  We report on the fabrication of conducting interfaces between LaAlO$_3$ and
SrTiO$_3$ by 90$^o$ off-axis sputtering in an Ar atmosphere. At a growth
pressure of 0.04 mbar the interface is metallic, with a carrier density of the
order of $10^{13}$ cm$^{-2}$ at 3 K. By increasing the growth pressure, we
observe an increase of the out-of-plane lattice constants of the LaAlO$_3$
films while the in-plane lattice constants do not change. Also, the
low-temperature sheet resistance increases with increasing growth pressure,
leading to an insulating interface when the growth pressure reaches 0.10 mbar.
We attribute the structural variations to an increase of the La/Al ratio, which
also explains the transition from metallic behavior to insulating behavior of
the interfaces. Our research emphasizes the key role of the cation
stoichiometry of LaAlO$_3$ in the formation of the conducting interface, and
also the control which is furnished by the Ar pressure in the growth process.


  In this paper, we present martingale decomposition on time scales. We
establish the related backward stochastic dynamic equations on time scales
(this paper BS$\nabla$E for short, concerning $\nabla$-integral on time scales)
which unify backward stochastic differential equations and backward stochastic
difference equations. We prove the existence and uniqueness theorem of
BS$\nabla$E. This work can be considered as a unification and a generalization
of similar results in backward stochastic difference equations and backward
stochastic differential equations.


  There was obtained a numerical external solution for the exact system of the
RTG equations with some natural boundary conditions in the static spherically
symmetric case. The properties of the solution are discussed.


  We show that the generic $1/f$ spectrum problem acquires a natural
explanation in a class of scale free solutions to the ordinary differential
equations. We prove the existence and uniqueness of this class of solutions and
show how this leads to a nonstandard, fuzzy extension of the ordinary framework
of calculus, and hence, that of the classical dynamics and quantum mechanics.
The exceptional role of the golden mean irrational number is also explained.


  Interactive Virtual Reality (VR) streaming over Wi-Fi networks encounters
significant challenges due to bandwidth fluctuations caused by channel
contention and user mobility. Adaptive BitRate (ABR) algorithms dynamically
adjust the video encoding bitrate based on the available network capacity,
aiming to maximize image quality while mitigating congestion and preserving the
user's Quality of Experience (QoE). In this paper, we experiment with ABR
algorithms for VR streaming using Air Light VR (ALVR), an open-source VR
streaming solution. We extend ALVR with a comprehensive set of metrics that
provide a robust characterization of the network's state, enabling more
informed bitrate adjustments. To demonstrate the utility of these performance
indicators, we develop and test the Network-aware Step-wise ABR algorithm for
VR streaming (NeSt-VR). Results validate the accuracy of the newly implemented
network performance metrics and demonstrate NeSt-VR's video bitrate adaptation
capabilities.


  We consider the problem of finding plausible knowledge that is missing from a
given ontology, as a generalisation of the well-studied taxonomy expansion
task. One line of work treats this task as a Natural Language Inference (NLI)
problem, thus relying on the knowledge captured by language models to identify
the missing knowledge. Another line of work uses concept embeddings to identify
what different concepts have in common, taking inspiration from cognitive
models for category based induction. These two approaches are intuitively
complementary, but their effectiveness has not yet been compared. In this
paper, we introduce a benchmark for evaluating ontology completion methods and
thoroughly analyse the strengths and weaknesses of both approaches. We find
that both approaches are indeed complementary, with hybrid strategies achieving
the best overall results. We also find that the task is highly challenging for
Large Language Models, even after fine-tuning.


  Chemisorption on ferromagnetic and non-magnetic surfaces is discussed within
the Newns-Anderson-Grimley model along with the Stoner model of ferromagnetism.
In the case of ferromagnetic surfaces, the adsorption energy is formulated in
terms of the change in surface magnetic moments. Using such a formulation, we
address the issue of how an adsorbate's binding strength depends on the
magnetic moments of the surface and how the adsorption process reduces/enhances
the magnetic moments of the surface. Our results indicates a possible
adsorption energy scaling relationship in terms surface magnetic moments. In
the case of non-magnetic surfaces, we formulate a modified stoner criterion and
discuss the condition for the appearance of magnetism due to chemisorption on
an otherwise non-magnetic surface.


  We prove that invariant subbundles of the Kontsevich-Zorich cocycle respect
the Hodge structure. In particular, we establish a version of Deligne
semisimplicity in this context. This implies that invariant subbundles must
vary polynomially on affine manifolds. All results apply to tensor powers of
the cocycle and this implies that the measurable and real-analytic algebraic
hulls coincide.
  We also prove that affine manifolds parametrize Jacobians with non-trivial
endomorphisms. Typically a factor has real multiplication.
  The tools involve curvature properties of the Hodge bundles and estimates
from random walks. In the appendix, we explain how methods from ergodic theory
imply some of the global consequences of Schmid's work on variations of Hodge
structures. We also derive the Kontsevich-Forni formula using differential
geometry.


  We study the stability of the antiferromagnetic all-in--all-out state
observed in dipolar-octupolar pyrochlores that have neodymium as the magnetic
species. Different types of disorder are considered, either affecting the
immediate environment of the Nd$^{3+}$ ion, or substituting it with a
non-magnetic ion. Starting from the well studied Nd$_2$Zr$_2$O$_7$ compound, Ti
substitution on the Zr site and dilution on the Nd magnetic site with La
substitution are investigated. The recently discovered entropy stabilized
compound NdMox, which exhibits a high degree of disorder on the non magnetic
site is also studied. Using a range of experimental techniques, especially very
low-temperature magnetization and neutron scattering, we show that the
all-in--all-out state is very robust and withstands substitutional disorder up
to large rates. From these measurements, we estimate the Hamiltonian parameters
and discuss their evolution in the framework of the phase diagram of
dipolar-octupolar pyrochlore magnets.


  We performed polarized Raman spectroscopy on mechanically exfoliated
few-layer MoTe2 samples and observed both 1T' and Td phases at room
temperature. Few-layer 1T' and Td MoTe2 exhibited a significant difference
especially in interlayer vibration modes, from which the interlayer coupling
strengths were extracted using the linear chain model: strong in-plane
anisotropy was observed in both phases. Furthermore, temperature-dependent
Raman measurements revealed a peculiar phase transition behavior in few-layer
1T' MoTe2. In contrast to bulk 1T' MoTe2 crystals where the phase transition to
the Td phase occurs at ~250 K, the temperature-driven phase transition to the
Td phase is increasingly suppressed as the thickness is reduced, and the
transition and the critical temperature varied dramatically from sample to
sample even for the same thickness. Raman spectra of intermediate phases that
correspond to neither 1T' nor Td phase with different interlayer vibration
modes were observed, which suggests that several metastable phases exist with
similar total energies.


  The purpose of this paper is two-fold: we present some matrix inequalities of
log-majorization type for eigenvalues indexed by a sequence; we then apply our
main theorem to generalize and improve the Hua-Marcus' inequalities. Our
results are stronger and more general than the existing ones.


  Asymptotic equivalence results for nonparametric regression experiments have
always assumed that the variances of the observations are known. In practice,
however the variance of each observation is generally considered to be an
unknown nuisance parameter. We establish an asymptotic approximation to the
nonparametric regression experiment when the value of the variance is an
additional parameter to be estimated or tested. This asymptotically equivalent
experiment has two components: the first contains all the information about the
variance and the second has all the information about the mean. The result can
be extended to regression problems where the variance varies slowly from
observation to observation.


  The advent of low field open magnetic resonance imaging (MRI) systems has
greatly expanded the accessibility of MRI technology to meet a wide range of
patient needs. However, the inherent challenges of low-field MRI, such as
limited signal-to-noise ratios and limited availability of dedicated RF coil,
have prompted the need for innovative coil designs that can improve imaging
quality and diagnostic capabilities. In response to these challenges, we
introduce the coupled stack-up volume coil, a novel RF coil design that
addresses the shortcomings of conventional birdcage in the context of low field
open MRI. The proposed coupled stack-up volume coil design utilizes a unique
architecture that optimizes both transmit/receive efficiency and RF field
homogeneity and offers the advantage of a simple design and construction,
making it a practical and feasible solution for low field MRI applications.
This paper presents a comprehensive exploration of the theoretical framework,
design considerations, and experimental validation of this innovative coil
design. Through rigorous analysis and empirical testing, we demonstrate the
superior performance of the coupled stack-up volume coil in achieving improved
transmit/receive efficiency and more uniform magnetic field distribution
compared to traditional birdcage coils.


  We theoretically show that an externally driven dipole placed inside a
cylindrical hollow waveguide can generate a train of ultrashort and
ultrafocused electromagnetic pulses. The waveguide encloses vacuum with perfect
electric conducting walls. A dipole driven by a single short pulse, which is
properly engineered to exploit the linear spectral filtering of the cylindrical
hollow waveguide, excites longitudinal waveguide modes that are coherently
re-focused at some particular instances of time. A dipole driven by a pulse
with a lower-bounded temporal width can thus generate, in principle, a finite
train of arbitrarily short and focused electromagnetic pulses. We numerically
show that such ultrafocused pulses persist outside the cylindrical waveguide at
distances comparable to its radius.


  We present new L' (3.8-micron) observations of stars in the nearby (~97 pc)
young (~6 Myr) compact cluster around eta Chamaeleontis, obtained with the
European Southern Observatory's Very Large Telescope in Paranal, Chile. Our
data, combined with J,H, Ks photometry from the 2-Micron All Sky Survey, reveal
that only two of the 12 members surveyed harbor L'-band excesses consistent
with optically thick inner disks; both are also likely accretors. Intriguingly,
two other stars with possible evidence for on-going accretion, albeit at very
low rates, do not show significant infrared excess: this may imply substantial
grain growth and/or partial clearing of the inner disk region, as expected in
planet formation scenarios. Our findings suggest that eta Cha stars are in an
epoch when disks are rapidly evolving, perhaps due to processes related to
planet building, and provide further constraints on inner disk lifetimes.


  We study the consistency of having Lorentz invariance as a low energy
approximation within the quantum field theory framework. A model with a scalar
and a fermion field is used to show how a Lorentz invariance violating high
momentum scale, a physical cutoff rendering the quantum field theory finite,
can be made compatible with a suppression of Lorentz invariance violations at
low momenta. The fine tuning required to get this suppression and to have a
light scalar particle in the spectrum is determined at one loop.


  Open-vocabulary object detection has benefited greatly from pretrained
vision-language models, but is still limited by the amount of available
detection training data. While detection training data can be expanded by using
Web image-text pairs as weak supervision, this has not been done at scales
comparable to image-level pretraining. Here, we scale up detection data with
self-training, which uses an existing detector to generate pseudo-box
annotations on image-text pairs. Major challenges in scaling self-training are
the choice of label space, pseudo-annotation filtering, and training
efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which
address these challenges. OWLv2 surpasses the performance of previous
state-of-the-art open-vocabulary detectors already at comparable training
scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples,
yielding further large improvement: With an L/14 architecture, OWL-ST improves
AP on LVIS rare classes, for which the model has seen no human box annotations,
from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale
training for open-world localization, similar to what has been seen for image
classification and language modelling.


  In this article, the minimum distance of the dual $C^{\bot}$ of a functional
code $C$ on an arbitrary dimensional variety $X$ over a finite field $\F_q$ is
studied. The approach consists in finding minimal configurations of points on
$X$ which are not in "general position". If $X$ is a curve, the result improves
in some situations the well-known Goppa designed distance.


  The n-type HgCr2Se4 exhibits a sharp semiconductor-to-metal transition (SMT)
in resistivity accompanying the ferromagnetic order at TC = 106 K. Here, we
investigate the effects of pressure and magnetic field on the concomitant SMT
and ferromagnetic order by measuring resistivity, dc and ac magnetic
susceptibility, as well as single-crystal neutron diffraction under various
pressures up to 8 GPa and magnetic fields up to 8 T. Our results demonstrate
that the ferromagnetic metallic ground state of n-type HgCr2Se4 is destabilized
and gradually replaced by an antiferromagnetic, most likely a spiral magnetic,
and insulating ground state upon the application of high pressure. On the other
hand, the application of external magnetic fields can restore the ferromagnetic
metallic state again at high pressures, resulting in a colossal
magnetoresistance (CMR) as high as ~ 3 * 10^11 % under 5 T and 2 K at 4 GPa.
The present study demonstrates that n-type HgCr2Se4 is located at a peculiar
critical point where the balance of competion between ferromagnetic and
antiferromagnetic interactions can be easily tipped by the external stimuli,
providing a new platform for achieving CMR in a single-valent system.


  We propose an efficient and model independent method for reconstructing the
primordial power spectrum from Cosmic Microwave Background (CMB) and large
scale structure observations. The algorithm is based on a Monte Carlo principle
and therefore very simple to incorporate into existing codes such as Markov
Chain Monte Carlo. The algorithm has been used on present cosmological data to
test for features in the primordial power spectrum. No significant evidence for
features is found, although there is a slight preference for an overall bending
of the spectrum, as well as a decrease in power at very large scales. We have
also tested the algorithm on mock high precision CMB data, calculated from
models with non-scale invariant primordial spectra. The algorithm efficiently
extracts the underlying spectrum, as well as the other cosmological parameters
in each case. Finally we have used the algorithm on a model where an artificial
glitch in the CMB spectrum has been imposed, like the ones seen in the WMAP
data. In this case it is found that, although the underlying cosmological
parameters can be extracted, the recovered power spectrum can show significant
spurious features, such as bending, even if the true spectrum is scale
invariant.


  We demonstrate that polarization measurements in exclusive semileptonic B
decays are powerful tools for unraveling non-standard model sources of
T-violation. Measurements of the transverse polarization of the tau lepton in
the B to D tau nu and B to D* tau nu decays probe separately effective scalar
and pseudoscalar CP-violating four-Fermi interactions, whereas the D*
polarization in the B to D* l nu (l=e,mu) decay is sensitive to an effective
right-handed quark current interaction. Two T-odd polarization structures exist
involving the D* polarization and they can be isolated and studied separately.
An estimate of these T-odd effects is also given in the context of
supersymmetric theories.


  The phase behavior of asymmetrically interacting ABC star triblock copolymer
melts is investigated by the self-consistent field theory (SCFT). Motivated by
the experimental systems, in this study, we focus on the systems in which the
Flory-Huggins interaction parameters satisfy $\chi_{AC}>\chi_{BC}\approx
\chi_{AB}$. Using various initialization strategies, a large number of periodic
structures have been obtained in our calculations. A fourth-order
pseudospectral algorithm combined with Anderson mixing method is used to
compute the free energy of candidate structures carefully. The stability has
been detailedly analyzed by splitting the free energy into internal and
entropic parts. A complete and complex triangular phase diagram is presented
for a model with $\chi_{AC}>\chi_{BC}= \chi_{AB}$ in which fifteen ordered
phases, including two-, and three-dimensional structures, have been predicted
to be stable from the SCFT calculations. Generally speaking, with the
asymmetrical interactions, the hierarchical structures tend to be formed near
the B-rich corner of the triangular phase diagram. This work broadens the
previous theoretical results from equal interaction systems to unequal
interaction systems. The predicted phase behavior is in good agreement with
experimental observations and previous theoretical results.


  Artificial intelligence (AI) represents a technological upheaval with the
potential to change human society. Because of its transformative potential, AI
is increasingly becoming subject to regulatory initiatives at the global level.
Yet, so far, scholarship in political science and international relations has
focused more on AI applications than on the emerging architecture of global AI
regulation. The purpose of this article is to outline an agenda for research
into the global governance of AI. The article distinguishes between two broad
perspectives: an empirical approach, aimed at mapping and explaining global AI
governance; and a normative approach, aimed at developing and applying
standards for appropriate global AI governance. The two approaches offer
questions, concepts, and theories that are helpful in gaining an understanding
of the emerging global governance of AI. Conversely, exploring AI as a
regulatory issue offers a critical opportunity to refine existing general
approaches to the study of global governance.


  We present a difference analogue of a result given by Hrushovski on
differential Galois groups under specialization. Let $k$ be an algebraically
closed field of characteristic zero and $\mathbb{X}$ an irreducible affine
algebraic variety over $k$. Consider the linear difference equation $$
  \sigma(Y)=AY $$ where $A\in \mathrm{GL}_n(k(\mathbb{X})(x))$ and $\sigma$ is
the shift operator $\sigma(x)=x+1$. Assume that the Galois group $G$ of the
above equation over $\overline{k(\mathbb{X})}(x)$ is defined over
$k(\mathbb{X})$ i.e. the vanishing ideal of $G$ is generated by a finite set
$S\subset k(\mathbb{X})[X,1/\det(X)]$. For a ${\bf c}\in \mathbb{X}$, denote by
$v_{\bf c}$ the map from $k[\mathbb{X}]$ to $k$ given by $v_{\bf c}(f)=f({\bf
c})$ for any $f\in k[\mathbb{X}]$. We prove that the set of ${\bf c}\in
\mathbb{X}$ satisfying that $v_{\bf c}(A)$ and $v_{\bf c}(S)$ are well-defined
and the affine variety in $\mathrm{GL}_n(k)$ defined by $v_{\bf c}(S)$ is the
Galois group of $\sigma(Y)=v_{\bf c}(A)Y$ over $k(x)$ is Zariski dense in
$\mathbb{X}$.
  We apply our result to van der Put-Singer's conjecture which asserts that an
algebraic subgroup $G$ of $\mathrm{GL}_n(k)$ is the Galois group of a linear
difference equation over $k(x)$ if and only if the quotient $G/G^\circ$ by the
identity component is cyclic. We show that if van der Put-Singer's conjecture
is true for $k=\mathbb{C}$ then it will be true for any algebraically closed
field $k$ of characteristic zero.


  The thermodynamics of a disordered planar vortex array is studied numerically
using a new polynomial algorithm which circumvents slow glassy dynamics. Close
to the glass transition, the anomalous vortex displacement is found to agree
well with the prediction of the renormalization-group theory. Interesting
behaviors such as the universal statistics of magnetic susceptibility
variations are observed in both the dense and dilute regimes of this mesoscopic
vortex system.


  We consider mechanisms of directed transport in a ratchet model comprising,
besides the external freedom where transport occurs, a chemical freedom that
replaces the familiar external driving by an autonomous dynamics providing
energy input, and an internal freedom representing a functional mode of a motor
molecule. The dependence of the current on various parameters is studied in
numerical simulations of our model. In particular, we point out the role of the
internal freedom as a buffer between energy input and output of mechanical work
that allows a temporary storage of injected energy and can contribute to the
efficiency of current generation.


  We calculate homological blocks for a knot in Seifert manifolds when the
gauge group is $SU(N)$. We obtain the homological blocks with a given
representation of the gauge group from the expectation value of the Wilson loop
operator by analytically continuing the Chern-Simons level. We also obtain
homological blocks with the analytically continued level and representation for
a knot in the Seifert integer homology spheres.


  Graph neural networks (GNNs) have gained significant popularity due to the
powerful capability to extract useful representations from graph data. As the
need for efficient GNN computation intensifies, a variety of programming
abstractions designed for optimizing GNN Aggregation have emerged to facilitate
acceleration. However, there is no comprehensive evaluation and analysis upon
existing abstractions, thus no clear consensus on which approach is better. In
this letter, we classify existing programming abstractions for GNN Aggregation
by the dimension of data organization and propagation method. By constructing
these abstractions on a state-of-the-art GNN library, we perform a thorough and
detailed characterization study to compare their performance and efficiency,
and provide several insights on future GNN acceleration based on our analysis.


  Measuring emotions is a real challenge for fundamental and applied research,
especially in ecological contexts. de Wijk and Noldus propose combining two
types of measures-explicit to characterize a specific food, and
implicit-physiological-to capture the whole experience of a meal in real-life
situations. This raises several challenges including development of new and
miniaturized sensors and devices but also developing new ways of data analysis.
We suggest a path to follow for future studies regarding data analysis: to
include Data Science in the game. This field of research may enable developing
predictive but also explicative models that link subjective experience of
emotions and physiological responses in real-life contexts. We suggest that
food scientists should go out of their comfort zone by collaborating with
computer scientists and then be trained with the new tools of Data Science,
which will undoubtedly enable them 1/ to better manage complex and
heterogeneous data sets, 2/ to extract knowledge that will be essential to this
field of research.


  We present one of the results of $BVRI$ photometry of the hosts of GRB for
the host galaxy of GRB 970508 and the theoretical modeling of its continuum
spectral energy distribution (SED) to show that it is important to take into
account internal extinction in the host galaxies. We compared the BVRI
broad-band flux spectrum of the host to template SEDs of local starburst
galaxies and found that there is a significant internal extintion in this host.
Moreover, this comparison allows us to derive the absolute magnitude
(M_{B_{rest}}) and rouhgly estimate reddening (A_V). Population synthesis
modeling of the continuum SED for different reddening laws demostrates that the
observational data of the host galaxy of GRB 970508 are best fitted by the
spectral properties of a model SED with extinction of A_V\approx 2.


  Tissues commonly consist of cells embedded within a fibrous biopolymer
network. Whereas cell-free reconstituted biopolymer networks typically soften
under applied uniaxial compression, various tissues, including liver, brain,
and fat, have been observed to instead stiffen when compressed. The mechanism
for this compression stiffening effect is not yet clear. Here, we demonstrate
that when a material composed of stiff inclusions embedded in a fibrous network
is compressed, heterogeneous rearrangement of the inclusions can induce tension
within the interstitial network, leading to a macroscopic crossover from an
initial bending-dominated softening regime to a stretching-dominated stiffening
regime, which occurs before and independently of jamming of the inclusions.
Using a coarse-grained particle-network model, we first establish a phase
diagram for compression-driven, stretching-dominated stress propagation and
jamming in uniaxially compressed 2- and 3-dimensional systems. Then, we
demonstrate that a more detailed computational model of stiff inclusions in a
subisostatic semiflexible fiber network exhibits quantitative agreement with
the predictions of our coarse-grained model as well as qualitative agreement
with experiments.


  Austrian Solitaire is a variation of Bulgarian Solitaire. It may be described
as a card game, a method of asset inventory management, or a discrete dynamical
system on integer partitions. We prove that the limit cycles in Austrian
Solitaire do not depend on the initial configuration; in other words, each
state space is connected. We show that a full Farey sequence completely
characterizes these unique (and balanced) cycles.


  We show that a newly proposed Shannon-like entropic measure of shape
complexity applicable to spatially-localized or periodic mathematical functions
known as configurational entropy (CE) can be used as a predictor of spontaneous
decay rates for one-electron atoms. The CE is constructed from the Fourier
transform of the atomic probability density. For the hydrogen atom with
degenerate states labeled with the principal quantum number n, we obtain a
scaling law relating the n-averaged decay rates to the respective CE. The
scaling law allows us to predict the n-averaged decay rate without relying on
the traditional computation of dipole matrix elements. We tested the predictive
power of our approach up to n=20, obtaining an accuracy better than 3.7% within
our numerical precision, as compared to spontaneous decay tables listed in the
literature.


  This work concerns the dynamical two-point spin correlation functions of the
transverse Ising quantum chain at finite (non-zero) temperature, in the
universal region near the quantum critical point. They are correlation
functions of twist fields in the massive Majorana fermion quantum field theory.
At finite temperature, these are known to satisfy a set of integrable partial
differential equations, including the sinh-Gordon equation. We apply the
classical inverse scattering method to study them, finding that the ``initial
scattering data'' corresponding to the correlation functions are simply related
to the one-particle finite-temperature form factors calculated recently by one
of the authors. The set of linear integral equations (Gelfand-Levitan-Marchenko
equations) associated to the inverse scattering problem then gives, in
principle, the two-point functions at all space and time separations, and all
temperatures. From them, we evaluate the large-time asymptotic expansion ``near
the light cone'', in the region where the difference between the space and time
separations is of the order of the correlation length.


  We consider the *adaptive influence maximization problem*: given a network
and a budget $k$, iteratively select $k$ seeds in the network to maximize the
expected number of adopters. In the *full-adoption feedback model*, after
selecting each seed, the seed-picker observes all the resulting adoptions. In
the *myopic feedback model*, the seed-picker only observes whether each
neighbor of the chosen seed adopts. Motivated by the extreme success of
greedy-based algorithms/heuristics for influence maximization, we propose the
concept of *greedy adaptivity gap*, which compares the performance of the
adaptive greedy algorithm to its non-adaptive counterpart. Our first result
shows that, for submodular influence maximization, the adaptive greedy
algorithm can perform up to a $(1-1/e)$-fraction worse than the non-adaptive
greedy algorithm, and that this ratio is tight. More specifically, on one side
we provide examples where the performance of the adaptive greedy algorithm is
only a $(1-1/e)$ fraction of the performance of the non-adaptive greedy
algorithm in four settings: for both feedback models and both the *independent
cascade model* and the *linear threshold model*. On the other side, we prove
that in any submodular cascade, the adaptive greedy algorithm always outputs a
$(1-1/e)$-approximation to the expected number of adoptions in the optimal
non-adaptive seed choice. Our second result shows that, for the general
submodular diffusion model with full-adoption feedback, the adaptive greedy
algorithm can outperform the non-adaptive greedy algorithm by an unbounded
factor. Finally, we propose a risk-free variant of the adaptive greedy
algorithm that always performs no worse than the non-adaptive greedy algorithm.


  We consider statistical patterns of variation in growth rates for over 400
species of breeding birds across North America surveyed from 1966 to 1998. We
report two results. First, the standard deviation of population growth rates
decays as a power-law function of total population size with an exponent $\beta
= 0.36\pm0.02$. Second, the number of subpopulations, measured as the number of
survey locations with non-zero counts, scales to the 3/4-power of total number
of birds counted in a given species. We show how these patterns may be related,
and discuss a simple stochastic growth model for a geographically subdivided
population that formalizes the relationship. We also examine reasons that may
explain why some species deviate from these scaling-laws.


  We calculate the ttbar forward-backward asymmetry, A_FB^t, in Randall-Sundrum
(RS) models taking into account the dominant next-to-leading order (NLO)
corrections in QCD. At Born level we include the exchange of Kaluza-Klein (KK)
gluons and photons, the Z boson and its KK excitations, as well as the Higgs
boson, whereas beyond the leading order (LO) we consider the interference of
tree-level KK-gluon exchange with one-loop QCD box diagrams and the
corresponding bremsstrahlungs corrections. We find that the strong suppression
of LO effects, that arises due to the elementary nature and the mostly
vector-like couplings of light quarks, is lifted at NLO after paying the price
of an additional factor of alpha_s/(4 pi). In spite of this enhancement, the
resulting RS corrections in A_FB^t remain marginal, leaving the predicted
asymmetry SM-like. As our arguments are solely based on the smallness of the
axial-vector couplings of light quarks to the strong sector, our findings are
model-independent and apply to many scenarios of new physics that address the
flavor problem via geometrical sequestering.


  The tomographic probability distribution on the phase space (cylinder)
related to a circle or an interval is introduced. The explicit relations of the
tomographic probability densities and the probability densities on the phase
space for the particle motion on a torus are obtained and the relation of the
suggested map to the Radon transform on the plane is elucidated. The
generalization to the case of a multidimensional torus is elaborated and the
geometrical meaning of the tomographic probability densities as marginal
distributions on the helix discussed.


  This is the second part of integrability analysis of cosmological models with
scalar fields. Here, we study systems with conformal coupling, and show that
apart from four cases, where explicit first integrals are known, the generic
system is not integrable. We also comment on some chaotic properties of the
system, and the issues of integrability restricted to the real domain.


  We study the energy and temperature relaxation of electrons in graphene on a
piezoelectric substrate. Scattering from the combined potential of extrinsic
piezoelectric surface acoustical (PA) phonons of the substrate and intrinsic
deformation acoustical (DA) phonons of graphene is considered in a
(non-)degenerate gas of Dirac fermions. It is shown that in the regime of low
energies or temperatures the PA phonons dominate the relaxation and change
qualitatively its character. This prediction is relevant for quantum metrology
and electronic applications using graphene devices and suggests a new
experimental setup for probing electron-phonon coupling in graphene.


  In this paper we investigate the Follow the Regularized Leader dynamics in
sequential imperfect information games (IIG). We generalize existing results of
Poincar\'e recurrence from normal-form games to zero-sum two-player imperfect
information games and other sequential game settings. We then investigate how
adapting the reward (by adding a regularization term) of the game can give
strong convergence guarantees in monotone games. We continue by showing how
this reward adaptation technique can be leveraged to build algorithms that
converge exactly to the Nash equilibrium. Finally, we show how these insights
can be directly used to build state-of-the-art model-free algorithms for
zero-sum two-player Imperfect Information Games (IIG).


  Training state-of-the-art models for human body pose and shape recovery from
images or videos requires datasets with corresponding annotations that are
really hard and expensive to obtain. Our goal in this paper is to study whether
poses from 3D Motion Capture (MoCap) data can be used to improve image-based
and video-based human mesh recovery methods. We find that fine-tune image-based
models with synthetic renderings from MoCap data can increase their
performance, by providing them with a wider variety of poses, textures and
backgrounds. In fact, we show that simply fine-tuning the batch normalization
layers of the model is enough to achieve large gains. We further study the use
of MoCap data for video, and introduce PoseBERT, a transformer module that
directly regresses the pose parameters and is trained via masked modeling. It
is simple, generic and can be plugged on top of any state-of-the-art
image-based model in order to transform it in a video-based model leveraging
temporal information. Our experimental results show that the proposed
approaches reach state-of-the-art performance on various datasets including
3DPW, MPI-INF-3DHP, MuPoTS-3D, MCB and AIST. Test code and models will be
available soon.


  Using graph neural networks for large graphs is challenging since there is no
clear way of constructing mini-batches. To solve this, previous methods have
relied on sampling or graph clustering. While these approaches often lead to
good training convergence, they introduce significant overhead due to expensive
random data accesses and perform poorly during inference. In this work we
instead focus on model behavior during inference. We theoretically model batch
construction via maximizing the influence score of nodes on the outputs. This
formulation leads to optimal approximation of the output when we do not have
knowledge of the trained model. We call the resulting method influence-based
mini-batching (IBMB). IBMB accelerates inference by up to 130x compared to
previous methods that reach similar accuracy. Remarkably, with adaptive
optimization and the right training schedule IBMB can also substantially
accelerate training, thanks to precomputed batches and consecutive memory
accesses. This results in up to 18x faster training per epoch and up to 17x
faster convergence per runtime compared to previous methods.


  Nearest neighbor classifier is arguably the most simple and popular
nonparametric classifier available in the literature. However, due to the
concentration of pairwise distances and the violation of the neighborhood
structure, this classifier often suffers in high-dimension, low-sample size
(HDLSS) situations, especially when the scale difference between the competing
classes dominates their location difference. Several attempts have been made in
the literature to take care of this problem. In this article, we discuss some
of these existing methods and propose some new ones. We carry out some
theoretical investigations in this regard and analyze several simulated and
benchmark datasets to compare the empirical performances of proposed methods
with some of the existing ones.


  It is known that reflection coefficients for bulk fields of a rational
conformal field theory in the presence of an elementary boundary condition can
be obtained as representation matrices of irreducible representations of the
classifying algebra, a semisimple commutative associative complex algebra.
  We show how this algebra arises naturally from the three-dimensional geometry
of factorization of correlators of bulk fields on the disk. This allows us to
derive explicit expressions for the structure constants of the classifying
algebra as invariants of ribbon graphs in the three-manifold S^2 x S^1. Our
result unravels a precise relation between intertwiners of the action of the
mapping class group on spaces of conformal blocks and boundary conditions in
rational conformal field theories.


  The different activities related to debugging such as program
instrumentation, representation of execution trace and analysis of trace are
not typically performed in an unified framework. We propose \textit{BOLD}, an
Ontology-based Log Debugger to unify and standardize the activities in
debugging. The syntactical information of programs can be represented in the
from of Resource Description Framework (RDF) triples. Using the BOLD framework,
the programs can be automatically instrumented by using declarative
specifications over these triples. A salient feature of the framework is to
store the execution trace of the program also as RDF triples called
\textit{trace triples}. These triples can be queried to implement the common
debug operations. The novelty of the framework is to abstract these triples as
\textit{spans} for high-level reasoning. A span gives a way of examining the
values of a particular variable over certain portion of the program execution.
The properties of the spans are defined formally as a Web Ontology Language
(OWL) ontology called \textit{Program Debug (PD) Ontology}. Using the span
abstraction and PD ontology, end-users can debug a given buggy program in a
standard manner. A notable feature of using ontology is that users can
accurately debug in some cases of missing information, which can be practically
useful. To demonstrate the feasibility of the proposed framework, we have
debugged the programs in a standard bug benchmark suite Software-artifact
Infrastructure Repository (SIR). Experiments show that the querying time is
almost the same as in \texttt{gdb}. The reasoning time depends on the
sub-language of OWL. We find that the expressibility offered by OWL-DL language
is sufficient for the bugs in SIR programs; but to achieve scalability in
reasoning, a restricted OWL-RL language is required.


  We earlier described two taggers for French, a statistical one and a
constraint-based one. The two taggers have the same tokeniser and morphological
analyser. In this paper, we describe aspects of this work concerned with the
definition of the tagset, the building of the lexicon, derived from an existing
two-level morphological analyser, and the definition of a lexical transducer
for guessing unknown words.


  It has been recently shown that Nambu-Goto action can be re-expressed in
terms of Cayley's hyperdeterminant with the manifest SL(2,R) X SL(2,R) X
SL(2,R) symmetry. In the present paper, we show that the same feature is shared
by Green-Schwarz sigma-model for N=2 superstring whose target space-time is
D=2+2. When its zweibein field is eliminated from the action, it contains the
Nambu-Goto action which is nothing but the square root of Cayley's
hyperdeterminant of the pull-back in superspace \sqrt{\hyperdet(\Pi_{i
\a\Dot\a})} manifestly invariant under SL(2,R) X SL(2,R) X SL(2,R). The target
space-time D=2+2 can accommodate self-dual supersymmetric Yang-Mills theory.
Our action has also fermionic kappa-symmetry, satisfying the criterion for its
light-cone equivalence to Neveu-Schwarz-Ramond formulation for N=2 superstring.


  The paper announces the new long-term challenge for improving the performance
of automatic speech recognition systems. The goal of the challenge is to
investigate methods of correcting the recognition results on the basis of
previously made errors by the speech processing system. The dataset prepared
for the task is described and evaluation criteria are presented.


  We have been investigating advanced students' learning of quantum mechanics
concepts and have developed interactive tutorials which strive to help students
learn these concepts. Two such tutorials, focused on the Mach-Zehnder
interferometer (MZI) and the double-slit experiment (DSE), help students learn
how to use the concept of "which-path" information to reason about the presence
or absence of interference in these two experiments in different situations.
After working on a pretest that asked students to predict interference in the
MZI with single photons and polarizers of various orientations placed in one or
both paths of the MZI, students worked on the MZI tutorial which, among other
things, guided them to reason in terms of which-path information in order to
predict interference in similar situations. We investigated the extent to which
students were able to use reasoning related to which-path information learned
in the MZI tutorial to answer analogous questions on the DSE (before working on
the DSE tutorial). After students worked on the DSE pretest they worked on a
DSE tutorial in which they learned to use the concept of which-path information
to answer questions about interference in the DSE with single particles with
mass sent through the two slits and a monochromatic lamp placed between the
slits and the screen. We investigated if this additional exposure to the
concept of which-path information promoted improved learning and performance in
the DSE questions with single photons and polarizers placed after one or both
slits. We find evidence that both tutorials promoted which-path information
reasoning and helped students use this reasoning appropriately in contexts
different from the ones in which they had learned it.


  Context. The Galactic chemical evolution of sulphur is still under debate. At
low metallicities some studies find no correlation between [S/Fe] and [Fe/H],
others find [S/Fe] increasing towards lower metallicities, and still others
find a combination of the two. Each scenario has different implications for the
Galactic chemical evolution of sulphur.
  Aims. To contribute to the discussion on the Galactic chemical evolution of
sulphur by deriving sulphur abundances from non-LTE insensitive spectral
diagnostics in Disk and Halo stars with homogeneously determined stellar
parameters.
  Methods. We derive Teff from photometric colours, logg from stellar
isochrones and Bayesian estimation, and [Fe/H] and [S/Fe] from spectrum
synthesis. We derive [S/Fe] from the [S i] 1082 nm line in 39 mostly cool and
metal-poor giants, using 1D LTE MARCS model atmospheres to model our
high-resolution NIR spectra obtained with the VLT, NOT and Gemini South
telescopes.
  Results. We derive homogeneous stellar parameters for 29 stars. Our results
argue for a chemical evolution of sulphur that is typical for alpha-elements,
contrary to some previous studies. Our abundances are systematically higher by
about 0.1 dex in comparison to other studies that arrived at similar
conclusions using other sulphur diagnostics.
  Conclusions. We find the [S i] line to be a valuable diagnostic of sulphur
abundances in cool giants down to [Fe/H] ~ -2.3. We argue that a homogeneous
determination of stellar parameters is necessary, since the derived abundances
are sensitive to them. Our results ([S/Fe]) show reasonable agreement with
predictions of contemporary models of Galactic chemical evolution. In these
models sulphur is predominantly created in massive stars by oxygen burning, and
ejected in the ISM during Type II SNe explosions. Systematic differences with
previous studies likely fall within modelling uncertainties.


  For dynamic borrowing to leverage external data to augment the control arm of
small RCTs, the key step is determining the amount of borrowing based on the
similarity of the outcomes in the controls from the trial and the external data
sources. A simple approach for this task uses the empirical Bayesian approach,
which maximizes the marginal likelihood (maxML) of the amount of borrowing,
while a likelihood-independent alternative minimizes the mean squared error
(minMSE). We consider two minMSE approaches that differ from each other in the
way of estimating the parameters in the minMSE rule. The classical one adjusts
for bias due to sample variance, which in some situations is equivalent to the
maxML rule. We propose a simplified alternative without the variance
adjustment, which has asymptotic properties partially similar to the maxML
rule, leading to no borrowing if means of control outcomes from the two data
sources are different and may have less bias than that of the maxML rule. In
contrast, the maxML rule may lead to full borrowing even when two datasets are
moderately different, which may not be a desirable property. For inference, we
propose a Bayesian bootstrap (BB) based approach taking the uncertainty of the
estimated amount of borrowing and that of pre-adjustment into account. The
approach can also be used with a pre-adjustment on the external controls for
population difference between the two data sources using, e.g., inverse
probability weighting. The proposed approach is computationally efficient and
is implemented via a simple algorithm. We conducted a simulation study to
examine properties of the proposed approach, including the coverage of 95 CI
based on the Bayesian bootstrapped posterior samples, or asymptotic normality.
The approach is illustrated by an example of borrowing controls for an AML
trial from another study.


  This paper has proposed a novel approach to classify the subjects' smoking
behavior by extracting relevant regions from a given image using deep learning.
After the classification, we have proposed a conditional detection module based
on Yolo-v3, which improves model's performance and reduces its complexity. As
per the best of our knowledge, we are the first to work on this dataset. This
dataset contains a total of 2,400 images that include smokers and non-smokers
equally in various environmental settings. We have evaluated the proposed
approach's performance using quantitative and qualitative measures, which
confirms its effectiveness in challenging situations. The proposed approach has
achieved a classification accuracy of 96.74% on this dataset.


  We investigate combinatorial dynamical systems on simplicial complexes
considered as {\em finite topological spaces}. Such systems arise in a natural
way from sampling dynamics and may be used to reconstruct some features of the
dynamics directly from the sample. We study the homological persistence of {\em
Morse decompositions} of such systems, an important descriptor of the dynamics,
as a tool for validating the reconstruction. Our framework can be viewed as a
step toward extending the classical persistence theory to "vector cloud" data.
We present experimental results on two numerical examples.


  We present two variants of Wythoff's game. The first game is a restriction of
Wythoff's game in which removing tokens from the smaller pile is not allowed if
the two entries are not equal. The second game is an extension of Wythoff's
game obtained by adjoining a move allowing players to remove k tokens from the
smaller pile and l tokens from the other pile provided l < k. We show that both
games preserve the P-positions of Wythoff's game. This resolves a question
raised by Duchene, Fraenkel, Nowakowski and Rigo. We give formulas for those
positions which have Sprague-Grundy value 1. We also prove several results on
the Sprague-Grundy functions.


  In this paper, we consider bivariate Cheney-Sharma operators which are not
the tensor product construction. Precisely, we show that these operators
preserve Lipschitz condition of a given Lipschitz continuous function f and
also the properties of the modulus of continuity function when f is a modulus
of continuity function.


  Our aim in this thesis is to use the language of deformation-quantization to
understand certain quantized algebras by looking at properties of the
corresponding commutative ones, and conversely to obtain results about the
commutative algebras (upon which a Poisson structure is induced) using existing
results for the non-commutative ones. We consider two main cases: firstly, the
division ring of fractions of the quantum plane, which we view as a deformation
of the commutative field of rational functions in two variables with respect to
the bracket $\{x,y\} = xy$, and secondly, quantum matrices and their
semi-classical limits. In particular, we use the theory of $H$-stratification
to study the Poisson-prime and Poisson-primitive ideals of $\mathcal{O}(GL_3)$
and $\mathcal{O}(SL_3)$, and compare this to the corresponding results for
quantum matrices.


  Let X be a variety over an algebraically closed field, \eta:\Omega^1_X\to L a
one-dimensional singular foliation, and C\subseteq X a projective leaf of \eta.
We prove that 2p_a(C)-2=\deg(L|C)+\lambda(C)-\deg(C\cap S) where p_a(C) is the
arithmetic genus, where \lambda(C) is the colength in the dualizing sheaf of
the subsheaf generated by the K\"ahler differentials, and where S is the
singular locus of \eta. We bound \lambda(C) and \deg(C\cap S), and then improve
and extend some recent results of Campillo, Carnicer, and de la Fuente, and of
du Plessis and Wall.


  We study the existence, the nonexistence, and the shape of the ground states
of a Nonlinear Schr\"odinger Equation on a manifold called hybrid plane, that
consists of a half-line whose origin is connected to a plane. The nonlinearity
is of power type, focusing and subcritical. The energy is the sum of the
Nonlinear Schr\"odinger energies with a contact interaction on the half-line
and on the plane with an additional quadratic term that couples the two
components. By ground state we mean every minimizer of the energy at a fixed
mass.
  As a first result, we single out the following rule: a ground state exists if
and only if the confinement near the junction is energetically more convenient
than escaping at infinity along the halfline, while escaping through the plane
is shown to be never convenient. The problem of existence reduces then to a
competition with the one-dimensional solitons.
  By this criterion, we prove existence of ground states for large and small
values of the mass. Moreover, we show that at given mass a ground state exists
if one of the following conditions is satisfied: the interaction at the origin
of the half-line is not too repulsive; the interaction at the origin of the
plane is sufficiently attractive; the coupling between the half-line and the
plane is strong enough. On the other hand, nonexistence holds if the contact
interactions on the half-line and on the plane are repulsive enough and the
coupling is not too strong.
  Finally, we provide qualitative features of ground states. In particular, we
show that in the presence of coupling every ground state is supported both on
the half-line and on the plane and each component has the shape of a ground
state at its mass for the related Nonlinear Schr\"odinger energy with a
suitable contact interaction.
  These are the first results for the Nonlinear Schr\"odinger Equation on a
manifold of mixed dimensionality.


  We address scaling up equilibrium computation in Mean Field Games (MFGs)
using Online Mirror Descent (OMD). We show that continuous-time OMD provably
converges to a Nash equilibrium under a natural and well-motivated set of
monotonicity assumptions. This theoretical result nicely extends to
multi-population games and to settings involving common noise. A thorough
experimental investigation on various single and multi-population MFGs shows
that OMD outperforms traditional algorithms such as Fictitious Play (FP). We
empirically show that OMD scales up and converges significantly faster than FP
by solving, for the first time to our knowledge, examples of MFGs with hundreds
of billions states. This study establishes the state-of-the-art for learning in
large-scale multi-agent and multi-population games.


  We studied the conduction noise spectrum in the vortex state of a
high-temperature superconductor, Bi2Sr2CaCu2Oy, subject to a uniform driving
force. Two characteristic features, a broadband noise (BBN) and a narrow-band
noise (NBN), were observed in the vortex-solid phase. The origin of the large
BBN was determined to be plastic motion of the vortices, whereas the NBN was
found to originate from the washboard modulation of the translational velocity
of the driven vortices. We believe this to be the first observation of


  Serre obtained the p-adic limit of the integral Fourier coefficient of
modular forms on $SL_2(\mathbb{Z})$ for $p=2,3,5,7$. In this paper, we extend
the result of Serre to weakly holomorphic modular forms of half integral weight
on $\Gamma_{0}(4N)$ for $N=1,2,4$. A proof is based on linear relations among
Fourier coefficients of modular forms of half integral weight. As applications
we obtain congruences of Borcherds exponents, congruences of quotient of
Eisentein series and congruences of values of $L$-functions at a certain point
are also studied. Furthermore, the congruences of the Fourier coefficients of
Siegel modular forms on Maass Space are obtained using Ikeda lifting.


  We demonstrate a fibre-based approach that generates mid-infrared femtosecond
pulses in the 3-4 {\mu}m spectral region with microjoule-level single pulse
energy. This is realised in a piece of gas-filled antiresonant hollow-core
fibre that is pumped by a two-micron light source. A rapid variation of the
dispersion near a structural resonance of the fibre creates a phase-matching
point in the mid-infrared, which mediates the frequency-down conversion. We
generate femtosecond pulses centred at 3.16 {\mu}m wavelength with the pulse
energy of more than 1 {\mu}J, achieving the conversion efficiency as high as
9.4%. The wavelength of the radiation is determined solely by the dielectric
wall thickness of the cladding elements, while the yield is subject to other
experimental parameters. This, combined with high power-handling capability of
hollow-core fibres, makes it possible to power scale the mid-infrared output by
either increasing the pulse energy or repetition rate of the pump. The
technique presents a new pathway to build an all-fibre-based mid-infrared
supercontinuum source, which promises to be a powerful new tool for ultrahigh
sensitivity molecular spectroscopy.


  The purpose of this paper is to expose and investigate natural phase-space
formulations of two longstanding problems in the restriction theory of the
Fourier transform. These problems, often referred to as the Stein and
Mizohata--Takeuchi conjectures, assert that Fourier extension operators
associated with rather general (codimension 1) submanifolds of euclidean space,
may be effectively controlled by the classical X-ray transform via weighted
$L^2$ inequalities. Our phase-space formulations, which have their origins in
recent work of Dendrinos, Mustata and Vitturi, expose close connections with a
conjecture of Flandrin from time-frequency analysis, and rest on the
identification of an explicit "geometric" Wigner transform associated with an
arbitrary (smooth strictly convex) submanifold $S$ of $\mathbb{R}^n$. Our main
results are certain natural "Sobolev variants" of the Stein and
Mizohata--Takeuchi conjectures, and involve estimating the Sobolev norms of
such Wigner transforms by geometric forms of classical bilinear fractional
integrals. Our broad geometric framework allows us to explore the role of the
curvature of the submanifold in these problems, and in particular we obtain
bounds that are independent of any lower bound on the curvature; a feature that
is uncommon in the wider restriction theory of the Fourier transform. Finally,
we provide a further illustration of the effectiveness of our analysis by
establishing a form of Flandrin's conjecture in the plane with an
$\varepsilon$-loss. While our perspective comes primarily from euclidean
harmonic analysis, the procedure used for constructing phase-space
representations of extension operators is well-known in optics.


  The behaviour of baby universes has been an important ingredient in
understanding and quantifying non-critical string theory or, equivalently,
models of two-dimensional Euclidean quantum gravity coupled to matter. Within a
regularized description based on dynamical triangulations, we amend an earlier
conjecture by Jain and Mathur on the scaling behaviour of genus-$g$ surfaces
containing particular baby universe `necks', and perform a nontrivial numerical
check on our improved conjecture.


  We describe a mainstream "universalist" approach to the understanding of
mathematics. We then conduct a systematic (but not exhaustive) review of the
academic literature on the decolonisation of mathematics and identify how this
challenges the universalist view. We examine evidence of whether the experience
of mathematics in the UK is systemically racist, examining both the decolonial
arguments and the empirical evidence. We find that there may be some benefit in
teaching the history of mathematics, but that this should be weighed against
the opportunity cost. We find some prima-facie evidence of discrimination in
the descriptive statistics on the representation of ethnic minorities in
academic roles in UK higher education.


  Inspired by the studies on the influence of transition metal impurities in
high Tc superconductors and what is already known about nonmagnetic suppression
of Tc in unconventional superconductors, we set out to investigate the behavior
of the nonmagnetic disordered elastic scattering for a realistic 2D anisotropic
high Tc superconductor with line nodes and a Fermi surface in the tight-binding
approximation. For this purpose, we performed a detailed self-consistent 2D
numerical study of the disordered averaged scattering matrix with nonmagnetic
impurities and a singlet line nodes order parameter, varying the concentration
and the strength of the impurities potential in the Born, intermediate and
unitary limits. In a high Tc anisotropic superconductor with a tight binding
dispersion law averaging over the Fermi surface, including hopping parameters
and an order parameter in agreement with experimental data, the tight-binding
approximation reflects the anisotropic effects. In this study, we also included
a detailed visualization of the behavior of the scattering matrix with
different sets of physical parameters involved in the nonmagnetic disorder,
which allowed us to model the dressed scattering behavior in different regimes
for very low and high energies. With this study, we demonstrate that the
scattering elastic matrix is affected by the non-magnetic disorder, as well as
the importance of an order parameter and a Fermi surface in agreement with
experiments when studying this effect in unconventional superconductors.


  The development of state-of-the-art generative large language models (LLMs)
disproportionately relies on English-centric tokenizers, vocabulary and
pre-training data. Despite the fact that some LLMs have multilingual
capabilities, recent studies have shown that their inference efficiency
deteriorates when generating text in languages other than English. This results
in increased inference time and costs. Cross-lingual vocabulary adaptation
(CVA) methods have been proposed for adapting models to a target language
aiming to improve downstream performance. However, the effectiveness of these
methods on increasing inference efficiency of generative LLMs has yet to be
explored. In this paper, we perform an empirical study of five CVA methods on
four generative LLMs (including monolingual and multilingual models) across
four typologically-diverse languages and four natural language understanding
tasks. We find that CVA substantially contributes to LLM inference speedups of
up to 271.5\%. We also show that adapting LLMs that have been pre-trained on
more balanced multilingual data results in downstream performance comparable to
the original models.


  This paper presents a novel trajectory tracker for autonomous quadrotor
navigation in dynamic and complex environments. The proposed framework
integrates a distributional Reinforcement Learning (RL) estimator for unknown
aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for
trajectory tracking. Aerodynamic effects derived from drag forces and moment
variations are difficult to model directly and accurately. Most current
quadrotor tracking systems therefore treat them as simple `disturbances' in
conventional control approaches. We propose Quantile-approximation-based
Distributional Reinforced-disturbance-estimator, an aerodynamic disturbance
estimator, to accurately identify disturbances, i.e., uncertainties between the
true and estimated values of aerodynamic effects. Simplified Affine Disturbance
Feedback is employed for control parameterization to guarantee convexity, which
we then integrate with a SMPC to achieve sufficient and non-conservative
control signals. We demonstrate our system to improve the cumulative tracking
errors by at least 66% with unknown and diverse aerodynamic forces compared
with recent state-of-the-art. Concerning traditional Reinforcement Learning's
non-interpretability, we provide convergence and stability guarantees of
Distributional RL and SMPC, respectively, with non-zero mean disturbances.


  Semantic segmentation in 3D indoor scenes has achieved remarkable performance
under the supervision of large-scale annotated data. However, previous works
rely on the assumption that the training and testing data are of the same
distribution, which may suffer from performance degradation when evaluated on
the out-of-distribution scenes. To alleviate the annotation cost and the
performance degradation, this paper introduces the synthetic-to-real domain
generalization setting to this task. Specifically, the domain gap between
synthetic and real-world point cloud data mainly lies in the different layouts
and point patterns. To address these problems, we first propose a clustering
instance mix (CINMix) augmentation technique to diversify the layouts of the
source data. In addition, we augment the point patterns of the source data and
introduce non-parametric multi-prototypes to ameliorate the intra-class
variance enlarged by the augmented point patterns. The multi-prototypes can
model the intra-class variance and rectify the global classifier in both
training and inference stages. Experiments on the synthetic-to-real benchmark
demonstrate that both CINMix and multi-prototypes can narrow the distribution
gap and thus improve the generalization ability on real-world datasets.


  Using a consistent quantum-mechanical treatment for the electromagnetic
radiation, we theoretically investigate the magnetic spin-flip scatterings of a
neutral two-level atom trapped in the vicinity of a superconducting body. We
derive a simple scaling law for the corresponding spin-flip lifetime for such
an atom trapped near a superconducting thick slab. For temperatures below the
superconducting transition temperature T_c, the lifetime is found to be
enhanced by several orders of magnitude in comparison to the case of a normal
conducting slab. At zero temperature the spin-flip lifetime is given by the
unbounded free-space value.


  In this paper we consider a phase space path integral for general
time-dependent quantum operations, not necessarily unitary. We obtain the path
integral for a completely positive quantum operation satisfied Lindblad
equation (quantum Markovian master equation). We consider the path integral for
quantum operation with a simple infinitesimal generator.


  Position scrambling (permutation) is widely used in multimedia encryption
schemes and some international encryption standards, such as the Data
Encryption Standard and the Advanced Encryption Standard. In this article, the
authors re-evaluate the security of a typical image-scrambling encryption
algorithm (ISEA). Using the internal correlation remaining in the cipher image,
they disclose important visual information of the corresponding plain image in
a ciphertext-only attack scenario. Furthermore, they found that the real
scrambling domain--the position-scrambling scope of ISEA's scrambled
elements--can be used to support an efficient known or chosen-plaintext attack
on it. Detailed experimental results have verified these points and demonstrate
that some advanced multimedia processing techniques can facilitate the
cryptanalysis of multimedia encryption algorithms.


  Low-temperature magnetization processes and magnetocaloric properties of a
geometrically frustrated spin-1 Blume-Capel model on a triangular lattice are
studied by Monte Carlo simulations. The model is found to display qualitatively
different behavior depending on the sign of the single-ion anisotropy D. For
positive values of D we observe two magnetization plateaus, similar to the
spin-1/2 Ising antiferromagnet, and negative isothermal entropy changes for any
field intensity. For a range of small negative values of D there are four
magnetization plateaus and the entropy changes can be either negative or
positive, depending on the field. If D is negative but large in absolute value
then the entropy changes are solely positive.


  In this paper, we show that for almost all primes p there is an integer
solution x in [2,p-1] to the congruence x^x == x mod p. The solutions can be
interpretated as fixed points of the map x -> x^x mod p, and we study
numerically and discuss some unexpected properties of the dynamical system
associated with this map.


  A feasibility to use the hadronic decay mode of $Z$ for the model independant
measurement of the total cross section of Higgs-strahlung process
$(\sigma_{Zh})$ at a future $e^+e^-$ collider was studied. For the recoil mass
measurement from hadronic decay of $Z$, a simple cut based analysis was applied
on samples produced by the ILD full detector simulation at $\sqrt{s}=350$ GeV
and 500 GeV using the ILC beam parameters. At $350$ GeV, a bump in the recoil
mass distribution was reconstructed, and $\Delta\sigma_{Zh}/\sigma_{Zh}$ = 3.4%
was obtained assuming 165 fb$^{-1}$ data with $e^-(e^+)$ beam polarization of
-80%(+30%) and +80%(-30%), respectively. At $500$ GeV, clear Higgs boson peak
in the recoil mass distribution was not seen, however, from the excess of the
events, $\Delta\sigma_{Zh}/\sigma_{Zh}=3.9%$ was obtained assuming 500
fb$^{-1}$ data with $e^-(e^+)$ beam polarization of -80%(+30%).


  Suspended nanowires are shown to provide mechanically-controlled coherent
mixing/splitting of the spin states of transmitted electrons, caused by the
Rashba spin-orbit interaction. The sensitivity of the latter to mechanical
bending makes the wire a tunable nano-electro-mechanical (NEM) weak link
between reservoirs. When the reservoirs are populated with misbalanced "spin
up/down" electrons, the wire becomes a source of split spin currents, which are
not associated with electric charge transfer and which do not depend on
temperature or driving voltages. The mechanical vibrations of the bended wires
allow for additional tunability of these splitters by applying a magnetic field
and varying the temperature. Clean metallic carbon nanotubes of a few microns
length are good candidates for generating spin conductance of the same order as
the charge conductance (divided by $e^2$) which would have been induced by
electric driving voltages.


  We consider multiphoton dynamics of a quantum system composed of a
three-state atom (a qutrit) and a single-mode photonic field in the ultrastrong
and deep strong coupling regimes, when the coupling strength is comparable to
or larger than the oscillator energy scale. We assume a qutrit to be in a
polar-$\Lambda $ configuration in which two lower levels have mean dipole
moments. Direct multiphoton resonant transitions revealing generalized Rabi
oscillations, collapse, and revivals in atomic excitation probabilities for the
ultrastrong couplings are studied. In the deep strong coupling regime
particular emphasis is placed on the ground state of considering system which
exhibits strictly nonclassical properties.


  The Stokes-parameter operators and the associated Poincare sphere, which
describe the quantum-optical polarization properties of light, are defined and
their basic properties are reviewed. The general features of the Stokes
operators are illustrated by evaluation of their means and variances for a
range of simple polarization states. Some of the examples show polarization
squeezing, in which the variances of one or more Stokes parameters are smaller
than the coherent-state value. The main object of the paper is the application
of these concepts to bright squeezed light. It is shown that a light beam
formed by interference of two orthogonally-polarized quadrature-squeezed beams
exhibits squeezing in some of the Stokes parameters. Passage of such a primary
polarization-squeezed beam through suitable optical components generates a pair
of polarization-entangled light beams with the nature of a two-mode squeezed
state. The use of pairs of primary polarization-squeezed light beams leads to
substantially increased entanglement and to the generation of EPR-entangled
light beams. The important advantage of these nonclassical polarization states
for quantum communication is the possibility of experimentally determining all
of the relevant conjugate variables of both squeezed and entangled fields using
only linear optical elements followed by direct detection.


  We discuss the Monge problem of mass transportation in the framework of
stochastic thermodynamics and revisit the problem of the Landauer limit for
finite-time thermodynamics, a problem that got the interest of Krzysztof
Gawedzki in the last years. We show that restricted to one dimension, optimal
transportation is efficiently solved numerically by well known methods from
differential equations. We add a brief discussion about the relevance this has
on optimising the processing in modern computers.


  Practical memristor came into picture just few years back and instantly
became the topic of interest for researchers and scientists. Memristor is the
fourth basic two-terminal passive circuit element apart from well known
resistor, capacitor and inductor. Recently, memristor based architectures has
been proposed by many researchers. In this paper, we have designed a hybrid
Memristor-CMOS (MeMOS) logic based adder circuit that can be used in numerous
logic computational architectures. We have also analyzed the transient response
of logic gates designed using MeMOS logic circuits. MeMOS use CMOS 180 nm
process with memristor to compute boolean logic operations. Various parameters
including speed, ares, delay and power dissipation are computed and compared
with standard CMOS 180 nm logic design. The proposed logic shows better area
utilization and excellent results from existing CMOS logic circuits at standard
1.8 V operating voltage.


  Electromagnetic properties of quark-like particles are examined in a
classical field model involving extended dual electromagnetic fields. These can
have fractional charges and a confining potential that derives essentially
completely from a short-range weaker potential. The combined potentials exhibit
an asymptotically free spherical surface and contribute to the masses of the
particles. The quarks are shown to have an intrinsic symmetry that describes
their structures in hadrons. Multi- quark solutions are easily obtained for
both stable and unstable particles. Each quark can undergo simple harmonic
motion in a range of frequencies.


  We give an overview of the circuit optimisation methods used by tket, a
compiler system for quantum software developed by Cambridge Quantum Computing
Ltd. We focus on a novel technique based around phase gadgets, a family of
multi-qubit quantum operations which occur naturally in a wide range of quantum
circuits of practical interest. The phase gadgets have a simple presentation in
the ZX-calculus, which makes it easy to reason about them. Taking advantage of
this, we present an efficient method to translate the phase gadgets back to
CNOT gates and single qubit operations suitable for execution on a quantum
computer with significant reductions in gate count and circuit depth. We
demonstrate the effectiveness of these methods on a quantum chemistry
benchmarking set based on variational circuits for ground state estimation of
small molecules.


  Several dissociated and two non-dissociated adsorption structures of the
phenol molecule on the Si(001)-(2 \times 1) surface are studied using density
functional theory with various exchange and correlation functionals. The
relaxed structures and adsorption energies are obtained and it is found that
the dissociated structures are energetically more favourable than the
non-dissociated structures. However, the ground state energies alone do not
determine which structure is obtained experimentally. To elucidate the
situation core level shift spectra for Si 2p and C 1s states are simulated and
compared with experimentally measured spectra. Several transition barriers were
calculated in order to determine which adsorption structures are kinetically
accessible. Based on these results we conclude that the molecule undergoes the
dissociation of two hydrogen atoms on adsorption.


  We propose a general approach for the description of multijet events in the
framework of QCD event generators. We introduce a new algorithm to match parton
showers and arbitrary matrix elements for the production of any number of jets
via the strong or electroweak interaction. We developped a new parton cascade
code capable to deal with multijet events at LEP energies and beyond.


  The Wheeler-DeWitt equation provides the probability distribution for the
curvature perturbation, the gauge invariant quantum fluctuation of the
inflaton. From this, we can find a tower of power spectra which is not found in
a perturbative approach. Since the power spectrum for the modes that cross the
horizon contributes to the uncertainty in the classical inflaton displacement,
we obtain new conditions for eternal inflation. In the presence of the patch in
the higher excitations, the bound on the slow-roll parameter allowing eternal
inflation is given by at most $\epsilon \lesssim (2n+1)(H/m_{\rm Pl})^2$ with
$n$ integer indicating the quantum number labelling the excitation. For large
$n$, the bound on $\epsilon$ is relaxed such that eternal inflation can take
place with even larger value of $\epsilon$. While the second law of
thermodynamics implies that $n=0$ state is preferred, we cannot ignore such
large $n$ effect since the nonlinear interaction inducing transitions to the
$n=0$ state is suppressed.


  We discuss the renormalon-based approach to power corrections in non-singlet
deep inelastic scattering structure functions and compare it with the general
operator product expansion. The renormalon technique and its variations relate
the power corrections directly to infrared-sensitive parameters such as the
position of the Landau pole \Lambda_{QCD} or the infinitesimal gluon mass
\lambda. In terms of the standard OPE these techniques unify evaluations of the
coefficient functions and of matrix elements. We argue that in case of deep
inelastic scattering there is a proliferation of competeing infrared sensitive
parameters. In particular we consider the gluon and quark masses, virtuality of
quarks and \Lambda_{QCD} as possible infrared cut offs and compare the emerging
results. In the standard renormalon technique where \Lambda_{QCD} is the
infrared parameter, the argument of the running coupling is crucial to obtain
the correct x dependance of the structure functions. Finally we discuss the
limitations of the use of the renormalon based methods for determining of the x
dependance of the power corrections.


  We propose a method of simulating the human process of foreign accentuation
using Generative Spoken Language Model (GSLM) only with native speech corpora.
When one listens to spoken words of a foreign language and repeats them, the
repeated speech is often with the accent of that listener's L1. This is said to
be because the spoken words are mentally represented as a sequence of
phonological units of the L1, and those units are used for oral reproduction.
We simulate this process by inputting speech of language A into GSLM of
language B to add B's accent onto the input speech. The process of running ASR
of the L1 for foreign input speech and giving the ASR result to TTS of the L1
can be viewed as a naive implementation of this approach. The results of our
experiments show that the synthesized accent of the output speech is highly
natural, compared to real samples of A generated by speakers whose L1 is B, and
that the degree of accentuation is controllable.


  It is shown that the variable bandwidth density estimator proposed by McKay
(1993a and b) following earlier findings by Abramson (1982) approximates
density functions in $C^4(\mathbb R^d)$ at the minimax rate in the supremum
norm over bounded sets where the preliminary density estimates on which they
are based are bounded away from zero. A somewhat more complicated estimator
proposed by Jones McKay and Hu (1994) to approximate densities in $C^6(\mathbb
R)$ is also shown to attain minimax rates in sup norm over the same kind of
sets. These estimators are strict probability densities.


  In this paper, we give a characterization of locally nilpotent derivations on
$\mathbb{A}^2$-fibrations over Noetherian domains containing $\mathbb{Q}$
having kernel isomorphic to an $\mathbb{A}^1$-fibration.


  We characterize absence of arbitrage with simple trading strategies in a
discounted market with a constant bond and several risky assets. We show that
if there is a simple arbitrage, then there is a 0-admissible one or an obvious
one, that is, a simple arbitrage which promises a minimal riskless gain of
\epsilon, if the investor trades at all. For continuous stock models, we
provide an equivalent condition for absence of 0-admissible simple arbitrage in
terms of a property of the fine structure of the paths, which we call "two-way
crossing." This property can be verified for many models by the law of the
iterated logarithm. As an application we show that the mixed fractional
Black-Scholes model, with Hurst parameter bigger than a half, is free of simple
arbitrage on a compact time horizon. More generally, we discuss the absence of
simple arbitrage for stochastic volatility models and local volatility models
which are perturbed by an independent 1/2-H\"{o}lder continuous process.


  In the disordered local moment picture, we calculated the magnetization (M)
and magnetic anisotropy energy (MAE) of FePt, CoPt, and MnAl ordered alloys and
body-centered tegragonal FeCo (bct-FeCo) disordered alloy, assuming spatially
fluctuated spin configurations at finite temperatures. All alloys exhibit the
relation K1(T)/K1(0)=(M(T)/M(0))^n with the exponent (n) around 2. This is
consistent with the two-ion anisotropy model, in contrast to the usual
single-ion anisotropy model exhibiting n=3. Because these systems have
different mechanisms of MAE, we suggest that this relation is a general rule
for itinerant electron systems.


  In ultrasound shear wave elasticity (SWE) imaging, a number of algorithms
exist for estimating the shear wave speed (SWS) from spatiotemporal
displacement data. However, no method provides a well-calibrated and practical
uncertainty metric, hindering SWE's clinical adoption and utility in downstream
decision-making. Here, we designed a deep learning SWS estimator that
simultaneously outputs a quantitative and well-calibrated uncertainty value for
each estimate. Our deep neural network (DNN) takes as input a single 2D
spatiotemporal plane of tracked displacement data and outputs the two
parameters $m$ and $\sigma$ of a log-normal probability distribution. For
training and testing, we used in vivo 2D-SWE data of the cervix collected from
30 pregnant subjects, totaling 551 acquisitions and >2 million space-time
plots. Points were grouped by uncertainty into bins to assess uncertainty
calibration: the predicted uncertainty closely matched the root-mean-square
estimation error, with an average absolute percent deviation of 3.84%. We
created a leave-one-out ensemble model that estimated uncertainty with better
calibration (1.45%) than any individual ensemble member on a held-out patient's
data. Lastly, we applied the DNN to an external dataset to evaluate its
generalizability. We have made the trained model, SweiNet, openly available to
provide the research community with a fast SWS estimator that also outputs a
well-calibrated estimate of the predictive uncertainty.


  The machine vision systems have been playing a significant role in visual
monitoring systems. With the help of stereovision and machine learning, it will
be able to mimic human-like visual system and behaviour towards the
environment. In this paper, we present a stereo vision based 3-DOF robot which
will be used to monitor places from remote using cloud server and internet
devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw,
pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This
video stream is sent to the user through any generic internet devices with VR
box support, i.e., smartphones giving the user a First-person real-time 3D
experience and transfers the head motion of the user to the robot also in
Real-time. The robot will also be able to track moving objects and faces as a
target using deep neural networks which enables it to be a standalone
monitoring robot. The user will be able to choose specific subjects to monitor
in a space. The stereovision enables us to track the depth information of
different objects detected and will be used to track human interest objects
with its distances and sent to the cloud. A full working prototype is developed
which showcases the capabilities of a monitoring system based on stereo vision,
robotics, and machine learning.


  We present an optical/infrared study of the dense molecular cloud, L935,
dubbed "The Gulf of Mexico", which separates the North America and the Pelican
nebulae, and we demonstrate that this area is a very active star forming
region. A wide-field imaging study with interference filters has revealed 35
new Herbig-Haro objects in the Gulf of Mexico. A grism survey has identified 41
Halpha emission-line stars, 30 of them new. A small cluster of partly embedded
pre-main sequence stars is located around the known LkHalpha 185-189 group of
stars, which includes the recently erupting FUor HBC 722.


  We have analysed an effect of the Bak-Sneppen predator-prey food-chain
self-organization on nucleotide content of evolving species. In our model,
genomes of the species under consideration have been represented by their
nucleotide genomic fraction and we have applied two-parameter Kimura model of
substitutions to include the changes of the fraction in time. The initial
nucleotide fraction and substitution rates were decided with the help of random
number generator. Deviation of the genomic nucleotide fraction from its
equilibrium value was playing the role of the fitness parameter, $B$, in
Bak-Sneppen model. Our finding is, that the higher is the value of the
threshold fitness, during the evolution course, the more frequent are large
fluctuations in number of species with strongly differentiated nucleotide
content; and it is more often the case that the oldest species, which survive
the food-chain competition, might have specific nucleotide fraction making
possible generating long genes


  Code-switching, the interleaving of two or more languages within a sentence
or discourse is pervasive in multilingual societies. Accurate language models
for code-switched text are critical for NLP tasks. State-of-the-art
data-intensive neural language models are difficult to train well from scarce
language-labeled code-switched text. A potential solution is to use deep
generative models to synthesize large volumes of realistic code-switched text.
Although generative adversarial networks and variational autoencoders can
synthesize plausible monolingual text from continuous latent space, they cannot
adequately address code-switched text, owing to their informal style and
complex interplay between the constituent languages. We introduce VACS, a novel
variational autoencoder architecture specifically tailored to code-switching
phenomena. VACS encodes to and decodes from a two-level hierarchical
representation, which models syntactic contextual signals in the lower level,
and language switching signals in the upper layer. Sampling representations
from the prior and decoding them produced well-formed, diverse code-switched
sentences. Extensive experiments show that using synthetic code-switched text
with natural monolingual data results in significant (33.06%) drop in
perplexity.


  Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.


  We provide the first example of continuous families of Poincar\'e-Einstein
metrics developing cusps on the trivial topology $\mathbb{R}^4$. We also
exhibit families of metrics with unexpected degenerations in their conformal
infinity only. These are obtained from the Riemannian version of an ansatz of
Debever and Pleba\'nski-Demia\'nski. We additionally indicate how to construct
similar examples on more complicated topologies.


  Popularity describes the dynamics of mass attention, and is a part of a
broader class of population dynamics in ecology and social science literature.
Studying accurate model of popularity is important for quantifying spreading of
novelty, memes, and influences in human society. Although logistic equation and
similar class of nonlinear differential equation formulates traditional
population dynamics well, part of the deviation in long-term prediction is
stated, yet fully understood. Recently, several studies hinted a long-term
memory effect on popularity whose response function follows a power-law,
especially that appears on online mass media such as YouTube, Twitter, or
Amazon book sales. Here, we investigate the ranking of most popular music,
\textit{the Billboard Hot 100 chart}, which is one of the largest popularity
dataset spanning several decades. Using a popularity model that comprises
logistic growth and a power-law decaying long-term memory, we showed that rank
history is mainly characterized by initial popularity and memory strength. With
this framework, we investigated temporal development of long-term memory on the
whole popularity dynamics. As a result, abrupt emergence of long-term memory
and broad initial popularity is illustrated, which was not clearly detected by
time-independent measures. We emphasize not only development of the mass media,
but also the difference of spreading and accumulated popularity affect dynamics
significantly when the popularity has long-term memory.


  This paper tackles the problem of designing efficient binary-level
verification for a subset of information flow properties encompassing
constant-time and secret-erasure. These properties are crucial for
cryptographic implementations, but are generally not preserved by compilers.
Our proposal builds on relational symbolic execution enhanced with new
optimizations dedicated to information flow and binary-level analysis, yielding
a dramatic improvement over prior work based on symbolic execution. We
implement a prototype, Binsec/Rel, for bug-finding and bounded-verification of
constant-time and secret-erasure, and perform extensive experiments on a set of
338 cryptographic implementations, demonstrating the benefits of our approach.
Using Binsec/Rel, we also automate two prior manual studies on preservation of
constant-time and secret-erasure by compilers for a total of 4148 and 1156
binaries respectively. Interestingly, our analysis highlights incorrect usages
of volatile data pointer for secret erasure and shows that scrubbing mechanisms
based on volatile function pointers can introduce additional register spilling
which might break secret-erasure. We also discovered that gcc -O0 and backend
passes of clang introduce violations of constant-time in implementations that
were previously deemed secure by a state-of-the-art constant-time verification
tool operating at LLVM level, showing the importance of reasoning at
binary-level.


  For a gas of N bosons interacting through a two-body Morse potential a
variational bound of the free energy of a confined system is obtained. The
calculation method is based on the Feynman-Kac functional projected on the
symmetric representation. Within the harmonic approximation a variational
estimate of the effect of the interaction range on the existence of
many-particle bound states, and on the N-T phase diagram is obtained.


  SIDH is a post-quantum key exchange algorithm based on the presumed
difficulty of finding isogenies between supersingular elliptic curves. However,
SIDH and related cryptosystems also reveal additional information: the
restriction of a secret isogeny to a subgroup of the curve (torsion point
information). Petit (2017) was the first to demonstrate that torsion point
information could noticeably lower the difficulty of finding secret isogenies.
In particular, Petit showed that "overstretched" parameterizations of SIDH
could be broken in polynomial time. However, this did not impact the security
of any cryptosystems proposed in the literature. The contribution of this paper
is twofold: First, we strengthen the techniques of Petit by exploiting
additional information coming from a dual and a Frobenius isogeny. This extends
the impact of torsion point attacks considerably. In particular, our techniques
yield a classical attack that completely breaks the n-party group key exchange
of Azarderakhsh et al. for 6 parties or more, and a quantum attack for 3
parties or more that improves on the best known asymptotic complexity. We also
provide a Magma implementation of our attack for 6 parties. We give the full
range of parameters for which our attacks apply. Second, we construct SIDH
variants designed to be weak against our attacks; this includes backdoor
choices of starting curve, as well as backdoor choices of base-field prime. We
stress that our results do not degrade the security of, or reveal any weakness
in, the NIST submission SIKE.


  The observation of scaling in processes in which a weakly interacting probe
delivers large momentum ${\bf q}$ to a many-body system reflects the dominance
of incoherent scattering off target constituents. While a suitably defined
scaling function can provide rich information on the internal dynamics of the
target, in general its extraction from the measured cross section requires
careful consideration of the nature of the interaction driving the scattering
process. The analysis of deep inelastic electron-proton scattering in the
target rest frame within standard many-body theory naturally leads to the
emergence of a scaling function that, unlike the commonly used structure
functions $F_1$ and $F_2$, can be identified with the intrinsic proton
response. The implications for the theoretical analysis of deep inelastic
scattering and the interpretation of the data are discussed.


  Wiener characterized the cyclic vectors (with respect to translations) in
$l^p(Z)$ and $L^p(R)$, $p=1,2$, in terms of the zero set of the Fourier
transform. He conjectured that a similar characterization should be true for
$1<p<2$. Our main result contradicts this conjecture.


  We present numerical three-body experiments that include the effects of
gravitational radiation reaction by using equations of motion that include the
2.5-order post-Newtonian force terms, which are the leading order terms of
energy loss from gravitational waves. We simulate binary-single interactions
and show that close approach cross sections for three 1 solar mass objects are
unchanged from the purely Newtonian dynamics except for close approaches
smaller than 1.0e-5 times the initial semimajor axis of the binary. We also
present cross sections for mergers resulting from gravitational radiation
during three-body encounters for a range of binary semimajor axes and mass
ratios including those of interest for intermediate-mass black holes (IMBHs).
Building on previous work, we simulate sequences of high-mass-ratio three-body
encounters that include the effects of gravitational radiation. The simulations
show that the binaries merge with extremely high eccentricity such that when
the gravitational waves are detectable by LISA, most of the binaries will have
eccentricities e > 0.9 though all will have circularized by the time they are
detectable by LIGO. We also investigate the implications for the formation and
growth of IMBHs and find that the inclusion of gravitational waves during the
encounter results in roughly half as many black holes ejected from the host
cluster for each black hole accreted onto the growing IMBH.


  The limited coexistence capabilities of current Internet-of-things (IoT)
wireless standards produce inefficient spectrum utilization and mutual
performance impairment. The entity of the issue escalates in industrial IoT
(IIoT) applications, which instead have stringent quality-of-service
requirements and exhibit very-low error tolerance. The constant growth of
wireless applications over unlicensed bands mandates then the adoption of
dynamic spectrum access techniques, which can greatly benefit from interference
mapping over multiple dimensions of the radio space. In this article, the
authors analyze the critical role of real-time interference detection and
classification mechanisms that rely on IIoT devices only, without the added
complexity of specialized hardware. The trade-offs between classification
performance and feasibility are analyzed in connection with the implementation
on low-complexity IIoT devices. Moreover, the authors explain how to use such
mechanisms for enabling IIoT networks to construct and maintain
multidimensional interference maps at run-time in an autonomous fashion.
Lastly, the authors give an overview of the opportunities and challenges of
using interference maps to enhance the performance of IIoT networks under
interference.


  We analyze 10 UV time series for 5 stars which fulfill specific sampling and
spectral criteria to constrain the origin of large-scale wind structure in O
stars. We argue that excited state lines must arise close to the stellar
surface and are an excellent diagnostic complement to resonance lines which,
due to radiative transfer effects, rarely show variability at low velocity.
Consequently, we splice dynamic spectra of the excited state line, N IV1718, at
low velocity to those of 1393 component of the Si IV 1400 doublet at high
velocity in order to examine the temporal evolution of wind line features.
These spliced time series reveal that nearly all of the features observed in
the time series originate at or very near the stellar surface. Further, we
positively identify the observational signature of equatorial co-rotating
interaction regions in two of the five stars and possibly two others. In
addition, we see no evidence for features originating further out in the wind.
We use our results to consolidate the fact that the features seen in dynamic
spectra must be huge, in order to remain in the line of sight for days,
persisting to very large velocity and that the photospheric footprint of the
features must also be quite large, ~ 15 - 20% of the stellar diameter.