  We demonstrate theoretically that an off-resonant circularly polarized
electromagnetic field can induce a persistent current in carbon nanotubes,
which corresponds to electron rotation about the nanotube axis. As a
consequence, the nanotubes acquire magnetic moment along the axis, which
depends on their crystal structure and can be detected in state-of-the-art
measurements. This effect and related phenomena are analyzed within the
developed Floquet theory describing the electronic properties of the nanotubes
irradiated by the field.


  Objective: The design of an Artificial Pancreas (AP) to regulate blood
glucose levels requires reliable control methods. Model Predictive Control has
emerged as a promising approach for glycemia control. However, model--based
control methods require computationally simple and identifiable mathematical
models that represent glucose dynamics accurately, which is challenging due to
the complexity of glucose homeostasis. Methods: In this work, a simple model is
deduced to estimate blood glucose concentration in subjects with Type 1
Diabetes Mellitus (T1DM). Novel features in the model are power--law kinetics
for intraperitoneal insulin absorption and a separate glucagon sensitivity
state. Profile likelihood and a method based on singular value decomposition of
the sensitivity matrix are carried out to assess parameter identifiability and
guide a model reduction for improving the identification of parameters.
Results: A reduced model with 10 parameters is obtained and calibrated, showing
good fit to experimental data from pigs where insulin and glucagon boluses were
delivered in the intraperitoneal cavity. Conclusion: A simple model with
power--law kinetics can accurately represent glucose dynamics submitted to
intraperitoneal insulin and glucagon injections. The reduced model was found to
exhibit local practical as well as structural identifiability. Importance: The
proposed model facilitates intraperitoneal bi-hormonal model-based closed-loop
control in animal trials.


  Cross-speaker style transfer in speech synthesis aims at transferring a style
from source speaker to synthesised speech of a target speaker's timbre. Most
previous approaches rely on data with style labels, but manually-annotated
labels are expensive and not always reliable. In response to this problem, we
propose Style-Label-Free, a cross-speaker style transfer method, which can
realize the style transfer from source speaker to target speaker without style
labels. Firstly, a reference encoder structure based on quantized variational
autoencoder (Q-VAE) and style bottleneck is designed to extract discrete style
representations. Secondly, a speaker-wise batch normalization layer is proposed
to reduce the source speaker leakage. In order to improve the style extraction
ability of the reference encoder, a style invariant and contrastive data
augmentation method is proposed. Experimental results show that the method
outperforms the baseline. We provide a website with audio samples.


  Let E be a nonarchimedean local field with residue characteristic l, and
suppose we have an n-dimensional representation of the absolute Galois group
G_E of E over a reduced complete Noetherian local ring A with finite residue
field k of characteristic p different from l. We consider the problem of
associating to any such representation an admissible A[GL_n(E)]-module in a
manner compatible with the local Langlands correspondence at characteristic
zero points of Spec A. In particular we give a set of conditions that uniquely
characterise such an A[GL_n(E)]-module if it exists, and show that such an
A[GL_n(E)]-module always exists when A is the ring of integers of a finite
extension of Q_p. We also use these results to define a "modified mod p local
Langlands correspondence" that is more compatible with specialization of Galois
representations than the mod p local Langlands correspondence of Vigneras.


  In this paper we study the Cauchy problem for diffusion equations associated
to a class of strongly hypoelliptic pseudo-differential operators on graded Lie
groups. To do so, we develop a global complex functional calculus on graded Lie
groups in order to analyse the corresponding energy estimates. One of the main
aspects of this complex functional calculus is that for the
$(\rho,\delta)$-Euclidean H\"ormander classes we recover the standard
functional calculus developed by Seeley [38]. In consequence the G\r{a}rding
inequality that we prove for arbitrary graded Lie groups absorbs the historical
1953's inequality due to G\r{a}rding [26].


  Graphene bilayers exhibit zero-energy flat bands at a discrete series of
magic twist angles. In the absence of intra-sublattice inter-layer hopping,
zero-energy states satisfy a Dirac equation with a non-abelian SU(2) gauge
potential that cannot be diagonalized globally. We develop a semiclassical WKB
approximation scheme for this Dirac equation by introducing a dimensionless
Planck's constant proportional to the twist angle, solving the linearized Dirac
equation around AB and BA turning points, and connecting Airy function
solutions via bulk WKB wavefunctions. We find zero energy solutions at a
discrete set of values of the dimensionless Planck's constant, which we obtain
analytically. Our analytic flat band twist angles correspond closely to those
determined numerically in previous work.


  We study the thermal transport in two-dimensional systems with a nontrivial
Berry curvature texture. The physical realizations are many: for a sake of
definiteness we consider undoped graphene gapped by the presence of an aligned
hexagonal-Boron-Nitride substrate. The same phenomenology applies, i.e., to
surface states of 3D topological insulators in the presence of a uniform
magnetization. We find that chiral valley-polarized second-sound collective
modes propagate along the edges of the system. The localization length of the
edge modes has topological origin stemming from the anomalous velocity term in
the quasiparticle current. At low temperature, the single-particle contribution
to the transverse thermal conductance is exponentially suppressed and only
second-sound modes carry heat along the boundary. A sharp change in the
behavior of the thermal Hall conductance, extracted from nonlocal measurements
of the temperature along the edge, marks the onset of ballistic heat transport
due to second-sound edge modes.


  Recently, there is a surge of interests on heterogeneous information network
analysis. As a newly emerging network model, heterogeneous information networks
have many unique features (e.g., complex structure and rich semantics) and a
number of interesting data mining tasks have been exploited in this kind of
networks, such as similarity measure, clustering, and classification. Although
evaluating the importance of objects has been well studied in homogeneous
networks, it is not yet exploited in heterogeneous networks. In this paper, we
study the ranking problem in heterogeneous networks and propose the HRank
framework to evaluate the importance of multiple types of objects and meta
paths. Since the importance of objects depends upon the meta paths in
heterogeneous networks, HRank develops a path based random walk process.
Moreover, a constrained meta path is proposed to subtly capture the rich
semantics in heterogeneous networks. Furthermore, HRank can simultaneously
determine the importance of objects and meta paths through applying the tensor
analysis. Extensive experiments on three real datasets show that HRank can
effectively evaluate the importance of objects and paths together. Moreover,
the constrained meta path shows its potential on mining subtle semantics by
obtaining more accurate ranking results.


  Superconductors with p+ip pairing symmetry are characterized by chiral edge
states, but these are difficult to detect in equilibrium since the resulting
magnetic field is screened by the Meissner effect. Nonequilibrium detection is
hindered by the fact that the edge excitations are unpaired Majorana fermions,
which cannot transport charge near the Fermi level. Here we show that the
boundary between p_x+ip_y and p_x-ip_y domains forms a one-way channel for
electrical charge. We derive a product rule for the domain wall conductance,
which allows to cancel the effect of a tunnel barrier between metal electrodes
and superconductor and provides a unique signature of topological
superconductors in the chiral p-wave symmetry class.


  In this paper, we show that the Gorenstein global dimension of trivial ring
extensions is often infinite. Also we study the transfer of Gorenstein
properties between a ring and its trivial ring extensions.
  We conclude with an example showing that, in general, the transfer of the
notion of Gorenstein projective module does not carry up to pullback
constructions.


  We study a pointwise inequality for submanifolds in real space forms
involving the scalar curvature, the normal scalar curvature and the mean
curvature. We translate it into an algebraic problem, allowing us to prove a
slightly weaker version of it. We also prove the conjecture for certain types
of submanifolds of $\mathbb C^n$.


  We study a one-dimensional three-state run-and-tumble model motivated by the
bacterium Caulobacter crescentus which displays a cell cycle between two
non-proliferation mobile phase and a proliferating sedentary phase. Our model
implements kinetic transitions between the two mobile and one sedentary states
described in terms of their number densities, where mobility is allowed with
different running speeds in forward and backward direction. We start by
analyzing the stationary states of the system and compute the mean and
squared-displacements for the distribution of all cells, as well as for the
number density of settled cells. The latter displays a surprising
super-ballistic scaling ~t^3 at early times. Including repulsive and attractive
interactions between the mobile cell populations and the settled cells, we
explore the stability of the system and then employ numerical methods to study
structure formation in the fully nonlinear system. We find traveling waves of
bacteria, whose occurrence is quantified in a nonequilibrium state diagram.


  Externally driven interstellar turbulence plays an important role in shaping
the density structure in molecular clouds. Here we study the dynamical role of
internally driven turbulence in a self-gravitating molecular cloud core.
Depending on the initial conditions and evolutionary stages, we find that a
self-gravitating core in the presence of gravity-driven turbulence can undergo
constant, decelerated, and accelerated infall, and thus has various radial
velocity profiles. In the gravity-dominated central region, a higher level of
turbulence results in a lower infall velocity, a higher density, and a lower
mass accretion rate. As an important implication of this study, efficient
reconnection diffusion of magnetic fields against the gravitational drag
naturally occurs due to the gravity-driven turbulence, without invoking
externally driven turbulence.


  Polarization selectivity of light-field-induced charge localization was
investigated in an organic metal alpha-(BEDT-TTF)2I3 with a triangular lattice.
Dependences of transient reflectivity spectra on polarizations of the 7-fs pump
and probe lights indicate that a short-range charge order (CO) is efficiently
induced from the metallic phase for the pump polarization perpendicular to the
1010-type CO axis. Numerical solution of a time-dependent Schrodinger equation
clarified that the 1010-CO is induced by field-induced re-distribution of
charges cooperating with competing inter-site Coulomb repulsions in the
triangular lattice.


  Stabilizing feedback operators are presented which depend only on the
orthogonal projection of the state onto the finite-dimensional control space. A
class of monotone feedback operators mapping the finite-dimensional control
space into itself is considered. The special case of the scaled identity
operator is included. Conditions are given on the set of actuators and on the
magnitude of the monotonicity, which guarantee the semiglobal stabilizing
property of the feedback for a class semilinear parabolic-like equations.
Subsequently an optimal feedback control minimizing the quadratic energy cost
is computed by a deep neural network, exploiting the fact that the feedback
depends only on a finite dimensional component of the state. Numerical
simulations demonstrate the stabilizing performance of explicitly scaled
orthogonal projection feedbacks, and of deep neural network feedbacks.


  We consider Sinai's random walk in random environment. We prove that the
logarithm of the local time is a good estimator of the random potential
associated to the random environment. We give a constructive method allowing us
to built the random environment from a single trajectory of the random walk.


  [Background] The MVP concept has influenced the way in which development
teams apply Software Engineering practices. However, the overall understanding
of this influence of MVPs on SE practices is still poor. [Objective] Our goal
is to characterize the publication landscape on practices that have been used
in the context of software MVPs and to gather practitioner insights on the
identified practices. [Method] We conducted a systematic mapping study and
discussed its results in two focus groups sessions involving twelve industry
practitioners that extensively use MVPs in their projects to capture their
perceptions on the findings of the mapping study. [Results] We identified 33
papers published between 2013 and 2020 and observed some trends related to MVP
ideation and evaluation practices. For instance, regarding ideation, we found
six different approaches and mainly informal end-user involvement practices.
Regarding evaluation, there is an emphasis on end-user validations based on
practices such as usability tests, A/B testing, and usage data analysis.
However, there is still limited research related to MVP technical feasibility
assessment and effort estimation. Practitioners of the focus group sessions
reinforced the confidence in our results regarding ideation and evaluation
practices, being aware of most of the identified practices. They also reported
how they deal with the technical feasibility assessments and effort estimation
in practice. [Conclusion] Our analysis suggests that there are opportunities
for solution proposals and evaluation studies to address literature gaps
concerning technical feasibility assessment and effort estimation. Overall,
more effort needs to be invested into empirically evaluating the existing
MVP-related practices.


  The local arrangement of atoms is one of the most important predictors of
mechanical and functional properties of materials. However, algorithms for
identifying the geometrical arrangements of atoms in complex materials systems
are lacking. To address this challenge, we present a point-pattern matching
algorithm that can detect instances of a `template' structure in a given set of
atom coordinates. To our knowledge this is the first geometrical comparison
technique for atomistic configurations with very different number of atoms, and
when the optimal rotations and translations required to align the
configurations are unknown. The pattern matching algorithm can be combined with
an appropriate set of metrics to quantify the similarity or dissimilarity
between configurations of atoms. We demonstrate the unique capabilities of the
point-pattern matching algorithm using two examples where the automated
analysis of local atomistic structure can help advance the understanding of
structure-property relationships in material science: (a) identifying local
three-dimensional polyhedral units along interfacial defects, and (b) the
analysis of quasi-icosahedral topologies in the atomistic structure of metallic
glasses. We anticipate that the pattern matching algorithm will be applicable
in the analysis of atomistic structures in broad areas of condensed matter
systems, including biological molecules, polymers and disordered metallic
systems. An online implementation of the algorithm is provided via the open
source code hosted on GitHub (https://github.com/spatala/ppm3d).


  The Riesz-Sobolev inequality provides an upper bound, in integral form, for
the convolution of indicator functions of subsets of Euclidean space. We
formulate and prove a sharper form of the inequality. This can be equivalently
phrased as a stability result, quantifying an inverse theorem of Burchard that
characterizes cases of equality.


  Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered
attention for its fast rendering speed and high rendering quality. However,
this comes with high memory consumption, e.g., a well-trained Gaussian field
may utilize three million Gaussian primitives and over 700 MB of memory. We
credit this high memory footprint to the lack of consideration for the
relationship between primitives. In this paper, we propose a memory-efficient
Gaussian field named SUNDAE with spectral pruning and neural compensation. On
one hand, we construct a graph on the set of Gaussian primitives to model their
relationship and design a spectral down-sampling module to prune out primitives
while preserving desired signals. On the other hand, to compensate for the
quality loss of pruning Gaussians, we exploit a lightweight neural network head
to mix splatted features, which effectively compensates for quality losses
while capturing the relationship between primitives in its weights. We
demonstrate the performance of SUNDAE with extensive results. For example,
SUNDAE can achieve 26.80 PSNR at 145 FPS using 104 MB memory while the vanilla
Gaussian splatting algorithm achieves 25.60 PSNR at 160 FPS using 523 MB
memory, on the Mip-NeRF360 dataset. Codes are publicly available at
https://runyiyang.github.io/projects/SUNDAE/.


  In this note we propose the exact artificial boundary conditions formula to
the fractional reaction-subdiffusion equation on an unbounded domain. With the
application of Laplace transformation, the exact artificial boundary conditions
(ABCs) are derived to reformulate the original problem on the unbounded domain
to an initial-boundary-value problem on the bounded computational domain. By
the Kreiss theory, we prove that the reduced initial-boundary value problem is
stability. Based on the properties of tempered fractional calculus, we obtain
that the reduced initial-boundary value problem is long-time stability.


  We prove Gaussian type bounds for the fundamental solution of the conjugate
heat equation evolving under the Ricci flow. As a consequence, for dimension 4
and higher, we show that the backward limit of type I $\kappa$-solutions of the
Ricci flow must be a non-flat gradient shrinking Ricci soliton. This extends
Perelman's previous result on backward limits of $\kappa$-solutions in
dimension 3, in which case that the curvature operator is nonnegative (follows
from Hamilton-Ivey curvature pinching estimate). The Gaussian bounds that we
obtain on the fundamental solution of the conjugate heat equation under
evolving metric might be of independent interest.


  Three-body continuum problems are investigated for light nuclei of
astrophysical relevance. We focus on three-body decays of resonances or
recombination via resonances or the continuum background. The concepts of
widths, decay mechanisms and dynamic evolution are discussed. We also discuss
results for the triple $\alpha$ decay in connection with $2^+$ resonances and
density and temperature dependence rates of recombination into light nuclei
from $\alpha$-particles and neutrons.


  Quark-model descriptions of the nucleon-nucleon interaction contain two main
ingredients, a quark-exchange mechanism for the short-range repulsion and
meson-exchanges for the medium- and long-range parts of the interaction. We
point out the special role played by higher partial waves, and in particular
the 1F3, as a very sensitive probe for the meson-exchange part employed in
these interaction models. In particular, we show that the presently available
models fail to provide a reasonable description of higher partial waves and
indicate the reasons for this shortcoming.


  The analysis of the entanglement entropy of a subsystem of a one-dimensional
quantum system is a powerful tool for unravelling its critical nature. For
instance, the scaling behaviour of the entanglement entropy determines the
central charge of the associated Virasoro algebra. For a free fermion system,
the entanglement entropy depends essentially on two sets, namely the set $A$ of
sites of the subsystem considered and the set $K$ of excited momentum modes. In
this work we make use of a general duality principle establishing the
invariance of the entanglement entropy under exchange of the sets $A$ and $K$
to tackle complex problems by studying their dual counterparts. The duality
principle is also a key ingredient in the formulation of a novel conjecture for
the asymptotic behavior of the entanglement entropy of a free fermion system in
the general case in which both sets $A$ and $K$ consist of an arbitrary number
of blocks. We have verified that this conjecture reproduces the numerical
results with excellent precision for all the configurations analyzed. We have
also applied the conjecture to deduce several asymptotic formulas for the
mutual and $r$-partite information generalizing the known ones for the single
block case.


  In his Ph.D. thesis, Michal Szabados conjectured that for a not fully
periodic configuration with a minimal periodic decomposition the nonexpansive
lines are exactly the lines that contain a period for some periodic
configuration in such decomposition. In this paper, we study Szabados's
conjecture. First, we show that we may consider a minimal periodic
decomposition where each periodic configuration is defined on a finite
alphabet. Then we prove that Szabados's conjecture holds for a wide class of
configurations, which includes all not fully periodic low convex pattern
complexity configurations.


  The modification of Amdahl's law for the case of increment of processor
elements in a computer system is considered. The coefficient $k$ linking
accelerations of parallel and parallel specialized computer systems is
determined. The limiting values of the coefficient are investigated and its
theoretical maximum is calculated. It is proved that $k$ > 1 for any positive
increment of processor elements. The obtained formulas are combined into a
single method allowing to determine the maximum theoretical acceleration of a
parallel specialized computer system in comparison with the acceleration of a
minimal parallel computer system. The method is tested on Apriori, k-nearest
neighbors, CDF 9/7, fast Fourier transform and naive Bayesian classifier
algorithms.


  The primary instrument of the proposed EXIST mission is a coded mask high
energy telescope (the HET), that must have a wide field of view and extremely
good sensitivity. It will be crucial to minimize systematic errors so that even
for very long total integration times the imaging performance is close to the
statistical photon limit. There is also a requirement to be able to reconstruct
images on-board in near real time in order to detect and localize gamma-ray
bursts. This must be done while the spacecraft is scanning the sky. The
scanning provides all-sky coverage and is key to reducing systematic errors.
The on-board computational problem is made even more challenging for EXIST by
the very large number of detector pixels. Numerous alternative designs for the
HET have been evaluated. The baseline concept adopted depends on a unique coded
mask with two spatial scales. Monte Carlo simulations and analytic analysis
techniques have been used to demonstrate the capabilities of the design and of
the proposed two-step burst localization procedure.


  Classical graph matching aims to find a node correspondence between two
unlabeled graphs of known topologies. This problem has a wide range of
applications, from matching identities in social networks to identifying
similar biological network functions across species. However, when the
underlying graphs are unknown, the use of conventional graph matching methods
requires inferring the graph topologies first, a process that is highly
sensitive to observation errors. In this paper, we tackle the blind graph
matching problem with unknown underlying graphs directly using observations of
graph signals, which are generated from graph filters applied to graph signal
excitations. We propose to construct sample covariance matrices from the
observed signals and match the nodes based on the selected sample eigenvectors.
Our analysis shows that the blind matching outcome converges to the result
obtained with known graph topologies when the signal sampling size is large and
the signal noise is small. Numerical results showcase the performance
improvement of the proposed algorithm compared to matching two estimated
underlying graphs learned from the graph signals.


  The research field of magnetic frustration is dominated by triangle-based
lattices but exotic phenomena can also be observed in pentagonal networks. A
peculiar noncollinear magnetic order is indeed known to be stabilized in
Bi2Fe4O9 materializing a Cairo pentagonal lattice. We present the spin wave
excitations in the magnetically ordered state, obtained by inelastic neutron
scattering. They reveal an unconventional excited state related to local
precession of pairs of spins. The magnetic excitations are then modeled to
determine the superexchange interactions for which the frustration is indeed at
the origin of the spin arrangement. This analysis unveils a hierarchy in the
interactions, leading to a paramagnetic state (close to the N\'eel temperature)
constituted of strongly coupled dimers separated by much less correlated spins.
This produces two types of response to an applied magnetic field associated
with the two nonequivalent Fe sites, as observed in the magnetization
distributions obtained using polarized neutrons.


  Shear-driven flow between a rotating cylinder and a stationary elliptical
enclosure is studied in this paper. Two-dimensional time-dependent Navier
Stokes equations are solved using a meshless method where interpolations are
done with Polyharmonic Spline Radial Basis Functions. The fluid flow is
analyzed for various aspect ratios of the ellipse and eccentric placements of
the inner cylinder. Contour plots of vorticity with streamlines, plots of
non-dimensional torque, and the angle of eye of the primary vortex are
presented in the paper for Reynolds numbers between 200 and 2000. Formation of
Moffatt like vortices in the wide-gap region of the model is observed and some
benchmark data are provided for various cases that are simulated.


  We investigated the superconducting fluctuation in FeSe, which is assumed to
be located in the BCS--BEC crossover region, via magnetic torque measurements.
In our method, the absolute cantilever displacement is measured by detecting
the interference intensity of the Fabry--Perot cavity formed between the
cantilever and optical fiber. Our findings are totally different from the
results of the previous torque magnetometry using a piezoresistive cantilever;
the "giant" fluctuation diamagnetism related to the BCS--BEC crossover does not
exist. Instead, a considerably smaller fluctuation signal originating from the
vortex liquid was observed that showed a qualitatively similar behavior to
those in cuprate superconductors. We also discuss the inconsistency between our
torque data and the existence of a pseudogap proposed by an NMR experiment.


  We present flavor SU(3) sum rules for $D \to PP$ and $D \to PV$ decay
amplitudes, that are valid to second order in symmetry breaking by the strange
quark mass spurion. Decay rate sum rules are also computed to this order.
Particular attention is given to sum rules arising from the isospin and U-spin
subgroups, the former providing sensitive tests for alternative sources of
SU(3) breaking. We apply the latter together with the postulated $\Delta U = 0$
rule for the large penguin picture to predict the ratio and difference of the
direct CP asymmetries for $D \to KK^*$ and $D \to \pi \rho$.


  We have collected a 43 event sample of the decay $K_L\to e^+e^-\mu^+\mu^-$
with negligible backgrounds and measured its branching ratio to be $(2.62 \pm
0.40 \pm 0.17)\times 10^{-9} $. We see no evidence for CP violation in this
decay. In addition, we set the 90% confidence upper limit on the combined
branching ratios for the lepton flavor violating decays $K_L\to
e^{\pm}e^{\pm}\mu^{\mp}\mu^{\mp}$ at ${\mathcal B}(K_L\to
e^{\pm}e^{\pm}\mu^{\mp}\mu^{\mp}) \leq 1.23\times10^{-10}$, assuming a uniform
phase space distribution.


  We consider entire solutions $u$ to the minimal surface equation in $R^N$,
with $ N\ge8,$ and we prove the following sharp result : if $N-7$ partial
derivatives $ \frac{\partial u }{\partial {x_j}}$ are bounded on one side (not
necessarily the same), then $u$ is necessarily an affine function.


  An alternative process is proposed for the diffractive Higgs boson production
in peripheral $pp$ collisions, exploring it through the photon-proton
interaction by Double Pomeron Exchange. It is estimated the event rate of the
diffractive Higgs production in central rapidity for Tevatron and LHC energies,
being of the order of 1 fb, in agreement to the predictions from other
diffractive processes. The results are confronted with those obtained from a
similar approach of the Durham group.


  The concept of a covering system was first introduced by Erd\H{o}s in 1950.
Since their introduction, a lot of the research regarding covering systems has
focused on the existence of covering systems with certain restrictions on the
moduli. Arguably, the most famous open question regarding covering systems is
the odd covering problem. In this paper, we explore a variation of the odd
covering problem, allowing a single odd prime to appear as a modulus in the
covering more than once, while all other moduli are distinct, odd, and greater
than $1$. We also consider this variation while further requiring the moduli of
the covering system to be square-free.


  The current searches for a heavy Higgs boson assume on-shell (stable)
Higgs-boson production. The Higgs-boson production cross section is then
sampled with a Breit-Wigner distribution (with fixed-width or running-width)
and implemented in MonteCarlo simulations. Therefore the question remains of
what is the limitation of the narrow Higgs-width approximation. The main focus
of this work is on the description of the Standard Model Higgs--boson-lineshape
in the heavy Higgs region, typically $\MH$ above $600\UGeV$. The framework
discussed in this paper is general enough and can be used for all processes and
for all kinematical regions. Numerical results are shown for the gluon-fusion
process. Issues of gauge invariance and residual theoretical uncertainties are
also discussed. Limitations due to a breakdown of the perturbative expansion
are comprehensively discussed, including a discussion of th equivalence theorem
for (off-shell) virtual vector-bosons. Analytic continuation in a theory with
unstable particles is thoroughly discussed.


  The focus of this paper is on trend estimation for a general state-space
model $Y_t=\mu_t+\varepsilon_t$, where the $d$th difference of the trend
$\{\mu_t\}$ is assumed to be i.i.d., and the error sequence $\{\varepsilon_t\}$
is assumed to be a mean zero stationary process. A fairly precise asymptotic
expression of the mean square error is derived for the estimator obtained by
penalizing the $d$th order differences. Optimal rate of convergence is
obtained, and it is shown to be "asymptotically equivalent" to a nonparametric
estimator of a fixed trend model of smoothness of order $d-0.5$. The results of
this paper show that the optimal rate of convergence for the stochastic and
nonstochastic cases are different. A criterion for selecting the penalty
parameter and degree of difference $d$ is given, along with an application to
the global temperature data, which shows that a longer term history has
nonlinearities that are important to take into consideration.


  Given the success of Large Language Models (LLMs), there has been
considerable interest in studying the properties of model activations. The
literature overwhelmingly agrees that LLM representations are dominated by a
few "outlier dimensions" with exceedingly high variance and magnitude. Several
studies in Natural Language Processing (NLP) have sought to mitigate the impact
of such outlier dimensions and force LLMs to be isotropic (i.e., have uniform
variance across all dimensions in embedding space). Isotropy is thought to be a
desirable property for LLMs that improves model performance and more closely
aligns textual representations with human intuition. However, many of the
claims regarding isotropy in NLP have been based on the average cosine
similarity of embeddings, which has recently been shown to be a flawed measure
of isotropy. In this paper, we propose I-STAR: IsoScore*-based STable
Anisotropic Regularization, a novel regularization method that can be used to
increase or decrease levels of isotropy in embedding space during training.
I-STAR uses IsoScore*, the first accurate measure of isotropy that is both
differentiable and stable on mini-batch computations. In contrast to several
previous works, we find that decreasing isotropy in contextualized embeddings
improves performance on the majority of tasks and models considered in this
paper.


  We report on the controlled coupling of a single nitrogen-vacancy (NV) center
to a surface plasmon mode propagating along a chemically grown silver nanowire.
We locate and optically characterize a single NV-center in a uniform dielectric
environment before we controllably position this emitter in the close proximity
of the nanowire. We are thus able to control the coupling of this particular
emitter to the nanowire and directly compare the photon emission properties
before and after the coupling. The excitation of single plasmonic modes is
witnessed and a total rate enhancement by a factor of up to 4.6 is
demonstrated.


  In a Hamiltonian Lie algebroid over a pre-symplectic manifold and over a
Poisson manifold, we introduce a map corresponding to a comomentum map, called
a comomentum section. We show that the comomentum section gives a Lie algebroid
morphism among Lie algebroids. Moreover, we prove that a momentum section on a
Hamiltonian Lie algebroid is a Poisson map between proper Poisson manifolds,
which is a generalization that a momentum map is a Poisson map between the
symplectic manifold to dual of the Lie algebra. Finally, a momentum section is
reinterpreted as a Dirac morphism on Dirac structures.


  Purpose:Generative Artificial Intelligence (GAI) models, such as ChatGPT, may
inherit or amplify societal biases due to their training on extensive datasets.
With the increasing usage of GAI by students, faculty, and staff in higher
education institutions (HEIs), it is urgent to examine the ethical issues and
potential biases associated with these technologies.
Design/Approach/Methods:This scoping review aims to elucidate how biases
related to GAI in HEIs have been researched and discussed in recent academic
publications. We categorized the potential societal biases that GAI might cause
in the field of higher education. Our review includes articles written in
English, Chinese, and Japanese across four main databases, focusing on GAI
usage in higher education and bias. Findings:Our findings reveal that while
there is meaningful scholarly discussion around bias and discrimination
concerning LLMs in the AI field, most articles addressing higher education
approach the issue superficially. Few articles identify specific types of bias
under different circumstances, and there is a notable lack of empirical
research. Most papers in our review focus primarily on educational and research
fields related to medicine and engineering, with some addressing English
education. However, there is almost no discussion regarding the humanities and
social sciences. Additionally, a significant portion of the current discourse
is in English and primarily addresses English-speaking contexts.
Originality/Value:To the best of our knowledge, our study is the first to
summarize the potential societal biases in higher education. This review
highlights the need for more in-depth studies and empirical work to understand
the specific biases that GAI might introduce or amplify in educational
settings, guiding the development of more ethical AI applications in higher
education.


  We prove various results about the largest exponent of a repetition in a
factor of the Thue-Morse word, when that factor is considered as a circular
word. Our results confirm and generalize previous results of Fitzpatrick and
Aberkane & Currie.


  It is shown that a significant amount of detectable gravitational
microlensing events that could potentially be found by MAssively Parallel
Photometry (MAPP) projects (such as the MACHO, EROS and OGLE collaborations)
will occur for stars too dim to be easily noticed individually by these
projects. This is the result of a large magnification bias effect, a bias of
including high magnification events in any flux-limited sample. The probability
of detecting these events may be as high as 2.3 times the lensing probability
of stars currently being monitored by MAPP collaborations.


  Recent work of Kaplan and Levy refining a nonsolvability criterion proved by
Thompson in his N-Groups paper prompts questions on whether certain conditions
on groups are equivalent to nonsolvability.


  Lung cancer is currently the leading cause of cancer deaths. Among various
subtypes, the number of patients diagnosed with stage I non-small cell lung
cancer (NSCLC), particularly adenocarcinoma, has been increasing. It is
estimated that 30 - 40\% of stage I patients will relapse, and 10 - 30\% will
die due to recurrence, clearly suggesting the presence of a subgroup that could
be benefited by additional therapy. We hypothesize that current attempts to
identify stage I NSCLC subgroup failed due to covariate effects, such as the
age at diagnosis and differentiation, which may be masking the results. In this
context, to stratify stage I NSCLC, we propose CEM-Co, a model-based clustering
algorithm that removes/minimizes the effects of undesirable covariates during
the clustering process. We applied CEM-Co on a gene expression data set
composed of 129 subjects diagnosed with stage I NSCLC and successfully
identified a subgroup with a significantly different phenotype (poor
prognosis), while standard clustering algorithms failed.


  We determine with positive probability the Hausdorff dimension of the level
sets of a class of Navier-Stokes \alpha-models at finite viscosity, forced by
mildly rough Gaussian white noise.


  We study mean curvature flow of Lagrangians in $\mathbb{C}^n$ that are
cohomogeneity-one with respect to a compact Lie group $G \leq \mathrm{SU}(n)$
acting linearly on $\mathbb{C}^n$. Each such Lagrangian necessarily lies in a
level set $\mu^{-1}(\xi)$ of the standard moment map $\mu \colon \mathbb{C}^n
\to \mathfrak{g}^*$, and mean curvature flow preserves this containment. We
classify all cohomogeneity-one self-similarly shrinking, expanding and
translating solutions to the flow, as well as cohomogeneity-one smooth special
Lagrangians lying in $\mu^{-1}(0)$. Restricting to the case of
almost-calibrated flows in the zero level set $\mu^{-1}(0)$, we classify
finite-time singularities, explicitly describing the Type I and Type II blowup
models. Finally, given any cohomogeneity-one special Lagrangian in
$\mu^{-1}(0)$, we show it occurs as the Type II blowup model of a Lagrangian
MCF singularity. Throughout, we give explicit examples of suitable group
actions, including a complete list in the case of $G$ simple. This yields
infinitely many new examples of shrinking and expanding solitons for Lagrangian
MCF, as well as infinitely many new singularity models.


  Motivated by quantum network applications over classical channels, we
initiate the study of $n$-party resource states from which LOCC protocols can
create EPR-pairs between any $k$ disjoint pairs of parties. We give
constructions of such states where $k$ is not too far from the optimal $n/2$
while the individual parties need to hold only a constant number of qubits. In
the special case when each party holds only one qubit, we describe a family of
$n$-qubit states with $k$ proportional to $\log n$ based on Reed-Muller codes,
as well as small numerically found examples for $k=2$ and $k=3$. We also prove
some lower bounds, for example showing that if $k=n/2$ then the parties must
have at least $\Omega(\log\log n)$ qubits each.


  The disk embedding lemma is a technique underlying the topological
classification results in 4-manifold topology for good fundamental groups. The
purpose of this paper is to develop new tools for disk embedding that work up
to s-cobordism, without restrictions on the fundamental group. As an
application we show that a surgery problem gives rise to a collection of capped
gropes that fit in the framework of control theory.


  Using the astrometry and integrated photometry from the Gaia Early Data
Release 3 (EDR3), we map the density variations in the distribution of young
Upper Main Sequence (UMS) stars, open clusters and classical Cepheids in the
Galactic disk within several kiloparsecs of the Sun. Maps of relative
over/under-dense regions for UMS stars in the Galactic disk are derived, using
both bivariate kernel density estimators and wavelet transformations. The
resulting overdensity maps exhibit large-scale arches, that extend in a clumpy
but coherent way over the entire sampled volume, indicating the location of the
spiral arms segments in the vicinity of the Sun. Peaks in the UMS overdensity
are well-matched by the distribution of young and intrinsically bright open
clusters. By applying a wavelet transformation to a sample of classical
Cepheids, we find that their overdensities possibly extend the spiral arm
segments on a larger scale (~10 kpc from the Sun). While the resulting map
based on the UMS sample is generally consistent with previous models of the
Sagittarius-Carina spiral arm, the geometry of the arms in the III quadrant
(galactic longitudes $180^\circ < l < 270^\circ$) differs significantly from
many previous models. In particular we find that our maps favour a larger pitch
angle for the Perseus arm, and that the Local Arm extends into the III quadrant
at least 4 kpc past the Sun's position, giving it a total length of at least 8
kpc.


  We present the discovery of a vast cloud of ionized gas 13$^{\prime}$ (32
kpc) north of the interacting system M51. We detected this cloud via deep
narrow-band imaging with the Burrell Schmidt Telescope, where it appears as an
extended, diffuse H$\alpha$-emitting feature with no embedded compact regions.
The Cloud spans $\sim$10$^{\prime}\times$3$^{\prime}$ (25$\times$7.5 kpc) in
size and has no stellar counterpart; comparisons with our previous deep
broadband imaging show no detected continuum light to a limit of $\mu_{\rm lim,
B} \sim$30 mag arcsec$^{-2}$. WIYN SparsePak observations confirm the cloud's
kinematic association with M51, and the high NII/H$\alpha$, SII/H$\alpha$, and
OI/H$\alpha$ line ratios we measure imply a hard ionization source such as AGN
photoionization or shock heating rather than photoionization due to young
stars. Given the strong NII emission, we infer roughly solar metallicity for
the cloud, ruling out an origin due to infall of primordial gas. Instead we
favor models where the gas has been expelled from the inner regions of the M51
system due to tidal stripping or starburst/AGN winds and has been subsequently
ionized either by shocks or a fading AGN. This latter scenario raises the
intriguing possibility that M51 may be the nearest example of an AGN fossil
nebula or light echo, akin to the famous "Hanny's Voorwerp" in the IC 2497
system.


  Politically sensitive topics are still a challenge for open-domain chatbots.
However, dealing with politically sensitive content in a responsible,
non-partisan, and safe behavior way is integral for these chatbots. Currently,
the main approach to handling political sensitivity is by simply changing such
a topic when it is detected. This is safe but evasive and results in a chatbot
that is less engaging. In this work, as a first step towards a politically safe
chatbot, we propose a group of metrics for assessing their political prudence.
We then conduct political prudence analysis of various chatbots and discuss
their behavior from multiple angles through our automatic metric and human
evaluation metrics. The testsets and codebase are released to promote research
in this area.


  We consider determining $\R$-minimizing solutions of linear ill-posed
problems $A x = y$, where $A: {\mathscr X} \to {\mathscr Y}$ is a bounded
linear operator from a Banach space ${\mathscr X}$ to a Hilbert space
${\mathscr Y}$ and ${\mathcal R}: {\mathscr X} \to [0, \infty]$ is a proper
strongly convex penalty function. Assuming that multiple repeated independent
identically distributed unbiased data of $y$ are available, we consider a dual
gradient method to reconstruct the ${\mathcal R}$-minimizing solution using the
average of these data. By terminating the method by either an {\it a priori}
stopping rule or a statistical variant of the discrepancy principle, we provide
the convergence analysis and derive convergence rates when the sought solution
satisfies certain variational source conditions. Various numerical results are
reported to test the performance of the method.


  The fermionic and bosonic sectors of the 2-site Hubbard model have been
exactly solved by means of the equation of motion and Green's function
formalism. The exact solution of the t-J model has been also reported to
investigate the low-energy dynamics. We have successfully searched for the
exact eigenoperators, and the corresponding eigenenergies, having in mind the
possibility to use them as an operatorial basis on the lattice. Many local,
single-particle, thermodynamical and response properties have been studied as
functions of the external parameters and compared between the two models and
with some numerical and exact results. It has been shown that the 2-site
Hubbard model already contains the most relevant energy scales of the Hubbard
model: the local Coulomb interaction $U$ and the spin-exchange one
$J=\frac{4t^2}U$. As a consequence of this, for some relevant properties
(kinetic energy, double occupancy, energy, specific heat and entropy) and as
regards the metal-insulator transition issue, it has resulted possible to
almost exactly mime the behavior of larger systems, sometimes using a higher
temperature to get a comparable level spacing. The 2-site models have been also
used as toy models to test the efficiency of the Green's function formalism for
composite operators. The capability to reproduce the exact solutions, obtained
by the exact diagonalization technique, gives a firm ground to the approximate
treatments based on this formalism.


  Colloidal nanocrystals are a promising fluorescent class of materials, whose
spontaneous emission features can be tuned over a broad spectral range via
their composition, geometry and size. However, towards embedding nanocrystals
films in elaborated device geometries, one significant drawback is the
sensitivity of their emission properties on further fabrication processes like
lithography, metal or oxide deposition, etc. In this work, we demonstrate how
bright emitting and robust thin films can be obtained by combining nanocrystal
deposition from solutions via spin2 coating with subsequent atomic layer
deposition of alumina. For the resulting composite films, the layer thickness
can be controlled on the nanoscale, and their refractive index can be finely
tuned by the amount of deposited alumina. Ellipsometry is used to measure the
real and imaginary part of the dielectric permittivity, which gives direct
access to the wavelength dependent refractive index absorbance of the film.
Detailed analysis of the photophysics of thin films of core-shell nanocrystal
with different shape and different shell thickness allow to correlate the
behavior of the photoluminescence and of the decay life time to the changes in
the non-radiative rate that are induced by the alumina deposition. We show that
the photoemission properties of such composite films are stable in wavelength
and intensity over several months, and that the photoluminescence completely
recovers from heating processes up to 240$^\circ$C. The latter is particularly
interesting, since it demonstrates robustness to the typical heat treatment
that is needed in several process steps like resist-based lithography and
deposition by thermal or electron beam evaporation of metals or oxides.


  We consider a five-dimensional Einstein--Cartan spacetime upon which Dirac
spinor fields can be defined. Dirac spinor fields in five and four dimensions
share many features, like the fact that both are described by four-component
spinor fields, but they are also characterized by strong differences, like the
fact that in five dimensions we do not have the possibility to project on
left-handed and right-handed chiral parts unlike what happens in the
four-dimensional instance: we conduct a polar decomposition of the spinorial
fields, so to highlight all similarities and discrepancies. As an application
of spinor fields in five dimensions, we study Bianchi-I spacetimes, verifying
whether the Dirac fields in five dimensions can give rise to inflation or
dark-energy dominated cosmological eras or not.


  Technology trends will cause data movement to account for the majority of
energy expenditure and execution time on emerging computers. Therefore,
computational complexity will no longer be a sufficient metric for comparing
algorithms, and a fundamental characterization of data access complexity will
be increasingly important. The problem of developing lower bounds for data
access complexity has been modeled using the formalism of Hong & Kung's
red/blue pebble game for computational directed acyclic graphs (CDAGs).
However, previously developed approaches to lower bounds analysis for the
red/blue pebble game are very limited in effectiveness when applied to CDAGs of
real programs, with computations comprised of multiple sub-computations with
differing DAG structure. We address this problem by developing an approach for
effectively composing lower bounds based on graph decomposition. We also
develop a static analysis algorithm to derive the asymptotic data-access lower
bounds of programs, as a function of the problem size and cache size.


  This work presents an extensive hyperparameter search on Image Diffusion
Models for Echocardiogram generation. The objective is to establish
foundational benchmarks and provide guidelines within the realm of ultrasound
image and video generation. This study builds over the latest advancements,
including cutting-edge model architectures and training methodologies. We also
examine the distribution shift between real and generated samples and consider
potential solutions, crucial to train efficient models on generated data. We
determine an Optimal FID score of $0.88$ for our research problem and achieve
an FID of $2.60$. This work is aimed at contributing valuable insights and
serving as a reference for further developments in the specialized field of
ultrasound image and video generation.


  From 2004 the Frascati Beam Test Facility (BTF) in the DAFNE accelerator
complex provides to the external user up to 1E10 electrons per bunch or up to
10E9 positrons per bunch to develop their detectors. After an upgrade program
terminated in 2020 of the beam test facility a description of the status and
available beam lines will be done.


  Relation Extraction (RE) has attracted increasing attention, but current RE
evaluation is limited to in-domain evaluation setups. Little is known on how
well a RE system fares in challenging, but realistic out-of-distribution
evaluation setups. To address this gap, we propose CrossRE, a new,
freely-available cross-domain benchmark for RE, which comprises six distinct
text domains and includes multi-label annotations. An additional innovation is
that we release meta-data collected during annotation, to include explanations
and flags of difficult instances. We provide an empirical evaluation with a
state-of-the-art model for relation classification. As the meta-data enables us
to shed new light on the state-of-the-art model, we provide a comprehensive
analysis on the impact of difficult cases and find correlations between model
and human annotations. Overall, our empirical investigation highlights the
difficulty of cross-domain RE. We release our dataset, to spur more research in
this direction.


  Shape transformation, a key mechanism for organismal survival and adaptation,
has gained importance across fields as diverse as electronics and medicine.
However, designing and controlling microscale shape-shifting materials remains
a fundamental challenge in various actuation modalities. Here, we introduce
SonoTransformer, an acoustically activated micromachine that delivers ultrafast
transformability using preprogrammed soft hinges. These hinges concentrate
energy through intensified oscillation and provide the necessary torque for
transformation. We have created new machine designs to predetermine the folding
state, enabling tailoring and milliseconds transformation. Additionally, we
have shown selective transformation by adjusting acoustic power, realizing high
degrees of control and functional versatility. Our findings open new research
avenues in acoustics, physics, and soft matter, offering new design paradigms
and development opportunities in robotics, metamaterials, adaptive optics, and
microtechnology.


  Pre-training of neural networks has recently revolutionized the field of
Natural Language Processing (NLP) and has before demonstrated its effectiveness
in computer vision. At the same time, advances around the detection of fake
news were mainly driven by the context-based paradigm, where different types of
signals (e.g. from social media) form graph-like structures that hold
contextual information apart from the news article to classify. We propose to
merge these two developments by applying pre-training of Graph Neural Networks
(GNNs) in the domain of context-based fake news detection. Our experiments
provide an evaluation of different pre-training strategies for graph-based
misinformation detection and demonstrate that transfer learning does currently
not lead to significant improvements over training a model from scratch in the
domain. We argue that a major current issue is the lack of suitable large-scale
resources that can be used for pre-training.


  We investigate the interplay of quantum one-dimensional discrete
$\mathbb{Z}_2$ gauge fields and fermion matter near full filling in terms of
deconfined fractionalized hole excitations that constitute mobile domain walls
between vacua that break spontaneously translation symmetry. In the limit of
strong string tension, we uncover emergent integrable correlated hopping
dynamics of holes which is complementary to the constrained XXZ description in
terms of bosonic dimers. We analyze numerically quantum dynamics of spreading
of an isolated hole together with the associated time evolution of entanglement
and provide analytical understanding of its salient features. We also study the
model enriched with a short-range interaction and clarify the nature of the
resulting ground state at low filling of holes and identify deconfined hole
excitations near the hole filling $\nu^h=1/3$.


  Here we present evolutionary models for a set of massive stars, introducing a
new prescription for the mass-loss rate obtained from hydrodynamical
calculations in which the wind velocity profile, $v(r)$, and the
line-acceleration, $g_\text{line}$, are obtained in a self consistently way.
Replacing mass-loss rates at the Main Sequence stage from the standard Vink's
formula by our new recipe, we generate a new set of evolutionary tracks for
$M_\text{ZAMS}=25,40,70$ and $120\,M_\odot$ and metallicities $Z=0.014$
(Galactic), $Z=0.006$ (LMC), and $Z=0.002$ (SMC).
  Our new derived formula for mass-loss rate predicts a dependence $\dot
M\propto Z^a$, where $a$ is not longer constant but dependent on the stellar
mass: ranging from $a\sim0.53$ when $M_*\sim120\;M_\odot$, to $a\sim1.02$ when
$M_*\sim25\;M_\odot$.
  We found that models adopting the new recipe for $\dot M$ retain more mass
during their evolution, which is expressed in larger radii and consequently
more luminous tracks over the Hertzsprung-Russell diagram. These differences
are more prominent for the cases of $M_\text{ZAMS}=70$ and 120 $M_\odot$ at
solar metallicity, where we found self-consistent tracks are $\sim0.1$ dex
brighter and keep extra mass up to 20 $M_\odot$, compared with the classical
models using the previous formulation for mass-loss rate.
  Moreover, we observed remarkable differences for the evolution of the
radionuclide isotope $^{26}$Al in the core and the surface of the star. Since
$\dot M_\text{sc}$ are weaker than the commonly adopted values for evolutionary
tracks, self-consistent tracks predict a later modification in the abundance
number of $^{26}$Al in the stellar winds. This new behaviour could provide
useful information about the real contribution of this isotope from massive
stars to the Galactic interstellar medium.


  In this paper, we improve the global existence result in [9] slightly. More
precisely, the global existence of strong solutions to the primitive equations
with only horizontal viscosity and diffusivity is obtained under the assumption
of initial data $(v_0,T_0) \in H^1$ with $\partial_z v_0 \in L^4$. Moreover, we
prove that the scaled Boussinesq equations with rotation strongly converge to
the primitive equations with only horizontal viscosity and diffusivity, in the
cases of $H^1$ initial data, $H^1$ initial data with additional regularity
$\partial_z v_0 \in L^4$ and $H^2$ initial data, respectively, as the aspect
ration parameter $\lambda$ goes to zero, and the rate of convergence is of the
order $O(\lambda^{{\eta}/2})$ with
$\eta=\min\{2,\beta-2,\gamma-2\}(2<\beta,\gamma<\infty)$. The convergence
result implies a rigorous justification of the hydrostatic approximation.


  Integral field optical spectroscopy with the INTEGRAL system has been used to
investigate for the first time the two-dimensional kinematic and ionization
properties of the extended, warm ionized gas in Arp 220 over an area of 28 kpc
by 15 kpc. The structure of the ionized gas is divided into well identified
regions associated with the X-ray emitting plumes and extended lobes. Changes
in the ionization state of the gas along the major axis of the plumes are
detected, in particular in the outer regions of the northwestern plume where
the transition between the main stellar body of the galaxy and a broad, low
surface brightness tidal tail is located. If the plumes are produced by a
starburst-driven galactic wind, the efficiency in the conversion of mechanical
to radiation energy is a factor of at least 10 smaller than in galactic winds
developed in edge-on spirals with well defined rotation and outflowing axis.
The kinematic properties of the lobes are to a first order in agreement with
the predictions of the merger scenario according to which the lobes are
tidal-induced gas condensations produced during the merging process. The
largest velocity gradients and velocity deviations from the systemic velocity
are not associated with the plumes, but with the outer stellar envelope and
broad tidal tails at distances of up to 7.5 kpc, indicating that the large
scale kinematics of the extended ionized gas in Arp 220, is most likely
dominated by the tidal-induced motions, and not by galactic winds associated
with nuclear starbursts.


  High order calculation at semi-hard scale is very important, but a
satisfactory calculation framework is still missing. We propose a systematic
method to regularize rapidity divergences in the CGC factorization, which makes
higher order calculation rigorous and straight forward. By applying this method
to single hadron production in pA collision, we find the kinematic constraint
effect introduced by hand in previous works comes out automatically, but with
different values. The difference is crucial for our next-to-leading order (NLO)
result to have a smaller theoretical uncertainty comparing with LO result,
which makes high order calculation in CGC factorization to be useful. As a
byproduct, the negativity problem found in literature can also be overcome in
our framework by a proper choosing of factorization scale.


  The well-known Tunguska-1908 phenomenon (TP) problems (the fast transfer of
the kinetic energy of the meteoroid W~10-50 Mt TNT to air, with its heating to
T>10^4 K at an altitude of 5-10 km, the final turn of the smoothly sloping,
~0-20^o to horizon, trajectory of the body through ~10^o to the West, the
pattern and area of the tree-fall and trees' scorching by heat radiation, etc.)
allow a simple solution within the New Explosive Cosmogony (NEC) of minor
bodies, as opposed to other approaches. The NEC considers the short-period (SP)
comet nuclei, to which the Tunguska body belonged, to be fragments produced in
explosions of massive icy envelopes of Ganymede-type bodies saturated by
products of bulk electrolysis of ices to the form of a 2H2+O2 solid solution.
The nearly tangent entry into the Earth's atmosphere with V~20 km/s of such a
nucleus, ~200-500 m in size and ~(5-50)x10^12 g in mass, also saturated by
2H2+O2, initiated detonation of its part of ~10^12 g at an altitude of 5-10 km.
This resulted in deflection of this fraction trajectory by 5^o-10^o, and fast
expansion with ~2 km/s of its detonation products brought about their fast
slowing down by the air, heating of the latter to T>10^4 K and a phenomenon of
high-altitude explosion. On crossing the Earth's atmosphere, the main part of
the unexploded nucleus escaped into space, and this body moving presently in an
SP orbit should eventually be identified in time. Its impact with W~250-3000 Mt
TNT on the Earth's surface (which could occur in 1908) would have produced a
crater up to ~3.5-8 km in size, with an ejection of dust that would have
brought about a climatic catastrophe. The processes involved in the TP are
resembling those accompanying falling P/Shoemaker-Levy 9 onto Jupiter and,
possibly, the impact-caused Younger Dryas cooling ~13 ka ago.


  Recently, several papers discussed the existence of a low mass leptonic
structure. It was suggested that the $\Sigma^{+}$ disintegration:
$\Sigma^{+}\to$pP$^{0}$, P$^{0}\to\mu^{-}\mu^{+}$ proceeds through an
intermediate particle P$^{0}$ having a mass close to M$\approx$~214.3 MeV. The
present work intends to look at other available data, in order to observe the
eventual existence of a small peak or shoulder, at a mass close to M=214.3 MeV,
which can strengthen the existence of a state produced by two leptons of
opposite electric charge.


  A brain-computer interface (BCI) is used not only to control external devices
for healthy people but also to rehabilitate motor functions for motor-disabled
patients. Decoding movement intention is one of the most significant aspects
for performing arm movement tasks using brain signals. Decoding movement
execution (ME) from electroencephalogram (EEG) signals have shown high
performance in previous works, however movement imagination (MI) paradigm-based
intention decoding has so far failed to achieve sufficient accuracy. In this
study, we focused on a robust MI decoding method with transfer learning for the
ME and MI paradigm. We acquired EEG data related to arm reaching for 3D
directions. We proposed a BCI-transfer learning method based on a Relation
network (BTRN) architecture. Decoding performances showed the highest
performance compared to conventional works. We confirmed the possibility of the
BTRN architecture to contribute to continuous decoding of MI using ME datasets.


  Differential machine learning combines automatic adjoint differentiation
(AAD) with modern machine learning (ML) in the context of risk management of
financial Derivatives. We introduce novel algorithms for training fast,
accurate pricing and risk approximations, online, in real-time, with
convergence guarantees. Our machinery is applicable to arbitrary Derivatives
instruments or trading books, under arbitrary stochastic models of the
underlying market variables. It effectively resolves computational bottlenecks
of Derivatives risk reports and capital calculations.
  Differential ML is a general extension of supervised learning, where ML
models are trained on examples of not only inputs and labels but also
differentials of labels wrt inputs. It is also applicable in many situations
outside finance, where high quality first-order derivatives wrt training inputs
are available. Applications in Physics, for example, may leverage differentials
known from first principles to learn function approximations more effectively.
  In finance, AAD computes pathwise differentials with remarkable efficacy so
differential ML algorithms provide extremely effective pricing and risk
approximations. We can produce fast analytics in models too complex for closed
form solutions, extract the risk factors of complex transactions and trading
books, and effectively compute risk management metrics like reports across a
large number of scenarios, backtesting and simulation of hedge strategies, or
regulations like XVA, CCR, FRTB or SIMM-MVA.
  TensorFlow implementation is available on
https://github.com/differential-machine-learning


  We study the effects of heavy Majorana neutrinos on the Higgs sector of the
MSSM via radiative corrections. We work within the SUSY context where the MSSM
particle content is enlarged with right handed neutrinos and their
corresponding SUSY partners, the sneutrinos, and where compatibility with
neutrino data is required. We compute the one-loop corrections to the mass of
the lightest MSSM CP-even neutral Higgs boson from Majorana neutrinos and their
SUSY partners and assume a seesaw mechanism of type I for neutrino mass
generation. A negative and sizeable Higgs mass correction of up to -5 GeV is
found for a heavy Majorana mass of up to 10^{15} GeV. This negative correction
can grow up to several tens of GeV if the soft SUSY breaking mass associated to
their sneutrino partners is simmilarly heavy as the Majorana mass.


  We consider the simultaneous measurement of the Higgs
($p_t^{\tiny{\mbox{H}}}$) and the leading jet ($p_t^{\tiny{\mbox{J}}}$)
transverse momentum in hadronic Higgs-boson production, and perform the
resummation of the large logarithmic corrections that originate in the limit
$p_t^{\tiny{\mbox{H}}}\,,p_t^{\tiny{\mbox{J}}} \ll m_{\tiny{\mbox{H}}}$ up to
next-to-next-to-leading-logarithmic order. This work constitutes the first
simultaneous (double differential) resummation for two kinematic observables of
which one involves a jet algorithm in hadronic collisions, and provides an
important milestone in the theoretical understanding of joint resummations. As
an application, we provide precise predictions for the Higgs
transverse-momentum distribution with a veto $p_t^{\tiny{\mbox{J}}} \leq
p_{t}^{\tiny{\mbox{J,v}}}$ on the accompanying jets, whose accurate description
is relevant to the Higgs precision programme at the Large Hadron Collider.


  The near-field diffraction of fs and sub-fs light pulses by nm-size slit-type
apertures and its implication for near-field scanning optical microscopy (NSOM)
is analyzed. The amplitude distributions of the diffracted wave-packets having
the central wavelengths in the visible spectral region are found by using the
Neerhoff and Mur coupled integral equations, which are solved numerically for
each Fourier's component of the wave-packet. In the case of fs pulses, the
duration and transverse dimensions of the diffracted pulse remain practically
the same as that of the input pulse. This demonstrates feasibility of the NSOM
in which a fs pulse is used to provide the fs temporal resolution together with
nm-scale spatial resolution. In the sub-fs domain, the Fourier spectrum of the
transmitted pulse experiences a considerable narrowing that leads to the
increase of the pulse duration in a few times. This imposes a limit on the
simultaneous resolutions in time and space.


  The possibility of realizing non-Abelian excitations (non-Abelions) in
two-dimensional (2D) Abelian states of matter has generated a lot of interest
recently. A well-known example of such non-Abelions are parafermion zeros modes
(PFZMs) which can be realized at the endpoints of the so called genons in
fractional quantum Hall (FQH) states or fractional Chern insulators (FCIs). In
this letter, we discuss some known signatures of PFZMs and also introduce some
novel ones. In particular, we show that the topological entanglement entropy
(TEE) shifts by a quantized value after crossing PFZMs. Utilizing those
signatures, we present the first large scale numerical study of PFZMs and their
stability against perturbations in both FQH states and FCIs within the
density-Matrix-Renormalization-Group (DMRG) framework. Our results can help
build a closer connection with future experiments on FQH states with genons.


  The mixed spectral element method (MSEM) is applied to solve the waveguide
problem with Bloch periodic boundary condition (BPBC). Based on the BPBC for
the original Helmholtz equation and the periodic boundary condition (PBC) for
the equivalent but modified Helmholtz equation, two equivalent mixed
variational formulations are applied for the MSEM. Unlike the traditional
finite element method and SEM, both these mixed SEM schemes are completely free
of spurious modes because of their use of the Gauss' law and the
curl-conforming vector basis functions structured by the Gauss-Legendre-Lobatto
(GLL) points. A simple implementation method is used to deal with the BPBC and
the PBC for the mixed variational formulations so that both schemes can save
computational costs over the traditional methods. Several numerical results are
also provided to verify that both schemes are free of spurious modes and have
high accuracy with the propagation constants.


  We study self-avoiding walks on the four-dimensional hypercubic lattice via
Monte Carlo simulations of walks with up to one billion steps. We study the
expected logarithmic corrections to scaling, and find convincing evidence in
support the scaling form predicted by the renormalization group, with an
estimate for the power of the logarithmic factor of 0.2516(14), which is
consistent with the predicted value of 1/4. We also characterize the behaviour
of the pivot algorithm for sampling four-dimensional self-avoiding walks, and
conjecture that the probability of a pivot move being successful for an
$N$-step walk is $O([ \log N ]^{-1/4})$.


  In this paper, we design a nonparametric online algorithm for estimating the
triggering functions of multivariate Hawkes processes. Unlike parametric
estimation, where evolutionary dynamics can be exploited for fast computation
of the gradient, and unlike typical function learning, where representer
theorem is readily applicable upon proper regularization of the objective
function, nonparametric estimation faces the challenges of (i) inefficient
evaluation of the gradient, (ii) lack of representer theorem, and (iii)
computationally expensive projection necessary to guarantee positivity of the
triggering functions. In this paper, we offer solutions to the above
challenges, and design an online estimation algorithm named NPOLE-MHP that
outputs estimations with a $\mathcal{O}(1/T)$ regret, and a $\mathcal{O}(1/T)$
stability. Furthermore, we design an algorithm, NPOLE-MMHP, for estimation of
multivariate marked Hawkes processes. We test the performance of NPOLE-MHP on
various synthetic and real datasets, and demonstrate, under different
evaluation metrics, that NPOLE-MHP performs as good as the optimal maximum
likelihood estimation (MLE), while having a run time as little as parametric
online algorithms.


  This paper considers space-time coding over several independently Rayleigh
faded blocks. In particular we will concentrate on giving upper bounds for the
coding gain of lattice space-time codes as the number of blocks grow. This
problem was previously considered in the single antenna case by Bayer et al. in
2006. Crucial to their work was Odlyzko's bound on the discriminant of an
algebraic number field, as this provides an upper bound for the normalized
coding gain of number field codes. In the MIMO context natural codes are
constructed from division algebras defined over number fields and the coding
gain is measured by the discriminant of the corresponding (non-commutative)
algebra. In this paper we will develop analogues of the Odlyzko bounds in this
context and show how these bounds limit the normalized coding gain of a very
general family of division algebra based space-time codes. These bounds can
also be used as benchmarks in practical code design and as tools to analyze
asymptotic bounds of performance as the number of independently faded blocks
increases.


  In a Bell experiment, it is natural to seek a causal account of correlations
wherein only a common cause acts on the outcomes. For this causal structure,
Bell inequality violations can be explained only if causal dependencies are
modelled as intrinsically quantum. There also exists a vast landscape of causal
structures beyond Bell that can witness nonclassicality, in some cases without
even requiring free external inputs. Here, we undertake a photonic experiment
realizing one such example: the triangle causal network, consisting of three
measurement stations pairwise connected by common causes and no external
inputs. To demonstrate the nonclassicality of the data, we adapt and improve
three known techniques: (i) a machine-learning-based heuristic test, (ii) a
data-seeded inflation technique generating polynomial Bell-type inequalities
and (iii) entropic inequalities. The demonstrated experimental and data
analysis tools are broadly applicable paving the way for future networks of
growing complexity.


  We study mimetic $F(R)$ gravity with potential and Lagrange multiplier
constraint. In the context of these theories, we introduce a reconstruction
technique which enables us to realize arbitrary cosmologies, given the Hubble
rate and an arbitrarily chosen $F(R)$ gravity. We exemplify our method by
realizing cosmologies that are in concordance with current observations (Planck
data) and also well known bouncing cosmologies. The attribute of our method is
that the $F(R)$ gravity can be arbitrarily chosen, so we can have the appealing
features of the mimetic approach combined with the known features of some
$F(R)$ gravities, which unify early-time with late-time acceleration. Moreover,
we study the existence and the stability of de Sitter points in the context of
mimetic $F(R)$ gravity. In the case of unstable de Sitter points, it is
demonstrated that graceful exit from inflation occurs. We also study the
Einstein frame counterpart theory of the Jordan frame mimetic $F(R)$ gravity,
we discuss the general properties of the theory and exemplify our analysis by
studying a quite interesting from a phenomenological point of view, model with
two scalar fields. We also calculate the observational indices of the two
scalar field model, by using the two scalar field formalism. Furthermore, we
extensively study the dynamical system that corresponds to the mimetic $F(R)$
gravity, by finding the fixed points and studying their stability. Finally, we
modify our reconstruction method to function in the inverse way and thus
yielding which $F(R)$ gravity can realize a specific cosmological evolution,
given the mimetic potential and the Lagrange multiplier.


  A one-dimensional system of bosons interacting with contact and
single-Gaussian forces is studied with an expansion in hyperspherical
harmonics. The hyperradial potentials are calculated using the link between the
hyperspherical harmonics and the single-particle harmonic-oscillator basis
while the coupled hyperradial equations are solved with the Lagrange-mesh
method. Extensions of this method are proposed to achieve good convergence with
small numbers of mesh points for any truncation of hypermomentum. The
convergence with hypermomentum strongly depends on the range of the two-body
forces: it is very good for large ranges but deteriorates as the range
decreases, being the worst for the contact interaction. In all cases, the
lowest-order energy is within 4.5$\%$ of the exact solution and shows the
correct cubic asymptotic behaviour at large boson numbers. Details of the
convergence studies are presented for 3, 5, 20 and 100 bosons. A special
treatment for three bosons was found to be necessary. For single-Gaussian
interactions, the convergence rate improves with increasing boson number,
similar to what happens in the case of three-dimensional systems of bosons.


  Offline reinforcement learning (RL) have received rising interest due to its
appealing data efficiency. The present study addresses behavior estimation, a
task that lays the foundation of many offline RL algorithms. Behavior
estimation aims at estimating the policy with which training data are
generated. In particular, this work considers a scenario where the data are
collected from multiple sources. In this case, neglecting data heterogeneity,
existing approaches for behavior estimation suffers from behavior
misspecification. To overcome this drawback, the present study proposes a
latent variable model to infer a set of policies from data, which allows an
agent to use as behavior policy the policy that best describes a particular
trajectory. This model provides with a agent fine-grained characterization for
multi-source data and helps it overcome behavior misspecification. This work
also proposes a learning algorithm for this model and illustrates its practical
usage via extending an existing offline RL algorithm. Lastly, with extensive
evaluation this work confirms the existence of behavior misspecification and
the efficacy of the proposed model.


  Due to the rapid growth of data transmissions in internet of vehicles (IoV),
finding schemes that can effectively alleviate access congestion has become an
important issue. Recently, many traffic control schemes have been studied.
Nevertheless, the dynamics of traffic and the heterogeneous requirements of
different IoV applications are not considered in most existing studies, which
is significant for the random access resource allocation. In this paper, we
consider a hybrid traffic control scheme and use proximal policy optimization
(PPO) method to tackle it. Firstly, IoV devices are divided into various
classes based on delay characteristics. The target of maximizing the successful
transmission of packets with the success rate constraint is established. Then,
the optimization objective is transformed into a markov decision process (MDP)
model. Finally, the access class barring (ACB) factors are obtained based on
the PPO method to maximize the number of successful access devices. The
performance of the proposal algorithm in respect of successful events and delay
compared to existing schemes is verified by simulations.


  In this paper, we prove abundance for 3-folds with non-trivial Albanese maps,
over an algebraically closed field of characteristic $p > 5$.


  The vehicle routing problem is a well known class of NP-hard combinatorial
optimisation problems in literature. Traditional solution methods involve
either carefully designed heuristics, or time-consuming metaheuristics. Recent
work in reinforcement learning has been a promising alternative approach, but
has found it difficult to compete with traditional methods in terms of solution
quality. This paper proposes a hybrid approach that combines reinforcement
learning, policy rollouts, and a satisfiability solver to enable a tunable
tradeoff between computation times and solution quality. Results on a popular
public data set show that the algorithm is able to produce solutions closer to
optimal levels than existing learning based approaches, and with shorter
computation times than meta-heuristics. The approach requires minimal design
effort and is able to solve unseen problems of arbitrary scale without
additional training. Furthermore, the methodology is generalisable to other
combinatorial optimisation problems.


  Swift and deep decarbonisation of electricity generation is central to
enabling a timely transition to net-zero emission energy systems. While future
power systems will likely be dominated by variable renewables (VRE), studies
have identified a need for low-carbon dispatchable power such as nuclear. We
use a cost-optimising power system model to examine the technoeconomic case for
investment in new nuclear capacity in the UK's net-zero emissions energy system
and consider four sensitivity dimensions: the capital cost of new nuclear, the
availability of competing technologies, the expansion of interconnection and
weather conditions. We conclude that new nuclear capacity is only
cost-effective if ambitious cost and construction times are assumed, competing
technologies are unavailable and interconnector expansion is not permitted. We
find that BECCS and long-term storage could reduce electricity system costs by
5-21% and that synchronous condensers can provide cost-effective inertia in
highly renewable systems with low amounts of synchronous generation. We show
that a nearly 100% variable renewable system with very little fossil fuels, no
new build nuclear and facilitated by long-term storage is the most
cost-effective system design. This suggests that the current favourable UK
Government policy towards nuclear is becoming increasingly difficult to
justify.


  We use time resolved spectroscopy of thermonuclear X-ray bursts observed from
SAX J1748.9-2021 to infer the mass and the radius of the neutron star in the
binary. Four X-ray bursts observed from the source with RXTE enable us to
measure the angular size and the Eddington limit on the neutron star surface.
Combined with a distance measurement to the globular cluster NGC 6440, in which
SAX J1748.9-2021 resides, we obtain two solutions for the neutron star radius
and mass, R = 8.18 +/- 1.62 km and M = 1.78 +/- 0.3 M_\sun or R = 10.93 +/-
2.09 km and M = 1.33 +/- 0.33 M_\sun.


  Existing user simulators (USs) for task-oriented dialogue systems only model
user behaviour on semantic and natural language levels without considering the
user persona and emotions. Optimising dialogue systems with generic user
policies, which cannot model diverse user behaviour driven by different
emotional states, may result in a high drop-off rate when deployed in the real
world. Thus, we present EmoUS, a user simulator that learns to simulate user
emotions alongside user behaviour. EmoUS generates user emotions, semantic
actions, and natural language responses based on the user goal, the dialogue
history, and the user persona. By analysing what kind of system behaviour
elicits what kind of user emotions, we show that EmoUS can be used as a probe
to evaluate a variety of dialogue systems and in particular their effect on the
user's emotional state. Developing such methods is important in the age of
large language model chat-bots and rising ethical concerns.


  Catheters are the second most common abnormal finding on radiographs. The
position of catheters must be assessed on all radiographs, as serious
complications can arise if catheters are malpositioned. However, due to the
large number of radiographs performed each day, there can be substantial delays
between the time a radiograph is performed and when it is interpreted by a
radiologist. Computer-aided approaches hold the potential to assist in
prioritizing radiographs with potentially malpositioned catheters for
interpretation and automatically insert text indicating the placement of
catheters in radiology reports, thereby improving radiologists' efficiency.
After 50 years of research in computer-aided diagnosis, there is still a
paucity of study in this area. With the development of deep learning
approaches, the problem of catheter assessment is far more solvable. Therefore,
we have performed a review of current algorithms and identified key challenges
in building a reliable computer-aided diagnosis system for assessment of
catheters on radiographs. This review may serve to further the development of
machine learning approaches for this important use case.


  Two forms of G4-DNA, with parallel and pairwise anti-parallel strands, are
studied using atomic force microscopy. The directionality of the strands
affects the molecules' structural properties (different height and length) and
their electrical polarizability. Parallel G4-DNA is twice as polarizable as
anti-parallel G4-DNA, suggesting it is a better electrical wire for
bio-nanoelectronics.


  One very promising technique for measuring the dense matter Equation of State
exploits hotspots that form on the neutron star surface due to the pulsar
mechanism, accretion streams, or during thermonuclear explosions in the neutron
star ocean. This article explains how Pulse Profile Modeling of hotspots is
being used by the Neutron Star Interior Composition Explorer (NICER), an X-ray
telescope installed on the International Space Station in 2017 - and why the
technique is a mission driver for the next, larger-area generation of
telescopes including the enhanced X-ray Timing and Polarimetry (eXTP) mission
and the Spectroscopic Time-Resolving Observatory for Broadband Energy X-rays
(STROBE-X).


  We introduce a novel, fast method for the numerical approximation of
parabolic partial differential equations (PDEs for short) based on model order
reduction techniques and the Laplace transform. We start by applying said
transform to the evolution problem, thus yielding a time-independent boundary
value problem solely depending on the complex Laplace parameter. In an offline
stage, we judiciously sample the Laplace parameter and numerically solve the
corresponding collection of high-fidelity or full-order problems. Next, we
apply a proper orthogonal decomposition (POD) to this collection of solutions
in order to obtain a reduced basis in the Laplace domain. We project the linear
parabolic problem onto this basis, and then using any suitable time-stepping
method, we solve the evolution problem. A key insight to justify the
implementation and analysis of the proposed method corresponds to resorting to
Hardy spaces of analytic functions and establishing, through the Paley-Wiener
theorem, an isometry between the solution of the time-dependent problem and its
Laplace transform. As a result, one may conclude that computing a POD with
samples taken in the Laplace domain produces an exponentially accurate reduced
basis for the time-dependent problem. Numerical experiments portray the
performance of the method in terms of accuracy and, in particular, speed-up
when compared to the solution obtained by solving the full-order model.


  Long-duration gamma-ray bursts (LGRBs), thought to be produced during
core-collapse supernovae, may have a prominent neutron component in the outflow
material. If present, neutrons can change how photons scatter in the outflow by
reducing its opacity, thereby allowing the photons to decouple sooner than if
there were no neutrons present. Understanding the details of this process could
therefore allow us to probe the central engine of LGRBs, which is otherwise
hidden. Here, we present results of the photospheric emission from an LGRB jet,
using a combination of relativistic hydrodynamic simulations and radiative
transfer post-processing using the Monte Carlo Radiation Transfer (MCRaT) code.
We control the size of the neutron component in the jet material by varying the
equilibrium electron fraction $Y_{e}$, and we find that the presence of
neutrons in the GRB fireball affects the Band parameters $\alpha$ and $E_{0}$,
while the picture with the $\beta$ parameter is less clear. In particular, the
break energy $E_{0}$ is shifted to higher energies. Additionally, we find that
increasing the size of the neutron component also increases the total radiated
energy of the outflow across multiple viewing angles. Our results not only shed
light on LGRBs, but are also relevant to short-duration gamma-ray bursts
associated with binary neutron star mergers, due to the likelihood of a
prominent neutron component in such systems.


  As the first step to extend our understanding of higher-derivative theories,
within the framework of analytic mechanics of point particles, we construct a
ghost-free theory involving third-order time derivatives in Lagrangian. While
eliminating linear momentum terms in the Hamiltonian is necessary and
sufficient to kill the ghosts associated with higher derivatives for Lagrangian
with at most second-order derivatives, we find that this is necessary but not
sufficient for the Lagrangian with higher than second-order derivatives. We
clarify a set of ghost-free conditions under which we show that the Hamiltonian
is bounded, and that equations of motion are reducible into a second-order
system.


  This letter illustrates the opinion of the molecular dynamics (MD) community
on the need to adopt a new FAIR paradigm for the use of molecular simulations.
It highlights the necessity of a collaborative effort to create, establish, and
sustain a database that allows findability, accessibility, interoperability,
and reusability of molecular dynamics simulation data. Such a development would
democratize the field and significantly improve the impact of MD simulations on
life science research. This will transform our working paradigm, pushing the
field to a new frontier. We invite you to support our initiative at the MDDB
community (https://mddbr.eu/community/)


  We propose Deep Autoencoding Predictive Components (DAPC) -- a
self-supervised representation learning method for sequence data, based on the
intuition that useful representations of sequence data should exhibit a simple
structure in the latent space. We encourage this latent structure by maximizing
an estimate of predictive information of latent feature sequences, which is the
mutual information between past and future windows at each time step. In
contrast to the mutual information lower bound commonly used by contrastive
learning, the estimate of predictive information we adopt is exact under a
Gaussian assumption. Additionally, it can be computed without negative
sampling. To reduce the degeneracy of the latent space extracted by powerful
encoders and keep useful information from the inputs, we regularize predictive
information learning with a challenging masked reconstruction loss. We
demonstrate that our method recovers the latent space of noisy dynamical
systems, extracts predictive features for forecasting tasks, and improves
automatic speech recognition when used to pretrain the encoder on large amounts
of unlabeled data.


  Conditional stochastic optimization has found applications in a wide range of
machine learning tasks, such as invariant learning, AUPRC maximization, and
meta-learning. As the demand for training models with large-scale distributed
data grows in these applications, there is an increasing need for
communication-efficient distributed optimization algorithms, such as federated
learning algorithms. This paper considers the nonconvex conditional stochastic
optimization in federated learning and proposes the first federated conditional
stochastic optimization algorithm (FCSG) with a conditional stochastic gradient
estimator and a momentum-based algorithm (FCSG-M). To match the lower bound
complexity in the single-machine setting, we design an accelerated algorithm
(Acc-FCSG-M) via the variance reduction to achieve the best sample and
communication complexity. Compared with the existing optimization analysis for
MAML in FL, federated conditional stochastic optimization considers the sample
of tasks. Extensive experimental results on various tasks validate the
efficiency of these algorithms.


  Phylogenetic PCA (p-PCA) is a version of PCA for observations that are leaf
nodes of a phylogenetic tree. P-PCA accounts for the fact that such
observations are not independent, due to shared evolutionary history. The
method works on Euclidean data, but in evolutionary biology there is a need for
applying it to data on manifolds, particularly shapes. We provide a
generalization of p-PCA to data lying on Riemannian manifolds, called Tangent
p-PCA. Tangent p-PCA thus makes it possible to perform dimension reduction on a
data set of shapes, taking into account both the non-linear structure of the
shape space as well as phylogenetic covariance. We show simulation results on
the sphere, demonstrating well-behaved error distributions and fast convergence
of estimators. Furthermore, we apply the method to a data set of mammal jaws,
represented as points on a landmark manifold equipped with the LDDMM metric.


  An ionization-induced plasma grating can be formed by spatially selective
ionization of gases by the interference of two intersecting ultra-short laser
pulses. The density modulation of a plasma grating can approach unity since the
plasma is produced only where the two pulses constructively interfere and
ionization does not occur in destructive interference regions. Such a large
density modulation leads to efficient Thomson scattering of a second
ultra-short probe pulse once the Bragg condition is satisfied. By measuring the
scattering efficiency, it is possible to determine the absolute electron
density in the plasma grating and thereby deduce the ionization degree for a
given neutral gas density. In this paper, we demonstrate the usefulness of this
concept by showing two applications: ionization degree measurement of
strong-field ionization of atoms and molecules and characterization of
extremely low-density gas jets. The former application is of particular
interest for ionization physics studies in dense gases where the collision of
the ionized electron with neighboring neutrals may become important-sometimes
referred to as many-body ionization, and the latter is useful for plasma-based
acceleration that requires extremely low-density plasmas.


  Hierarchically hyperbolic spaces (HHSs) are a large class of spaces that
provide a unified framework for studying the mapping class group, right-angled
Artin and Coxeter groups, and many 3--manifold groups. We investigate strongly
quasiconvex subsets in this class and characterize them in terms of their
contracting properties, relative divergence, the coarse median structure, and
the hierarchical structure itself. Along the way, we obtain new tools to study
HHSs, including two new equivalent definitions of hierarchical quasiconvexity
and a version of the bounded geodesic image property for strongly quasconvex
subsets. Utilizing our characterization, we prove that the hyperbolically
embedded subgroups of hierarchically hyperbolic groups are precisely those
which are almost malnormal and strongly quasiconvex, producing a new result in
the case of the mapping class group. We also apply our characterization to
study strongly quasiconvex subsets in several specific examples of HHSs. We
show that while many commonly studied HHSs have the property that that every
strongly quasiconvex subset is either hyperbolic or coarsely covers the entire
space, right-angled Coxeter groups exhibit a wide variety of strongly
quasiconvex subsets.


  We analyze the constraints on neutrino mass spectra with extra sterile
neutrinos as implied by the LSND experiment. The various mass related
observables in neutrinoless double beta decay, tritium beta decay and cosmology
are discussed. Both neutrino oscillation results as well as recent cosmological
neutrino mass bounds are taken into account. We find that some of the allowed
mass patterns are severely restricted by the current constraints, in particular
by the cosmological constraints on the total sum of neutrino masses and by the
non-maximality of the solar neutrino mixing angle. Furthermore, we estimate the
form of the four neutrino mass matrices and also comment on the situation in
scenarios with two additional sterile neutrinos.


  Magnetic silicene superlattices (MSSLs) are versatile structures with
spin-valley polarization and tunneling magnetoresistance (TMR) capabilities.
However, the oscillating transport properties related to the superlattice
periodicity impede stable spin-valley polarization states reachable by
reversing the magnetization direction. Here, we show that aperiodicity can be
used to improve the spin-valley polarization and TMR by reducing the
characteristic conductance oscillations of periodic MSSLs (P-MSSLs). Using the
Landauer-B\"uttiker formalism and the transfer matrix method, we investigate
the spin-valley polarization and the TMR of Fibonacci (F-) and Thue-Morse (TM-)
MSSLs as typical aperiodic superlattices. Our findings indicate that aperiodic
superlattices with higher disorder provide better spin-valley polarization and
TMR values. In particular, TM-MSSLs reduce considerably the conductance
oscillations giving rise to two well-defined spin-valley polarization states
and a better TMR than F- and P-MSSLs. F-MSSLs also improve the spin-valley
polarization and TMR, however they depend strongly on the parity of the
superlattice generation.


  We consider a sequence of identically independently distributed random
samples from an absolutely continuous probability measure in one dimension with
unbounded density. We establish a new rate of convergence of the
$\infty-$Wasserstein distance between the empirical measure of the samples and
the true distribution, which extends the previous convergence result by
Trilllos and Slep\v{c}ev to the case that the true distribution has an
unbounded density.


  Let A be a del Pezzo order on the projective plane over the field of complex
numbers. We prove that every torsion-free A-module of rank one can be deformed
into a locally free A-module of rank one.