  The random hopping models exhibit many fascinating features, such as
diverging localization length and density of states as energy approaches the
bandcenter, due to its particle-hole symmetry. Nevertheless, such models are
yet to be realized experimentally because the particle-hole symmetry is easily
destroyed by diagonal disorder. Here we propose that a pure random hopping
model can be effectively realized in ultracold atoms by modulating a disordered
onsite potential in particular frequency ranges. This idea is motivated by the
recent development of the phenomena called "dynamical localization" or
"coherent destruction of tunneling". Investigating the application of this idea
in one dimension, we find that if the oscillation frequency of the disorder
potential is gradually increased from zero to infinity, one can tune a
non-interacting system from an Anderson insulator to a random hopping model
with diverging localization length at the band center, and eventually to a
uniform-hopping tight-binding model.


  A self-consistent system of interacting spinor and scalar fields is
considered within the scope of Bianchi type VI cosmological model filled with a
perfect fluid. The contribution of the cosmological constant ($\Lambda$-term)
is taken into account as well. Exact self-consistent solutions to the field
equations are obtained for a special choice of spatial inhomogeneity and the
interaction terms of the spinor and scalar fields. It has been found that some
special choice of metric functions can give rise to a singularity-free
solutions independent of the value and sign of the $\Lambda$ term. It is also
shown that the introduction of a positive $\Lambda$, the most widespread kind
of dark energy, leads to the rapid growth of the universe, while the negative
one, corresponding to an additional gravitational energy gives rise to an
oscillatory or non-periodic mode of expansion. The role of the spatial
inhomogeneity in the evolution of the universe is clarified within the scope of
the considered models.


  Mottness is at the heart of the essential physics in a strongly correlated
system as many novel quantum phenomena occur in the metallic phase near the
Mott metal-insulator transition. We investigate the Mott transition in a
Hubbard model by using the dynamical mean-field theory and introduce the local
quantum state fidelity to depict the Mott metal-insulator transition. The local
quantum state fidelity provides a convenient approach for determining the
critical point of the Mott transition. Additionally, it presents a consistent
description of the two distinct forms of the Mott transition points.


  Despite recent calls for including artificial intelligence (AI) literacy in
K-12 education, not enough attention has been paid to studying youths' everyday
knowledge about machine learning (ML). Most research has examined how youths
attribute intelligence to AI/ML systems. Other studies have centered on youths'
theories and hypotheses about ML highlighting their misconceptions and how
these may hinder learning. However, research on conceptual change shows that
youths may not have coherent theories about scientific phenomena and instead
have knowledge pieces that can be productive for formal learning. We
investigate teens' everyday understanding of ML through a knowledge-in-pieces
perspective. Our analyses reveal that youths showed some understanding that ML
applications learn from training data and that applications recognize patterns
in input data and depending on these provide different outputs. We discuss how
these findings expand our knowledge base and implications for the design of
tools and activities to introduce youths to ML.


  We examine the infrared properties of five low surface brightness galaxies
(LSBGs) and compare them with related but higher surface brightness galaxies,
using Spitzer Space Telescope images and spectra. All the LSBGs are detected in
the 3.6 and 4.5um bands, representing the stellar population. All but one are
detected at 5.8 and 8.0um, revealing emission from hot dust and aromatic
molecules, though many are faint or point-like at these wavelengths. Detections
of LSBGs at the far-infrared wavelengths, 24, 70, and 160um, are varied in
morphology and brightness, with only two detections at 160um, resulting in
highly varied spectral energy distributions. Consistent with previous
expectations for these galaxies, we find that detectable dust components exist
for only some LSBGs, with the strength of dust emission dependent on the
existence of bright star forming regions. However, the far-infrared emission
may be relatively weak compared with normal star-forming galaxies.


  Direct Preference Optimization (DPO) improves the alignment of large language
models (LLMs) with human values by training directly on human preference
datasets, eliminating the need for reward models. However, due to the presence
of cross-domain human preferences, direct continual training can lead to
catastrophic forgetting, limiting DPO's performance and efficiency. Inspired by
intraspecific competition driving species evolution, we propose a Online
Fast-Slow chasing DPO (OFS-DPO) for preference alignment, simulating
competition through fast and slow chasing among models to facilitate rapid
adaptation. Specifically, we first derive the regret upper bound for online
learning, validating our motivation with a min-max optimization pattern. Based
on this, we introduce two identical modules using Low-rank Adaptive (LoRA) with
different optimization speeds to simulate intraspecific competition, and
propose a new regularization term to guide their learning. To further mitigate
catastrophic forgetting in cross-domain scenarios, we extend the OFS-DPO with
LoRA modules combination strategy, resulting in the Cross domain Online
Fast-Slow chasing DPO (COFS-DPO). This method leverages linear combinations of
fast modules parameters from different task domains, fully utilizing historical
information to achive continual value alignment. Experimental results show that
OFS-DPO outperforms DPO in in-domain alignment, while COFS-DPO excels in
cross-domain continual learning scenarios.


  Merger rates of binary black holes, binary neutron stars, and neutron
star-black hole binaries in the local Universe (i.e., redshift $z=0$), inferred
from the Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo,
are $16-130\, \mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$, $13-1900\,
\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$, and
$7.4-320\,\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$, respectively. These rates
suggest that there is a significant chance that two or more of these signals
will overlap with each other during their lifetime in the sensitivity-band of
future gravitational-wave detectors such as the Cosmic Explorer and Einstein
Telescope. The detection pipelines provide the coalescence time of each signal
with an accuracy $\mathcal{O}(10\,\rm ms)$. We show that using a prior on the
coalescence time from a detection pipeline, it is possible to correctly infer
the properties of these overlapping signals with the current data-analysis
infrastructure. We study different configurations of two overlapping signals
created by non-spinning binaries, varying their time and phase at coalescence,
as well as their signal-to-noise ratios. We conclude that, for the scenarios
considered in this work, parameter inference is robust provided that their
coalescence times in the detector frame are more than $\sim 1-2 \,\mathrm{s}$.
Signals whose coalescence epochs lie within $\sim 0.5\,\mathrm{s}$ of each
other suffer from significant biases in parameter inference, and new strategies
and algorithms would be required to overcome such biases.


  Graphene is a powerful playground for studying a plethora of quantum
phenomena. One of the remarkable properties of graphene arises when it is
strained in particular geometries and the electrons behave as if they were
under the influence of a magnetic field. Previously, these strain-induced
pseudomagnetic fields have been explored on the nano- and micrometer-scale
using scanning probe and transport measurements. Heteroepitaxial strain, in
contrast, is a wafer-scale engineering method. Here, we show that
pseudomagnetic fields can be generated in graphene through wafer-scale
epitaxial growth. Shallow triangular nanoprisms in the SiC substrate generate
strain-induced uniform fields of 41 T. This enables the observation of
strain-induced Landau levels at room temperature, as detected by angle-resolved
photoemission spectroscopy, and confirmed by model calculations and scanning
tunneling microscopy measurements. Our work demonstrates the feasibility of
exploiting strain-induced quantum phases in two-dimensional Dirac materials on
a wafer-scale platform, opening the field to new applications.


  Smooth behaviors are preferable for many contact-rich manipulation tasks.
Impedance control arises as an effective way to regulate robot movements by
mimicking a mass-spring-damping system. Consequently, the robot behavior can be
determined by the impedance gains. However, tuning the impedance gains for
different tasks is tricky, especially for unstructured environments. Moreover,
online adapting the optimal gains to meet the time-varying performance index is
even more challenging. In this paper, we present Safe Online Gain Optimization
for Variable Impedance Control (Safe OnGO-VIC). By reformulating the dynamics
of impedance control as a control-affine system, in which the impedance gains
are the inputs, we provide a novel perspective to understand variable impedance
control. Additionally, we innovatively formulate an optimization problem with
online collected force information to obtain the optimal impedance gains in
real-time. Safety constraints are also embedded in the proposed framework to
avoid unwanted collisions. We experimentally validated the proposed algorithm
on three manipulation tasks. Comparison results with a constant gain baseline
and an adaptive control method prove that the proposed algorithm is effective
and generalizable to different scenarios.


  Mullineux defined an involution on the set of $e$-regular partitions of $n$.
When $e=p$ is prime, these partitions label irreducible symmetric group modules
in characteristic $p$. Mullineux's conjecture, since proven, was that this
``Mullineux map" described the effect on the labels of taking the tensor
product with the one-dimensional signature representation. Counting irreducible
modules fixed by this tensor product is related to counting irreducible modules
for the alternating group $A_n$ in prime characteristic. In 1991, Andrews and
Olsson worked out the generating function counting fixed points of Mullineux's
map when $e=p$ is an odd prime (providing evidence in support of Mullineux's
conjecture). In 1998, Bessenrodt and Olsson counted the fixed points in a
$p$-block of weight $w$. We extend both results to arbitrary $e$, and determine
the corresponding generating functions. When $e$ is odd but not prime the
extension is immediate, while $e$ even requires additional work and the
results, which are different, have not appeared in the literature.


  On solving a convex-concave bilinear saddle-point problem (SPP), there have
been many works studying the complexity results of first-order methods. These
results are all about upper complexity bounds, which can determine at most how
many efforts would guarantee a solution of desired accuracy. In this paper, we
pursue the opposite direction by deriving lower complexity bounds of
first-order methods on large-scale SPPs. Our results apply to the methods whose
iterates are in the linear span of past first-order information, as well as
more general methods that produce their iterates in an arbitrary manner based
on first-order information. We first work on the affinely constrained smooth
convex optimization that is a special case of SPP. Different from gradient
method on unconstrained problems, we show that first-order methods on affinely
constrained problems generally cannot be accelerated from the known convergence
rate $O(1/t)$ to $O(1/t^2)$, and in addition, $O(1/t)$ is optimal for convex
problems. Moreover, we prove that for strongly convex problems, $O(1/t^2)$ is
the best possible convergence rate, while it is known that gradient methods can
have linear convergence on unconstrained problems. Then we extend these results
to general SPPs. It turns out that our lower complexity bounds match with
several established upper complexity bounds in the literature, and thus they
are tight and indicate the optimality of several existing first-order methods.


  We use the magnetic butterfly diagram to determine the speed of the magnetic
flux transport on the solar surface towards the poles. The manifestation of the
flux transport is clearly visible as elongated structures extended from the
sunspot belt to the polar regions. The slopes of these structures are measured
and interpreted as meridional magnetic flux transport speed. Comparison with
the time-distance helioseismology measurements of the mean speed of the
meridional flows at the depth of 3.5--12 Mm shows a generally good agreement,
but the speeds of the flux transport and the meridional flow are significantly
different in areas occupied by the magnetic field. The local circulation flows
around active regions, especially the strong equatorward flows on the
equatorial side of active regions affect the mean velocity profile derived by
helioseismology, but do not influence the magnetic flux transport. The results
show that the mean longitudinally averaged meridional flow measurements by
helioseismology may not be used directly in solar dynamo models for describing
the magnetic flux transport, and that it is necessary to take into account the
longitudinal structure of these flows.


  The problem of synthesis of gate-level descriptions of digital circuits from
behavioural specifications written in higher-level programming languages
(hardware compilation) has been studied for a long time yet a definitive
solution has not been forthcoming. The argument of this essay is mainly
methodological, bringing a perspective that is informed by recent developments
in programming-language theory. We argue that one of the major obstacles in the
way of hardware compilation becoming a useful and mature technology is the lack
of a well defined function interface model, i.e. a canonical way in which
functions communicate with arguments. We discuss the consequences of this
problem and propose a solution based on new developments in programming
language theory. We conclude by presenting a prototype implementation and some
examples illustrating our principles.


  Real-time novel-view image synthesis on mobile devices is prohibitive due to
the limited computational power and storage. Using volumetric rendering
methods, such as NeRF and its derivatives, on mobile devices is not suitable
due to the high computational cost of volumetric rendering. On the other hand,
recent advances in neural light field representations have shown promising
real-time view synthesis results on mobile devices. Neural light field methods
learn a direct mapping from a ray representation to the pixel color. The
current choice of ray representation is either stratified ray sampling or
Plucker coordinates, overlooking the classic light slab (two-plane)
representation, the preferred representation to interpolate between light field
views. In this work, we find that using the light slab representation is an
efficient representation for learning a neural light field. More importantly,
it is a lower-dimensional ray representation enabling us to learn the 4D ray
space using feature grids which are significantly faster to train and render.
Although mostly designed for frontal views, we show that the light-slab
representation can be further extended to non-frontal scenes using a
divide-and-conquer strategy. Our method offers superior rendering quality
compared to previous light field methods and achieves a significantly improved
trade-off between rendering quality and speed.


  The non-linear dynamics of irrotational dust in General Relativity is studied
in synchronous and comoving coordinates. All the equations are written in terms
of the metric tensor of spatial sections orthogonal to the flow, which allows
an unambiguous expansion in powers of $1/c^2$. To lowest order, the Newtonian
approximation in Lagrangian form is derived. At this level the evolution is
governed by the Raychaudhuri equation for the Lagrangian-to-Eulerian Jacobian
matrix. The Lagrangian spatial metric reduces to that of Euclidean 3-space in
time-dependent curvilinear coordinates. A Lagrangian version of the Bernoulli
equation for the evolution of the `velocity potential' is also given. At the
post-Newtonian (PN) level, an exact and general formula is derived for
gravitational-wave emission from non-linear perturbations. It is shown that, in
the anisotropic collapse of homogeneous ellipsoids, the ratio of the PN tensor
modes to the Newtonian metric tends to diverge like the mass density. It is
finally argued that a stochastic gravitational-wave background is produced,
with present-day closure density $\Omega_{gw} \sim 10^{-5}$ -- $10^{-6}$ on $1$
- $10$ Mpc scales.


  This letter discusses rotating magnetohydrodynamics (MHD) of a thin layer of
astrophysical plasma. To describe a thin plasma layer with a free surface in a
vertical external magnetic field we use the shallow water ap- proximation. The
presence of a vertical magnetic field essentially changed the wave processes
dynamics in astrophysical plasma compared to the neu- tral uid and plasma layer
in a thoroidal magnetic field. In present case thre are three-waves nonlinear
interactions. Using the asymptotic mul- tiscale we deduced nonlinear wave
packets interaction equations: three magneto-Poincare waves interaction, three
magnetostrophic waves inter- action, the interaction of two magneto-Poincare
and one magnetostrophic wave and two magnetostrophic and one magneto-Poincare
wave interac- tion. The existence of decay instabilities and parametric
amplifications is predicted. We found following four types of decay
instabilities: magneto- Poincare wave decays into two magneto-Poincare waves,
magnetostrophic wave decays into two magnetostrophic waves, magneto-Poincare
wave de- cays into one magneto-Poincare wave and one magnetostrophic wave,
magnetostrophic wave decays into one magnetostrophic wave and one
magneto-Poincare wave. Also following mechanisms of parametric amplifications
were found: parametric amplification of magneto-Poincare waves, parametric
amplification of magnetostrophic waves, magneto-Poincare wave amplification in
magnetostrophic wave presence nd magnetostrophic wave amplification in
magneto-Poincare wave presence. The instabilities growth rates and parametrical
amplifications factors were found respectively.


  We consider point-to-point directed paths in a random environment on the
two-dimensional integer lattice. For a general independent environment under
mild assumptions we show that the quenched energy of a typical path satisfies a
central limit theorem as the mesh of the lattice goes to zero. Our proofs rely
on concentration of measure techniques and some combinatorial bounds on
families of paths.


  We study the dynamical behavior of spinor particles, and get the
energy-momentum relation of charged particles by solving the Dirac equation.
Based on the energy-momentum relation, we investigate the laws of
thermodynamics and the weak cosmic censorship conjecture for the
(2+1)-dimensional regular black hole with nonlinear electrodynamics sources in
the normal phase space and the extended phase space. Our results show that the
first law of thermodynamics as well as the weak cosmic censorship conjecture
are valid in both the phase spaces. However, the second law of thermodynamics
is violated in the extended phase space, implying that the first law and weak
cosmic censorship conjecture do not depend on the phase space while the second
law depends. In addition, in the extended phase space, we find the
configurations of the extremal and near-extremal black holes will not be
changed for the final states and initial states are the same.


  In the transient state of d-wave superconductors, we investigate the temporal
variation of photoinduced changes in the superfluid weight. We derive the
formula that relates the nonlinear response function to the nonequilibrium
distribution function. The latter qunatity is obtained by solving the kinetic
equation with the electron-electron and the electron-phonon interaction
included. By numerical calculations, a nonexponential decay is found at low
temperatures in contrast to the usual exponential decay at high temperatures.
The nonexponential decay originates from the nonmonotonous temporal variation
of the nonequilibrium distribution function at low energies. The main physical
process that causes this behavior is not the recombination of quasiparticles as
previous phenomenological studies suggested, but the absorption of phonons.


  Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle
classification systems will catalyze data-driven traffic flow optimization in
future smart cities and will transform the road infrastructure itself into a
dynamically sensing Cyber-physical System (CPS). Although a wide range of
different traffic sensing systems has been proposed, the existing solutions are
not yet able to simultaneously satisfy the multitude of requirements, e.g.,
accuracy, robustness, cost-efficiency, and privacy preservation. In this paper,
we present a novel approach, which exploits radio fingerprints -
multidimensional attenuation patterns of wireless signals - for accurate and
robust vehicle detection and classification. The proposed system can be
deployed in a highly cost-efficient manner as it relies on off-the-shelf
embedded devices which are installed into existing delineator posts. In a
comprehensive field evaluation campaign, the performance of the radio
fingerprinting-based approach is analyzed within an experimental live
deployment on a German highway, where it is able to achieve a binary
classification success ratio of more than 99% and an overall accuracy of 93.83%
for a classification task with seven different classes.


  In the stoichiometric Fe-pnictides the same d-electrons form the local
moments and participate in the metallic conductivity. In the manuscript the
emergence of the magnetic moments on the Fe-sites, their interaction with the
itinerant electrons are studied in the paramagnetic phase at the different
relations between the onsite Coulomb repulsion and the intraatomic Hund's
exchange. The energy of the local center that is due to its interaction with
the conduction electrons is calculated exactly. It is found that the spin
density wave transition is controlled by the RKKY interactions between the
centers rather than by the nesting mechanism. In the paramagnetic phase the
electrons scatter on the randomly oriented moments. The calculated inverse mean
free time provides the interpretation for few available experimental data on
the conductivity. The multi-band structure of the energy spectrum in iron
pnictides is taken into account.


  We propose a new approach to the mathematical description of light
propagation in a single-mode fiber light-guide (SMFLG) with random
inhomogeneities. We investigate statistics of complex amplitudes of the
electric field of light wave by methods of the random group theory. We have
analyzed the behavior of the coherence matrix of a monochromatic light wave and
the polarization degree of a nonmonochromatic radiation in SMFLG with random
inhomogeneities as the length of the fiber tends to infinity; in particular, we
prove that limit polarization degree is equal to zero.


  Experimental observation of highly reduced thermal conductivity in
surface-roughness dominated silicon nanowires have generated renewed interest
in low-dimensional thermoelectric devices. Using a previous work where the
scattering of phonons from a rough surface is mapped to scattering from
randomly situated localized phonons in the bulk of a smooth nanowire, we
consider the thermal current across a nanowire for various strengths of surface
disorder. We use non-equilibrium Green's function techniques that allow us to
evaluate the thermal current beyond the linear response regime, for arbitrary
cold and hot temperatures of the two semi-infinite connecting leads. We show
how the surface-roughness affects the frequency dependence of the thermal
current, eventually leading to a temperature dependent reduction of the net
current at high temperatures. We use a universal disorder parameter to describe
the surface-roughness as has been proposed, and show that the dependence of the
net current on this parameter provides a natural explanation for the
experimentally observed differences between smooth vs rough surfaces. We argue
that a systematic study of the thermal current for different values of the
temperature difference between the two sides of a surface-roughness dominated
nanowire for various strengths of disorder would help in our understanding of
how best to optimize the thermoelectric efficiency.


  The Astropy Project supports and fosters the development of open-source and
openly-developed Python packages that provide commonly needed functionality to
the astronomical community. A key element of the Astropy Project is the core
package $\texttt{astropy}$, which serves as the foundation for more specialized
projects and packages. In this article, we summarize key features in the core
package as of the recent major release, version 5.0, and provide major updates
for the Project. We then discuss supporting a broader ecosystem of
interoperable packages, including connections with several astronomical
observatories and missions. We also revisit the future outlook of the Astropy
Project and the current status of Learn Astropy. We conclude by raising and
discussing the current and future challenges facing the Project.


  The afterglow of a gamma-ray burst (GRB) has been widely argued to arise from
the interaction of a relativistic outflow with its ambient medium. During such
an interaction, a pair of shocks are generated: a forward shock that propagates
into the medium, and a reverse shock that propagates into the outflow.
Extensive studies have been conducted on the emission from the forward shock
viewed off-axis. Furthermore, the observation of a reverse shock in an on-axis
short GRB suggests that the reverse shock can produce an electromagnetic
counterpart to a gravitational wave-detected merger. In this paper, we
investigate the contribution of the reverse shock to the afterglow from a
top-hat jet viewed off-axis, and apply our model to some short GRBs previously
modeled by an off-axis emission. We employ the Markov Chain Monte Carlo (MCMC)
method to get the model parameters (i.e., the jet's half-opening angle
$\theta_j$, the viewing angle $\theta_\text{obs}$, the initial Lorentz factor
$\Gamma_0$, and the isotropic energy $E_\mathrm{iso}$). Our model successfully
reproduces off-axis afterglow emission without a structured jet. In addition,
our calculations suggest that the reverse shock may produce a prominent feature
in an early afterglow, which can be potentially observed in an orphan optical
afterglow.


  Recently, Masjed-Jamei-Beyki-Koepf studied the so called new type Euler
polynomials without making use of Euler polynomials of complex variable. Here
we study degenerate and type 2 versions of these new type Euler polynomials,
namely the type 2 degenerate cosine-Euler and type 2 degenerate sine-Euler
polynomials and also the corresponding ones for Bernoulli polynomials, namely
the type 2 degenerate cosine-Bernoulli and type 2 degenerate sine-Bernoulli
polynomials by considering the degenerate Euler and degenerate Bernoulli
polynomials of complex variable and by treating the real and imaginary parts
separately. We derive some explicit expressions for those new polynomials and
some identities relating to them. Here we note that the idea of separating the
real and imaginary parts separately gives an affirmative answer to the question
asked by Belbachir.


  We show how to prepare magnonic squeezed states in an optomagnomechanical
system, in which magnetostriction induced mechanical displacement couples to an
optical cavity via radiation pressure. We discuss two scenarios depending on
whether the magnomechanical coupling is linear or dispersive. We show that in
both cases the strong mechanical squeezing obtained via two-tone driving of the
optical cavity can be efficiently transferred to the magnon mode. In the linear
coupling case, stationary magnon squeezing is achieved; while in the dispersive
coupling case, a transient magnonic squeezed state is prepared in a two-step
protocol. The proposed magnonic squeezed states find promising applications in
quantum information processing and quantum sensing using magnons.


  Consider estimating a structured signal $\mathbf{x}_0$ from linear,
underdetermined and noisy measurements
$\mathbf{y}=\mathbf{A}\mathbf{x}_0+\mathbf{z}$, via solving a variant of the
lasso algorithm: $\hat{\mathbf{x}}=\arg\min_\mathbf{x}\{
\|\mathbf{y}-\mathbf{A}\mathbf{x}\|_2+\lambda f(\mathbf{x})\}$. Here, $f$ is a
convex function aiming to promote the structure of $\mathbf{x}_0$, say
$\ell_1$-norm to promote sparsity or nuclear norm to promote low-rankness. We
assume that the entries of $\mathbf{A}$ are independent and normally
distributed and make no assumptions on the noise vector $\mathbf{z}$, other
than it being independent of $\mathbf{A}$. Under this generic setup, we derive
a general, non-asymptotic and rather tight upper bound on the $\ell_2$-norm of
the estimation error $\|\hat{\mathbf{x}}-\mathbf{x}_0\|_2$. Our bound is
geometric in nature and obeys a simple formula; the roles of $\lambda$, $f$ and
$\mathbf{x}_0$ are all captured by a single summary parameter
$\delta(\lambda\partial((f(\mathbf{x}_0)))$, termed the Gaussian squared
distance to the scaled subdifferential. We connect our result to the literature
and verify its validity through simulations.


  Pre training of language models on large text corpora is common practice in
Natural Language Processing. Following, fine tuning of these models is
performed to achieve the best results on a variety of tasks. In this paper we
question the common practice of only adding a single output layer as a
classification head on top of the network. We perform an AutoML search to find
architectures that outperform the current single layer at only a small compute
cost. We validate our classification architecture on a variety of NLP
benchmarks from the GLUE dataset.


  The lattice properties at low temperatures of two samples of NdFeAsO1-xFx
(x=0.05 and 0.25) have been examined in order to investigate possible
structural phase transition that may occur in the optimally doped
superconducting sample with respect to the non-superconducting low-F
concentration compound. In order to detect small modifications in the ion
displacements with temperature micro-Raman and high resolution synchrotron
powder diffraction measurements were carried out. No increase of the width of
the (220) or (322) tetragonal diffraction peaks and microstrains could be found
in the superconducting sample from synchrotron XRD measurements. On the other
hand, the atomic displacement parameters deviate from the expected behavior, in
agreement with modifications in the phonon width, as obtained by Raman
scattering. These deviations occur around 150 K for both F dopings, with
distinct differences among the two compounds, i.e., they decrease at low doping
and increase for the superconducting sample. The data do not support a hidden
phase transition to an orthorhombic phase in the superconducting compound, but
point to an isostructural lattice deformation. Based on the absence of magnetic
effects in this temperature range for the superconducting sample, we attribute
the observed lattice anomalies to the formation of local lattice distortions
that, being screened by the carriers, can only acquire long-range coherence by
means of a structural phase transition at low doping levels.


  We submitted the available photometric V data of all the known galactic
Double Mode Cepheids (DMCs) to a careful frequency analysis with the aim of
detecting in each case the importance of the harmonics and of the cross
coupling terms. For each object, starting from different data subsets, we
progressively built a homogenous set of data, checking the consistency of the
results step by step. It was demonstrated that each star displays a different
content, showing that no a priori fit can be applied. Up to 4 harmonics were
found for the fundamental radial mode (F); in every case, 2 harmonics were
found for the first overtone radial mode (1 O). We also proceeded to a
preliminar analysis of the Fourier parameters of the DMC light curves and we
found a very close similarity between i) the light curves of the Classical
Cepheids and those of the F-mode of the DMCs; ii) the light curves of the
s-Cepheids and those of the 1 O-mode of the DMCs. The analysis of DMC light
curves offers the possibility of unifying the light curves of Classical and
s-Cepheids. The case of the unique DMC CO Aur is also discussed.


  For the first time, the $(d,{}^2\text{He})$ reaction was successfully used in
inverse kinematics to extract the Gamow-Teller transition strength in the
$\beta^{+}$ direction from an unstable nucleus. The nucleus studied was
$^{14}$O, and the Gamow-Teller transition strength to $^{14}$N was extracted up
to an excitation energy of 22 MeV. The measurement of the $(d,{}^2\text{He})$
reaction in inverse kinematics was made possible by the combination of an
active target time projection chamber and a magnetic spectrometer. The data
were used to test shell-model and state-of-the-art coupled cluster
calculations. Shell-model calculations reproduce the measured Gamow-Teller
strength distribution up to about 15 MeV reasonably well, after the application
of a phenomenological quenching factor. Coupled-cluster calculation reproduces
the full strength distribution well without such quenching, owing to the large
model space, the inclusion of strong correlations, and the coupling of the weak
interaction to two nucleons through two-body currents. This indicates that such
calculations provide a very promising path for answering long-standing
questions about the observed quenching of Gamow-Teller strengths in nuclei.


  A general methodology is proposed to engineer a system of interacting
components (particles) which is able to self-regulate their concentrations in
order to produce any prescribed output in response to a particular input. The
methodology is based on the mathematical equivalence between artificial neurons
in neural networks and species in autocatalytic reactions, and it specifies the
relationship between the artificial neural network's parameters and the rate
coefficients of the reactions between particle species. Such systems are
characterised by a high degree of robustness as they are able to reach the
desired output despite disturbances and perturbations of the concentrations of
the various species.


  This work adopts the notion of Ceteris Paribus (CP) as an interpretation of
the Decision Maker (DM) preferences and incorporates it in a constrained
multiobjective problem known as virtual machine placement (VMP). VMP is an
essential multiobjective problem in the design and operation of cloud data
centers concerned about placing each virtual machine to a physical machine (a
server) in the data center. We analyze the effectiveness of CP interpretation
on VMP problems and propose an NSGA-II variant with which preferred solutions
are returned at almost no extra time cost.


  For combining different single photon channels into single path, we use an
effective and reliable technique which is known as quantum multiple access. We
take advantage of an add-drop multiplexer capable of pushing and withdrawing a
single photon into an optical fiber cable which carries quantum bits from
multiusers. In addition to this, spreading spreads the channel noise at
receiver side and use of filters stop the overlapping of adjacent channels,
which helps in reducing the noise level and improved signal-to-noise ratio. In
this way, we obtain enhanced performance of code division multiple access-based
QKD links with a single photon without necessity of amplifiers and modulators.


  Let $\Gamma$ be the Littlewood-Richardson tableau corresponding to an
embedding $M$ of a subgroup in a finite abelian $p$-group. Each individual
entry in $\Gamma$ yields information about the homomorphisms from $M$ into a
particular subgroup embedding, and hence determines the position of $M$ within
the category of subgroup embeddings. Conversely, this category provides a
categorification for LR-tableaux in the sense that all subgroup embeddings
corresponding to a given LR-tableau share certain homological properties.


  Let $X$ be a projective surface and let $L$ be an ample line bundle on $X$.
The global Seshadri constant $\varepsilon(L)$ of $L$ is defined as the infimum
of Seshadri constants $\varepsilon(L,x)$ as $x\in X$ varies. It is an
interesting question to ask if $\varepsilon(L)$ is a rational number for any
pair $(X, L)$. We study this question when $X$ is a blow up of $\mathbb{P}^2$
at $r \ge 0$ very general points and $L$ is an ample line bundle on $X$. For
each $r$ we define a $\textit{submaximality threshold}$ which governs the
rationality or irrationality of $\varepsilon(L)$. We state a conjecture which
strengthens the SHGH Conjecture and assuming that this conjecture is true we
determine the submaximality threshold.


  The goal of this work is to model the peering arrangements between Autonomous
Systems (ASes). Most existing models of the AS-graph assume an undirected
graph. However, peering arrangements are mostly asymmetric Customer-Provider
arrangements, which are better modeled as directed edges. Furthermore, it is
well known that the AS-graph, and in particular its clustering structure, is
influenced by geography.
  We introduce a new model that describes the AS-graph as a directed graph,
with an edge going from the customer to the provider, but also models symmetric
peer-to-peer arrangements, and takes geography into account. We are able to
mathematically analyze its power-law exponent and number of leaves. Beyond the
analysis we have implemented our model as a synthetic network generator we call
GdTang. Experimentation with GdTang shows that the networks it produces are
more realistic than those generated by other network generators, in terms of
its power-law exponent, fractions of customer-provider and symmetric peering
arrangements, and the size of its dense core. We believe that our model is the
first to manifest realistic regional dense cores that have a clear geographic
flavor. Our synthetic networks also exhibit path inflation effects that are
similar to those observed in the real AS graph.


  We have developed an application called Wikipedia Live Monitor that monitors
article edits on different language versions of Wikipedia, as they happen in
realtime. Wikipedia articles in different languages are highly interlinked. For
example, the English article en:2013_Russian_meteor_event on the topic of the
February 15 meteoroid that exploded over the region of Chelyabinsk Oblast,
Russia, is interlinked with the Russian article on the same topic. As we
monitor multiple language versions of Wikipedia in parallel, we can exploit
this fact to detect concurrent edit spikes of Wikipedia articles covering the
same topics, both in only one, and in different languages. We treat such
concurrent edit spikes as signals for potential breaking news events, whose
plausibility we then check with full-text cross-language searches on multiple
social networks. Unlike the reverse approach of monitoring social networks
first, and potentially checking plausibility on Wikipedia second, the approach
proposed in this paper has the advantage of being less prone to false-positive
alerts, while being equally sensitive to true-positive events, however, at only
a fraction of the processing cost.


  As a measure to ascertain whether a system is metallic or insulating,
localization length $\lambda_N$, which represents the spread of electron
distribution, can be a useful quantity, especially for approaching a
metal-insulator transition from the insulator side. We try to calculate
$\lambda_N$ using a variational Monte Carlo method for normal (paramagnetic),
superconducting and antiferromagnetic states in the square-lattice Hubbard
model. It is found that the behavior of $\lambda_N$ is consistent with what is
expected from other quantities, and gives information complementary to another
measure, the Drude weight.


  About ten years ago, various notions of preferential entailment have been
introduced. The main reference is a paper by Kraus, Lehmann and Magidor (KLM),
one of the main competitor being a more general version defined by Makinson
(MAK). These two versions have already been compared, but it is time to revisit
these comparisons. Here are our three main results: (1) These two notions are
equivalent, provided that we restrict our attention, as done in KLM, to the
cases where the entailment respects logical equivalence (on the left and on the
right). (2) A serious simplification of the description of the fundamental
cases in which MAK is equivalent to KLM, including a natural passage in both
ways. (3) The two previous results are given for preferential entailments more
general than considered in some of the original texts, but they apply also to
the original definitions and, for this particular case also, the models can be
simplified.


  In the unfortunate event of current ongoing pandemic COVID-19, where
vaccination development is still at the initial stage, several preventive
control measures such as social distancing, hand-hygiene, and personal
protective equipment have been recommended by health professionals and
organizations. Among them, the safe wearing of facemasks has played a vital
role in reducing the likelihood and severity of infectious respiratory disease
transmission. The reported research in facemasks has covered many of their
material types, fabrication techniques, mechanism characterization, and
application aspects. However, in more recent times, the focus has shifted
towards the theoretical investigations of fluid flow mechanisms involved in the
virus-laden particles prevention by facemasks. This exciting research domain
aims to address the complex fluid transport that led to designing a facemask
with a better performance. This review paper discusses the recent updates on
fluid flow dynamics through the facemasks. Key design aspects such as thermal
comfort and flow resistance are discussed. Furthermore, the recent progress in
the investigations on the efficacy of facemasks for prevention of COVID 19
spread and the impact of wearing facemasks are presented. Finally, the
potential research directions for analyzing the fluid flow behavior are
highlighted.


  The Kondo effect may be observable in some atom-surface scattering
experiments, in particular, those involving alkaline-earth atoms. By combining
Keldysh techniques with the NCA approximation to solve the time-dependent
Newns-Anderson Hamiltonian in the infinite-U limit, Shao, Nordlander and
Langreth found an anomalously strong surface-temperature dependence of the
outgoing charge state fractions. Here we employ the dynamical 1/N expansion
with finite Coulomb interaction U to provide a more realistic description of
the scattering process. We test the accuracy of the 1/N expansion in the
spinless N = 1 case against the exact independent-particle solution. We then
compare results obtained in the infinite-U limit with the NCA approximation and
recover qualitative features found previously. Finally, we analyze the
realistic situation of Ca atoms with U = 5.8 eV scattered off Cu(001) surfaces.
Although the presence of the doubly-ionized Ca species can change the absolute
scattered positive Ca yields, the temperature dependence is qualitatively the
same as that found in the infinite-U limit. One of the main difficulties that
experimentalists face in attempting to detect this effect is that the atomic
velocity must be kept small enough to reduce possible kinematic smearing of the
metal's Fermi surface.


  We propose a new method for creating far-field optical barrier potentials for
ultracold atoms with widths that are narrower than the diffraction limit and
can approach tens of nanometers. The reduced widths stem from the nonlinear
atomic response to control fields that create spatially varying dark
resonances. The subwavelenth barrier is the result of the geometric scalar
potential experienced by an atom prepared in such a spatially varying dark
state. The performance of this technique, as well as its applications to the
study of many-body physics and to the implementation of quantum information
protocols with ultracold atoms, are discussed, with a focus on the
implementation of tunnel junctions.


  Sarcasm Detection has enjoyed great interest from the research community,
however the task of predicting sarcasm in a text remains an elusive problem for
machines. Past studies mostly make use of twitter datasets collected using
hashtag based supervision but such datasets are noisy in terms of labels and
language. To overcome these shortcoming, we introduce a new dataset which
contains news headlines from a sarcastic news website and a real news website.
Next, we propose a hybrid Neural Network architecture with attention mechanism
which provides insights about what actually makes sentences sarcastic. Through
experiments, we show that the proposed model improves upon the baseline by ~ 5%
in terms of classification accuracy.


  The intensive flow of personal data associated with the trend of
computerizing aspects of people's diversity in their daily lives is associated
with issues concerning not only people protection and their trust in new
technologies, but also bias in the analysis of data and problems in their
management and reuse. Faced with a complex problem, the strategies adopted,
including technologies and services, often focus on individual aspects, which
are difficult to integrate into a broader framework, which can be of effective
support for researchers and developers. Therefore, we argue for the development
of an end-to-end research infrastructure (RI) that enables trustworthy
diversity-aware data within a citizen science community.


  Let $G$ be a connected graph. The Steiner distance $d(S)$ of a set $S$ of
vertices is the minimum size of a connected subgraph of $G$ containing all
vertices of $S$. For $k\in \mathbb{N}$, the Steiner $k$-Wiener index $SW_k(G)$
is defined as $\sum_S d(S)$, where the sum is over all $k$-element subsets of
the vertex set of $G$. The average Steiner $k$-distance $\mu_k(G)$ of $G$ is
defined as $\binom{n}{k}^{-1} SW_k(G)$.
  In this paper we prove upper bounds on the Steiner Wiener index and the
average Steiner distance of graphs with given order $n$ and minimum degree
$\delta$. Specifically we show that $SW_k(G) \leq
\frac{k-1}{k+1}\frac{3n}{\delta+1} \binom{n}{k} + O(n^{k})$, and that $\mu_k(G)
\leq \frac{k-1}{k+1}\frac{3n}{\delta+1} + O(1)$. We improve this bound for
triangle-free graphs to $SW_k(G) \leq \frac{k-1}{k+1}\frac{2n}{\delta}
\binom{n}{k} + O(n^{k})$, and $\mu_k(G) \leq \frac{k-1}{k+1}\frac{2n}{\delta} +
O(1)$. All bounds are best possible.


  We have investigated phenomenological implications on the neutrino flavor
mixings in the $S_{3L}\times S_{3R}$ symmetric mass matrices including symmetry
breaking terms. We have shown how to get the large mixing angle MSW solution,
$\sin^2 2 \th_\odot=0.65\sim 0.97$ and $\Delta m_{\odot}^2= 10^{-5}\sim
10^{-4}\eV^2$, in this model. It is found that the structure of the lepton mass
matrix in our model is stable against radiative corrections although the model
leads to nearly degenerate neutrinos.


  In recent years, the interest growth in the Blockchains (BC) and
Internet-of-Things (IoT) integration -- termed as BIoT -- for more trust via
decentralization has led to great potentials in various use cases such as
health care, supply chain tracking, and smart cities. A key element of BIoT
ecosystems is the data transactions (TX) that include the data collected by IoT
devices. BIoT applications face many challenges to comply with the European
General Data Protection Regulation (GDPR) i.e., enabling users to hold on to
their rights for deleting or modifying their data stored on publicly accessible
and immutable BCs. In this regard, this paper identifies the requirements of
BCs for being GDPR compliant in BIoT use cases. Accordingly, an on-chain
solution is proposed that allows fine-grained modification (update and erasure)
operations on TXs' data fields within a BC. The proposed solution is based on a
cryptographic primitive called Chameleon Hashing. The novelty of this approach
is manifold. BC users have the authority to update their data, which are
addressed at the TX level with no side-effects on the block or chain. By
performing and storing the data updates, all on-chain, traceability and
verifiability of the BC are preserved. Moreover, the compatibility with TX
aggregation mechanisms that allow the compression of the BC size is maintained.


  This research provides a characterization of centralizer-like subgroups
associated with the $n$-Engel word in a direct product of groups. Specifically,
properties of the set of right $n$-Engel elements inside of direct products are
explored. A proof is given to demonstrate the equivalence between the set of
right $n$-Engel elements of a direct product of two groups and a direct product
of the set of right $n$-Engel elements of each direct factor. This work was
inspired by the study of centralizer-like subgroups in paper written by
Luise-Charlotte Kappe and Patrick Ratchford. We present additional questions
explored during this project, and we propose future research possibilities.


  This paper introduces Ta'keed, an explainable Arabic automatic fact-checking
system. While existing research often focuses on classifying claims as "True"
or "False," there is a limited exploration of generating explanations for claim
credibility, particularly in Arabic. Ta'keed addresses this gap by assessing
claim truthfulness based on retrieved snippets, utilizing two main components:
information retrieval and LLM-based claim verification. We compiled the
ArFactEx, a testing gold-labelled dataset with manually justified references,
to evaluate the system. The initial model achieved a promising F1 score of 0.72
in the classification task. Meanwhile, the system's generated explanations are
compared with gold-standard explanations syntactically and semantically. The
study recommends evaluating using semantic similarities, resulting in an
average cosine similarity score of 0.76. Additionally, we explored the impact
of varying snippet quantities on claim classification accuracy, revealing a
potential correlation, with the model using the top seven hits outperforming
others with an F1 score of 0.77.


  Regularized empirical risk minimization including support vector machines
plays an important role in machine learning theory. In this paper regularized
pairwise learning (RPL) methods based on kernels will be investigated. One
example is regularized minimization of the error entropy loss which has
recently attracted quite some interest from the viewpoint of consistency and
learning rates. This paper shows that such RPL methods have additionally good
statistical robustness properties, if the loss function and the kernel are
chosen appropriately. We treat two cases of particular interest: (i) a bounded
and non-convex loss function and (ii) an unbounded convex loss function
satisfying a certain Lipschitz type condition.


  We construct the moduli space, $M_d$, of degree $d$ rational maps on
$\mathbb{P}^1$ in terms of invariants of binary forms. We apply this
construction to give explicit invariants and equations for $M_3$. Using
classical invariant theory, we give solutions to the following problems: (1)
explicitly construct, from a moduli point $P\in M_d(k)$, a rational map $\phi$
with the given moduli; (2) find a model for $\phi$ over the field of definition
(i.e. explicit descent). We work out the method in detail for the cases
$d=2,3$.


  The hydrodynamics of Newtonian fluids has been the subject of a tremendous
amount of work over the past eighty years, both in physics and mathematics.
Sadly, however, a mutual feeling of incomprehension has often hindered
scientific contacts. This article provides a dictionary that allows
mathematicians to define and study the spectral properties of Kolmogorov-Obukov
turbulence in a simple deterministic manner. In other words, this approach fits
turbulence into the mathematical framework of studying the qualitative
properties of solutions of PDEs, independently from any a-priori model of the
structure of the flow. To check that this new approach is correct, this article
proves some of the classical statements that can be found in physics textbooks.
This is followed by an investigation of the compatibility between turbulence
and the smoothness of solutions of Navier-Stokes in 3D, which was the initial
motivation of this study.


  Recently, there has been a renewed interest in the machine learning community
for variants of a sparse greedy approximation procedure for concave
optimization known as {the Frank-Wolfe (FW) method}. In particular, this
procedure has been successfully applied to train large-scale instances of
non-linear Support Vector Machines (SVMs). Specializing FW to SVM training has
allowed to obtain efficient algorithms but also important theoretical results,
including convergence analysis of training algorithms and new characterizations
of model sparsity.
  In this paper, we present and analyze a novel variant of the FW method based
on a new way to perform away steps, a classic strategy used to accelerate the
convergence of the basic FW procedure. Our formulation and analysis is focused
on a general concave maximization problem on the simplex. However, the
specialization of our algorithm to quadratic forms is strongly related to some
classic methods in computational geometry, namely the Gilbert and MDM
algorithms.
  On the theoretical side, we demonstrate that the method matches the
guarantees in terms of convergence rate and number of iterations obtained by
using classic away steps. In particular, the method enjoys a linear rate of
convergence, a result that has been recently proved for MDM on quadratic forms.
  On the practical side, we provide experiments on several classification
datasets, and evaluate the results using statistical tests. Experiments show
that our method is faster than the FW method with classic away steps, and works
well even in the cases in which classic away steps slow down the algorithm.
Furthermore, these improvements are obtained without sacrificing the predictive
accuracy of the obtained SVM model.


  The work distribution function for a non-relativistic, non-interacting
quantum many-body system interacting with classical external sources is
investigated. Exact expressions for the characteristic function corresponding
to the work distribution function is obtained for arbitrary switching function
and coupling functions. The many-body frequencies are assumed to be generally
time-dependent in order to take into account the possibility of moving the
boundaries of the system in a predefined process linking the characteristic
function to the fluctuation-induced energies in confined geometries. Some
limiting cases are considered and discussed.


  This paper is to accompany the Census of Edge-Transitive Tetravalent Graphs,
available at jan.ucc.nau.edu/~swilson/C4FullSite/index.html, which is a
collection of all known edge-transitive graphs of valence 4 up to 512 vertices.
The Census contains information for each graph. This information includes
parameters such as group order, diameter, girth etc., all known constructions,
relations to other graphs in the Census and intersting substructures such as
colorings, cycle structures, and dissections. We try to present most graphs as
members of one or more parameterized families, and one purpose of this paper is
to gather together, here in one place, descriptions of each of these families,
to show how each is constructed, what the history of each is and how one family
is related to another. We also discuss in this paper the theory and techniques
behind computer searches leading to many entries in the Census.


  Recent progress in language model pre-training has led to important
improvements in Named Entity Recognition (NER). Nonetheless, this progress has
been mainly tested in well-formatted documents such as news, Wikipedia, or
scientific articles. In social media the landscape is different, in which it
adds another layer of complexity due to its noisy and dynamic nature. In this
paper, we focus on NER in Twitter, one of the largest social media platforms,
and construct a new NER dataset, TweetNER7, which contains seven entity types
annotated over 11,382 tweets from September 2019 to August 2021. The dataset
was constructed by carefully distributing the tweets over time and taking
representative trends as a basis. Along with the dataset, we provide a set of
language model baselines and perform an analysis on the language model
performance on the task, especially analyzing the impact of different time
periods. In particular, we focus on three important temporal aspects in our
analysis: short-term degradation of NER models over time, strategies to
fine-tune a language model over different periods, and self-labeling as an
alternative to lack of recently-labeled data. TweetNER7 is released publicly
(https://huggingface.co/datasets/tner/tweetner7) along with the models
fine-tuned on it.


  The extreme energetics of the gamma-ray burst (GRB) 990123 has revealed that
some of GRBs emit quite a large amount of energy, and total energy release from
GRBs seems to change from burst to burst by a factor of 10^{2-3} as E_{gamma,
iso} \sim 10^{52-55} erg, where E_{gamma, iso} is the observed GRB energy when
radiation is isotropic. If all GRBs are triggered by similar events, such a
wide dispersion in energy release seems odd. Here we propose a unified picture
for these various energetics of GRBs, in which all GRB events release roughly
the same amount of energy of E_{iso} \sim 10^{55-56} erg as relativistic
motion, with the baryon load problem almost resolved. A mild dispersion in the
initial Lorentz factor (\Gamma) results in difference of E_{gamma, iso} up to a
factor of m_p/m_e \sim 10^3. Protons work as ``a hidden energy reservoir'' of
the total GRB energy, and E_{gamma, iso} depends on the energy transfer
efficiency from protons into electrons (or positrons) in the internal shock. We
show that this transfer occurs via e^{+-} pair-creation by very high energy
photons of proton-synchrotron radiation, and this efficiency depends quite
sensitively on \Gamma. We also show that, in spite of the wide dispersion in
E_{gamma, iso}, this model predicts roughly a constant photon energy range of
the e^{+-} pair-synchrotron at \sim MeV, which is well consistent with GRB
observations. The optical flash of GRB 990123 can be explained by the internal
shock origin in our model. The apparent no-correlation between E_{gamma, iso}
and observed afterglow luminosity is consistent with the expectation of our
scenario.


  This work is a generalization of the results in [Gul] to bi-disc case. As in
[Gul], quasi-parabolic composition operators on the Hilbert-Hardy space of the
bi-disc are written as a linear combination of Toeplitz operators and Fourier
multipliers. The C*-algebra generated by Toeplitz operators and Fourier
multipliers on the Hilbert-Hardy space of the bi-disc is written as the tensor
product of the similar C*-algebra in one variable with itself. As a result we
find a nontrivial set lying inside the essential spectra of quasi-parabolic
composition operators.


  Deep functional maps have recently emerged as a successful paradigm for
non-rigid 3D shape correspondence tasks. An essential step in this pipeline
consists in learning feature functions that are used as constraints to solve
for a functional map inside the network. However, the precise nature of the
information learned and stored in these functions is not yet well understood.
Specifically, a major question is whether these features can be used for any
other objective, apart from their purely algebraic role in solving for
functional map matrices. In this paper, we show that under some mild
conditions, the features learned within deep functional map approaches can be
used as point-wise descriptors and thus are directly comparable across
different shapes, even without the necessity of solving for a functional map at
test time. Furthermore, informed by our analysis, we propose effective
modifications to the standard deep functional map pipeline, which promote
structural properties of learned features, significantly improving the matching
results. Finally, we demonstrate that previously unsuccessful attempts at using
extrinsic architectures for deep functional map feature extraction can be
remedied via simple architectural changes, which encourage the theoretical
properties suggested by our analysis. We thus bridge the gap between intrinsic
and extrinsic surface-based learning, suggesting the necessary and sufficient
conditions for successful shape matching. Our code is available at
https://github.com/pvnieo/clover.


  We give an improved bound on the famed sum-product estimate in a field of
residue class modulo $p$ ($\mathbb{F}_{p}$) by Erd\H{o}s and Szemeredi, and a
non-empty set $A \subset \mathbb{F}_{p}$ such that: $$ \max \{|A+A|,|A A|\} \gg
\min \left\{\frac{|A|^{15 / 14} \max \left\{1,|A|^{1 / 7} p^{-1 /
14}\right\}}{(\log |A|)^{2 / 7}}, \frac{|A|^{11 / 12} p^{1 / 12}}{(\log |A|)^{1
/ 3}}\right\}, $$ and more importantly: $$\max \{|A+A|,|A A|\} \gg
\frac{|A|^{15 / 14}}{(\log |A|)^{2 / 7}}.$$


  We consider the effect of stratification on systematic, large-scale flows
generated in anelastic convection. We present results from three-dimensional
numerical simulations of convection in a rotating plane layer in which the
angle between the axis of rotation and gravity is allowed to vary. This model
is representative of different latitudes of a spherical body. We consider two
distinct parameter regimes: (i) weakly rotating and (ii) rapidly rotating. In
each case, we examine the effect of stratification on the flow structure and
heat transport properties focussing on the difference between Boussinesq and
anelastic convection. Furthermore, we show that regimes (i) and (ii) generate
very different large-scale flows and we investigate the role stratification has
in modifying these flows. The stratified flows possess a net helicity not
present in the Boussinesq cases which we suggest, when combined with the
self-generated shear flows, could be important for dynamo action.


  Superconductivity-induced changes of phonon lineshapes in niobium have been
re-investigated by high-resolution inelastic neutron scattering. We show that
the changes go beyond a simple change in lifetime and frequency when the phonon
frequency is close to the superconducting energy gap $2\Delta$. The observed
lineshapes in elemental niobium are qualitatively similar to those found
previously in borocarbide superconductors and agree very well with those
predicted by the theory of Allen et al. [PRB 56, 5552 (1997)]. Our results
indicate that the peculiar phonon lineshapes in the superconducting state
predicted by the theory of Allen et al. are a general phenomenon and not
restricted to a particular class of compounds.


  Sparse Bayesian learning (SBL) can be implemented with low complexity based
on the approximate message passing (AMP) algorithm. However, it is vulnerable
to `difficult' measurement matrices as AMP can easily diverge. Damped AMP has
been used to alleviate the problem at the cost of slowing the convergence
speed. In this work, we propose an SBL algorithm based on the AMP with unitary
transformation (UTAMP), where the shape parameter of the hyperprior is tuned
automatically. It is shown that, compared to the state-of-the-art AMP based SBL
algorithm, the proposed UTAMP-SBL is much more robust and much faster, leading
to remarkably better performance. It is shown that in many cases, UTAMP-SBL can
approach the support-oracle bound closely.


  Event-based systems lie at the heart of many cloud-based Internet-of-Things
(IoT) platforms. This combination of the Broker architectural style and the
Publisher-Subscriber design pattern provides a way for smart devices to
communicate and coordinate with one another. The present design of these
cloud-based IoT frameworks lacks measures to (i) protect devices against
malicious cloud disconnections, (ii) impose information flow control among
communicating parties, and (iii) enforce coordination protocols in the presence
of compromised devices. In this work, we propose to extend the modular
event-based system architecture of Fiege et al., to incorporate brokering
policies and execution monitors, in order to address the three protection
challenges mentioned above. We formalized the operational semantics of our
protection scheme, explored how the scheme can be used to enforce BLP-style
information flow control and RBAC-style protection domains, implemented the
proposal in an open-source MQTT broker, and evaluated the performance impact of
the protection mechanisms.


  We analyse the reconstruction of an unknown pure qubit state. We derive the
optimal guess that can be inferred from any set of measurements on N identical
copies of the system with the fidelity as a figure of merit. We study in detail
the estimation process with individual von Neumann measurements and demonstrate
that they are very competitive as compared to (complicated) collective
measurements. We compute the expressions of the fidelity for large $N$ and show
that individual measurement schemes can perform optimally in the asymptotic
regime.


  In this paper, we develop a graphical calculus to rewrite photonic circuits
involving light-matter interactions and non-linear optical effects. We
introduce the infinite ZW calculus, a graphical language for linear operators
on the bosonic Fock space which captures both linear and non-linear photonic
circuits. This calculus is obtained by combining the QPath calculus, a
diagrammatic language for linear optics, and the recently developed qudit ZXW
calculus, a complete axiomatisation of linear maps between qudits. It comes
with a 'lifting' theorem allowing to prove equalities between infinite
operators by rewriting in the ZXW calculus. We give a method for representing
bosonic and fermionic Hamiltonians in the infinite ZW calculus. This allows us
to derive their exponentials by diagrammatic reasoning. Examples include phase
shifts and beam splitters, as well as non-linear Kerr media and Jaynes-Cummings
light-matter interaction.


  We construct a large set of dynamical models of the galactic bulge, bar and
inner disk using the Made-to-Measure method. Our models are constrained to
match the red clump giant density from a combination of the VVV, UKIDSS and
2MASS infrared surveys together with stellar kinematics in the bulge from the
BRAVA and OGLE surveys, and in the entire bar region from the ARGOS survey. We
are able to recover the bar pattern speed and the stellar and dark matter mass
distributions in the bar region, thus recovering the entire galactic effective
potential. We find a bar pattern speed of $39.0 \pm 3.5
\,\rm{km\,s^{-1}\,kpc^{-1}}$, placing the bar corotation radius at $6.1 \pm 0.5
\rm{kpc}$ and making the Milky Way bar a typical fast rotator. We evaluate the
stellar mass of the long bar and bulge structure to be $M_{\rm{bar/bulge}} =
1.88 \pm 0.12 \times 10^{10} \, \rm{M}_{\odot}$, larger than the mass of disk
in the bar region, $M_{\rm{inner\ disk}} = 1.29\pm0.12 \times 10^{10} \,
\rm{M}_{\odot}$. The total dynamical mass in the bulge volume is
$1.85\pm0.05\times 10^{10} \, \rm{M}_{\odot}$. Thanks to more extended
kinematic data sets and recent measurement of the bulge IMF our models have a
low dark matter fraction in the bulge of $17\%\pm2\%$. We find a dark matter
density profile which flattens to a shallow cusp or core in the bulge region.
Finally, we find dynamical evidence for an extra central mass of
$\sim0.2\times10^{10} \,\rm{M}_{\odot}$, probably in a nuclear disk or disky
pseudobulge.


  This work is a continuation of [Fittouhi and Joseph, Parabolic adjoint
action, Weierstrass Sections and components of the nilfibre in type $A$]. Let
$P$ be a parabolic subgroup of an irreducible simple algebraic group $G$, $P'$
its derived group and $\mathfrak m$ be the nilradical to its Lie algebra. A
theorem of Richardson implies that the subalgebra $\mathbb C[\mathfrak
m]^{P'}$, spanned by the $P$ semi-invariants in $\mathbb C[\mathfrak m]$, is
polynomial. A linear subvariety $e+V$ of $\mathfrak m$ is is called a
Weierstrass section for the action of $P'$ on $\mathfrak m$, if the restriction
map induces an isomorphism of $\mathbb C[\mathfrak m]^{P'}$ onto $\mathbb
C[e+V]$. Thus a Weierstrass section can exist only if the latter is polynomial,
but even when this holds its existence is far from assured. The existence of a
Weierstrass section $e+V$ in $\mathfrak m$ was established by a general
combinatorial construction. Notably $e \in \mathscr N$ and is a sum of root
vectors with linearly independent roots. The Weierstraass section $e+V$ looks
very different for different choices of parabolics but nevertheless has a
uniform construction and exists in all cases. It is called the "canonical
Weierstrass section".
  It was announced in [Fittouhi and Joseph, loc. cit.] that one may augment $e$
to an element $e_{VS}$ by adjoining root vectors. Then the linear span $E_{VS}$
of these root vectors lies in $\mathscr N^e$ and its closure is just $\mathscr
N^e$. Yet this result shows that $\mathscr N^e$ need not admit a dense $P$
orbit. However this theorem was only verified in the special case needed to
obtain the example showing that $\mathscr N^e$ may fail to admit a dense $P$
orbit. Here a general proof is given. Finally a map from compositions to the
set of distinct non-negative integers is defined. Its image is shown to
determine the canonical Weierstrass section.


  We show that the equilibrium interfaces in the disordered phase have critical
percolation fractal dimension over a wide range of length scales. We confirm
that the system falls out of equilibrium at a temperature that depends on the
cooling rate as predicted by the Kibble-Zurek argument and we prove that the
dynamic growing length once the cooling reaches the critical point satisfies
the same scaling. We determine the dynamic scaling properties of the interface
winding angle variance and we show that the crossover between critical Ising
and critical percolation properties is determined by the growing length reached
when the system fell out of equilibrium.


  We have carried out first principles all-electron calculations of the
(001)-projected 2D electron momentum density and the directional Compton
profiles along the [100], [001] and [110] directions in the Fe-based
superconductor LaOFeAs within the framework of the local density approximation.
We identify Fermi surface features in the 2D electron momentum density and the
directional Compton profiles, and discuss issues related to the observation of
these features via Compton scattering experiments.


  We demonstrate that a large class of one-dimensional quantum and classical
exchange models can be described by the same type of graphs, namely Cayley
graphs of the permutation group. Their well-studied spectral properties allow
us to derive crucial information about those models of fundamental importance
in both classical and quantum physics, and to completely characterize their
algebraic structure. Notably, we prove that the spectral gap can be obtained in
polynomial computational time, which has strong implications in the context of
adiabatic quantum computing with quantum spin-chains. This quantity also
characterizes the rate to stationarity of some important classical random
processes such as interchange and exclusion processes. Reciprocally, we use
results derived from the celebrated Bethe ansatz to obtain original
mathematical results about these graphs in the unweighted case. We also discuss
extensions of this unifying framework to other systems, such as asymmetric
exclusion processes -- a paradigmatic model in non-equilibrium physics, or the
more exotic non-Hermitian quantum systems.


  Space missions such as Kepler and CoRoT have led to large numbers of eclipse
or transit measurements in nearly continuous time series. This paper shows how
to obtain the period error in such measurements from a basic linear
least-squares fit, and how to correctly derive the timing error in the
prediction of future transit or eclipse events. Assuming strict periodicity, a
formula for the period error of such time series is derived: sigma_P = sigma_T
(12/( N^3-N))^0.5, where sigma_P is the period error; sigma_T the timing error
of a single measurement and N the number of measurements. Relative to the
iterative method for period error estimation by Mighell & Plavchan (2013), this
much simpler formula leads to smaller period errors, whose correctness has been
verified through simulations. For the prediction of times of future periodic
events, the usual linear ephemeris where epoch errors are quoted for the first
time measurement, are prone to overestimation of the error of that prediction.
This may be avoided by a correction for the duration of the time series. An
alternative is the derivation of ephemerides whose reference epoch and epoch
error are given for the centre of the time series. For long continuous or
near-continuous time series whose acquisition is completed, such central epochs
should be the preferred way for the quotation of linear ephemerides. While this
work was motivated from the analysis of eclipse timing measures in space-based
light curves, it should be applicable to any other problem with an
uninterrupted sequence of discrete timings for which the determination of a
zero point, of a constant period and of the associated errors is needed.


  In this paper we consider the Erd\H{o}s-R\'enyi random graph in the sparse
regime in the limit as the number of vertices $n$ tends to infinity. We are
interested in what this graph looks like when it contains many triangles, in
two settings. First, we derive asymptotically sharp bounds on the probability
that the graph contains a large number of triangles. We show that conditionally
on this event, with high probability the graph contains an almost complete
subgraph, i.e., the triangles form a near-clique, and has the same local limit
as the original Erd\H{o}s-R\'enyi random graph. Second, we derive
asymptotically sharp bounds on the probability that the graph contains a large
number of vertices that are part of a triangle. If order $n$ vertices are in
triangles, then the local limit (provided it exists) is different from that of
the Erd\H{o}s-R\'enyi random graph. Our results shed light on the challenges
that arise in the description of real-world networks, which often are sparse,
yet highly clustered, and on exponential random graphs, which often are used to
model such networks.


  In the framework of seesaw mechanism embedded in the constrained Minimal
Supersymmetric Standard Model (cMSSM), phases of neutrino Yukawa coupling,
$\mu$-term and $A$-terms can all contribute to the Electric Dipole Moment (EDM)
of the electron. We discuss and classify the situations for which by combined
analysis of the upcoming results on $d_e$, $d_{{\rm Hg}}$ and $d_D$
discriminating between these sources will be possible.


  We empirically investigate the functional link between the variance swap rate
and the spot variance. Using S\&P500 data over the period 2006-2018, we find
overwhelming empirical evidence supporting the affine link analytically found
by Kallsen et al. (2011) in the context of exponentially affine stochastic
volatility models. Tests on yearly subsamples suggest that exponentially
mean-reverting variance models provide a good fit during periods of extreme
volatility, while polynomial models, introduced in Cuchiero (2011), are suited
for years characterized by more frequent price jumps.


  Lepton Flavour Universality tests with semileptonic $\Lambda_b\to\Lambda_c^*$
decays are important to corroborate the present anomalies in the similar ratios
$R_{D^{(*)}}$, and can provide complementary constraints on possible origins of
these anomalies beyond the Standard Model. In this paper we provide - for the
first time - all the necessary theoretical ingredients to perform and interpret
measurements of $R_{\Lambda_c^*}$ at the LHCb experiment. For this, we revisit
the heavy-quark expansion of the relevant hadronic matrix elements, and provide
their expressions to order $\alpha_s$ and $1/m$ accuracy. Moreover, we study
the sensitivity to the form factor parameters given the projected size and
purity of upcoming and future LHCb datasets of $\Lambda_b\to
\Lambda_c^*\mu\bar{\nu}$ decays. We demonstrate explicitly the need to perform
a simultaneous fit to both $\Lambda_c^*$ final states. Finally, we provide
projections for the uncertainty of $R_{\Lambda_c^*}$ based on the form factor
analysis from semimuonic decays and theoretical relations based on the
heavy-quark expansion.


  The impact factor has been extensively used in the last years to assess
journals visibility and prestige. While the impact factor is useful to compare
journals, the specificities of subfields visibility in journals are overlooked
whenever visibility is measured only at the journal level. In this paper, we
analyze the subfields visibility in a subset of over 450,000 Physics papers. We
show that the visibility of subfields is not regular in the considered dataset.
In particular years, the variability in subfields impact factor in a journal
reached 75% of the average subfields impact factor. We also found that the
difference of subfields visibility in the same journal can be even higher than
the difference of visibility between different journals. Our results show that
subfields impact is an important factor accounting for journals visibility.


  With uncertain injections from Renewable Energy Sources (RESs) and loads,
deterministic AC Optimal Power Flow (OPF) often fails to provide optimal
setpoints of conventional generators. A computationally time-efficient,
economical, and robust solution is essential for ACOPF with short-term
injection uncertainties. Usually, applying Robust Optimization (RO) for
conventional non-linear ACOPF results in computationally intractable Robust
Counterpart (RC), which is undesirable as ACOPF is an operational problem.
Hence, this paper proposes a single-stage non-integer non-recursive RC of
ACOPF, using a dual transformation, for short-term injection uncertainties. The
proposed RC is convex, tractable, and provides base-point active power
generations and terminal voltage magnitudes (setpoints) of conventional
generators that satisfy all constraints for all realizations of defined
injection uncertainties. The non-linear impact of uncertainties on other
variables is inherently modeled without using any affine policy. The proposed
approach also includes the budget of uncertainty constraints for low
conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based
participation factored AC power flows validate the robustness of the obtained
setpoints on NESTA and case9241pegase systems for different injection
uncertainties. Comparison with previous approaches indicates the efficacy of
the proposed approach in terms of low operational cost and computation time.


  The Inverse-Wishart (IW) distribution is a standard and popular choice of
priors for covariance matrices and has attractive properties such as
conditional conjugacy. However, the IW family of priors has crucial drawbacks,
including the lack of effective choices for non-informative priors. Several
classes of priors for covariance matrices that alleviate these drawbacks, while
preserving computational tractability, have been proposed in the literature.
These priors can be obtained through appropriate scale mixtures of IW priors.
However, the high-dimensional posterior consistency of models which incorporate
such priors has not been investigated. We address this issue for the
multi-response regression setting ($q$ responses, $n$ samples) under a wide
variety of IW scale mixture priors for the error covariance matrix. Posterior
consistency and contraction rates for both the regression coefficient matrix
and the error covariance matrix are established in the ``large $q$, large $n$"
setting under mild assumptions on the true data-generating covariance matrix
and relevant hyperparameters. In particular, the number of responses $q_n$ is
allowed to grow with $n$, but with $q_n = o(n)$. Also, some results related to
the inconsistency of posterior mean for $q_n/n \to \gamma$, where $\gamma \in
(0,\infty)$ are provided.


  Cataract is one of the most prevalent protein aggregation disorders and still
the biggest cause of vision loss worldwide. The human lens, in its core region,
lacks turnover of any cells or cellular components; it has therefore evolved
remarkable mechanisms for resisting protein aggregation for a lifetime. We now
report that one such mechanism relies on an unusually abundant metabolite,
myo-inositol, to suppress light-scattering aggregation of lens proteins. We
quantified aggregation suppression by in vitro turbidimetry and characterized
both macroscopic and microscopic mechanisms of myo-inositol action using
negative-stain electron microscopy, differential scanning fluorometry, and a
thermal scanning Raman spectroscopy apparatus. Given recent metabolomic
evidence that it is dramatically depleted in human cataractous lenses compared
to age-matched controls, we suggest that maintaining or restoring healthy
levels of myo-inositol in the lens may be a simple, safe, and widely available
strategy for reducing the global burden of cataract.


  Tunneling metal-insulator-metal (MIM) junctions can exhibit an open-circuit
photovoltage (OCPV) response under illumination that may be useful for
photodetection. One mechanism for photovoltage generation is hot carrier
tunneling, in which photoexcited carriers generate a net photocurrent that must
be balanced by a drift current in the open-circuit configuration. We present
experiments in electromigrated planar MIM structures, designed with asymmetric
plasmonic properties using Au and Pt electrodes. Decay of optically excited
local plasmonic modes preferentially creates hot carriers on the Au side of the
junction, leading to a clear preferred directionality of the hot electron
photocurrent and hence a preferred polarity of the resulting OCPV. In contrast,
in an ensemble of symmetric devices constructed from only one Au, polarity of
the OCPV has no preferred direction.


  For an $(n-1)$-Auslander algebra $\Lambda$ with global dimension $n$, we give
some necessary conditions for $\Lambda$ admitting a maximal $(n-1)$-orthogonal
subcategory in terms of the properties of simple $\Lambda$-modules with
projective dimension $n-1$ or $n$. For an almost hereditary algebra $\Lambda$
with global dimension 2, we prove that $\Lambda$ admits a maximal 1-orthogonal
subcategory if and only if for any non-projective indecomposable
$\Lambda$-module $M$, $M$ is injective is equivalent to that the reduced grade
of $M$ is equal to 2. We give a connection between the Gorenstein Symmetric
Conjecture and the existence of maximal $n$-orthogonal subcategories of
$^{\bot}T$ for a cotilting module $T$. For a Gorenstein algebra, we prove that
all non-projective direct summands of a maximal $n$-orthogonal module are
$\Omega ^n\tau$-periodic. In addition, we study the relation between the
complexity of modules and the existence of maximal $n$-orthogonal subcategories
for the tensor product of two finite-dimensional algebras.


  It is shown that the recently introduced lower cone distribution function and
the associated set-valued multivariate quantile generate a Galois connection
between a complete lattice of closed convex sets and the intervall [0,1]. This
generalizes the (not so well-known) corresponding univariate result. It is also
shown that an extension of the lower cone distribution function and the
set-valued quantile characterize the capacity functional of a random set
extension of the original multivariate variable along with its distribution.


  We consider ultraluminous X-ray sources (ULXs) where the accretor is a
neutron star rather than a black hole. We show that the recently-discovered
example (M82 X-2) fits naturally into the simple picture of ULXs as beamed
X-ray sources fed at super-Eddington rates, provided that its magnetic field is
weaker ($\simeq 10^{11}{\rm G}$) than a new-born X-ray pulsar, as expected if
there has been mass gain. Continuing accretion is likely to weaken the field to
the point that pulsing stops, and make the system indistinguishable from a ULX
containing a black hole. Accordingly we suggest that a significant fraction of
all ULXs may actually contain neutron star accretors rather than black holes,
reflecting the neutron-star fraction among their X-ray binary progenitors. We
emphasize that neutron-star ULXs are likely to have {\it higher} apparent
luminosities than black hole ULXs for a given mass transfer rate, as their
tighter beaming outweighs their lower Eddington luminosities. This further
increases the likely proportion of neutron-star accretors among all ULXs.
Cygnus X-2 is probably a typical descendant of neutron-star ULXs, which may
therefore ultimately end as millisecond pulsar binaries with massive white
dwarf companions.


  We study the impact of the coupling of neutrinos with a new light neutral
gauge boson, $Z^\prime$, with a mass of less than 500 {\rm MeV} in FASER$\nu$
experiment. Scenarios in which a light gauge boson is coupled to neutrinos are
motivated within numerous contexts which are designed to explain various
anomalies in particle physics and cosmology. This interaction leads to a new
decay mode for charged mesons to a light lepton plus neutrino and $Z^{\prime}$,
($\pi^+(K^+)\to e^+ \nu Z^\prime$) followed by the subsequent decay of
$Z^\prime$ into the pair of neutrino and anti-neutrino, ($Z^\prime \to
\nu\bar{\nu}$). FASER$\nu$, the Forward Search Experiment at the LHC, has the
potential to detect collider neutrinos for the first time. In particular, the
FASER$\nu$ emulsion detector will provide the opportunity to detect
$\tau$-neutrinos and to measure their energies. Using this ability of
FASER$\nu$ emulsion detector, we investigate the potential of FASER$\nu$
experiment and the proposed upgraded version of this experiment, FASER2$\nu$,
to constrain the coupling of a neutrino with the light gauge boson.


  We drop the complexification procedure from the Newman-Janis algorithm and
introduce more physical arguments and symmetry properties, and we show how one
can generate regular and singular rotating black hole and non-black-hole
solutions in Boyer-Lindquist coordinates. We focus on generic rotating regular
black holes and show that they are regular on the Kerr-like ring but physical
entities are undefined there. We show that rotating regular black holes have
much smaller electric charges, and, with increasing charge, they turn into
regular non-black-hole solutions well before their Kerr-Newman counterparts
become naked singularities. No causality violations occur in the region inside
a rotating regular black hole. The separability of the Hamilton-Jacobi equation
for neutral particles is also carried out in the generic case, and the
innermost boundaries of circular orbits for particles are briefly discussed.
Other, but special, properties pertaining to the rotating regular counterpart
of the Ay\'on-Beato--Garc\'{\i}a regular static black hole are also
investigated.


  Lens-free Shadow Imaging Technique (LSIT) is a well-established technique for
the characterization of microparticles and biological cells. Due to its
simplicity and cost-effectiveness, various low-cost solutions have been
evolved, such as automatic analysis of complete blood count (CBC), cell
viability, 2D cell morphology, 3D cell tomography, etc. The developed auto
characterization algorithm so far for this custom-developed LSIT cytometer was
based on the hand-crafted features of the cell diffraction patterns from the
LSIT cytometer, that were determined from our empirical findings on thousands
of samples of individual cell types, which limit the system in terms of
induction of a new cell type for auto classification or characterization.
Further, its performance is suffering from poor image (cell diffraction
pattern) signatures due to its small signal or background noise. In this work,
we address these issues by leveraging the artificial intelligence-powered auto
signal enhancing scheme such as denoising autoencoder and adaptive cell
characterization technique based on the transfer of learning in deep neural
networks. The performance of our proposed method shows an increase in accuracy
>98% along with the signal enhancement of >5 dB for most of the cell types,
such as Red Blood Cell (RBC) and White Blood Cell (WBC). Furthermore, the model
is adaptive to learn new type of samples within a few learning iterations and
able to successfully classify the newly introduced sample along with the
existing other sample types.


  Nd_0.67Sr_0.33MnO_3 nanoparticles with the grain size of about 30 nm are
prepared by sol-gel method.These nanopowders are annealed at four different
temperatures viz. 800 oC, 900 oC, 1000 oC and 1100 oC to study the effect of
particle size on magnetic, transport and electron magnetic resonance (EMR)
spectral parameters. The samples are characterized by XRD, SEM, EDAX and TEM.
The ac susceptibility experiments show that as the particle size increases the
ferromagnetic to paramagnetic transition temperature (Tc) decreases. The
metal-insulator transition temperature also changes with the particle size as
revealed by resistivity measurements. EMR spectra of the nanopowders are
recorded from room temperature down to 4K using an X-band EPR spectrometer. the
spectra could be fitted using two broad-Gaussian lineshapes below Tc and
suggested the ferromagnetic nature of the samples. Above Tc a single Lorenzian
fits the signals as expetced for paramagnetic samples. The EMR spectral
parameters are found to be different from the bulk (polycrystalline)sample
data. The spectral parameters show variation with the particle size. The
presence of the two signals in the ferromagnetic phase is attributed to core
and shell regions in the nanoparticles. We could estimate the shell thickness
from the EMR intensity data as 0.7 - 1 nm which agrees with other measurements.


  Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar
induction. However, to reduce computational complexity, they make a strong
independence assumption on the generation of the child word and thus bilexical
dependencies are ignored. In this paper, we propose an approach to parameterize
L-PCFGs without making implausible independence assumptions. Our approach
directly models bilexical dependencies and meanwhile reduces both learning and
representation complexities of L-PCFGs. Experimental results on the English WSJ
dataset confirm the effectiveness of our approach in improving both running
speed and unsupervised parsing performance.


  Based on an identified quantum relativity symmetry the contraction of which
gives the Newtonian approximation of Galilean relativity, a quantum model of
the physical space can be formulated with the Newtonian space seen in a way as
the classical approximation. Matching picture for the observable algebra as the
corresponding representation of the group C* -algebra, describes the full
dynamical pictures equally successfully. Extension of the scheme to a Lorentz
covariant setting and beyond will also be addressed.The formulation of quantum
mechanics allows the theory to be seen in a new picture in line with the notion
of a noncommutative spacetime.


  To distinguish between regular and chaotic orbits in Hamiltonian systems, the
Global Symplectic Integrator (GSI) has been introduced, based on the symplectic
integration of both Hamiltonian equations of motion and variational equations.
In the present contribution, we show how to compute efficiently the MEGNO
indicator jointly with the GSI. Moreover, we discuss the choice of symplectic
integrator, in fact we point out that a particular attention has to be paid to
the structure of the Hamiltonian system associated to the variational
equations. The performances of our method is illustrated through the study of
the Arnold diffusion problem.


  In this work we investigate the possibility to completely rotate away proton
decay. We show that by choosing specific mass matrices for fermions it is
possible to accomplish this in flipped SU(5).


  Test-time adaptation (TTA) has emerged as a promising solution to address
performance decay due to unforeseen distribution shifts between training and
test data. While recent TTA methods excel in adapting to test data variations,
such adaptability exposes a model to vulnerability against malicious examples,
an aspect that has received limited attention. Previous studies have uncovered
security vulnerabilities within TTA even when a small proportion of the test
batch is maliciously manipulated. In response to the emerging threat, we
propose median batch normalization (MedBN), leveraging the robustness of the
median for statistics estimation within the batch normalization layer during
test-time inference. Our method is algorithm-agnostic, thus allowing seamless
integration with existing TTA frameworks. Our experimental results on benchmark
datasets, including CIFAR10-C, CIFAR100-C and ImageNet-C, consistently
demonstrate that MedBN outperforms existing approaches in maintaining robust
performance across different attack scenarios, encompassing both instant and
cumulative attacks. Through extensive experiments, we show that our approach
sustains the performance even in the absence of attacks, achieving a practical
balance between robustness and performance.


  We present data obtained with the Infrared Array Camera (IRAC) aboard the
Spitzer Space Telescope (Spitzer) for a sample of 74 young (t < 30 Myr old)
Sun-like (0.7 < M(star)/M(Sun) < 1.5) stars. These are a sub-set of the
observations that comprise the Spitzer Legacy science program entitled the
Formation and Evolution of Planetary Systems (FEPS). Using IRAC we study the
fraction of young stars that exhibit 3.6-8.0 micron infrared emission in excess
of that expected from the stellar photosphere, as a function of age from 3-30
Myr. The most straightforward interpretation of such excess emission is the
presence of hot (300-1000K) dust in the inner regions (< 3 AU) of a
circumstellar disk. Five out of the 74 young stars show a strong infrared
excess, four of which have estimated ages of 3-10 Myr. While we detect excesses
from 5 optically thick disks, and photospheric emission from the remainder of
our sample, we do not detect any excess emission from optically thin disks at
these wavelengths. We compare our results with accretion disk fractions
detected in previous studies, and use the ensemble results to place additional
constraints on the dissipation timescales for optically-thick, primordial
disks.


  Scenario reduction is an important topic in stochastic programming problems.
Due to the random behavior of load and renewable energy, stochastic programming
becomes a useful technique to optimize power systems. Thus, scenario reduction
gets more attentions in recent years. Many scenario reduction methods have been
proposed to reduce the scenario set in a fast speed. However, the speed of
scenario reduction is still very slow, in which it takes at least several
seconds to several minutes to finish the reduction. This limitation of speed
prevents stochastic programming to be implemented in real-time optimal control
problems. In this paper, a fast scenario reduction method based on deep
learning is proposed to solve this problem. Inspired by the deep learning based
image process, recognition and generation methods, the scenario data are
transformed into a 2D image-like data and then to be fed into a deep
convolutional neural network (DCNN). The output of the DCNN will be an "image"
of the reduced scenario set. Since images can be processed in a very high speed
by neural networks, the scenario reduction by neural network can also be very
fast. The results of the simulation show that the scenario reduction with the
proposed DCNN method can be completed in very high speed.


  We present molecular line observations of the protostellar outflow associated
with HH270mms1 in the Orion B molecular cloud with ALMA. The 12CO(J = 3 - 2)
emissions show that the outflow velocity structure consists of four distinct
components of low ($\gtrsim$ 10 km s-1), intermediate (~ 10 - 25 km s-1) and
high ($\gtrsim$ 40 km s-1) velocities in addition to the entrained gas velocity
(~ 25 - 40 km s-1). The high- and intermediate-velocity flows have
well-collimated structures surrounded by the low-velocity flow. The chain of
knots is embedded in the high-velocity flow or jet, which is the evidence of
episodic mass ejections induced by time-variable mass accretion. We could
detect the velocity gradients perpendicular to the outflow axis in both the
low- and intermediate-velocity flows. We confirmed the rotation of the envelope
and disk in the 13CO and C17O emission and found that their velocity gradients
are the same as those of the outflow. Thus, we concluded that the velocity
gradients in the low- and intermediate-velocity flows are due to the outflow
rotation. Using observational outflow properties, we estimated the outflow
launching radii to be 67.1 - 77.1 au for the low-velocity flow and 13.3 - 20.8
au for the intermediate-velocity flow. Although we could not detect the
rotation in the jets due to the limited spatial resolution, we estimated the
jet launching radii to be (2.36 - 3.14) x 10^-2 au using the observed velocity
of each knots. Thus, the jet is driven from the inner disk region. We could
identify the launching radii of distinct velocity components within a single
outflow with all the prototypical characteristics expected from recent
theoretical works.