  We investigate the degenerate fractional Schr\"{o}dinger-Kirchhoff-Poisson
equation in $\mathbb{R}^3$ with critical nonlinearity and electromagnetic
fields $\varepsilon^{2s} M([u]_{s,A}^2)(-\Delta)_{A}^su + V(x)u + \phi u =
k(x)|u|^{r-2}u +
\left(\mathcal{I}_\mu*|u|^{2_s^\sharp}\right)|u|^{2_s^\sharp-2}u$ and
$(-\Delta)^t\phi = u^2,$ where $\varepsilon > 0$ is a parameter, $3/4<s<1$, $0
< t < 1$, $V$ is an electric potential satisfying some suitable assumptions, $0
< k_\ast \leq k(x) \leq k^\ast$, $\mathcal{I}_\mu(x) = |x|^{3-\mu}$ with
$0<\mu<3$, $2_s^\sharp =\frac{3+\mu}{3-2s},$ and $2 < r < 2_s^\sharp$. With the
help of the concentration compactness principle and variational methods,
together with some fine analytical tools, we establish the existence and
multiplicity of solutions for the above problem when $\varepsilon \rightarrow
0$ in the degenerate cases, i.e. when the Kirchhoff term $M$ vanishes at zero.


  In this article, we discuss a novel approach to solving number sequence
problems, in which sequences of numbers following unstated rules are given, and
missing terms are to be inferred. We develop a methodology of decomposing test
sequences into linear combinations of known base sequences, and using the
decomposition weights to predict the missing term. We show that if assumptions
are made ahead of time of the expected base sequences, then a Model Vector can
be created, where a dot-product with the input will produce the result. This is
surprising since it means sequence problems can be solved with no knowledge of
the hidden rule. Model vectors can be created either by matrix inversion or by
a novel combination function applied to primitive vectors. A heuristic
algorithm to compute the most likely model vector from the input is described.
Finally we evaluate the algorithm on a suite of number sequence problem tests.


  A near-infrared (0.85-2.5 microns) camera in use at the Canada-France-Hawaii
Telescope and at the 1.6m telescope of the Observatoire du Mont-Megantic is
described. The camera is based on a Hawaii-1 1024x1024 HgCdTe array detector.
Its main feature is to acquire three simultaneous images at three wavelengths
(simultaneous differential imaging) across the methane absorption bandhead at
1.6 micron, enabling an accurate subtraction of the stellar point spread
function (PSF) and the detection of faint close methanated companions. The
instrument has no coronagraph and features a fast (1 MHz) data acquisition
system without reset anomaly, yielding high observing efficiencies on bright
stars. The performance of the instrument is described, and it is illustrated by
CFHT images of the nearby star Ups And. TRIDENT can detect (3 sigma) a
methanated companion with DeltaH=10 at 0.5 arcsec from the star in one hour of
observing time. Non-common path aberrations between the three optical paths are
the limiting factors preventing further PSF attenuation. Reference star
subtraction and instrument rotation improve the detection limit by one order of
magnitude.


  This paper deals with a generalized Sudoku problem and investigates the
unicity of a given solution. We introduce constraint sets, which is a
generalization of the rows, columns and blocks of a classical Sudoku puzzle.
The unicity property is characterized by three different properties. We
describe unicity by permutations, by unicity cells and by rectangles. These
terms are defined in this paper and are illustrated with examples. Throughout
this paper we are not concerned with the existence of a solution.


  We present a hierarchical neural message passing architecture for learning on
molecular graphs. Our model takes in two complementary graph representations:
the raw molecular graph representation and its associated junction tree, where
nodes represent meaningful clusters in the original graph, e.g., rings or
bridged compounds. We then proceed to learn a molecule's representation by
passing messages inside each graph, and exchange messages between the two
representations using a coarse-to-fine and fine-to-coarse information flow. Our
method is able to overcome some of the restrictions known from classical GNNs,
like detecting cycles, while still being very efficient to train. We validate
its performance on the ZINC dataset and datasets stemming from the MoleculeNet
benchmark collection.


  Let $\left( \mathcal{M},g\right) $ be a $d$-dimensional compact connected
Riemannian manifold and let $\left\{ \varphi_{m}\right\}_{m=0}^{+\infty}$ be a
complete sequence of orthonormal eigenfunctions of the Laplace-Beltrami
operator on $\mathcal{M}$. We show that there exists a positive constant $C$
such that for all integers $N$ and $X$ and for all finite sequences of $N$
points in $\mathcal{M}$, $\left\{ x\left( j\right) \right\}_{j=1}^{N}$, and
positive weights $\left\{ a_{j}\right\}_{j=1}^{N}$ we have \[ \sum_{m=0}^{X} |
\sum_{j=1}^{N} a_{j} \varphi_{m} ( x( j) ) | ^{2}\geq \max \{
CX\sum_{j=1}^{N}a_{j}^{2},( \sum_{j=1}^{N}a_{j}) ^{2}\}.\]


  We show that the maximal globally hyperbolic development of near-FLRW initial
data for the Einstein scalar-field Vlasov system exhibits stable Big Bang
formation in the collapsing direction, i.e., stable Kretschmann scalar blow-up,
causing the spacetime to become past incomplete. This is the first stability
result towards the collapsing direction in the presence of Vlasov matter.
Furthermore, while the asymptotic behaviour of Vlasov matter is close to that
of the FLRW solution up to a small change in asymptotic order controlled by
initial data, generically, the distribution function itself asymptotically
concentrates in certain preferred directions that are teleologically
determined. To ensure that this behaviour is sufficiently mitigated by the
scalar field, we crucially exploit a scaling hierarchy between horizontal and
vertical derivatives in the Vlasov equation.


  We introduce cs-topologies, or topologies of complemented subsets, as a new
approach to constructive topology that preserves the duality between open and
closed subsets of classical topology. Complemented subsets were used
successfully by Bishop in his constructive formulation of the Daniell approach
to measure and integration. Here we use complemented subsets in topology, in
order to describe simulataneously an open set, the first-component of an open
complemented subset, and its complement as a closed set, the second component
of an open complemented subset. We analyse the canonical cs-topology induced by
a metric, and we introduce the notion of a modulus of openness for a cs-open
subset of a metric space. Pointwise and uniform continuity of functions between
metric spaces are formulated with respect to the way these functions inverse
open complemented subsets together with their moduli of openness. The addition
of moduli of openness in the concept of an open subset, given a base for the
cs-topology, makes possible to define the notion of uniform continuity of
functions between csb-spaces, that is cs-spaces with a given base. In this way,
the notions of pointwise and uniform continuity of functions between metric
spaces are directly generalised to the notions of pointwise and uniform
continuity between csb-topological spaces. Constructions and facts from the
classical theory of topologies of open subsets are translated to our
constructive theory of topologies of open complemented subsets.


  We propose a novel relation between the low-mass enhancement of dielectrons
observed at PHENIX and transport coefficients of QGP such as the charge
diffusion constant $D$ and the relaxation time $\tau_{\rm J}$. We parameterize
the transport peak in the spectral function using the second-order relativistic
dissipative hydrodynamics by Israel and Stewart. Combining the spectral
function and the full (3+1)-dimensional hydrodynamical evolution with the
lattice EoS, theoretical dielectron spectra and the experimental data are
compared. Detailed analysis suggests that the low-mass dilepton enhancement
originates mainly from the high-temperature QGP phase where there is a large
electric charge fluctuation as obtained from lattice QCD simulations.


  The non-supersymmetric and supersymmetric versions of hybrid inflation are
summarized. It is emphasized that supersymmetric hybrid inflation (with its
extensions) is an extremely "natural" inflationary scenario since it does not
require "tiny" parameters, its superpotential has the most general form allowed
by the symmetries, and it can be protected against radiative or supergravity
corrections. A concrete supersymmetric grand unified theory which leads to
hybrid inflation, solves the mu problem and generates seesaw masses for the
light neutrinos is constructed. The "reheating" which follows hybrid inflation
is studied in this theory. It is shown that the gravitino constraint on the
"reheat" temperature can be "naturally" satisfied. Also, the observed baryon
asymmetry of the universe can be generated via a primordial leptogenesis
consistently with the requirements from solar and atmospheric neutrino
oscillations. Extensions of the standard supersymmetric hybrid inflationary
scenario which are still consistent with all these requirements but can also
avoid the cosmological disaster from the possible copious monopole production
at the abrupt termination of standard hybrid inflation are constructed. They
rely on utilizing the leading non-renormalizable correction to the standard
hybrid inflationary superpotential and are necessary for higher unified gauge
groups which predict the existence of monopoles.


  We extend a result of Patodi for closed Riemannian manifolds to the context
of closed contact manifolds by showing the condition that a manifold is an
$\eta$-Einstein Sasakian manifold is spectrally determined. We also prove that
the condition that a Sasakian space form has constant $\phi$-sectional
curvature $c$ is spectrally determined.


  In this paper we derive control charts for the variance of a Gaussian process
using the likelihood ratio approach, the generalized likelihood ratio approach,
the sequential probability ratio method and a generalized sequential
probability ratio procedure, the Shiryaev-Roberts procedure and a generalized
Shiryaev-Roberts ap- proach. Recursive presentations for the calculation of the
control statistics are given for autoregressive processes of order 1. In an
extensive simulation study these schemes are compared with existing control
charts for the variance. In order to asses the performance of the schemes both
the average run length and the average delay are used.


  The breakup and coalescence of drops are elementary topological transitions
in interfacial flows. The breakup of a drop changes dramatically when polymers
are added to the fluid. With the strong elongation of the polymers during the
process, long threads connecting the two droplets appear prior to their
eventual pinch-off. Here, we demonstrate how elasticity affects drop
coalescence, the complement of the much studied drop pinch-off. We reveal the
emergence of an elastic singularity, characterised by a diverging interface
curvature at the point of coalescence. Intriguingly, while the polymers dictate
the \emph{spatial} features of coalescence, they hardly affect the
\emph{temporal} evolution of the bridge. These results are explained using a
novel viscoelastic similarity analysis and are relevant for drops created in
biofluids, coating sprays and inkjet printing.


  We consider the noncommutative space $\mathbb{R}^3_\theta$, a deformation of
$\mathbb{R}^3$ for which the star product is closed for the trace functional.
We study one-loop IR and UV properties of the 2-point function for real and
complex noncommutative scalar field theories with quartic interactions and
Laplacian on $\mathbb{R}^3$ as kinetic operator. We find that the 2-point
functions for these noncommutative scalar field theories have no IR
singularities in the external momenta, indicating the absence of UV/IR mixing.
We also find that the 2-point functions are UV finite with the deformation
parameter $\theta$ playing the role of a natural UV cut-off. The possible
origin of the absence of UV/IR mixing in noncommutative scalar field theories
on $\mathbb{R}^3_\theta$ as well as on $\mathbb{R}^3_\lambda $, another
deformation of $\mathbb{R}^3$, is discussed.


  This paper addresses the problem of defect segmentation in semiconductor
manufacturing. The input of our segmentation is a scanning-electron-microscopy
(SEM) image of the candidate defect region. We train a U-net shape network to
segment defects using a dataset of clean background images. The samples of the
training phase are produced automatically such that no manual labeling is
required. To enrich the dataset of clean background samples, we apply defect
implant augmentation. To that end, we apply a copy-and-paste of a random image
patch in the clean specimen. To improve the robustness of the unlabeled data
scenario, we train the features of the network with unsupervised learning
methods and loss functions. Our experiments show that we succeed to segment
real defects with high quality, even though our dataset contains no defect
examples. Our approach performs accurately also on the problem of supervised
and labeled defect segmentation.


  Deep generative models for symbolic music are typically designed to model
temporal dependencies in music so as to predict the next musical event given
previous events. In many cases, such models are expected to learn abstract
concepts such as harmony, meter, and rhythm from raw musical data without any
additional information. In this study, we investigate the effects of explicitly
conditioning deep generative models with musically relevant information.
Specifically, we study the effects of four different conditioning inputs on the
performance of a recurrent monophonic melody generation model. Several
combinations of these conditioning inputs are used to train different model
variants which are then evaluated using three objective evaluation paradigms
across two genres of music. The results indicate musically relevant
conditioning significantly improves learning and performance, and reveal how
this information affects learning of musical features related to pitch and
rhythm. An informal subjective evaluation suggests a corresponding improvement
in the aesthetic quality of generations.


  We introduce and study a cone which consists of a class of generalized
polynomial functions and which provides a common framework for recent
non-negativity certificates of polynomials in sparse settings. Specifically,
this $\mathcal{S}$-cone generalizes and unifies sums of arithmetic-geometric
mean exponentials (SAGE) and sums of non-negative circuit polynomials (SONC).
We provide a comprehensive characterization of the dual cone of the
$\mathcal{S}$-cone, which even for its specializations provides novel and
projection-free descriptions. As applications of this result, we give an exact
characterization of the extreme rays of the $\mathcal{S}$-cone and thus also of
its specializations, and we provide a subclass of functions for which
non-negativity coincides with membership in the $\mathcal{S}$-cone.
  Moreover, we derive from the duality theory an approximation result of
non-negative univariate polynomials and show that a SONC analogue of Putinar's
Positivstellensatz does not exist even in the univariate case.


  In this work, we present a discrete event mix network simulator, which allows
analysing how anonymity, latency, and bandwidth overhead are affected by
various scenarios of deployment and design choices. These design choices
include network topology, mixing technique, volume of traffic, latency
requirements, packet size or use of cover traffic. To the best of our
knowledge, this is the first such simulator as work on it began in 2017 to
analyze the Loopix mix network, and the code of our simulator is available
under an open-source license. To demonstrate the capabilities of our simulator,
we perform an empirical analysis of the impact of core design choices on
anonymity, scalability and latency in Elixxir, HOPR and Nym, currently deployed
mix network infrastructures that make a variety of different choices in their
design.


  We outline a simple way to include heavy quark vacuum polarization in lattice
QCD simulations. The method, based on effective field theory, requires only a
trivial modification of the gluon action and has no impact on simulation times.
We assess the range of validity for this procedure, and the impact that it may
have.


  The ground-state energy per particle $E/N$ and condensate density $n_0$ of a
dilute Bose gas are studied with a self-consistent perturbation expansion
satisfying the Hugenholtz-Pines theorem and conservation laws simultaneously. A
class of Feynman diagrams for the self-energy, which has escaped consideration
so far, is shown to add an extra constant $c_{ip}\sim O(1)$ to the expressions
reported by Lee, Huang, and Yang [Phys. Rev. 106, 1135 (1957) ] as
$E/N=(2\pi\hbar^2 an/m)[1+(128/15\sqrt{{\pi}}+16c_{ip}/5)\sqrt{{a^3n}}]$ and
$n_0/n=1-(8/3\sqrt{{\pi}}+c_{ip})\sqrt{{a^3n}}$, where $a$, $n$, and $m$ are
are the s-wave scattering length, particle density, and particle mass,
respectively. We present a couple of estimates for $c_{ip}$; the third-order
perturbation expansion yields $c_{ip}=0.412$.


  With the advent of new technologies, Augmented Reality (AR) has become an
effective tool in remote collaboration. Narrow field-of-view (FoV) and motion
blur can offer an unpleasant experience with limited cognition for remote
viewers of AR headsets. In this article, we propose a two-stage pipeline to
tackle this issue and ensure a stable viewing experience with a larger FoV. The
solution involves an offline 3D reconstruction of the indoor environment,
followed by enhanced rendering using only the live poses of AR device. We
experiment with and evaluate the two different 3D reconstruction methods, RGB-D
geometric approach and Neural Radiance Fields (NeRF), based on their data
requirements, reconstruction quality, rendering, and training times. The
generated sequences from these methods had smoother transitions and provided a
better perspective of the environment. The geometry-based enhanced FoV method
had better renderings as it lacked blurry outputs making it better than the
other attempted approaches. Structural Similarity Index (SSIM) and Peak Signal
to Noise Ratio (PSNR) metrics were used to quantitatively show that the
rendering quality using the geometry-based enhanced FoV method is better. Link
to the code repository -
https://github.com/MixedRealityETHZ/ImageStabilization.


  The CMS experiment at CERN has reported a possible signal for a resonance at
146 GeV decaying into the $e\mu$ final state which, presently, is the only
experimental hint for lepton flavour violation in any low- and high-energy
experiment. The Froggatt-Nielsen mechanism naturally predicts the existence of
new scalars, the flavons, with flavour off-diagonal couplings. We study this
framework in the context of the CMS result and find that the minimal, purely
leptophilic model is too restricted to match the claimed signal. Thereafter we
show how models with additional flavon couplings to quarks can explain the
claimed signal while satisfying all the existing constraints on lepton flavour
violation.


  Edwards et al question aspects of the methods used in two of our published
papers that report results showing Levy walk like and Levy flight movement
patterns of marine predators.The criticisms are focused on the applicability of
some statistical methodologies used to detect power law distributions.We reply
to the principal criticisms levelled at each of these papers in turn including
our own reanalysis of specific datasets and find that neither of our papers
conclusions are overturned in any part by the issues raised.Indeed, in addition
to the findings of our research reported in these papers there is strong
evidence accumulating from studies worldwide that organisms show movements and
behaviour consistent with scale invariant patterns such as Levy flights.


  This paper presents Ellogon, a multi-lingual, cross-platform, general-purpose
text engineering environment. Ellogon was designed in order to aid both
researchers in natural language processing, as well as companies that produce
language engineering systems for the end-user. Ellogon provides a powerful
TIPSTER-based infrastructure for managing, storing and exchanging textual data,
embedding and managing text processing components as well as visualising
textual data and their associated linguistic information. Among its key
features are full Unicode support, an extensive multi-lingual graphical user
interface, its modular architecture and the reduced hardware requirements.


  Politicians often have underlying agendas when reacting to events. Arguments
in contexts of various events reflect a fairly consistent set of agendas for a
given entity. In spite of recent advances in Pretrained Language Models (PLMs),
those text representations are not designed to capture such nuanced patterns.
In this paper, we propose a Compositional Reader model consisting of encoder
and composer modules, that attempts to capture and leverage such information to
generate more effective representations for entities, issues, and events. These
representations are contextualized by tweets, press releases, issues, news
articles, and participating entities. Our model can process several documents
at once and generate composed representations for multiple entities over
several issues or events. Via qualitative and quantitative empirical analysis,
we show that these representations are meaningful and effective.


  Dwarf spiral galaxies - and in particular the prototypical DDO 154 - are
known to be completely dominated by an unseen component. The putative
neutralinos - so far the favored explanation for the astronomical dark matter -
fail to reproduce the well measured rotation curves of those systems because
these species tend to form a central cusp whose presence is not supported by
observation. We have considered here a self-coupled charged scalar field as an
alternative to neutralinos and investigated whether a Bose condensate of that
field could account for the dark matter inside DDO 154 and more generally
inside dwarf spirals. The size of the condensate turns out to be precisely
determined by the scalar mass m and self-coupling lambda of the field. We find
actually that for m^4 / lambda = 50 - 75 eV^4, the agreement with the
measurements of the circular speed of DDO 154 is impressive whereas it lessens
for larger systems. The cosmological behavior of the field is also found to be
consistent - yet marginally - with the limits set by BBN on the effective
number of neutrino families. We conclude that classical configurations of a
scalar and self-coupled field provide a possible solution to the astronomical
dark matter problem and we suggest further directions of research.


  We initiate a study of algorithms with a focus on the computational
complexity of individual elements, and introduce the fragile complexity of
comparison-based algorithms as the maximal number of comparisons any individual
element takes part in. We give a number of upper and lower bounds on the
fragile complexity for fundamental problems, including Minimum, Selection,
Sorting and Heap Construction. The results include both deterministic and
randomized upper and lower bounds, and demonstrate a separation between the two
settings for a number of problems. The depth of a comparator network is a
straight-forward upper bound on the worst case fragile complexity of the
corresponding fragile algorithm. We prove that fragile complexity is a
different and strictly easier property than the depth of comparator networks,
in the sense that for some problems a fragile complexity equal to the best
network depth can be achieved with less total work and that with randomization,
even a lower fragile complexity is possible.


  A generating function is derived that counts the number of diagrams in an
arbitrary scalar field theory. The number of graphs containing any number $n_j$
of $j$-point vertices is given. The count is also used to obtain the number of
classical graphs in gauge theory and gravity.


  A recent study of the diffuse $\gamma$-ray emission in the Central Molecular
Zone using very high energy (VHE, E $>$ 0.1 TeV) H.E.S.S. data suggests that
the Galactic Center (GC) is the most plausible supplier of Galactic
ultra-relativistic cosmic-rays (CRs) up to the knee at about 10$^{15}$ eV
(PeV). However, the GC might not be the only source capable to accelerate CRs
up to PeV energies in the Galaxy. Here we present H.E.S.S. data analysis
results and interpretation of three H.E.S.S. sources, with spectra extending
beyond 10 TeV and relatively hard spectral indices compared with the average
spectral index of H.E.S.S. sources, namely HESS J1641-463, HESS J1741-302 and
HESS J1826-130. Although the nature of these VHE $\gamma$-ray sources is still
open, their spectra suggest that the astrophysical objects producing such
emission must be capable of accelerating the parental particle population up to
energies of at least several hundreds of TeV. Assuming a hadronic scenario,
dense gas regions can provide rich target material for accelerated particles to
produce VHE $\gamma$-ray emission via proton-proton interactions followed by a
subsequent $\pi^{0}$ decay. Thus, detailed investigations of the interstellar
medium along the line of sight to all of these sources have been performed by
using data from available atomic and molecular hydrogen surveys. The results
point out the existence of dense interstellar gas structures coincident with
the best fit positions of these sources. One can find possible hadronic models
with CRs being accelerated close to the PeV energies to explain the
$\gamma$-ray emission from all of these sources, which opens up the possibility
that a population of PeV CR accelerators might be active in the Galaxy.


  A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA),
was proposed. The idea of proposing this algorithm belongs to the hunting
behavior of the humpback whale. However, WOA suffers from poor performance in
the exploitation phase and stagnates in the local best solution. Grey Wolf
Optimization (GWO) is a very competitive algorithm comparing to other common
metaheuristic algorithms as it has a super performance in the exploitation
phase while it is tested on unimodal benchmark functions. Therefore, the aim of
this paper is to hybridize GWO with WOA to overcome the problems. GWO can
perform well in exploiting optimal solutions. In this paper, a hybridized WOA
with GWO which is called WOAGWO is presented. The proposed hybridized model
consists of two steps. Firstly, the hunting mechanism of GWO is embedded into
the WOA exploitation phase with a new condition which is related to GWO.
Secondly, a new technique is added to the exploration phase to improve the
solution after each iteration. Experimentations are tested on three different
standard test functions which are called benchmark functions: 23 common
functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO
is also evaluated against original WOA, GWO and three other commonly used
algorithms. Results show that WOAGWO outperforms other algorithms depending on
the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an
engineering problem such as pressure vessel design. Then the results prove that
WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent
Optimizer (FDO).


  In addition to the biologically active monomer of the protein Insulin
circulating in human blood, the molecule also exists in dimeric and hexameric
forms that are used as storage. The Insulin monomer contains two distinct
surfaces, namely the dimer forming surface (DFS) and the hexamer forming
surface (HFS) that are specifically designed to facilitate the formation of the
dimer and the hexamer, respectively. In order to characterize the structural
and dynamical behaviour of interfacial water molecules near these two surfaces
(DFS and HFS), we performed atomistic molecular dynamics simulations of Insulin
with explicit water. Dynamical characterization reveals that the structural
relaxation of the hydrogen bonds formed between the residues of DFS and the
interfacial water molecules is faster than those formed between water and that
of the HFS. Furthermore, the residence times of water molecules in the protein
hydration layer for both the DFS and HFS are found to be significantly higher
than those for some of the other proteins studied so far, such as HP-36 and
lysozyme. The surface topography and the arrangement of amino acid residues
work together to organize the water molecules in the hydration layer in order
to provide them with a preferred orientation. HFS having a large polar solvent
accessible surface area and a convex extensive nonpolar region, drives the
surrounding water molecules to acquire predominantly a clathrate-like
structure. In contrast, near the DFS, the surrounding water molecules acquire
an inverted orientation owing to the flat curvature of hydrophobic surface and
interrupted hydrophilic residual alignment. We have followed escape trajectory
of several such quasi-bound water molecules from both the surfaces and
constructed free energy surfaces of these water molecules.These free energy
surfaces reveal the differences between the two hydration layers.


  We investigate a renewal scheme for non-uniformly hyperbolic semiflows that
closely resembles the renewal scheme developed in the discrete time case, in
order to obtain sharp estimates for the correlation function. Also, the
involved observables are supported on a flow-box of unbounded length. The
present abstract setting does not require the use of Markov structure. However,
the classes of examples covered here are rather restrictive. In these examples,
it is easier to exploit the full force of the method and get optimal results
for observables supported on finite length flow-boxes.


  JPEG is one of the most popular image compression methods. It is beneficial
to compress those existing JPEG files without introducing additional
distortion. In this paper, we propose a deep learning based method to further
compress JPEG images losslessly. Specifically, we propose a Multi-Level
Parallel Conditional Modeling (ML-PCM) architecture, which enables parallel
decoding in different granularities. First, luma and chroma are processed
independently to allow parallel coding. Second, we propose pipeline parallel
context model (PPCM) and compressed checkerboard context model (CCCM) for the
effective conditional modeling and efficient decoding within luma and chroma
components. Our method has much lower latency while achieves better compression
ratio compared with previous SOTA. After proper software optimization, we can
obtain a good throughput of 57 FPS for 1080P images on NVIDIA T4 GPU.
Furthermore, combined with quantization, our approach can also act as a lossy
JPEG codec which has obvious advantage over SOTA lossy compression methods in
high bit rate (bpp$>0.9$).


  Ground state of the periodic Anderson model on a cubic lattice is
investigated by mean-field calculations, with focusing on the possibility of
charge ordering. We show that three different charge-ordered phases appear at
3/2 filling, which are accompanied by ferromagnetic, noncoplanar triple-Q, and
ferrimagnetic order, respectively. The triple-Q magnetic order is likely
stabilized by emergent geometrical frustration in the charge-ordered phase with
the ordering wave vector (pi,pi,pi). We show that the nearest-neighbor hopping
between localized electrons stabilizes the triple-Q phase relative to the other
two phases. We also discuss the electronic structure in the triple-Q
charge-ordered phase.


  Theories in physics usually do not address ``the present''or ``the now''.
However, they usually have a precise notion of an ``instant'' (or state). I
review how this notion appears in relational point mechanics and how it
suffices to determine durations - a fact that is often ignored in modern
presentations of analytical dynamics. An analogous discussion is attempted for
General Relativity. Finally I critically remark on the difference between
relationalism in point mechanics and field theory and the problematic
foundational dependencies between fields and spacetime.


  Superconductivity arises mostly at energy and temperature scales that are
much smaller than the typical bare electronic energies. Since the computational
effort of diagrammatic many-body techniques increases with the number of
required Matsubara frequencies and thus with the inverse temperature, phase
transitions that occur at low temperatures are typically hard to address
numerically. In this work, we implement a fluctuation exchange (FLEX) approach
to spin fluctuations and superconductivity using the "intermediate
representation basis" (IR) [Shinaoka et al., PRB 96, 2017] for Matsubara Green
functions. This FLEX+IR approach is numerically very efficient and enables us
to reach temperatures on the order of $10^{-4}$ in units of the electronic band
width in multi-orbital systems. After benchmarking the method in the doped
repulsive Hubbard model on the square lattice, we study the possibility of
spin-fluctuation-mediated superconductivity in the hydrated sodium cobalt
material Na$_x$CoO$_2\cdot y$H$_2$O reaching the scale of the experimental
transition temperature $T_{\mathrm{c}}=4.5$ K and below.


  Long-range quasi-static gauge-boson interactions lead to anomalous
(non-Fermi-liquid) behavior of the specific heat in the low-temperature limit
of an electron or quark gas with a leading $T\ln T^{-1}$ term. We obtain
perturbative results beyond the leading log approximation and find that
dynamical screening gives rise to a low-temperature series involving also
anomalous fractional powers $T^{(3+2n)/3}$. We determine their coefficients in
perturbation theory up to and including order $T^{7/3}$ and compare with exact
numerical results obtained in the large-$N_f$ limit of QED and QCD.


  In recent years, emotion detection in text has become more popular due to its
vast potential applications in marketing, political science, psychology,
human-computer interaction, artificial intelligence, etc. In this work, we
argue that current methods which are based on conventional machine learning
models cannot grasp the intricacy of emotional language by ignoring the
sequential nature of the text, and the context. These methods, therefore, are
not sufficient to create an applicable and generalizable emotion detection
methodology. Understanding these limitations, we present a new network based on
a bidirectional GRU model to show that capturing more meaningful information
from text can significantly improve the performance of these models. The
results show significant improvement with an average of 26.8 point increase in
F-measure on our test data and 38.6 increase on the totally new dataset.


  In this study we compared the temporal and periodic variations of the Maximum
CME Speed Index (MCMESI) and the number of different class (C, M, and X) solar
X-Ray flares for the last two solar cycles (Cycle 23 and 24). To obtain the
correlation between the MCMESI and solar flare numbers the cross correlation
analysis was applied to monthly data sets. Also to investigate the periodic
behavior of all data sets the Multi Taper Method (MTM) and the Morlet wavelet
analysis method were performed with daily data from 2009 to 2018. To evaluate
our wavelet analysis Cross Wavelet Transform (XWT) and Wavelet Transform
Coherence (WTC) methods were performed. Causal relationships between datasets
were further examined by Convergence Cross Mapping (CCM) method. In results of
our analysis we found followings; 1) The C class X-Ray flare numbers increased
about 16 % during the solar cycle 24 compared to cycle 23, while all other data
sets decreased; the MCMESI decreased about 16 % and the number of M and X class
flares decreased about 32 %. 2) All the X-Ray solar flare classes show
remarkable positive correlation with the MCMESI. While the correlation between
the MCMESI and C class flares comes from the general solar cycle trend, it
mainly results from the fluctuations in the data in case of the X class flares.
3) In general, all class flare numbers and the MCMESI show similar periodic
behavior. 4) The 546 days periodicity detected in the MCMESI may not be of
solar origin or at least the solar flares are not the source of this
periodicity. 5) C and M Class solar flares have a stronger causative effect on
the MCMESI compared to X class solar flares. However the only bidirectional
causal relationship is obtained between the MCMESI and C class flare numbers.


  Cellular automata are widely used to model natural or artificial systems.
Classically they are run with perfect synchrony, i.e., the local rule is
applied to each cell at each time step. A possible modification of the updating
scheme consists in applying the rule with a fixed probability, called the
synchrony rate. For some particular rules, varying the synchrony rate
continuously produces a qualitative change in the behaviour of the cellular
automaton. We investigate the nature of this change of behaviour using
Monte-Carlo simulations. We show that this phenomenon is a second-order phase
transition, which we characterise more specifically as belonging to the
directed percolation or to the parity conservation universality classes studied
in statistical physics.


  We argue that the geodesic rule, for global defects, is a consequence of the
randomness of the values of the Goldstone field $\phi$ in each causally
connected volume. As these volumes collide and coalescence, $\phi$ evolves by
performing a random walk on the vacuum manifold $\mathcal{M}$. We derive a
Fokker-Planck equation that describes the continuum limit of this process. Its
fundamental solution is the heat kernel on $\mathcal{M}$, whose leading
asymptotic behavior establishes the geodesic rule.


  In a Comment by Perez et al (2014) it is claimed that recently published
simulations of Beresnyak (2014) are grossly underresolved, compared to theirs,
and that Beresnyak (2014) failed to estimate numerical error. Both claims are
contrary to the fact. Firstly, as far as numerical resolution is concerned,
Beresnyak (2014) was using $k_{\rm max}\eta_{41}$=1.06 resolution criterion,
while Perez et al has been using $k_{\rm max}\eta_{41}$=0.8. Obviously,
$1.06>0.8$. Secondly, Beresnyak (2014) have estimated numerical error and found
it to be below $3\times 10^{-3}$, which is properly explained in the paper. On
close inspection of the Comment I have not found a single numerical value or
parameter pertaining to the criticized paper, Beresnyak (2014), and it is
completely unclear how the Authors came to their conclusions.


  Large-scale fully interconnected ring carbon nanotube (CNT) networks were
first prepared using thermo chemical vapor deposition in nano-channel network
templates of porous anodic alumina. This conductive CNT network film consists
of billions of nanotube segments with a single layer, and could be made as
large as the centimeter size of the template, with a uniform two-dimensional
ring topological structure. These CNT networks could be grown from tiny Fe/Co
catalyst particles which were electro-deposited into the templates, or totally
without additional metallic catalysts. The common interconnected ring topology
suggests a both-tip growth mechanism in which the growth of every CNT should
occurs at both ends by the incorporation of carbon clusters until they connect
into other CNTs. Comparing the different morphology of CNT networks grown with
and without catalyst particles, we found that the local concentration of carbon
clusters should play the key role for the continued growth of CNTs.


  We consider a general one-factor short rate model, in which the instantaneous
interest rate is driven by a univariate diffusion with time independent drift
and volatility. We construct recursive formula for the coefficients of the
Taylor expansion of the bond price and its logarithm around $\tau=0$, where
$\tau$ is time to maturity. We provide numerical examples of convergence of the
partial sums of the series and compare them with the known exact values in the
case of Cox-Ingersoll-Ross and Dothan model.


  Motivic local systems over a curve in finite characteristic form a countable
set endowed with an action of the absolute Galois group of rational numbers
commuting with the Frobenius map. I will discuss three series of conjectures
about such sets, based on an analogy with algebraic dynamics, on a formalism of
commutative algebras of motivic integral operators, and on an analogy with
  2-dimensional lattice models.


  We study theoretically the behavior of thermal massless Dirac fermions inside
graphene-based Ferromagnetic/Insulator/d-wave (s-wave) superconductor (F/I/d
and F/I/S) junctions in the ballistic regime. Using the Dirac-BdG wave
functions within the three regions and appropriate boundary conditions, the
Andreev and Normal reflection coefficients are derived. By employing the
obtained Andreev and Normal reflection coefficients the characteristics of heat
current through the F/I/d and F/I/S junctions are investigated within the thin
barrier approximation. We find that for s-wave superconductors, thermal
conductance oscillates sinusoidally vs barrier strength. The finding persist
for the values of $\alpha$, the orientation of d-wave superconductor crystal in
the $k$-space, below $\pi/4$. By increasing temperature, the thermal
conductance is increased exponentially for small values of $\alpha$ and for
larger values the quantity is modified to exhibit a linear behavior at
$\alpha=\pi/4$ which is similar to Wiedemann-Franz law for metals in low
temperatures.


  Markov random field (MRF) learning is intractable, and its approximation
algorithms are computationally expensive. We target a small subset of MRF that
is used frequently in computer vision. We characterize this subset with three
concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as
an alternative. Our goal is robust learning from small datasets. Our learning
algorithm uses vector quantization and, at time complexity O(U log U) for a
dataset of U pixels, is much faster than that of general-purpose MRF.


  Recurrent Neural Network (RNN) is one of the most popular architectures used
in Natural Language Processsing (NLP) tasks because its recurrent structure is
very suitable to process variable-length text. RNN can utilize distributed
representations of words by first converting the tokens comprising each text
into vectors, which form a matrix. And this matrix includes two dimensions: the
time-step dimension and the feature vector dimension. Then most existing models
usually utilize one-dimensional (1D) max pooling operation or attention-based
operation only on the time-step dimension to obtain a fixed-length vector.
However, the features on the feature vector dimension are not mutually
independent, and simply applying 1D pooling operation over the time-step
dimension independently may destroy the structure of the feature
representation. On the other hand, applying two-dimensional (2D) pooling
operation over the two dimensions may sample more meaningful features for
sequence modeling tasks. To integrate the features on both dimensions of the
matrix, this paper explores applying 2D max pooling operation to obtain a
fixed-length representation of the text. This paper also utilizes 2D
convolution to sample more meaningful information of the matrix. Experiments
are conducted on six text classification tasks, including sentiment analysis,
question classification, subjectivity classification and newsgroup
classification. Compared with the state-of-the-art models, the proposed models
achieve excellent performance on 4 out of 6 tasks. Specifically, one of the
proposed models achieves highest accuracy on Stanford Sentiment Treebank binary
classification and fine-grained classification tasks.


  LTLf synthesis is the automated construction of a reactive system from a
high-level description, expressed in LTLf, of its finite-horizon behavior. So
far, the conversion of LTLf formulas to deterministic finite-state automata
(DFAs) has been identified as the primary bottleneck to the scalabity of
synthesis. Recent investigations have also shown that the size of the DFA state
space plays a critical role in synthesis as well.
  Therefore, effective resolution of the bottleneck for synthesis requires the
conversion to be time and memory performant, and prevent state-space explosion.
Current conversion approaches, however, which are based either on
explicit-state representation or symbolic-state representation, fail to address
these necessities adequately at scale: Explicit-state approaches generate
minimal DFA but are slow due to expensive DFA minimization. Symbolic-state
representations can be succinct, but due to the lack of DFA minimization they
generate such large state spaces that even their symbolic representations
cannot compensate for the blow-up.
  This work proposes a hybrid representation approach for the conversion. Our
approach utilizes both explicit and symbolic representations of the
state-space, and effectively leverages their complementary strengths. In doing
so, we offer an LTLf to DFA conversion technique that addresses all three
necessities, hence resolving the bottleneck. A comprehensive empirical
evaluation on conversion and synthesis benchmarks supports the merits of our
hybrid approach.


  We leverage game theory and a new vehicle modeling approach to compute
overtaking maneuvers for racecars on a nonplanar surface. We solve for
equilibria between noncooperative racing agents and demonstrate that by
leveraging the novel nonplanar vehicle dynamics, overtaking can be achieved in
situations where simpler models can do not provide a winning strategy.


  We propose a model of inference and heuristic decision-making in groups that
is rooted in the Bayes rule but avoids the complexities of rational inference
in partially observed environments with incomplete information, which are
characteristic of group interactions. Our model is also consistent with a
dual-process psychological theory of thinking: the group members behave
rationally at the initiation of their interactions with each other (the slow
and deliberative mode); however, in the ensuing decision epochs, they rely on a
heuristic that replicates their experiences from the first stage (the fast
automatic mode). We specialize this model to a group decision scenario where
private observations are received at the beginning, and agents aim to take the
best action given the aggregate observations of all group members. We study the
implications of the information structure together with the properties of the
probability distributions which determine the structure of the so-called
"Bayesian heuristics" that the agents follow in our model. We also analyze the
group decision outcomes in two classes of linear action updates and log-linear
belief updates and show that many inefficiencies arise in group decisions as a
result of repeated interactions between individuals, leading to overconfident
beliefs as well as choice-shifts toward extremes. Nevertheless, balanced
regular structures demonstrate a measure of efficiency in terms of aggregating
the initial information of individuals. These results not only verify some
well-known insights about group decision-making but also complement these
insights by revealing additional mechanistic interpretations for the group
declension-process, as well as psychological and cognitive intuitions about the
group interaction model.


  We derive estimates of the Babu\u{s}ka-Brezzi inf-sup constant $\beta$ for
two-dimensional incompressible flow in a periodic channel with one flat
boundary and the other given by a periodic, Lipschitz continuous function $h$.
If $h$ is a constant function (so the domain is rectangular), we show that
periodicity in one direction but not the other leads to an interesting
connection between $\beta$ and the unitary operator mapping the Fourier sine
coefficients of a function to its Fourier cosine coefficients. We exploit this
connection to determine the dependence of $\beta$ on the aspect ratio of the
rectangle. We then show how to transfer this result to the case that $h$ is
$C^{1,1}$ or even $C^{0,1}$ by a change of variables. We avoid non-constructive
theorems of functional analysis in order to explicitly exhibit the dependence
of $\beta$ on features of the geometry such as the aspect ratio, the maximum
slope, and the minimum gap thickness (if $h$ passes near the substrate). We
give an example to show that our estimates are optimal in their dependence on
the minimum gap thickness in the $C^{1,1}$ case, and nearly optimal in the
Lipschitz case.


  A new method for exact quantization of general bound Hamiltonian systems is
presented. It is the quantum analogue of the classical Poincare Surface Of
Section (SOS) reduction of classical dynamics. The quantum Poincare mapping is
shown to be the product of the two generalized (non-unitary but compact)
on-shell scattering operators of the two scattering Hamiltonians which are
obtained from the original bound one by cutting the f-dim configuration space
(CS) along (f-1)-dim configurational SOS and attaching the flat
quasi-one-dimensional waveguides instead. The quantum Poincare mapping has
fixed points at the eigenenergies of the original bound Hamiltonian. The energy
dependent quantum propagator (E - H)^(-1) can be decomposed in terms of the
four energy dependent propagators which propagate from and/or to CS to and/or
from configurational SOS (which may generally be composed of many disconnected
parts). I show that in the semiclassical limit (hbar ---> 0) the quantum
Poincare mapping converges to the Bogomolny's propagator and explain how the
higher order semiclassical corrections can be obtained systematically.


  Saddle point problems, ubiquitous in optimization, extend beyond game theory
to diverse domains like power networks and reinforcement learning. This paper
presents novel approaches to tackle saddle point problem, with a focus on
continuous-time contexts. In this paper we propose a continuous time dynamics
to tackle saddle point problem utilizing projected dynamical system in
non-Euclidean domain. This involves computing the (sub/super) gradient of the
min-max function within a Riemannian metric. Additionally, we establish viable
Caratheodory solutions also prove the Lyapunov stability and asymptotic set
stability of the proposed continuous time dynamical system. Next, we present
the Stochastic Saddle Point Mirror Descent (SSPMD) algorithm and establish its
equivalence with the proposed continuous-time dynamics. Leveraging stability
analysis of the continuous-time dynamics, we demonstrate the almost sure
convergence of the algorithm's iterates. Furthermore, we introduce the
Zeroth-Order Saddle Point Mirror Descent (SZSPMD) algorithm, which approximates
gradients using Nesterov's Gaussian Approximation, showcasing convergence to a
neighborhood around saddle points. The analysis in this paper provides
geometric insights into the mirror descent algorithm and demonstrates how these
insights offer theoretical foundations for various practical applications of
the mirror descent algorithm in diverse scenarios.


  Composed video retrieval (CoVR) is a challenging problem in computer vision
which has recently highlighted the integration of modification text with visual
queries for more sophisticated video search in large databases. Existing works
predominantly rely on visual queries combined with modification text to
distinguish relevant videos. However, such a strategy struggles to fully
preserve the rich query-specific context in retrieved target videos and only
represents the target video using visual embedding. We introduce a novel CoVR
framework that leverages detailed language descriptions to explicitly encode
query-specific contextual information and learns discriminative embeddings of
vision only, text only and vision-text for better alignment to accurately
retrieve matched target videos. Our proposed framework can be flexibly employed
for both composed video (CoVR) and image (CoIR) retrieval tasks. Experiments on
three datasets show that our approach obtains state-of-the-art performance for
both CovR and zero-shot CoIR tasks, achieving gains as high as around 7% in
terms of recall@K=1 score. Our code, models, detailed language descriptions for
WebViD-CoVR dataset are available at
\url{https://github.com/OmkarThawakar/composed-video-retrieval}


  We have examined the stacked radio and X-ray emission from UV-selected
galaxies spectroscopically confirmed to lie between redshifts 1.5 < z < 3.0 in
the GOODS-North field to determine their average extinction and star formation
rates (SFRs). The X-ray and radio data are obtained from the Chandra 2 Msec
survey and the Very Large Array, respectively. There is a good agreement
between the X-ray, radio, and de-reddened UV estimates of the average SFR for
our sample of z~2 galaxies of ~50 solar masses per year, indicating that the
locally-calibrated SFR relations appear to be statistically valid from
redshifts 1.5 < z < 3.0. We find that UV-estimated SFRs (uncorrected for
extinction) underestimate the bolometric SFRs as determined from the 2-10 keV
X-ray luminosity by a factor of ~4.5 to 5.0 for galaxies over a large range in
redshift from 1.0 < z < 3.5.


  For a stable process, we give an explicit formula for the potential measure
of the process killed outside a bounded interval and the joint law of the
overshoot, undershoot and undershoot from the maximum at exit from a bounded
interval. We obtain the equivalent quantities for a stable process reflected in
its infimum. The results are obtained by exploiting a simple connection with
the Lamperti representation and exit problems of stable processes.


  Elastic constants of splay K_11, twist K_22, and bend K_33 of nematic liquid
crystals are often assumed to be equal to each other in order to simplify the
theoretical description of complex director fields. Here we present examples of
how the disparity of K_11 and K_33 produces effects that cannot be described in
a one-constant approximation. In a lyotropic chromonic liquid crystal, nematic
droplets coexisting with the isotropic phase change their shape from a
simply-connected tactoid to a topologically distinct toroid as a result of
temperature or concentration variation. The transformation is caused by the
increase of the splay-to-bend ratio K_11/K_33. A phase transition from a
conventional nematic to a twist-bend nematic implies that the ratio K_11/K_33
changes from very large to very small. As a result, the defects caused by an
externally applied electric field change the deformation mode of optic axis
from bend to splay. In the paraelectric-ferroelectric nematic transition, one
finds an inverse situation: K_11/K_33 changes from small to large, which shapes
the domain walls in the spontaneous electric polarization field as conic
sections. The polarization field tends to be solenoidal, or divergence-free, a
behavior complementary to irrotational curl-free director textures of a smectic
A.


  We develop an approach to construct Poisson algebras for non-linear scalar
field theories that is based on the Cahiers topos model for synthetic
differential geometry. In this framework the solution space of the field
equation carries a natural smooth structure and, following Zuckerman's ideas,
we can endow it with a presymplectic current. We formulate the Hamiltonian
vector field equation in this setting and show that it selects a family of
observables which forms a Poisson algebra. Our approach provides a clean
splitting between geometric and algebraic aspects of the construction of a
Poisson algebra, which are sufficient to guarantee existence, and analytical
aspects that are crucial to analyze its properties.


  Measurements of the dependence of the J/Psi production cross section on its
kinematic variables as well as on the target atomic numbers for 920 GeV/c
protons incident on different targets have been made with the Hera-B detector.
The large collected di-lepton sample allows to study the production ratio of
Psi(2S) to J/Psi and of Chic to J/Psi . We also report on measurements of the
b-bbar and Upsilon production cross section.


  We derive the partition function of {\cal N}=4 supersymmetric Yang-Mills
theory on orbifold-T^4/{\bf Z}_2 for gauge group SU(N). We generalize the
method of our previous work for the SU(2) case to the SU(N) case. The resulting
partition function is represented as the sum of the product of G\"ottche
formula of singular quotient space $T^4/{\bf Z}_2 $ and of blow-up formulas
including A_{N-1} theta series with level N.


  We present the discovery of two new quasars, that initially appeared as
candidate cataclysmic variables (CVs) in a list of the Calan-Tololo objective
prism survey. However, additional spectroscopic and photometric observations
revealed their true nature. Furthermore we present data for a third quasar,
CTCV J0322-4653, which has also been found in our study, but which has already
been included in the list of Maza et al. (1996) as CTS 0639. Here we present
the spectra for the first time.


  We demonstrate the ability to load, cool and detect singly-charged calcium
ions in a surface electrode trap using only visible and infrared lasers for the
trapped-ion control. As opposed to the standard methods of cooling using
dipole-allowed transitions, we combine power broadening of a quadrupole
transition at 729 nm with quenching of the upper level using a dipole allowed
transition at 854 nm. By observing the resulting 393 nm fluorescence we are
able to perform background-free detection of the ion. We show that this system
can be used to smoothly transition between the Doppler cooling and sideband
cooling regimes, and verify theoretical predictions throughout this range. We
achieve scattering rates which reliably allow recooling after collision events
and allow ions to be loaded from a thermal atomic beam. This work is compatible
with recent advances in optical waveguides, and thus opens a path in current
technologies for large-scale quantum information processing. In situations
where dielectric materials are placed close to trapped ions, it carries the
additional advantage of using wavelengths which do not lead to significant
charging, which should facilitate high rate optical interfaces between remotely
held ions.


  The rise-up in boron neutrino spectrum at low energies has been studied
within the framework of `pure LMA' scenario. Indirect bounds on the spectral
`upturn' have been obtained from the available solar neutrino data. These
bounds have been used to demonstrate the efficacy of the precision measurements
of the `upturn' for further constraining the neutrino parameter space allowed
by SNO salt phase data. The sterile neutrino flux has been constrained in the
light of the recent 766.3 Ty KamLAND spectral data.


  The Continuum Shell Model is an old but recently revived method that
traverses the boundary between nuclear many-body structure and nuclear
reactions. The method is based on the non-Hermitian energy-dependent effective
Hamiltonian. The formalism, interpretation of solutions and practical
implementation of calculations are discussed in detail. The results of the
traditional shell model are fully reproduced for bound states; resonance
parameters and cross section calculations are presented for decaying states.
Particular attention is given to one- and two-nucleon reaction channels
including sequential and direct two-body decay modes. New calculations of
reaction cross sections and comparisons with experimental data for helium and
oxygen isotope chains are presented.


  When the energy content of a resonant mode of a crystalline solid in
thermodynamic equilibrium is directly measured, assuming that quantum effects
can be neglected it coincides with temperature except for a proportionality
factor. This is due to the principle of energy equipartition and the
equilibrium hypothesis. However, most natural systems found in nature are not
in thermodynamic equilibrium and thus the principle cannot be granted. We
measured the extent to which the low-frequency modes of vibration of a solid
can defy energy equipartition, in presence of a steady state heat flux, even
close to equilibrium. We found, experimentally and numerically, that the energy
separately associated with low frequency normal modes strongly depends on the
heat flux, and decouples sensibly from temperature. A 4% in the relative
temperature difference across the object around room temperature suffices to
excite two modes of a macroscopic oscillator, as if they were at equilibrium,
respectively, at temperatures about 20% and a factor 3.5 higher. We interpret
the result in terms of new flux-mediated correlations between modes in the
nonequilibrium state, which are absent at equilibrium.


  The concept of Galactic Habitable Zone (GHZ) was introduced a few years ago
as an extension of the much older concept of Circumstellar Habitable Zone.
However, the physical processes underlying the former concept are hard to
identify and even harder to quantify. That difficulty does not allow us, at
present, to draw any significant conclusions about the extent of the GHZ: it
may well be that the entire Milky Way disk is suitable for complex life.


  The aim of this paper is to describe a basic idea to introduce formal
modeling in development and presentation of e-learning systems. By introducing
formalism, reasoning on e-learning systems and processes through them can be
more easily understood. In particular, we propose to use Harel automata
(statecharts) in modeling sequencing and navigation through learning content
and learning process.


  We propose solution methods for previously-unsolved constrained MDPs in which
actions can continuously modify the transition probabilities within some
acceptable sets. While many methods have been proposed to solve regular MDPs
with large state sets, there are few practical approaches for solving
constrained MDPs with large action sets. In particular, we show that the
continuous action sets can be replaced by their extreme points when the rewards
are linear in the modulation. We also develop a tractable optimization
formulation for concave reward functions and, surprisingly, also extend it to
non- concave reward functions by using their concave envelopes. We evaluate the
effectiveness of the approach on the problem of managing delinquencies in a
portfolio of loans.


  A search for the standard model Higgs boson (H) decaying to a b-quark pair
when produced in association with weak vector bosons (V) is reported for the
following modes: W(mu,nu)H, W(e,nu)H, Z(mu,mu)H, Z(e,e)H and Z(nu,nu)H. The
search is performed in a data sample corresponding to an integrated luminosity
of 4.7 inverse femtobarns, recorded by the CMS detector in proton-proton
collisions at the LHC with a center-of-mass energy of 7 TeV. No significant
excess of events above the expectation from background is observed. Upper
limits on the VH production cross section times the H to b-quark pair branching
ratio, with respect to the expectations for a standard model Higgs boson, are
derived for a Higgs boson in the mass range 110-135 GeV. In this range, the
observed 95% confidence level upper limits vary from 3.4 to 7.5 times the
standard model prediction; the corresponding expected limits vary from 2.7 to
6.7.


  An analytical solution of the collective Bohr equation with a Coulomb-like
and a Kratzer-like $\gamma-$unstable potential in quadrupole deformation space
is presented. Eigenvalues and eigenfunctions are given in closed form and
transition rates are calculated for the two cases. The corresponding
SO(2,1)$\times$SO(5) algebraic structure is discussed.


  In this note we consider the independent domination polynomial problem along
with their unimodal and log-concave properties which were earlier studied by
G\"ursoy, \"Ulker and G\"ursoy (Soft Comp. 2022). We show that the independent
domination polynomial of zero divisor graphs of $\mathbb{Z}_{n}$ for $n\in \{
pq, p^{2}q, pqr, p^{\alpha}\}$ where $p,q,r$ are primes with $2<p<q<r$ are not
unimodal thereby contradicting the main result of G\"ursoy, \"Ulker and
G\"ursoy \cite{gursoy}. Besides the authors show that the zero of the
independent domination polynomial of these graphs have only real zero and used
concept of Newton's inequalities to establish the log-concave property for the
afore said polynomials. We show that these polynomials have complex zeros and
the technique of Newton's inequalities are not applicable. Finally, by
definition of log-concave, we prove that these polynomials are log-concave and
fix the flaws in Theorem 10 of G\"ursoy, \"Ulker and G\"ursoy \cite{gursoy}.


  When the rare earth mononitrides (RENs) first burst onto the scientific scene
in the middle of last century, there were feverish dreams that their strong
magnetic moment would afford a wide range of applications. For decades research
was frustrated by poor stoichiometry and the ready reaction of the materials in
ambient conditions, and only recently have these impediments finally been
overcome by advances in thin film fabrication with ultra-high vacuum based
growth technology. Currently, the field of research into the RENs is growing
rapidly, motivated by the materials demands of proposed electronic and
spintronic devices. Both semiconducting and ferromagnetic properties have been
established in some of the RENs which thus attract interest for the potential
to exploit the spin of charge carriers in semiconductor technologies for both
fundamental and applied science. In this review, we take stock of where
progress has occurred within the last decade in both theoretical and
experimental fields, and which has led to the point where a proof-of-concept
spintronic device based on RENs has already been demonstrated. The article is
organized into three major parts. First, we describe the epitaxial growth of
REN thin films and their structural properties, with an emphasis on their
prospective spintronic applications. Then, we conduct a critical review of the
different advanced theoretical calculations utilised to determine both the
electronic structure and the origins of the magnetism in these compounds. The
rest of the review is devoted to the recent experimental results on optical,
electrical and magnetic properties and their relation to current theoretical
descriptions. These results are discussed particularly with regard to the
controversy about the exact nature of the magnetic state and conduction
processes in the RENs.


  Consider a multiuser multiple-input multiple-output (MU-MIMO) downlink system
in which the base station (BS) sends multiple data streams to multi-antenna
users via symbol-level precoding (SLP), where the optimization of receive
combining matrix becomes crucial, unlike in the single-antenna user scenario.
We begin by introducing a joint optimization problem on the symbol-level
transmit precoder and receive combiner. The problem is solved using the
alternating optimization (AO) method, and the optimal solution structures for
transmit precoding and receive combining matrices are derived by using
Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the
original problem is transformed into an equivalent quadratic programming
problem, enabling more efficient solutions. To address the challenge that the
above joint design is difficult to implement, we propose a more practical
scheme where the receive combining optimization is replaced by the interference
rejection combiner (IRC), which is however difficult to directly use because of
the rank-one transmit precoding matrix. Therefore, we introduce a new
regularized IRC (RIRC) receiver to circumvent the above issue. Numerical
results demonstrate that the practical SLP-RIRC method enjoys only a slight
communication performance loss compared to the joint transmit precoding and
receive combining design, both offering substantial performance gains over the
conventional BD-based approaches.


  We elucidate the feature of gravitational waves (GWs) from binary neutron
star merger collapsing to a black hole by general relativistic simulation. We
show that GW spectrum imprints the coalescence dynamics, formation process of
disk, equation of state for neutron stars, total masses, and mass ratio. A
formation mechanism of the central engine of short $\gamma$-ray bursts, which
are likely to be composed of a black hole and surrounding disk, therefore could
be constrained by GW observation.


  We present results from detailed imaging of the centrally dominant radio
elliptical galaxy in the cooling flow cluster Abell 2597, using data obtained
with the Wide Field and Planetary Camera 2 (WFPC2) on the Hubble Space
Telescope (HST). This object is one of the archetypal "blue-lobed" cooling flow
radio elliptical galaxies, also displaying a luminous emission-line nebula, a
compact radio source, and a significant dust lane and evidence of molecular gas
in its center. We show that the radio source is surrounded by a complex network
of emission-line filaments, some of which display a close spatial association
with the outer boundary of the radio lobes. We present a detailed analysis of
the physical properties of ionized and neutral gas associated with the radio
lobes, and show that their properties are strongly suggestive of direct
interactions between the radio plasma and ambient gas. We resolve the blue
continuum emission into a series of knots and clumps, and present evidence that
these are most likely due to regions of recent star formation. We investigate
several possible triggering mechanisms for the star formation, including direct
interactions with the radio source, filaments condensing from the cooling flow,
or the result of an interaction with a gas-rich galaxy, which may also have
been responsible for fueling the active nucleus. We propose that the properties
of the source are plausibly explained in terms of accretion of gas by the cD
during an interaction with a gas-rich galaxy, which combined with the fact that
this object is located at the center of a dense, high-pressure ICM can account
for the high rates of star formation and the strong confinement of the radio
source.


  Temporal action detection (TAD) aims to determine the semantic label and the
temporal interval of every action instance in an untrimmed video. It is a
fundamental and challenging task in video understanding. Previous methods
tackle this task with complicated pipelines. They often need to train multiple
networks and involve hand-designed operations, such as non-maximal suppression
and anchor generation, which limit the flexibility and prevent end-to-end
learning. In this paper, we propose an end-to-end Transformer-based method for
TAD, termed TadTR. Given a small set of learnable embeddings called action
queries, TadTR adaptively extracts temporal context information from the video
for each query and directly predicts action instances with the context. To
adapt Transformer to TAD, we propose three improvements to enhance its locality
awareness. The core is a temporal deformable attention module that selectively
attends to a sparse set of key snippets in a video. A segment refinement
mechanism and an actionness regression head are designed to refine the
boundaries and confidence of the predicted instances, respectively. With such a
simple pipeline, TadTR requires lower computation cost than previous detectors,
while preserving remarkable performance. As a self-contained detector, it
achieves state-of-the-art performance on THUMOS14 (56.7% mAP) and HACS Segments
(32.09% mAP). Combined with an extra action classifier, it obtains 36.75% mAP
on ActivityNet-1.3. Code is available at https://github.com/xlliu7/TadTR.


  Let $ (X,d) $ be a metric space. We study a metric $ d_0 $ on $ X $ naturally
derived from $ d $. If $ (X,d) $ is complete and locally compact, or if it is
complete and $ (d_0)_0=d_0 $, then $ d_0 $ coincides with the length metric
induced by $ d $. Counterexamples are constructed when any of the hypotheses is
absent. The behavior of the iterates of $ d_0 $ (the metrics $ d_0^n $
inductively defined as $ (d_0^{n-1})_0 $) is also considered.


  This paper deals with the problem of testing for dispersion parameter change
in discretely observed diffusion processes when the observations are
contaminated by outliers. To lessen the impact of outliers, we first calculate
residuals using a robust estimate and then propose a trimmed-residual based
CUSUM test. The proposed test is shown to converge weakly to a function of the
Brownian bridge under the null hypothesis of no parameter change. We conduct
simulations to evaluate performances of the proposed test in the presence of
outliers. Numerical results confirm that the proposed test posses a strong
robust property against outliers. In real data analysis, we fit the
Ornstein-Uhlenbeck process to KOSPI200 volatility index data and locate some
change points that are not detected by a naive CUSUM test.


  We present a finite-temperature theory of the anisotropic spin-spin
correlations in magnetic metallic monolayers, deposited on a suitable
substrate. The `spins' are the local moments set up by the itinerant electrons,
and the key concept is the relativistic disordered local moment state, which
represents the paramagnetic state of a set of local moments. The spin-spin
correlations between these local moments are then extracted using the linear
response formalism. The anisotropy is included in a fully relativistic
treatment, based on the Dirac equation, and has a qualitative impact on
noncollinear magnetic states, by lifting their chiral degeneracy. The theory is
applied to Mn monolayers on the hexagonal (111) surfaces of Pd, Pt, Ag and Au.
The presence of competing exchange interactions is highlighted by choosing
different substrates, which favour either the row-wise antiferromagnetic state
or the chiral triangular N\'eel state. We correlate the electronic structure
with the magnetic properties, by comparing filled with partially filled
substrate d--bands, and low vs high atomic number. The disagreement between
theory and experiment for Mn$_1$/Ag(111) is addressed, and the nature of the
magnetic domains found experimentally is suggested to be chiral.


  Geometric quantum speed limits quantify the trade-off between the rate with
which quantum states can change and the resources that are expended during the
evolution. Counterdiabatic driving is a unique tool from shortcuts to
adiabaticity to speed up quantum dynamics while completely suppressing
nonequilibrium excitations. We show that the quantum speed limit for
counterdiabatically driven systems undergoing quantum phase transitions fully
encodes the Kibble-Zurek mechanism by correctly predicting the transition from
adiabatic to impulse regimes. Our findings are demonstrated for three
scenarios, namely the transverse field Ising, the Landau-Zener, and the
Lipkin-Meshkov-Glick models.


  Recent research has shown the potential utility of Deep Gaussian Processes.
These deep structures are probability distributions, designed through
hierarchical construction, which are conditionally Gaussian. In this paper, the
current published body of work is placed in a common framework and, through
recursion, several classes of deep Gaussian processes are defined. The
resulting samples generated from a deep Gaussian process have a Markovian
structure with respect to the depth parameter, and the effective depth of the
resulting process is interpreted in terms of the ergodicity, or non-ergodicity,
of the resulting Markov chain. For the classes of deep Gaussian processes
introduced, we provide results concerning their ergodicity and hence their
effective depth. We also demonstrate how these processes may be used for
inference; in particular we show how a Metropolis-within-Gibbs construction
across the levels of the hierarchy can be used to derive sampling tools which
are robust to the level of resolution used to represent the functions on a
computer. For illustration, we consider the effect of ergodicity in some simple
numerical examples.


  A new class of core-collapse supernovae (SNe) has been discovered in recent
years by optical/infrared surveys; these SNe suggest the presence of one or
more extremely dense (~10^5-10^11 cm^-3) shells of circumstellar material (CSM)
on 10^2-10^4 AU scales. We consider the collisions of the SN ejecta with these
massive CSM shells as potential cosmic-ray (CR) accelerators. If ~10% of the SN
energy goes into CRs, multi-TeV neutrinos and/or GeV-TeV gamma rays almost
simultaneous with the optical/infrared light curves are detectable for SNe at
<20-30 Mpc. A new type of coordinated multi-messenger search for such
transients of duration ~1 - 10 months is required; these may give important
clues to the physical origin of such SNe and to CR acceleration mechanisms.


  The thesis addresses the problem of scalability in decentralized blockchains
in the context of the trade-off between transaction throughput and hardware
requirements to participate in the network. Rollups are presented, that is
technologies to verify on-chain blocks executed off-chain by minimizing the
assumptions of trust. The variant of the Optimistic Rollups, in particular of
Optimism and the use of invalidity proofs through interactive binary search and
of the Validity Rollups, in particular of StarkNet, and the use of validity
proofs through STARKs are discussed. Finally, the two solutions are compared on
withdrawal time, on the cost of transactions and techniques to minimize it, on
the possibility of applying the technology recursively, on compatibility with
Ethereum and on the licenses used.


  We consider the problem of recovering an unknown $k$-factor, hidden in a
weighted random graph. For $k=1$ this is the planted matching problem, while
the $k=2$ case is closely related to the planted travelling salesman problem.
The inference problem is solved by exploiting the information arising from the
use of two different distributions for the weights on the edges inside and
outside the planted sub-graph. We argue that, in the large size limit, a phase
transition can appear between a full and a partial recovery phase as function
of the signal-to-noise ratio. We give a criterion for the location of the
transition.


  Meteor showers occur when streams of meteoroids originating from a common
source intersect the Earth. There will be small dissimilarities between the
direction of motion of different meteoroids within a stream, and these small
differences will act to broaden the radiant, or apparent point of origin, of
the shower. This dispersion in meteor radiant can be particularly important
when considering the effect of the Earth's gravity on the stream, as it limits
the degree of enhancement of the stream's flux due to gravitational focusing.
In this paper, we present measurements of the radiant dispersion of twelve
showers using observations from the Global Meteor Network. We find that the
median offset of individual meteors from the shower radiant ranges from
0.32$^\circ$ for the eta Aquariids to 1.41$^\circ$ for the Southern Taurids. We
also find that there is a small but statistically significant drift in
Sun-centered ecliptic radiant and/or geocentric speed over time for most
showers. Finally, we compare radiant dispersion with shower duration and find
that, in contrast with previous results, the two quantities are not correlated
in our data.


  Electron energy loss spectroscopy is consolidating as a powerful tool to
explore electronic (as well as vibrational) excitations of matter, including
molecules. Performed in a scanning transmission electron microscope, this
technique is based on inelastic scattering of fast electrons in a thin
specimen. Very recently, new electron optics configuration have been
introduced, opening the way to the analysis of the single components of orbital
angular momentum of the outcoming electrons, that convey additional information
on the spatial features of the investigated excitations: innovative
double-dispersed spectroscopic experiments for metallic nanostructures have
been therefore suggested. We propose here to extend this technology to probe
molecular and supra-molecular systems, devising new kind of experiments: using
state of the art quantum chemical methods to describe the molecular system in
presence of an electron beam in a configuration that avoid molecular damage, we
show that scattered electrons acquire the different azimuthal components of
induced molecular transition potentials. Numerical simulations performed for
systems of increasing size, point out that the conceived new technique can open
up the possibility of probing the multipolar components and even the chirality
of molecular transitions, superseding the usual optical spectroscopies for
those cases that are problematic, such as dipole-forbidden transitions, at a
very high spatial resolution.


  Several recently developed methods have the potential to harness machine
learning in the pursuit of target quantities inspired by causal inference,
including inverse weighting, doubly robust estimating equations and
substitution estimators like targeted maximum likelihood estimation. There are
even more recent augmentations of these procedures that can increase
robustness, by adding a layer of cross-validation (cross-validated targeted
maximum likelihood estimation and double machine learning, as applied to
substitution and estimating equation approaches, respectively). While these
methods have been evaluated individually on simulated and experimental data
sets, a comprehensive analysis of their performance across ``real-world''
simulations have yet to be conducted.
  In this work, we benchmark multiple widely used methods for estimation of the
average treatment effect using ten different nutrition intervention studies
data. A realistic set of simulations, based on a novel method, highly adaptive
lasso, for estimating the data-generating distribution that guarantees a
certain level of complexity (undersmoothing) is used to better mimic the
complexity of the true data-generating distribution. We have applied this novel
method for estimating the data-generating distribution by individual study and
to subsequently use these fits to simulate data and estimate treatment effects
parameters as well as their standard errors and resulting confidence intervals.
Based on the analytic results, a general recommendation is put forth for use of
the cross-validated variants of both substitution and estimating equation
estimators. We conclude that the additional layer of cross-validation helps in
avoiding unintentional over-fitting of nuisance parameter functionals and leads
to more robust inferences.


  Using a framework in which all elements are constrained by Dyson-Schwinger
equation studies in QCD, and therefore incorporates a consistent, direct and
simultaneous description of light- and heavy-quarks and the states they
constitute, we analyze the accuracy of SU(4)-flavor symmetry relations between
{\pi}{\rho}{\pi}, K{\rho}K and D{\rho}D couplings. Such relations are widely
used in phenomenological analyses of the interactions between matter and
charmed mesons. We find that whilst SU(3)-flavor symmetry is accurate to 20%,
SU(4) relations underestimate the D{\rho}D coupling by a factor of five.


  We prove that many completeness properties coincide in metric spaces,
precompact groups and dense subgroups of products of separable metric groups.
We apply these results to function spaces C_p(X,G) of G-valued continuous
functions on a space X with the topology of pointwise convergence, for a
separable metric group G. Not only the results but also the proofs themselves
are novel even in the classical case when G is the real line.
  A space X is weakly pseudocompact if it is G_delta-dense in at least one of
its compactifications. A topological group G is precompact if it is
topologically isomorphic to a subgroup of a compact group. We prove that every
weakly pseudocompact precompact topological group is pseudocompact, thereby
answering positively a question of Tkachenko.


  This paper is concerned with the study of a diffusive perturbation of the
linear LSW model introduced by Carr and Penrose. A main subject of interest is
to understand how the presence of diffusion acts as a selection principle,
which singles out a particular self-similar solution of the linear LSW model as
determining the large time behavior of the diffusive model. A selection
principle is rigorously proven for a model which is a semi-classical
approximation to the diffusive model. Upper bounds on the rate of coarsening
are also obtained for the full diffusive model.


  A sequence in the additive group ${\mathbb Z}_n$ of integers modulo $n$ is
called $n$-zero-free if it does not contain subsequences with length $n$ and
sum zero. The article characterizes the $n$-zero-free sequences in ${\mathbb
Z}_n$ of length greater than $3n/2-1$. The structure of these sequences is
completely determined, which generalizes a number of previously known facts.
The characterization cannot be extended in the same form to shorter sequence
lengths. Consequences of the main result are best possible lower bounds for the
maximum multiplicity of a term in an $n$-zero-free sequence of any given length
greater than $3n/2-1$ in ${\mathbb Z}_n$, and also for the combined
multiplicity of the two most repeated terms. Yet another application is finding
the values in a certain range of a function related to the classic theorem of
Erd\H{o}s, Ginzburg and Ziv.


  A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as
the trunk network is devised to predict full-field highly nonlinear
elastic-plastic stress response for complex geometries obtained from topology
optimization under variable loads. The proposed DeepONet uses a ResUNet in the
trunk to encode complex input geometries, and a fully-connected branch network
encodes the parametric loads. Additional information fusion is introduced via
an element-wise multiplication of the encoded latent space to improve
prediction accuracy further. The performance of the proposed DeepONet was
compared to two baseline models, a standalone ResUNet and a DeepONet with fully
connected networks as the branch and trunk. The results show that ResUNet and
the proposed DeepONet share comparable accuracy; both can predict the stress
field and accurately identify stress concentration points. However, the novel
DeepONet is more memory efficient and allows greater flexibility with framework
architecture modifications. The DeepONet with fully connected networks suffers
from high prediction error due to its inability to effectively encode the
complex, varying geometry. Once trained, all three networks can predict the
full stress distribution orders of magnitude faster than finite element
simulations. The proposed network can quickly guide preliminary optimization,
designs, sensitivity analysis, uncertainty quantification, and many other
nonlinear analyses that require extensive forward evaluations with variable
geometries, loads, and other parameters. This work marks the first time a
ResUNet is used as the trunk network in the DeepONet architecture and the first
time that DeepONet solves problems with complex, varying input geometries under
parametric loads and elasto-plastic material behavior.


  We present a new publicly available code, $\texttt{class_rot}$, which
modifies $\texttt{class}$ to enable fast non-perturbative calculation of cosmic
microwave background polarization power spectra due to both isotropic and
anisotropic polarization rotation from cosmic birefringence. Cosmic
birefringence can arise from new parity-violating physics such as axion dark
matter with a Chern-Simons coupling to photons or Faraday rotation due to a
primordial magnetic field. Constraints on these effects can be obtained by
comparing measurements to precise numerical calculations of the polarization
power spectra. We describe the implementation of $\texttt{class_rot}$ in terms
of both mathematical formalism and coding architecture. We also provide usage
examples and demonstrate the accuracy of the code by comparing with
simulations.


  In the next decades millimeter and sub-mm astronomy requires large format
imaging arrays and broad-band spectrometers to complement the high spatial and
spectral resolution of the Atacama Large Millimeter/sub-millimeter Array. The
desired sensors for these instruments should have a background limited
sensitivity and a high optical efficiency and enable arrays thousands of pixels
in size. Hybrid microwave kinetic inductance detectors consisting of NbTiN and
Al have shown to satisfy these requirements. We present the second generation
hybrid NbTiN-Al MKIDs, which are photon noise limited in both phase and
amplitude readout for loading levels $P_{850GHz} \geq 10$ fW. Thanks to the
increased responsivity, the photon noise level achieved in phase allows us to
simultaneously read out approximately 8000 pixels using state-of-the-art
electronics. In addition, the choice of superconducting materials and the use
of a Si lens in combination with a planar antenna gives these resonators the
flexibility to operate within the frequency range $0.09 < \nu < 1.1$ THz. Given
these specifications, hybrid NbTiN-Al MKIDs will enable astronomically usable
kilopixel arrays for sub-mm imaging and moderate resolution spectroscopy.


  It has been shown recently that the strong cosmic censorship conjecture is
violated by near-extremal Reissner-Nordstr\"om-de Sitter black holes. We
investigate whether the introduction of a charged scalar field can rescue
strong cosmic censorship. We find that such a field improves the situation but
there is always a neighbourhood of extremality in which strong cosmic
censorship is violated by perturbations arising from smooth initial data.


  Weak gravitational lensing provides a sensitive probe of cosmology by
measuring the mass distribution and the geometry of the low redshift universe.
We show how an all-sky weak lensing tomographic survey can jointly constrain
different sets of cosmological parameters describing dark energy, massive
neutrinos (hot dark matter), and the primordial power spectrum. In order to put
all sectors on an equal footing, we introduce a new parameter $\beta$, the
second order running spectral index. Using the Fisher matrix formalism with and
without CMB priors, we examine how the constraints vary as the parameter set is
enlarged. We find that weak lensing with CMB priors provides robust constraints
on dark energy parameters and can simultaneously provide strong constraints on
all three sectors. We find that the dark energy sector is largely insensitive
to the inclusion of the other cosmological sectors. Implications for the
planning of future surveys are discussed.


  We investigate the spectrum of the generator of a self-adjoint transition
semigroup of a (symmetric) L\'{e}vy process taking values in $d$--dimensional
space.


  Coping with malware is getting more and more challenging, given their
relentless growth in complexity and volume. One of the most common approaches
in literature is using machine learning techniques, to automatically learn
models and patterns behind such complexity, and to develop technologies to keep
pace with malware evolution. This survey aims at providing an overview on the
way machine learning has been used so far in the context of malware analysis in
Windows environments, i.e. for the analysis of Portable Executables. We
systematize surveyed papers according to their objectives (i.e., the expected
output), what information about malware they specifically use (i.e., the
features), and what machine learning techniques they employ (i.e., what
algorithm is used to process the input and produce the output). We also outline
a number of issues and challenges, including those concerning the used
datasets, and identify the main current topical trends and how to possibly
advance them. In particular, we introduce the novel concept of malware analysis
economics, regarding the study of existing trade-offs among key metrics, such
as analysis accuracy and economical costs.


  We discuss possibilities of generating a Majorana mass for the neutron from
topological quantum gravity effects which survive at mesoscopic scales from
decoherence. We show how virtual micro-black hole (BH) pairs with skyrme/baryon
hairs induce a neutron-antineutron transition which can be tested in next
generation of experiments. Such effects do not destabilize the proton. We also
discuss how BHs with mix ordinary and mirror baryon hairs can mediate
neutron-mirror neutron mixings.


  In the present investigation, certain subclasses of close-to-convex functions
are investigated. In particular, we obtain an estimate for the Fekete-Szeg\"o
functional for functions belonging to the class, distortion, growth estimates
and covering theorems.


  Deep learning has become a powerful and popular tool for a variety of machine
learning tasks. However, it is challenging to understand the mechanism of deep
learning from a theoretical perspective. In this work, we propose a random
active path model to study collective properties of deep neural networks with
binary synapses, under the removal perturbation of connections between layers.
In the model, the path from input to output is randomly activated, and the
corresponding input unit constrains the weights along the path into the form of
a $p$-weight interaction glass model. A critical value of the perturbation is
observed to separate a spin glass regime from a paramagnetic regime, with the
transition being of the first order. The paramagnetic phase is conjectured to
have a poor generalization performance.


  In this paper, we investigate homotopy invariance for homology of SL_4. For
any commutative ring, the group E_4(R[t]) acts on a simplicial complex whose
contractibility implies homotopy invariance. We show that for a local factorial
ring R, this complex satisfies the CAT(0)-property for the induced length
metric from the Bruhat-Tits building.


  The modern S-Matrix Bootstrap provides non-perturbative bounds on low-energy
aspects of scattering amplitudes, leveraging the constraints of unitarity,
analyticity and crossing. Typically, the solutions saturating such bounds also
saturate the unitarity constraint as much as possible, meaning that they are
almost exclusively elastic. This is expected to be unphysical in $d>2$ because
of Aks' theorem. We explore this issue by adding inelasticity as an additional
input, both using a primal approach in general dimensions which extends the
usual ansatz, and establishing a dual formulation in the 2d case. We then
measure the effects on the low-energy observables where we observe stronger
bounds than in the standard setup.


  Named Entity Recognition (NER) serves as a fundamental task in natural
language understanding, bearing direct implications for web content analysis,
search engines, and information retrieval systems. Fine-tuned NER models
exhibit satisfactory performance on standard NER benchmarks. However, due to
limited fine-tuning data and lack of knowledge, it performs poorly on unseen
entity recognition. As a result, the usability and reliability of NER models in
web-related applications are compromised. Instead, Large Language Models (LLMs)
like GPT-4 possess extensive external knowledge, but research indicates that
they lack specialty for NER tasks. Furthermore, non-public and large-scale
weights make tuning LLMs difficult. To address these challenges, we propose a
framework that combines small fine-tuned models with LLMs (LinkNER) and an
uncertainty-based linking strategy called RDC that enables fine-tuned models to
complement black-box LLMs, achieving better performance. We experiment with
both standard NER test sets and noisy social media datasets. LinkNER enhances
NER task performance, notably surpassing SOTA models in robustness tests. We
also quantitatively analyze the influence of key components like uncertainty
estimation methods, LLMs, and in-context learning on diverse NER tasks,
offering specific web-related recommendations.


  In the leading log approximation and at large $N_C$ the interaction of two
fermionic and one gluonic reggeons is described by an integrable system
corresponding to an open spin chain.


  In this paper, we propose several calculus rules for the generalized concave
Kurdyka-\L ojasiewicz (KL) property, which generalize Li and Pong's results for
KL exponents. The optimal concave desingularizing function has various forms
and may be nondifferentiable. Our calculus rules do not assume desingularizing
functions to have any specific form nor differentiable, while the known results
do. Several examples are also given to show that our calculus rules are
applicable to a broader class of functions than the known ones.


  In our continuing attempts to understand theoretically various surface
properties such as corrosion and potential catalytic activity of actinide
surfaces in the presence of environmental gases, we report here the first ab
initio study of molecular adsorption on the double hexagonal packed (dhcp)
americium (0001) surface. Dissociative adsorption is found to be energetically
more favorable compared to molecular adsorption. The most stable configuration
corresponds to a horizontal approach molecular dissociation with the oxygen
atoms occupying neighboring h3 sites, with chemisorption energies at the NSOC
and SOC theoretical levels being 9.395 eV and 9.886 eV, respectively. The
corresponding distances of the oxygen molecule from the surface and
oxygen-oxygen distance were found to be 0.953 Ang. and 3.731 Ang.,
respectively. Overall our calculations indicate that chemisorption energies in
cases with SOC are slightly more stable than the cases with NSOC in the
0.089-0.493 eV range. The work functions and net magnetic moments respectively
increased and decreased in all cases compared with the corresponding quantities
of the bare dhcp Am (0001) surface. The adsorbate-substrate interactions have
been analyzed in detail using the partial charges inside the muffin-tin
spheres, difference charge density distributions, and the local density of
states. The effects, if any, of chemisorption on the Am 5f electron
localization-delocalization characteristics in the vicinity of the Fermi level
are also discussed.