  In the current study, a brand-new SINARS(1) model is proposed for stationary
discrete time series defined on $\boldsymbol{Z}$, based on extended binomial
distribution and the Pegram's operator. The model effectively characterizes the
series of positive and negative integer values generated after differencing
some non-stationary time series. The model's attributes are addressed. For the
parameter estimation of the model, the conditional maximum likelihood method
and Yule-Walker method are taken into consideration. And we prove the
asymptotic normality of CML method. By using these two methods, we simulate our
model comparing with some relevant ones proposed before. The model can deal
with positive or negative autocorrelation data. The analysis of the number of
differenced daily new cases in Barbados is done using the suggested model.


  We study a transverse electron-hole focusing effect in a
normal-superconductor system. The spectrum of the quasiparticles is calculated
both quantum mechanically and in semiclassical approximation, showing an
excellent agreement. A semiclassical conductance formula is derived which takes
into account the effect of electron-like as well as hole-like quasiparticles.
At low magnetic field the semiclassical conductance shows characteristic
oscillations due to the Andreev reflection, while at higher fields it goes to
zero. These findigs are in line with the results of previous quantum
calculations and with the expectations based on the classical dynamics of the
quasiparticles.


  In this article we present a comprehensive study of the totally asymmetric
simple exclusion process with pausing particles (pTASEP), a model initially
introduced to describe RNAP dynamics during transcription. We extend previous
mean-field approaches and demonstrate that the pTASEP is equivalent to the
exclusion process with dynamical defects (ddTASEP), thus broadening the scope
of our investigation to a larger class of problems related to transcription and
translation. We extend the mean-field theory to the open boundary case,
revealing the system's phase diagram and critical values of entry and exit
rates. However, we identify a significant discrepancy between theory and
simulations in a region of the parameter space, indicating severe finite-size
effects. To address this, we develop a single-cluster approximation that
captures the relationship between current and lattice size, providing a more
accurate representation of the system's dynamics. Finally, we extend our
approach to open boundary conditions, demonstrating its applicability in
different scenarios. Our findings underscore the importance of considering
finite-size effects, often overlooked in the literature, when modelling
biological processes such as transcription and translation.


  We present a quantum secure direct communication scheme achieved by swapping
quantum entanglement. In this scheme a set of ordered Einstein-Podolsky-Rosen
(EPR) pairs is used as a quantum information channel for sending secret
messages directly. After insuring the safety of the quantum channel, the sender
Alice encodes the secret messages directly by applying a series local
operations on her particles sequence according to their stipulation.
  Using three EPR pairs, three bits of secret classical information can be
faithfully transmitted from Alice to remote Bob without revealing any
information to a potential eavesdropper.
  By both Alice and Bob's GHZ state measurement results, Bob is able to read
out the encoded secret messages directly. The protocol is completely secure if
perfect quantum channel is used, because there is not a transmission of the
qubits carrying the secret message between Alice and Bob in the public channel.


  In conventional metals, modification of electron trajectories under magnetic
field gives rise to a magnetoresistance that varies quadratically at low field,
followed by a saturation at high field for closed orbits on the Fermi surface.
Deviations from the conventional behaviour, e.g. the observation of a linear
magnetoresistance, or a non-saturating magnetoresistance, have been attributed
to exotic electron scattering mechanisms. Recently, linear magnetoresistance
has been observed in many Dirac materials, in which the electron-electron
correlation is relatively weak. The strongly correlated helimagnet CrAs
undergoes a quantum phase transition to a nonmagnetic superconductor under
pressure. Near the magnetic instability, we observe a large and non-saturating
quasilinear magnetoresistance from the upper critical field to 14 T at low
temperatures. We show that the quasilinear magnetoresistance arises from an
intricate interplay between a nontrivial band crossing protected by
nonsymmorphic crystal symmetry and strong magnetic fluctuations


  NIO1 is a compact and flexible radiofrequency H- ion source, developed by
Consorzio RFX and INFN-LNL. Aim of the experimentation on NIO1 is the
optimization of both the production of negative ions and their extraction and
beam optics. In the initial phase of its commissioning, NIO1 was operated with
nitrogen, but now the source is regularly operated also with hydrogen. To
evaluate the source performances an optical emission spectroscopy diagnostic
was installed. The system includes a low resolution spectrometer in the
spectral range of 300-850 nm and a high resolution (50 pm) one, to study
respectively the atomic and the molecular emissions in the visible range. The
spectroscopic data have been interpreted also by means of a
collisional-radiative model developed at IPP Garching. Besides the diagnostic
hardware and the data analysis methods, the paper presents the first plasma
measurements across a transition to the full H mode, in a hydrogen discharge.
The characteristic signatures of this transition in the plasma parameters are
described, in particular the sudden increase of the light emitted from the
plasma above a certain power threshold.


  Object Re-Identification (ReID) is pivotal in computer vision, witnessing an
escalating demand for adept multimodal representation learning. Current models,
although promising, reveal scalability limitations with increasing modalities
as they rely heavily on late fusion, which postpones the integration of
specific modality insights. Addressing this, we introduce the \textbf{Gradual
Fusion Transformer (GraFT)} for multimodal ReID. At its core, GraFT employs
learnable fusion tokens that guide self-attention across encoders, adeptly
capturing both modality-specific and object-specific features. Further
bolstering its efficacy, we introduce a novel training paradigm combined with
an augmented triplet loss, optimizing the ReID feature embedding space. We
demonstrate these enhancements through extensive ablation studies and show that
GraFT consistently surpasses established multimodal ReID benchmarks.
Additionally, aiming for deployment versatility, we've integrated neural
network pruning into GraFT, offering a balance between model size and
performance.


  The effects of soft errors in processor cores have been widely studied.
However, little has been published about soft errors in uncore components, such
as memory subsystem and I/O controllers, of a System-on-a-Chip (SoC). In this
work, we study how soft errors in uncore components affect system-level
behaviors. We have created a new mixed-mode simulation platform that combines
simulators at two different levels of abstraction, and achieves 20,000x speedup
over RTL-only simulation. Using this platform, we present the first study of
the system-level impact of soft errors inside various uncore components of a
large-scale, multi-core SoC using the industrial-grade, open-source OpenSPARC
T2 SoC design. Our results show that soft errors in uncore components can
significantly impact system-level reliability. We also demonstrate that uncore
soft errors can create major challenges for traditional system-level checkpoint
recovery techniques. To overcome such recovery challenges, we present a new
replay recovery technique for uncore components belonging to the memory
subsystem. For the L2 cache controller and the DRAM controller components of
OpenSPARC T2, our new technique reduces the probability that an application run
fails to produce correct results due to soft errors by more than 100x with
3.32% and 6.09% chip-level area and power impact, respectively.


  We consider the azimuthal distribution of the final observed hadron in
semi-inclusive deep-inelastic scattering and the lepton pair in the Drell-Yan
process. In particular, we focus on the $\cos \phi$ modulation of the
unpolarized cross section and on its dependence upon transverse momentum. At
low transverse momentum, for these observables we propose a factorized
expression based on tree-level approach and conjecture that the same formula is
valid in transverse-momentum dependent (TMD) factorization when written in
terms of subtracted TMD parton distributions. Our formula correctly matches
with the collinear factorization results at high transverse momentum, solves a
long-standing problem and is a necessary step towards the extension of the TMD
factorization theorems up to the subleading twist.


  We introduce the "selPT" perturbative approach, based on ab initio molecular
dynamics (AIMD), for computing accurate finite-temperature properties by
efficiently using correlated wave-function methods. We demonstrate the power of
the method by computing prototypical molecular enthalpies of adsorption in
zeolite (CH$_4$ and CO$_2$ on protonated chabazite at 300~K) using the random
phase approximation. Results are in excellent agreement with experiment. The
improved accuracy provided by selPT represents a crucial step towards the goal
of truly quantitative AIMD prediction of experimental observables at finite
temperature.


  We study additive models built with trend filtering, i.e., additive models
whose components are each regularized by the (discrete) total variation of
their $k$th (discrete) derivative, for a chosen integer $k \geq 0$. This
results in $k$th degree piecewise polynomial components, (e.g., $k=0$ gives
piecewise constant components, $k=1$ gives piecewise linear, $k=2$ gives
piecewise quadratic, etc.). Analogous to its advantages in the univariate case,
additive trend filtering has favorable theoretical and computational
properties, thanks in large part to the localized nature of the (discrete)
total variation regularizer that it uses. On the theory side, we derive fast
error rates for additive trend filtering estimates, and show these rates are
minimax optimal when the underlying function is additive and has component
functions whose derivatives are of bounded variation. We also show that these
rates are unattainable by additive smoothing splines (and by additive models
built from linear smoothers, in general). On the computational side, as per the
standard in additive models, backfitting is an appealing method for
optimization, but it is particularly appealing for additive trend filtering
because we can leverage a few highly efficient univariate trend filtering
solvers. Going one step further, we describe a new backfitting algorithm whose
iterations can be run in parallel, which (as far as we know) is the first of
its kind. Lastly, we present experiments to examine the empirical performance
of additive trend filtering.


  Let $X$ be a Banach space or more generally a complete metric space admitting
a conical geodesic bicombing. We prove that every closed $L$-Lipschitz curve
$\gamma:S^1\rightarrow X$ may be extended to an $L$-Lipschitz map defined on
the hemisphere $f:H^2\rightarrow X$. This implies that $X$ satisfies a
quadratic isoperimetric inequality (for curves) with constant $\frac{1}{2\pi}$.
We discuss how this fact controls the regularity of minimal discs in Finsler
manifolds when applied to the work of Alexander Lytchak and Stefan Wenger.


  In earlier work with C.~Monical, we introduced the notion of a K-crystal,
with applications to K-theoretic Schubert calculus and the study of Lascoux
polynomials. We conjectured that such a K-crystal structure existed on the set
of semistandard set-valued tableaux of any fixed rectangular shape. Here, we
establish this conjecture by explicitly constructing the K-crystal operators.
As a consequence, we establish the first combinatorial formula for Lascoux
polynomials $L_{w\lambda}$ when $\lambda$ is a multiple of a fundamental weight
as the sum over flagged set-valued tableaux. Using this result, we then prove
corresponding cases of conjectures of Ross--Yong (2015) and Monical (2016) by
constructing bijections with the respective combinatorial objects.


  These notes are loosely based on lectures given at the CERN Winter School on
Supergravity, Strings and Gauge theories, February 2009 and at the IPM String
School in Tehran, April 2009. I have focused on a few concrete topics and also
on addressing questions that have arisen repeatedly. Background condensed
matter physics material is included as motivation and easy reference for the
high energy physics community. The discussion of holographic techniques
progresses from equilibrium, to transport and to superconductivity.


  This paper proposes a proximal variant of the alternating direction method of
multipliers (ADMM) for distributed optimization. Although the current versions
of ADMM algorithm provide promising numerical results in producing solutions
that are close to optimal for many convex and non-convex optimization problems,
it remains unclear if they can converge to a stationary point for weakly convex
and locally non-smooth functions. Through our analysis using the Moreau
envelope function, we demonstrate that MADM can indeed converge to a stationary
point under mild conditions. Our analysis also includes computing the bounds on
the amount of change in the dual variable update step by relating the gradient
of the Moreau envelope function to the proximal function. Furthermore, the
results of our numerical experiments indicate that our method is faster and
more robust than widely-used approaches.


  In this paper I will describe some results that have been recently obtained
in the study of random Euclidean matrices, i.e. matrices that are functions of
random points in Euclidean space. In the case of translation invariant matrices
one generically finds a phase transition between a phonon phase and a saddle
phase. If we apply these considerations to the study of the Hessian of the
Hamiltonian of the particles of a fluid, we find that this phonon-saddle
transition corresponds to the dynamical phase transition in glasses, that has
been studied in the framework of the mode coupling approximation. The Boson
peak observed in glasses at low temperature is a remanent of this transition.


  In this paper we prove that the trapezoidal $H^{4}$ and the $H^{6}$ families
of quad-equations are Darboux integrable systems. This result sheds light on
the fact that such equations are linearizable as it was proved using the
Algebraic Entropy test [G. Gubbiotti, C. Scimiterna and D. Levi, Algebraic
entropy, symmetries and linearization for quad equations consistent on the
cube, \emph{J. Nonlinear Math. Phys.}, 23(4):507543, 2016]. We conclude with
some suggestions on how first integrals can be used to obtain general
solutions.


  We introduce the adjoint homological Selmer module for an
SL$_2$-representation of a knot group, which may be seen as a knot theoretic
analogue of the dual adjoint Selmer module for a Galois representation. We then
show finitely generated torsion-ness of our adjoint Selmer module, which are
widely known as conjectures in number theory, and give some concrete examples.


  Obesity increases significantly cancer risk in various organs. Although this
has been recognized for decades, the mechanism through which this happens has
never been explained. Here, we show that the volumes of kidneys, pancreas, and
liver are strongly correlated (median correlation = 0.625; P-value<10-47) with
the body mass index (BMI) of an individual. We also find a significant
relationship between the increase in organ volume and the increase in cancer
risk (P-value<10-12). These results provide a mechanism explaining why obese
individuals have higher cancer risk in several organs: the larger the organ
volume the more cells at risk of becoming cancerous. These findings are
important for a better understanding of the effects obesity has on cancer risk
and, more generally, for the development of better preventive strategies to
limit the mortality caused by obesity.


  $N=2$ coset models of the type $SU(m+1)/SU(m)\times U(1)$ with nondiagonal
modular invariants for both $SU(m+1)$ and $SU(m)$ are considered. Poincar\'e
polynomials of the corresponding chiral rings of these algebras are
constructed. They are used to compute the number of chiral generations of the
associated string compactifications. Moddings by discrete symmetries are also
discussed.


  Using isochronous mass spectrometry at the experimental storage ring CSRe in
Lanzhou, the masses of $^{82}$Zr and $^{84}$Nb were measured for the first time
with an uncertainty of $\sim 10$ keV, and the masses of $^{79}$Y, $^{81}$Zr,
and $^{83}$Nb were re-determined with a higher precision. %The latter differ
significantly from their literature values. The latter are significantly less
bound than their literature values. Our new and accurate masses remove the
irregularities of the mass surface in this region of the nuclear chart. Our
results do not support the predicted island of pronounced low $\alpha$
separation energies for neutron-deficient Mo and Tc isotopes, making the
formation of Zr-Nb cycle in the $rp$-process unlikely. The new proton
separation energy of $^{83}$Nb was determined to be 490(400)~keV smaller than
that in the Atomic Mass Evaluation 2012. This partly removes the overproduction
of the $p$-nucleus $^{84}$Sr relative to the neutron-deficient molybdenum
isotopes in the previous $\nu p$-process simulations.


  This paper investigates an approximation scheme of the optimal nonlinear
Bayesian filter based on the Gaussian mixture representation of the state
probability distribution function. The resulting filter is similar to the
particle filter, but is different from it in that, the standard weight-type
correction in the particle filter is complemented by the Kalman-type correction
with the associated covariance matrices in the Gaussian mixture. We show that
this filter is an algorithm in between the Kalman filter and the particle
filter, and therefore is referred to as the particle Kalman filter (PKF). In
the PKF, the solution of a nonlinear filtering problem is expressed as the
weighted average of an "ensemble of Kalman filters" operating in parallel.
Running an ensemble of Kalman filters is, however, computationally prohibitive
for realistic atmospheric and oceanic data assimilation problems. For this
reason, we consider the construction of the PKF through an "ensemble" of
ensemble Kalman filters (EnKFs) instead, and call the implementation the
particle EnKF (PEnKF). We show that different types of the EnKFs can be
considered as special cases of the PEnKF. Similar to the situation in the
particle filter, we also introduce a re-sampling step to the PEnKF in order to
reduce the risk of weights collapse and improve the performance of the filter.
Numerical experiments with the strongly nonlinear Lorenz-96 model are presented
and discussed.


  When subject to a weak magnetic impurity potential, the order parameter and
quasi-particle energy gap of a bulk singlet superconductor are suppressed.
According to the conventional mean-field theory of Abrikosov and Gor'kov, the
integrity of the energy gap is maintained up to a critical concentration of
magnetic impurities. In this paper, a field theoretic approach is developed to
critically analyze the validity of the mean field theory. Using the
supersymmetry technique we find a spatially homogeneous saddle-point that
reproduces the Abrikosov-Gor'kov theory, and identify instanton contributions
to the density of states that render the quasi-particle energy gap soft at any
non-zero magnetic impurity concentration. The sub-gap states are associated
with supersymmetry broken field configurations of the action. An analysis of
fluctuations around these configurations shows how the underlying supersymmetry
of the action is restored by zero modes. An estimate of the density of states
is given for all dimensionalities. To illustrate the universality of the
present scheme we apply the same method to study `gap fluctuations' in a normal
quantum dot coupled to a superconducting terminal. Using the same instanton
approach, we recover the universal result recently proposed by Vavilov et al.
Finally, we emphasize the universality of the present scheme for the
description of gap fluctuations in d-dimensional superconducting/normal
structures.


  The upcoming p+Pb run at the LHC will probe the nuclear gluon distribution at
very small Bjorken x (from $x \sim 10^{-4}$ at mid-rapidity down to $x \sim
10^{-6}$ in the proton fragmentation region) and will allow to test approaches
based on parton saturation. Here, we present the predictions of the KLN model
for hadron multiplicities and multiplicity distributions in p+Pb collisions at
a center-of-mass energy of 4.4 TeV. We also compare the model to the existing
pp, dA and AA data from RHIC and LHC.


  When a strong magnetic field is applied perpendicularly (along z) to a sheet
confining electrons to two dimensions (x-y), highly correlated states emerge as
a result of the interplay between electron-electron interactions, confinement
and disorder. These so-called fractional quantum Hall (FQH) liquids form a
series of states which ultimately give way to a periodic electron solid that
crystallizes at high magnetic fields. This quantum phase of electrons has been
identified previously as a disorder-pinned two-dimensional Wigner crystal with
broken translational symmetry in the x-y plane. Here, we report our discovery
of a new insulating quantum phase of electrons when a very high magnetic field,
up to 45T, is applied in a geometry parallel (y-direction) to the
two-dimensional electron sheet. Our data point towards this new quantum phase
being an electron solid in a "quasi-3D" configuration induced by orbital
coupling with the parallel field.


  We analyze the recent electron-proton scattering data from Mainz using a
dispersive framework that respects the constraints from analyticity and
unitarity on the nucleon structure. We also perform a continued fraction
analysis of these data. We find a small electric proton charge radius, r_E^p =
0.84_{-0.01}^{+0.01} fm, consistent with the recent determination from muonic
hydrogen measurements and earlier dispersive analyses. We also extract the
proton magnetic radius, r_M^p = 0.86_{-0.03}^{+0.02} fm, consistent with
earlier determinations based on dispersion relations.


  It is well known that there exist statistical and structural differences
between the stock markets of developed and emerging countries. In this work, we
present an analysis of the variations and autocorrelations of the Mexican Stock
Market index (IPC) for different periods of its historical daily data, showing
evidence that the Mexican Stock Market has been increasing its efficiency in
recent times. We have analyzed the returns autocorrelation function (ACF) and
used detrended fluctuation analysis (DFA) methods. We also analyze the
volatility of the IPC and the Dow Jones Industrial Average (DJIA) and compare
their evolution. The data samples analyzed here, correspond to daily values of
the IPC and DJIA for the period 10/30/1978 to 02/28/2006.


  We have constructed a large, uniform galaxy cluster catalog from the Sloan
Digital Sky Survey data. By studying the morphology--cluster-centric-radius
relation, we have found two characteristic environments where galaxy
morphologies change dramatically, indicating there exist two different physical
mechanisms responsible for the cluster galaxy evolution. We also found an
unusual population of galaxies, which have spiral morphology but do not have
any emission lines, indicating these spiral galaxies do not have any on-going
star formation activity. More interestingly, these passive spiral galaxies
preferentially exist in the cluster outskirts. Therefore, these passive spiral
galaxies are likely to be a key galaxy population being transformed from blue,
star-forming galaxies into red, early-type galaxies.


  Static code analysis is a powerful approach to detect quality deficiencies
such as performance bottlenecks, safety violations or security vulnerabilities
already during a software system's implementation. Yet, as current software
systems continue to grow, current static-analysis systems more frequently face
the problem of insufficient scalability. We argue that this is mainly due to
the fact that current static analyses are implemented fully manually, often in
general-purpose programming languages such as Java or C, or in declarative
languages such as Datalog. This design choice predefines the way in which the
static analysis evaluates, and limits the optimizations and extensions
static-analysis designers can apply.
  To boost scalability to a new level, we propose to fuse static-analysis with
just-in-time-optimization technology, introducing for the first time static
analyses that are managed and inherently self-adaptive. Those analyses
automatically adapt themselves to yield a performance/precision tradeoff that
is optimal with respect to the analyzed software system and to the analysis
itself.
  Self-adaptivity is enabled by the novel idea of designing a dedicated
intermediate representation, not for the analyzed program but for the analysis
itself. This representation allows for an automatic optimization and adaptation
of the analysis code, both ahead-of-time (through static analysis of the static
analysis) as well as just-in-time during the analysis' execution, similar to
just-in-time compilers.


  While live 360 degree video streaming delivers immersive viewing experience,
it poses significant bandwidth and latency challenges for content delivery
networks. Edge servers are expected to play an important role in facilitating
live streaming of 360 degree videos. In this paper, we propose a novel
predictive edge caching algorithm (Coffee) for live 360 degree video that
employ collaborative FoV prediction and predictive tile prefetching to reduce
bandwidth consumption, streaming cost and improve the streaming quality and
robustness. Our light-weight caching algorithms exploit the unique tile
consumption patterns of live 360 degree video streaming to achieve high tile
caching gains. Through extensive experiments driven by real 360 degree video
streaming traces, we demonstrate that edge caching algorithms specifically
designed for live 360 degree video streaming can achieve high streaming cost
savings with small edge cache space consumption. Coffee, guided by viewer FoV
predictions, significantly reduces back-haul traffic up to 76% compared to
state-of-the-art edge caching algorithms. Furthermore, we develop a
transcoding-aware variant (TransCoffee) and evaluate it using comprehensive
experiments, which demonstrate that TransCoffee can achieve 63\% lower cost
compared to state-of-the-art transcoding-aware approaches.


  Under the presence of strong electromagnetic fields and radiation reaction,
plasmas develop anisotropic momentum distributions, characterized by a
population inversion. This is a general property of collisionless plasmas when
the radiation reaction force is taken into account. We study the case of a
plasma in a strong magnetic field and demonstrate the development of ring
momentum distributions. The timescales for ring formation are derived for this
configuration. The analytical results for the ring properties and the
timescales for ring formation are confirmed with particle-in-cell simulations.
The resulting momentum distributions are kinetically unstable and are known to
lead to coherent radiation emission in astrophysical plasmas and laboratory
setups.


  We introduce the concept of coverage risk as an error measure for density
ridge estimation. The coverage risk generalizes the mean integrated square
error to set estimation. We propose two risk estimators for the coverage risk
and we show that we can select tuning parameters by minimizing the estimated
risk. We study the rate of convergence for coverage risk and prove consistency
of the risk estimators. We apply our method to three simulated datasets and to
cosmology data. In all the examples, the proposed method successfully recover
the underlying density structure.


  As the latest video coding standard, versatile video coding (VVC) has shown
its ability in retaining pixel quality. To excavate more compression potential
for video conference scenarios under ultra-low bitrate, this paper proposes a
bitrate adjustable hybrid compression scheme for face video. This hybrid scheme
combines the pixel-level precise recovery capability of traditional coding with
the generation capability of deep learning based on abridged information, where
Pixel wise Bi-Prediction, Low-Bitrate-FOM and Lossless Keypoint Encoder
collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s.
Without introducing any additional bitrate, our method has a clear advantage
over VVC under a completely fair comparative experiment, which proves the
effectiveness of our proposed scheme. Moreover, our scheme can adapt to any
existing encoder / configuration to deal with different encoding requirements,
and the bitrate can be dynamically adjusted according to the network condition.


  We investigate the collapse of a spherical shell of matter in an anti-de
Sitter space. We search for a holographic description of the collapsing shell
in the boundary theory. It turns out that in the boundary theory it is possible
to find information about the radial size of the shell. The shell deforms the
background spacetime, and the deformed background metric enters into the action
of a generic bulk field. As a consequence, the correlators of operators
coupling to the bulk field are modified. By studying the analytic structure of
the correlators, we find that in the boundary theory there are unstable
excitations ("shellons") whose masses are multiples of a scale set by the
radius of the shell. We also comment on the relation between black hole
formation in the bulk and thermalization in the boundary.


  Let $X$ and $Y$ be nonsingular projective varieties over an algebraically
closed field $k$ of positive characteristic. If $X$ and $Y$ are birational, we
show their $S$-fundamental group schemes are isomorphic.


  The foundations are laid for the numerical computation of the actual
worldline for a particle orbiting a black hole and emitting gravitational
waves. The essential practicalities of this computation are here illustrated
for a scalar particle of infinitesimal size and small but finite scalar charge.
This particle deviates from a geodesic because it interacts with its own
retarded field $\psi^\ret$. A recently introduced Green's function $G^\SS$
precisely determines the singular part, $\psi^\SS$, of the retarded field. This
part exerts no force on the particle. The remainder of the field $\psi^\R =
\psi^\ret - \psi^\SS$ is a vacuum solution of the field equation and is
entirely responsible for the self-force. A particular, locally inertial
coordinate system is used to determine an expansion of $\psi^\SS$ in the
vicinity of the particle. For a particle in a circular orbit in the
Schwarzschild geometry, the mode-sum decomposition of the difference between
$\psi^\ret$ and the dominant terms in the expansion of $\psi^\SS$ provide a
mode-sum decomposition of an approximation for $\psi^\R$ from which the
self-force is obtained. When more terms are included in the expansion, the
approximation for $\psi^\R$ is increasingly differentiable, and the mode-sum
for the self-force converges more rapidly.


  We discuss two novel approaches to the classical two-sample problem. Our
starting point are properly standardized and combined, very popular in several
areas of statistics and data analysis, ordinal dominance and receiver
characteristic curves, denoted by ODC and ROC, respectively. The proposed new
curves are termed the comparison curves. Their estimates, being weighted rank
processes on (0,1), form the basis of inference. These weighted processes are
intuitive, well-suited for visual inspection of data at hand, and are also
useful for constructing some formal inferential procedures. They can be applied
to several variants of two-sample problem. Their use can help to improve some
existing procedures both in terms of power and the ability to identify the
sources of departures from the postulated model. To simplify interpretation of
finite sample results we restrict attention to values of the processes on a
finite grid of points. This results in the so-called bar plots (B-plots) which
readably summarize the information contained in the data. What is more, we show
that B-plots along with adjusted simultaneous acceptance regions provide
principled information about where the model departs from the data. This leads
to a framework which facilitates identification of regions with locally
significant differences.
  We show an implementation of the considered techniques to a standard
stochastic dominance testing problem. Some min-type statistics are introduced
and investigated. A simulation study compares two tests pertinent to the
comparison curves to well-established tests in the literature and demonstrates
the strong and competitive performance of the former in many typical
situations. Some real data applications illustrate simplicity and practical
usefulness of the proposed approaches. A range of other applications of
considered weighted processes is briefly discussed too.


  In this paper we present a constructive proof of cut elimination for a system
of full second order logic with the structural rules absorbed and using sets
instead of sequences. The standard problem of the cutrank growth is avoided by
using a new parameter for the induction, the cutweight. This technique can also
be applied to first order logic.


  Lately, concepts such as lockdown, quarantine, and social distancing have
become very relevant since they have been associated with essential measures in
the prevention and mitigation of COVID-19. While some conclusions about the
effectiveness of these measures could be drawn from field observations, many
mathematical models aimed to provide some clues. However, the reliability of
these models is questioned, especially if the social structure is not included
in them. In this work, we propose a mesoscopic model that allows the evaluation
of the effect of measures such as social distancing and lockdown when the
social topology is taken into account. The model is able to predict successive
waves of infections without the need to account for reinfections, and it can
qualitatively reproduce the wave patterns observed across many countries during
the COVID-19 pandemic. Subsequent waves can have a higher peak of infections if
the restrictiveness of the lockdown is above a certain threshold. The model is
flexible and can implement various social distancing strategies by adjusting
the restrictiveness and the duration of lockdown measures or specifying whether
they occur once or repeatedly. It also includes the option to consider
essential workers that do not isolate during a lockdown.


  Illicit object detection is a critical task performed at various
high-security locations, including airports, train stations, subways, and
ports. The continuous and tedious work of examining thousands of X-ray images
per hour can be mentally taxing. Thus, Deep Neural Networks (DNNs) can be used
to automate the X-ray image analysis process, improve efficiency and alleviate
the security officers' inspection burden. The neural architectures typically
utilized in relevant literature are Convolutional Neural Networks (CNNs), with
Vision Transformers (ViTs) rarely employed. In order to address this gap, this
paper conducts a comprehensive evaluation of relevant ViT architectures on
illicit item detection in X-ray images. This study utilizes both Transformer
and hybrid backbones, such as SWIN and NextViT, and detectors, such as DINO and
RT-DETR. The results demonstrate the remarkable accuracy of the DINO
Transformer detector in the low-data regime, the impressive real-time
performance of YOLOv8, and the effectiveness of the hybrid NextViT backbone.


  We consider a sequence of repeated interactions between an agent and an
environment. Uncertainty about the environment is captured by a probability
distribution over a space of hypotheses, which includes all computable
functions. Given a utility function, we can evaluate the expected utility of
any computational policy for interaction with the environment. After making
some plausible assumptions (and maybe one not-so-plausible assumption), we show
that if the utility function is unbounded, then the expected utility of any
policy is undefined.


  Quantum key distribution (QKD) based on the laws of quantum physics allows
the secure distribution of secret keys over an insecure channel. Unfortunately,
imperfect implementations of QKD compromise its information-theoretical
security. Measurement-device-independent quantum key distribution (MDI-QKD) is
a promising approach to remove all side channels from the measurement unit,
which is regarded as the "Achilles' heel" of QKD. An essential assumption in
MDI-QKD is however that the sources are trusted. Here we experimentally
demonstrate that a practical source based on a semiconductor laser diode is
vulnerable to a laser seeding attack, in which light injected from the
communication line into the laser results in an increase of the intensities of
the prepared states. The unnoticed increase of intensity may compromise the
security of QKD, as we show theoretically for the prepare-and-measure
decoy-state BB84 and MDI-QKD protocols. Our theoretical security analysis is
general and can be applied to any vulnerability that increases the intensity of
the emitted pulses. Moreover, a laser seeding attack might be launched as well
against decoy-state based quantum cryptographic protocols beyond QKD.


  The adiabatic groupoid $\mathcal{G}_{ad}$ of a smooth groupoid $\mathcal{G}$
is a deformation relating $\mathcal{G}$ with its algebroid. In a previous work,
we constructed a natural action of $\mathbb{R}$ on the C*-algebra of zero order
pseudodifferential operators on $\mathcal{G}$ and identified the crossed
product with a natural ideal $J(\mathcal{G})$ of $C^*(\mathcal{G}_{ad})$. In
the present paper we show that $C^*(\mathcal{G}_{ad})$ itself is a
pseudodifferential extension of this crossed product in a sense introduced by
Saad Baaj. Let us point out that we prove our results in a slightly more
general situation: the smooth groupoid $\mathcal{G}$ is assumed to act on a
C*-algebra $A$. We construct in this generalized setting the extension of order
$0$ pseudodifferential operators $\Psi(A,\mathcal{G})$ of the associated
crossed product $A\rtimes \mathcal{G}$. We show that $\mathbb{R}$ acts
naturally on $\Psi(A,\mathcal{G})$ and identify the crossed product of $A$ by
the action of the adiabatic groupoid $\mathcal{G}_{ad}$ with an extension of
the crossed product $\Psi(A,\mathcal{G})\rtimes \mathbb{R}$. Note that our
construction of $\Psi(A,\mathcal{G})$ unifies the ones of Connes (case
$A=\mathbb{C} $) and of Baaj ($\mathcal{G}$ is a Lie group).


  Photonuclear reaction is described with an approach based on the
quasideuteron nuclear photoabsorption model followed by the process of
competition between light particle evaporation and fission for the excited
nucleus. Thus fission process is considered as a decay mode. The
evaporation-fission process of the compound nucleus is simulated in a
Monte-Carlo framework. Photofission reaction cross sections are analysed in a
systematic manner in the energy range $\sim$~50-70 MeV for the actinides
$^{232}$Th, $^{233}$U, $^{235}$U, $^{238}$U and $^{237}$Np and the pre-actinide
nuclei $^{208}$Pb and $^{209}$Bi. The study reproduces satisfactorily well the
available experimental data of photofission cross sections at energies
$\sim$~50-70 MeV and the increasing trend of nuclear fissility with the
fissility parameter $Z^2/A$ for the actinides and pre-actinides at intermediate
energies [$\sim$~20-140 MeV].


  Software refactoring aims at improving code quality while preserving the
system's external behavior. Although in principle refactoring is a
behavior-preserving activity, a study presented by Bavota et al. in 2012
reported the proneness of some refactoring actions (e.g., pull up method) to
induce faults. The study was performed by mining refactoring activities and
bugs from three systems. Taking profit of the advances made in the mining
software repositories field (e.g., better tools to detect refactoring actions
at commit-level granularity), we present a differentiated replication of the
work by Bavota et al. in which we (i) overcome some of the weaknesses that
affect their experimental design, (ii) answer the same research questions of
the original study on a much larger dataset (3 vs 103 systems), and (iii)
complement the quantitative analysis of the relationship between refactoring
and bugs with a qualitative, manual inspection of commits aimed at verifying
the extent to which refactoring actions trigger bug-fixing activities. The
results of our quantitative analysis confirm the findings of the replicated
study, while the qualitative analysis partially demystifies the role played by
refactoring actions in the bug introduction.


  We propose a new incremental aggregation algorithm for multi-image deblurring
with automatic image selection. The primary motivation is that current bursts
deblurring methods do not handle well situations in which misalignment or
out-of-context frames are present in the burst. These real-life situations
result in poor reconstructions or manual selection of the images that will be
used to deblur. Automatically selecting best frames within the burst to improve
the base reconstruction is challenging because the amount of possible images
fusions is equal to the power set cardinal. Here, we approach the multi-image
deblurring problem as a two steps process. First, we successfully learn a
comparison function to rank a burst of images using a deep convolutional neural
network. Then, an incremental Fourier burst accumulation with a reconstruction
degradation mechanism is applied fusing only less blurred images that are
sufficient to maximize the reconstruction quality. Experiments with the
proposed algorithm have shown superior results when compared to other similar
approaches, outperforming other methods described in the literature in
previously described situations. We validate our findings on several synthetic
and real datasets.


  Recent developments have established the vulnerability of deep Reinforcement
Learning (RL) to policy manipulation attacks via adversarial perturbations. In
this paper, we investigate the robustness and resilience of deep RL to
training-time and test-time attacks. Through experimental results, we
demonstrate that under noncontiguous training-time attacks, Deep Q-Network
(DQN) agents can recover and adapt to the adversarial conditions by reactively
adjusting the policy. Our results also show that policies learned under
adversarial perturbations are more robust to test-time attacks. Furthermore, we
compare the performance of $\epsilon$-greedy and parameter-space noise
exploration methods in terms of robustness and resilience against adversarial
perturbations.


  We provide a simplified proof of a theorem proved by Tavanfar and Shimomoto
which states that a quasi-Gorenstein deformation of a $3$-dimensional
quasi-Gorenstein local ring $(A,m,k)$ with $H^2_m(A)=k$ admits a small
Cohen-Macaulay module.


  Let $K_n$ be the set of all nonsingular $n\times n$ lower triangular
$(0,1)$-matrices. Hong and Loewy (2004) introduced the numbers $$ c_n={\rm
min}\{\lambda\mid \lambda~\text{is an eigenvalue of}~XX^{\rm T},~X\in
K_n\},\quad n\in\mathbb{Z}_+. $$ A related family of numbers was considered by
Ilmonen, Haukkanen, and Merikoski (2008): $$ C_n={\rm max}\{\lambda\mid
\lambda~\text{is an eigenvalue of}~XX^{\rm T},~X\in K_n\},\quad
n\in\mathbb{Z}_+. $$ These numbers can be used to bound the singular values of
matrices belonging to $K_n$ and they appear, e.g., in eigenvalue bounds for
power GCD matrices, lattice-theoretic meet and join matrices, and related
number-theoretic matrices. In this paper, it is shown that for $n$ odd, one has
the lower bound $$ c_n\geq
\frac{1}{\sqrt{\frac{1}{25}\varphi^{-4n}+\frac{2}{25}\varphi^{-2n}-\frac{2}{5\sqrt{5}}n\varphi^{-2n}-\frac{23}{25}+n+\frac{2}{25}\varphi^{2n}+\frac{2}{5\sqrt{5}}n\varphi^{2n}+\frac{1}{25}\varphi^{4n}}},
$$ and for $n$ even, one has $$ c_n\geq
\frac{1}{\sqrt{\frac{1}{25}\varphi^{-4n}+\frac{4}{25}\varphi^{-2n}-\frac{2}{5\sqrt{5}}n\varphi^{-2n}-\frac{2}{5}+n+\frac{4}{25}\varphi^{2n}+\frac{2}{5\sqrt{5}}n\varphi^{2n}+\frac{1}{25}\varphi^{4n}}},
$$ where $\varphi$ denotes the golden ratio. These lower bounds improve the
estimates derived previously by Mattila (2015) and Altini\c{s}ik et al. (2016).
The sharpness of these lower bounds is assessed numerically and it is
conjectured that $c_n\sim 5\varphi^{-2n}$ as $n\to\infty$. In addition, a new
closed form expression is derived for the numbers $C_n$, viz. $$ C_n=\frac14
\csc^2\bigg(\frac{\pi}{4n+2}\bigg)=\frac{4n^2}{\pi^2}+\frac{4n}{\pi^2}+\bigg(\frac{1}{12}+\frac{1}{\pi^2}\bigg)+\mathcal{O}\bigg(\frac{1}{n^2}\bigg),\quad
n\in\mathbb{Z}_+. $$


  The enhanced principal rank characteristic sequence (epr-sequence) of a
symmetric matrix $B \in \mathbb{F}^{n \times n}$ is defined as $\ell_1 \ell_2
\cdots \ell_n$, where $\ell_j \in \{\tt{A}, \tt{S}, \tt{N}\}$ according to
whether all, some but not all, or none of the principal minors of order $j$ of
$B$ are nonzero. Building upon the second author's recent classification of the
epr-sequences of symmetric matrices over the field $\mathbb{F}=\mathbb{F}_2$,
we initiate a study of the case $\mathbb{F}=\mathbb{F}_3$. Moreover,
epr-sequences over finite fields are shown to have connections to Ramsey theory
and coding theory.


  The merits of machine learning in information security have primarily focused
on bolstering defenses. However, machine learning (ML) techniques are not
reserved for organizations with deep pockets and massive data repositories; the
democratization of ML has lead to a rise in the number of security teams using
ML to support offensive operations. The research presented here will explore
two models that our team has used to solve a single offensive task, detecting a
sandbox. Using process list data gathered with phishing emails, we will
demonstrate the use of Decision Trees and Artificial Neural Networks to
successfully classify sandboxes, thereby avoiding unsafe execution. This paper
aims to give unique insight into how a real offensive team is using machine
learning to support offensive operations.


  The effective mass m*, and the Lande g-factor of the uniform 2-D electron
fluid (2DEF) are calculated as a function of the spin polarization zeta, and
the density parameter r_s, using a non-perturbative analytic approach. Our
theory is in good accord with the m*g* data of Zhu et al. for zeta=0 for the
GaAs-2DEF, and striking agreement with the data of Shashkin et al for the
Si-2DEF.
  While g* is enhanced in GaAs, m* is enhanced in Si. The latter arises from
singlet-pair excitations in the two valleys forming a coupled-valley state
occurring at the critical density of ~1.10^{11}$ e/cm^2.


  We discuss the de Sitter swampland and trans-Planckian censorship conjectures
(TCC) from the entropy bound with quantum corrections, namely quantum version
of Bousso bound and energy conditions. We include the typical contributions
from the entanglement entropy in quasi-de Sitter spacetime. The TCC is not much
corrected, whereas the bounds on de Sitter swampland conjecture from energy
conditions can be relaxed due to quantum corrections.


  Traditional single-factor authentication possesses several critical security
vulnerabilities due to single-point failure feature. Multi-factor
authentication (MFA), intends to enhance security by providing additional
verification steps. However, in practical deployment, users often experience
dissatisfaction while using MFA, which leads to non-adoption. In order to
understand the current design and usability issues with MFA, we analyze
aggregated user generated comments (N = 12,500) about application-based MFA
tools from major distributors, such as, Amazon, Google Play, Apple App Store,
and others. While some users acknowledge the security benefits of MFA, majority
of them still faced problems with initial configuration, system design
understanding, limited device compatibility, and risk trade-offs leading to
non-adoption of MFA. Based on these results, we provide actionable
recommendations in technological design, initial training, and risk
communication to improve the adoption and user experience of MFA.


  Euclidean space and linear algebra do not characterize dynamic electronic
orbitals satisfactorily for even the motion of both electrons in an inert
helium atom cannot be defined in reasonable details. Here the author puts
forward a novel two-dimensional spacetime model from scratch in the context of
defining both electrons in a helium atom. Space and time are treated as two
orthogonal, symmetric and complementary quantities under the atomic spacetime.
Electronic motion observed the rule of differential and integral operations
that were implemented by dynamic trigonometric functions. It is demonstrated
that the atomic spacetime is not a linear vector space with Newtonian time, and
within which calculus has non-classical definition, and complex wave functions
have fresh physical significances. This alternative approach is original,
informative and refreshing but still compatible with quantum mechanics in the
formulation. The description of electronic resonance in helium is also
comparable with classical mechanics such as an oscillating pendulum and with
classical electromagnetism such as an LC oscillator. The study has effectively
unified complex function, calculus, and trigonometry in mathematics, and
provided a prospect for unifying particle physics with classical physics on the
novel spacetime platform.


  We study the $\mathcal N=2$ field theory realized by D3-branes on the
${\mathbb C}^2/{\mathbb Z}_2$ orbifold. The dual supergravity solution exhibits
a repulson singularity cured by the enhancon mechanism. By comparing the open
and closed string descriptions of a probe D-instanton, we can compute the exact
non-perturbative profile of the supergravity twisted field, which determines
the supergravity background. We then show how the non-trivial IR physics of the
field theory translates into the stringy effects that give rise to the enhancon
mechanism and the associated excision procedure.


  The problem of cache enabled private information retrieval (PIR) is
considered in which a user wishes to privately retrieve one out of $K$
messages, each of size $L$ bits from $N$ distributed databases. The user has a
local cache of storage $SL$ bits which can be used to store any function of the
$K$ messages. The main contribution of this work is the exact characterization
of the capacity of cache aided PIR as a function of the storage parameter $S$.
In particular, for a given cache storage parameter $S$, the
information-theoretically optimal download cost $D^{*}(S)/L$ (or the inverse of
capacity) is shown to be equal to $(1- \frac{S}{K})\left(1+ \frac{1}{N}+ \ldots
+ \frac{1}{N^{K-1}}\right)$. Special cases of this result correspond to the
settings when $S=0$, for which the optimal download cost was shown by Sun and
Jafar to be $\left(1+ \frac{1}{N}+ \ldots + \frac{1}{N^{K-1}}\right)$, and the
case when $S=K$, i.e., cache size is large enough to store all messages
locally, for which the optimal download cost is $0$. The intermediate points
$S\in (0, K)$ can be readily achieved through a simple memory-sharing based PIR
scheme. The key technical contribution of this work is the converse, i.e., a
lower bound on the download cost as a function of storage $S$ which shows that
memory sharing is information-theoretically optimal.


  In the standard model of particle physics, the chiral anomaly can occur in
relativistic plasmas and plays a role in the early Universe, protoneutron
stars, heavy-ion collisions, and quantum materials. It gives rise to a magnetic
instability if the number densities of left- and right-handed electrically
charged fermions are unequal. Using direct numerical simulations, we show this
can result just from spatial fluctuations of the chemical potential, causing a
chiral dynamo instability, magnetically driven turbulence, and ultimately a
large-scale magnetic field through the magnetic alpha effect.


  We study numerical simulations of satellite galaxy disruption in a potential
resembling that of the Milky Way. Our goal is to assess whether a merger origin
for the stellar halo would leave observable fossil structure in the phase-space
distribution of nearby stars. We show how mixing of disrupted satellites can be
quantified using a coarse-grained entropy. Although after 10 Gyr few obvious
asymmetries remain in the distribution of particles in configuration space,
strong correlations are still present in velocity space. We give a simple
analytic description of these effects, based on a linearised treatment in
action-angle variables, which shows how the kinematic and density structure of
the debris stream changes with time. By applying this description we find that
a single satellite of current luminosity $10^8 L_\sun$ disrupted 10 Gyr ago
from an orbit circulating in the inner halo (mean apocentre $\sim 12$ kpc)
would contribute about $\sim 30$ kinematically cold streams with internal
velocity dispersions below 5 km/s to the local stellar halo. If the whole
stellar halo were built by disrupted satellites, it should consist locally of
300 - 500 such streams. Clear detection of all these structures would require a
sample of a few thousand stars with 3-D velocities accurate to better than 5
km/s. Even with velocity errors several times worse than this, the expected
clumpiness should be quite evident. We apply our formalism to a group of stars
detected near the North Galactic Pole, and derive an order of magnitude
estimate for the initial properties of the progenitor system.


  The anomalous transport properties of Heusler compounds become a hotspot of
research in recent years due to their unique band structure and possible
application in spintronics. In this paper, we report the anomalous Hall effect
in polycrystalline NiCoMnGa quaternary Heusler compound by experimental means
and theoretical calculations. The experimental anomalous Hall conductivity
(AHC) was found at about 256 S/cm at 10K with an intrinsic contribution of ~
121 S/cm. The analysis of Hall data reveals the presence of both extrinsic and
intrinsic contributions in AHE. Our theoretical calculations show that a pair
of spin-orbit coupled band formed by the band splitting due to spin-orbit
interaction (SOI) at the Fermi level produces a finite Berry flux in the system
that provides the intrinsic AHC about 100 S/cm, which is in good agreement with
the experiment.


  We present an elementary argument that one can shield linearised
gravitational fields using linearised gravitational fields. This is done by
using third-order potentials for the metric, which avoids the need to solve
singular equations in shielding or gluing constructions for the linearised
metric.


  Pattern recognition systems implemented using deep neural networks achieve
better results than linear models. However, their drawback is the black box
property. This property means that one with no experience utilising nonlinear
systems may need help understanding the outcome of the decision. Such a
solution is unacceptable to the user responsible for the final decision. He
must not only believe in the decision but also understand it. Therefore,
recognisers must have an architecture that allows interpreters to interpret the
findings. The idea of post-hoc explainable classifiers is to design an
interpretable classifier parallel to the black box classifier, giving the same
decisions as the black box classifier. This paper shows that the explainable
classifier completes matching classification decisions with the black box
classifier on the MNIST and FashionMNIST databases if Zadeh`s fuzzy logic
function forms the classifier and DeconvNet importance gives the truth values.
Since the other tested significance measures achieved lower performance than
DeconvNet, it is the optimal transformation of the feature values to their
truth values as inputs to the fuzzy logic function for the databases and
recogniser architecture used.


  In the present effort we show that $S_{\kappa}=-k_B \int d^3p
(n^{1+\kappa}-n^{1-\kappa})/(2\kappa)$ is the unique existing entropy obtained
by a continuous deformation of the Shannon-Boltzmann entropy $S_0=-k_B \int
d^3p n \ln n$ and preserving unaltered its fundamental properties of concavity,
additivity and extensivity. Subsequently, we explain the origin of the
deformation mechanism introduced by $\kappa$ and show that this deformation
emerges naturally within the Einstein special relativity. Furthermore, we
extend the theory in order to treat statistical systems in a time dependent and
relativistic context. Then, we show that it is possible to determine in a self
consistent scheme within the special relativity the values of the free
parameter $\kappa$ which results to depend on the light speed $c$ and reduces
to zero as $c \to \infty$ recovering in this way the ordinary statistical
mechanics and thermodynamics. The novel statistical mechanics constructed
starting from the above entropy, preserves unaltered the mathematical and
epistemological structure of the ordinary statistical mechanics and is suitable
to describe a very large class of experimentally observed phenomena in low and
high energy physics and in natural, economic and social sciences. Finally, in
order to test the correctness and predictability of the theory, as working
example we consider the cosmic rays spectrum, which spans 13 decades in energy
and 33 decades in flux, finding a high quality agreement between our
predictions and observed data.
  PACS number(s): 05.20.-y, 51.10.+y, 03.30.+p, 02.20.-a


  In this paper, we present a novel application of matrix game theory for
optimization of link scheduling in wireless ad-hoc networks. Optimum scheduling
is achieved by soft coloring of network graphs. Conventional coloring schemes
are based on assignment of one color to each region or equivalently each link
is member of just one partial topology. These algorithms based on coloring are
not optimal when links are not activated with the same rate. Soft coloring,
introduced in this paper, solves this problem and provide optimal solution for
any requested link usage rate. To define the game model for optimum scheduling,
first all possible components of the graph are identified. Components are
defined as sets of the wireless links can be activated simultaneously without
suffering from mutual interference. Then by switching between components with
appropriate frequencies (usage rate) optimum scheduling is achieved. We call
this kind of scheduling as soft coloring because any links can be member of
more than one partial topology, in different time segments. To simplify this
problem, we model relationship between link rates and components selection
frequencies by a matrix game which provides a simple and helpful tool to
simplify and solve the problem. This proposed game theoretic model is solved by
fictitious playing method. Simulation results prove the efficiency of the
proposed technique compared to conventional scheduling based on coloring


  We derive and solve the full set of scalar perturbation equations for a class
of $Z_2$-symmetric five-dimensional geometries generated by a bulk cosmological
constant and by a 3-brane non-minimally coupled to a bulk dilaton field. The
massless scalar modes, like their tensor analogues, are localized on the brane,
and provide long-range four-dimensional dilatonic interactions, which are
generically present even when matter on the brane carries no dilatonic charge.
The shorter-range corrections induced by the continuum of massive scalar modes
are always present: they persist even in the case of a trivial dilaton
background (the standard Randall--Sundrum configuration) and vanishing
dilatonic charges.


  Let $ L/K $ be a finite separable extension of local or global fields in any
characteristic, let $ H_{1}, H_{2} $ be two Hopf algebras giving Hopf-Galois
structures on the extension, and suppose that the actions of $ H_{1}, H_{2} $
on $ L $ commute. We show that a fractional ideal $ {\mathfrak B} $ of $ L $ is
free over its associated order in $ H_{1} $ if and only if it is free over its
associated order in $ H_{2} $. We also study which properties these associated
orders share.


  As protostars evolve from optically faint / infrared bright (Class I) sources
to optically bright / infrared faint (Class II) the solid material in their
surrounding disks accumulates into planetesimals and protoplanets. The nearby,
young Ophiuchus star-forming region contains hundreds of protostars in a range
of evolutionary states. Using the Atacama Large Millimeter Array to observe
their millimeter continuum emission, we have measured masses of, or placed
strong upper limits on, the dust content of 279 disks. The masses follow a
log-normal distribution with a clear trend of decreasing mass from less to more
evolved protostellar infrared class. The (logarithmic) mean Class I disk mass,
M = 3.8 M_Earth, is about 5 times greater than the mean Class II disk mass, but
the dispersion in each class is so high, sigma(logM) ~ 0.8-1, that there is a
large overlap between the two distributions. The disk mass distribution of
flat-spectrum protostars lies in between Classes I and II. In addition, three
Class III sources with little to no infrared excess are detected with low disk
masses, M ~ 0.3 M_Earth. Despite the clear trend of decreasing disk mass with
protostellar evolutionary state in this region, a comparison with surveys of
Class II disks in other regions shows that masses do not decrease monotonically
with age. This suggests that the cloud-scale environment may determine the
initial disk mass scale or that there is substantial dust regeneration after 1
Myr.


  We consider stochastic differential equations driven by a general L\'evy
processes (SDEs) with infinite activity and the related, via the Feynman-Kac
formula, Dirichlet problem for parabolic integro-differential equation (PIDE).
We approximate the solution of PIDE using a numerical method for the SDEs. The
method is based on three ingredients: (i) we approximate small jumps by a
diffusion; (ii) we use restricted jump-adaptive time-stepping; and (iii)
between the jumps we exploit a weak Euler approximation. We prove weak
convergence of the considered algorithm and present an in-depth analysis of how
its error and computational cost depend on the jump activity level. Results of
some numerical experiments, including pricing of barrier basket currency
options, are presented.


  The recent success of neural machine translation models relies on the
availability of high quality, in-domain data. Domain adaptation is required
when domain-specific data is scarce or nonexistent. Previous unsupervised
domain adaptation strategies include training the model with in-domain copied
monolingual or back-translated data. However, these methods use generic
representations for text regardless of domain shift, which makes it infeasible
for translation models to control outputs conditional on a specific domain. In
this work, we propose an approach that adapts models with domain-aware feature
embeddings, which are learned via an auxiliary language modeling task. Our
approach allows the model to assign domain-specific representations to words
and output sentences in the desired domain. Our empirical results demonstrate
the effectiveness of the proposed strategy, achieving consistent improvements
in multiple experimental settings. In addition, we show that combining our
method with back translation can further improve the performance of the model.


  The theory of direct integral decompositions of both bounded and unbounded
operators is further developed; in particular, results about spectral
projections, functional calculus and affiliation to von Neumann algebras are
proved. For operators belonging to or affiliated to a tracial von Neumann
algebra that is a direct integral von Neumann algebra, the Brown measure is
shown to be given by the corresponding integral of Brown measures.


  We study convergence of a generative modeling method that first estimates the
score function of the distribution using Denoising Auto-Encoders (DAE) or
Denoising Score Matching (DSM) and then employs Langevin diffusion for
sampling. We show that both DAE and DSM provide estimates of the score of the
Gaussian smoothed population density, allowing us to apply the machinery of
Empirical Processes.
  We overcome the challenge of relying only on $L^2$ bounds on the score
estimation error and provide finite-sample bounds in the Wasserstein distance
between the law of the population distribution and the law of this sampling
scheme. We then apply our results to the homotopy method of arXiv:1907.05600
and provide theoretical justification for its empirical success.


  To discuss the possible contribution of parafermions to the dark matter
abundance, we extend the Boltzmann equation for fermionic dark matter to
include parafermions. Parafermions can accommodate $r$ particles per quantum
state $(2\le r<\infty)$, where the parafermion of order $r=1$ is identical to
the ordinary fermion. It is found that the parafermionic dark matter can be
more abundant than the fermionic dark matter in the present universe.


  In this article, shear viscosity, bulk viscosity, and thermal conductivity of
a hot QCD medium have been studied in the presence of a strong magnetic field.
To model the hot magnetized QCD matter, an extended quasi-particle description
of the hot QCD equation of state in the presence of the magnetic field has been
adopted. The effects of higher Landau levels on the temperature dependence of
viscous coefficients (bulk and shear viscosities) and thermal conductivity have
been obtained by considering the $1\rightarrow 2$ processes in the presence of
the strong magnetic field. An effective covariant kinetic theory has been set
up in (1+1)-dimensional that includes mean field contributions in terms of
quasi-particle dispersions and magnetic field to describe the Landau level
dynamics of quarks. The sensitivity of these parameters to the magnitude of the
magnetic field has also been explored. Both the magnetic field and mean field
contributions have seen to play a significant role in obtaining the temperature
behaviour of the transport coefficients of hot QCD medium.


  The one-dimensional Ising spin-glass model with power-law long-range
interactions is a useful proxy model for studying spin glasses in higher space
dimensions and for finding the dimension at which the spin-glass state changes
from having broken replica symmetry to that of droplet behavior. To this end we
have calculated the exponent that describes the difference in free energy
between periodic and antiperiodic boundary conditions. Numerical work is done
to support some of the assumptions made in the calculations and to determine
the behavior of the interface free-energy exponent of the power law of the
interactions. Our numerical results for the interface free-energy exponent are
badly affected by finite-size problems.


  In this paper we first construct an analytic realization of the
$C_\lambda$-extended oscillator algebra with the help of
difference-differential operators. Secondly, we study families of
$d$-orthogonal polynomials which are extensions of the Hermite and Laguerre
polynomials. The underlying algebraic framework allowed us a systematic
derivation of their main properties such as recurrence relations,
difference-differential equations, lowering and rising operators and generating
functions. Finally, we use these polynomials to construct a realization of the
$C_\lambda$-extended oscillator by block matrices.


  Deep learning has proven effective for various application tasks, but its
applicability is limited by the reliance on annotated examples. Self-supervised
learning has emerged as a promising direction to alleviate the supervision
bottleneck, but existing work focuses on leveraging co-occurrences in unlabeled
data for task-agnostic representation learning, as exemplified by masked
language model pretraining. In this chapter, we explore task-specific
self-supervision, which leverages domain knowledge to automatically annotate
noisy training examples for end applications, either by introducing labeling
functions for annotating individual instances, or by imposing constraints over
interdependent label decisions. We first present deep probabilistic logic(DPL),
which offers a unifying framework for task-specific self-supervision by
composing probabilistic logic with deep learning. DPL represents unknown labels
as latent variables and incorporates diverse self-supervision using
probabilistic logic to train a deep neural network end-to-end using variational
EM. Next, we present self-supervised self-supervision(S4), which adds to DPL
the capability to learn new self-supervision automatically. Starting from an
initial seed self-supervision, S4 iteratively uses the deep neural network to
propose new self supervision. These are either added directly (a form of
structured self-training) or verified by a human expert (as in feature-based
active learning). Experiments on real-world applications such as biomedical
machine reading and various text classification tasks show that task-specific
self-supervision can effectively leverage domain expertise and often match the
accuracy of supervised methods with a tiny fraction of human effort.


  The precise modeling of binary black hole coalescences in generic planar
orbits is a crucial step to disentangle dynamical and isolated binary formation
channels through gravitational-wave observations. The merger regime of such
coalescences exhibits a significantly higher complexity compared to the
quasicircular case, and cannot be readily described through standard
parameterizations in terms of eccentricity and anomaly. In the spirit of the
Effective One Body formalism, we build on the study of the test-mass limit, and
introduce a new modelling strategy to describe the general-relativistic
dynamics of two-body systems in generic orbits. This is achieved through
gauge-invariant combinations of the binary energy and angular momentum, such as
a dynamical "impact parameter" at merger. These variables reveal simple
"quasi-universal" structures of the pivotal merger parameters, allowing to
build an accurate analytical representation of generic (bounded and
dynamically-bounded) orbital configurations. We demonstrate the validity of
these analytical relations using 311 numerical simulations of bounded
noncircular binaries with progenitors from the RIT and SXS catalogs, together
with a custom dataset of dynamical captures generated using the Einstein
Toolkit, and test-mass data in bound orbits. Our modeling strategy lays the
foundations of accurate and complete waveform models for systems in arbitrary
orbits, bolstering observational explorations of dynamical formation scenarios
and the discovery of new classes of gravitational wave sources.


  The explosion of people networking in cyberspace, disseminating terabytes of
information, is being promoted through the use of broadband, bluetooth
technology, and wireless mobile computing facilities. New communities within
such venues as virtual chat rooms discussion groups, newsgroups etc are being
created daily and even hourly. This is raising issues of cyberethics concerning
privacy,security, crime, human needs, e-business, e-healthcare, e-government
and intellectual property among others that need to be evaluated and reflected
upon. With this new freedom come new moral and ethical responsibilities, which
raise questions as to whether anything can be published or whether there should
be restrictions. This paper addresses one specific area, that has come into the
public eye, the closure by Microsoft of all of its free chat rooms.


  We have studied the impact of time-dependent learning capacities of players
in the framework of spatial prisoner's dilemma game. In our model, this
capacity of players may decrease or increase in time after strategy adoption
according to a step-like function. We investigated both possibilities
separately and observed significantly different mechanisms that form the
stationary pattern of the system. The time decreasing learning activity helps
cooperator domains to recover the possible intrude of defectors hence supports
cooperation. In the other case the temporary restrained learning activity
generates a cyclic dominance between defector and cooperator strategies, which
helps to maintain the diversity of strategies via propagating waves. The
results are robust and remain valid by changing payoff values, interaction
graphs or functions characterizing time-dependence of learning activity. Our
observations suggest that dynamically generated mechanisms may offer
alternative ways to keep cooperators alive even at very larger temptation to
defect.


  We give an explicit formula showing how the double Poisson algebra introduced
in \cite{VdB} appears as a particular part of a pre-Calabi-Yau structure, i.e.
cyclically invariant, with respect to the natural inner form, solution of the
Maurer-Cartan equation on $A\oplus A^*$. Specific part of this solution is
described, which is in one-to-one correspondence with the double Poisson
algebra structures. The result holds for any associative algebra $A$ and
emphasizes the special role of the fourth component of a pre-Calabi-Yau
structure in this respect. As a consequence we have that appropriate
pre-Calabi-Yau structures induce a Poisson brackets on representation spaces
$({\rm Rep}_n A)^{Gl_n}$ for any associative algebra $A$.


  OpenAI's advanced large language models (LLMs) have revolutionized natural
language processing and enabled developers to create innovative applications.
As adoption grows, understanding the experiences and challenges of developers
working with these technologies is crucial. This paper presents a comprehensive
analysis of the OpenAI Developer Forum, focusing on (1) popularity trends and
user engagement patterns, and (2) a taxonomy of challenges and concerns faced
by developers. We first employ a quantitative analysis of the metadata from
29,576 forum topics, investigating temporal trends in topic creation, the
popularity of topics across different categories, and user contributions at
various trust levels. We then qualitatively analyze content from 9,301 recently
active topics on developer concerns. From a sample of 886 topics, we construct
a taxonomy of concerns in the OpenAI Developer Forum. Our findings uncover
critical concerns raised by developers in creating AI-powered applications and
offer targeted recommendations to address them. This work not only advances
AI-assisted software engineering but also empowers developer communities to
shape the responsible evolution and integration of AI technology in society.


  Based on a simple geometrical approach, we analyze the evolution of the
Kozai-Lidov mechanism for stars around shrinking massive black hole binaries on
circular orbits. We find that, due to a peculiar bifurcation pattern induced by
the Newtonian potential of stellar clusters, the orbit of stars could become
highly eccentric. This transition occurs abruptly for stars with small initial
eccentricities. Our approach would be also useful for studying the Kozai-Lidov
mechanism in various astrophysical contexts.


  A new version of the R-matrix Floquet theory for laser-assisted electron-atom
scattering is presented. The theory is non-perturbative and applicable to a
non-relativistic many-electron atom or ion in a homogeneous linearly polarized
field. It is based on the use of channel functions built from field-dressed
target states, which greatly simplifies the general formalism.


  Indoor scene recognition is a multi-faceted and challenging problem due to
the diverse intra-class variations and the confusing inter-class similarities.
This paper presents a novel approach which exploits rich mid-level
convolutional features to categorize indoor scenes. Traditionally used
convolutional features preserve the global spatial structure, which is a
desirable property for general object recognition. However, we argue that this
structuredness is not much helpful when we have large variations in scene
layouts, e.g., in indoor scenes. We propose to transform the structured
convolutional activations to another highly discriminative feature space. The
representation in the transformed space not only incorporates the
discriminative aspects of the target dataset, but it also encodes the features
in terms of the general object categories that are present in indoor scenes. To
this end, we introduce a new large-scale dataset of 1300 object categories
which are commonly present in indoor scenes. Our proposed approach achieves a
significant performance boost over previous state of the art approaches on five
major scene classification datasets.


  The transverse momentum technique is used to analyse charged-particle
exclusive data in central C-Ne and C-Cu interactions at energy of 3.7 GeV per
nucleon. Clear evidence of in-plane and out-of-plane (squeeze-out) flow effects
for protons and pi^{-} mesons have been obtained. In C-Ne interactions in-plane
flow of pi^{-} mesons is in the same direction as for the protons, while in
C-Cu collisions pions show antuflow behaviour. From the transverse momentum and
azimuthal distributions of protons and pi^{-} mesons with respect to the
reaction plane, the flow (the measure of the amount of collective transverse
momentum transfer in the reaction plane) and the parameter a_{2} (the measure
of the anisotropic emission strength) have been extracted. The flow effects
increase with the mass of the particle and the mass number of target A_{T}. The
comparison of our in-plane flow results with flow data for various
projectile/target configurations was made using the scaled flow F_{S}=F/(A_{P}
^{1/3}+A_{T}^{1/3}). F_{S} demonstrates a common scaling behaviour for flow
values from different systems. The Quark Gluon String Model (QGSM) was used for
the comparison with the experimental data. The QGSM yields a signature of
in-plane and out-of-plane flow effects in C-ne and C-Cu collisions for protons.


  Crowd counting, i.e., estimation number of the pedestrian in crowd images, is
emerging as an important research problem with the public security
applications. A key component for the crowd counting systems is the
construction of counting models which are robust to various scenarios under
facts such as camera perspective and physical barriers. In this paper, we
present an adaptive scenario discovery framework for crowd counting. The system
is structured with two parallel pathways that are trained with different sizes
of the receptive field to represent different scales and crowd densities. After
ensuring that these components are present in the proper geometric
configuration, a third branch is designed to adaptively recalibrate the
pathway-wise responses by discovering and modeling the dynamic scenarios
implicitly. Our system is able to represent highly variable crowd images and
achieves state-of-the-art results in two challenging benchmarks.


  We present a simplified solution to orbit determination of a binary system
from astrometric observations. An exact solution was found by Asada, Akasaka
and Kasai by assuming no observational errors. We extend the solution
considering observational data. The generalized solution is expressed in terms
of elementary functions, and therefore requires neither iterative nor numerical
methods.


  A probabilistic method for solving the Monge-Kantorovich mass transport
problem on $R^d$ is introduced. A system of empirical measures of independent
particles is built in such a way that it obeys a doubly indexed large deviation
principle with an optimal transport cost as its rate function. As a
consequence, new approximation results for the optimal cost function and the
optimal transport plans are derived. They follow from the Gamma-convergence of
a sequence of normalized relative entropies toward the optimal transport cost.
A wide class of cost functions including the standard power cost functions
$|x-y|^p$ enter this framework.


  We introduce an approach to quickly and accurately approximate the cumulative
distribution function of multivariate Gaussian distributions arising from
spatial Gaussian processes. This approximation is trivially parallelizable and
simple to implement using standard software. We demonstrate its accuracy and
computational efficiency in a series of simulation experiments and apply it to
analyzing the joint tail of a large precipitation dataset using a
recently-proposed scale mixture model for spatial extremes. This dataset is
many times larger than what was previously considered possible to fit using
preferred inferential techniques.


  We revisit the question of classification of balanced circular words and
focus on the case of a ternary alphabet. We propose a $3$-dimensional
generalisation of the discrete approximation representation of Christoffel
words. By considering the minimal bound $3$ for abelian complexity of balanced
circular words over a ternary alphabet, we provide a classification of all
circular words over a ternary alphabet with abelian complexity subject to this
bound. This result also allows us to construct an uncountable set of
bi-infinite aperiodic words with abelian complexity equal to $3$.


  We report the first results from our comprehensive lattice tool set to
explore non-perturbative aspects of Higgs physics in the Standard Model. We
demonstrate in Higgs-Yukawa models that Higgs mass lower bounds and upper
bounds can be determined in lattice simulations when triviality requires the
necessity of a finite cutoff to maintain non-zero interactions. The vacuum
instability problem is investigated and the lattice approach is compared with
the traditional renormalization group procedure which sets similar goals to
correlate lower and upper Higgs mass bounds with the scale of new physics. A
novel feature of our lattice simulations is the use of Ginsparg-Wilson fermions
to represent the effects of Top quark loops in Higgs dynamics. The need for
chiral lattice fermions is discussed and the approach is extended to full
Top-Higgs-QCD dynamics. We also report results from our large $N_F$ analysis of
Top-Higgs Yukawa models to gain analytic insight and to verify our new lattice
tool set which is deployed in the simulations. The role of non-perturbative
lattice studies to investigate heavy Higgs particle scenarios is illustrated in
extensions of the Standard Model.


  Influenced by breakthroughs in LLMs, single-cell foundation models are
emerging. While these models show successful performance in cell type
clustering, phenotype classification, and gene perturbation response
prediction, it remains to be seen if a simpler model could achieve comparable
or better results, especially with limited data. This is important, as the
quantity and quality of single-cell data typically fall short of the standards
in textual data used for training LLMs. Single-cell sequencing often suffers
from technical artifacts, dropout events, and batch effects. These challenges
are compounded in a weakly supervised setting, where the labels of cell states
can be noisy, further complicating the analysis. To tackle these challenges, we
present sc-OTGM, streamlined with less than 500K parameters, making it
approximately 100x more compact than the foundation models, offering an
efficient alternative. sc-OTGM is an unsupervised model grounded in the
inductive bias that the scRNAseq data can be generated from a combination of
the finite multivariate Gaussian distributions. The core function of sc-OTGM is
to create a probabilistic latent space utilizing a GMM as its prior
distribution and distinguish between distinct cell populations by learning
their respective marginal PDFs. It uses a Hit-and-Run Markov chain sampler to
determine the OT plan across these PDFs within the GMM framework. We evaluated
our model against a CRISPR-mediated perturbation dataset, called CROP-seq,
consisting of 57 one-gene perturbations. Our results demonstrate that sc-OTGM
is effective in cell state classification, aids in the analysis of differential
gene expression, and ranks genes for target identification through a
recommender system. It also predicts the effects of single-gene perturbations
on downstream gene regulation and generates synthetic scRNA-seq data
conditioned on specific cell states.


  While mixture density networks (MDNs) have been extensively used for
regression tasks, they have not been used much for classification tasks. One
reason for this is that the usability of MDNs for classification is not clear
and straightforward. In this paper, we propose two MDN-based models for
classification tasks. Both models fit mixtures of Gaussians to the the data and
use the fitted distributions to classify a given sample by evaluating the
learnt cumulative distribution function for the given input features. While the
proposed MDN-based models perform slightly better than, or on par with, five
baseline classification models on three publicly available datasets, the real
utility of our models comes out through a real-world product bundling
application. Specifically, we use our MDN-based models to learn the
willingness-to-pay (WTP) distributions for two products from synthetic sales
data of the individual products. The Gaussian mixture representation of the
learnt WTP distributions is then exploited to obtain the WTP distribution of
the bundle consisting of both the products. The proposed MDN-based models are
able to approximate the true WTP distributions of both products and the bundle
well.


  We study theoretically how electrons, coherently injected at one point on the
boundary of a two-dimensional electron system, are focused by a perpendicular
magnetic field $B$ onto another point on the boundary. Using the
non-equilibrium Green's function approach, we calculate the generalized 4-point
Hall resistance $R_{xy}$ as a function of B. In weak fields, $R_{xy}$ shows the
characteristic equidistant peaks observed in the experiment and explained by
classical cyclotron motion along the boundary. In strong fields, $R_{xy}$ shows
a single extended plateau reflecting the quantum Hall effect. In intermediate
fields, we find superimposed upon the lower Hall plateaus anomalous
oscillations, which are neither periodic in 1/B (quantum Hall effect) nor in
$B$ (classical cyclotron motion). The oscillations are explained by the
interference between the occupied edge channels, which causes beatings in
$R_{xy}$. In the case of two occupied edge channels, these beatings constitute
a new commensurability between the magnetic flux enclosed within the edge
channels and the flux quantum. Introducing decoherence and a partially specular
boundary shows that this new effect is quite robust.


  We present the first investigation of excited state dynamics by resonant
Auger-Meitner spectroscopy (also known as resonant Auger spectroscopy) using
the nucleobase thymine as an example. Thymine is photoexcited in the UV and
probed with X-ray photon energies at and below the oxygen K-edge. After initial
photoexcitation to a {\pi}{\pi}* excited state, thymine is known to undergo
internal conversion to an n{\pi}* excited state with a strong resonance at the
oxygen K-edge, red-shifted from the ground state {\pi}* resonances of thymine
(see our previous study Wolf et al., Nat. Commun., 2017, 8, 29). We resolve and
compare the Auger-Meitner electron spectra associated both with the excited
state and ground state resonances, and distinguish participator and spectator
decay contributions. Furthermore, we observe simultaneously with the decay of
the n{\pi}* state signatures the appearance of additional resonant
Auger-Meitner contributions at photon energies between the n{\pi}* state and
the ground state resonances. We assign these contributions to population
transfer from the n{\pi}* state to a {\pi}{\pi}* triplet state via intersystem
crossing on the picosecond timescale based on simulations of the X-ray
absorption spectra in the vibrationally hot triplet state. Moreover, we
identify signatures from the initially excited {\pi}{\pi}* singlet state which
we have not observed in our previous study.


  The performance of earth-to-satellite link largely depends on various
environmental factors like rain, fog, cloud, and atmospheric effects like
ionospheric and tropospheric scintillation. In this paper, the tropospheric
scintillation of Bangladesh, a subtropical country, is predicted based on
measured climatic parameters, like relative humidity, temperature. In this
prediction, ITU scintillation model are used. Four major cities, named Dhaka,
Chittagong, Rajshahi and Sylhet, of Bangladesh are selected for prediction of
scintillation. From the simulation result, Rajshahi is found to be the most
badly affected region by the scintillation fade depth (SFD), which is followed
by Chittagong and the SFD is minimum in Dhaka and Sylhet. The difference in
SFDs among the considered cities does not vary heavily. It is also found that
the SFD varies from 3 dB to 13 dB depending on the frequency in used. Moreover,
higher scintillation is found in rainy season of Bangladesh. During this
period, the scintillation becomes double of the average figure.


  Multicopters are among the most versatile mobile robots. Their applications
range from inspection and mapping tasks to providing vital reconnaissance in
disaster zones and to package delivery. The range, endurance, and speed a
multirotor vehicle can achieve while performing its task is a decisive factor
not only for vehicle design and mission planning, but also for policy makers
deciding on the rules and regulations for aerial robots. To the best of the
authors' knowledge, this work proposes the first approach to estimate the
range, endurance, and optimal flight speed for a wide variety of multicopters.
This advance is made possible by combining a state-of-the-art first-principles
aerodynamic multicopter model based on blade-element-momentum theory with an
electric-motor model and a graybox battery model. This model predicts the cell
voltage with only 1.3% relative error (43.1 mV), even if the battery is
subjected to non-constant discharge rates. Our approach is validated with
real-world experiments on a test bench as well as with flights at speeds up to
65 km/h in one of the world's largest motion-capture systems. We also present
an accurate pen-and-paper algorithm to estimate the range, endurance and
optimal speed of multicopters to help future researchers build drones with
maximal range and endurance, ensuring that future multirotor vehicles are even
more versatile.


  We outline an algorithm for the Quantum Counting problem using Adiabatic
Quantum Computation (AQC). We show that using local adiabatic evolution, a
process in which the adiabatic procedure is performed at a variable rate, the
problem is solved with the same complexity as the analogous circuit-based
algorithm, i.e., quadratically faster than the corresponding classical
algorithm. The above algorithm provides further evidence for the potentially
powerful capabilities of AQC as a paradigm for more efficient problem solving
on a quantum computer, and may be used as the basis for solving more
sophisticated problems.


  We construct new classes of modified theories in which the matter sector
couples with the Einstein tensor, namely we consider direct couplings of the
latter to the energy-momentum tensor, and to the derivatives of its trace. We
extract the general field equations, which do not contain higher-order
derivatives, and we apply them in a cosmological framework, obtaining the
Friedmann equations, whose extra terms give rise to an effective dark energy
sector. At the background level we show that we can successfully describe the
usual thermal history of the universe, with the sequence of matter and
dark-energy epochs, while the dark-energy equation-of-state parameter can lie
in the phantom regime, tending progressively to $-1$ at present and future
times. Furthermore, we confront the theory with Cosmic Chronometer data,
showing that the agreement is very good. Finally, we perform a detailed
investigation of scalar and tensor perturbations, and extracting an approximate
evolution equation for the matter overdensity we show that the predicted
behavior is in agreement with observations.


  Binary ruthenium pnictides, RuP and RuAs, with an orthorhombic MnP structure,
were found to show a metal to a non-magnetic insulator transition at TMI = 270
K and 200 K, respectively. In the metallic region above TMI, a structural phase
transition, accompanied by a weak anomaly in the resistivity and the magnetic
susceptibility, indicative of a pseudo-gap formation, was identified at Ts =
330 K and 280 K, respectively. These two transitions were suppressed by
substituting Ru with Rh. We found superconductivity with a maximum Tc = 3.7 K
and Tc =1.8 K in a narrow composition range around the critical point for the
pseudo-gap phase, Rh content xc = 0.45 and xc = 0.25 for Ru1-xRhxP and
Ru1-xRhxAs, respectively, which may provide us with a novel non-magnetic route
to superconductivity at a quantum critical point.


  We address the validity of the momentum sum rule for deep inelastic nuclear
structure functions.


  Trace formulae for d-regular graphs are derived and used to express the
spectral density in terms of the periodic walks on the graphs under
consideration. The trace formulae depend on a parameter w which can be tuned
continuously to assign different weights to different periodic orbit
contributions. At the special value w=1, the only periodic orbits which
contribute are the non back- scattering orbits, and the smooth part in the
trace formula coincides with the Kesten-McKay expression. As w deviates from
unity, non vanishing weights are assigned to the periodic walks with
back-scatter, and the smooth part is modified in a consistent way. The trace
formulae presented here are the tools to be used in the second paper in this
sequence, for showing the connection between the spectral properties of
d-regular graphs and the theory of random matrices.


  Modern wireless communication systems are expected to provide improved
latency and reliability. To meet these expectations, a short packet length is
needed, which makes the first-order Shannon rate an inaccurate performance
metric for such communication systems. A more accurate approximation of the
achievable rates of finite-block-length (FBL) coding regimes is known as the
normal approximation (NA). It is therefore of substantial interest to study the
optimization of the FBL rate in multi-user multiple-input multiple-output
(MIMO) systems, in which each user may transmit and/or receive multiple data
streams. Hence, we formulate a general optimization problem for improving the
spectral and energy efficiency of multi-user MIMO-aided ultra-reliable
low-latency communication (URLLC) systems, which are assisted by reconfigurable
intelligent surfaces (RISs). We show that a RIS is capable of substantially
improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the
benefits of RIS increase as the packet length and/or the tolerable bit error
rate are reduced. This reveals that RISs can be even more beneficial in URLLC
systems for improving the FBL rates than in conventional systems approaching
Shannon rates.


  Autonomous driving relies on a variety of sensors, especially on radars,
which have unique robustness under heavy rain/fog/snow and poor light
conditions. With the rapid increase of the amount of radars used on modern
vehicles, where most radars operate in the same frequency band, the risk of
radar interference becomes a compelling issue. This article analyses automotive
radar interference and proposes several new approaches, which combine
industrial and academic expertise, toward the path of interference-free
autonomous driving.


  Two-fluid flowing equilibrium configurations of a helicity-driven spherical
torus (HD-ST) are numerically determined by using the combination of the finite
difference and the boundary element methods. It is found from the numerical
results that electron fluids near the central conductor are tied to an external
toroidal field and ion fluids are not. The magnetic configurations change from
the high-$q$ HD-ST ($q$>1) with paramagnetic toroidal field and low-$\beta$
(volume average $\beta$ value, ($<\beta>\approx$ 2 %) through the
helicity-driven spheromak and RFP to the ultra low-$q$ HD-ST (0<q<1) with
diagmagnetic toroidal field and high-$\beta$ ($\beta\approx 18\%) as the
external toroidal field at the inner edge region decreases and reverses the
sign. The two-fluid effecs are more significant in this equilibrium transition
when the ion diagmagnetic drift is dominant in the flowing two-fluid.


  We investigate the dynamics of classical and quantum correlations between two
qubits. Each qubit is implemented by a pair of phosphorous impurities embedded
in a silicon substrate. The main decoherence mechanism affecting these types of
qubits is provided by the coupling of the phosphorous impurities to the
acoustical vibrations of the silicon lattice. We find that depending on the
temperature of the substrate and the initial state, three different dynamics
can be found. These are characterized by the number of abrupt changes in both
classical and quantum correlations. We also show that the correlations do not
disappear. Moreover, before the classical correlations reach a constant value,
they may experience successive abrupt changes associated with the apparition of
metastable pointer states basis. Then, a constant value for the classical
correlations is reached when the preferred basis is established.