  One of the ways natural and synthetic systems regulate temperature is via
circulating fluids through vasculatures embedded within their bodies. Because
of the flexibility and availability of proven fabrication techniques,
vascular-based thermal regulation is attractive for thin microvascular systems.
Although preliminary designs and experiments demonstrate the feasibility of
thermal modulation by pushing fluid through embedded micro-vasculatures, one
has yet to optimize the performance before translating the concept into
real-world applications. It will be beneficial to know how two vital design
variables -- host material's thermal conductivity and fluid's heat capacity
rate -- affect a thermal regulation system's performance, quantified in terms
of the mean surface temperature. This paper fills the remarked inadequacy by
performing adjoint-based sensitivity analysis and unravels a surprising
non-monotonic trend. Increasing thermal conductivity can either increase or
decrease the mean surface temperature; the increase happens if countercurrent
heat exchange -- transfer of heat from one segment of the vasculature to
another -- is significant. In contrast, increasing the heat capacity rate will
invariably lower the mean surface temperature, for which we provide
mathematical proof. The reported results (a) dispose of some misunderstandings
in the literature, especially on the effect of the host material's thermal
conductivity, (b) reveal the role of countercurrent heat exchange in altering
the effects of design variables, and (c) guide designers to realize efficient
microvascular active-cooling systems. The analysis and findings will advance
the field of thermal regulation both on theoretical and practical fronts.


  (abridged) We report results from a Chandra study of the central regions of
the nearby, X-ray bright, Ophiuchus Cluster (z = 0.03), the second-brightest
cluster in the sky. Our study reveals a dramatic, close-up view of the
stripping and potential destruction of a cool core within a rich cluster. The
X-ray emission from the Ophiuchus Cluster core exhibits a comet-like morphology
extending to the north, driven by merging activity, indicative of ram-pressure
stripping caused by rapid motion through the ambient cluster gas. A cold front
at the southern edge implies a velocity of 1000$\pm$200 km/s (M~0.6). The X-ray
emission from the cluster core is sharply peaked. As previously noted, the peak
is offset by 4 arcsec (~2 kpc) from the optical center of the associated cD
galaxy, indicating that ram pressure has slowed the core, allowing the
relatively collisionless stars and dark matter to carry on ahead. The cluster
exhibits the strongest central temperature gradient of any massive cluster
observed to date: the temperature rises from 0.7 keV within 1 kpc of the
brightness peak, to 10 keV by 30 kpc. A strong metallicity gradient is also
observed within the same region. This supports a picture in which the outer
parts of the cool core have been stripped by ram-pressure due to its rapid
motion. The cooling time of the innermost gas is very short, ~5$\times10^7$
yrs. Within the central 10 kpc radius, multiple small-scale fronts and a
complex thermodynamic structure are observed, indicating significant motions.
Beyond the central 50 kpc, and out to a radius ~150 kpc, the cluster appears
relatively isothermal and has near constant metallicity. The exception is a
large, coherent ridge of enhanced metallicity observed to trail the cool core,
and which is likely to have been stripped from it.


  We describe an efficient bottom-up parser that interleaves syntactic and
semantic structure building. Two techniques are presented for reducing search
by reducing local ambiguity: Limited left-context constraints are used to
reduce local syntactic ambiguity, and deferred sortal-constraint application is
used to reduce local semantic ambiguity. We experimentally evaluate these
techniques, and show dramatic reductions in both number of chart-edges and
total parsing time. The robust processing capabilities of the parser are
demonstrated in its use in improving the accuracy of a speech recognizer.


  We present a deep learning method for composite and task-driven motion
control for physically simulated characters. In contrast to existing
data-driven approaches using reinforcement learning that imitate full-body
motions, we learn decoupled motions for specific body parts from multiple
reference motions simultaneously and directly by leveraging the use of multiple
discriminators in a GAN-like setup. In this process, there is no need of any
manual work to produce composite reference motions for learning. Instead, the
control policy explores by itself how the composite motions can be combined
automatically. We further account for multiple task-specific rewards and train
a single, multi-objective control policy. To this end, we propose a novel
framework for multi-objective learning that adaptively balances the learning of
disparate motions from multiple sources and multiple goal-directed control
objectives. In addition, as composite motions are typically augmentations of
simpler behaviors, we introduce a sample-efficient method for training
composite control policies in an incremental manner, where we reuse a
pre-trained policy as the meta policy and train a cooperative policy that
adapts the meta one for new composite tasks. We show the applicability of our
approach on a variety of challenging multi-objective tasks involving both
composite motion imitation and multiple goal-directed control.


  We present the first 3-dimensional simulations following the evolution of
supernova shocks from their inception in the stellar core through the
development of a supernova remnant into the Sedov phase. Our set of simulations
use two different progenitors and two different conditions for the structure of
the circumstellar environment. These calculations demonstrate the role that
supernova instabilities (the instabilities that develop as the shock drive
through the star) play in defining the structure and long-term development of
instabilities in supernova remnants. We also present a first investigation of
the mixing between stellar and interstellar matter as the supernova evolves
into a young supernova remnant.


  Revisiting the notion of m-almost equicontinuous cellular automata introduced
by R. Gilman, we show that the sequence of image measures of a shift ergodic
measure m by iterations of a m-almost equicontinuous automata F, converges in
Cesaro mean to an invariant measure mc. If the initial measure m is a
Bernouilli measure, we prove that the Cesaro mean limit measure mc is shift
mixing. Therefore we also show that for any shift ergodic and F-invariant
measure m, the existence of m-almost equicontinuous points implies that the set
of periodic points is dense in the topological support S(m) of the invariant
measure m. Finally we give a non trivial example of a couple (m-equicontinuous
cellular automata F, shift ergodic and F-invariant measure m) which has no
equicontinuous point in S(m).


  Doped HfO2 thin films exhibit robust ferroelectric properties even for
nanometric thicknesses, are compatible with current Si technology and thus have
great potential for the revival of integrated ferroelectrics. Phase control and
reliability are core issues for their applications. Here we show that, in
(111)-oriented 5%La:HfO2 (HLO) epitaxial thin films deposited on
(La0.3Sr0.7)(Al0.65Ta0.35)O3 substrates, the flexoelectric effect, arising from
the strain gradient along the films normal, induces a rhombohedral distortion
in the otherwise Pca21 orthorhombic structure. Density functional calculations
reveal that the distorted structure is indeed more stable than the pure Pca21
structure, when applying an electric field mimicking the flexoelectric field.
This rhombohedral distortion greatly improves the fatigue endurance of HLO thin
films by further stabilizing the metastable ferroelectric phase against the
transition to the thermodynamically stable non-polar monoclinic phase during
repetitive cycling. Our results demonstrate that the flexoelectric effect,
though negligibly weak in bulk, is crucial to optimize the structure and
properties of doped HfO2 thin films with nanometric thicknesses for integrated
ferroelectric applications.


  Based on numerical results of a Silnikov equation, three period-doubling
cascades, corresponding respectively to three different characters of the
rotation number of a limit closed orbit, are studied, and the Feigenbaum
constant is used successfully in the estimation of the critical parameter
values for the period-doubling bifurcation. The conceptions of separaror and
pseudo-attractor are also introduced in the discussion part of this paper.


  A neural network model that exhibits stochastic population bursting is
studied by simulation. First return maps of inter-burst intervals exhibit
recurrent unstable periodic orbit (UPO)-like trajectories similar to those
found in experiments on hippocampal slices. Applications of various control
methods and surrogate analysis for UPO-detection also yield results similar to
those of experiments. Our results question the interpretation of the
experimental data as evidence for deterministic chaos and suggest caution in
the use of UPO-based methods for detecting determinism in time-series data.


  We prove a couple of related theorems including Legendre's and Andrica's
conjecture.
  Key to the proofs is an algorithm that delivers the exact upper bound on the
greatest gap that can occur in a combinatorial game with the set of P primes <=
p in their doubled primorial interval 0..p#..2p# where we relax a constraint
that the primes usually follow: if the bound g(P)=2p-5 for maximizing the gap
length applies with more degrees of freedom, it also applies in the more
constrained prime game as g(P)<=2p-5, at least in the subregion $[1,{p'}^2]$
where no other primes have influence (p' notates the next other prime). From
here proving the mentioned theorems is straightforward, for example Legendre's
interval $]n^2,(n+1)^2[$ is located completely inside the valid subregion and
is greater than the greatest possible gap. Another consequence is that there
must be a prime within n+-(sqrt(n)-1) for all n>1. For small numbers the proofs
are verified using the R statistical language.


  We introduce and study pawn games, a class of two-player zero-sum turn-based
graph games. A turn-based graph game proceeds by placing a token on an initial
vertex, and whoever controls the vertex on which the token is located, chooses
its next location. This leads to a path in the graph, which determines the
winner. Traditionally, the control of vertices is predetermined and fixed. The
novelty of pawn games is that control of vertices changes dynamically
throughout the game as follows. Each vertex of a pawn game is owned by a pawn.
In each turn, the pawns are partitioned between the two players, and the player
who controls the pawn that owns the vertex on which the token is located,
chooses the next location of the token. Control of pawns changes dynamically
throughout the game according to a fixed mechanism. Specifically, we define
several grabbing-based mechanisms in which control of at most one pawn
transfers at the end of each turn. We study the complexity of solving pawn
games, where we focus on reachability objectives and parameterize the problem
by the mechanism that is being used and by restrictions on pawn ownership of
vertices. On the positive side, even though pawn games are
exponentially-succinct turn-based games, we identify several natural classes
that can be solved in PTIME. On the negative side, we identify several
EXPTIME-complete classes, where our hardness proofs are based on a new class of
games called Lock & Key games, which may be of independent interest.


  The original idea of quantum optical spring arises from the requirement of
quantization of the frequency of oscillations in the Hamiltonian of harmonic
oscillator. This purpose is achieved by considering a spring whose constant
(and so its frequency) depends on the quantum states of another system.
Recently, it is realized that by the assumption of frequency modulation of
$\omega$ to $\omega\sqrt{1+\mu a^\dagger a}$ the mentioned idea can be
established. In the present paper, we generalize the approach of quantum
optical spring with particular attention to the {\it dependence of frequency to
the intensity of radiation field} that {\it naturally} observes in the {\it
nonlinear coherent states}, from which we arrive at a physical system has been
called by us as {\it nonlinear quantum optical spring}. Then, after the
introduction of the generalized Hamiltonian of nonlinear quantum optical spring
and it's solution, we will investigate the nonclassical properties of the
obtained states. Specially, typical collapse and revival in the distribution
functions and squeezing parameters, as particular quantum features, will be
revealed.


  A quantum engine with n qubits performing thermodynamic cycles with two
thermal reservoirs is presented. While such constructions have been aplenty,
here we show the existence of what we term as "limit cycle" at a purely quantum
level of description owing to the properties of superoperators governing the
evolution of states. It is shown that the limit cycle is the same under forward
and reverse protocol of cycle operations. This limit cycle becomes the basis of
the quantum engine. One dimensional Ising model has been used to illustrate
these ideas.


  In this paper, we consider unsupervised partitioning problems, such as
clustering, image segmentation, video segmentation and other change-point
detection problems. We focus on partitioning problems based explicitly or
implicitly on the minimization of Euclidean distortions, which include
mean-based change-point detection, K-means, spectral clustering and normalized
cuts. Our main goal is to learn a Mahalanobis metric for these unsupervised
problems, leading to feature weighting and/or selection. This is done in a
supervised way by assuming the availability of several potentially partially
labelled datasets that share the same metric. We cast the metric learning
problem as a large-margin structured prediction problem, with proper definition
of regularizers and losses, leading to a convex optimization problem which can
be solved efficiently with iterative techniques. We provide experiments where
we show how learning the metric may significantly improve the partitioning
performance in synthetic examples, bioinformatics, video segmentation and image
segmentation problems.


  We consider the behavior of an interacting many particle system under slow
external driving -- a many body generalization of the Landau-Zener paradigm. We
find that a conspiracy of interactions and driving leads to physics profoundly
different from that of the single particle limit: for practically all values of
the driving rate the particle distributions in Hilbert space are very broad, a
phenomenon caused by a strong amplification of quantum fluctuations in the
driving process. These fluctuations are 'non-adiabatic' in that even at very
slow driving it is exceedingly difficult to push the center of the distribution
towards the limit of full ground state occupancy. We obtain these results by a
number of complementary theoretical approaches, including diagrammatic
perturbation theory, semiclassical analysis, and exact diagonalization.


  The total entropy production is the sum of two contributions, the so-called
adiabatic and nonadiabatic entropy production, each of which is non-negative.
We derive their explicit expressions for continuous Markov processes, discuss
their properties and illustrate their behavior on two exactly solvable models.


  Surgery to treat elderly hip fracture patients may cause complications that
can lead to early mortality. An early warning system for complications could
provoke clinicians to monitor high-risk patients more carefully and address
potential complications early, or inform the patient. In this work, we develop
a multimodal deep-learning model for post-operative mortality prediction using
pre-operative and per-operative data from elderly hip fracture patients.
Specifically, we include static patient data, hip and chest images before
surgery in pre-operative data, vital signals, and medications administered
during surgery in per-operative data. We extract features from image modalities
using ResNet and from vital signals using LSTM. Explainable model outcomes are
essential for clinical applicability, therefore we compute Shapley values to
explain the predictions of our multimodal black box model. We find that i)
Shapley values can be used to estimate the relative contribution of each
modality both locally and globally, and ii) a modified version of the chain
rule can be used to propagate Shapley values through a sequence of models
supporting interpretable local explanations. Our findings imply that a
multimodal combination of black box models can be explained by propagating
Shapley values through the model sequence.


  We start presenting an $L^{\infty}$-gradient bound for solutions to
non-homogeneous $p$-Laplacean type systems and equations, via suitable
non-linear potentials of the right hand side. Such a bound implies a Lorentz
space characterization of Lipschitz regularity of solutions which surprisingly
turns out to be independent of $p$, and that reveals to be the same classical
one for the standard Laplacean operator. In turn, the a priori estimates
derived imply the existence of locally Lipschitz regular solutions to certain
degenerate systems with critical growth of the type arising when considering
geometric analysis problems, as recently emphasized by Rivi\`ere


  Self-consistent factorization of two-body residual interaction is proposed
for arbitrary density- and current-dependent energy functionals. Following this
procedure, a separable RPA (SRPA) method is constructed. SRPA dramatically
simplifies the calculations and demonstrates quick convergence to exact
results. The method is tested for SkM* forces.


  We consider particle systems with mean-field interactions whose distribution
is invariant by translations. Under the assumption that the system seen from
its centre of mass be reversible with respect to a Gibbs measure, we establish
large deviation principles for its empirical measure at equilibrium. Our study
covers the cases of McKean-Vlasov particle systems without external potential,
and systems of rank-based interacting diffusions. Depending on the strength of
the interaction, the large deviation principles are stated in the space of
centered probability measures endowed with the Wasserstein topology of
appropriate order, or in the orbit space of the action of translations on
probability measures. An application to the study of atypical capital
distribution is detailed.


  We define the notion of a smooth pseudo-Riemannian algebraic variety $(X,g)$
over a field $k$ of characteristic $0$, which is an algebraic analogue of the
notion of Riemannian manifold and we study, from a model-theoretic perspective,
the algebraic differential equation describing the geodesics on $(X,g)$.
  When $k$ is the field of real numbers, we prove that if the real points of
$X$ are Zariski-dense in $X$ and if the real analytification of $(X,g)$ is a
compact Riemannian manifold with negative curvature, then the algebraic
differential equation describing the geodesics on $(X,g)$ is absolutely
irreducible and its generic type is orthogonal to the constants.


  We perform total energy calculations based on the tight-binding Hamiltonian
scheme (i) to study the structural properties and energetics of the extended
{311} defects depending upon their dimensions and interstitial concentrations
and (ii) to find possible mechanisms of interstitial capture by and release
from the {311} defects. The generalized orbital-based linear-scaling method
implemented on Cray-T3D is used for supercell calculations of large scale
systems containing more than 1000 Si atoms.


  We present thermoelectric properties of Be$_2$C monolayer based on density
functional theory and semi-classical Boltzmann transport theory. Electronic
structure calculations predict this material as a semiconductor with a direct
bandgap of 2.0 eV computed using Gaussian-attenuating Perdew-Burke-Ernzerhof
(Gau-PBE) hybrid functional. The Gau-PBE band structure is used to compute
transport properties by solving the Boltzmann transport equation under the
constant relaxation time approximation. In this work, we have explicitly
determined the relaxation time by studying the electron-phonon interactions in
the system to estimate absolute transport coefficients. Our results show that
the monolayer possesses a high power factor ($\sim$ 3.44 mW/mK$^2$ @300K),
similar to the commercial TE materials doped-Bi$_2$Te$_3$ and PbTe, suggesting
that Be$_2$C monolayer is a promising thermoelectric material.


  We generalize the elementary methods presented in several examples in the
book \cite{[FZ]} to obtain the Thomae formulae for general fully ramified
$Z_{n}$ curves.


  This is a review article on the quantum toroidal algebras, focusing on their
roles in various solvable structures of 2d conformal field theory,
supersymmetric gauge theory, and string theory. Using $\mathcal{W}$-algebras as
our starting point, we elucidate the interconnection of affine Yangians,
quantum toroidal algebras, and double affine Hecke algebras.
  Our exploration delves into the representation theory of the quantum toroidal
algebra of $\mathfrak{gl}_1$ in full detail, highlighting its connections to
partitions, $\mathcal{W}$-algebras, Macdonald functions, and the notion of
intertwiners. Further, we also discuss integrable models constructed on Fock
spaces and associated $\mathcal{R}$-matrices, both for the affine Yangian and
the quantum toroidal algebra of $\mathfrak{gl}_1$.
  The article then demonstrates how quantum toroidal algebras serve as a
unifying algebraic framework that bridges different areas in physics. Notably,
we cover topological string theory and supersymmetric gauge theories with eight
supercharges, incorporating the AGT duality. Drawing upon the representation
theory of the quantum toroidal algebra of $\mathfrak{gl}_1$, we provide a
rather detailed review of its role in the algebraic formulations of topological
vertex and $qq$-characters. Additionally, we briefly touch upon the corner
vertex operator algebras and quiver quantum toroidal algebras.


  This paper introduces a new variant of hypercubes, which we call Z-cubes. The
n-dimensional Z-cube $H_n$ is obtained from two copies of the (n-1)-dimensional
Z-cube $H_{n-1}$ by adding a special perfect matching between the vertices of
these two copies of $H_{n-1}$. We prove that the n-dimensional Z-cubes $H_n$
has diameter $(1+o(1))n/\log_2 n$. This greatly improves on the previous known
variants of hypercube of dimension n, whose diameters are all larger than n/3.
Moreover, any hypercube variant of dimension $n$ is an n-regular graph on $2^n$
vertices, and hence has diameter greater than $n/\log_2 n$. So the Z-cubes are
optimal with respect to diameters, up to an error of order $o(n/\log_2n)$.
Another type of Z-cubes $Z_{n,k}$ which have similar structure and properties
as $H_n$ are also discussed in the last section.


  In this paper, we consider the effect of Planck scale operators on neutrino
magnetic moments. We assume that the main part of neutrino masses and mixings
arise through GUT scale operators. We further assume that additional discrete
symmetries make the neutrino mixing bi-maximal. Quantum gravitational (Planck
scale) effects lead to an effective $SU(2)_{L}\times U(1)$ invariant
dimension-5 Lagrangian involving neutrino and Higgs fields, which gives rise to
additional terms in neutrino mass matrix. These additional terms can be
considered to be perturbation of the GUT scale bi-maximal neutrino mass matrix.
We assume that the gravitational interaction is flavour blind and we study the
neutrino mixings and magnetic moments due to the physics above the GUT scale.


  We study the relation between violation of Bell inequalities and
distillability properties of quantum states. Recently, D\"ur has shown that
there are some multiparticle bound entangled states, non-separable and
non-distillable, that violate a Bell inequality. We prove that for all the
states violating this inequality there exist at least one splitting of the
parties into two groups such that some pure-state entanglement can be
distilled, obtaining a connection between Bell inequalities and bipartite
distillable entanglement.


  A prediction model is most useful if it generalizes beyond the development
data with external validations, but to what extent should it generalize remains
unclear. In practice, prediction models are externally validated using data
from very different settings, including populations from other health systems
or countries, with predictably poor results. This may not be a fair reflection
of the performance of the model which was designed for a specific target
population or setting, and may be stretching the expected model
generalizability. To address this, we suggest to externally validate a model
using new data from the target population to ensure clear implications of
validation performance on model reliability, whereas model generalizability to
broader settings should be carefully investigated during model development
instead of explored post-hoc. Based on this perspective, we propose a roadmap
that facilitates the development and application of reliable, fair, and
trustworthy artificial intelligence prediction models.


  C(X) denotes the space of continuous complex-valued functions on the compact
Hausdorff space X. X has the CSWP if every subalgebra of C(X) which separates
points and contains the constant functions is dense in C(X). W. Rudin showed
that all scattered X have the CSWP. We describe a class of non-scattered X with
the CSWP; by another result of Rudin, such X cannot be metrizable.


  Mahalanobis metrics are widely used in machine learning in conjunction with
methods like $k$-nearest neighbors, $k$-means clustering, and $k$-medians
clustering. Despite their importance, there has not been any prior work on
applying sketching techniques to speed up algorithms for Mahalanobis metrics.
In this paper, we initiate the study of dimension reduction for Mahalanobis
metrics. In particular, we provide efficient data structures for solving the
Approximate Distance Estimation (ADE) problem for Mahalanobis distances. We
first provide a randomized Monte Carlo data structure. Then, we show how we can
adapt it to provide our main data structure which can handle sequences of
\textit{adaptive} queries and also online updates to both the Mahalanobis
metric matrix and the data points, making it amenable to be used in conjunction
with prior algorithms for online learning of Mahalanobis metrics.


  We consider a state-dependent full-duplex relay channel with the state of the
channel non-causally available at only the relay. In the framework of
cooperative wireless networks, some specific terminals can be equipped with
cognition capabilities, i.e, the relay in our model. In the discrete memoryless
(DM) case, we derive lower and upper bounds on channel capacity. The lower
bound is obtained by a coding scheme at the relay that consists in a
combination of codeword splitting, Gel'fand-Pinsker binning, and a
decode-and-forward scheme. The upper bound is better than that obtained by
assuming that the channel state is available at the source and the destination
as well. For the Gaussian case, we also derive lower and upper bounds on
channel capacity. The lower bound is obtained by a coding scheme which is based
on a combination of codeword splitting and Generalized dirty paper coding. The
upper bound is also better than that obtained by assuming that the channel
state is available at the source, the relay, and the destination. The two
bounds meet, and so give the capacity, in some special cases for the degraded
Gaussian case.


  The local Seyfert 2 galaxy NGC5135, thanks to its almost face-on appearance,
a bulge overdensity of stars, the presence of a large-scale bar, an AGN and a
Supernova Remnant, is an excellent target to investigate the dynamics of
inflows, outflows, star formation and AGN feedback. Here we present a
reconstruction of the gas morphology and kinematics in the inner regions of
this galaxy, based on the analysis of Atacama Large Millimeter Array (ALMA)
archival data. To our purpose, we combine the available $\sim$100 pc resolution
ALMA 1.3 and 0.45 mm observations of dust continuum emission, the spectroscopic
maps of two transitions of the CO molecule (tracer of molecular mass in star
forming and nuclear regions), and of the CS molecule (tracer of the dense star
forming regions) with the outcome of the SED decomposition. By applying the
$^{\rm 3D}$BAROLO software (3D-Based Analysis of Rotating Object via Line
Observations), we have been able to fit the galaxy rotation curves
reconstructing a 3D tilted-ring model of the disk. Most of the observed
emitting features are described by our kinematic model. We also attempt an
interpretation for the emission in few regions that the axisymmetric model
fails to reproduce. The most relevant of these is a region at the northern edge
of the inner bar, where multiple velocity components overlap, as a possible
consequence of the expansion of a super-bubble.


  We study a family of classical solutions of modified sinh-Gordon equation,
$\partial_z\partial_{{\bar z}} \eta-\re^{2\eta}+p(z)\,p({\bar z})\
\re^{-2\eta}=0$ with $p(z)=z^{2\alpha}-s^{2\alpha}$. We show that certain
connection coefficients for solutions of the associated linear problem coincide
with the $Q$-function of the quantum sine-Gordon $(\alpha>0)$ or sinh-Gordon
$(\alpha<-1)$ models.


  Low-spin states in the neutron-rich, N = 90 nuclide $^{146}$Ba were populated
following $\beta$-decay of $^{146}$Cs, with the goal of clarifying the
development of deformation in Ba isotopes through delineation of their
non-yrast structures. Fission fragments of $^{146}$Cs were extracted from a
1.7-Ci $^{252}$Cf source and mass-selected using the CARIBU facility.
Low-energy ions were deposited at the center of a box of thin $\beta$
detectors, surrounded by a high-efficiency HPGe array. The new $^{146}$Ba decay
scheme now contains 31 excited levels extending up to ~2.5 MeV excitation
energy, double what was previously known. These data are compared to
predictions from the Interacting Boson Approximation (IBA) model. It appears
that the abrupt shape change found at N = 90 in Sm and Gd is much more gradual
in Ba and Ce, due to an enhanced role of the $\gamma$ degree of freedom.


  I shall consider each of the 18 claims made by Mohrhoff, and explain, in each
case, why I take the path opposite to the one by which he seeks to remove the
effects of our thoughts on the activities of our quantum mechanically described
brains.


  An amateur astronomer in the modern world has the opportunity not only to
make visual observations for own interest, but can make scientific astronomical
observations and new discoveries in astronomy.
  In my example, as amateur astronomer and only through self-education, I
inform about my discoveries: of the possible dwarf nova on the old digitized
photographic plates and of new variable stars from sky surveys data by means of
data mining; how I discovered (in the images of the sky surveys): astronomical
transients, supernovae, planetary nebula candidates and new binary systems in
the data of Gaia DR2; I describe my discoveries of three novae in the Andromeda
Galaxy.
  I report about some of my scientific observations using remote telescopes: of
superhumps of cataclysmic variable stars; of echo outburst of AM CVn star; of
maximum brightness of blazars; of optical afterglows of gamma-ray bursts
(including GRB 221009A); of microlensing events; of rotation of near-Earth
asteroid 2022 AB. I also describe my photometric follow-up observations of
novae (including V1405 Cas and V1674 Her) and my astrometric observations of
Solar System objects (including the confirmation of objects posted at the
Confirmation Pages of the Minor Planet Center) including observations of comet
2I/Borisov, asteroids 2020 AV2 and (65803) Didymos. I also describe some of my
observations of occultations: of the star by asteroid (159) Aemilia, of the
star by Saturn's moon Titan and of Uranus by the Moon during total lunar
eclipse on November 8, 2022; and visual observations of variable stars, meteors
and sunspots (including during the transit of Venus in 2012).
  Some of my data already used in scientific papers, others were sent to the
databases. I share my experience of discovery and research of astronomical
objects and in my example, I show that an amateur astronomer can make a real
contribution to the science.


  We investigate the effects of stochasticity in the spatial and temporal
distribution of supernova remnants on the anisotropy of cosmic rays observed at
Earth. The calculations are carried out for different choices of the diffusion
coefficient D(E) for propagation in the Galaxy. The propagation and spallation
of nuclei are taken into account. At high energies we assume that
$D(E)\sim(E/Z)^{\delta}$, with $\delta=1/3$ and $\delta=0.6$ being the
reference scenarios. The large scale distribution of supernova remnants in the
Galaxy is modeled following the distribution of pulsars with and without
accounting for the spiral structure of the Galaxy. Our calculations allow us to
determine the contribution to anisotropy resulting from both the large scale
distribution of SNRs in the Galaxy and the random distribution of the nearest
remnants. The naive expectation that the anisotropy amplitude scales as D(E) is
shown to be an oversimplification which does not reflect in the predicted
anisotropy for any realistic distribution of the sources. The fluctuations in
the anisotropy pattern are dominated by nearby sources, so that predicting or
explaining the observed anisotropy amplitude and phase becomes close to
impossible. We find however that the very weak energy dependence of the
anisotropy amplitude below $10^{5}$ GeV and the rise at higher energies, can
best be explained if the diffusion coefficient is $D(E)\sim E^{1/3}$. Faster
diffusion, for instance with $\delta=0.6$, leads in general to an exceedingly
large anisotropy amplitude. The spiral structure introduces interesting trends
in the energy dependence of the anisotropy pattern, which qualitatively reflect
the trend seen in the data. For large values of the halo size we find that the
anisotropy becomes dominated by the large scale regular structure of the source
distribution, leading indeed to a monotonic increase of $\delta_A$ with energy.


  We propose a mechanism for cogenesis of baryon and dark matter (DM) in the
universe via the Affleck-Dine (AD) route. An AD field which breaks the lepton
number symmetry, leads to the generation of lepton asymmetry by virtue of its
cosmic evolution, which then gets transferred into lepton and dark sectors.
While the lepton asymmetry gets converted into baryon asymmetry via sphalerons,
the dark sector asymmetry leads to the final DM abundance with the symmetric
part being annihilated away due to resonantly enhanced annihilation, which we
choose to be provided by a gauged $B-L$ portal. Stringent constraints from DM
direct detection forces DM and $B-L$ gauge boson masses to be light, in the few
GeV ballpark. While a large portion of the model parameter space is already
ruled out, the remaining parameter space is within sensitivity of laboratory as
well as cosmology based experiments. The AD field also plays the role of
inflaton with the required dynamics by virtue of its non-minimal coupling to
gravity, consistent with observations.


  Machine learning techniques can reveal hidden structure in large data amounts
and can potentially extent or even replace analytical scientific methods. In
nanophotonics, modes can increase the light yield from emitters located inside
the nanostructure or near the surface. Optimizing such systems enforces to
systematically analyze large amounts of three-dimensional field distribution
data. We present a method based on finite element simulations and machine
learning for the identification of modes with large field energies and specific
spatial properties. By clustering we reduce the field distribution data to a
minimal subset of prototypes. The predictive power of the approach is
demonstrated using an analysis of experimentally measured fluorescence
enhancement of quantum dots on a photonic crystal surface. The clustering
method can be used for any optimization task that depends on three-dimensional
field data, and is therefore relevant for biosensing, quantum dot solar cells
or photon upconversion.


  In this work, we address a novel, but potentially emerging, problem of
discriminating the natural human voices and those played back by any kind of
audio devices in the context of interactions with in-house voice user
interface. The tackled problem may find relevant applications in (1) the
far-field voice interactions of vocal interfaces such as Amazon Echo, Google
Home, Facebook Portal, etc, and (2) the replay spoofing attack detection. The
detection of loudspeaker emitted speech will help avoid false wake-ups or
unintended interactions with the devices in the first application, while
eliminating attacks involve the replay of recordings collected from enrolled
speakers in the second one. At first we collect a real-world dataset under
well-controlled conditions containing two classes: recorded speeches directly
spoken by numerous people (considered as the natural speech), and recorded
speeches played back from various loudspeakers (considered as the loudspeaker
emitted speech). Then from this dataset, we build prediction models based on
Deep Neural Network (DNN) for which different combination of audio features
have been considered. Experiment results confirm the feasibility of the task
where the combination of audio embeddings extracted from SoundNet and VGGish
network yields the classification accuracy up to about 90%.


  We propose a generalization of spin algebra using three-index objects. There
is a possibility that a triple commutation relation among three-index objects
implies a kind of uncertainty relation among their expectation values.


  Angle-resolved photoemission spectra of alkali metals exhibit a puzzling,
non-dispersing peak in the apparent density of states near the Fermi energy. We
argue that the holes left behind a significant fraction of photoejected
electrons are not wavepacket-like objects used to describe excitations of an
equilibrium Fermi liquid but, instead, are relatively localized entities
resulting from a photon-induced cavitation in the electron fluid. At the same
time, these special localized holes can be thought of as vacancies in a
transient Wigner solid. The corresponding contribution to the photoemission
current is non-dispersive and is tied to the Fermi level; it exhibits certain
similarities to photoemission from localized core orbitals such as the presence
of recoil currents. Calculated spectra are consistent with experiment. We
briefly discuss the present findings in the context of quantum measurement.


  Femtocells are assuming an increasingly important role in the coverage and
capacity of cellular networks. In contrast to existing cellular systems,
femtocells are end-user deployed and controlled, randomly located, and rely on
third party backhaul (e.g. DSL or cable modem). Femtocells can be configured to
be either open access or closed access. Open access allows an arbitrary nearby
cellular user to use the femtocell, whereas closed access restricts the use of
the femtocell to users explicitly approved by the owner. Seemingly, the network
operator would prefer an open access deployment since this provides an
inexpensive way to expand their network capabilities, whereas the femtocell
owner would prefer closed access, in order to keep the femtocell's capacity and
backhaul to himself. We show mathematically and through simulations that the
reality is more complicated for both parties, and that the best approach
depends heavily on whether the multiple access scheme is orthogonal (TDMA or
OFDMA, per subband) or non-orthogonal (CDMA). In a TDMA/OFDMA network,
closed-access is typically preferable at high user densities, whereas in CDMA,
open access can provide gains of more than 200% for the home user by reducing
the near-far problem experienced by the femtocell. The results of this paper
suggest that the interests of the femtocell owner and the network operator are
more compatible than typically believed, and that CDMA femtocells should be
configured for open access whereas OFDMA or TDMA femtocells should adapt to the
cellular user density.


  Pressure ulcers have high prevalence in ICU patients but are preventable if
identified in initial stages. In practice, the Braden scale is used to classify
high-risk patients. This paper investigates the use of machine learning in
electronic health records data for this task, by using data available in
MIMIC-III v1.4. Two main contributions are made: a new approach for evaluating
models that considers all predictions made during a stay, and a new training
method for the machine learning models. The results show a superior performance
in comparison to the state of the art; moreover, all models surpass the Braden
scale in every operating point in the precision-recall curve. -- --
  Les\~oes por press\~ao possuem alta preval\^encia em pacientes de UTI e s\~ao
preven\'iveis ao serem identificadas em est\'agios iniciais. Na pr\'atica
utiliza-se a escala de Braden para classifica\c{c}\~ao de pacientes em risco.
Este artigo investiga o uso de aprendizado de m\'aquina em dados de registros
eletr\^onicos para este fim, a partir da base de dados MIMIC-III v1.4. S\~ao
feitas duas contribui\c{c}\~oes principais: uma nova abordagem para a
avalia\c{c}\~ao dos modelos e da escala de Braden levando em conta todas as
predi\c{c}\~oes feitas ao longo das interna\c{c}\~oes, e um novo m\'etodo de
treinamento para os modelos de aprendizado de m\'aquina. Os resultados obtidos
superam o estado da arte e verifica-se que os modelos superam
significativamente a escala de Braden em todos os pontos de opera\c{c}\~ao da
curva de precis\~ao por sensibilidade.


  Using path integral techniques, we compute exactly the distribution of the
maximal height H_p of p nonintersecting Brownian walkers over a unit time
interval in one dimension, both for excursions (p-watermelons with a wall) and
bridges (p-watermelons without a wall), for all integer p\ge 1. For large p, we
show that < H_p > \sim \sqrt{2p} (excursions) whereas < H_p > \sim \sqrt{p}
(bridges). Our exact results prove that previous numerical experiments only
measured the pre-asymptotic behaviors and not the correct asymptotic ones. In
addition, our method establishes a physical connection between vicious walkers
and random matrix theory.


  Botnets and malware continue to avoid detection by static rules engines when
using domain generation algorithms (DGAs) for callouts to unique, dynamically
generated web addresses. Common DGA detection techniques fail to reliably
detect DGA variants that combine random dictionary words to create domain names
that closely mirror legitimate domains. To combat this, we created a novel
hybrid neural network, Bilbo the `bagging` model, that analyses domains and
scores the likelihood they are generated by such algorithms and therefore are
potentially malicious. Bilbo is the first parallel usage of a convolutional
neural network (CNN) and a long short-term memory (LSTM) network for DGA
detection. Our unique architecture is found to be the most consistent in
performance in terms of AUC, F1 score, and accuracy when generalising across
different dictionary DGA classification tasks compared to current
state-of-the-art deep learning architectures. We validate using
reverse-engineered dictionary DGA domains and detail our real-time
implementation strategy for scoring real-world network logs within a large
financial enterprise. In four hours of actual network traffic, the model
discovered at least five potential command-and-control networks that commercial
vendor tools did not flag.


  We prove a metric statement about approximation of a $n$-dimensional linear
subspace $A$ in $\mathbb{R}^d$ by $n$-dimensional rational subspaces. We
consider the problem of finding a rational subspace $B$ of bounded height
$H=H(B)$ for which the angle of inclination $\psi (A,B) $ is small in terms of
$H$. In the simplest case $d=4, n=2$ we give a partial solution of a problem
formulated by W.M. Schmidt in 1967.


  We have explored the impact of sterile neutrino dark matter on core-collapse
supernova explosions. We have included oscillations between electron neutrinos
or mixed $\mu,\tau$ neutrinos and right-handed sterile neutrinos into a
supernova model. We have chosen sterile neutrino masses and mixing angles that
are consistent with sterile neutrino dark matter candidates as indicated by
recent x-ray flux measurements. Using these simulations, we have explored the
impact of sterile neutrinos on the core bounce and shock reheating. We find
that, for ranges of sterile neutrino mass and mixing angle consistent with most
dark matter constraints, the shock energy can be significantly enhanced and
even a model that does not explode can be made to explode. In addition, we have
found that the presence of a sterile neutrino may lead to detectable changes in
the observed neutrino luminosities.


  We fit the single-hadron transverse-momentum spectra measured in Pb+Pb
collisions at $\sqrt{s_{NN}} = 2.76$ TeV with the blast-wave model that
includes production via resonance decays. Common fit to pions, kaons,
(anti)protons, and lambdas yields centrality dependence of the freeze-out
temperature and transverse expansion velocity. In the most central collisions
we see $T=98$ MeV and $\langle v_t \rangle = 0.654$. The $K^*$ resonance fits
into this picture but the $\phi$ meson might freeze-out a little earlier.
Multistrange baryons seem to decouple at higher temperature and weaker
transverse flow. Within our model we observe hints of chemical potential for
the charged pions.


  A theoretical study is carried out for the cavity cooling of a $\Lambda$-type
three level atom in a high-finesse optical cavity with a weakly driven field.
Analytical expressions for the friction, diffusion coefficients and the
equilibrium temperatures are obtained by using the Heisenberg equations, then
they are calculated numerically and shown graphically as a function of
controlling parameters. For a suitable choice of these parameters, the dynamics
of the cavity field interaction with the $\Lambda$-type three-level atom
introduces a sisyphus cooling mechanism yielding lower temperatures below the
Doppler limit and allowing larger cooling rate, avoiding the problems induced
by spontaneous emission.


  We study some particular modifications of gravity in search for a natural way
to unify the gravitational and electromagnetic interaction. The certain
components of connection in the appearing variants of the theory can be
identified with electromagnetic potential. The methods of adding matter in the
form of scalar and spinor fields are studied. In particular, the expansion of
the local symmetry group up to $GL(2,C)$ is explored, in which equations of
Einstein, Maxwell and Dirac are reproduced for the theory with Weyl spinor.


  The influence of large amplitude pairing fluctuations is investigated in the
framework of beyond mean field symmetry conserving configuration mixing
calculations. In the numerical application the finite range density dependent
Gogny force is used. We investigate the nucleus $^{54}$Cr with particle number
and angular momentum projected wave functions considering the axial quadrupole
deformation and the pairing gap degree of freedom as generator coordinates. We
find that the effects of the pairing fluctuations increase with the excitation
energy and the angular momentum. The self-consistency in the determination of
the basis states plays an important role.


  The soft current describes the factorization behavior of quantum
chromodynamics (QCD) scattering amplitudes in the limit of vanishing energy of
one of the external partons. It is process-independent and can be expanded in a
perturbative series in the coupling constant. To all orders in the dimensional
regularization parameter, we compute the two-loop correction to the soft
current for processes involving two hard partons.


  Charge inversion occurring for smeared-out ions is accompanied by an
oscillatory charge density profile, indicating a layering of an alternating
charge. The layering effect, however, is not coextensive with charge inversion,
and charge inversion accompanied by a monotonically decaying profile is
possible. The present work focuses on the structure of a double-layer of
overscreened charged surfaces by smeared-out charges and probes the link
between the structure of a double-layer and the bulk properties of an
electrolyte, with special view to the role of the Kirkwood crossover.


  We show that for a connected Lie group $G$, its Fourier algebra $A(G)$ is
weakly amenable only if $G$ is abelian. Our main new idea is to show that weak
amenability of $A(G)$ implies that the anti-diagonal,
$\check{\Delta}_G=\{(g,g^{-1}):g\in G\}$, is a set of local synthesis for
$A(G\times G)$. We then show that this cannot happen if $G$ is non-abelian. We
conclude for a locally compact group $G$, that $A(G)$ can be weakly amenable
only if it contains no closed connected non-abelian Lie subgroups. In
particular, for a Lie group $G$, $A(G)$ is weakly amenable if and only if its
connected component of the identity $G_e$ is abelian.


  This paper is a continuation of a previous work about the study of the
survival probability modelizing the molecular predissociation in the
Born-Oppenheimer framework. Here we consider the critical case where the
reference energy corresponds to the value of a crossing of two electronic
levels, one of these two levels being confining while the second dissociates.
We show that the survival probability associated to a certain initial state is
a sum of the usual time-dependent exponential contribution, and a reminder term
that is jointly polynomially small with respect to the time and the
semiclassical parameter. We also compute explicitly the main contribution of
the remainder.


  We describe a portable CCD-based instrumentation system designed to
efficiently undertake high time precision fast photometry. The key components
of the system are (1) an externally triggered commercial frame-transfer CCD,
(2) a custom GPS-derived time source, and (3) flexible software for both
instrument control and online analysis/display. Two working instruments that
implement this design are described. The New Zealand based instrument employs a
Princeton Instruments (PI) 1k x 1k CCD and has been used with the 1 m telescope
at Mt John University Observatory, while the other uses a newer 1k x 1k
electron-multiplying CCD supplied by PI and is based at the University of Texas
at Austin. We include some recent observations that illustrate the capabilities
of the instruments.


  $*$-structures on quantum and braided spaces of the type defined via an
R-matrix are studied. These include $q$-Minkowski and $q$-Euclidean spaces as
additive braided groups. The duality between the $*$-braided groups of vectors
and covectors is proved and some first applications to braided geometry are
made.


  On the basis of a fit to the Sivers effect in deep-inelastic scattering, we
make predictions for single-spin asymmetries in the Drell-Yan process at RHIC.


  The spectral hardenings of cosmic ray nuclei above $\sim 200$ GV followed by
softenings around 10 TV, the knee of the all-particle spectrum around PeV
energies, as well as the pattern change of the amplitude and phase of the
large-scale anisotropies around 100 TeV indicate the complexities of the origin
and transportation of Galactic cosmic rays. It has been shown that nearby
source(s) are most likely to be the cause of such spectral features of both the
spectra and the anisotropies. In this work, we study the anisotropy features of
different mass composition (or mass groups) of cosmic rays in this nearby
source model. We show that even if the spectral features from the nearby source
component is less distinctive compared with the background component from e.g.,
the population of distant sources, the anisotropy features are more remarkable
to be identified. Measurements of the anisotropies of each mass composition
(group) of cosmic rays by the space experiments such as DAMPE and HERD and the
ground-based experiments such as LHAASO in the near future are expected to be
able to critically test this scenario.


  We derive bounds on the electric and chromo-electric dipole moments of the
charm quark. The second one turns out to be particularly strong, and we
quantify its impact on models that allow for a sizeable flavour violation in
the up quark sector, like flavour alignment and Generic U(2)^3. In particular
we show how the bounds coming from the charm and up CEDMs constrain the size of
new physics contributions to direct flavour violation in D decays. We also
specialize our analysis to the cases of Supersymmetry with split families and
composite Higgs models. The results exposed in this paper motivate both an
increase in experimental sensitivity to fundamental hadronic dipoles, and a
further exploration of the SM contribution to flavour violating D decays.


  We study the equilibrium properties of a ferromagnetic
insulator/superconductor structure near a magnetic domain wall. We show how the
domain wall size is affected by the superconductivity in such structures.
Moreover, we calculate several physical quantities altered due to the magnetic
domain wall, such as the spin current density and local density of states, as
well as the resulting tunneling conductance into a structure with a magnetic
domain wall.


  Over-fitting-based image compression requires weights compactness for
compression and fast convergence for practical use, posing challenges for deep
convolutional neural networks (CNNs) based methods. This paper presents a
simple re-parameterization method to train CNNs with reduced weights storage
and accelerated convergence. The convolution kernels are re-parameterized as a
weighted sum of discrete cosine transform (DCT) kernels enabling direct
optimization in the frequency domain. Combined with L1 regularization, the
proposed method surpasses vanilla convolutions by achieving a significantly
improved rate-distortion with low computational cost. The proposed method is
verified with extensive experiments of over-fitting-based image restoration on
various datasets, achieving up to -46.12% BD-rate on top of HEIF with only 200
iterations.


  We propose a new conjectural way to calculate the local $L$-factor
$L=L_\chi(\pi,\rho,s)$ where $\pi$ is a representation of a $p$-adic group $G$,
$\rho$ is an algebraic representation of the dual group $G^{\vee}$ and $\chi$
is an algebraic character of $G$ satisfying a positivity condition.
  A method going back to Godement and Jacquet yields a description of $L$ using
as an input a certain space ${\mathcal S}_\rho$ of functions on $G$ depending
on $\rho$. A (partly conjectural) description of ${\mathcal S}_\rho$ involving
trace of Frobenius functions associated to perverse sheaves on the loop space
of a semigroup containing $G$ was developed %by Bouthier, Ngo and Sakellaridis,
partly based on an earlier work of Braverman and Kazhdan. Here we propose a
different, more general conjectural description of ${\mathcal S}_\rho$: it also
refers to trace of Frobenius functions but instead of the loop space of a
semi-group we work with the ramified global Grassmannian fibering over the
configuration space of points on a global curve defined by Beilinson-Drinfeld
and Gaitsgory (a relation between two approaches is discussed in the appendix).
  Our main result asserts validity of our conjectures where $\pi$ is generated
by an Iwahori fixed vector: we show that in this case it is compatible with the
standard formula for $L$ involving local Langlands correspondence which is
known for such representations $\pi$.
  The proof is based on properties of the coherent realization of the affine
Hecke category.


  In human interactions, emotion recognition is crucial. For this reason, the
topic of computer-vision approaches for automatic emotion recognition is
currently being extensively researched. Processing multi-channel
electroencephalogram (EEG) information is one of the most researched methods
for automatic emotion recognition. This paper presents a new model for an
affective computing-driven Quality of Experience (QoE) prediction. In order to
validate the proposed model, a publicly available dataset is used. The dataset
contains EEG, ECG, and respiratory data and is focused on a multimedia QoE
assessment context. The EEG data are retained on which the differential entropy
and the power spectral density are calculated with an observation window of
three seconds. These two features were extracted to train several deep-learning
models to investigate the possibility of predicting QoE with five different
factors. The performance of these models is compared, and the best model is
optimized to improve the results. The best results were obtained with an
LSTM-based model, presenting an F1-score from 68% to 78%. An analysis of the
model and its features shows that the Delta frequency band is the least
necessary, that two electrodes have a higher importance, and that two other
electrodes have a very low impact on the model's performances.


  Clustering large spatial databases is an important problem, which tries to
find the densely populated regions in a spatial area to be used in data mining,
knowledge discovery, or efficient information retrieval. However most
algorithms have ignored the fact that physical obstacles such as rivers, lakes,
and highways exist in the real world and could thus affect the result of the
clustering. In this paper, we propose CPO, an efficient clustering technique to
solve the problem of clustering in the presence of obstacles. The proposed
algorithm divides the spatial area into rectangular cells. Each cell is
associated with statistical information used to label the cell as dense or
non-dense. It also labels each cell as obstructed (i.e. intersects any
obstacle) or nonobstructed. For each obstructed cell, the algorithm finds a
number of non-obstructed sub-cells. Then it finds the dense regions of
non-obstructed cells or sub-cells by a breadthfirst search as the required
clusters with a center to each region.


  The concept of the moduli space allows for a simple, universally applicable
description of the low-energy dynamics of topological solitons. This
description is remarkably insensitive to the properties of the underlying
theory, whose details only manifest themselves via the moduli space metric.
This article presents a generalization of this concept, which allows to
transfer its most intriguing features to configurations of any energy captured
by the theory giving rise to the soliton, given that these are localized
sufficiently close to the soliton's center. The resulting theory is capable of
describing all dynamics within its range of applicability by just one family of
fields, with all the information about the underlying theory entering via a
finite number of background functions, which can be linked to physical
properties of the present soliton.


  Hamiltonian Monte Carlo provides efficient Markov transitions at the expense
of introducing two free parameters: a step size and total integration time.
Because the step size controls discretization error it can be readily tuned to
achieve certain accuracy criteria, but the total integration time is left
unconstrained. Recently Hoffman and Gelman proposed a criterion for tuning the
integration time in certain systems with their No U-Turn Sampler, or NUTS. In
this paper I investigate the dynamical basis for the success of NUTS and
generalize it to Riemannian Manifold Hamiltonian Monte Carlo.


  Ultra High Energy Cosmic Rays are the most energetic particles directly
measured, reaching orders of magnitude above those attained in the LHC. The
Pierre Auger Observatory is shedding light onto the long-standing mysteries of
their nature and origin. The present paper briefly reviews the latest results:
the energy spectrum and mass composition of UHECR, limits on the fluxes of
photons and neutrinos, the study of anisotropies in the arrival directions of
UHECS, and finally the constraints on high energy hadronic models. The paper
will finish with the upgrade of the Observaotry: AugerPrime.


  Achieving super-human performance in recognizing human speech has been a goal
for several decades, as researchers have worked on increasingly challenging
tasks. In the 1990's it was discovered, that conversational speech between two
humans turns out to be considerably more difficult than read speech as
hesitations, disfluencies, false starts and sloppy articulation complicate
acoustic processing and require robust handling of acoustic, lexical and
language context, jointly. Early attempts with statistical models could only
reach error rates over 50% and far from human performance (WER of around 5.5%).
Neural hybrid models and recent attention-based encoder-decoder models have
considerably improved performance as such contexts can now be learned in an
integral fashion. However, processing such contexts requires an entire
utterance presentation and thus introduces unwanted delays before a recognition
result can be output. In this paper, we address performance as well as latency.
We present results for a system that can achieve super-human performance (at a
WER of 5.0%, over the Switchboard conversational benchmark) at a word based
latency of only 1 second behind a speaker's speech. The system uses multiple
attention-based encoder-decoder networks integrated within a novel low latency
incremental inference approach.


  We determine the top quark mass m_t using t-tbar pairs produced in the D0
detector by \sqrt{s} = 1.8 TeV p-pbar collisions in a 125 pb^-1 exposure at the
Fermilab Tevatron. We make a two constraint fit to m_t in t-tbar -> b W^+bbar
W^- final states with one W boson decaying to q-qbar and the other to e-nu or
mu-nu. Likelihood fits to the data yield m_t(l+jets) = 173.3 +- 5.6 (stat) +-
5.5 (syst) GeV/c^2. When this result is combined with an analysis of events in
which both W bosons decay into leptons, we obtain m_t = 172.1 +- 5.2 (stat) +-
4.9 (syst) GeV/c^2. An alternate analysis, using three constraint fits to fixed
top quark masses, gives m_t(l+jets) = 176.0 +- 7.9 (stat) +- 4.8 (syst)
GeV/C^2, consistent with the above result. Studies of kinematic distributions
of the top quark candidates are also presented.


  Kepler-20 is a solar-type star (V = 12.5) hosting a compact system of five
transiting planets, all packed within the orbital distance of Mercury in our
own Solar System. A transition from rocky to gaseous planets with a planetary
transition radius of ~1.6 REarth has recently been proposed by several
publications in the literature (Rogers 2015;Weiss & Marcy 2014). Kepler-20b (Rp
~ 1.9 REarth) has a size beyond this transition radius, however previous mass
measurements were not sufficiently precise to allow definite conclusions to be
drawn regarding its composition. We present new mass measurements of three of
the planets in the Kepler-20 system facilitated by 104 radial velocity
measurements from the HARPS-N spectrograph and 30 archival Keck/HIRES
observations, as well as an updated photometric analysis of the Kepler data and
an asteroseismic analysis of the host star (MStar = 0.948+-0.051 Msun and Rstar
= 0.964+-0.018 Rsun). Kepler-20b is a 1.868+0.066-0.034 REarth planet in a 3.7
day period with a mass of 9.70+1.41-1.44 MEarth resulting in a mean density of
8.2+1.5-1.3 g/cc indicating a rocky composition with an iron to silicate ratio
consistent with that of the Earth. This makes Kepler-20b the most massive
planet with a rocky composition found to date. Furthermore, we report the
discovery of an additional non-transiting planet with a minimum mass of
19.96+3.08-3.61 MEarth and an orbital period of ~34 days in the gap between
Kepler-20f (P ~ 11 days) and Kepler-20d (P ~ 78 days).


  In the last decade, transition-metal-doped ZnO has been intensively
investigated as a route to room-temperature diluted magnetic semiconductors
(DMSs). However, the origin for the reported ferromagnetism in ZnO-based DMS
remains questionable. Possible options are diluted magnetic semiconductors,
spinodal decomposition, or secondary phases. In order to clarify this question,
we have performed a thorough characterization of the structural and magnetic
properties of Co- and Ni-implanted ZnO single crystals. Our measurements reveal
that Co or Ni nanocrystals (NCs) are the major contribution of the measured
ferromagnetism. Already in the as-implanted samples, Co or Ni NCs have formed
and they exhibit superparamagnetic properties. The Co or Ni NCs are
crystallographically oriented with respect to the ZnO matrix. Their magnetic
properties, e.g., the anisotropy and the superparamagnetic blocking
temperature, can be tuned by annealing. We discuss the magnetic anisotropy of
Ni NCs embedded in ZnO concerning the strain anisotropy.


  We have detected extraplanar cold dust at distances out to > 10 kpc, situated
in the halo of the interacting galaxy NGC 4631. The dust emission disk is much
thinner than the warped HI disk and new structures emerge. In particular, a
giant arc has been found that is linked to anomalies in the kinematical
structure of the atomic gas. Most of the extraplanar dust is closely associated
with HI spurs that have been found earlier. These spurs obviously are traces of
the interaction. The dust emission within the plane reaches the border of the
optical disk.
  The activity of the disk of NGC 4631 is moderately enhanced by the
interaction, but no gas moving in the z-direction could be found. Hence, it
seems unlikely that strong winds have deposited the high-z dust. Instead, the
coincidence with the HI features suggests that we see a track left behind by
the interaction. In addition, the HI shows a supershell formed by an impact in
the zone where the dust trail crosses the disk. This region is also
characterized by disturbances in the distribution of the Ha light. The masses
associated with the dust can be estimated only very roughly on the basis of the
existing data; they are of the order of a few 10^9 M_o of gas.


  Multilingual sentence representations from large models encode semantic
information from two or more languages and can be used for different
cross-lingual information retrieval and matching tasks. In this paper, we
integrate contrastive learning into multilingual representation distillation
and use it for quality estimation of parallel sentences (i.e., find
semantically similar sentences that can be used as translations of each other).
We validate our approach with multilingual similarity search and corpus
filtering tasks. Experiments across different low-resource languages show that
our method greatly outperforms previous sentence encoders such as LASER,
LASER3, and LaBSE.


  We compute the component group of the derived $G$-geometric fixed points of
the real topological Hochschild homology of a ring with anti-involution, where
$G$ denotes the Galois group Gal($\mathbb{C}$/$\mathbb{R}$) of order 2.


  In purely c-axis oriented PbZr$_{0.2}$Ti$_{0.8}$O$_3$ ferroelectric thin
films, a lateral piezoresponse force microscopy signal is observed at the
position of 180{\deg}domain walls, where the out-of-plane oriented polarization
is reversed. Using electric force microscopy measurements we exclude
electrostatic effects as the origin of this signal. Moreover, our mechanical
simulations of the tip/cantilever system show that the small tilt of the
surface at the domain wall below the tip does not satisfactorily explain the
observed signal either. We thus attribute this lateral piezoresponse at domain
walls to their sideways motion (shear) under the applied electric field. From
simple elastic considerations and the conservation of volume of the unit cell,
we would expect a similar lateral signal more generally in other ferroelectric
materials, and for all types of domain walls in which the out-of-plane
component of the polarization is reversed through the domain wall. We show that
in BiFeO$_3$ thin films, with 180, 109 and 71{\deg}domain walls, this is indeed
the case.


  Near-Infrared (NIR) observations are presented for five Type IIn supernovae
(SN 1995N, SN 1997ab, SN 1998S, SN 1999Z, and SN 1999el) that exhibit strong
infrared excesses at late times (t >= 100 d). H- and K-band emission from these
objects is dominated by a continuum that rises toward longer wavelengths. The
data are interpreted as thermal emission from dust, probably situated in a
pre-existing circumstellar nebula. The IR luminosities implied by single
temperature blackbody fits are quite large,> 10^(41 - 42) erg s^-1, and the
emission evolves slowly, lasting for years after maximum light. For SN 1995N,
the integrated energy release via IR dust emission was 0.5 -- 1 * 10^50 erg. A
number of dust heating scenarios are considered, the most likely being an
infrared echo poweredby X-ray and UV emissions from the shock interaction with
a dense circumstellar medium.


  The main result of this paper is that if $X$ is a Peano continuum such that
its $n$-th cone $C^n(X)$ embeds into $\RR^{n+2}$ then $X$ embeds into $S^2$.
This solves a problem proposed by W. Rosicki.


  We compute the structure and degree of neutronization of general relativistic
magnetohydrodynamic (GRMHD) outflows originating from the inner region of
neutrino-cooled disks. We consider both, outflows expelled from a hydrostatic
disk corona and outflows driven by disk turbulence. We show that in outflows
driven thermally from a static disk the electron fraction quickly evolves to
its equilibrium value which is dominated by neutrino capture. Those outflows
are generally proton rich and, under certain conditions, can be magnetically
dominated. They may also provide sites for effective production of 56Ni.
Centrifugally driven outflows and outflows driven by disk turbulence, on the
other hand, can preserve the large in-disk neutron excess. Those outflows are,
quite generally, subrelativistic by virtue of the large mass flux driven by the
additional forces.


  We present recent results from searches by the LIGO Science Collaboration for
bursts of gravitational-wave radiation, as well as the status of other ongoing
searches. These include directed searches for bursts associated with observed
sources (gamma-ray bursts, soft gamma repeaters) and untriggered searches for
bursts from unknown sources. We also present the status of some newer
investigations, such as coherent network methods. We show methods for
interpreting our search results in terms of astrophysical source distributions
that improve their accessibility to the wider community.


  Fleming's inequality is generalized to the decay function of mixed states. We
show that for any symmetric hamiltonian $h$ and for any density operator $\rho$
on a finite dimensional Hilbert space with the orthogonal projection $\Pi$ onto
the range of $\rho$ there holds the estimate $\Tr(\Pi \rme^{-\rmi ht}\rho
\rme^{\rmi ht}) \geq\cos^{2}((\Delta h)_{\rho}t) $ for all real $t$ with
$(\Delta h)_{\rho}| t| \leq\pi/2.$ We show that equality either holds for all
$t\in\mathbb{R}$ or it does not hold for a single $t$ with $0<(\Delta
h)_{\rho}| t| \leq\pi/2.$ All the density operators saturating the bound for
all $t\in\mathbb{R},$ i.e. the mixed intelligent states, are determined.


  Industrial accidents, chemical spills, and structural fires can release large
amounts of harmful materials that disperse into urban atmospheres and impact
populated areas. Computer models are typically used to predict the transport of
toxic plumes by solving fluid dynamical equations. However, these models can be
computationally expensive due to the need for many grid cells to simulate
turbulent flow and resolve individual buildings and streets. In emergency
response situations, alternative methods are needed that can run quickly and
adequately capture important spatiotemporal features. Here, we present a novel
deep learning model called ST-GasNet that was inspired by the mathematical
equations that govern the behavior of plumes as they disperse through the
atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set
of temporal sequences of ground-level toxic urban plumes generated by a
high-resolution large eddy simulation model. On independent sequences,
ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the
early-time behavior as an input, even for cases when a building splits a large
plume into smaller plumes. By incorporating large-scale wind boundary condition
information, ST-GasNet achieves a prediction accuracy of at least 90% on test
data for the entire prediction period.


  Three-dimensional generative adversarial networks (3D-GAN) have attracted
widespread attention in three-dimension (3D) visual tasks. 3D deconvolution
(DeConv), as an important computation of 3D-GAN, significantly increases
computational complexity compared with 2D DeConv. 3D DeConv has become a
bottleneck for the acceleration of 3D-GAN. Previous accelerators suffer from
several problems, such as large memory requirements and resource
underutilization. To handle the above issues, a fast algorithm for 3D DeConv
(F3DC) is proposed in this paper. F3DC applies a fast algorithm to reduce the
number of multiplications and achieves a significant algorithmic strength
reduction. Besides, F3DC removes the extra memory requirement for overlapped
partial sums and avoids computational imbalance to fully utilize resources.
Moreover, we design an F3DC-based hardware architecture, which consists of four
fast processing units (FPUs). Each FPU includes a pre-process module, a EWMM
module and a post-process module for F3DC transformation. By implementing our
design on the Xilinx VC709 platform for 3D-GAN, we achieve a throughput up to
1700 GOPS and 4$\times$ computational efficiency improvement compared with
prior works.


  We propose a formalism which uses boundary conditions imposed on the
Luttinger liquid (LL) to describe the transport properties of a LL coupled to
reservoirs. The various boundary conditions completely determine linear
transport in the joint system reservoirs+LL. As an illustration we consider an
exactly solvable microscopic model in a multi-terminal geometry for which such
boundary conditions can be explicitly derived; in this model the
Landauer-B\"uttiker formalism fails: if it were valid, the relation between the
conductance matrix elements and the reflection and transmission coefficients
could yield negative probabilities. We then apply our formalism to a discussion
of shot noise through an impurity in a LL connected to two reservoirs.


  Unconventional flat band (FB) superconductivity, as observed in van der Waals
heterostructures, could open promising avenues towards high-T$_c$ materials. In
FBs, pairings and superfluid weight scale linearly with the interaction
parameter, such an unusual behaviour justifies and encourages strategies to
promote FB engineering. Bipartite lattices (BLs) which naturally host FBs could
be particularly interesting candidates. Within Bogoliubov de Gennes theory and
in the framework of the attractive Hubbard model in BLs, a hidden symmetry of
the quasi-particle eigenstates is revealed. As a consequence, we demonstrate
universal relations for the pairings and the superfluid weight that are
independent of the characteristics of the hopping term. Remarkably, it is shown
that these general properties are insensitive to disorder as long as the
bipartite character is protected.


  Echo chambers and opinion polarization recently quantified in several
sociopolitical contexts and across different social media, raise concerns on
their potential impact on the spread of misinformation and on openness of
debates. Despite increasing efforts, the dynamics leading to the emergence of
these phenomena stay unclear. We propose a model that introduces the dynamics
of radicalization, as a reinforcing mechanism driving the evolution to extreme
opinions from moderate initial conditions. Inspired by empirical findings on
social interaction dynamics, we consider agents characterized by heterogeneous
activities and homophily. We show that the transition between a global
consensus and emerging radicalized states is mostly governed by social
influence and by the controversialness of the topic discussed. Compared with
empirical data of polarized debates on Twitter, the model qualitatively
reproduces the observed relation between users' engagement and opinions, as
well as opinion segregation in the interaction network. Our findings shed light
on the mechanisms that may lie at the core of the emergence of echo chambers
and polarization in social media.


  We report heat capacity measurements of SrCu$_2$(BO$_3$)$_2$ under high
pressure along with simulations of relevant quantum spin models and map out the
$(P,T)$ phase diagram of the material. We find a first-order quantum phase
transition between the low-pressure quantum dimer paramagnet and a phase with
signatures of a plaquette-singlet state below T = $2$ K. At higher pressures,
we observe a transition into a previously unknown antiferromagnetic state below
$4$ K. Our findings can be explained within the two-dimensional
Shastry-Sutherland quantum spin model supplemented by weak inter-layer
couplings. The possibility to tune SrCu$_2$(BO$_3$)$_2$ between the
plaquette-singlet and antiferromagnetic states opens opportunities for
experimental tests of quantum field theories and lattice models involving
fractionalized excitations, emergent symmetries, and gauge fluctuations.


  We show that for every vector bundle E over any given symplectic manifold M
there exists an explicitly given super-Poisson bracket on the space of sections
of the dual Grassmann bundle associated to E built out of symplectic structure
of M, a fibre metric on E, and a connection in E compatible with the given
fibre metric. Moreover, we construct a deformation quantization for this space
of sections by means of a Fedosov type procedure.


  We investigate the generalizability of learned binary relations: functions
that map pairs of instances to a logical indicator. This problem has
application in numerous areas of machine learning, such as ranking, entity
resolution and link prediction. Our learning framework incorporates an example
labeler that, given a sequence $X$ of $n$ instances and a desired training size
$m$, subsamples $m$ pairs from $X \times X$ without replacement. The challenge
in analyzing this learning scenario is that pairwise combinations of random
variables are inherently dependent, which prevents us from using traditional
learning-theoretic arguments. We present a unified, graph-based analysis, which
allows us to analyze this dependence using well-known graph identities. We are
then able to bound the generalization error of learned binary relations using
Rademacher complexity and algorithmic stability. The rate of uniform
convergence is partially determined by the labeler's subsampling process. We
thus examine how various assumptions about subsampling affect generalization;
under a natural random subsampling process, our bounds guarantee
$\tilde{O}(1/\sqrt{n})$ uniform convergence.


  We define a subdivision network $\Gamma^S$ of a given network $\Gamma,$ by
inserting a new vertex in every edge, so that each edge is replaced by two new
edges with conductances that fulfill electrical conditions on the new network.
In this work, we firstly obtain an expression for the Green kernel of the
subdivision network in terms of the Green kernel of the base network. Moreover,
we also obtain the effective resistance and the Kirchhoff index of the
subdivision network in terms of the corresponding parameters on the base
network. Finally, as an example, we carry out the computations in the case of a
wheel.


  With new advances in machine learning and in particular powerful learning
libraries, we illustrate some of the new possibilities they enable in terms of
nonlinear system identification. For a large class of hybrid systems, we
explain how these tools allow for identification of complex dynamics using
neural networks. We illustrate the method by examining the performance on a
quad-rotor example.


  We consider several protostellar systems where either a precessing jet or at
least two misaligned jets have been observed. We assume that the precession of
jets is caused by tidal interactions in noncoplanar binary systems. For Cep E,
V1331 Cyg and RNO 15-FIR the inferred orbital separations and disk radii are in
the range 4-160 AU and 1-80 AU, respectively, consistent with those expected
for pre-main sequence stars. Furthermore, we assume or use the fact that the
source of misaligned outflows is a binary, and evaluate the lengthscale over
which the jets should precess as a result of tidal interactions. For T Tau, HH1
VLA 1/2 and HH 24 SVS63, it may be possible to detect a bending of the jets
rather than 'wiggling'. In HH 111 IRS and L1551 IRS5, 'wiggling' may be
detected on the current observed scale. Our results are consistent with the
existence of noncoplanar binary systems in which tidal interactions induce jets
to precess.


  We carried out a comprehensive study of the structural, electrical transport,
thermal and thermodynamic properties in ferrimagnetic Mn$_3$Si$_2$Te$_6$ single
crystals. Mn and Te $K$-edge X-ray absorption spectroscopy and synchrotron
powder X-ray diffraction were measured to provide information on the local
atomic environment and the average crystal structure. The dc and ac magnetic
susceptibility measurements indicate a second-order paramagnetic to
ferrimagnetic transition at $T_c$ $\sim$ 74 K, which is further confirmed by
the specific heat measurement. Mn$_3$Si$_2$Te$_6$ exhibits semiconducting
behavior along with a large negative magnetoresistance of -87\% at $T_c$ and
relatively high value of thermopower up to $\sim$ 10 mV/K at 5 K. Besides the
rapidly increasing resistivity $\rho(T)$ and thermopower $S(T)$ below 20 K, the
large discrepancy between activation energy for resistivity $E_\rho$ and
thermopower $E_S$ above 20 K indicates the polaronic transport mechanism.
Furthermore, the thermal conductivity $\kappa(T)$ of Mn$_3$Si$_2$Te$_6$ is
notably rather low, comparable to Cr$_2$Si$_2$Te$_6$, and is strongly
suppressed in magnetic field across $T_c$, indicating the presence of strong
spin-lattice coupling, also similar with Cr$_2$Si$_2$Te$_6$.


  With the growing integration of AI in daily life, ensuring the robustness of
systems to inference-time attacks is crucial. Among the approaches for
certifying robustness to such adversarial examples, randomized smoothing has
emerged as highly promising due to its nature as a wrapper around arbitrary
black-box models. Previous work on randomized smoothing in natural language
processing has primarily focused on specific subsets of edit distance
operations, such as synonym substitution or word insertion, without exploring
the certification of all edit operations. In this paper, we adapt Randomized
Deletion (Huang et al., 2023) and propose, CERTified Edit Distance defense
(CERT-ED) for natural language classification. Through comprehensive
experiments, we demonstrate that CERT-ED outperforms the existing Hamming
distance method RanMASK (Zeng et al., 2023) in 4 out of 5 datasets in terms of
both accuracy and the cardinality of the certificate. By covering various
threat models, including 5 direct and 5 transfer attacks, our method improves
empirical robustness in 38 out of 50 settings.


  We present the first linear multicolor polarization observations for a sample
of 35 stars in the direction of the Galactic cluster NGC 6231. We have found a
complex pattern in the angles of the polarimetric vectors. Near the core of
this cluster the structure shows a semi-circular pattern that we have
interpreted as a re-orientation of the dust particles, showing the morphology
of the magnetic field. We propose that a supernova event has occurred some time
ago and produced a shock on the local ISM. We discuss in this paper independent
confirmations othis event, both from the studies on the diffuse interstellar
absorptions Crawford (2001) and the results of the pre-main sequence stars
(PMS) given by Sung et al. (1998). We also show that a supernova is supported
by the evolutive status of the cluster.


  We consider wireless transmission of images in the presence of channel output
feedback. From a Shannon theoretic perspective feedback does not improve the
asymptotic end-to-end performance, and separate source coding followed by
capacity-achieving channel coding, which ignores the feedback signal, achieves
the optimal performance. It is well known that separation is not optimal in the
practical finite blocklength regime; however, there are no known practical
joint source-channel coding (JSCC) schemes that can exploit the feedback signal
and surpass the performance of separation-based schemes. Inspired by the recent
success of deep learning methods for JSCC, we investigate how noiseless or
noisy channel output feedback can be incorporated into the transmission system
to improve the reconstruction quality at the receiver. We introduce an
autoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits the
channel output feedback, and provides considerable improvements in terms of the
end-to-end reconstruction quality for fixed-length transmission, or in terms of
the average delay for variable-length transmission. To the best of our
knowledge, this is the first practical JSCC scheme that can fully exploit
channel output feedback, demonstrating yet another setting in which modern
machine learning techniques can enable the design of new and efficient
communication methods that surpass the performance of traditional structured
coding-based designs.


  We explore the rich and unique magnetic quantization of Si-doped graphene
defect systems with various concentrations and configurations using the
generalized tight-binding model. This model takes into account simultaneously
the non-uniform bond lengths, site energies and hopping integrals, and a
uniform perpendicular magnetic field (${B_z\hat z}$). The magnetic quantized
Landau levels (LLs) are classified into four different kinds based on the
probability distributions and oscillation modes. The main characteristics of
LLs are clearly reflected in the magneto-optical selection rules which cover
the dominating ${\Delta\,n=|n^v-n^c|=0}$, the coexistent ${\Delta\,n=0}$ $\&$
${\Delta\,n=1}$, and the specific ${\Delta\,n=1}$. These rules for inter-LLs
excitations come from the non-equivalence or equivalence of the A$_i$ and B$_i$
sublattices in a supercell.


  This paper describes a conditional neural network architecture for Mandarin
Chinese polyphone disambiguation. The system is composed of a bidirectional
recurrent neural network component acting as a sentence encoder to accumulate
the context correlations, followed by a prediction network that maps the
polyphonic character embeddings along with the conditions to corresponding
pronunciations. We obtain the word-level condition from a pre-trained
word-to-vector lookup table. One goal of polyphone disambiguation is to address
the homograph problem existing in the front-end processing of Mandarin Chinese
text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly
available polyphonic character dataset. To further validate our choices on the
conditional feature, we investigate polyphone disambiguation systems with
multi-level conditions respectively. The experimental results show that both
the sentence-level and the word-level conditional embedding features are able
to attain good performance for Mandarin Chinese polyphone disambiguation.


  The purpose of this report is presentation of the main modifications of the
standard Kibble-Zurek formalism caused by the existence of the unperfections in
the system. We know that the distribution of kinks created during a second
order phase transition in pure systems is determined solely by the correlation
length at freeze-out time. The correlation length at that instant of time
intuitively describes the size of the defect and therefore the number density
of defects is limited by the possibility of holding kinks in a unit volume. On
the other hand if the system is populated by the impurities then kinks emerge
mainly in knots of the force distribution which correspond to extremes of the
impurity potential i.e. positions of imperfections. The purpose of this report
is to show that, due to existence of the strong gradients of the impurity
potential, kinks can be created mainly in the close vicinity of the impurities.
It seems that this simple mechanism can be responsible for occurrence, in the
number density formula, the additional length scale describing the impurity
distribution. We know that in pure systems, as a consequence of kink-antikink
annihilation, the number density of kinks decrease in time. In contradiction to
pure systems, kinks produced in the systems populated by impurities could be
confined by the impurity centers and therefore they may not disappear from the
system and may remain above the level established by thermal nucleation of
pairs.


  In this talk we present an update of model predictions for the $\gamma
\gamma$ total cross-section in the TeV region. The update includes preliminary
results for $\gamma \gamma $ cross-sections using the Bloch-Nordsieck model for
the overlap function of the partons in the transverse space, use of the CJLK
parametrisation of the photonic parton densities that has recently become
available and extension to the higher $\gamma \gamma$ energies relevant to the
planned CLIC collider.


  Let $F$ be a field with at least three elements and $G$ a locally finite
group. This paper aims to show that if either $F$ is algebraically closed or
the characteristic of $F$ is positive, then an element in the group algebra
$FG$ is a product of unipotent elements if, and only if, it? lies in the first
derived subgroup of the unit group of $FG$. In addition, it? is a product of at
most three unipotent elements.
  Moreover, we explore some crucial properties satisfied by certain algebras
like the connection between unipotent elements of index 2 and commutators as
well as we investigate the unipotent radical of a group algebra by showing that
the group algebra of a finite group over an infinite field cannot have a
unipotent maximal subgroup. In particular, we apply these results to twisted
group algebras.


  Heisenberg's uncertainty principle is a fundamental element in quantum
mechanics. It sets a bound on our ability to predict the measurement outcomes
of two incompatible observables simultaneously. In quantum information theory,
the uncertainty principle can be expressed using entropic measures. The
entropic uncertainty relation can be improved by considering an additional
particle as a memory particle. The presence of quantum correlation between the
memory particle and the measured particle reduces the uncertainty. In a curved
space-time, the presence of the Hawking radiation can reduce quantum
correlation. Therefore, concerning the relationship between the quantum
correlation and entropic uncertainty lower bound, we expect that the Hawking
radiation increases the entropic uncertainty lower bound. In this work, we
investigate the entropic uncertainty relation in Garfinkle-Horowitz-Strominger
(GHS) dilation black hole. We consider a model in which the memory particle is
located near the event horizon outside the black hole, while the measured
particle is free falling. To study the proposed model, we will consider
examples with Dirac fields. We also explore the effect of the Hawking radiation
on the quantum secret key rate.


  State-of-the-art methods for relation extraction consider the sentential
context by modeling the entire sentence. However, syntactic indicators, certain
phrases or words like prepositions that are more informative than other words
and may be beneficial for identifying semantic relations. Other approaches
using fixed text triggers capture such information but ignore the lexical
diversity. To leverage both syntactic indicators and sentential contexts, we
propose an indicator-aware approach for relation extraction. Firstly, we
extract syntactic indicators under the guidance of syntactic knowledge. Then we
construct a neural network to incorporate both syntactic indicators and the
entire sentences into better relation representations. By this way, the
proposed model alleviates the impact of noisy information from entire sentences
and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8
benchmark dataset show that our model significantly outperforms the
state-of-the-art methods.


  Cross-modality images that integrate visible-infrared spectra cues can
provide richer complementary information for object detection. Despite this,
existing visible-infrared object detection methods severely degrade in severe
weather conditions. This failure stems from the pronounced sensitivity of
visible images to environmental perturbations, such as rain, haze, and snow,
which frequently cause false negatives and false positives in detection. To
address this issue, we introduce a novel and challenging task, termed
visible-infrared object detection under adverse weather conditions. To foster
this task, we have constructed a new Severe Weather Visible-Infrared Dataset
(SWVID) with diverse severe weather scenes. Furthermore, we introduce the
Cross-modality Fusion Mamba with Weather-removal (CFMW) to augment detection
accuracy in adverse weather conditions. Thanks to the proposed Weather Removal
Diffusion Model (WRDM) and Cross-modality Fusion Mamba (CFM) modules, CFMW is
able to mine more essential information of pedestrian features in
cross-modality fusion, thus could transfer to other rarer scenarios with high
efficiency and has adequate availability on those platforms with low computing
power. To the best of our knowledge, this is the first study that targeted
improvement and integrated both Diffusion and Mamba modules in cross-modality
object detection, successfully expanding the practical application of this type
of model with its higher accuracy and more advanced architecture. Extensive
experiments on both well-recognized and self-created datasets conclusively
demonstrate that our CFMW achieves state-of-the-art detection performance,
surpassing existing benchmarks. The dataset and source code will be made
publicly available at https://github.com/lhy-zjut/CFMW.


  In the last years, Deep Learning technology has been proposed in different
fields, bringing many advances in each of them, but identifying new threats in
these solutions regarding cybersecurity. Those implemented models have brought
several vulnerabilities associated with Deep Learning technology. Moreover,
those allow taking advantage of the implemented model, obtaining private
information, and even modifying the model's decision-making. Therefore,
interest in studying those vulnerabilities/attacks and designing defenses to
avoid or fight them is gaining prominence among researchers. In particular, the
widely known evasion attack is being analyzed by researchers; thus, several
defenses to avoid such a threat can be found in the literature. Since the
presentation of the L-BFG algorithm, this threat concerns the research
community. However, it continues developing new and ingenious countermeasures
since there is no perfect defense for all the known evasion algorithms. In this
work, a novel detector of evasion attacks is developed. It focuses on the
information of the activations of the neurons given by the model when an input
sample is injected. Moreover, it puts attention to the topology of the targeted
deep learning model to analyze the activations according to which neurons are
connecting. This approach has been decided because the literature shows that
the targeted model's topology contains essential information about if the
evasion attack occurs. For this purpose, a huge data preprocessing is required
to introduce all this information in the detector, which uses the Graph
Convolutional Neural Network (GCN) technology. Thus, it understands the
topology of the target model, obtaining promising results and improving the
outcomes presented in the literature related to similar defenses.


  An understanding of the hydrophobicity of complex heterogeneous molecular
assemblies is crucial to characterize and predict interactions between
biomolecules. As such, uncovering the subtleties of assembly processes hinges
on an accurate classification of the relevant interfaces involved, and much
effort has been spent on developing so-called "hydrophobicity maps." In this
work, we introduce a novel electrostatics-based mapping of aqueous interfaces
that focuses on the collective, long-wavelength electrostatic response of water
to the presence of nearby surfaces. In addition to distinguishing between
hydrophobic and hydrophilic regions of heterogeneous surfaces, this
electrostatic mapping can also differentiate between hydrophilic regions that
polarize nearby waters in opposing directions. We therefore expect this
approach to find use in predicting the location of possible water-mediated
hydrophilic interactions, in addition to the more commonly emphasized
hydrophobic interactions that can also be of significant importance.


  We use theory and first-principles calculations to investigate how structural
changes induced by ultrafast optical excitation of infrared-active phonons
change with hydrostatic pressure in LaAlO$_3$. Our calculations show that the
observed structural changes are sensitive to pressure, with the largest changes
occurring at pressures near the boundary between the cubic perovskite and
rhombohedral phases. We rationalize our findings by defining a figure of merit
that depends only on intrinsic materials quantities, and show that the peak
response near the phase boundary is dictated by different microscopic materials
properties depending on the particular phonon mode being excited. Our work
demonstrates how it is possible to systematically identify materials that may
exhibit particularly large changes in structure and properties due to optical
excitation of infrared-active phonons.