuclide and to effective
interactions in the energy region of the crossover. Depending on the excitation
energy, the observed low-energy $E1$ excitations are not necessarily dominated
by the skin mode, as exemplified for $^{90}$Zr. fied for $^{90}$Zr.


  Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.


  Centralized systems in the Internet of Things---be it local middleware or
cloud-based services---fail to fundamentally address privacy of the collected
data. We propose an architecture featuring secure multiparty computation at its
core in order to realize data processing systems which already incorporate
support for privacy protection in the architecture.


  Medical Triage is of paramount importance to healthcare systems, allowing for
the correct orientation of patients and allocation of the necessary resources
to treat them adequately. While reliable decision-tree methods exist to triage
patients based on their presentation, those trees implicitly require human
inference and are not immediately applicable in a fully automated setting. On
the other hand, learning triage policies directly from experts may correct for
some of the limitations of hard-coded decision-trees. In this work, we present
a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage
patients using curated clinical vignettes. The dataset, consisting of 1374
clinical vignettes, was created by medical doctors to represent real-life
cases. Each vignette is associated with an average of 3.8 expert triage
decisions given by medical doctors relying solely on medical history. We show
that this approach is on a par with human performance, yielding safe triage
decisions in 94% of cases, and matching expert decisions in 85% of cases. The
trained agent learns when to stop asking questions, acquires optimized decision
policies requiring less evidence than supervised approaches, and adapts to the
novelty of a situation by asking for more information. Overall, we demonstrate
that a Deep Reinforcement Learning approach can learn effective medical triage
policies directly from expert decisions, without requiring expert knowledge
engineering. This approach is scalable and can be deployed in healthcare
settings or geographical regions with distinct triage specifications, or where
trained experts are scarce, to improve decision making in the early stage of
care.


  Adsorption of the molecule CO on metallic surfaces is an important unsolved
problem in Kohn-Sham density functional theory (KS-DFT). We present a detailed
study of carbon monoxide adsorption on fcc (111) surfaces of 3d, 4d and 5d
metals using nonempirical semilocal density functionals for the
exchange-correlation energy: the local-density approximation (LDA), two
generalized gradient approximations or GGAs (PBE and PBEsol), and a meta-GGA
(SCAN). The typical error pattern (as found earlier for free molecules and for
free transition metal surfaces), in which results improve from LDA to PBE or
PBEsol to SCAN, due to the satisfaction of more exact constraints, is not found
here. Instead, for CO adsorption on transition metal surfaces, we find that,
while SCAN overbinds much less than LDA, it overbinds slightly more than PBE.
Moreover, the tested functionals often predict the wrong adsorption site, as
first pointed out for LDA and GGA in the ``CO/Pt (111) puzzle". This abnormal
pattern leads us to suspect that the errors of PBE and SCAN for this problem
are density-driven self-interaction errors associated with incorrect charge
transfer between molecule and metal surface. We point out that, by the
variational principle, overbinding by an approximate functional would be
reduced if that functional were applied not to its selfconsistent density for
the adsorbed system but to an exact or more correct density for that system.
Finally, we show for CO on Pt(111) that the site preference is corrected and
the adsorption energy is improved for the PBE functional by using not the
selfconsistent PBE density but a PBE+U density. The resulting correction to the
PBE total energy is much larger for the adsorbed system than for its desorbed
components, showing that the error is in the density of the adsorbed system.
This seems to solve the ``CO/Pt (111) puzzle", in principle if not fully in
practice.


  The absolute magnitude of the brightest galaxy of clusters varies remarkably
little and is nearly independent of all other physical properties of the
cluster as, e.g., its spatial extension or its richness. The question arises
whether the observed small scatter is compatible with the assumption of
dynamical evolution of the cluster. This is investigated with the help of
statistical analysis of the results of cluster simulation. The underlying
interaction process is merging (and also destruction) of smaller galaxies
forming the giant galaxy. The cluster itself is supposed to be in virial
equilibrium.
  We find that the evolutionary importance of merger processes grows with
decreasing scale. Rich clusters as well as their brightest members evolve
merely slowly whereas compact groups as well as their brightest members evolve
more rapidly and more violently. We also find that the number of merger
processes leading to the growth of the brightest cluster galaxy (BCG) is small
enough to keep the BCG mass dispersion below the measured value. Our
simulations substantiate that just the combination of the initial distribution
function and the following merging to form the BCG can explain the remarkably
small variance of mean BCG masses between clusters of different size and
different number of galaxies.


  Before its demise DIANA Hi-TECH, LLC, demonstrated the use of two 50 kJoule
Plasma Focus devices for the copius production of fast neutrons, x-rays and
radio-isotopes. Such a device is suitable for fast neutron non invasive
interogation of contra-band materials including hidden nuclear materials. It
could be particularly useful for a fast and fail safe interogation of large
cargo containers, or in merchant marine port of entries. The performance and
fast neutron production (2.5 or 14 MeV at 10^11 or 10^13 neutrons per pulse,
respectively) of the two PF50 Plasma Focus devices produced by DIANA HiTECH,
LLC, are discussed.


  Innovation in the world of today is mainly driven by software. Companies need
to continuously rejuvenate their product portfolios with new features to stay
ahead of their competitors. For example, recent trends explore the application
of blockchains to domains other than finance. This paper analyzes the
state-of-the-art for safety-critical systems as found in modern vehicles like
self-driving cars, smart energy systems, and home automation focusing on
specific challenges where key ideas behind blockchains might be applicable.
Next, potential benefits unlocked by applying such ideas are presented and
discussed for the respective usage scenario. Finally, a research agenda is
outlined to summarize remaining challenges for successfully applying
blockchains to safety-critical cyber-physical systems.


  The inclusive cross section for the production of $J/\psi$ in $e^+ e^-$
annihilation is studied using a recently developed factorization formalism. In
addition to the conventional color-singlet contribution, we include
contributions from $c \bar c$ pairs that are produced at short distances in
color-octet states. The color-octet contributions are suppressed by the smaller
probability for the color-octet $c \bar c$ pair to evolve into a state that
includes a $\psi$, but they may dominate near the upper endpoint of the $\psi$
energy spectrum. The signature for the color-octet contribution is a dramatic
change in the angular distribution of the $\psi$ near the endpoint.


  In the field of medical imaging, particularly in tasks related to early
disease detection and prognosis, understanding the reasoning behind AI model
predictions is imperative for assessing their reliability. Conventional
explanation methods encounter challenges in identifying decisive features in
medical image classifications, especially when discriminative features are
subtle or not immediately evident. To address this limitation, we propose an
agent model capable of generating counterfactual images that prompt different
decisions when plugged into a black box model. By employing this agent model,
we can uncover influential image patterns that impact the black model's final
predictions. Through our methodology, we efficiently identify features that
influence decisions of the deep black box. We validated our approach in the
rigorous domain of medical prognosis tasks, showcasing its efficacy and
potential to enhance the reliability of deep learning models in medical image
classification compared to existing interpretation methods. The code will be
publicly available at https://github.com/ayanglab/DiffExplainer.


  Many theoretical results for the lasso require the samples to be iid. Recent
work has provided guarantees for the lasso assuming that the time series is
generated by a sparse Vector Auto-Regressive (VAR) model with Gaussian
innovations. Proofs of these results rely critically on the fact that the true
data generating mechanism (DGM) is a finite-order Gaussian VAR. This assumption
is quite brittle: linear transformations, including selecting a subset of
variables, can lead to the violation of this assumption. In order to break free
from such assumptions, we derive non-asymptotic inequalities for estimation
error and prediction error of the lasso estimate of the best linear predictor
without assuming any special parametric form of the DGM. Instead, we rely only
on (strict) stationarity and geometrically decaying \b{eta}-mixing coefficients
to establish error bounds for the lasso for subweibull random vectors. The
class of subweibull random variables that we introduce includes subgaussian and
subexponential random variables but also includes random variables with tails
heavier than an exponential. We also show that, for Gaussian processes, the
\b{eta}-mixing condition can be relaxed to summability of the {\alpha}-mixing
coefficients. Our work provides an alternative proof of the consistency of the
lasso for sparse Gaussian VAR models. But the applicability of our results
extends to non-Gaussian and non-linear times series models as the examples we
provide demonstrate.


  The ARGO-YBJ experiment, a full coverage extensive air shower (EAS) detector
located at high altitude (4300 m a.s.l.) in Tibet, China, has smoothly taken
data, with very high stability, since November 2007 to the beginning of 2013.
The array consisted of a carpet of about 7000 m$^2$ Resistive Plate Chambers
(RPCs) operated in streamer mode and equipped with both digital and analog
readout, providing the measurement of particle densities up to few particles
per cm$^2$. The unique detector features (full coverage, readout granularity,
wide dynamic range, etc) and location (very high altitude) allowed a detailed
study of the lateral density profile of charged particles at ground very close
to the shower axis and its description by a proper lateral distribution
function (LDF). In particular, the information collected in the first 10 m from
the shower axis have been shown to provide a very effective tool for the
determination of the shower development stage ("age") in the energy range 50
TeV - 10 PeV. The sensitivity of the age parameter to the mass composition of
primary Cosmic Rays is also discussed.


  We construct new supersymmetric solutions corresponding to M5-branes wrapped
on a topological disc by turning on additional scalars in the background. The
presence of such scalar fields breaks one of the U(1) isometries of the
internal space, explicitly realising the breaking by the Stuckelberg mechanism
observed previously. In addition, we construct a consistent truncation of
maximal seven-dimensional gauged supergravity on the disc to five-dimensional
Romans' SU (2) x U(1) gauged supergravity, allowing us to construct a plethora
of new supergravity solutions corresponding to more general states in the dual
SCFTs as well as solutions corresponding to M5-branes wrapping four-dimensional
orbifolds.


  For understanding the origin of life, it is essential to explain the
development of a compartmentalized structure, which undergoes growth and
division, from a set of chemical reactions. In this study, a hypercycle with
two chemicals that mutually catalyze each other is considered in order to show
that the reproduction of a protocell with a growth-division process naturally
occurs when the replication speed of one chemical is considerably slower than
that of the other chemical. It is observed that the protocell divides after a
minority molecule is replicated at a slow synthesis rate, and thus, a synchrony
between the reproduction of a cell and molecule replication is achieved. The
robustness of such protocells against the invasion of parasitic molecules is
also demonstrated.