  The application of adiabatic protocols in quantum technologies is severely
limited by environmental sources of noise and decoherence. Shortcuts to
adiabaticity by counterdiabatic driving constitute a powerful alternative that
speed up time-evolution while mimicking adiabatic dynamics. Here we present the
first experimental implementation of counterdiabatic driving in a continuous
variable system, a shortcut to the adiabatic transport of a trapped ion in the
phase space. The resulting dynamics is equivalent to a "fast-motion video" of
the adiabatic trajectory. The robustness of this protocol is shown to surpass
that of competing schemes based on classical local controls and Fourier
optimization methods. Our results demonstrate that shortcuts to adiabaticity
provide a robust speedup of quantum protocols of wide applicability in quantum
technologies.


  Virtual memory has been a standard hardware feature for more than three
decades. At the price of increased hardware complexity, it has simplified
software and promised strong isolation among colocated processes. In modern
computing systems, however, the costs of virtual memory have increased
significantly. With large memory workloads, virtualized environments, data
center computing, and chips with multiple DMA devices, virtual memory can
degrade performance and increase power usage. We therefore explore the
implications of building applications and operating systems without relying on
hardware support for address translation. Primarily, we investigate the
implications of removing the abstraction of large contiguous memory segments.
Our experiments show that the overhead to remove this reliance is surprisingly
small for real programs. We expect this small overhead to be worth the benefit
of reducing the complexity and energy usage of address translation. In fact, in
some cases, performance can even improve when address translation is avoided.


  Under the Riemann Hypothesis, we show that as $t$ varies in $T\leq t \leq
2T$, the distribution of $\log|\zeta(1/2+it)|$ with respect to the measure
$|\zeta(1/2+it)|^2dt$ is approximately normal with mean $\log\log T$ and
variance $\frac{1}{2}\log\log T$.


  We analyze, on a random graph, a diffusive strategic dynamics with pairwise
interactions, where nor Glauber prescription, neither detailed balance hold. We
observe numerically that such a dynamics reaches a well defined steady state
that fulfills a shift property: the critical temperature of the canonical
ferromagnetic phase transition is higher with respect to the expected
equilibrium one, known both numerically via Glauber relaxation or Monte Carlo
simulations as well as analytically via cavity techniques or replica
approaches. We show how the relaxed states of this kind of dynamics can be
described by statistical mechanics equilibria of a diluted p-spin model, for a
suitable non-integer real p. Several implications from both theoretical physics
and quantitative sociology points of view are discussed.


  Social media has become a major information platform where people consume and
share news. However, it has also enabled the wide dissemination of false news,
i.e., news posts published on social media that are verifiably false, causing
significant negative effects on society. In order to help prevent further
propagation of false news on social media, we set up this competition to
motivate the development of automated real-time false news detection
approaches. Specifically, this competition includes three sub-tasks: false-news
text detection, false-news image detection and false-news multi-modal
detetcion, which aims to motivate participants to further explore the
efficiency of multiple modalities in detecting false news and reasonable fusion
approaches of multi-modal contents. To better support this competition, we also
construct and publicize a multi-modal data repository about False News on Weibo
Social platform(MCG-FNeWS}) to help evaluate the performance of different
approaches from participants.


  Star formation in strongly self-gravitating cloud cores should be similar at
all redshifts, forming single or multiple stars with a range of masses
determined by local magneto-hydrodynamics and gravity. The formation processes
for these cores, however, as well as their structures, temperatures, Mach
numbers, etc., and the boundedness and mass distribution functions of the
resulting stars, should depend on environment, as should the characteristic
mass, density, and column density at which cloud self-gravity dominates other
forces. Because the environments for high and low redshift star formation
differ significantly, we expect the resulting gas to stellar conversion details
to differ also. At high redshift, the universe is denser and more gas-rich, so
the active parts of galaxies are denser and more gas rich too, leading to
slightly shorter gas consumption timescales, higher cloud pressures, and
denser, more massive, bound stellar clusters at the high mass end. With shorter
consumption times corresponding to higher relative cosmic accretion rates, and
with the resulting higher star formation rates and their higher feedback
powers, the ISM has greater turbulent speeds relative to the rotation speeds,
thicker gas disks, and larger cloud and star complex sizes at the
characteristic Jeans length. The result is a more chaotic appearance at high
redshift, bridging the morphology gap between today's quiescent spirals and
today's major-mergers, with neither spiral nor major-merger processes actually
in play at that time. The result is also a thick disk at early times, and after
in-plane accretion from relatively large clump torques, a classical bulge.
Today's disks are thinner, and torque-driven accretion is slower outside of
inner barred regions. This paper reviews the basic processes involved with star
formation in order to illustrate its evolution over time and environment.


  Let $\{q_n\}_{n=0}^\infty\subset [0,1]$ satisfy $q_0=0$, $\sum_{n=0}^\infty
q_n=1$, and $\gcd\{n\geq 1\mid q_n\neq 0\}=1$. We consider the following
process: Let $x$ be a real number. We first set $x=0$. Then $x$ is increased by
$i$ with probability $q_i~(i=0,1,2,\cdots)$ every time. For $n\geq 0$, let
$p_n$ be the probability such that $x=n$ occurs, so we have $p_0=1$ and
$p_n=q_1p_{n-1}+q_2p_{n-2}+\cdots+q_np_0~(n\geq 1)$. In this setting, we have
$\lim_n p_n=1/\sum_{i=0}^\infty iq_i$, where we define $1/\sum_{i=0}^\infty
iq_i=0$ if $\sum_{i=0}^\infty iq_i=+\infty$. This result is known as (discrete
case of) Blackwell's renewal theorem. The proof of $\lim_n
p_n=1/\sum_{i=0}^\infty iq_i$ is not trivial, while the meaning of $\lim_n
p_n=1/\sum_{i=0}^\infty iq_i$ is clear since the expected value of increasing
number $i$ is $\sum_{i=0}^\infty iq_i$. Many proofs of this result have been
given. In this paper, we will also provide a proof of this result. The idea of
our proof is based on Fourier-analytic methods and Tauberian theorems for
almost convergent sequences, while we actually need only elementary analysis.


  Linear and nonlinear optical properties of low dimensional nanostructures
have attracted a large interest in the scientific community as tools to probe
the strong confinement of the electrons and for possible applications in
optoelectronic devices. In particular it has been shown that the linear optical
response of carbon nanotubes [Science 308, 838 (2005)] and graphene nanoribbons
[Nat. Comm. 5, 4253 (2014)] is dominated by bounded electron-hole pairs, the
excitons. The role of excitons in linear response has been widely studied, but
still little is known on their effect on nonlinear susceptibilities. Using a
recently developed methodology [Phys. Rev. B 88, 235113 (2013)] based on
well-established ab-initio many-body perturbation theory approaches, we find
that quasiparticle shifts and excitonic effects significantly modify the
third-harmonic generation in carbon nanotubes and graphene nanoribbons. For
both systems the net effect of many-body effects is to reduce the intensity of
the main peak in the independent particle spectrum and redistribute the
spectral weight among several excitonic resonances.


  Our experiments and three-dimensional molecular dynamics simulations of
particles confined to a vertical monolayer by closely spaced frictional walls
(sidewalls) yield velocity distributions with non-Gaussian tails and a peak
near zero velocity. Simulations with frictionless sidewalls are not peaked.
Thus interactions between particles and their container are an important
determinant of the shape of the distribution and should be considered when
evaluating experiments on a tightly constrained monolayer of particles.


  The behavior of accelerating Unruh-DeWitt detectors coupled with a spinor
field in (3+1)-dimensional spacetime is investigated. For a single point-like
detector with Gaussian switching function, the transition probability increases
with the acceleration and thus the antiUnruh effect effect cannot occur. Due to
the spinor structure of the Dirac field, UV divergences are encountered in the
calculation of the entanglement between the detectors. After introducing some
UV cutoff $\Lambda$, the logarithmic negativity of detectors is shown to behave
nonmonotonically with respect to the acceleration. Besides, the logarithmic
negativity increases with the cutoff $\Lambda$ and decreases with the distance
between the detectors. The mutual information between the two detectors is also
discussed.


  A free semigroupoid algebra is the weak operator topology closed algebra
generated by the left regular representation of a directed graph. We establish
lattice isomorphisms between ideals and invariant subspaces, and this leads to
a complete description of the weak operator topology closed ideal structure for
these algebras. We prove a distance formula to ideals, and this gives an
appropriate version of the Caratheodory interpolation theorem. Our analysis
rests on an investigation of predual properties, specifically the $A_n$
properties for linear functionals, together with a general Wold Decomposition
for $n$-tuples of partial isometries. A number of our proofs unify proofs for
subclasses appearing in the literature.


  Hybrid superconductor-semiconductor nanowires are predicted to undergo a
field-induced phase transition from a trivial to a topological superconductor,
marked by the closure and re-opening of the excitation gap, followed by the
emergence of Majorana bound states at the nanowire ends. Many local
density-of-states measurements have reported signatures of the topological
phase, however this interpretation has been challenged by alternative
explanations. Here, by measuring nonlocal conductance, we identify the closure
of the excitation gap in the bulk of the semiconductor before the emergence of
zero-bias peaks. This observation is inconsistent with scenarios where
zero-bias peaks occur due to end-states with a trivially gapped bulk, which
have been extensively considered in the theoretical and experimental
literature. We observe that after the gap closes, nonlocal signals fluctuate
strongly and persist irrespective of the presence of local-conductance
zero-bias peaks. Thus, our observations are also incompatible with a simple
picture of clean topological superconductivity. This work presents a new
experimental approach for probing the spatial extent of states in Majorana
wires, and reveals the presence of a regime with a continuum of spatially
extended states and uncorrelated zero-bias peaks.


  In the field of computational molecule generation, an essential task in the
discovery of new chemical compounds, fragment-based deep generative models are
a leading approach, consistently achieving state-of-the-art results in
molecular design benchmarks as of 2023. We present a detailed comparative
assessment of their architectures, highlighting their unique approaches to
molecular fragmentation and generative modeling. This review also includes
comparisons of output quality, generation speed, and the current limitations of
specific models. We also highlight promising avenues for future research that
could bridge fragment-based models to real-world applications.


  In recent years, there has been a remarkable development of simulation-based
inference (SBI) algorithms, and they have now been applied across a wide range
of astrophysical and cosmological analyses. There are a number of key
advantages to these methods, centred around the ability to perform scalable
statistical inference without an explicit likelihood. In this work, we propose
two technical building blocks to a specific sequential SBI algorithm, truncated
marginal neural ratio estimation (TMNRE). In particular, first we develop
autoregressive ratio estimation with the aim to robustly estimate correlated
high-dimensional posteriors. Secondly, we propose a slice-based nested sampling
algorithm to efficiently draw both posterior samples and constrained prior
samples from ratio estimators, the latter being instrumental for sequential
inference. To validate our implementation, we carry out inference tasks on
three concrete examples: a toy model of a multi-dimensional Gaussian, the
analysis of a stellar stream mock observation, and finally, a proof-of-concept
application to substructure searches in strong gravitational lensing. In
addition, we publicly release the code for both the autoregressive ratio
estimator and the slice sampler.


  Formal expressions are derived for the multipole expansion of the structure
functions of a general polarization observable of exclusive
electrodisintegration of the deuteron using a longitudinally polarized beam
and/or an oriented target. This allows one to exhibit explicitly the angular
dependence of the structure functions by expanding them in terms of the small
rotation matrices $d^j_{m'm}(\theta)$, whose coefficients are given in terms of
the electromagnetic multipole matrix elements. Furthermore, explicit
expressions for the coefficients of the angular distributions of the
differential cross section including multipoles up to $L_{max}=3$ are listed in
tabular form.


  Abridged abstract: We present HI synthesis observations of the weak blue
compact dwarf (BCD) galaxy NGC~2915. NGC~2915 has the HI properties of a late
type spiral galaxy (Sd - Sm), including a double horn global profile, and HI
spiral arms. The HI extends out to over five times the Holmberg radius. The
optical counterpart corresponds to a central HI bar. The rotation curve is
derived and fitted with a mass model consisting of a stellar disk, a neutral
gas disk, and a dark matter (DM) halo. The DM halo dominates at nearly all
radii. The total mass to blue light ratio is 76 within the last measured point,
making NGC~2915 one of the darkest disk galaxies known. The core of the DM halo
is unusually dense (rho_0 ~ 0.1, M_sun pc^{-3}) and compact (R_core approx 1
kpc). The neutral gas component, with mass M_g = 1.27x10^9 M_sun is probably
more massive than the stellar component. Split and broad HI lines (line of
sight velocity dispersion, sigma ~ 35 km/s) are seen in the central region.
Pressure support is probably significant, and it is not clear whether the core
is in equilibrium. Beyond the Holmberg radius, the average HI sigma is 8 km/s,
which is normal for disk galaxies. NGC~2915 does not obey the Tully-Fisher
(1977) relation, being underluminous by a factor of nine. A simple HI surface
density threshold of about 10^{21} cm^{-2} adequately describes the location of
current star formation. The HI properties are compared to "normal" disk
galaxies and other BCDS.


  Spin dynamics is calculated in the ferromagnetic (FM) state of the
generalized Kondo lattice model taking into account strong on-site correlations
between e_g electrons and antiferromagnetic (AFM) exchange among t_{2g} spins.
Our study suggests that competing FM double-exchange and AFM super-exchange
interaction lead to a rather nontrivial spin-wave spectrum. While spin
excitations have a conventional Dq^2 spectrum in the long-wavelength limit,
there is a strong deviation from the spin-wave spectrum of the isotropic
Heisenberg model close to the zone boundary. The relevance of our results to
the experimental data are discussed.


  We review the application of BRST and BRST-BV approaches to construct the
generic off-shell local Lorentz covariant cubic interaction vertices for
irreducible massless and massive higher integer spin fields (as the candidates
for massive particles in the Dark Matter problem) on $d$-dimensional Minkowski
spaces. It is shown that equivalence among two Lagrangian dynamics for the same
cubically interacting fields with given masses and spins obtained by means of
the approach with complete BRST, $Q$, operator and of one with incomplete BRST,
$Q_c$, operator in presence of consistent off-shell holonomic (traceless)
constraints can be uplifted from the equivalence of the Lagrangians for free
higher spin fields. We found that to get non-contradictory Lagrangians for
irreducible interacting higher-spin fields within approach with $Q_c$ operator,
together with off-shell algebraic constraints in addition to necessary
condition of superconmmuting of $Q_c$ with appropriate holonomic constraints on
the field and gauge parameter vectors, these constraints should form Abelian
superalgebra both with BRST operator above and with operators of cubic
vertices.


  The strongest constraint on Vtb presently comes from the 3 x 3 unitarity of
the CKM matrix, which fixes Vtb to be very close to one. If the unitarity is
relaxed, current information from top production at Tevatron still leaves open
the possibility that Vtb is sizably smaller than one. In minimal extensions of
the standard model with extra heavy quarks, the unitarity constraints are much
weaker and the EW precision parameters entail the strongest bounds on Vtb. We
discuss the experimental perspectives of discovering and identifying such new
physics models at the Tevatron and the LHC, through a precise measurement of
Vtb from the single top cross sections and by the study of processes where the
extra heavy quarks are produced.


  Recent interpretability methods propose using concept-based explanations to
translate the internal representations of deep learning models into a language
that humans are familiar with: concepts. This requires understanding which
concepts are present in the representation space of a neural network. One
popular method for finding concepts is Concept Activation Vectors (CAVs), which
are learnt using a probe dataset of concept exemplars. In this work, we
investigate three properties of CAVs. CAVs may be: (1) inconsistent between
layers, (2) entangled with different concepts, and (3) spatially dependent.
Each property provides both challenges and opportunities in interpreting
models. We introduce tools designed to detect the presence of these properties,
provide insight into how they affect the derived explanations, and provide
recommendations to minimise their impact. Understanding these properties can be
used to our advantage. For example, we introduce spatially dependent CAVs to
test if a model is translation invariant with respect to a specific concept and
class. Our experiments are performed on ImageNet and a new synthetic dataset,
Elements. Elements is designed to capture a known ground truth relationship
between concepts and classes. We release this dataset to facilitate further
research in understanding and evaluating interpretability methods.


  Medical Image-to-image translation is a key task in computer vision and
generative artificial intelligence, and it is highly applicable to medical
image analysis. GAN-based methods are the mainstream image translation methods,
but they often ignore the variation and distribution of images in the frequency
domain, or only take simple measures to align high-frequency information, which
can lead to distortion and low quality of the generated images. To solve these
problems, we propose a novel method called frequency domain decomposition
translation (FDDT). This method decomposes the original image into a
high-frequency component and a low-frequency component, with the high-frequency
component containing the details and identity information, and the
low-frequency component containing the style information. Next, the
high-frequency and low-frequency components of the transformed image are
aligned with the transformed results of the high-frequency and low-frequency
components of the original image in the same frequency band in the spatial
domain, thus preserving the identity information of the image while destroying
as little stylistic information of the image as possible. We conduct extensive
experiments on MRI images and natural images with FDDT and several mainstream
baseline models, and we use four evaluation metrics to assess the quality of
the generated images. Compared with the baseline models, optimally, FDDT can
reduce Fr\'echet inception distance by up to 24.4%, structural similarity by up
to 4.4%, peak signal-to-noise ratio by up to 5.8%, and mean squared error by up
to 31%. Compared with the previous method, optimally, FDDT can reduce Fr\'echet
inception distance by up to 23.7%, structural similarity by up to 1.8%, peak
signal-to-noise ratio by up to 6.8%, and mean squared error by up to 31.6%.


  A partial semigroup is a set with restricted binary operation. In this work
we will extend a result due to V. Bergelson and N. Hindman concerning the rich
structure presented in the product space of semigroups to partial semigroup. An
$IP^{\star}$ set in a semigroup is a set that intersect every set of the form
$\left\{ FS(x_{n})_{n=1}^{\infty}:x_{n}\in S\right\} $. V. Bergelson and N.
Hindman proved that if $S_{1},S_{2},\ldots,S_{l}$ are finite collection of
commutative semigroup, then under certain condition, an $IP^{\star}$ set in
$S_{1}\times S_{2}\times\ldots\times S_{l}$ contains cartesian products of
arbitrarily large finite substructures of the form
$FS\left(x_{1,n}\right)_{n=1}^{\infty}\times
FS\left(x_{2,n}\right)_{n=1}^{\infty}\times\ldots\times
FS\left(x_{l,n}\right)_{n=1}^{\infty}$. In this work we will extend this result
to countable adequate commutative partial semigroup.


  We study the effect of a small density $n_v$ of quenched non-magnetic
impurities, i.e. vacancy disorder, in gapped short-range resonating valence
bond (RVB) spin liquid states and valence bond solid (VBS) states of quantum
magnets. We argue that a large class of short-range RVB liquids are stable at
small $n_v$ on the kagome lattice, while the corresponding states on
triangular, square, and honeycomb lattices are unstable at any nonzero $n_v$
due to the presence of emergent vacancy-induced local moments. In contrast, VBS
states are argued to be generically unstable (independent of lattice geometry)
at nonzero $n_v$ due to such a local-moment instability. Our arguments rely in
part on an analysis of the statistical mechanics of maximally-packed dimer
covers of the diluted lattice, and are fully supported by our computational
results on $O(N)$ symmetric designer Hamiltonians.


  We study the breaking of parity in the spin-boson model and demonstrate
unique scaling behavior of the magnetization and entanglement around the
critical points for the parity breaking after suppressing the infrared
divergence existing inherently in the spectral functions for Ohmic and
sub-Ohmic dissipations. Our treatment is basically analytical and of generality
for all types of the bath. We argue that the conventionally employed spectral
function is not fully reasonable and the previous justification of quantum
phase transition for localization needs to be more seriously reexamined.


  We propose a solution to the `cuspy-core' problem by extending the geodesic
equations of motion using the Dark Energy length scale
$\lambda_{DE}=c/(\Lambda_{DE} G)^{1/2}$. This extension does not affect the
motion of photons; gravitational lensing is unchanged. A cosmological check of
the theory is made, and $\sigma_8$ is calculated to be $0.68_{\pm0.11}$,
compared to $0.761_{-0.048}^{+0.049}$ for WMAP. We estimate the fractional
density of matter that cannot be determined through gravity at $0.197_{\pm
0.017}$, compared to $0.196^{+0.025}_{-0.026}$, the fractional density of
nonbaryonic matter. The fractional density of matter that can be determined
through gravity is estimated at $0.041_{-0.031}^{+0.030}$, compared to
$0.0416_{-0.0039}^{+0.0038}$ for $\Omega_B$.


  It has been shown that a water drop can bounce persistently, when thrown on a
super-hydrophobic substrate. We present here scaling arguments which allow us
to predict the maximal deformation and the contact time of the drop. This
approach is completed by a model describing the flow inside the drop, and by
original experimental data.


  Invertible maps from operators of quantum obvservables onto functions of
c-number arguments and their associative products are first assessed. Different
types of maps like Weyl-Wigner-Stratonovich map and s-ordered quasidistribution
are discussed. The recently introduced symplectic tomography map of observables
(tomograms) related to the Heisenberg-Weyl group is shown to belong to the
standard framework of the maps from quantum observables onto the c-number
functions. The star-product for symbols of the quantum-observable for each one
of the maps (including the tomographic map) and explicit relations among
different star-products are obtained. Deformations of the Moyal star-product
and alternative commutation relations are also considered.


  The linear absorption spectra in monolayers of transition metal
dichalcogenides show pronounced signatures of the exceptionally strong
exciton-phonon interaction in these materials. To account for both exciton and
phonon physics in such optical signals, we compare different theoretical
methods to calculate the absorption spectra using the example of
$\mathrm{MoSe_2}$. In this paper, we derive the equations of motion for the
polarization either using a correlation expansion up to 4th Born approximation
or a time convolutionless master equation. We show that the Born approximation
might become problematic when not treated in high enough order, especially at
high temperatures. In contrast, the time convolutionless formulation gives
surprisingly good results despite its simplicity when compared to higher-order
corrrelation expansion and therefore provides a powerful tool to calculate the
lineshape of linear absorption spectra in the very popular monolayer materials.


  The precise origins of the millisecond radio pulsars, discovered in the early
1980s, remain uncertain until this day. They plausibly evolve from accreting
low magnetic-field neutron stars in X-ray binary systems. If so, these stars
should spin at millisecond rates. In accordance with this idea, quasi-periodic
oscillations discovered in X-ray binaries around 50 Hz and 1 kHz, and drifting
oscillations at several 100 Hz in X-ray bursts have all been interpreted in
terms of millisecond spins of weakly magnetized neutron stars. However, in 15
years of searching, the expected coherent millisecond signals from X-ray
binaries remained elusive. In this Letter, we report the discovery of the first
example of such a signal. Using the Rossi X-ray Timing Explorer we find
persistent 2.49 millisecond X-ray pulsations in an X-ray binary, which we
interpret to come from an accretion-powered millisecond X-ray pulsar in the
system. This is the first known object of its kind. It is likely to switch on
as a millisecond radio pulsar when the accretion turns off completely. The
source is positionally coincident with the known transient X-ray burster SAX
J1808.4-3658, which also makes this the first X-ray pulsar which exhibits
thermonuclear X-ray bursts.


  In these proceedings we report our progress in the development of the
publicly available C++ library NJet for accurate calculations of
high-multiplicity one-loop amplitudes. As a phenomenological application we
present the first complete next-to-leading order (NLO) calculation of five jet
cross section at hadron colliders.


  Due to the large number of submissions that more and more conferences
experience, finding an automatized way to well distribute the submitted papers
among reviewers has become necessary. We model the peer-reviewing matching
problem as a {\it bilevel programming (BP)} formulation. Our model consists of
a lower-level problem describing the reviewers' perspective and an upper-level
problem describing the editors'. Every reviewer is interested in minimizing
their overall effort, while the editors are interested in finding an allocation
that maximizes the quality of the reviews and follows the reviewers'
preferences the most. To the best of our knowledge, the proposed model is the
first one that formulates the peer-reviewing matching problem by considering
two objective functions, one to describe the reviewers' viewpoint and the other
to describe the editors' viewpoint. We demonstrate that both the upper-level
and lower-level problems are feasible and that our BP model admits a solution
under mild assumptions. After studying the properties of the solutions, we
propose a heuristic to solve our model and compare its performance with the
relevant state-of-the-art methods. Extensive numerical results show that our
approach can find fairer solutions with competitive quality and less effort
from the reviewers.


  We detail a technique to produce actuators able to bear large strain and
respond to an external magnetic field. The material used is PDMS reinforced
with nickel coated carbon fibres. Thanks to the nickel functionalisation, the
fibre orientation can be achieved by embedding the viscous solution into a low
external magnetic field ($<0.2$~T). It is shown that both mechanical and
magnetic properties can be controlled by tailoring the material anisotropy
through properly orientating the reinforcing fibres in the pre-curing phase.
The large strain behaviour is investigated by tensile testing up to 60 % of
deformation and shows a strong dependence on the fibre orientation. The
magnetic properties are investigated by placing beam-like specimens into a
uniform magnetic field. The results show a multistable behaviour with a
transition from a bending-only deformed configuration for the 0$^\circ$ fibres
specimen, to a twisting only configuration, achieved for fibres at 90$^\circ$
whereas all the intermediate angles show both bending and twisting. This
behaviour is accurately captured by the large rotations beam model introduced.
Such an actuator can be used in all applications which require fast response
times and large strain.


  We present Atacama Large Millimeter/submillimeter Array (ALMA) observations
of GSC 6214-210 A and B, a solar-mass member of the 5-10 Myr Upper Scorpius
association with a 15 $\pm$ 2 Mjup companion orbiting at $\approx$330 AU
(2.2"). Previous photometry and spectroscopy spanning 0.3-5 $\mu$m revealed
optical and thermal excess as well as strong H$\alpha$ and Pa~$\beta$ emission
originating from a circum-substellar accretion disk around GSC 6214-210 B,
making it the lowest mass companion with unambiguous evidence of a subdisk.
Despite ALMA's unprecedented sensitivity and angular resolution, neither
component was detected in our 880 $\mu$m (341 GHz) continuum observations down
to a 3-$\sigma$ limit of 0.22 mJy/beam. The corresponding constraints on the
dust mass and total mass are <0.15 Mearth and <0.05 Mjup, respectively, or
<0.003% and <0.3% of the mass of GSC 6214-210 B itself assuming a 100:1
gas-to-dust ratio and characteristic dust temperature of 10-20 K. If the host
star possesses a putative circum-stellar disk then at most it is a meager
0.0015% of the primary mass, implying that giant planet formation has certainly
ceased in this system. Considering these limits and its current accretion rate,
GSC 6214-210 B appears to be at the end stages of assembly and is not expected
to gain any appreciable mass over the next few Myr.


  Flow instabilities are widely studied because of their economical and
theoretical interest, however few results have been published about the polymer
electrification during the extrusion. Nevertheless the generation of the
electrical charges is characteristic of the interaction between the polymer
melt and the die walls. In our study, the capillary extrusion of a metallocene
polyethylene (mPE) through a tungsten carbide die is characterized through
accurate electrical measurements thanks a Faraday pail. No significant charges
are observed since the extrudate surface remains smooth. However, as soon as
the sharkskin distortion appears, measurable charges are collected (around 5
10-8 C/m2). Higher level of charges are measured during the spurt or the
gross-melt fracture (g.m.f) defects. This work is focused on the electrical
charging during the sharkskin instability. The variation of the electrical
charges versus the apparent wall shear stress is investigated for different die
geometries. This curve exhibits a linear increase, followed by a sudden growth
just before the onset of the spurt instability. This abrupt charging
corresponds also to the end of the sharkskin instability. It is also well-known
that wall slip appears just at the same time, with smaller velocity values than
during spurt flow. Our results indicate that electrification could be a
signature of the wall slip. We show also that the electrification curves can be
shifted according to the time-temperature superposition principle, leading to
the conclusion that molecular features of the polymer are also involved in this
process.


  We show that the axiom of choice, a basic yet controversial postulate of set
theory, leads to revise the standard understanding of one of the pillars of our
best physical theories, namely the no-signaling principle. While it is well
known that probabilistic no-signaling resources (such as quantum non-locality)
are stronger than deterministic ones, we show-by invoking the axiom of
choice-the opposite: Functional (deterministic) no-signaling resources can be
stronger than probabilistic ones. To prove this, we devise a Bell-like game
that shows a systematic advantage of functional no-signaling with respect to
any probabilistic no-signaling resource.


  Recent progress on many imaging and vision tasks has been driven by the use
of deep feed-forward neural networks, which are trained by propagating
gradients of a loss defined on the final output, back through the network up to
the first layer that operates directly on the image. We propose
back-propagating one step further---to learn camera sensor designs jointly with
networks that carry out inference on the images they capture. In this paper, we
specifically consider the design and inference problems in a typical color
camera---where the sensor is able to measure only one color channel at each
pixel location, and computational inference is required to reconstruct a full
color image. We learn the camera sensor's color multiplexing pattern by
encoding it as layer whose learnable weights determine which color channel,
from among a fixed set, will be measured at each location. These weights are
jointly trained with those of a reconstruction network that operates on the
corresponding sensor measurements to produce a full color image. Our network
achieves significant improvements in accuracy over the traditional Bayer
pattern used in most color cameras. It automatically learns to employ a sparse
color measurement approach similar to that of a recent design, and moreover,
improves upon that design by learning an optimal layout for these measurements.


  In this paper we analyze the finite element approximation of the Stokes
equations with non-smooth Dirichlet boundary data. To define the discrete
solution, we first approximate the boundary datum by a smooth one and then
apply a standard finite element method to the regularized problem.
  We prove almost optimal order error estimates for two regularization
procedures in the case of general data in fractional order Sobolev spaces, and
for the Lagrange interpolation (with appropriate modifications at the
discontinuities) for piecewise smooth data. Our results apply in particular to
the classic lid-driven cavity problem improving the error estimates obtained in
[Z. Cai and Y. Wang, Math. Comp., 78(266):771-787, 2009].
  Finally, we introduce and analyze an a posteriori error estimator. We prove
its reliability and efficiency, and show some numerical examples which suggest
that optimal order of convergence is obtained by an adaptive procedure based on
our estimator.


  Functional brain dynamics is supported by parallel and overlapping functional
network modes that are associated with specific neural circuits. Decomposing
these network modes from fMRI data and finding their temporal characteristics
is challenging due to their time-varying nature and the non-linearity of the
functional dynamics. Dynamic Mode Decomposition (DMD) algorithms have been
quite popular for solving this decomposition problem in recent years. In this
work, we apply GraphDMD -- an extension of the DMD for network data -- to
extract the dynamic network modes and their temporal characteristics from the
fMRI time series in an interpretable manner. GraphDMD, however, regards the
underlying system as a linear dynamical system that is sub-optimal for
extracting the network modes from non-linear functional data. In this work, we
develop a generalized version of the GraphDMD algorithm -- DeepGraphDMD --
applicable to arbitrary non-linear graph dynamical systems. DeepGraphDMD is an
autoencoder-based deep learning model that learns Koopman eigenfunctions for
graph data and embeds the non-linear graph dynamics into a latent linear space.
We show the effectiveness of our method in both simulated data and the HCP
resting-state fMRI data. In the HCP data, DeepGraphDMD provides novel insights
into cognitive brain functions by discovering two major network modes related
to fluid and crystallized intelligence.


  Magnetars are conjectured to be highly magnetized neutron stars (NSs). Strong
internal magnetic field and elasticity in the crust may deform the stars and
lead to free precession. We study the precession dynamics of
triaxially-deformed NSs incorporating the near-field and the far-field
electromagnetic torques. We obtain timing residuals for different NS geometries
and torques. We also investigate the polarized X-ray and radio signals from
precessing magnetars. The modulations on the Stokes parameters are obtained for
thermal X-rays emitted from the surface of magnetars. For radio signals, we
apply the simple rotating vector model (RVM) to give the modulations on the
position angle (PA) of the polarization. Our results are comprehensive, ready
to be used to search for magnetar precession with timing data and polarizations
of X-ray and radio emissions. Future observations of precessing magnetars will
give us valuable information on the geometry and the strength of the strong
magnetic fields, the emission geometry, as well as the equation of state (EoS)
of NSs.


  Given a countable model of set theory, we study the structure of its generic
multiverse, the collection of its forcing extensions and ground models, ordered
by inclusion. Mostowski showed that any finite poset embeds into the generic
multiverse while preserving the nonexistence of upper bounds. We obtain several
improvements of his result, using what we call the blockchain construction to
build generic objects with varying degrees of mutual genericity. The method
accommodates certain infinite posets, and we can realize these embeddings via a
wide variety of forcing notions, while providing control over lower bounds as
well. We also give a generalization to class forcing in the context of
second-order set theory, and exhibit some further structure in the generic
multiverse, such as the existence of exact pairs.


  We study the refraction effect of electronic wave in hole-doped lateral
heterojunctions of metallic and semiconducting transition-metal dichalcogenide
monolayers. This effect is theoretically investigated in 2$H$-type
MoSe$_2$-NbS$_2$ and WSe$_2$-NbS$_2$ junctions by combining the
first-principles calculation and the lattice Green's function method. We show
that the electronic waves change the direction of motion at the interface and
collimate the velocity along two different directions depending on the spin. We
find that the transmission probability increases with the charge density and
that the direction of refracted electron beams is close to $\pm30^\circ$ with
respect to the perpendicular axis to the interface. The metallic
transition-metal dichalcogenide is essential for the refraction effect because
of the strong trigonal-warping effect, the large Fermi surface, and the
Zeeman-type spin-orbit coupling. The refraction effect enables to generate the
spin-polarized electronic current by using a simple fabrication of
transition-metal dichalcogenide monolayers.


  We obtain a CLT for $\log|\det(M_n-s_n)|$ where $M_n$ is a scaled Laguerre
$\beta$ ensemble and $s_n=d_++\sigma_n n^{-2/3}$ with $d_+$ denoting the upper
edge of the limiting spectrum of $M_n$ and $\sigma_n$ a slowly growing function
($\log\log^2 n\ll\sigma_n\ll\log^2 n$). In the special cases of LUE and LOE, we
prove that the CLT also holds for $\sigma_n$ of constant order. A similar
result was proved for Wigner matrices by Johnstone, Klochkov, Onatski, and
Pavlyshyn. Obtaining this type of CLT of Laguerre matrices is of interest for
statistical testing of critically spiked sample covariance matrices as well as
free energy of bipartite spherical spin glasses at critical temperature.


  We present FLUKA and MARS simulation studies of the pion production and
energy deposition in the Neutrino Factory baseline target station, which
consists of a 4 MW proton beam interacting with a liquid mercury jet target
within a 20 T solenoidal magnetic field. We show that a substantial increase in
the shielding is needed to protect the superconducting coils from too much
energy deposition. Investigations reveal that it is possible to reduce the
magnetic field in the solenoid capture system without adversely affecting the
pion production efficiency. We show estimates of the amount of concrete
shielding that will be required to protect the environment from the high
radiation doses generated by the target station facility. We also present yield
and energy deposition results for alternative targets: gallium liquid jet,
tungsten powder jet and solid tungsten bars.


  The formation of small droplets and bubbles in turbulent flows is a crucial
process in geophysics and engineering, whose underlying physical mechanism
remains a puzzle. In this letter, we address this problem by means of
high-resolution numerical simulations, comparing a realistic multiphase
configuration with a numerical experiment in which we attenuate the presence of
strong velocity gradients either across the whole mixture or in the disperse
phase only. Our results show unambiguously that the formation of small droplets
is governed by the internal dynamics which occur during the break-up of large
drops and that the high vorticity and the extreme dissipation associated to
these events are the consequence and not the cause of the breakup.


  Highly accurate methods such as coupled cluster (CC) techniques can be used
for periodic systems within the framework of the method of increments. Its
extension to low-dimensional conducting system is considered. To demonstrate
the presented approach a clean Mg (0001) surface is selected, where the CC
treatment with single and double excitations and perturbative triples is used
for calculation of the surface energy. Further example concerns the adsorption
energy of Xe on the metal surface. The obtained results can be used to verify
performance of the approximate methods. Along with the computational speed-up
at the high-level of accuracy, application of the method of increments provides
for a possibility to analyze influence of individual correlation-energy
increments on the studied property.


  After shortly reviewing the fundamentals of approach theory as introduced by
R. Lowen in 1989, we show that this theory is intimately related with the
well-known Wasserstein metric on the space of probability measures with a
finite first moment on a complete and separable metric space. More precisely,
we introduce a canonical approach structure, called the contractive approach
structure, and prove that it is metrized by the Wasserstein metric. The key
ingredients of the proof of this result are Dini's Theorem, Ascoli's Theorem,
and the fact that the class of real-valued contractions on a metric space has
some nice stability properties. We then combine the obtained result with
Prokhorov's Theorem to establish inequalities between the relative Hausdorff
measure of non-compactness for the Wasserstein metric and a canonical measure
of non-uniform integrability.


  We calculate the thermal diffusivity $D = \kappa/c_{\rho}$ and butterfly
velocity $v_B$ in holographic models that flow to AdS$_2 \times R^{d}$ fixed
points in the infra-red. We show that both these quantities are governed by the
same irrelevant deformation of AdS$_2$ and hence establish a simple
relationship between them. When this deformation corresponds to a universal
dilaton mode of dimension $\Delta = 2$ then this relationship is always given
by $D = v_B^2/(2 \pi T)$.


  The distorted spin-dependent spectral function of a nucleon inside an A=3
nucleus is introduced as a novel tool for investigating the polarized electron
scattering off polarized $^3$He in semi-inclusive DIS regime (SiDIS), going
beyond the standard plane wave impulse approximation. This distribution
function is applied to the study of the spectator SiDIS, $\vec{^3{\rm He}}(\vec
e, e' ~{^2}{\rm H})X$, in order to properly take into account the final state
interaction between the hadronizing quark and the detected deuteron, with the
final goal of a more reliable extraction of the polarized parton-distribution
$g_1(x)$ inside a bound proton. Our analysis allows to single out two
well-defined kinematical regions where the experimental asymmetries could yield
very interesting information: the region where the final state effects can be
minimized, and therefore the direct access to the parton distributions in the
proton is feasible, and the one where the final state interaction dominates,
and the spectator SiDIS reactions can elucidate the mechanism of the quark
hadronization itself. The perspectives of extending our approach i) to the
mirror nucleus, $^3$H, for achieving a less model-dependent flavor
decomposition, and ii) to the asymmetries measured in the standard SiDIS
reactions, $\vec e + \vec{^3 {\rm He}} \to e' + h+X$ with $h$ a detected fast
hadron, with the aim of extracting the neutron transversity, are discussed.


  Machine Learning (ML) and Algorithmic Information Theory (AIT) look at
Complexity from different points of view. We explore the interface between AIT
and Kernel Methods (that are prevalent in ML) by adopting an AIT perspective on
the problem of learning kernels from data, in kernel ridge regression, through
the method of Sparse Kernel Flows. In particular, by looking at the differences
and commonalities between Minimal Description Length (MDL) and Regularization
in Machine Learning (RML), we prove that the method of Sparse Kernel Flows is
the natural approach to adopt to learn kernels from data. This approach aligns
naturally with the MDL principle, offering a more robust theoretical basis than
the existing reliance on cross-validation. The study reveals that deriving
Sparse Kernel Flows does not require a statistical approach; instead, one can
directly engage with code-lengths and complexities, concepts central to AIT.
Thereby, this approach opens the door to reformulating algorithms in machine
learning using tools from AIT, with the aim of providing them a more solid
theoretical foundation.


  Discs of gas and dust around Myr-old stars are a by-product of the star
formation process and provide the raw material to form planets. Hence, their
evolution and dispersal directly impact what type of planets can form and
affect the final architecture of planetary systems. Here, we review empirical
constraints on disc evolution and dispersal with special emphasis on transition
discs, a subset of discs that appear to be caught in the act of clearing out
planet-forming material. Along with observations, we summarize theoretical
models that build our physical understanding of how discs evolve and disperse
and discuss their significance in the context of the formation and evolution of
planetary systems. By confronting theoretical predictions with observations, we
also identify the most promising areas for future progress.


  A highly accurate self-consistent particle code to simulate the beam-beam
collision in $e^+e^-$ storage rings has been developed. It adopts a method of
solving the Poisson equation with an open boundary. The method consists of two
steps: assigning the potential on a finite boundary using the Green's function,
and then solving the potential inside the boundary with a fast Poisson solver.
Since the solution of the Poisson's equation is unique, our solution is exactly
the same as the one obtained by simply using the Green's function. The method
allows us to select much smaller region of mesh and therefore increase the
resolution of the solver. The better resolution makes more accurate the
calculation of the dynamics in the core of the beams. The luminosity simulated
with this method agrees quantitatively with the measurement for the PEP-II
B-factory ring in the linear and nonlinear beam current regimes, demonstrating
its predictive capability in detail.


  Recent Transformer-based 3D object detectors learn point cloud features
either from point- or voxel-based representations. However, the former requires
time-consuming sampling while the latter introduces quantization errors. In
this paper, we present a novel Point-Voxel Transformer for single-stage 3D
detection (PVT-SSD) that takes advantage of these two representations.
Specifically, we first use voxel-based sparse convolutions for efficient
feature encoding. Then, we propose a Point-Voxel Transformer (PVT) module that
obtains long-range contexts in a cheap manner from voxels while attaining
accurate positions from points. The key to associating the two different
representations is our introduced input-dependent Query Initialization module,
which could efficiently generate reference points and content queries. Then,
PVT adaptively fuses long-range contextual and local geometric information
around reference points into content queries. Further, to quickly find the
neighboring points of reference points, we design the Virtual Range Image
module, which generalizes the native range image to multi-sensor and
multi-frame. The experiments on several autonomous driving benchmarks verify
the effectiveness and efficiency of the proposed method. Code will be available
at https://github.com/Nightmare-n/PVT-SSD.


  We examine the origins of the bimodality observed in the global properties of
galaxies by comparing the environmental dependencies of star-formation for
giant and dwarf galaxy populations. Using Sloan Digital Sky Survey (SDSS) DR4
spectroscopic data to create a volume-limited sample complete to M*+3, we find
that the environmental dependences of giant and dwarf galaxies are quite
different, implying fundamental differences in their evolution. Whereas the
star-formation histories of giant galaxies are determined primarily by their
merger history, resulting in passively-evolving giant galaxies being found in
all environments, we show that this is not the case for dwarf galaxies. In
particular, we find that old or passive dwarf galaxies are only found as
satellites within massive halos (clusters, groups or giant galaxies), with none
in the lowest density regions. This implies that star-formation in dwarf
galaxies must be much more resilient to the effects of mergers, and that the
evolution of dwarf galaxies is primarily driven by the mass of their host halo,
through effects such as suffocation, ram-pressure stripping or galaxy
harassment.


  Neural implicit functions have recently shown promising results on surface
reconstructions from multiple views. However, current methods still suffer from
excessive time complexity and poor robustness when reconstructing unbounded or
complex scenes. In this paper, we present RegSDF, which shows that proper point
cloud supervisions and geometry regularizations are sufficient to produce
high-quality and robust reconstruction results. Specifically, RegSDF takes an
additional oriented point cloud as input, and optimizes a signed distance field
and a surface light field within a differentiable rendering framework. We also
introduce the two critical regularizations for this optimization. The first one
is the Hessian regularization that smoothly diffuses the signed distance values
to the entire distance field given noisy and incomplete input. And the second
one is the minimal surface regularization that compactly interpolates and
extrapolates the missing geometry. Extensive experiments are conducted on DTU,
BlendedMVS, and Tanks and Temples datasets. Compared with recent neural surface
reconstruction approaches, RegSDF is able to reconstruct surfaces with fine
details even for open scenes with complex topologies and unstructured camera
trajectories.


  Despite excellent progress has been made, the performance on action
recognition still heavily relies on specific datasets, which are difficult to
extend new action classes due to labor-intensive labeling. Moreover, the high
diversity in Spatio-temporal appearance requires robust and representative
action feature aggregation and attention. To address the above issues, we focus
on atomic actions and propose a novel model for semi-supervised few-shot atomic
action recognition. Our model features unsupervised and contrastive video
embedding, loose action alignment, multi-head feature comparison, and
attention-based aggregation, together of which enables action recognition with
only a few training examples through extracting more representative features
and allowing flexibility in spatial and temporal alignment and variations in
the action. Experiments show that our model can attain high accuracy on
representative atomic action datasets outperforming their respective
state-of-the-art classification accuracy in full supervision setting.


  Correlations that signal direct CP violation in the bottom sector are
suggested. They need data on $Z\to b\bar b$ events which consist of a bottom
hadron decay into an exclusive semileptonic channel $h \ell \nu $ (where $h$ is
a hadron), and the other bottom hadron decay into a multi-body final state that
manifests as a jet in the opposite hemisphere.


  The subjects which are discussed in this Thesis include: the self energy of a
bound electron and the spin-dependence of QED corrections in bound systems,
convergence acceleration techniques, and resummation methods for divergent
series with an emphasis on physical applications.


  Scale-free networks, in which the distribution of the degrees obeys a
power-law, are ubiquitous in the study of complex systems. One basic network
property that relates to the structure of the links found is the degree
assortativity, which is a measure of the correlation between the degrees of the
nodes at the end of the links. Degree correlations are known to affect both the
structure of a network and the dynamics of the processes supported thereon,
including the resilience to damage, the spread of information and epidemics,
and the efficiency of defence mechanisms. Nonetheless, while many studies focus
on undirected scale-free networks, the interactions in real-world systems often
have a directionality. Here, we investigate the dependence of the degree
correlations on the power-law exponents in directed scale-free networks. To
perform our study, we consider the problem of building directed networks with a
prescribed degree distribution, providing a method for proper generation of
power-law-distributed directed degree sequences. Applying this new method, we
perform extensive numerical simulations, generating ensembles of directed
scale-free networks with exponents between~2 and~3, and measuring ensemble
averages of the Pearson correlation coefficients. Our results show that
scale-free networks are on average uncorrelated across directed links for three
of the four possible degree-degree correlations, namely in-degree to in-degree,
in-degree to out-degree, and out-degree to out-degree. However, they exhibit
anticorrelation between the number of outgoing connections and the number of
incoming ones. The findings are consistent with an entropic origin for the
observed disassortativity in biological and technological networks.


  Let $\Omega $ be a bounded domain of $\mathbb{R}^{N}(N\geq 2)$. We obtain a
necessary and a sufficient condition, expressed in terms of capacities, for
existence of a solution to the porous medium equation with absorption
\begin{equation*} \left\{ \begin{array}{l} {u_{t}}-{\Delta
}(|u|^{m-1}u)+|u|^{q-1}u=\mu ~ \text{in }\Omega \times (0,T), \\
{u}=0~~~\text{on }\partial \Omega \times (0,T), \\ u(0)=\sigma , \end{array}
\right. \end{equation*} where $\sigma$ and $\mu$ are bounded Radon measures,
$q>\max (m,1)$, $m>\frac{N-2}{N}$. We also obtain a sufficient condition for
existence of a solution to the $p$-Laplace evolution equation \begin{equation*}
\left\{ \begin{array}{l} {u_{t}}-{\Delta _{p}}u+|u|^{q-1}u=\mu ~~\text{in
}\Omega \times (0,T), \\ {u}=0 ~ \text{on }\partial \Omega \times (0,T), \\
u(0)=\sigma . \end{array} \right. \end{equation*} where $q>p-1$ and $p>2$.


  Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.


  Strangeness and multi-strangeness particles production can be used to explore
the initial transient field fluctuations in heavy ion collisions. We emphasize
the role played by Junction anti-Junction (JJbar) loops and strong color
electric fields (SCF) in these collisions. Transient field fluctuations of SCF
on the baryon production in central (0-5%) Pb+Pb collisions at nucleon-nucleon
centre of mass energy of 5.5 TeV will be discussed in the framework of
HIJING/BBbar v2.0 model, looking in particular to the predicted evolution of
nuclear modification factors R_AA from RHIC to LHC energies. Our results
indicate the importance of a good description of the baseline elementary p+p
collisions at this energy.


  While semantic communication succeeds in efficiently transmitting due to the
strong capability to extract the essential semantic information, it is still
far from the intelligent or human-like communications. In this paper, we
introduce an essential component, memory, into semantic communications to mimic
human communications. Particularly, we investigate a deep learning (DL) based
semantic communication system with memory, named Mem-DeepSC, by considering the
scenario question answer task. We exploit the universal Transformer based
transceiver to extract the semantic information and introduce the memory module
to process the context information. Moreover, we derive the relationship
between the length of semantic signal and the channel noise to validate the
possibility of dynamic transmission. Specially, we propose two dynamic
transmission methods to enhance the transmission reliability as well as to
reduce the communication overhead by masking some unessential elements, which
are recognized through training the model with mutual information. Numerical
results show that the proposed Mem-DeepSC is superior to benchmarks in terms of
answer accuracy and transmission efficiency, i.e., number of transmitted
symbols.


  We study a dipolar boson-fermion mixture in a pancake geometry at absolute
zero temperature, generalizing our previous work on the stability of polar
condensates and the formation of a density-wave state in cylindrical traps.
After examining the dependence of the polar condensate stability on the
strength of the fermion-induced interaction, we determine the transition point
from a ground-state Gaussian to a hexagonal density-wave state. We use a
variational principle to analyze the stability properties of those density-wave
state.


  The use of visual sensors is flourishing, driven among others by the several
applications in detection and prevention of crimes or dangerous events. While
the problem of optimal camera placement for total coverage has been solved for
a decade or so, that of the arrangement of cameras maximizing the recognition
of objects "in-transit" is still open. The objective of this paper is to attack
this problem by providing an adversarial method of proven optimality based on
the resolution of Hamilton-Jacobi equations. The problem is attacked by first
assuming the perspective of an adversary, i.e. computing explicitly the path
minimizing the probability of detection and the quality of reconstruction.
Building on this result, we introduce an optimality measure for camera
configurations and perform a simulated annealing algorithm to find the optimal
camera placement.


  Adaptive methods are a crucial component widely used for training generative
adversarial networks (GANs). While there has been some work to pinpoint the
"marginal value of adaptive methods" in standard tasks, it remains unclear why
they are still critical for GAN training. In this paper, we formally study how
adaptive methods help train GANs; inspired by the grafting method proposed in
arXiv:2002.11803 [cs.LG], we separate the magnitude and direction components of
the Adam updates, and graft them to the direction and magnitude of SGDA updates
respectively. By considering an update rule with the magnitude of the Adam
update and the normalized direction of SGD, we empirically show that the
adaptive magnitude of Adam is key for GAN training. This motivates us to have a
closer look at the class of normalized stochastic gradient descent ascent
(nSGDA) methods in the context of GAN training. We propose a synthetic
theoretical framework to compare the performance of nSGDA and SGDA for GAN
training with neural networks. We prove that in that setting, GANs trained with
nSGDA recover all the modes of the true distribution, whereas the same networks
trained with SGDA (and any learning rate configuration) suffer from mode
collapse. The critical insight in our analysis is that normalizing the
gradients forces the discriminator and generator to be updated at the same
pace. We also experimentally show that for several datasets, Adam's performance
can be recovered with nSGDA methods.


  We obtain analytical expressions for the electron self-energy and the
electron-phonon coupling in electron-doped graphene using electron-phonon
matrix elements extracted from density functional theory simulations. From the
electron self-energies we calculate angle resolved photoemission spectra. We
demonstrate that the measured kink at $\approx -0.2$ eV from the Fermi level is
actually composed of two features, one at $\approx -0.195$ eV due to the
twofold degenerate E$_{2g}$ mode, and a second one at $\approx -0.16$ eV due to
the A$_{1}^{'}$ mode. The electron-phonon coupling extracted from the kink
observed in ARPES experiments is roughly a factor of 5.5 larger than the
calculated one. This disagreement can only be partially reconciled by the
inclusion of resolution effects. Indeed we show that a finite resolution
increases the apparent electron-phonon coupling by underestimating the
renormalization of the electron velocity at energies larger than the kinks
positions. The discrepancy between theory and experiments is thus reduced to a
factor of $\approx$ 2.2. From the linewidth of the calculated ARPES spectra we
obtain the electron relaxation time. A comparison with available experimental
data in graphene shows that the electron relaxation time detected in ARPES is
almost two orders of magnitudes smaller than what measured by other
experimental techniques.


  Effective Field Theories (EFTs) constructed as derivative expansions in
powers of momentum, in the spirit of Chiral Perturbation Theory (ChPT), are a
controllable approximation to strong dynamics as long as the energy of the
interacting particles remains small, as they do not respect exact elastic
unitarity. This limits their predictive power towards new physics at a higher
scale if small separations from the Standard Model are found at the LHC or
elsewhere. Unitarized chiral perturbation theory techniques have been devised
to extend the reach of the EFT to regimes where partial waves are saturating
unitarity, but their uncertainties have hitherto not been addressed thoroughly.
Here we take one of the best known of them, the Inverse Amplitude Method (IAM),
and carefully following its derivation, we quantify the uncertainty introduced
at each step. We compare its hadron ChPT and its electroweak sector Higgs EFT
applications. We find that the relative theoretical uncertainty of the IAM at
the mass of the first resonance encountered in a partial-wave is of the same
order in the counting as the starting uncertainty of the EFT at near-threshold
energies, so that its unitarized extension should \textit{a priori} be expected
to be reasonably successful. This is so provided a check for zeroes of the
partial wave amplitude is carried out and, if they appear near the resonance
region, we show how to modify adequately the IAM to take them into account.


  Are P and NP provably inseparable ? Take a look at some unorthodox, guilty
mentioned folklore and related unpublished results.


  I review several topics involving CP violation with heavy hadrons. In
particular, I discuss (i) Hyperons: CP violation in the decay $\Lambda^0 \to p
\pi^-$, (ii) Charm: indirect CP violation in the D^0 system, both within and
beyond the SM, and (iii) Beauty: indirect CP violation in the neutral B-meson
system beyond the SM.
  Talk presented at the 2nd International Conference on Hyperons, Charm and
Beauty Hadrons, Montr\'eal, Canada, August 1996.


  In this paper the authors study quotients of the product of elliptic curves
by a rigid diagonal action of a finite group $G$. It is shown that only for $G
= \operatorname{He(3)}, \mathbb Z_3^2$, and only for dimension $\geq 4$ such an
action can be free. A complete classification of the singular quotients in
dimension 3 and the smooth quotients in dimension $4$ is given. For the other
finite groups a strong structure theorem for rigid quotients is proven.


  The study started in a former work about the Dilaton mean field stabilization
thanks to the effective potential generated by the existence of massive
fermions, is here extended. Three loop corrections are evaluated in addition to
the previously calculated two loop terms. The results indicate that the Dilaton
vacuum field tend to be fixed at a high value close to the Planck scale, in
accordance with the need for predicting Einstein gravity from string theory.
The mass of the Dilaton is evaluated to be also a high value close to the
Planck mass, which implies the absence of Dilaton scalar signals in modern
cosmological observations. These properties arise when the fermion mass is
chosen to be either at a lower bound corresponding to the top quark mass, or
alternatively, at a very much higher value assumed to be in the grand
unification energy range. One of the three 3-loop terms is exactly evaluated in
terms of Master integrals. The other two graphs are however evaluated in their
leading logarithm correction in the perturbative expansion. The calculation of
the non leading logarithmic contribution and the inclusion of higher loops
terms could made more precise the numerical estimates of the vacuum field value
and masses, but seemingly are expected not to change the qualitative behavior
obtained. The validity of the here employed Yukawa model approximation is
argued for small value of the fermion masses with respect to the Planck one. A
correction to the two loop calculation done in the previous work is here
underlined.


  A robust drift-safe rendezvous trajectory optimization tool is developed in
this work, with applications to orbital rendezvous and proximity operations.
The method is based on direct collocation and utilizes a sequential convex
programming framework, and is extended from previous work to include passive
safety constraints. The tool is then paired with a dispersion analysis
framework to allow trajectories to be optimized subject to plant, navigation,
and actuator uncertainties. The timing, direction, and magnitude of orbital
maneuvers are optimized subject to the expected propellant usage, for a given
navigation system performance. Representative trajectories are presented for
the LEO flight regime, but the approach can also be applied to GEO and NRHO
with minimal modification.


  In most of standard Finite Element (FE) codes it is not easy to calculate
dispersion relations from periodic materials. Here we propose a new strategy to
calculate such dispersion relations with available FE codes using user element
subroutines. Typically, the Bloch boundary conditions are applied to the global
assembled matrices of the structure through a transformation matrix or
row-and-column operations. Such a process is difficult to implement in standard
FE codes since the user does not have access to the global matrices. In this
work, we apply those Bloch boundary conditions directly at the elemental level.
The proposed strategy can be easily implemented in any FE code. This strategy
can be used either in real or complex algebra solvers. It is general enough to
permit any spatial dimension and physical phenomena involving periodic
structures. A detailed process of calculation and assembly of the elemental
matrices is shown. We verify our method with available analytical solutions and
external numerical results, using different material models and unit cell
geometries


  The transition of the binary gravitational lens from the equal mass case to
small (planetary) mass ratios q is studied. It is shown how the limit of a
(pure shear) Chang-Refsdal lens is approached, under what conditions the
Chang-Refsdal approximation is valid, and how the 3 different topologies of the
critical curves and caustics for a binary lens are mapped onto the 2 different
topologies for a Chang-Refsdal lens with pure shear. It is shown that for wide
binaries, the lensing in the vicinity of both lens objects can be described by
a Taylor-expansion of the deflection term due to the other object, where the
Chang-Refsdal approximation corresponds to a truncation of this series. For
close binaries, only the vicinity of the secondary, less massive, object can be
described in this way. However, for image distances much larger than the
separation of the lens objects, any binary lens can be approximated by means of
multipole expansion, where the first non-trivial term is the quadrupole term.
It is shown that an ambiguity exists between wide and close binary lenses,
where the shear at one of the objects due to the other object for the wide
binary is equal to the absolute value of the eigenvalues of the quadrupole
moment for the close binary. This analysis provides the basis for a
classification of binary lens microlensing events, especially of planetary
events, and an understanding of present ambiguities.


  There has been a lot of interest in understanding what information is
captured by hidden representations of language models (LMs). Typically,
interpretation methods i) do not guarantee that the model actually uses the
encoded information, and ii) do not discover small subsets of neurons
responsible for a considered phenomenon. Inspired by causal mediation analysis,
we propose a method that discovers within a neural LM a small subset of neurons
responsible for a particular linguistic phenomenon, i.e., subsets causing a
change in the corresponding token emission probabilities. We use a
differentiable relaxation to approximately search through the combinatorial
space. An $L_0$ regularization term ensures that the search converges to
discrete and sparse solutions. We apply our method to analyze subject-verb
number agreement and gender bias detection in LSTMs. We observe that it is fast
and finds better solutions than the alternative (REINFORCE). Our experiments
confirm that each of these phenomenons is mediated through a small subset of
neurons that do not play any other discernible role.


  Entanglement in the ground state of a many-body quantum system may arise when
the local terms in the system Hamiltonian fail to commute with the interaction
terms in the Hamiltonian. We quantify this phenomenon, demonstrating an analogy
between ground-state entanglement and the phenomenon of frustration in spin
systems. In particular, we prove that the amount of ground-state entanglement
is bounded above by a measure of the extent to which interactions frustrate the
local terms in the Hamiltonian. As a corollary, we show that the amount of
ground-state entanglement is bounded above by a ratio between parameters
characterizing the strength of interactions in the system, and the local energy
scale. Finally, we prove a qualitatively similar result for other energy
eigenstates of the system.


  One possible low energy process due to beyond the Standard Model (BSM)
physics is the neutron-antineutron transition, where baryon number changes by
two units. In addition to providing a source of baryon number violation in the
early universe, interactions of this kind are natural in grand unified theories
(GUTs) with Majorana neutrinos that violate lepton number. Bounds on these
oscillations can greatly restrict a variety of GUTs, while a non-zero signal
would be a "smoking gun" for new physics; however, to make a reliable
prediction, the six-quark nucleon-antinucleon matrix elements must first be
calculated non-perturbatively via lattice QCD. We review the current
understanding of this quantity, describe the lattice formalism, and present
preliminary results from $32^3\times256$ clover-Wilson lattices with a pion
mass of 390 MeV.


  Boundary element methods for elliptic partial differential equations
typically lead to boundary integral operators with translation-invariant kernel
functions. Taking advantage of this property is fairly simple for particle
methods, e.g., Nystrom-type discretizations, but more challenging if the
supports of basis functions have to be taken into account.
  In this article, we present a modified construction for
$\mathcal{H}^2$-matrices that uses translation-invariance to significantly
reduce the storage requirements. Due to the uniformity of the boxes used for
the construction, we need only a few uncomplicated assumptions to prove
estimates for the resulting storage complexity.


  Transport across heterogeneous, patchy environments is a ubiquitous
phenomenon spanning fields of study including ecological movement,
intracellular transport and regions of specialised function in a cell. These
regions or patches may be highly heterogeneous in their properties, and often
exhibit anomalous behaviour (resulting from e.g. crowding or viscoelastic
effects) which necessitates the inclusion of non-Markovian dynamics in their
study. However, many such processes are also subject to an internal
self-regulating or tempering process due to concurrent competing functions
being carried out. In this work we develop a model for anomalous transport
across a heterogeneous, patchy environment subject to tempering. We show that
in the long-time an equilibrium may be reached with constant effective
transport rates between the patches. This result is qualitatively different
from untempered systems where subdiffusion results in the long-time
accumulation of all particles in the patch with lowest anomalous exponent,
$0<\mu<1$.


  We explore the possible formation of ordered phases in neutron star matter.
In the framework of a quantum hadrodynamics model where neutrons, protons and
Lambda hyperons interact via the exchange of mesons, we compare the energy of
the usually assumed uniform, liquid phase, to that of a configuration in which
di-lambda pairs immersed in an uniform nucleon fluid are localized on the nodes
of a regular lattice. The confining potential is calculated self-consistently
as resulting from the combined action of the nucleon fluid and the other
hyperons, under the condition of beta equilibrium. We are able to obtain stable
ordered phases for some reasonable sets of values of the model parameters. This
could have important consequences on the structure and cooling of neutron
stars.


  We theoretically study the effects of trap-confinement and interatomic
interactions on Josephson oscillations (JO) and macroscopic quantum
self-trapping (MQST) for a Bose-Einstein condensate (BEC) confined in a trap
which has a symmetric double-well (DW) potential along z-axis and 2D harmonic
potentials along x- and y-axis. We consider three types of model interaction
potentials: contact, long-range dipolar and finite-range potentials. Our
results show that by changing the aspect ratio between the axial and radial
trap sizes, one can induce a transition from JO to MQST for contact
interactions with a small scattering length. For long-range dipolar interatomic
interactions, we analyze transition from Rabi to Josephson regime and Josephson
to MQST regime by changing the aspect ratio of the trap for a particular
dipolar orientation. For a finite-range interaction, we study the effects of
relatively large scattering length and effective range on JO and MQST. We show
that JO and MQST are possible even if scattering length is relatively large,
particularly near a narrow Feshbach resonance due to the finite-range effects.


  Understanding visual question answering is going to be crucial for numerous
human activities. However, it presents major challenges at the heart of the
artificial intelligence endeavor. This paper presents an update on the rapid
advancements in visual question answering using images that have occurred in
the last couple of years. Tremendous growth in research on improving visual
question answering system architecture has been published recently, showing the
importance of multimodal architectures. Several points on the benefits of
visual question answering are mentioned in the review paper by Manmadhan et al.
(2020), on which the present article builds, including subsequent updates in
the field.


  Machine learning algorithms have shown remarkable performance in diverse
applications. However, it is still challenging to guarantee performance in
distribution shifts when distributions of training and test datasets are
different. There have been several approaches to improve the performance in
distribution shift cases by learning invariant features across groups or
domains. However, we observe that the previous works only learn invariant
features partially. While the prior works focus on the limited invariant
features, we first raise the importance of the sufficient invariant features.
Since only training sets are given empirically, the learned partial invariant
features from training sets might not be present in the test sets under
distribution shift. Therefore, the performance improvement on distribution
shifts might be limited. In this paper, we argue that learning sufficient
invariant features from the training set is crucial for the distribution shift
case. Concretely, we newly observe the connection between a) sufficient
invariant features and b) flatness differences between groups or domains.
Moreover, we propose a new algorithm, Adaptive Sharpness-aware Group
Distributionally Robust Optimization (ASGDRO), to learn sufficient invariant
features across domains or groups. ASGDRO learns sufficient invariant features
by seeking common flat minima across all groups or domains. Therefore, ASGDRO
improves the performance on diverse distribution shift cases. Besides, we
provide a new simple dataset, Heterogeneous-CMNIST, to diagnose whether the
various algorithms learn sufficient invariant features.


  We prove lower bounds for the minimum distance of algebraic geometry codes
over surfaces whose canonical divisor is either nef or anti-strictly nef and
over surfaces without irreducible curves of small genus. We sharpen these lower
bounds for surfaces whose arithmetic Picard number equals one, surfaces without
curves with small self-intersection and fibered surfaces. Finally we specify
our bounds to the case of surfaces of degree $d\geq 3$ embedded in
$\mathbb{P)^3$.


  Language models such as GPT-3 have caused a furore in the research community.
Some studies found that GPT-3 has some creative abilities and makes mistakes
that are on par with human behaviour. This paper answers a related question:
Who is GPT-3? We administered two validated measurement tools to GPT-3 to
assess its personality, the values it holds and its self-reported demographics.
Our results show that GPT-3 scores similarly to human samples in terms of
personality and - when provided with a model response memory - in terms of the
values it holds. We provide the first evidence of psychological assessment of
the GPT-3 model and thereby add to our understanding of this language model. We
close with suggestions for future research that moves social science closer to
language models and vice versa.


  We investigate correlations between the optical linear polarization position
angle and the orientation of the host galaxy/extended emission of Type 1 and
Type 2 Radio-Loud (RL) and Radio-Quiet (RQ) quasars. We have used high
resolution Hubble Space Telescope (HST) data and deconvolution process to
obtain a good determination of the host galaxy orientation. With these new
measurements and a compilation of data from the literature, we find a
significant correlation between the polarization position angle and the
position angle of the major axis of the host galaxy/extended emission. The
correlation appears different for Type 1 and Type 2 objects and depends on the
redshift of the source. Interpretations in the framework of the unification
model are discussed.


  Inspired by the importance of inhibitory and excitatory couplings in the
brain, we analyze the largest eigenvalue statistics of random networks
incorporating such features. We find that the largest real part of eigenvalues
of a network, which accounts for the stability of an underlying system,
decreases linearly as a function of inhibitory connection probability up to a
particular threshold value, after which it exhibits rich behaviors with the
distribution manifesting generalized extreme value statistics. Fluctuations in
the largest eigenvalue remain somewhat robust against an increase in system
size but reflect a strong dependence on the number of connections, indicating
that systems having more interactions among its constituents are likely to be
more unstable.


  Network structures are extremely important to the study of political science.
Much of the data in its subfields are naturally represented as networks. This
includes trade, diplomatic and conflict relationships. The social structure of
several organization is also of interest to many researchers, such as the
affiliations of legislators or the relationships among terrorist. A key aspect
of studying social networks is understanding the evolutionary dynamics and the
mechanism by which these structures grow and change over time. While current
methods are well suited to describe static features of networks, they are less
capable of specifying models of change and simulating network evolution. In the
following paper I present a new method for modeling network growth and
evolution. This method relies on graph motifs to generate simulated network
data with particular structural characteristic. This technique departs notably
from current methods both in form and function. Rather than a closed-form
model, or stochastic implementation from a single class of graphs, the proposed
"graph motif model" provides a framework for building flexible and complex
models of network evolution. The paper proceeds as follows: first a brief
review of the current literature on network modeling is provided to place the
graph motif model in context. Next, the graph motif model is introduced, and a
simple example is provided. As a proof of concept, three classic random graph
models are recovered using the graph motif modeling method: the Erdos-Renyi
binomial random graph, the Watts-Strogatz "small world" model, and the
Barabasi-Albert preferential attachment model. In the final section I discuss
the results of these simulations and subsequent advantage and disadvantages
presented by using this technique to model social networks.


  We share the source code and a 121 GB dataset of 96 long-term N-body
simulations of the Solar System. This dataset can be analyzed by itself to
study the dynamics of the Solar System. In addition, our simulations can be the
starting point for future studies wishing to explore different initial
conditions or additional physical effects on the Solar System. Our simulations
can also be used as a comparison and benchmark for new numerical algorithms.


  Discrete-value time series are sequences of measurements where each
measurement is a discrete (categorical or integer) value. These time series are
widely used in various fields, and their classification and clustering are
essential for data analysis. This article presents the possibility of applying
diagnostic test methods to such time series and estimates the probability of
finding ``matching tests''.


  A thorough characterization of the {\gamma}, {\beta} and glass phases of
deuterated 1,1,2,2 Tetrachloroethane (C2D2Cl4) via Nuclear Quadrupole Resonance
and Molecular Dynamic Simulations (MDS) is reported. The presence of molecular
reorientations was experimentally observed in the glass phase and in the
{\beta} phase. In the {\beta} phase, and from MDS, these reorientations are
attributed to two possible movements, i.e. a $180^o$ reorientation around the
C2 molecular symmetry axis and a reorientation of the molecule between
non-equivalent positions. In the glass phase, the spin-lattice relaxation time
T1 is of the order of 16 times lower that T1 in the crystalline phase and
varies as $T^{-1}$ below 100 K in good agreement with the strong quadrupolar
relaxation observed in amorphous materials and in the glassy state of molecular
organic systems. The activation energy of molecular reorientations in the glass
phase (19 kJ/mol) is comparable to that observed in the glassy crystal of a
"molecular cousin" compound, Freon 112 (C2F2Cl4), for the secondary
{\beta}-relaxation. Moreover, the on-site orientational motion of
Tetrachloroethane molecules offers a new indirect evidence of the prominent
role of such orientational disorder in glassy dynamics.


  Textual data from financial filings, e.g., the Management's Discussion &
Analysis (MDA) section in Form 10-K, has been used to improve the prediction
accuracy of bankruptcy models. In practice, however, we cannot obtain the MDA
section for all public companies, which limits the use of MDA data in
traditional bankruptcy models, as they need complete data to make predictions.
The two main reasons for the lack of MDA are: (i) not all companies are obliged
to submit the MDA and (ii) technical problems arise when crawling and scrapping
the MDA section. To solve this limitation, this research introduces the
Conditional Multimodal Discriminative (CMMD) model that learns multimodal
representations that embed information from accounting, market, and textual
data modalities. The CMMD model needs a sample with all data modalities for
model training. At test time, the CMMD model only needs access to accounting
and market modalities to generate multimodal representations, which are further
used to make bankruptcy predictions and to generate words from the missing MDA
modality. With this novel methodology, it is realistic to use textual data in
bankruptcy prediction models, since accounting and market data are available
for all companies, unlike textual data. The empirical results of this research
show that if financial regulators, or investors, were to use traditional models
using MDA data, they would only be able to make predictions for 60% of the
companies. Furthermore, the classification performance of our proposed
methodology is superior to that of a large number of traditional classifier
models, taking into account all the companies in our sample.


  In this paper the strong metric dimension of generalized Petersen graphs
$GP(n,2)$ is considered. The exact value is determined for cases $n=4k$ and
$n=4k+2$, while for $n=4k+1$ an upper bound of the strong metric dimension is
presented.


  This paper presents a trajectory generation method for contact-constrained
robotic systems such as manipulators and legged robots. Contact-constrained
systems are affected by the interaction forces between the robot and the
environment. In turn, these forces determine and constrain state reachability
of the robot parts or end effectors. Our study subdivides the trajectory
generation problem and the supporting reachability analysis into tractable
subproblems consisting of a sampling problem, a convex optimization problem,
and a nonlinear programming problem. Our method leads to significant reduction
of computational cost. The proposed approach is validated using a realistic
simulated contact-constrained robotic system.


  In this work, we have obtained exact solutions of Einstein equations for
static and axially symmetric magnetized matter, specifically in plane-symmetric
and almost-plane symmetric cases. Although these solutions impose constraints
on the components of the energy-momentum tensor, some physically interesting
situations, like the magnetized vacuum, might be described. Plane-symmetric
solutions in presence of a non-vanishing cosmological constant have remarkable
features. In particular, the system can be driven continuously to the isotropic
case by an appropriate tuning of the cosmological constant, sweeping the
magnetic field from weak to strong magnetic field regimes. The role of the
cosmological constant in magnetic collapse is discussed with an emphasis in the
description of jets from compact objects. For illustrative purposes, specific
calculations for a magnetized electron gas have been developed.


  In this paper we discuss a closed-form approximation of the likelihood
functions of an arbitrary diffusion process. The approximation is based on an
exponential ansatz of the transition probability for a finite time step $\Delta
t$, and a series expansion of the deviation of its logarithm from that of a
Gaussian distribution. Through this procedure, dubbed {\em exponent expansion},
the transition probability is obtained as a power series in $\Delta t$. This
becomes asymptotically exact if an increasing number of terms is included, and
provides remarkably accurate results even when truncated to the first few (say
3) terms. The coefficients of such expansion can be determined
straightforwardly through a recursion, and involve simple one-dimensional
integrals.
  We present several examples of financial interest, and we compare our results
with the state-of-the-art approximation of discretely sampled diffusions
[A\"it-Sahalia, {\it Journal of Finance} {\bf 54}, 1361 (1999)]. We find that
the exponent expansion provides a similar accuracy in most of the cases, but a
better behavior in the low-volatility regime. Furthermore the implementation of
the present approach turns out to be simpler.
  Within the functional integration framework the exponent expansion allows one
to obtain remarkably good approximations of the pricing kernels of financial
derivatives. This is illustrated with the application to simple path-dependent
interest rate derivatives. Finally we discuss how these results can also be
used to increase the efficiency of numerical (both deterministic and
stochastic) approaches to derivative pricing.


  We consider the one-dimensional compressible Navier-Stokes system with
constant viscosity and the nonlinear heat conductivity being proportional to a
positive power of the temperature which may be degenerate. This problem is
imposed on the stress-free boundary condition, which reveals the motion of a
viscous heat-conducting perfect polytropic gas with adiabatic ends putting into
a vacuum. We prove that the solution of one dimensional compressible
Navier-Stokes system with the stress-free boundary condition shares the same
large-time behavior as the case of constant heat conductivity.


  We show that every even number $>\exp\exp 36$ can be represented as the sum
of a prime and a product of at most two primes.


  In the presence of D-branes, fermionic N=2 strings in 2+2 dimensions can be
coupled to a K"ahler NS-NS two-form B. We present the corresponding action
which produces N=2 supersymmetric boundary conditions and discuss the
Seiberg-Witten zero-slope limit. After recalling the constraints on the
Chan-Paton gauge group, we demonstrate for U(n) groups that the open N=2 string
with a nonzero B-field coincides on tree level with noncommutative self-dual
Yang-Mills. Several misconceptions of hep-th/0011206 are corrected.


  We derive new identities for the thermodynamic variables of five-dimensional,
asymptotically flat, stationary and biaxisymmetric vacuum black holes. These
identities depend on the topology of the solution and include contributions
arising from certain topological charges. The proof employs the harmonic map
formulation of the vacuum Einstein equations for solutions with these
symmetries.


  In this work we revisit the construction of theories for a massive vector
field with derivative self-interactions such that only the 3 desired
polarizations corresponding to a Proca field propagate. We start from the
decoupling limit by constructing healthy interactions containing second
derivatives of the Stueckelberg field with itself and also with the transverse
modes. The resulting interactions can then be straightforwardly generalized
beyond the decoupling limit. We then proceed to a systematic construction of
the interactions by using the Levi-Civita tensors. Both approaches lead to a
finite family of allowed derivative self-interactions for the Proca field. This
construction allows us to show that some higher order terms recently introduced
as new interactions trivialize in 4 dimensions by virtue of the Cayley-Hamilton
theorem. Moreover, we discuss how the resulting derivative interactions can be
written in a compact determinantal form, which can also be regarded as a
generalization of the Born-Infeld lagrangian for electromagnetism. Finally, we
generalize our results for a curved background and give the necessary
non-minimal couplings guaranteeing that no additional polarizations propagate
even in the presence of gravity.


  The Big Bang should have created equal amounts of matter and antimatter;
however today matter remains in the Universe. The European Spallation Source
neutrino Super Beam's (ESSnuSB) main objective is to demonstrate the
feasibility of using the European Spallation Source (ESS) proton linac to
produce the world's most intense neutrino beam simultaneously with the 5 MW
proton generation for neutron production and measure the parameters of the
neutrino oscillation, leading to the determination of the value of
${\delta}CP$. Once it is constructed ESSnuSB project aims to explain the
matter/antimatter asymmetry in the Universe. The Cost Action EuroNuNet's
(Combining forces for a novel European facility for neutrino-antineutrino
symmetry-violation discovery ) major goals are to bring together the European
neutrino physicists to study this concept in a spirit of inclusiveness and to
impact the priority list of High Energy Physics policy makers and of funding
agencies to this new approach to the experimental discovery of leptonic CP
violation.


  We provide analytic solutions for the power spectrum and bispectrum of
curvature fluctuations produced by a step feature in the inflaton potential,
valid in the limit that the step is short and sharp. In this limit, the
bispectrum is strongly scale dependent and its effective non-linearity attains
a large oscillatory amplitude. The perturbations to the curvature power
spectrum, on the other hand, remain a small component on top of the usual
spectrum of fluctuations generated by slow roll. We utilize our analytic
solutions to assess the observability of the predicted non-Gaussian signatures
and show that, if present, only very sharp steps on scales larger than ~ 2 Gpc
are likely to be able to be detected by Planck. Such features are not only
consistent with WMAP7 data, but can also improve its likelihood by 2 Delta ln L
~ 12 for two extra parameters, the step location and height. If this
improvement were due to a slow roll violating step as considered here, a
bispectrum or corresponding polarization power spectrum detection would provide
definitive checks as to its primordial origin.