  We study the dimensional reduction of a ten-dimensional supersymmetric E_8
gauge theory over six-dimensional coset spaces. We find that the coset space
dimensional reduction over a symmetric coset space leaves the four dimensional
gauge theory without any track of the original supersymmetry. On the contrary
the dimensional reduction over a non symmetric coset space leads to a softly
broken supersymmetric gauge theory in four dimensions. The SO_7/SO_6 and
G_2/SU(3) are used as representative prototypes of symmetric and non symmetric
coset spaces respectively.


  A central question in derandomization is whether randomized logspace (RL)
equals deterministic logspace (L). To show that RL=L, it suffices to construct
explicit pseudorandom generators (PRGs) that fool polynomial-size read-once
(oblivious) branching programs (roBPs). Starting with the work of Nisan,
pseudorandom generators with seed-length $O(\log^2 n)$ were constructed.
Unfortunately, improving on this seed-length in general has proven challenging
and seems to require new ideas.
  A recent line of inquiry has suggested focusing on a particular limitation of
the existing PRGs, which is that they only fool roBPs when the variables are
read in a particular known order, such as $x_1<\cdots<x_n$. In comparison,
existentially one can obtain logarithmic seed-length for fooling the set of
polynomial-size roBPs that read the variables under any fixed unknown
permutation $x_{\pi(1)}<\cdots<x_{\pi(n)}$. While recent works have established
novel PRGs in this setting for subclasses of roBPs, there were no known
$n^{o(1)}$ seed-length explicit PRGs for general polynomial-size roBPs in this
setting.
  In this work, we follow the "bounded independence plus noise" paradigm of
Haramaty, Lee and Viola, and give an improved analysis in the general roBP
unknown-order setting. With this analysis we obtain an explicit PRG with
seed-length $O(\log^3 n)$ for polynomial-size roBPs reading their bits in an
unknown order. Plugging in a recent Fourier tail bound of Chattopadhyay,
Hatami, Reingold, and Tal, we can obtain a $\widetilde{O}(\log^2 n)$
seed-length when the roBP is of constant width.


  During the epoch of reionisation the first galaxies were enshrouded in
pristine neutral gas, with one of the brightest emission lines in star-forming
galaxies, Lyman-$\alpha$ (Ly$\alpha$), expected to remain undetected until the
Universe became ionised. Providing an explanation for the surprising detection
of Ly$\alpha$ in these early galaxies is a major challenge for extra-galactic
studies. Recent JWST observations have reignited the debate on whether
residence in an overdensity of galaxies is a it sufficient and necessary
condition for Ly$\alpha$ to escape. Here, we take unique advantage of both
high-resolution and high-sensitivity images from the JWST instrument NIRCam to
reveal that all galaxies in a sample of z>7 Ly$\alpha$ emitters have close
companions. We exploit novel on-the-fly radiative transfer
magnetohydrodynamical simulations with cosmic ray feedback to show that
galaxies with frequent mergers have very bursty star formation which drives
episodes of high intrinsic Ly$\alpha$ emission and facilitates the escape of
Ly$\alpha$ photons along channels cleared of neutral gas. We conclude that the
rapid build up of stellar mass through mergers presents a compelling solution
to the long-standing puzzle of the detection of Ly$\alpha$ emission deep into
the epoch of reionisation.


  We have presented an introductory study on surface states of topological
insulator, Bi2Se3 based on the first principle


  We consider the causal structure of generalized uncharged McVittie spacetimes
with increasing central mass $m (t)$ and positive Hubble factor $H (t)$. Under
physically reasonable conditions, namely, a big bang singularity in the past, a
positive cosmological constant and an upper limit to the central mass, we prove
that the patch of the spacetime described by the cosmological time and areal
radius coordinates is always geodesically incomplete, which implies the
presence of event horizons in the spacetime. We also show that, depending on
the asymptotic behavior of the $m$ and $H$ functions, the generalized McVittie
spacetime can have a single black hole, a black-hole/white-hole pair or,
differently from classic fixed-mass McVittie, a single white hole. A simple
criterion is given to distinguish the different causal structures.


  Pinboard on Pinterest is an emerging media to engage online social media
users, on which users post online images for specific topics. Regardless of its
significance, there is little previous work specifically to facilitate
information discovery based on pinboards. This paper proposes a novel pinboard
recommendation system for Twitter users. In order to associate contents from
the two social media platforms, we propose to use MultiLabel classification to
map Twitter user followees to pinboard topics and visual diversification to
recommend pinboards given user interested topics. A preliminary experiment on a
dataset with 2000 users validated our proposed system.


  The purpose of this paper is to give some new identities for the Bernoulli,
the Euler and the Genocchi numbers and polynomials.


  We study experimentally and numerically the thinning of a Newtonian
leaky-dielectric filament subject to an axial electric field. We consider
moderately viscous liquids with high permittivity. The experiments show that
satellite droplets are produced from the breakup of the filaments with high
electrical permittivity due to the action of the electric field. Two
electrified filaments with the same minimum radius thin at the same speed
regardless of when the voltage was applied. The numerical simulations show that
the polarization stress is responsible for the pinching delay observed in the
experiments. Asymptotically close to the pinching point, the filament pinching
is dominated by the diverging hydrodynamic forces. The polarization stress
becomes subdominant even if this stress also diverges at this finite-time
singularity.


  We calculate to the leading twist 2 accuracy the rate for the decay $ B_{u}
\rightarrow l \nu \gamma $ using the light cone QCD sume rules. We find $ Br(
B_{u} \rightarrow l \nu \gamma) \simeq 2 \cdot 10^{-6} $. The results are used
to test the applicability of the constituent quark model approximation to the
same process.The latter estimate is proportional to $ 1/m_{u}^{2} $, where $
m_{u} \simeq \bar{\Lambda}_{u} $ is the "constituent quark mass", indicating
that the process is of long distance type. We find that the two approaches
yield similar results for the rate with the choice $ m_{u} \simeq 430-480 \;MeV
$. This indicates that the constituent quark model may be used for estimates of
the radiative "annihilation" contribution to this and other radiative decays.
We point out that this decay may be useful for the measurement of $ |V_{ub}|$.


  In this paper, we present an efficient algorithm for the long time behavior
of plasma simulations. We will focus on 4D drift-kinetic model, where the
plasma's motion occurs in the plane perpendicular to the magnetic field and can
be governed by the 2D guiding-center model.
  Hermite WENO reconstructions, already proposed in \cite{YF15}, are applied
for solving the Vlasov equation. Here we consider an arbitrary computational
domain with an appropriate numerical method for the treatment of boundary
conditions.
  Then we apply this algorithm for plasma turbulence simulations. We first
solve the 2D guiding-center model in a D-shape domain and investigate the
numerical stability of the steady state. Then, the 4D drift-kinetic model is
studied with a mixed method, i.e. the semi-Lagrangian method in linear phase
and finite difference method during the nonlinear phase. Numerical results show
that the mixed method is efficient and accurate in linear phase and it is much
stable during the nonlinear phase. Moreover, in practice it has better
conservation properties.


  Gait is an essential manifestation of depression. Laboratory gait
characteristics have been found to be closely associated with depression.
However, the gait characteristics of daily walking in real-world scenarios and
their relationships with depression are yet to be fully explored. This study
aimed to explore associations between depression symptom severity and
daily-life gait characteristics derived from acceleration signals in real-world
settings. In this study, we used two ambulatory datasets: a public dataset with
71 elder adults' 3-day acceleration signals collected by a wearable device, and
a subset of an EU longitudinal depression study with 215 participants and their
phone-collected acceleration signals (average 463 hours per participant). We
detected participants' gait cycles and force from acceleration signals and
extracted 20 statistics-based daily-life gait features to describe the
distribution and variance of gait cadence and force over a long-term period
corresponding to the self-reported depression score. The gait cadence of faster
steps (75th percentile) over a long-term period has a significant negative
association with the depression symptom severity of this period in both
datasets. Daily-life gait features could significantly improve the goodness of
fit of evaluating depression severity relative to laboratory gait patterns and
demographics, which was assessed by likelihood-ratio tests in both datasets.
This study indicated that the significant links between daily-life walking
characteristics and depression symptom severity could be captured by both
wearable devices and mobile phones. The gait cadence of faster steps in
daily-life walking has the potential to be a biomarker for evaluating
depression severity, which may contribute to clinical tools to remotely monitor
mental health in real-world settings.


  Near the flavor SU(3) limit, we propose an analytical description for
color-flavor-locked-type Bardeen-Cooper-Schrieffer (BCS) phase in the Nambu
Jona-Lasinio (NJL) model. The diquark behaviors in light-flavor and
strange-flavor-involved channels and Bose-Einstein condensation (BEC) of bound
diquark states are studied. When the attractive interaction between quarks is
strong enough, a BCS-BEC crossover is predicted in the environment with
color-flavor-locked pairing pattern. The resulting Bose-Einstein condensed
phase is found to be an intergrade phase before the emergence of the
previous-predicted BEC phase in two-flavor quark superconductor.


  This letter focuses on the pilot contamination problem in the uplink and
downlink of cell-free massive multiple-input multiple-output networks with
different degrees of cooperation between access points. The optimum minimum
mean square error processing can take advantage of large-scale fading
coefficients for canceling the interference of pilot-sharing user-equipments
and thus achieves asymptotically unbounded capacity. However, it is
computationally demanding and can only be implemented in a fully centralized
network. Here, sub-optimal schemes are derived that provide unbounded capacity
with linear-growing complexity and using only local channel estimates but
global channel statistics. This makes them suited for both centralized and
distributed networks. In this latter case, the best performance is achieved
with a generalized maximum ratio combiner that maximizes a capacity bound based
on channel statistics only.


  Consider a randomly oriented graph $G=(V,E)$ and let $a$, $s$ and $b$ be
three distinct vertices in $V$. We study the correlation between the events
$\{a\to s\}$ and $\{s\to b\}$. We show that, when $G$ is the complete graph
$K_n$, the correlation is negative for $n=3$, zero for $n=4$, and that,
counter-intuitively, it is positive for $n\ge 5$. We also show that the
correlation is always negative when $G$ is a cycle, $C_n$, and negative or zero
when $G$ is a tree (or a forest).


  We numerically examine the driven transport of an overdamped self-propelled
particle through a two-dimensional array of circular obstacles. A detailed
analysis of transport quantifiers (mobility and diffusivity) has been performed
for two types of channels, {\it channel I} and {\it channel II}, that
respectively correspond to the parallel and diagonal drives with respect to the
array axis. Our simulation results show that the signatures of pinning actions
and depinning processes in the array of obstacles are manifested through excess
diffusion peaks or sudden drops in diffusivity, and abrupt jumps in mobility
with varying amplitude of the drive. The underlying depinning mechanisms and
the associated threshold driving strength largely depend on the persistent
length of self-propulsion. For low driving strength, both diffusivity and
mobility are noticeably suppressed by the array of obstacles, irrespective of
the self-propulsion parameters and direction of the drive. When self-propulsion
length is larger than a channel compartment size, transport quantifiers are
insensitive to the rotational relaxation time. Transport with diagonal drives
features self-propulsion-dependent negative differential mobility. The
amplitude of the negative differential mobility of an active particle is much
larger than that of a passive one. The present analysis aims at understanding
the driven transport of active species like, bacteria, virus, Janus Particle
etc. in porous medium.


  The main subject is the difference between the coarse moduli space and the
stack of hyperelliptic curves. In particular, we compute their Picard groups,
giving explicit description of the generators. We also study how many families
of hyperelliptic curves may have the same modular map as well as the existence
of the tautological family over the coarse moduli space of hyperelliptic
curves.


  Given two graphs $H_1$ and $H_2$, a graph $G$ is $(H_1,H_2)$-free if it
contains no induced subgraph isomorphic to $H_1$ or $H_2$. Let $P_t$ be the
path on $t$ vertices. A graph $G$ is $k$-vertex-critical if $G$ has chromatic
number $k$ but every proper induced subgraph of $G$ has chromatic number less
than $k$. The study of $k$-vertex-critical graphs for graph classes is an
important topic in algorithmic graph theory because if the number of such
graphs that are in a given hereditary graph class is finite, then there is a
polynomial-time algorithm to decide if a graph in the class is
$(k-1)$-colorable.
  In this paper, we initiate a systematic study of the finiteness of
$k$-vertex-critical graphs in subclasses of $P_5$-free graphs. Our main result
is a complete classification of the finiteness of $k$-vertex-critical graphs in
the class of $(P_5,H)$-free graphs for all graphs $H$ on 4 vertices. To obtain
the complete dichotomy, we prove the finiteness for four new graphs $H$ using
various techniques -- such as Ramsey-type arguments and the dual of Dilworth's
Theorem -- that may be of independent interest.


  The idea of atoms is old but X-rays provided the first probe into the
physical atom. Photographs of X-ray scattering from crystals -Laue spots- were
the first visual proof for the physical existence of atoms arranged in a
perfect geometric pattern. Thereby conclusively established the stability and
physical reality of atoms. The Braggs developed Laue technique to study atoms.
Moseley applied (Bragg) X-ray spectroscopy to determine the nuclear charge
number of Rutherford atom. We argue that Bohr also at Manchester and
contemporary of Moseley likely was inspired by Laue discovery to get busy with
the mechanics of the nuclear atom. Roentgens discovery was awarded the first
Nobel prize ever in 1901, Laue was honored in 1914, the Braggs in 1915, making
Lawrence Bragg then at 25 the youngest ever. Eleven of the cited authors (Bohr
himself included) in the trilogy (but not Nicholson the most cited), were later
recognized by ten Noble prize awards, seven Laureates in physics and four in
chemistry. The ensuing synergy between science and Nobel honors has been
critical.


  Wireless powered backscatter communications (WPBC) is capable of implementing
ultra-low-power communication, thus promising in the Internet of Things (IoT)
networks. In practice, however, it is challenging to apply WPBC in large-scale
IoT networks because of its short communication range. To address this
challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC
in large-scale IoT networks. In particular, we investigate the joint design of
network planning and dynamic resource allocation of the access point (AP), tag
reader, and UGV to minimize the total energy consumption. Also, the AP can
operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under
HD mode, the optimal cell radius is derived and the optimal power allocation
and transmit/receive beamforming are obtained in closed form. Under FD mode,
the optimal resource allocation, as well as two suboptimal ones with low
computational complexity, is developed. Simulation results disclose that
dynamic power allocation at the tag reader rather than at the AP dominates the
network energy efficiency while the AP operating in FD mode outperforms that in
HD mode concerning energy efficienc


  The behavior of two interacting populations, ``hosts''and ``parasites'', is
investigated on Cayley trees and scale-free networks. In the former case
analytical and numerical arguments elucidate a phase diagram, whose most
interesting feature is the absence of a tri-critical point as a function of the
two independent spreading parameters. For scale-free graphs, the parasite
population can be described effectively by
Susceptible-Infected-Susceptible-type dynamics in a host background. This is
shown both by considering the appropriate dynamical equations and by numerical
simulations on Barab\'asi-Albert networks with the major implication that in
the termodynamic limit the critical parasite spreading parameter vanishes.


  BACKGROUND: An important question is whether evolution favors properties such
as mutational robustness or evolvability that do not directly benefit any
individual, but can influence the course of future evolution. Functionally
similar proteins can differ substantially in their robustness to mutations and
capacity to evolve new functions, but it has remained unclear whether any of
these differences might be due to evolutionary selection for these properties.
  RESULTS: Here we use laboratory experiments to demonstrate that evolution
favors protein mutational robustness if the evolving population is sufficiently
large. We neutrally evolve cytochrome P450 proteins under identical selection
pressures and mutation rates in populations of different sizes, and show that
proteins from the larger and thus more polymorphic population tend towards
higher mutational robustness. Proteins from the larger population also evolve
greater stability, a biophysical property that is known to enhance both
mutational robustness and evolvability. The excess mutational robustness and
stability is well described by existing mathematical theories, and can be
quantitatively related to the way that the proteins occupy their neutral
network.
  CONCLUSIONS: Our work is the first experimental demonstration of the general
tendency of evolution to favor mutational robustness and protein stability in
highly polymorphic populations. We suggest that this phenomenon may contribute
to the mutational robustness and evolvability of viruses and bacteria that
exist in large populations.


  The semiconductor Bloch equations for a two-band model including inter- and
intraband excitation are used to study the nonlinear absorption of single and
multiple light pulses by direct-gap semiconductors. For a consistent analysis
the contributions to the absorption originating from both the interband
polarization and the intraband current need to be included. In the Bloch
equation approach theses contributions as well as different excitation pathways
in terms of sequences of inter- and intraband excitations can be evaluated
separately which allows for a transparent analysis, the identification of the
dominant terms, and analyzing their dependence on the excitation conditions. In
the perturbative regime, we obtain analytical expressions for the multi-photon
absorption coefficients for continuous-wave excitation. These results are shown
to agree well with numerical results for short pulses and/or finite dephasing
and relaxation times and we confirm the previously predicted strong enhancement
of two-photon absorption for non-degenerate conditions for pulsed excitation.
We discuss the dependencies on the light frequencies, initial band populations,
and the time delay between the pulses. The frequency dependence of the
two-photon absorption coefficient for non-degenerate excitation is evaluated
perturbatively in third-order. The higher-order contributions to the optical
absorption include three- and four-photon absorption and show a rich frequency
dependence including negative regions and dispersive lineshapes.
Non-perturbative solutions of the Bloch equations demonstrate a strongly
non-monotonous behavior of the intensity-dependent optical absorption for a
single incident pulse and in a pump-probe set-up.


  In this article we show the existence of a random-field solution to linear
stochastic partial differential equations whose partial differential operator
is hyperbolic and has variable coefficients that may depend on the temporal and
spatial argument. The main tools for this, pseudo-differential and Fourier
integral operators, come from microlocal analysis. The equations that we treat
are second-order and higher-order strictly hyperbolic, and second-order weakly
hyperbolic with uniformly bounded coefficients in space. For the latter one we
show that a stronger assumption on the correlation measure of the random noise
might be needed. Moreover, we show that the well-known case of the stochastic
wave equation can be embedded into the theory presented in this article.


  Surfaces capable of delaying the frosting passively and facilitating its
removal are highly desirable in fields where ice introduces inefficiencies and
risks. Coalescence jumping, enabled by highly hydrophobic surfaces, is already
exploited to slow down the frosting but it is insufficient to completely
eliminate the propagation by ice-bridging. We show how the self-ejection of
single condensation droplets can frustrate the ice bridges of all the
condensation droplets leading to a frost velocity lower than 0.5 um/s thus
dropping below the current limits of passive surfaces by a factor of at least
2. Arrays of truncated microcones, covered by uniformly hydrophobic
nanostructures, enable individual condensation droplets to growth and
self-propel towards the top of the microstructures and to self-eject once a
precise volume is reached. The independency of self-ejection on the neighbour
droplets allows a precise control on the droplets' size and distance
distributions and the ice-bridging frustration. The most performant
microstructures tend to cones with a sharp tip on which the percentage of
self-ejection is maximum. Looking towards applications, tapered microstructures
allow maximising the percentage of self-ejecting drops while maintaining a
certain mechanical strength. Further, it is shown that inserted pinning sites
are not essential, which greatly facilitates manufacturing.


  We demonstrate particle clustering on macroscopic scales in a coupled
nonequilibrium system where two species of particles are advected by a
fluctuating landscape and modify the landscape in the process. The phase
diagram generated by varying the particle-landscape coupling, valid for all
particle density and in both one and two dimensions, shows novel nonequilibrium
phases. While particle species are completely phase separated, the landscape
develops macroscopically ordered regions coexisting with a disordered region,
resulting in coarsening and steady state dynamics on time scales which grow
algebraically with size, not seen earlier in systems with pure domains.


  In this paper, system identification approach has been adopted to develop a
novel dynamical model for describing the relationship between light as an
environmental stimulus and the electrical response as the measured output for a
bay leaf (Laurus nobilis) plant. More specifically, the target is to predict
the characteristics of the input light stimulus (in terms of on-off timing,
duration and intensity) from the measured electrical response - leading to an
inverse problem. We explored two major classes of system estimators to develop
dynamical models - linear and nonlinear - and their several variants for
establishing a forward and also an inverse relationship between the light
stimulus and plant electrical response. The best class of models are given by
the Nonlinear Hammerstein-Wiener (NLHW) estimator showing good data fitting
results over other linear and nonlinear estimators in a statistical sense.
Consequently, a few set of models using different functional variants of NLHW
has been developed and their accuracy in detecting the on-off timing and
intensity of the input light stimulus are compared for 19 independent plant
datasets (including 2 additional species viz. Zamioculcas zamiifolia and
Cucumis sativus) under similar experimental scenario.


  The well-known Gouy-Stodola theorem states that a device produces maximum
useful power when working reversibly, that is with no entropy production inside
the device. This statement then leads to a method of thermodynamic optimization
based on entropy production minimization. Exergy destruction (difference
between exergy of fuel and exhausts) is also given by entropy production inside
the device. Therefore, assessing efficiency of a device by exergy analysis is
also based on the Gouy-Stodola theorem. However, assumptions that had led to
the Gouy-Stodola theorem are not satisfied in several optimization scenarios,
e.g. non-isothermal steady-state fuel cells, where both entropy production
minimization and exergy analysis should be used with caution. We demonstrate,
using non-equilibrium thermodynamics, a few cases where entropy production
minimization and exergy analysis should not be applied.


  We show that if F is a totally real field in which p splits completely and f
is a mod p Hilbert modular form with parallel weight 2<k<p, which is totally
ordinary at p and has tamely ramified Galois representation at all primes
dividing p, then there is a "companion form" of parallel weight k':=p+1-k. This
work generalises results of Gross and Coleman-Voloch for modular forms over Q.


  Motivated by establishing theoretical foundations for various manifold
learning algorithms, we study the problem of Mahalanobis distance (MD), and the
associated precision matrix, estimation from high-dimensional noisy data. By
relying on recent transformative results in covariance matrix estimation, we
demonstrate the sensitivity of \MD~and the associated precision matrix to
measurement noise, determining the exact asymptotic signal-to-noise ratio at
which MD fails, and quantifying its performance otherwise. In addition, for an
appropriate loss function, we propose an asymptotically optimal shrinker, which
is shown to be beneficial over the classical implementation of the MD, both
analytically and in simulations. The result is extended to the manifold setup,
where the nonlinear interaction between curvature and high-dimensional noise is
taken care of. The developed solution is applied to study a multiscale
reduction problem in the dynamical system analysis.


  Magnetic insulators can be characterized by a gap separating the singlet
ground state from the lowest energy triplet, S=1 excitation. If the gap can be
closed by the Zeeman interaction in applied magnetic field, the resulting S=1
quasiparticles, triplons, can have concentrations sufficient to undergo the
Bose-Einstein condensates transition. We consider macroscopic properties of the
triplon Bose-Einstein condensates in the Hartree-Fock-Bogoliubov approximation
taking into account the anomalous averages. We prove that these averages play
the qualitative role in the condensate properties. As a result, we show that
with the increase in the external magnetic field at a given temperature, the
condensate demonstrates an instability related to the appearance of nonzero
phonon damping and a change in the characteristic dependence of the speed of
sound on the magnetic field. The calculated magnetic susceptibility diverges
when the external magnetic field approaches this instability threshold,
providing a tool for the experimental verification of this approach.


  Interferometric out-of-focus imaging of ice particles is realized. The
overlapping defocused images of two nearby ice particles are analyzed. If one
particle is much smaller than the other one, the pattern constitutes an
off-axis hologram whose analysis gives the exact size and shape of the biggest
particle. Using two angles of view, we obtain a 3D description of the biggest
ice particle.


  Speckle patterns are a powerful tool for high-precision metrology, as they
allow remarkable performance in relatively simple setups. Nonetheless,
researchers in this field follow rather distinct paths due to underappreciated
general principles underlying speckle phenomena. Here, we advise on a universal
metric of intrinsic speckle sensitivity, and on the advantages and
disadvantages of multiple scattering. This will catalyse progress in speckle
metrology but will also translate to other domains of disordered optics which
are undergoing rapid developments at present.


  Recently, it was showed that there is a large N phase transition in Nflation,
in which when the number of fields is large enough, the slow roll inflation
phase will disappear. In this brief report, we illustrate the phase diagram for
Nflation, and discuss the entropy bound and some relevant results. It is found
that near the critical point the number of fields saturates dS entropy.


  The possibility of measuring the 85Kr activity with a liquid scintillation
spectrometer (LSS) by registering the scintillations in krypton was
investigated. It was found that at partial pressure of krypton less than
atmospheric, the conversion efficiency of krypton scintillators amounts to 3
keV/photon and is 20 times lower than the conversion efficiency of liquid
scintillators. The amplitude of the pulses due to scintillations in krypton is
approximately equal to the amplitude of the pulses from Cherenkov radiation
which occurs in the walls of the vial under the influence of 85Kr beta
particles.


  By allowing users to erase their data's impact on federated learning models,
federated unlearning protects users' right to be forgotten and data privacy.
Despite a burgeoning body of research on federated unlearning's technical
feasibility, there is a paucity of literature investigating the considerations
behind users' requests for data revocation. This paper proposes a
non-cooperative game framework to study users' data revocation strategies in
federated unlearning. We prove the existence of a Nash equilibrium. However,
users' best response strategies are coupled via model performance and
unlearning costs, which makes the equilibrium computation challenging. We
obtain the Nash equilibrium by establishing its equivalence with a much simpler
auxiliary optimization problem. We also summarize users' multi-dimensional
attributes into a single-dimensional metric and derive the closed-form
characterization of an equilibrium, when users' unlearning costs are
negligible. Moreover, we compare the cases of allowing and forbidding partial
data revocation in federated unlearning. Interestingly, the results reveal that
allowing partial revocation does not necessarily increase users' data
contributions or payoffs due to the game structure. Additionally, we
demonstrate that positive externalities may exist between users' data
revocation decisions when users incur unlearning costs, while this is not the
case when their unlearning costs are negligible.


  Efficient numerical optimization methods can improve performance and reduce
the environmental impact of computing in many applications. This work presents
a proof-of-concept study combining primitive state representations and
agent-environment interactions as first-order optimizers in the setting of
budget-limited optimization. Through reinforcement learning (RL) over a set of
training instances of an optimization problem class, optimal policies for
sequential update selection of algorithmic iteration steps are approximated in
generally formulated low-dimensional partial state representations that
consider aspects of progress and resource use. For the investigated case
studies, deployment of the trained agents to unseen instances of the quadratic
optimization problem classes outperformed conventional optimal algorithms with
optimized hyperparameters. The results show that elementary RL methods combined
with succinct partial state representations can be used as heuristics to manage
complexity in RL-based optimization, paving the way for agentic optimization
approaches.


  In recent work a general solution of the Ornstein Zernike equation for a
general Yukawa closure for a single component fluid was found. Because of the
complexity of the equations a simplifying assumption was made, namely that the
main scaling matrix $\bGamma$ had to be diagonal. While in principle this is
mathematically correct, it is not physical because it will violate symmetry
conditions when different Yukawas are assigned to different components. In this
work we show that by using the symmetry conditions the off diagonal elements of
$\bGamma$ can be computed explicitly for the case of two Yukawas, and that
although the solution is different than in the diagonal case, the excess
entropy is formally the same as in the diagonal case. Analytical expressions
for the Laplace transforms of the pair distribution functions are derived.


  The hybrid approach to quantum computation simultaneously utilizes both
discrete and continuous variables which offers the advantage of higher density
encoding and processing powers for the same physical resources. Trapped ions,
with discrete internal states and motional modes which can be described by
continuous variables in an infinite dimensional Hilbert space, offer a natural
platform for this approach. A nonlinear gate for universal quantum computing
can be implemented with the conditional beam splitter Hamiltonian $|e\rangle
\langle e| ( a^{\dagger} b + a b^{\dagger})$ that swaps the quantum states of
two motional modes, depending on the ion's internal state. We realize such a
gate and demonstrate its applications for quantum state overlap measurements,
single-shot parity measurement, and generation of NOON states.


  Using the bosonization method, we study the low temperature behavior of the
Kondo effect in the Tomonaga-Luttinger liquid and clearly show that the power
law temperature dependence of the impurity susceptibility is completely
determined by the repulsive electron-electron interaction existing in the total
spin channel and is independent of the electron-electron interaction existing
in the charge channels.


  In jet quenching, a hard QCD parton, before fragmenting into a jet of
hadrons, deposits a fraction of its energy in the medium. As the parton moves
nearly with speed of light, much greater that the speed of sound of the medium,
quenching jet can generate Mach shock wave. We have examined the possibility of
Mach shock wave formation due to jet quenching. Assuming that the deposited
energy quickly thermalize, we simulate the hydrodynamic evolution of the QGP
fluid with a quenching jet and subsequent particle production. Angular
distribution of pions, averaged over all the jet trajectories, resembles
'conical flow' due to Mach shock wave formation.
  However, speed of sound dependence of the simulated Mach angles are at
variance with that due to shock wave formation in a static medium or in a
medium with finite velocity.


  Dyadic data on pairs of objects, such as relational or social network data,
often exhibit strong statistical dependencies. Certain types of second-order
dependencies, such as degree heterogeneity and reciprocity, can be
well-represented with additive random effects models. Higher-order
dependencies, such as transitivity and stochastic equivalence, can often be
represented with multiplicative effects. The "amen" package for the R
statistical computing environment provides estimation and inference for a class
of additive and multiplicative random effects models for ordinal, continuous,
binary and other types of dyadic data. The package also provides methods for
missing, censored and fixed-rank nomination data, as well as longitudinal
dyadic data. This tutorial illustrates the "amen" package via example
statistical analyses of several of these different data types.


  Atmospheric retrieval of exoplanets from spectroscopic observations requires
an extensive exploration of a highly degenerate and high-dimensional parameter
space to accurately constrain atmospheric parameters. Retrieval methods
commonly conduct Bayesian parameter estimation and statistical inference using
sampling algorithms such as Markov Chain Monte Carlo (MCMC) or Nested Sampling.
Recently several attempts have been made to use machine learning algorithms
either to complement or replace fully Bayesian methods. While much progress has
been made, these approaches are still at times unable to accurately reproduce
results from contemporary Bayesian retrievals. The goal of our present work is
to investigate the efficacy of machine learning for atmospheric retrieval. As a
case study, we use the Random Forest supervised machine learning algorithm
which has been applied previously with some success for atmospheric retrieval
of the hot Jupiter WASP-12b using its near-infrared transmission spectrum. We
reproduce previous results using the same approach and the same semi-analytic
models, and subsequently extend this method to develop a new algorithm that
results in a closer match to a fully Bayesian retrieval. We combine this new
method with a fully numerical atmospheric model and demonstrate excellent
agreement with a Bayesian retrieval of the transmission spectrum of another hot
Jupiter, HD 209458b. Despite this success, and achieving high computational
efficiency, we still find that the machine learning approach is computationally
prohibitive for high-dimensional parameter spaces that are routinely explored
with Bayesian retrievals with modest computational resources. We discuss the
trade offs and potential avenues for the future.


  For a positive integer $n$, let $\mathcal T(n)$ be the set of all integers
greater than or equal to $n$. An integral quadratic form $f$ is called tight
$\mathcal T(n)$-universal if the set of nonzero integers that are represented
by $f$ is exactly $\mathcal T(n)$. The smallest possible rank over all tight
$\mathcal T(n)$-universal quadratic forms is defined by $t(n)$. In this
article, we find all tight $\mathcal T(n)$-universal diagonal quadratic forms.
We also prove that $t(n) \in \Omega(\log_2(n)) \cap O(\sqrt{n})$. Explicit
lower and upper bounds for $t(n)$ will be provided for some small integer $n$.


  Neutrino flavor evolution in core-collapse supernovae, neutron-star mergers,
or the early universe is dominated by neutrino-neutrino refraction, often
spawning "self-induced flavor conversion", i.e., shuffling of flavor among
momentum modes. This effect is driven by collective run-away modes of the
coupled "flavor oscillators" and can spontaneously break the initial symmetries
such as axial symmetry, homogeneity, isotropy, and even stationarity. Moreover,
the growth rates of unstable modes can be of the order of the neutrino-neutrino
interaction energy instead of the much smaller vacuum oscillation frequency:
self-induced flavor conversion does not always require neutrino masses. We
illustrate these newly found phenomena in terms of simple toy models. What
happens in realistic astrophysical settings is up to speculation at present.


  We experimentally study for the first time the self-heating normal domain in
magnetic field using nanowires made of granular NbTiN films with sub-10~nm
thicknesses. Specifically, we found that at temperatures below 10 K, the heat
capacity of phonons in such films is reduced with respect to predictions of the
Debye model. Moreover, as the temperature decreases, the phonon heat capacity
reduces quicker than the Debye prediction. These effects strengthen when the
film thickness decreases. We attribute the observed reduction in the heat
capacity to the size effect, which arises from the confinement of phonon modes
within the grains; a phenomenon that has been predicted but not yet observed.
These findings hold great importance in understanding the role of heat
transport in superconducting electronic devices and have the potential for
practical applications in mid-infrared photon sensing with superconducting
nanowire detectors.


  HgTe-based quantum wells (QWs) recently attracted a lot of attention for the
realization of a two-dimensional topological insulator with protected helical
edge states. Another class of topological systems are topological
superconductors (TSCs) with Majorana edge states. In this paper, we show how
proximity induced s-wave superconductivity in the bulk of HgTe-QWs and in the
presence of a Zeeman field can exhibit a TSC with chiral Majorana edge states.
We calculate the topological invariants and the corresponding Majorana edge
states explicitly within a four-band model accounting for inversion symmetry
breaking terms due to Rashba spin-orbit coupling and bulk inversion asymmetry
present in these QWs.


  The fast-growing demand for fully autonomous aerial operations in shared
spaces necessitates developing trustworthy agents that can safely and
seamlessly navigate in crowded, dynamic spaces. In this work, we propose Social
Robot Tree Search (SoRTS), an algorithm for the safe navigation of mobile
robots in social domains. SoRTS aims to augment existing socially-aware
trajectory prediction policies with a Monte Carlo Tree Search planner for
improved downstream navigation of mobile robots. To evaluate the performance of
our method, we choose the use case of social navigation for general aviation.
To aid this evaluation, within this work, we also introduce X-PlaneROS, a
high-fidelity aerial simulator, to enable more research in full-scale aerial
autonomy. By conducting a user study based on the assessments of 26 FAA
certified pilots, we show that SoRTS performs comparably to a competent human
pilot, significantly outperforming our baseline algorithm. We further
complement these results with self-play experiments in scenarios with
increasing complexity.


  Existing strategies for determining the optimal treatment or monitoring
strategy typically assume unlimited access to resources. However, when a health
system has resource constraints, such as limited funds, access to medication,
or monitoring capabilities, medical decisions must balance impacts on both
individual and population health outcomes. That is, decisions should account
for competition between individuals in resource usage. One simple solution is
to estimate the (counterfactual) resource usage under the possible
interventions and choose the optimal strategy for which resource usage is
within acceptable limits. We propose a method to identify the optimal dynamic
intervention strategy that leads to the best expected health outcome accounting
for a health system's resource constraints. We then apply this method to
determine the optimal dynamic monitoring strategy for people living with HIV
when resource limits on monitoring exist using observational data from the
HIV-CAUSAL Collaboration.


  We carefully study how well minimizing convex surrogate loss functions,
corresponds to minimizing the misclassification error rate for the problem of
binary classification with linear predictors. In particular, we show that
amongst all convex surrogate losses, the hinge loss gives essentially the best
possible bound, of all convex loss functions, for the misclassification error
rate of the resulting linear predictor in terms of the best possible margin
error rate. We also provide lower bounds for specific convex surrogates that
show how different commonly used losses qualitatively differ from each other.


  We analyze the vacuum structure of a generalized lattice Nambu--Jona-Lasinio
model with two flavors of Wilson fermions, such that its continuum action is
the most general four-fermion action with 'trivial' color interactions, and
having a $SU(2)_V x SU(2)_A$ symmetry in the chiral limit. The phase structure
of this model in the space of the two four-fermion couplings shows, in addition
to the standard Aoki phases, new phases with $<\bar\psi \gamma_5\psi> != 0$, in
close analogy to similar results recently suggested by some of us for lattice
QCD with two degenerate Wilson fermions. This result shows how the phase
structure of an effective model for low energy QCD cannot be entirely
understood from Wilson Chiral Perturbation Theory, based on the standard QCD
chiral effective Lagrangian approach.


  We introduce LightIt, a method for explicit illumination control for image
generation. Recent generative methods lack lighting control, which is crucial
to numerous artistic aspects of image generation such as setting the overall
mood or cinematic appearance. To overcome these limitations, we propose to
condition the generation on shading and normal maps. We model the lighting with
single bounce shading, which includes cast shadows. We first train a shading
estimation module to generate a dataset of real-world images and shading pairs.
Then, we train a control network using the estimated shading and normals as
input. Our method demonstrates high-quality image generation and lighting
control in numerous scenes. Additionally, we use our generated dataset to train
an identity-preserving relighting model, conditioned on an image and a target
shading. Our method is the first that enables the generation of images with
controllable, consistent lighting and performs on par with specialized
relighting state-of-the-art methods.


  This paper studies $n$-person simultaneous-move games with linear best
response function, where individuals interact within a given network structure.
This class of games have been used to model various settings, such as, public
goods, belief formation, peer effects, and oligopoly. The purpose of this paper
is to study the effect of the network structure on Nash equilibrium outcomes of
this class of games. Bramoull\'{e} et al. derived conditions for uniqueness and
stability of a Nash equilibrium in terms of the smallest eigenvalue of the
adjacency matrix representing the network of interactions. Motivated by this
result, we study how local structural properties of the network of interactions
affect this eigenvalue, influencing game equilibria. In particular, we use
algebraic graph theory and convex optimization to derive new bounds on the
smallest eigenvalue in terms of the distribution of degrees, cycles, and other
relevant substructures. We illustrate our results with numerical simulations
involving online social networks.


  We study current-induced dynamics of a magnetic domain wall by solving a
time-dependent Schr\"{o}dinger equation combined with Landau-Lifshitz-Gilbert
equation in a one-dimensional electron system coupled to localized spins. Two
types of domain-wall motions are observed depending on the hard-axis
anisotropy, $K_{\perp}$, of the localized spin system. For small values of
$K_{\perp}$, the magnetic domain wall shows a streaming motion driven by spin
transfer. In contrast, for large values of $K_{\perp}$, a stick-slip motion
driven by momentum transfer is obtained. We clarify the origin of these
characters of domain-wall motions in terms of the dynamics of one-particle
energy levels and distribution functions.


  Femto-second optical pair breaking spectroscopy is performed on optimally
doped YBa2Cu3O7-delta near 1.5eV. Dramatic photon energy dependence with a
sharp triplet fine structure is seen for the first time. The peak separations
are attributable to the reported phonon and magnetic excitations in the system.
The power dependence is non-linear with differing exponents for the three
contributions. These results imply the presence of insulating antiferromagnetic
domains in the superconducting state of YBCO over sub-picosecond time scale,
and thereby favor an electronic phase separation picture.


  The long story of the oscillatory approach to the initial cosmological
singularity and its more recent incarnation in multidimensional universe models
is told.


  Point cloud registration is a common step in many 3D computer vision tasks
such as object pose estimation, where a 3D model is aligned to an observation.
Classical registration methods generalize well to novel domains but fail when
given a noisy observation or a bad initialization. Learning-based methods, in
contrast, are more robust but lack in generalization capacity. We propose to
consider iterative point cloud registration as a reinforcement learning task
and, to this end, present a novel registration agent (ReAgent). We employ
imitation learning to initialize its discrete registration policy based on a
steady expert policy. Integration with policy optimization, based on our
proposed alignment reward, further improves the agent's registration
performance. We compare our approach to classical and learning-based
registration methods on both ModelNet40 (synthetic) and ScanObjectNN (real
data) and show that our ReAgent achieves state-of-the-art accuracy. The
lightweight architecture of the agent, moreover, enables reduced inference time
as compared to related approaches. In addition, we apply our method to the
object pose estimation task on real data (LINEMOD), outperforming
state-of-the-art pose refinement approaches.


  Iridium is a very promising material for spintronic applications due to its
interesting magnetic properties such as large RKKY exchange coupling as well as
its large spin-orbit coupling value. Ir is for instance used as a spacer layer
for perpendicular synthetic antiferromagnetic or ferrimagnet systems. However,
only a few studies of the spintronic parameters of this material have been
reported. In this paper, we present inverse spin Hall effect - spin pumping
ferromagnetic resonance measurements on CoFeB/Ir based bilayers to estimate the
values of the effective spin Hall angle, the spin diffusion length within
iridium, and the spin mixing conductance in the CoFeB/Ir bilayer. In order to
have reliable results, we performed the same experiments on CoFeB/Pt bilayers,
which behavior is well known due to numerous reported studies. Our experimental
results show that the spin diffusion length within iridium is 1.3 nm for
resistivity of 250 n$\Omega$.m, the spin mixing conductance $g_{eff}^{\uparrow
\downarrow}$ of the CoFeB/Ir interface is 30 nm$^{-2}$, and the spin Hall angle
of iridium has the same sign than the one of platinum and is evaluated at 26%
of the one of platinum. The value of the spin Hall angle found is 7.7% for Pt
and 2% for Ir. These relevant parameters shall be useful to consider Ir in new
concepts and devices combining spin-orbit torque and spin-transfer torque.


  The multidimensional N=4 supersymmetric quantum mechanics (SUSY QM) is
constructed using the superfield approach. As a result, the component form of
the classical and quantum Lagrangian and Hamiltonian is obtained. In the
considered SUSY QM both classical and quantum N=4 algebras include central
charges, and this opens various possibilities for partial supersymmetry
breaking. It is shown that quantum mechanical models with one quarter, one half
and three quarters of unbroken(broken) supersymmetries can exist in the
framework of the multidimensional N=4 SUSY QM, while the one-dimensional N=4
SUSY QM, constructed earlier, admits only one half or total supersymmetry
breakdown. We illustrate the constructed general formalism, as well as all
possible cases of the partial SUSY breaking taking as an example a direct
multidimensional generalization of the one-dimensional N=4 superconformal
quantum mechanical model. Some open questions and possible applications of the
constructed multidimensional N=4 SUSY QM to the known exactly integrable
systems and problems of quantum cosmology are briefly discussed.


  The task of retrieving video content relevant to natural language queries
plays a critical role in effectively handling internet-scale datasets. Most of
the existing methods for this caption-to-video retrieval problem do not fully
exploit cross-modal cues present in video. Furthermore, they aggregate
per-frame visual features with limited or no temporal information. In this
paper, we present a multi-modal transformer to jointly encode the different
modalities in video, which allows each of them to attend to the others. The
transformer architecture is also leveraged to encode and model the temporal
information. On the natural language side, we investigate the best practices to
jointly optimize the language embedding together with the multi-modal
transformer. This novel framework allows us to establish state-of-the-art
results for video retrieval on three datasets. More details are available at
http://thoth.inrialpes.fr/research/MMT.


  The currently accepted mathematical description of the fundamental
constituents and interactions of matter is the Standard Model of particle
physics. Its last missing particle, the famous Higgs boson, was observed at the
Large Hadron Collider at CERN in 2012. However, it is clear that the Standard
Model cannot be the ultimate theory of Nature, and e.g. cannot account for Dark
Matter or non-vanishing neutrino masses (and does not include gravity). In
fact, searches for physics beyond the SM have been intensified since the Higgs
boson discovery. In this article, we review the hints for new physics, called
``anomalies'', obtained in particle physics experiments within the last years.
We consider both direct high-energy searches for new resonances at the LHC and
indirect low-energy precision experiments. These anomalies range from the
nuclear scale (approximately the mass of the proton) to the electroweak scale
(i.e. the mass of the Higgs boson) to the TeV scale (the highest scale directly
accessible at the LHC), therefore spanning over four orders of magnitude. After
discussing the experimental and theoretical status of the anomalies, we
summarize possible explanations in terms of new particles and new interactions.
In particular, new Higgs bosons and leptoquarks are promising candidates.
Discovery prospects and implications for future colliders are discussed.


  Weakly interacting Fermi gases exhibit rich collective dynamics in
spin-dependent potentials, arising from correlations between spin degrees of
freedom and conserved single atom energies, offering broad prospects for
simulating many-body quantum systems by engineering energy-space "lattices,"
with controlled energy landscapes and site to site interactions. Using quantum
degenerate clouds of $^6$Li, confined in a spin-dependent harmonic potential,
we measure complex, time-dependent spin-density profiles, varying on length
scales much smaller than the cloud size. We show that a one-dimensional mean
field model, without additional simplifying approximations, quantitatively
predicts the observed fine structure. We measure the magnetic fields where the
scattering lengths vanish for three different hyperfine state mixtures to
provide new constraints on the collisional (Feshbach) resonance parameters.


  While self-supervised learning has enabled effective representation learning
in the absence of labels, for vision, video remains a relatively untapped
source of supervision. To address this, we propose Pixel-level Correspondence
(PiCo), a method for dense contrastive learning from video. By tracking points
with optical flow, we obtain a correspondence map which can be used to match
local features at different points in time. We validate PiCo on standard
benchmarks, outperforming self-supervised baselines on multiple dense
prediction tasks, without compromising performance on image classification.


  These days more companies are shifting towards using cloud environments to
provide their services to their client. While it is easy to set up a cloud
environment, it is equally important to monitor the system's runtime behaviour
and identify anomalous behaviours that occur during its operation. In recent
years, the utilisation of \ac{rnn} and \ac{dnn} to detect anomalies that might
occur during runtime has been a trending approach. However, it is unclear how
to explain the decisions made by these networks and how these networks should
be interpreted to understand the runtime behaviour that they model. On the
contrary, state machine models provide an easier manner to interpret and
understand the behaviour that they model. In this work, we propose an approach
that learns state machine models to model the runtime behaviour of a cloud
environment that runs multiple microservice applications. To the best of our
knowledge, this is the first work that tries to apply state machine models to
microservice architectures. The state machine model is used to detect the
different types of attacks that we launch on the cloud environment. From our
experiment results, our approach can detect the attacks very well, achieving a
balanced accuracy of 99.2% and an F1 score of 0.982.


  Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs)
are able to complete a number of tasks in cooperation with each other without
need for human intervention. In recent years, UAVs, which are widely utilized
in military missions, have begun to be deployed in civilian applications and
mostly for commercial purposes. With their growing numbers and range of
applications, UAVs are becoming more and more popular; on the other hand, they
are also the target of various threats which can exploit various
vulnerabilities of UAV systems in order to cause destructive effects. It is
therefore critical that security is ensured for UAVs and the networks that
provide communication between UAVs. This survey seeks to provide a
comprehensive perspective on security within the domain of UAVs and FANETs. Our
approach incorporates attack surface analysis and aligns it with the
identification of potential threats. Additionally, we discuss countermeasures
proposed in the existing literature in two categories: preventive and detection
strategies. Our primary focus centers on the security challenges inherent to
FANETs, acknowledging their susceptibility to insider threats due to their
unique characteristics. Consequently, our study involves the simulation and
analysis of four distinct routing attacks on FANETs. Hence, this study
transcends a standard review by integrating an attack analysis based on
extensive simulations. Finally, we rigorously examine open issues, and propose
research directions to guide future endeavors in this field.


  The stochastic methods in Hilbert space have been used both from a
fundamental and a practical point of view. The result we report here concerns
only the idea of applying these methods to model the evolution of quantum
systems and does not enter into the question of their fundamental or practical
status. It can be easily stated as follows: Once a quantum stochastic evolution
scheme is assumed, the incompatibility between the Markov property and the
notion of quantum jump is rapidly established.


  The big empirical success of group equivariant networks has led in recent
years to the sprouting of a great variety of equivariant network architectures.
A particular focus has thereby been on rotation and reflection equivariant CNNs
for planar images. Here we give a general description of $E(2)$-equivariant
convolutions in the framework of Steerable CNNs. The theory of Steerable CNNs
thereby yields constraints on the convolution kernels which depend on group
representations describing the transformation laws of feature spaces. We show
that these constraints for arbitrary group representations can be reduced to
constraints under irreducible representations. A general solution of the kernel
space constraint is given for arbitrary representations of the Euclidean group
$E(2)$ and its subgroups. We implement a wide range of previously proposed and
entirely new equivariant network architectures and extensively compare their
performances. $E(2)$-steerable convolutions are further shown to yield
remarkable gains on CIFAR-10, CIFAR-100 and STL-10 when used as a drop-in
replacement for non-equivariant convolutions.


  Magnetic vortices in soft ferromagnetic nano-disks have been extensively
studied for at least several decades both for their fundamental (as a "live"
macroscopic realization of a field theory model of an elementary particle) as
well as applied value for high-speed high-density power-independent information
storage. Here it is shown that there is another stable vortex state with large
thickness-dependent core profile in nano-scale ferromagnetic disks of several
exchange lengths in size. Its energy is computed numerically (in the framework
of Magnetism@home distributed computing project) and its stability is studied
analytically, which allows to plot it on magnetic phase diagram. In cylinders
of certain geometries large vortices exist on par with classical ones, while
being separated by an energy barrier, controllable by tuning the geometry and
material of ferromagnetic disk. They can be an excellent candidate for magnetic
information storage not only because the resulting disk sizes are among the
smallest, able to support magnetic vortices, but also because it is the closest
to the classical vortex state of all other known metastable states of magnetic
nano-cylinder. It means that memory, based on switching between these two types
of magnetic vortices, may, potentially, achieve the highest possible rate of
switching.


  Understanding small neutrino masses in type II seesaw models with TeV scale
SM triplet Higgs bosons requires that its coupling with the standard model
Higgs doublet H be dialed down to be order eV to KeV, which is a fine-tuning by
a factor of $10^{-11}-10^{-8}$ with respect to the weak scale. We present a
SUSY extension of the type II seesaw model where this dimensionful small
coupling is radiatively induced, thus making its smallness natural. This model
has an exotic vector-like quark doublet which contains a quark X with electric
charge 5/3 and a top partner t'. We discuss in details the phenomenology of the
model paying special attention to the consequences of the interactions of the
the exotic heavy quarks and the scalars of the model. Implications for
neutrinoless double beta decay and for the LHC experiments are discussed in
detail. Remarkably, in this model both the seesaw triplet and the heavy quarks
can manifest at colliders in a host of different signatures, including some
that significantly differ from those of the minimal models. Depending on the
choice of the hierarchy of couplings, the decay of the heavy quarks and of the
seesaw triplet may be subject to bounds that can be tighter or looser than the
bounds from standard LHC searches. Furthermore we point out a new
short-distance contribution to neutrinoless double beta decay mediated by the
simultaneous propagation of the type II triplet and exotic fermions. Remarkably
this contribution to the neutrinoless double beta decay is parametrically quite
independent from the scale of the generated neutrino mass.


  Complementary relationships exist regarding interference properties of
particles such as pattern visibility, predictability and distinguishability.
Additionally, relationships are known between information gain $G$ and
measurement disturbance $F$ for entangled spin pairs. The question of whether a
similar complementary relationship between entanglement and measurement occurs
is examined herein. For qubit systems, both measurement on a single system and
measurements on a bipartite system are considered in regards to the
entanglement. It is proven that $\overline{E}+D\le 1$ holds where
$\overline{E}$ is the average entanglement after a measurement is made and for
which $D$ is a measure of the measurement disturbance of a single measurement.
For measurements on a bipartite system shared by Alice and Bob ,it is shown
that $\overline{E}+G\le 1$ where $G$ is the maximum information gain regarding
Alice's result that can be obtained by Bob. These results are generalized for
arbitrary initial mixed states and as well to non-Hermitian operators. In the
case of maximally entangled initial states, it is found that $D\le E_{L}$ and
$G\le E_{L}$ where $E_{L}$ is the entanglement loss due to measurement by
Alice. We conclude that the amount of disturbance and information gain that one
can gain are strictly limited by entanglement.


  We propose a scheme for parameter estimation with cluster states. We find
that phase estimation with cluster states under a many-body Hamiltonian and
separable measurements leads to a precision at the Heisenberg limit. As noise
models we study the dephasing, depolarizing, and pure damping channels.
Decoherence reduces the sensitivity but our scheme remains superior over
several reference schemes with states such as maximally entangled states and
product states. For small cluster states and fixed evolution times it remains
at the Heisenberg limit for approximately 2 times as many qubits than
alternative schemes.


  It is shown, that the hard component of quarks distribution functions induced
by instantons gives the essential contribution to hard diffractive processes.
We predict large polarization effects in these processes.


  In this paper we prove that there exists an asymptotical diagonalization
algorithm for a class of sparse Hermitian (or real symmetric) matrices if and
only if the matrices become Hessenberg matrices after some permutation of rows
and columns. The proof is based on Morse theory, Roberts' theorem on
indifference graphs, toric topology, and computer-based homological
calculations.


  Projections for the earliest interstellar mission possibilities are
calculated based on 27 years of data on historic energy trends, societal
priorities, required mission energy, and the implications of the Incessant
Obsolescence Postulate (Where newer probes pass prior probes). Two sample
missions are considered: launching a minimal colony ship where destination is
irrelevant, and sending a minimal probe to Alpha Centauri with a 75 year
mission duration. The colony ship is assumed to have a mass of 10^7 kg, and the
probe 10^4 kg. It is found that the earliest interstellar missions could not
begin for roughly another 2 centuries, or 1 century at best. Even when
considering only the kinetic energy of the vehicles without any regard for
propellant, the colony ship cannot launch until around the year 2200, and the
probe cannot launch until around 2500. Examination of the Incessant Obsolesce
Postulate shows that it becomes irrelevant under several conditions.


  This paper proposes a theoretical framework to evaluate and compare the
performance of gradient-descent algorithms for distributed learning in relation
to their behavior around local minima in nonconvex environments. Previous works
have noticed that convergence toward flat local minima tend to enhance the
generalization ability of learning algorithms. This work discovers two
interesting results. First, it shows that decentralized learning strategies are
able to escape faster away from local minimizers and favor convergence toward
flatter minima relative to the centralized solution in the large-batch training
regime. Second, and importantly, the ultimate classification accuracy is not
solely dependent on the flatness of the local minimizer but also on how well a
learning algorithm can approach that minimum. In other words, the
classification accuracy is a function of both flatness and optimization
performance. The paper examines the interplay between the two measures of
flatness and optimization error closely. One important conclusion is that
decentralized strategies of the diffusion type deliver enhanced classification
accuracy because it strikes a more favorable balance between flatness and
optimization performance.


  Originating from the premise that Generative Adversarial Networks (GANs)
enrich creative processes rather than diluting them, we describe an ongoing PhD
project that proposes to study GANs in a co-creative context. By asking How can
GANs be applied in co-creation, and in doing so, how can they contribute to
fashion design processes? the project sets out to investigate co-creative GAN
applications and further develop them for the specific application area of
fashion design. We do so by drawing on the field of mixed-initiative
co-creation. Combined with the technical insight into GANs' functioning, we aim
to understand how their algorithmic properties translate into interactive
interfaces for co-creation and propose new interactions.


  This paper describes the short-term competition on the Components
Segmentation Task of Document Photos that was prepared in the context of the
16th International Conference on Document Analysis and Recognition (ICDAR
2021). This competition aims to bring together researchers working in the field
of identification document image processing and provides them a suitable
benchmark to compare their techniques on the component segmentation task of
document images. Three challenge tasks were proposed entailing different
segmentation assignments to be performed on a provided dataset. The collected
data are from several types of Brazilian ID documents, whose personal
information was conveniently replaced. There were 16 participants whose results
obtained for some or all the three tasks show different rates for the adopted
metrics, like Dice Similarity Coefficient ranging from 0.06 to 0.99. Different
Deep Learning models were applied by the entrants with diverse strategies to
achieve the best results in each of the tasks. Obtained results show that the
currently applied methods for solving one of the proposed tasks (document
boundary detection) are already well established. However, for the other two
challenge tasks (text zone and handwritten sign detection) research and
development of more robust approaches are still required to achieve acceptable
results.


  This chapter discusses the current state of the art, and emerging research
challenges, for metamodelling. In the state-of-the-art review on metamodelling,
we review approaches, abstractions, and tools for metamodelling, evaluate them
with respect to their expressivity, investigate what role(s) metamodels may
play at run-time and how semantics can be assigned to metamodels and the domain
specific modeling languages they could define. In the emerging challenges
section on metamodelling we highlight research issues regarding the management
of complexity, consistency, and evolution of metamodels, and how the semantics
of metamodels impacts each of these.


  Using Monte Carlo model of biological evolution we have discovered that
populations can switch between two different strategies of their genomes'
evolution; Darwinian purifying selection and complementing the haplotypes. The
first one is exploited in the large panmictic populations while the second one
in the small highly inbred populations. The choice depends on the crossover
frequency. There is a power law relation between the critical value of
crossover frequency and the size of panmictic population. Under the constant
inbreeding this critical value of crossover does not depend on the population
size and has a character of phase transition. Close to this value sympatric
speciation is observed.


  The assumption of collisionless cold dark matter on its own cannot reconcile
several astrophysical discrepancies (cusp-vs-core problem, missing satellite
problem, too-big-to-fail problem). Self-interacting dark matter provides a
promising framework for solving all these problems, and self-interaction cross
sections are duly constrained in the literature. Following the work of Tulin,
Yu, and Zurek [1], we can constrain the dark matter mass and the mass of a
light mediator assuming a generic scalar Yukawa-type interaction. In
particular, we constrain the strongly coupled inflationary dark matter of the
luminogenesis model, a unification model with the gauge group $SU(3)_C \times
SU(6) \times U(1)_Y$, which breaks to the Standard Model with an extra gauge
group for dark matter when the inflaton rolls into its true vacuum. The
luminogenesis model is additionally subject to constraints on inflation, and we
find an upper bound on the scale of symmetry breaking of the inflaton and the
decoupling scale $M_1$ of certain representations of the gauge group. We
emphasize that the luminogenesis model enables a unique connection between
astrophysical constraints, the nature of dark matter, and inflation.


  We have studied the effects of the isoelectronic substitution of Ru for Fe in
polycrystalline samples of the spin density wave (SDW) material PrFeAsO.
Crystal structures from powder x-ray diffraction at room temperature and
transport properties from 2-300 K are reported. The SDW is completely
suppressed upon Ru substitution. The distortion of the tetrahedral coordination
environment of the transition metal site increases as the Ru concentration
increases, which may be related to the absence of superconductivity above 2 K.
Band structure calculations show that the larger size of Ru 4$d$ orbitals is
primarily responsible for the suppression of magnetism as Ru is substituted
into the Fe layer. The experimental results indicate that long-ranged magnetic
order of Pr moments is suppressed at Ru concentrations as low as 10%.


  We study the global existence and decay to spherical equilibrium of Hele-Shaw
flows with surface tension. We prove that without injection of fluid,
perturbations of the sphere decay to zero exponentially fast. On the other
hand, with a time-dependent rate of fluid injection into the Hele-Shaw cell,
the distance from the moving boundary to an expanding sphere (with
time-dependent radius) also decays to zero but with an algebraic rate, which
depends on the injection rate of the fluid.


  The Joint Research Centre in collaboration with the European standardization
bodies CEN and CENELEC launched a scoping exercise on standardization needs in
response to COVID-19 and future pandemics. The purpose of the exercise was to
identify ongoing harmonization initiatives, as was well as further
standardization needs in relevant sectors such as artisanal reusable face
masks, medical face masks, and social distancing in closed public or commercial
spaces. An overview of already ongoing standardization activities relevant to
COVID-19 in Spain and Italy illustrate, although fragmented and partially
complete, the importance of standardization in key sectors for combatting
pandemics, such as in health, social, safety and security. This report informs
colleagues in European institutions and Member States about the crucial role
standardization plays in the common efforts to overcome the COVID-19 pandemic.
Examples include potential inputs to the drafting of guidelines, methods and or
interoperability standards. Finally, the report also provides practical
examples of agile standardization activities and deliverables that have the
potential to enable the EU to respond more effectively and multilaterally to
future crises. With this report we aim to raise awareness about the
opportunities that standardization and harmonization can bring in the context
of the COVID-19 pandemic.


  In this modern era of technology with e-commerce developing at a rapid pace,
it is very important to understand customer requirements and details from a
business conversation. It is very crucial for customer retention and
satisfaction. Extracting key insights from these conversations is very
important when it comes to developing their product or solving their issue.
Understanding customer feedback, responses, and important details of the
product are essential and it would be done using Named entity recognition
(NER). For extracting the entities we would be converting the conversations to
text using the optimal speech-to-text model. The model would be a two-stage
network in which the conversation is converted to text. Then, suitable entities
are extracted using robust techniques using a NER BERT transformer model. This
will aid in the enrichment of customer experience when there is an issue which
is faced by them. If a customer faces a problem he will call and register his
complaint. The model will then extract the key features from this conversation
which will be necessary to look into the problem. These features would include
details like the order number, and the exact problem. All these would be
extracted directly from the conversation and this would reduce the effort of
going through the conversation again.


  Automated Valet Parking (AVP) is a crucial component of advanced autonomous
driving systems, focusing on the endpoint task within the "human-vehicle
interaction" process to tackle the challenges of the "last mile".The perception
module of the automated parking algorithm has evolved from local perception
using ultrasonic radar and global scenario precise map matching for
localization to a high-level map-free Birds Eye View (BEV) perception
solution.The BEV scene places higher demands on the real-time performance and
safety of automated parking planning tasks. This paper proposes an improved
automated parking algorithm based on the A* algorithm, integrating vehicle
kinematic models, heuristic function optimization, bidirectional search, and
Bezier curve optimization to enhance the computational speed and real-time
capabilities of the planning algorithm.Numerical optimization methods are
employed to generate the final parking trajectory, ensuring the safety of the
parking path. The proposed approach is experimentally validated in the commonly
used industrial CARLA-ROS joint simulation environment. Compared to traditional
algorithms, this approach demonstrates reduced computation time with more
challenging collision-risk test cases and improved performance in comfort
metrics.


  In this article we investigate the percolative properties of Brownian
interlacements, a model introduced by Alain-Sol Sznitman in arXiv:1209.4531,
and show that: the interlacement set is "well-connected", i.e., any two
"sausages" in $d$-dimensional Brownian interlacements, $d\geq 3$, can be
connected via no more than $\lceil (d-4)/2 \rceil$ intermediate sausages almost
surely; while the vacant set undergoes a non-trivial percolation phase
transition when the level parameter varies.


  Let $\mathfrak{g}$ be a semisimple simply-laced Lie algebra of finite type.
Let $\mathcal{C}$ be an abelian categorical representation of the quantum group
$U_q(\mathfrak{g})$ categorifying an integrable representation $V$. The Artin
braid group $B$ of $\mathfrak{g}$ acts on $D^b(\mathcal{C})$ by Rickard
complexes, providing a triangulated equivalence
$\Theta_{w_0}:D^b(\mathcal{C}_\mu) \to D^b(\mathcal{C}_{w_0(\mu)})$, where
$\mu$ is a weight of $V$ and $\Theta_{w_0}$ is a positive lift of the longest
element of the Weyl group.
  We prove that this equivalence is t-exact up to shift when $V$ is isotypic,
generalising a fundamental result of Chuang and Rouquier in the case
$\mathfrak{g}=\mathfrak{sl}_2$. For general $V$, we prove that $\Theta_{w_0}$
is a perverse equivalence with respect to a Jordan-H\"older filtration of
$\mathcal{C}$.
  Using these results we construct, from the action of $B$ on $V$, an action of
the cactus group on the crystal of $V$. This recovers the cactus group action
on $V$ defined via generalised Sch\"utzenberger involutions, and provides a new
connection between categorical representation theory and crystal bases. We also
use these results to give new proofs of theorems of Berenstein-Zelevinsky,
Rhoades, and Stembridge regarding the action of symmetric group on the
Kazhdan-Lusztig basis of its Specht modules.


  Recently, neural networks have produced state-of-the-art results for
density-ratio estimation (DRE), a fundamental technique in machine learning.
However, existing methods bear optimization issues that arise from the loss
functions of DRE: a large sample requirement of Kullback--Leibler
(KL)-divergence, vanishing of train loss gradients, and biased gradients of the
loss functions. Thus, an $\alpha$-divergence loss function ($\alpha$-Div) that
offers concise implementation and stable optimization is proposed in this
paper. Furthermore, technical justifications for the proposed loss function are
presented. The stability of the proposed loss function is empirically
demonstrated and the estimation accuracy of DRE tasks is investigated.
Additionally, this study presents a sample requirement for DRE using the
proposed loss function in terms of the upper bound of $L_1$ error, which
connects a curse of dimensionality as a common problem in high-dimensional DRE
tasks.


  We investigate the entanglement dynamics and purity of a two-level atom,
which is additionally driven by a classical field, interacting with a coherent
field in a dissipative environment. It is shown that the amount of entanglement
and the purity of the system can be improved by controlling the classical
field.


  We study the implementation of a correlation measurement technique for the
characterization of squeezed light which is nearly free of electronic noise.
With two different sources of squeezed light, we show that the sign of the
covariance coefficient, revealed from the time resolved correlation data, is
witnessing the presence of squeezing in the system. Furthermore, we estimate
the degree of squeezing using the correlation method and compare it to the
standard homodyne measurement scheme. We show that the role of electronic
detector noise is minimized using the correlation approach as opposed to
homodyning where it often becomes a crucial issue.


  For a finite group $G$, a semi-Mackey (resp. Tambara) functor is regarded as
a $G$-bivariant analog of a commutative monoid (resp. ring). As such, some
naive algebraic constructions are generalized to this $G$-bivariant setting. In
this article, as a $G$-bivariant analog of the fraction of a ring, we consider
{\it fraction} of a Tambara (and a semi-Mackey) functor, by a multiplicative
semi-Mackey subfunctor.


  Languages are continuously undergoing changes, and the mechanisms that
underlie these changes are still a matter of debate. In this work, we approach
language evolution through the lens of causality in order to model not only how
various distributional factors associate with language change, but how they
causally affect it. In particular, we study slang, which is an informal
language that is typically restricted to a specific group or social setting. We
analyze the semantic change and frequency shift of slang words and compare them
to those of standard, nonslang words. With causal discovery and causal
inference techniques, we measure the effect that word type (slang/nonslang) has
on both semantic change and frequency shift, as well as its relationship to
frequency, polysemy and part of speech. Our analysis provides some new insights
in the study of language change, e.g., we show that slang words undergo less
semantic change but tend to have larger frequency shifts over time.


  The studies of the associated production processes of a top-quark pair with a
colour-singlet boson, e.g. Higgs, W or Z, are among the highest priorities of
the LHC programme. Correspondingly, improvements in precision of theoretical
predictions for these processes are of central importance. In this talk, we
review our latest results on resummation of soft gluon corrections. The
resummation is carried out using the direct QCD Mellin space technique in
three-particle invariant mass kinematics. We discuss the impact of the soft
gluon corrections on predictions for total cross sections and differential
distributions.


  We have performed two-dimensional multicomponent decomposition of 144 local
barred spiral galaxies using 3.6 $\mu {\rm m}$ images from the Spitzer Survey
of Stellar Structure in Galaxies. Our model fit includes up to four components
(bulge, disk, bar, and a point source) and, most importantly, takes into
account disk breaks. We find that ignoring the disk break and using a single
disk scale length in the model fit for Type II (down-bending) disk galaxies can
lead to differences of 40% in the disk scale length, 10% in bulge-to-total
luminosity ratio (B/T), and 25% in bar-to-total luminosity ratios. We find that
for galaxies with B/T $\geq$ 0.1, the break radius to bar radius, $r_{\rm
br}/R_{\rm bar}$, varies between 1 and 3, but as a function of B/T the ratio
remains roughly constant. This suggests that in bulge-dominated galaxies the
disk break is likely related to the outer Lindblad Resonance (OLR) of the bar,
and thus moves outwards as the bar grows. For galaxies with small bulges, B/T
$<$ 0.1, $r_{\rm br}/R_{\rm bar}$ spans a wide range from 1 to 6. This suggests
that the mechanism that produces the break in these galaxies may be different
from that in galaxies with more massive bulges. Consistent with previous
studies, we conclude that disk breaks in galaxies with small bulges may
originate from bar resonances that may be also coupled with the spiral arms, or
be related to star formation thresholds.


  Survival is poor for patients with metastatic cancer, and it is vital to
examine new biomarkers that can improve patient prognostication and identify
those who would benefit from more aggressive therapy. In metastatic prostate
cancer, two new assays have become available: one that quantifies the number of
cancer cells circulating in the peripheral blood, and the other a marker of the
aggressiveness of the disease. It is critical to determine the magnitude of the
effect of these biomarkers on the discrimination of a model-based risk score.
To do so, most analysts frequently consider the discrimination of two separate
survival models: one that includes both the new and standard factors and a
second that includes the standard factors alone. However, this analysis is
ultimately incorrect for many of the scale-transformation models ubiquitous in
survival, as the reduced model is misspecified if the full model is specified
correctly. To circumvent this issue, we developed a projection-based approach
to estimate the impact of the two prostate cancer biomarkers. The results
indicate that the new biomarkers can influence model discrimination and justify
their inclusion in the risk model; however, the hunt remains for an applicable
model to risk-stratify patients with metastatic prostate cancer.


  We show that in cases of marginal detections (~ 3\sigma), such as that of
Baryonic Acoustic Oscillations (BAO) in cosmology, the often-used Gaussian
approximation to the full likelihood is very poor, especially beyond ~3\sigma.
This can radically alter confidence intervals on parameters and implies that
one cannot naively extrapolate 1\sigma-errorbars to 3\sigma, and beyond. We
propose a simple fitting formula which corrects for this effect in posterior
probabilities arising from marginal detections. Alternatively the full
likelihood should be used for parameter estimation rather than the Gaussian
approximation of a just mean and an error.


  Abdominal multi-organ segmentation of computed tomography (CT) images has
been the subject of extensive research interest. It presents a substantial
challenge in medical image processing, as the shape and distribution of
abdominal organs can vary greatly among the population and within an individual
over time. While continuous integration of novel datasets into the training set
provides potential for better segmentation performance, collection of data at
scale is not only costly, but also impractical in some contexts. Moreover, it
remains unclear what marginal value additional data have to offer. Herein, we
propose a single-pass active learning method through human quality assurance
(QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ
segmentation and augmented the dataset either with outlier data (e.g.,
exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars
for which the baseline algorithm worked). The new models were trained using the
augmented datasets with 5-fold cross-validation (for outlier data) and withheld
outlier samples (for inlier data). Manual labeling of outliers increased Dice
scores with outliers by 0.130, compared to an increase of 0.067 with inliers
(p<0.001, two-tailed paired t-test). By adding 5 to 37 inliers or outliers to
training, we find that the marginal value of adding outliers is higher than
that of adding inliers. In summary, improvement on single-organ performance was
obtained without diminishing multi-organ performance or significantly
increasing training time. Hence, identification and correction of baseline
failure cases present an effective and efficient method of selecting training
data to improve algorithm performance.


  Planetary climates are strongly affected by planetary orbital parameters such
as obliquity, eccentricity, and precession. In exoplanetary systems,
exo-terrestrial planets should have various obliquities. High-obliquity planets
would have extreme seasonal cycles due to the seasonal change of the
distribution of the insolation. Here, we introduce the Non-hydrostatic
ICosahedral Atmospheric Model(NICAM), a global cloud-resolving model, to
investigate the climate of high-obliquity planets. This model can explicitly
simulate a three-dimensional cloud distribution and vertical transports of
water vapor. We simulated exo-terrestrial climates with high resolution using
the supercomputer FUGAKU. We assumed aqua-planet configurations with 1 bar of
air as a background atmosphere, with four different obliquities ($0^{\circ}$,
$23.5^{\circ}$, $45^{\circ}$, and $60^{\circ}$). We ran two sets of
simulations: 1) low-resolution (~ 220 km-mesh as the standard resolution of a
general circulation model for exoplanetary science) with parametrization for
cloud formation, and 2) high-resolution (~ 14 km-mesh) with an explicit cloud
microphysics scheme. Results suggest that high-resolution simulations with an
explicit treatment of cloud microphysics reveal warmer climates due to less low
cloud fraction and a large amount of water vapor in the atmosphere. It implies
that treatments of cloud-related processes lead to a difference between
different resolutions in climatic regimes in cases with high obliquities.


  3D ultrasound (US) can facilitate detailed prenatal examinations for fetal
growth monitoring. To analyze a 3D US volume, it is fundamental to identify
anatomical landmarks of the evaluated organs accurately. Typical deep learning
methods usually regress the coordinates directly or involve heatmap-matching.
However, these methods struggle to deal with volumes with large sizes and the
highly-varying positions and orientations of fetuses. In this work, we exploit
an object detection framework to detect landmarks in 3D fetal facial US
volumes. By regressing multiple parameters of the landmark-centered bounding
box (B-box) with a strict criteria, the proposed model is able to pinpoint the
exact location of the targeted landmarks. Specifically, the model uses a 3D
region proposal network (RPN) to generate 3D candidate regions, followed by
several 3D classification branches to select the best candidate. It also adopts
an IoU-balance loss to improve communications between branches that benefits
the learning process. Furthermore, it leverages a distance-based graph prior to
regularize the training and helps to reduce false positive predictions. The
performance of the proposed framework is evaluated on a 3D US dataset to detect
five key fetal facial landmarks. Results showed the proposed method outperforms
some of the state-of-the-art methods in efficacy and efficiency.


  Production of high-energy muons in cosmic-ray air showers, relevant for
underground detectors, depends on the properties of the primary cosmic ray as
well as the atmospheric temperature through the competition between decay and
re-interaction of charged pions and kaons. We present a parameterization of
muon production profiles based on simulations as a function of the primary
cosmic-ray energy, mass and zenith angle, the minimum energy for a muon to
reach the detector and an atmospheric temperature profile. We illustrate how
this can be used to calculate muon bundle properties such as multiplicity and
transverse size and their seasonal variations in the context of underground
measurements in coincidence with a surface detector which fixes the primary
cosmic-ray energy.


  Maximum bound principle (MBP) is an important property for a large class of
semilinear parabolic equations, in the sense that the time-dependent solution
of the equation with appropriate initial and boundary conditions and nonlinear
operator preserves for all time a uniform pointwise bound in absolute value. It
has been a challenging problem on how to design unconditionally MBP-preserving
high-order accurate time-stepping schemes for these equations. In this paper,
we combine the integrating factor Runge-Kutta (IFRK) method with the linear
stabilization technique to develop a stabilized IFRK (sIFRK) method, and
successfully derive sufficient conditions for the proposed method to preserve
MBP unconditionally in the discrete setting. We then elaborate some sIFRK
schemes with up to the third-order accuracy, which are proven to be
unconditionally MBP-preserving by verifying these conditions. In addition, it
is shown that many classic strong stability-preserving sIFRK schemes do not
satisfy these conditions except the first-order one. Extensive numerical
experiments are also carried out to demonstrate the performance of the proposed
method.


  It has recently been suggested that the existence of bare strange stars is
incompatible with low scale gravity scenarios. It has been claimed that in such
models, high energy neutrinos incident on the surface of a bare strange star
would lead to catastrophic black hole growth. We point out that for the flat
large extra dimensional case, the parts of parameter space which give rise to
such growth are ruled out by other methods. We then go on to show in detail how
black holes evolve in the the Randall-Sundrum two brane scenario where the
extra dimensions are curved. We find that catastrophic black hole growth does
not occur in this situation either. We also present some general expressions
for the growth of five dimensional black holes in dense media.


  We study a braneworld model in which a phantom-like behaviour occurs with
only cold dark matter and a cosmological constant, due to a large distance
modification of gravity. With the addition of curvature, the geometrical tests
are not strict enough to rule out models in which gravity is modified
significantly on large scales. We show that this degeneracy in the parameter
space is broken by the structure formation tests, such as the integrated
Sachs-Wolfe effect, which can probe general relativity on large scales.


  We apply the vortex path model of critical currents to a comprehensive
analysis of contemporary data on defect-engineered superconductors, showing
that it provides a consistent and detailed interpretation of the experimental
data for a diverse range of materials. We address the question of whether
electron mass anisotropy plays a role of any consequence in determining the
form of this data and conclude that it does not. By abandoning this false
interpretation of the data, we are able to make significant progress in
understanding the real origin of the observed behavior. In particular, we are
able to explain a number of common features in the data including shoulders at
intermediate angles, a uniform response over a wide angular range and the
greater discrimination between individual defect populations at higher fields.
We also correct several misconceptions including the idea that a peak in the
angular dependence of the critical current is a necessary signature of strong
correlated pinning, and conversely that the existence of such a peak implies
the existence of correlated pinning aligned to the particular direction. The
consistency of the vortex path model with the principle of maximum entropy is
introduced.


  Thick-section plates made from a recently developed Al-Cu-Mg-Li alloy have
been evaluated to understand the influence of microstructure on the anisotropy
of tensile strengths after natural and artificial ageing treatment.
Pancake-shaped grains with a coarse substructure and strong crystallographic
texture with a beta-fibre orientation at the mid-thickness position are
observed. In addition, an inhomogeneous distribution of T1 precipitates through
the plate thickness has been revealed with the volume fraction of intragranular
precipitates ~ 40% higher at the plate centre than the thickness position.
Altogether these microstructural features contribute to the in-plane anisotropy
of tensile strengths that is ~ 5% higher at the mid-thickness position than the
1/4 thickness position. The variation of ageing-induced T1 precipitates through
the plate thickness further contributes to the through-thickness anisotropy
that is ~ 3% higher in T8 temper as compared to T3 temper.


  Using exact Bethe ansatz (BA) solutions, we show that a spin-down fermion
immersed into a fully polarized spin-up Fermi sea with a weak attraction is
dressed by the surrounding spin-up fermions to form the one-dimensional analog
of a polaron. As the attraction becomes strong, the spin-down fermion binds
with one spin-up fermion to form a tightly bound molecule. Throughout the whole
interaction regime, a crossover from the polaron to a molecule state is fully
demonstrated through exact results of the excitation spectrum, the effective
mass, binding energy and kinetic energy. Furthermore, a clear distinction
between the polaron and molecule is conceived by the probability distribution,
single particle reduced density matrix and density-density correlations, which
are calculated directly from the Bethe ansatz wave function. Such a
polaron-molecule crossover presents a universal nature of an impurity immersed
into a fermionic medium with an attraction in one dimension.