  We introduce a comprehensive data-driven framework aimed at enhancing the
modeling of physical systems, employing inference techniques and machine
learning enhancements. As a demonstrative application, we pursue the modeling
of cathodic electrophoretic deposition (EPD), commonly known as e-coating. Our
approach illustrates a systematic procedure for enhancing physical models by
identifying their limitations through inference on experimental data and
introducing adaptable model enhancements to address these shortcomings. We
begin by tackling the issue of model parameter identifiability, which reveals
aspects of the model that require improvement. To address generalizability , we
introduce modifications which also enhance identifiability. However, these
modifications do not fully capture essential experimental behaviors. To
overcome this limitation, we incorporate interpretable yet flexible
augmentations into the baseline model. These augmentations are parameterized by
simple fully-connected neural networks (FNNs), and we leverage machine learning
tools, particularly Neural Ordinary Differential Equations (Neural ODEs), to
learn these augmentations. Our simulations demonstrate that the machine
learning-augmented model more accurately captures observed behaviors and
improves predictive accuracy. Nevertheless, we contend that while the model
updates offer superior performance and capture the relevant physics, we can
reduce off-line computational costs by eliminating certain dynamics without
compromising accuracy or interpretability in downstream predictions of
quantities of interest, particularly film thickness predictions. The entire
process outlined here provides a structured approach to leverage data-driven
methods. Firstly, it helps us comprehend the root causes of model inaccuracies,
and secondly, it offers a principled method for enhancing model performance.


  Manipulation of mechanical motion at the micro-scale has been attracting
continuous attention, leading to the successful implementation of various
strategies with potential impact on classical and quantum information
processing. We here propose an approach based on the interplay between a pair
of localized mechanical resonators and travelling surface acoustic waves (SAW).
We demonstrate the existence of a two-sided interaction, allowing to use SAW to
trigger and control the resonator oscillation, and to manipulate the elastic
energy distribution on the substrate through resonator coupling. Observation of
the vectorial structure of the resonator motion reveals the existence of two
coupling regimes, a dipole-dipole-like interaction at small separation distance
versus a surface-mediated mechanical coupling at larger separation. These
results illustrate the potential of this platform for coherent control of
mechanical vibration at a resonator level, and reciprocally for manipulating
SAW propagation using sub-wavelength elements.


  We propose a variant of elliptic multiple polylogarithms that have at most
logarithmic singularities in all variables and satisfy a differential equation
without homogeneous term. We investigate several non-trivial elliptic two-loop
Feynman integrals with up to three external legs and express them in terms of
our functions. We observe that in all cases they evaluate to pure combinations
of elliptic multiple polylogarithms of uniform weight. This is the first time
that a notion of uniform weight is observed in the context of Feynman integrals
that evaluate to elliptic polylogarithms.


  We begin a systematic study of finite semigroups that generate join
irreducible members of the lattice of pseudovarieties of finite semigroups,
which are important for the spectral theory of this lattice. Finite semigroups
$S$ that generate join irreducible pseudovarieties are characterized as
follows: whenever $S$ divides a direct product $A \times B$ of finite
semigroups, then $S$ divides either $A^n$ or $B^n$ for some $n \geq 1$. We
present a new operator ${ \mathbf{V} \mapsto \mathbf{V}^\mathsf{bar} }$ that
preserves the property of join irreducibility, as does the dual operator, and
show that iteration of these operators on any nontrivial join irreducible
pseudovariety leads to an infinite hierarchy of join irreducible
pseudovarieties. We also describe all join irreducible pseudovarieties
generated by a semigroup of order up to five. It turns out that there are $30$
such pseudovarieties, and there is a relatively easy way to remember them. In
addition, we survey most results known about join irreducible pseudovarieties
to date and generalize a number of results in Sec. 7.3 of The $q$-theory of
Finite Semigroups, Springer Monographs in Mathematics (Springer, Berlin, 2009).


  The multipartite non-Hermitian Su-Schrieffer-Heeger model is explored as a
prototypical example of one-dimensional systems with several sublattice sites
for unveiling intriguing insulating and metallic phases with no Hermitian
counterparts. These phases are characterized by composite cyclic loops of
multiple complex-energy bands encircling single or multiple exceptional points
(EPs) on the parametric space of real and imaginary energy. We show the
topology of these composite loops is similar to well-known topological objects
like M\"obius strips and Penrose triangles, and can be quantified by a
nonadiabatic cyclic geometric phase which includes contributions only from the
participating bands. We analytically derive a complete phase diagram with the
phase boundaries of the model. We further examine the connection between the
encircling of multiple EPs by complex-energy bands on parametric space and
associated topology.


  Polynomials whose zeros are symmetric either to the real line or to the unit
circle are very important in mathematics and physics. We can classify them into
three main classes: the self-conjugate polynomials, whose zeros are symmetric
to the real line; the self-inversive polynomials, whose zeros are symmetric to
the unit circle; and the self-reciprocal polynomials, whose zeros are symmetric
by an inversion with respect to the unit circle followed by a reflection in the
real line. Real self-reciprocal polynomials are simultaneously self-conjugate
and self-inversive so that their zeros are symmetric to both the real line and
the unit circle. In this survey, we present a short review of these
polynomials, focusing on the distribution of their zeros.


  The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this representation and benchmark it on a large dataset of indoor scenes. Our
experiments evaluate a number of practical design questions, demonstrate that
we can infer this representation, and quantitatively and qualitatively
demonstrate its merits compared to alternate representations.


  The ${\cal N}=1$ SUSY nonlinear sigma models in four spacetime dimensions are
studied to obtain BPS walls and junctions. A nonlinear sigma model with a
single chiral scalar superfield is found which has a moduli space of the
topology of $S^1$ and admits BPS walls and junctions connecting arbitrary
points in moduli space. New BPS junction solutions connecting $N$ discrete
vacua are also found for nonlinear sigma models with several chiral scalar
superfields. More detailed exposition of our results can be found in
Ref.\cite{NNS}.


  The ubiquity of mobile devices has led to the proliferation of mobile
services that provide personalized and context-aware content to their users.
Modern mobile services are distributed between end-devices, such as
smartphones, and remote servers that reside in the cloud. Such services thrive
on their ability to predict future contexts to pre-fetch content or make
context-specific recommendations. An increasingly common method to predict
future contexts, such as location, is via machine learning (ML) models. Recent
work in context prediction has focused on ML model personalization where a
personalized model is learned for each individual user in order to tailor
predictions or recommendations to a user's mobile behavior. While the use of
personalized models increases efficacy of the mobile service, we argue that it
increases privacy risk since a personalized model encodes contextual behavior
unique to each user. To demonstrate these privacy risks, we present several
attribute inference-based privacy attacks and show that such attacks can leak
privacy with up to 78% efficacy for top-3 predictions. We present Pelican, a
privacy-preserving personalization system for context-aware mobile services
that leverages both device and cloud resources to personalize ML models while
minimizing the risk of privacy leakage for users. We evaluate Pelican using
real world traces for location-aware mobile services and show that Pelican can
substantially reduce privacy leakage by up to 75%.


  The hyperfine structure of the interstitial muonium (Mu) center in rutile
(TiO$_2$, weakly $n$-type) has been identified by means of muon spin rotation
technique. The angle-resolved hyperfine parameter has a tetragonal anisotropy
within the $ab$ plane and axial anisotropy along the $c$ axis, strongly
suggesting that Mu simulates the known local structure of interstitial hydrogen
(H) located at an off-center position within a channel along $c$ axis, and the
electron wave function bound to Mu is highly delocalized (~1.5 nm along $c$
axis, ~0.8 nm for $a$ axis). The ionization energy of Mu ($\rightarrow \mu^+ +
e^-$) due to thermal activation is deduced to be 1.2(4) meV, as is directly
inferred from the disappearance of Mu signal above ~8 K. These observations
suggest that electronic level associated with Mu (as well as H) is situated
near the bottom of the conduction band, serving as a shallow donor state in
rutile.


  We present a comprehensive study of a low-mass star-forming region,L1251B, at
wavelengths from the near-infrared to the millimeter. L1251B, where only one
protostar, IRAS 22376+7455, was known previously, is confirmed to be a small
group of protostars based on observations with the Spitzer Space Telescope. The
most luminous source of L1251B is located 5" north of the IRAS position. A
near-infrared bipolar nebula, which is not associated with the brightest object
and is located at the southeast corner of L1251B, has been detected in the IRAC
bands. OVRO and SMA interferometric observations indicate that the brightest
source and the bipolar nebula source in the IRAC bands are deeply embedded disk
sources.Submillimeter continuum observations with single-dish telescopes and
the SMA interferometric observations suggest two possible prestellar objects
with very high column densities. Outside of the small group, many young stellar
object candidates have been detected over a larger region of 12' x 12'.
Extended emission to the east of L1251B has been detected at 850 micron; this
"east core" may be a site for future star formation since no point source has
been detected with IRAC or MIPS. This region is therefore a possible example of
low-mass cluster formation, where a small group of pre- and protostellar
objects (L1251B) is currently forming, alongside a large starless core (the
east core).


  New approach to the life time estimation for recoil alpha detected times is
presented. Brief description of the Dubna Gas Filled Recoil Separator detection
module is presented too. This approach will be used for real time detecting
ctive correlationsmethod with nearest future commissioning of new FLNR DC280
extremely high intense heavy ion cyclotron in the year 2017.


  The Water Cherenkov Detector Array (WCDA) is one of the key parts in the
Large High Altitude Air Shower Observatory (LHAASO). In the WCDA, 3600
Photomultiplier Tubes (PMTs) and the Front End Electronics (FEEs) are scattered
within a 90000 m2 area, while a time measurement resolution better than 0.5 ns
is required in the readout electronics. To achieve such time measurement
precision, high quality clock distribution and synchronization among the 400
FEEs (each FEE for 9 PMTs readout) is required. To simplify the electronics
system architecture, data, commands, and clock are transmitted simultaneously
through fibers over a 400-meter distance between FEEs and the Clock and Data
Transfer Modules (CDTMs). In this article, we propose a new method based on the
White Rabbit (WR) to achieve completely automatic clock phase alignment between
different FEEs. The original WR is enhanced to overcome the clock delay
fluctuations due to ambient temperature variations. This paper presents the
general scheme, the design of prototype electronics, and initial test results.
These indicate that a clock synchronization precision better than 50 ps is
achieved over 1 km fibers, which is well beyond the application requirement.


  The first-year WMAP data release showed that the reionization optical depth
to CMB photons is greater than previously thought. This follows from
unexpectedly high values of the C^TE_l spectrum, for l up to ~30, presumably
allowing a measurement of the E-mode polarization spectrum C^E_l sooner than
expected. This note aims to test the capability of large-angle polarization
experiments to explore the history of the cosmological reionization,
considering also the impact of cosmic variance. In particular, here we discuss
how well the ionized fraction x_e and the reionization redshift z_r can be
separately measured, at various levels of instrumental noise.


  We present a characterization of one-component inner functions in terms of
the location of their zeros and their associated singular measure. As
consequence we answer several questions posed by J. Cima and R. Mortini. In
particular we prove that for any inner function $\Theta$ whose singular set has
measure zero, one can find a Blaschke product $B$ such that $\Theta B$ is
one-component. We also obtain a characterization of one-component singular
inner functions which is used to produce examples of discrete and continuous
one-component singular inner functions.


  In this report, we present our solution to the challenge provided by the SFI
Centre for Machine Learning (ML-Labs) in which the distance between two phones
needs to be estimated. It is a modified version of the NIST Too Close For Too
Long (TC4TL) Challenge, as the time aspect is excluded. We propose a
feature-based approach based on Bluetooth RSSI and IMU sensory data, that
outperforms the previous state of the art by a significant margin, reducing the
error down to 0.071. We perform an ablation study of our model that reveals
interesting insights about the relationship between the distance and the
Bluetooth RSSI readings.


  With the use of theory developed earlier, bulk effects in ultracold neutron
coherent inelastic scattering are considered both for solid and liquid target
samples related to energy and momentum exchange with phonon and diffusion-like
modes. For the neutron in a material trap, differential and integral
probabilities for the energy transfer per bounce are presented in a simple
analytic form which exhibits the parameter dependence. As an example, the
theoretical values for the ultracold neutron loss rate from a storage bottle
with Fomblin coated walls and stainless steel walls are evaluated. Possible
contribution from incoherent inelastic scattering on hydrogen contamination is
discussed.


  In this paper, we study the minimum mean square estimator for a sublinear
operator. Under some mild assumptions, we prove the existence and uniqueness of
the minimum mean square estimator. Several characterizations of the minimum
mean square estimator are obtained. We also explore the relationship between
the minimum mean square estimator and the conditional coherent risk measure and
conditional g-expectation.


  This paper presents a framework for solving the pure-state preparation
problem using numerical optimal control. As an example, we consider the case
where a number of qubits are dispersively coupled to a readout cavity. We model
open system quantum dynamics using the Markovian Lindblad master equation,
driven by external control pulses. The main result of this paper develops a
basis of density matrices (a parameterization) where each basis element is a
density matrix itself. Utilizing a specific objective function, we show how an
ensemble of the basis elements can be used as a single initial state throughout
the optimization process - independent of the system dimension. We apply the
general framework to the specific application of ground-state reset of one and
two qubits coupled to a readout cavity.


  The aim of this paper is to give new characterizations of some fundamental
issues about idempotents. In the general setting of adjointable operators on
Hilbert $C^*$-modules, a new term of quasi-projection pair is introduced. For
each idempotent $Q$, a projection $m(Q)$, called the matched projection of $Q$,
is constructed. It is shown that $Q$ and $m(Q)$ as idempotents are homotopic,
and $\big(m(Q),Q\big)$ is a quasi-projection pair. Some formulas for $m(Q)$ are
derived. Based on these formulas, representations and norm estimations
associated with $m(Q)$ are dealt with.


  We consider the following data perturbation model, where the covariates incur
multiplicative errors. For two $n \times m$ random matrices $U, X$, we denote
by $U \circ X$ the Hadamard or Schur product, which is defined as $(U \circ
X)_{ij} = (U_{ij}) \cdot (X_{ij})$. In this paper, we study the subgaussian
matrix variate model, where we observe the matrix variate data $X$ through a
random mask $U$:
  $$
  {\mathcal X} = U \circ X \; \; \; \text{ where} \; \; \;X = B^{1/2}
{\mathbb{Z}} A^{1/2}, $$
  where ${\mathbb{Z}}$ is a random matrix with independent subgaussian entries,
and $U$ is a mask matrix with either zero or positive entries, where ${\mathbb
E} U_{ij} \in [0, 1]$ and all entries are mutually independent. Subsampling in
rows, or columns, or random sampling of entries of $X$ are special cases of
this model. Under the assumption of independence between $U$ and $X$, we
introduce componentwise unbiased estimators for estimating covariance $A$ and
$B$, and prove the concentration of measure bounds in the sense of guaranteeing
the restricted eigenvalue($\textsf{RE}$) conditions to hold on the unbiased
estimator for $B$, when columns of data matrix $X$ are sampled with different
rates. We further develop multiple regression methods for estimating the
inverse of $B$ and show statistical rate of convergence. Our results provide
insight for sparse recovery for relationships among entities (samples,
locations, items) when features (variables, time points, user ratings) are
present in the observed data matrix ${\mathcal X}$ with heterogeneous rates.
Our proof techniques can certainly be extended to other scenarios. We provide
simulation evidence illuminating the theoretical predictions.


  We present a lattice QCD calculation of the low energy constants of the
leading order chiral Lagrangian. In these simulations the epsilon regime is
reached by using tree-level improved nHYP Wilson fermions combined with
reweighting in the quark mass. We analyze two point functions on two ensembles
with lattices of size (1.85fm)^4 and (2.8fm)^4, and at several quark mass
values between 4 and 20 MeV. The data are well fitted with next-to-leading
order chiral perturbative formulae and predict F=90(4)MeV and
Sigma^(1/3)=248(6)MeV in the MS-bar scheme at 2 GeV.


  In social networks the {\sc Strong Triadic Closure} is an assignment of the
edges with strong or weak labels such that any two vertices that have a common
neighbor with a strong edge are adjacent. The problem of maximizing the number
of strong edges that satisfy the strong triadic closure was recently shown to
be NP-complete for general graphs. Here we initiate the study of graph classes
for which the problem is solvable. We show that the problem admits a
polynomial-time algorithm for two unrelated classes of graphs: proper interval
graphs and trivially-perfect graphs. To complement our result, we show that the
problem remains NP-complete on split graphs, and consequently also on chordal
graphs. Thus we contribute to define the first border between graph classes on
which the problem is polynomially solvable and on which it remains NP-complete.


  We consider a class of matrix integrals over the unitary group $U(N)$ with an
infinite set of couplings characterized by a series $f(q)=\sum_{n \ge 1} a_n
q^n$, with $a_n \in \mathbb{Z}$. Such integrals arise in physics as the
partition functions of free four-dimensional gauge theories on $S^3$ and, in
particular, as the superconformal index of super Yang-Mills theory. We show
that any such model can be expressed in terms of a system of free fermions in
an ensemble parameterized by the infinite set of couplings. Integrating out the
fermions in a given quantum state leads to a convergent expansion as a series
of determinants, as shown by Borodin-Okounkov many years ago. By further
averaging over the ensemble, we obtain a formula for the matrix integral as a
$q$-series with successive terms suppressed by $q^{\alpha N + \beta}$ where
$\alpha$, $\beta$ do not depend on $N$. This provides a matrix-model
explanation of the giant graviton expansion that has been observed recently in
the literature.


  We study wave propagation in interacting Bose liquid, where the short range
part of the interaction between atoms is of a hard core type, and its long
range part scales with a distance as a power law. The cases of Coulomb,
dipole-dipole and Van der Waals interaction are considered. We employ a
hydrodynamic approach, based on the exact solution of Lieb-Liniger model, and
study the evolution of a density pulse instantly released from a potential
trap. We analyze semi-classical Euler and continuity equations and construct
the corresponding Riemann invariants. We supplement our analysis with numerical
calculations and discuss experimental applications for ultacold atom
experiments.


  In this paper, the numbers of rational curves on general complete
intersection Calabi-Yau threefolds in complex projective spaces are computed up
to degree six. The results are all in agreement with the predictions made from
mirror symmetry.


  We considered the characteristic features of SU(3) partial dynamical symmetry
in the interacting boson model framework to demonstrate the relevance of this
technique in the nuclear spectroscopy of Dy (A=160) nucleus. The predictions of
SU(3)-PDS for energy spectrum and B(E2)transition probabilities were compared
with the most recent experimental data which an acceptable degree of agreement
is achieved.


  The rise of "big data" has led to the frequent need to process and store
datasets containing large numbers of high dimensional observations. Due to
storage restrictions, these observations might be recorded in a
lossy-but-sparse manner, with information collapsed onto a few entries which
are considered important. This results in informative missingness in the
observed data. Our motivating application comes from retail analytics, where
the behaviour of product sales is summarised by the price elasticity of each
product with respect to a small number of its top competitors. The resulting
data are vectors of order statistics, due to only the top few entries being
observed. Interest lies in characterising the behaviour of a product's
competitors, and clustering products based on how their competition is spread
across the market. We develop nonparametric Bayesian methodology for modelling
vectors of order statistics that utilises a Dirichlet Process Mixture Model
with an Exponentiated Weibull kernel. Our approach allows us added flexibility
for the distribution of each vector, while providing parameters that
characterise the decay of the leading entries. We implement our methods on a
retail analytics dataset of the cross-elasticity coefficients, and our analysis
reveals distinct types of behaviour across the different products of interest.


  Capsule Networks (CapsNets) have been proposed as an alternative to
Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are
more capable than CNNs for autonomous agent exploration of realistic scenarios.
In real world navigation, rewards external to agents may be rare. In turn,
reinforcement learning algorithms can struggle to form meaningful policy
functions. This paper's approach Capsules Exploration Module (Caps-EM) pairs a
CapsNets architecture with an Advantage Actor Critic algorithm. Other
approaches for navigating sparse environments require intrinsic reward
generators, such as the Intrinsic Curiosity Module (ICM) and Augmented
Curiosity Modules (ACM). Caps-EM uses a more compact architecture without need
for intrinsic rewards. Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer
trainable network parameters than the ICM and Depth-Augmented Curiosity Module
(D-ACM), respectively, for 1141% and 437% average time improvement over the ICM
and D-ACM, respectively, for converging to a policy function across "My Way
Home" scenarios.


  We study the surface $\mathcal{W}_k : x^2 + y^2 + z^2 + x^2 y^2 z^2 = k x y
z$ in $(\mathbb{P}^1)^3$, a tri-involutive K3 (TIK3) surface. We explain a
phenomenon noticed by Fuchs, Litman, Silverman, and Tran: over a finite field
of order $\equiv 1$ mod $8$, the points of $\mathcal{W}_4$ do not form a single
large orbit under the group $\Gamma$ generated by the three involutions fixing
two variables and a few other obvious symmetries, but rather admit a partition
into two $\Gamma$-invariant subsets of roughly equal size. The phenomenon is
traced to an explicit double cover of the surface.


  Vehicle tracking is an integral part of intelligent traffic management
systems. Previous implementations of vehicle tracking used Global Positioning
System(GPS) based systems that gave location of the vehicle of an individual on
their smartphones.The proposed system uses a novel approach to vehicle tracking
using Vehicle License plate detection and recognition (VLPR) technique, which
can be integrated on a large scale with traffic management systems. Initial
methods of implementing VLPR used simple image processing techniques which were
quite experimental and heuristic. With the onset of Deep learning and Computer
Vision, one can create robust VLPR systems that can produce results close to
human efficiency. Previous implementations, based on deep learning, made use of
object detection and support vector machines for detection and a heuristic
image processing based approach for recognition. The proposed system makes use
of scene text detection model architecture for License plate detection and for
recognition it uses the Optical character recognition engine (OCR) Tesseract.
The proposed system obtained extraordinary results when it was tested on a
highway video using NVIDIA Ge-force RTX 2080ti GPU, results were obtained at a
speed of 30 frames per second with accuracy close to human.


  In this work we present a new approach to threshold resummation in processes
with heavy quarks. In particular we will focus on the differential decay rate
of a color-singlet particle into a $b \bar b$ pair and we will show how to
resum in a consistent way both the logarithms of the mass and the logarithms of
the heavy flavor energy fraction. Within this framework, we match the two
different approaches existing in literature to threshold resummation, the main
difference of which is the way in which the mass is treated (5-flavor scheme vs
4-flavor scheme).


  This paper is devoted to studying abstract stochastic semilinear evolution
equations with additive noise in Hilbert spaces. First, we prove the existence
of unique local mild solutions and show their regularity. Second, we show the
regular dependence of the solutions on initial data. Finally, some applications
to stochastic partial differential equations are presented.


  The metastable lifetime of the square-lattice and simple-cubic-lattice
kinetic Ising models are studied in the low-temperature limit. The simulations
are performed using Monte Carlo with Absorbing Markov Chain algorithms to
simulate extremely long low-temperature lifetimes. The question being addressed
is at what temperatures the mathematically rigorous low-temperature results
become valid. It is shown that the answer depends partly on how close the
system is to fields at which the prefactor for the metastable decay either has
a discontinuity or diverges.


  In this paper we study two finite trigonometric sums $\sum a_l\csc\big(\pi
l/n\big)\,,$ where $a_l$ are equal either to $\cos(2\pi l \nu/n)$ or to
$(-1)^{l+1}$, and where the summation index $l$ and the discrete parameter
$\nu$ both run through $1$ to $n-1$ (marginally, cases $a_l=\ln\csc\big(\pi
l/n\big)$ and $a_l=\Psi\big(l/n\big)$ also appear in the paper). These sums
occur in various problems in mathematics, physics and engineering, and play an
important part in some number-theoretic problems. Formally, the first of these
sums is also the so-called Dowker sum of order one half. In the paper, we
obtain several integral and series representations for the above-mentioned
sums, investigate their properties, derive their complete asymptotical
expansions and deduce very accurate upper and lower bounds for them (both
bounds are asymptotically vanishing). In addition, we obtain a useful
approximate formula containing only three terms, which is also very accurate
and can be particularly appreciated in applications. Both trigonometric sums
appear to be closely related with the digamma function and with the square of
the Bernoulli numbers. Finally, we also derive several advanced summation
formulae for the gamma and the digamma functions, in which the first on these
sums, as well as the product of a sequence of cosecants $\,\prod\big(\csc(\pi
l/n)\big)^{\csc(\pi l/n)}$, play an important role.


  Individual differences in human intelligence can be modeled and predicted
from in vivo neurobiological connectivity. Many established modeling frameworks
for predicting intelligence, however, discard higher-order information about
individual differences in brain network topology, and show only moderate
performance when generalized to make predictions in out-of-sample subjects. In
this paper, we propose that connectome-based predictive modeling, a common
predictive modeling framework for neuroscience data, can be productively
modified to incorporate information about brain network topology and individual
differences via the incorporation of bagged decision trees and the network
based statistic. These modifications produce a novel predictive modeling
framework that leverages individual differences in cortical tractography to
generate accurate regression predictions of intelligence scores. Network
topology-based feature selection provides for natively interpretable networks
as input features, increasing the model's explainability. Investigating the
proposed modeling framework's efficacy, we find that advanced connectome-based
predictive modeling generates neuroscience predictions that account for a
significantly greater proportion of variance in general intelligence scores
than previously established methods, advancing our scientific understanding of
the network architecture that underlies human intelligence.


  We report on the first observation of a pronounced re-entrant
superconductivity phenomenon in superconductor/ferromagnetic layered systems.
The results were obtained using a superconductor/ferromagnetic-alloy bilayer of
Nb/Cu(1-x)Ni(x). The superconducting transition temperature T_{c} drops sharply
with increasing thickness d_{CuNi} of the ferromagnetic layer, until complete
suppression of superconductivity is observed at d_{CuNi}= 4 nm. Increasing the
Cu(1-x)Ni(x) layer thickness further, superconductivity reappears at
d_{CuNi}=13 nm. Our experiments give evidence for the pairing function
oscillations associated with a realization of the quasi-one dimensional
Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) like state in the ferromagnetic layer.


  For exponents $p,q\in (1,\infty),$ we study the $L^p$-to-$L^q$ boundedness
and compactness of the commutator $[b,H_{\gamma}] = bH_{\gamma} - H_{\gamma}b,$
where $H_{\gamma}$ is the Hilbert transform along the monomial curve $\gamma$
and the function $b$ is complex valued.
  We obtain a sparse form domination of the commutator and show that $b\in
\operatorname{BMO}^{\gamma,\alpha}$ is a sufficient condition for boundedness
in a half-open range of exponents $q\in[p,p+p(\Xi));$ in doing so we obtain a
genuinely fractional case $q>p$ and also give a new proof of the case $q=p,$
due to Bongers, Guo, J. Li and Wick. Involving the $L^p$ improving phenomena of
single scale averages, we show that $b\in \operatorname{VMO}^{\gamma,\alpha}$
is a sufficient condition for compactness in the same range of exponents
$q\in[p,p+p(\Xi))$ as in the case of boundedness.
  For $q<p,$ we show that $b\in \dot L^r$ is a sufficient condition for
compactness; the argument is new and we use it to obtain a new short proof of
the same implication for commutators of Calder\'on-Zygmund operators, recently
due to Hyt\"{o}nen, K. Li, Tao and Yang.
  For necessity, and now restricting to the plane, we develop the approximate
weak factorization argument to the extent that it now works for all monomial
curves, thus removing the extra assumption present in our earlier work that the
graph $\gamma(\mathbb{R})\subset\mathbb{R}^2$ intersects adjacent quadrants. As
a consequence in the plane, we obtain that all of the above sufficiency
conditions are necessary.
  We present several open problems.


  Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved
substantial success in the field of medical image segmentation. However, most
of these methods fail to enable the diffusion model to learn edge features and
non-edge features effectively and to inject them efficiently into the diffusion
backbone. Additionally, the domain gap between the images features and the
diffusion model features poses a great challenge to prostate segmentation. In
this paper, we proposed CriDiff, a two-stage feature injecting framework with a
Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach
for prostate segmentation. The CIS maximizes the use of multi-level features by
efficiently harnessing the complementarity of high and low-level features. To
effectively learn multi-level of edge features and non-edge features, we
proposed two parallel conditioners in the CIS: the Boundary Enhance Conditioner
(BEC) and the Core Enhance Conditioner (CEC), which discriminatively model the
image edge regions and non-edge regions, respectively. Moreover, the GP
approach eases the inconsistency between the images features and the diffusion
model without adding additional parameters. Extensive experiments on four
benchmark datasets demonstrate the effectiveness of the proposed method and
achieve state-of-the-art performance on four evaluation metrics.


  We analyze some features of alternative Hermitian and quasi-Hermitian quantum
descriptions of simple and bipartite compound systems. We show that alternative
descriptions of two interacting subsystems are possible if and only if the
metric operator of the compound system can be obtained as tensor product of
positive operators on component spaces. Some examples also show that such
property could be strictly connected with symmetry properties of the
non-Hermitian Hamiltonian.


  We applied the FCI to 646 engineering students from Bogota when they began
your first year physics, we found that the relative frequency of the number of
correct answers has a random pattern of two levels, also we found that they
don't have clear mental models about physical world.


  Single crystals of the layered perovskite GdBaCo_{2}O_{5+x} (GBCO) have been
grown by the floating-zone method, and their transport, magnetic, and
structural properties have been studied in detail over a wide range of oxygen
contents. The obtained data are used to establish a rich phase diagram centered
at the "parent'' compound GdBaCo_{2}O_{5.5} -- an insulator with Co ions in the
3+ state. An attractive feature of GBCO is that it allows a precise and
continuous doping of CoO_{2} planes with either electrons or holes, spanning a
wide range from the charge-ordered insulator at 50% electron doping (x=0) to
the undoped band insulator (x=0.5), and further towards the heavily hole-doped
metallic state. This continuous doping is clearly manifested in the behavior of
thermoelectric power which exhibits a spectacular divergence with approaching
x=0.5, where it reaches large absolute values and abruptly changes its sign. At
low temperatures, the homogeneous distribution of doped carriers in GBCO
becomes unstable, and both the magnetic and transport properties point to an
intriguing nanoscopic phase separation. We also find that throughout the
composition range the magnetic behavior in GBCO is governed by a delicate
balance between ferromagnetic (FM) and antiferromagnetic (AF) interactions,
which can be easily affected by temperature, doping, or magnetic field,
bringing about FM-AF transitions and a giant magnetoresistance (MR) phenomenon.
An exceptionally strong uniaxial anisotropy of the Co spins, which dramatically
simplifies the possible spin arrangements, together with the possibility of
continuous ambipolar doping turn GBCO into a model system for studying the
competing magnetic interactions, nanoscopic phase separation and accompanying
magnetoresistance phenomena.


  Given $n$ noisy samples with $p$ dimensions, where $n \ll p$, we show that
the multi-step thresholding procedure based on the Lasso -- we call it the {\it
Thresholded Lasso}, can accurately estimate a sparse vector $\beta \in {\mathbb
R}^p$ in a linear model $Y = X \beta + \epsilon$, where $X_{n \times p}$ is a
design matrix normalized to have column $\ell_2$-norm $\sqrt{n}$, and $\epsilon
\sim N(0, \sigma^2 I_n)$. We show that under the restricted eigenvalue (RE)
condition, it is possible to achieve the $\ell_2$ loss within a logarithmic
factor of the ideal mean square error one would achieve with an {\em oracle }
while selecting a sufficiently sparse model -- hence achieving {\it sparse
oracle inequalities}; the oracle would supply perfect information about which
coordinates are non-zero and which are above the noise level. We also show for
the Gauss-Dantzig selector (Cand\`{e}s-Tao 07), if $X$ obeys a uniform
uncertainty principle, one will achieve the sparse oracle inequalities as
above, while allowing at most $s_0$ irrelevant variables in the model in the
worst case, where $s_0 \leq s$ is the smallest integer such that for $\lambda =
\sqrt{2 \log p/n}$, $\sum_{i=1}^p \min(\beta_i^2, \lambda^2 \sigma^2) \leq s_0
\lambda^2 \sigma^2$. Our simulation results on the Thresholded Lasso match our
theoretical analysis excellently.


  Empowering the creation of thermodynamic and property databases, the CALPHAD
(CALculation of PHAse Diagrams) methodology plays a vital role in enhancing
materials and manufacturing process design. In this study, we propose a deep
learning approach to train parameters in CALPHAD models solely based on
chemical formula. We demonstrate its application through an example of
calculating the mixing parameter of liquids. This work showcases the
integration of CALPHAD and deep learning, highlighting its potential for
achieving automated comprehensive CALPHAD modeling.


  Surface mount technology (SMT) is an enhanced method in electronic packaging
in which electronic components are placed directly on soldered printing circuit
board (PCB) and are permanently attached on PCB with the aim of reflow
soldering process. During reflow process, once deposited solder pastes start
melting, electronic components move in a direction that achieve their highest
symmetry. This motion is known as self-alignment since can correct potential
mounting misalignment. In this study, two noticeable machine learning
algorithms, including support vector regression (SVR) and random forest
regression (RFR) are proposed as a prediction technique to (1) diagnose the
relation among component self-alignment, deposited solder paste status and
placement machining parameters, (2) predict the final component position on PCB
in x, y, and rotational directions before entering in the reflow process. Based
on the prediction result, a non-linear optimization model (NLP) is developed to
optimize placement parameters at initial stage. Resultantly, RFR outperforms in
terms of prediction model fitness and error. The optimization model is run for
6 samples in which the minimum Euclidean distance from component position after
reflow process from ideal position (i.e., the center of pads) is outlined as
25.57 ({\mu}m) regarding defined boundaries in model.


  An ideal experiment is designed to determine the past of a particle in the
nested Mach-Zehnder interferometer (MZI) by using standard quantum mechanics
with quantum non-demolition measurements. We find that when the photon reaches
the detector, it only follows one arm of the outer interferometer and leaves no
trace in the inner MZI; while when it goes through the inner MZI, it cannot
reach the detector. Our result obtained from the standard quantum mechanics is
contradict to the statement based on two state vector formulism, "the photon
did not enter the (inner) interferometer, the photon never left the
interferometer, but it was there". Therefore, the statement and also the
overlap claim are incorrect.


  This paper presents the results of the irradiation, performed with
atmospheric-like neutrons and heavy-ions, of Commercial Off-the Shelf
Components (COTS), which can be used in space missions. In such cases, it is
crucial to perform tests in a radiation environment that emulates the
environment of different orbits around Earth. In our study we used
atmosphericlike neutrons with fluences up to 1011 neutrons/cm-2 and Kr ions of
fluences up to 107 ions/cm-2. These intensities are augmented with respect to
the atmospheric one in order to shorten the irradiation time while simulating a
long-time exposure during a possible mission in Low Earth Orbit (LEO). A
similar radiation environment to LEO can also be present during High-Energy
Physics experiments. Therefore, the study herby reported can also be helpful
for accelerator physics. In this paper we show in detail procedures, setup and
results we have obtained on a commercial device normally exploited in
automotive environments.


  Every real algebraic variety determines a Voronoi decomposition of its
ambient Euclidean space. Each Voronoi cell is a convex semialgebraic set in the
normal space of the variety at a point. We compute the algebraic boundaries of
these Voronoi cells.


  In this paper, we formulate and analyze generalizations of the quantum penny
flip game. In the penny flip game, one coin has two states, heads or tails, and
two players apply alternating operations on the coin. In the original Meyer
game, the first player is allowed to use quantum (i.e., non-commutative)
operations, but the second player is still only allowed to use classical (i.e.,
commutative) operations. In our generalized games, both players are allowed to
use non-commutative operations, with the second player being partially
restricted in what operators they use. We show that even if the second player
is allowed to use "phase-variable" operations, which are non-Abelian in
general, the first player still has winning strategies. Furthermore, we show
that even when the second player is allowed to choose one from two or more
elements of the group $U(2)$, the second player has winning strategies under
certain conditions. These results suggest that there is often a method for
restoring the quantum state disturbed by another agent.


  A well known argument in cosmology gives that the power spectrum (or
structure function) $P(k)$ of mass density fluctuations produced from a uniform
initial state by physics which is causal (i.e. moves matter and momentum only
up to a finite scale) has the behaviour $P(k) \propto k^4$ at small $k$. Noting
the assumption of analyticity at $k=0$ of $P(k)$ in the standard derivation of
this result, we introduce a class of solvable one dimensional models which
allows us to study the relation between the behaviour of $P(k)$ at small $k$
and the properties of the probability distribution $f(l)$ for the spatial
extent $l$ of mass and momentum conserving fluctuations. We find that the $k^4$
behaviour is obtained in the case that the first {\it six} moments of $f(l)$
are finite. Interestingly the condition that the fluctuations be localised -
taken to correspond to the convergence of the first two moments of $f(l)$ -
imposes only the weaker constraint $P(k) \propto k^n$ with $n$ anywhere in the
range $0< n \leq 4$. We interpret this result to suggest that the causality
bound will be loosened in this way if quantum fluctuations are permitted.


  Cooper pairs occupy the ground state of superconductors and are typically
composed of maximally entangled electrons with opposite spin. In order to study
the spin and entanglement properties of these electrons, one must separate them
spatially via a process known as Cooper pair splitting (CPS). Here we provide
the first demonstration of CPS in a semiconductor two-dimensional electron gas
(2DEG). By coupling two quantum dots to a superconductor-semiconductor hybrid
region we achieve efficient Cooper pair splitting, and clearly distinguish it
from other local and non-local processes. When the spin degeneracy of the dots
is lifted, they can be operated as spin-filters to obtain information about the
spin of the electrons forming the Cooper pair. Not only do we observe a near
perfect splitting of Cooper pairs into opposite-spin electrons (i.e.
conventional singlet pairing), but also into equal-spin electrons, thus
achieving triplet correlations between the quantum dots. Importantly, the
exceptionally large spin-orbit interaction in our 2DEGs results in a strong
triplet component, comparable in amplitude to the singlet pairing. The
demonstration of CPS in a scalable and flexible platform provides a credible
route to study on-chip entanglement and topological superconductivity in the
form of artificial Kitaev chains.


  We introduce dual curvature measures for log-concave functions, which in the
case of characteristic functions recover the dual curvature measures for convex
bodies introduced by Huang-Lutwak-Yang-Zhang in 2016. Variational formulas are
shown. The associated Minkowski problem for these dual curvature measures is
considered and sufficient conditions in the symmetric setting are demonstrated.


  Rotational superradiance affects the dynamics of many rotating systems in
nature, through either stimulated or spontaneous extraction of energy and
angular momentum. By now, this process is well-studied in the relativistic
setting, where systems are intrinsically dispersion-free. In many condensed
matter systems, however, dispersion is an unavoidable aspect of the description
for the short wavelength modes. For these systems, how might one expect
superradiance to be modified? In this work, an answer to this question is
provided using an illustrative example. The scattering of linear excitations of
a Bose-Einstein condensate are studied in the presence of a rotating, draining
vortex flow using the full Bogoliubov dispersion relation. It is shown that
dispersion suppresses the extraction of energy and angular momentum, firstly,
by decreasing the superradiant bandwidth, and secondly, by preventing
high-angular momentum modes from superradiating.


  We construct bona fide one- and two-center supersymmetric solutions to N=2,
d=4 supergravity coupled to SU(2) non-Abelian vector multiplets. The solutions
describe black holes and global monopoles alone or in equilibrium with each
other and exhibit non-Abelian hairs of different kinds.


  Background: The half-life of the famous $^{14}$C $\beta$ decay is anomalously
long, with different mechanisms: the tensor force, cross-shell mixing, and
three-body forces, proposed to explain the cancellations that lead to a small
transition matrix element. Purpose: We revisit and analyze the role of the
tensor force for the $\beta$ decay of $^{14}$C as well as of neighboring
isotopes. Methods: We add a tensor force to the Gogny interaction, and derive
an effective Hamiltonian for shell-model calculations. The calculations were
carried out in a $p$-$sd$ model space to investigate cross-shell effects.
Furthermore, we decompose the wave functions according to the total orbital
angular momentum $L$ in order to analyze the effects of the tensor force and
cross-shell mixing.
  Results: The inclusion of the tensor force significantly improves the
shell-model calculations of the $\beta$-decay properties of carbon isotopes. In
particular, the anomalously slow $\beta$ decay of $^{14}$C can be explained by
the isospin $T=0$ part of the tensor force, which changes the components of
$^{14}$N with the orbital angular momentum $L=0,1$, and results in a dramatic
suppression of the Gamow-Teller transition strength. At the same time, the
description of other nearby $\beta$ decays are improved.
  Conclusions: Decomposition of wave function into $L$ components illuminates
how the tensor force modifies nuclear wave functions, in particular suppression
of $\beta$-decay matrix elements. Cross-shell mixing also has a visible impact
on the $\beta$-decay strength. Inclusion of the tensor force does not seem to
significantly change, however, binding energies of the nuclei within the
phenomenological interaction.


  The jump of the Milnor number of an isolated singularity $f_0$ is the minimal
non-zero difference between the Milnor numbers of $f_0$ and one of its
deformations $(f_s)$. We give a formula for the jump in some class of surface
singularities in the case deformations are non-degenerate.


  This paper aims to explore the temporal-spatial spreading and asymptotic
behaviors of West Nile virus by a reaction-advection-diffusion system with free
boundaries, especially considering the impact of advection term on the
extinction and persistence of West Nile virus. We define the spatial-temporal
risk index $R^{F}_{0}(t)$ with the advection rate and the general basic disease
reproduction number $R^D_0$ to get the vanishing-spreading dichotomy regimes of
West Nile virus. We show that there exists a threshold value $\mu^{*}$ of the
advection rate, and obtain the threshold results of it. When the spreading
occurs, we investigate the asymptotic dynamical behaviors of the solution in
the long run and first give a sharper estimate that the asymptotic spreading
speed of the leftward front is less than the rightward front for $0<\mu<\mu^*$.
At last, we give some numerical simulations to identify the significant effects
of the advection.


  In two dimensions polymer collapse has been shown to be complex with multiple
low temperature states and multi-critical points. Recently, strong numerical
evidence has been provided for a long-standing prediction of universal scaling
of winding angle distributions, where simulations of interacting self-avoiding
walks show that the winding angle distribution for N-step walks is compatible
with the theoretical prediction of a Gaussian with a variance growing
asymptotically as C log N . Here we extend this work by considering interacting
self-avoiding trails which are believed to be a model representative of some of
the more complex behaviour. We provide robust evidence that, while the high
temperature swollen state of this model has a winding angle distribution that
is also Gaussian, this breaks down at the polymer collapse point and at low
temperatures. Moreover, we provide some evidence that the distributions are
well modelled by stretched/compressed exponentials, in contradistinction to the
behaviour found in interacting self-avoiding walks.


  We study $p$-harmonic maps, $p$-harmonic morphisms, biharmonic maps, and
quasiregular mappings into submanifolds of warped product Riemannian manifolds
${I}\times_f S^{m-1}(k)\, $ of an open interval and a complete
simply-connecteded $(m-1)$-dimensional Riemannian manifold of constant
sectional curvature $k$. We establish an existence theorem for $p$-harmonic
maps and give a classification of complete stable minimal surfaces in certain
three dimensional warped product Riemannian manifolds ${\bf R}\times_f
S^{2}(k)\, ,$ building on our previous work. When $f \equiv\, $ Const. and
$k=0$, we recapture a generalized Bernstein Theorem and hence the Classical
Bernstein Theorem in $R^3$. We then extend the classification to parabolic
stable minimal hypersurfaces in higher dimensions.


  This paper is concerned with inverse source problems for the time-dependent
Lam\'e system in an unbounded domain corresponding to the exterior of a bounded
cavity or the full space $\R^3$. If the time and spatial variables of the
source term can be separated with compact support, we prove that the vector
valued spatial source term can be uniquely determined by boundary Dirichlet
data in the exterior of a given cavity. Uniqueness and stability for recovering
some class of time-dependent source terms are also obtained using partial
boundary data.


  We study the reliability of dark matter (DM) profile determinations for rich
clusters of galaxies based on weak gravitational lensing measurements. We
consider three functional forms for the mass distribution: a softened
isothermal sphere (SIS), the Navarro, Frenk, & White model (NFW) and the
Klypin, Kravstov, Bullock, & Primack model (KKBP). A maximum likelihood
parametric method is developed and applied to simulated images of background
galaxies (z=2.0) lensed by clusters at z=0.2, 0.5, and 0.8. The images have
field size (~10 arcmin) and angular resolution (~0.5 arcsec) characteristic of
the new generation of 8-10 meter-class ground-based telescopes. The method
recovers the correct virial mass of the DM halo, M_{200}, to within ~10% at all
redshifts. The recovered values of \delta_c and r_s are only accurate to ~25%;
however, their correlated errors are such that greater accuracy is obtained for
the mass. Clusters with an SIS density profile are recognized by the method,
while NFW and KKBP clusters are recognized as not being SIS. The method does
not discriminate between NFW and KKBP profiles for clusters of a given mass,
because they are too similar in their outer regions. Strongly lensed arcs near
the cluster centers would be needed to distinguish NFW from KKBP.


  We study the $(a:b)$ Maker-Breaker subgraph game played on the edges of the
complete graph $K_n$ on $n$ vertices, $n,a,b \in \mathbb{N}$ where the goal of
Maker is to build a copy of a specific fixed subgraph $H$. In our work this is
a spanning graph with minimum degree $k=k(n)$, a connected spanning subgraph or
a Hamiltonian subgraph. In the $(a:b)$ game in each round Maker chooses $a$
unclaimed edges of $K_n$ and Breaker chooses $b$ unclaimed edges. Maker wins,
if he succeeds to build a copy of the subgraph under consideration, otherwise
Breaker wins. For the $k$-minimum-degree, we present a winning strategy for
Maker leading to a bound that generalizes a bound of Gebauer and Szab{\'o} for
the $(1:b)$ case. Moreover, we give an explicit strategy for Breaker for $b
>(1+o(1)) \frac{an}{a+\ln(n)}$ in case of
$a=o\left(\sqrt{\frac{n}{\ln(n)}}\right)$ and $k=o(\ln(n))$. Note that this
bound is the same as the Maker bound presented by Hefetz et al. (2012) for the
$(a:b)$ connectivity game, which implies that the asymptotic optimal bias for
this game is $\frac{an}{a+\ln(n)}$. This resolves the open problem stated by
these authors. We also study the $(a:b)$ Hamiltonicity game in which Maker's
goal is to create a Hamiltonian subgraph. For the $(1:b)$ variant Krivelevich
proved that $\left(1+o(1) \right)\frac{n}{\ln n}$ is the exact threshold bias.
Controlling Breaker's vertex degree in the $(a:b)$ Maker-Breaker minimum degree
game enables us to the asymptotic optimal generalized threshold bias for the
$(a:b)$-game, both for $a=o\left(\sqrt{\frac{n}{\ln n}} \right)$ and
$a=\Omega\left(\sqrt{\frac{n}{\ln n}} \right)$.


  Controlled text generation (CTG) seeks to guide large language model (LLM)
output to produce text that conforms to desired criteria. The current study
presents a novel CTG algorithm that enforces adherence toward specific
rhetorical relations in an LLM sentence-completion context by a parser-driven
decoding scheme that requires no model fine-tuning. The method is validated
both with automatic and human evaluation. The code is accessible on GitHub.


  Let V be a simple vertex operator algebra and let G be a finite automorphism
group of V. In [DY], it was shown that any irreducible V-module is a completely
reducible V^G-module where V^G is the G-invariant sub-vertex operator algebra
of V. In this paper, we give an alternative proof of this fact using the theory
of skew group algebras. We also extend this result to any irreducible g-twisted
V-module when g is in the center of G and V is a g-rational vertex operator
algebra.


  We report on high resolution spectroscopy of four giant stars in the Galactic
old open clusters Berkeley~29 and Saurer~1 obtained with HIRES at the Keck
telescope. These two clusters possess the largest galactocentric distances
insofar known for open star clusters, and therefore are crucial objects to
probe the chemical pattern and evolution of the outskirts of the Galactic disk.
We find that $[Fe/H]=-0.38\pm0.14$ and $[Fe/H]=-0.44\pm0.18$ for Saurer 1 and
Berkeley~29, respectively. Based on these data, we first revise the fundamental
parameters of the clusters, and then discuss them in the context of the
Galactic disk radial abundance gradients. Both clusters seem to significantly
deviate from the general trend, suggesting that the outer part of the Galactic
disk underwent a completely different evolution compared to the inner disk. In
particular Berkeley~29 is clearly associated with the Monoceros stream, while
Saurer~1 exhibits very different properties. The abundance ratios suggest that
the chemical evolution of the outer disk was dominated by the Galactic halo.


  In this paper, we present a refined approach to establish a global Lipschitz
stability for an inverse source problem concerning the determination of forcing
terms in the wave equation with mixed boundary conditions. It consists of
boundary conditions incorporating a dynamic boundary condition and Dirichlet
boundary condition on disjoint subsets of the boundary. The primary
contribution of this article is the rigorous derivation of a sharp Carleman
estimate for the wave system with a dynamic boundary condition. In particular,
our findings complete and drastically improve the earlier results established
by Gal and Tebou [SIAM J. Control Optim., 55 (2017), 324-364]. This is achieved
by using a different weight function to overcome some relevant difficulties. As
for the stability proof, we extend to dynamic boundary conditions a recent
argument avoiding cut-off functions. Finally, we also show that our developed
Carleman estimate yields a sharp boundary controllability result.


  Recent ALMA observations have revealed that a large fraction of
protoplanetary discs contain bright rings at (sub)millimeter wavelengths. Dust
trapping induced by pressure maxima in the gas disc is a popular explanation
for these rings. However, it is unclear whether such pressure bumps can survive
for evolutionary time-scales of the disc. In this work, we investigate an
alternative scenario, which involves only dust-gas interactions in a smooth gas
disc. We postulate that ALMA rings are a manifestation of a dense, clumpy
mid-plane that is actively forming planetesimals. The clumpy medium itself
hardly experiences radial drift, but clumps lose mass by disintegration and
vertical transport and planetesimal formation. Starting from a seed ring, we
numerically solve the transport equations to investigate the ring's survival.
In general, rings move outward, due to diffusion of the clump component.
Without pressure support, rings leak material at rates $\sim$40
$M_\oplus\,\mathrm{Myr}^{-1}$ and in order for rings to survive, they must feed
from an external mass reservoir of pebbles. In the case where the pebble size
is constant in the disk, a cycle between ring formation and dispersion emerges.
Rings produce large quantities of planetesimals, which could be material for
planet formation and explain the massive budget inferred debris disc. Mock
images of ALMA observations compare well to the rings of Elias 24 and AS 209
from DSHARP's sample.


  Lateral effects are analyzed in the antibunching of a beam of free
non-interacting fermions. The emission of particles from a source is
dynamically described in a 3D full quantum field-theoretical framework. The
size of the source and the detectors, as well as the temperature of the source
are taken into account and the behavior of the visibility is scrutinized as a
function of these parameters.


  I review the ability of the LHC (large hadron collider), NLC (next linear
lepton collider) and FMC (first muon collider) to detect and study Higgs
bosons, with emphasis on the Higgs bosons of extended Higgs sectors, especially
those of the minimal supersymmetric standard model (MSSM). Particular attention
is given to means for distinguishing the lightest neutral CP-even Higgs boson
of the MSSM from the single Higgs boson of the minimal Standard Model (SM).


  In Supplemental Information (SI) to the article [A. M. Fleshman, G. E.
Forsythe, M. Petrowsky, R. Frech, J. Phys. Chem. B 2016, 120, 37, 9959-68] the
temperature dependences (TDs) of the dielectric and dynamic properties of
polyhydric alcohols, as well as the activation energy (Ea) obtained using the
compensated Arrhenius formalism (CAF) are given. The authors present CAF as an
approach provides physical significance into the molecular level nature of the
transport process. However, the CAF logic is physically inadequate and analysis
of TDS polyhydric alcohols using CAF cannot provide physically significant
correlations useful for understanding the molecular mechanisms of fluid
dynamics. However, such correlations from SI can be obtained by applying
physically adequate Arrhenius approximations of TDs properties of liquids [A.
Kholmanskiy, N. Zaytseva, J. Mol. Liq. 275 (2019) 741-8].


  We present a new inner-outer iterative algorithm for edge enhancement in
imaging problems. At each outer iteration, we formulate a Tikhonov-regularized
problem where the penalization is expressed in the 2-norm and involves a
regularization operator designed to improve edge resolution as the outer
iterations progress, through an adaptive process. An efficient hybrid
regularization method is used to project the Tikhonov-regularized problem onto
approximation subspaces of increasing dimensions (inner iterations), while
conveniently choosing the regularization parameter (by applying well-known
techniques, such as the discrepancy principle or the ${\mathcal L}$-curve
criterion, to the projected problem). This procedure results in an automated
algorithm for edge recovery that does not involve regularization parameter
tuning by the user, nor repeated calls to sophisticated optimization
algorithms, and is therefore particularly attractive from a computational point
of view. A key to the success of the new algorithm is the design of the
regularization operator through the use of an adaptive diagonal weighting
matrix that effectively enforces smoothness only where needed. We demonstrate
the value of our approach on applications in X-ray CT image reconstruction and
in image deblurring, and show that it can be computationally much more
attractive than other well-known strategies for edge preservation, while
providing solutions of greater or equal quality.


  Variance-Invariance-Covariance Regularization (VICReg) is a self-supervised
learning (SSL) method that has shown promising results on a variety of tasks.
However, the fundamental mechanisms underlying VICReg remain unexplored. In
this paper, we present an information-theoretic perspective on the VICReg
objective. We begin by deriving information-theoretic quantities for
deterministic networks as an alternative to unrealistic stochastic network
assumptions. We then relate the optimization of the VICReg objective to mutual
information optimization, highlighting underlying assumptions and facilitating
a constructive comparison with other SSL algorithms and derive a generalization
bound for VICReg, revealing its inherent advantages for downstream tasks.
Building on these results, we introduce a family of SSL methods derived from
information-theoretic principles that outperform existing SSL techniques.


  Recent network research has focused on the cascading failures in a system of
interdependent networks and the necessary preconditions for system collapse. An
important question that has not been addressed is how to repair a failing
system before it suffers total breakdown. Here we introduce a recovery strategy
of nodes and develop an analytic and numerical framework for studying the
concurrent failure and recovery of a system of interdependent networks based on
an efficient and practically reasonable strategy. Our strategy consists of
repairing a fraction of failed nodes, with probability of recovery $\gamma$,
that are neighbors of the largest connected component of each constituent
network. We find that, for a given initial failure of a fraction $1-p$ of
nodes, there is a critical probability of recovery above which the cascade is
halted and the system fully restores to its initial state and below which the
system abruptly collapses. As a consequence we find in the plane $\gamma-p$ of
the phase diagram three distinct phases. A phase in which the system never
collapses without being restored, another phase in which the recovery strategy
avoids the breakdown, and a phase in which even the repairing process cannot
avoid the system collapse.


  In this paper, we present the results of the NeurIPS-2022 Neural MMO
Challenge, which attracted 500 participants and received over 1,600
submissions. Like the previous IJCAI-2022 Neural MMO Challenge, it involved
agents from 16 populations surviving in procedurally generated worlds by
collecting resources and defeating opponents. This year's competition runs on
the latest v1.6 Neural MMO, which introduces new equipment, combat, trading,
and a better scoring system. These elements combine to pose additional
robustness and generalization challenges not present in previous competitions.
This paper summarizes the design and results of the challenge, explores the
potential of this environment as a benchmark for learning methods, and presents
some practical reinforcement learning training approaches for complex tasks
with sparse rewards. Additionally, we have open-sourced our baselines,
including environment wrappers, benchmarks, and visualization tools for future
research.


  In recent years, the task of video prediction-forecasting future video given
past video frames-has attracted attention in the research community. In this
paper we propose a novel approach to this problem with Vector Quantized
Variational AutoEncoders (VQ-VAE). With VQ-VAE we compress high-resolution
videos into a hierarchical set of multi-scale discrete latent variables.
Compared to pixels, this compressed latent space has dramatically reduced
dimensionality, allowing us to apply scalable autoregressive generative models
to predict video. In contrast to previous work that has largely emphasized
highly constrained datasets, we focus on very diverse, large-scale datasets
such as Kinetics-600. We predict video at a higher resolution on unconstrained
videos, 256x256, than any other previous method to our knowledge. We further
validate our approach against prior work via a crowdsourced human evaluation.


  In this study, we investigate the spin susceptibility in the superconducting
state of a multilayer system with a layer-dependent Rashba spin-orbit coupling,
representing a locally non-centrosymmetric superconductor. We show that the
charge imbalance between different layers yields a strong layer dependence on
the susceptibility, most significantly for a weak spin-orbit coupling, which is
in contrast to a situation with an equally distributed charge. These results
can be relevant for experimental test in multilayer high-Tc cuprates as well as
in superconducting CeCoIn5/YbCoIn5 superlattices with more than two layers.


  This study is focused on the mechanical properties and stress transfer
behavior of multiscale composites containing nano- and micro-scale
reinforcements. The distinctive feature of construction of this composite is
such that the carbon nanostructures (CNS) are dispersed in the matrix around
the continuous microscale fiber to modify microfiber-matrix interfacial
adhesion. Such CNS are considered to be made of aligned CNTs (A-CNTs).
Accordingly, multiscale models are developed for such hybrid composites. First,
molecular dynamics simulations in conjunction with the Mori-Tanaka method are
used to determine the effective elastic properties of nano-engineered
interphase layer composed of CNS and epoxy. Subsequently, a micromechanical
pull-out model for a continuous fiber multi-scale composite is developed, and
stress transfer behavior is studied for different orientations of CNS
considering their perfect and imperfect interfacial bonding conditions with the
surrounding epoxy. Such interface condition was modeled using the linear spring
layer model with a continuous traction but a displacement jump. The current
pull-out model accounts for the radial as well as the axial deformations of
different orthotropic constituent phases of the multiscale composite. The
results from the developed pull-out model are compared with those of the finite
element analyses and are found to be in good agreement. Our results reveal that
the stress transfer characteristics of the multiscale composite are
significantly improved by controlling the CNT morphology around the fiber,
particularly, when they are aligned along the axial direction of the microscale
fiber. The results also show that the CNS-epoxy interface weakening
significantly influences the radial stress along the length of the microscale
fiber.


  Matrix Factorization (MF) on large scale data takes substantial time on a
Central Processing Unit (CPU). While Graphical Processing Unit (GPU)s could
expedite the computation of MF, the available memory on a GPU is finite.
Leveraging GPUs require alternative techniques that allow not only parallelism
but also address memory limitations. Synchronization between computation units,
isolation of data related to a computational unit, sharing of data between
computational units and identification of independent tasks among computational
units are some of the challenges while leveraging GPUs for MF. We propose a
block based approach to matrix factorization using Stochastic Gradient Descent
(SGD) that is aimed at accelerating MF on GPUs. The primary motivation for the
approach is to make it viable to factorize extremely large data sets on limited
hardware without having to compromise on results. The approach addresses
factorization of large scale data by identifying independent blocks, each of
which are factorized in parallel using multiple computational units. The
approach can be extended to one or more GPUs and even to distributed systems.
The RMSE results of the block based approach are with in acceptable delta in
comparison to the results of CPU based variant and multi-threaded CPU variant
of similar SGD kernel implementation. The advantage, of the block based
variant, in-terms of speed are significant in comparison to other variants.


  We explore a family of nested recurrence relations with arbitrary levels of
nesting, which have an interpretation in terms of fixed points of morphisms
over a countably infinite alphabet. Recurrences in this family are related to a
number of well-known sequences, including Hofstadter's G sequence and the
Conolly and Tanny sequences. For a recurrence a(n) in this family with only
finitely terms, we provide necessary and sufficient conditions for the limit
a(n)/n to exist.


  The NUCLEUS experiment aims at measuring the coherent elastic scattering of
nuclear reactor antineutrinos off nuclei using cryogenic calorimeters.
Operating at an overburden of 3m.w.e., muon-induced backgrounds are expected to
be dominant. It is therefore essential to develop an efficient muon veto, with
a detection efficiency of more than 99%. This will be realized in NUCLEUS
through a compact cube assembly of plastic scintillator panels. In order to
prevent a large unshielded area where the cryostat intersects the shielding
arrangement without unnecessarily increasing the induced detector dead time, a
novel concept has been investigated, featuring a plastic scintillator based
active muon veto operating inside the NUCLEUS cryostat at sub-Kelvin
temperatures. The verification of the key physical aspects of this cryogenic
muon veto detector led to the first reported measurements of organic plastic
scintillators at sub-Kelvin temperatures. The functionality of the principal
scintillation process of organic plastic scintillators at these temperatures
has been confirmed. On the basis of these findings, a disc-shape plastic
scintillator equipped with wavelength shifting fibers and a silicon
photomultiplier to guide and detect the scintillation light has been developed.
The NUCLEUS cryogenic muon veto will be the first of its kind to be operated at
sub-Kelvin temperatures.


  In this paper, we introduce the Causal Cognitive Interference Channel With
Delay (CC-IFC-WD) in which the cognitive user transmission can depend on $L$
future received symbols as well as the past ones. Taking the effect of the link
delays into account, CC-IFC-WD fills the gap between the genie-aided and causal
1cognitive radio channels. We study three special cases: 1) Classical CC-IFC
(L=0), 2) CC-IFC without delay (L=1) and 3) CC-IFC with a block length delay
(L=n). In each case, we obtain an inner bound on the capacity region. Our
coding schemes make use of cooperative strategy by generalized block Markov
superposition coding, collaborative strategy by rate splitting, and
Gel'fand-Pinsker coding in order to pre-cancel part of the interference.
Moreover, instantaneous relaying and non-causal partial Decode-and-Forward
strategies are employed in the second and third cases, respectively. The
derived regions under special conditions, reduce to several previously known
results. Moreover, we show that the coding strategy which we use to derive
achievable rate region for the classical CC-IFC achieves capacity for a special
case of this channel. Furthermore, we extend our achievable rate regions to
Gaussian case. Providing a numerical example for Gaussian CC-IFC-WD, we
investigate the rate gain of the cognitive link for different delay values.


  We systematically study 3d $\mathcal{N}=2$ dualities for $U(N_c)$ gauge
theories with different CS levels for the abelian and the non-abelian factors.
We derive such dualities by a gauging/ungauging procedure on other known
dualities and by adding an opportune CS contact term for the baryonic symmetry.
This allows us to follow the various steps on the three sphere partition
function, obtaining exact identities among the new dual phases proposed here.
We apply the procedure to various cases, namely SQCD without and with adjoint,
chiral dualities and some sporadic cases arising from the real mass flow of
models with an $E_7 \times U(1)$ enhancement of the global symmetry.


  To develop efficient thermal management strategies for wide bandgap (WBG)
semiconductor devices, it is essential to have a clear understanding of the
heat transport process within the device and accurately predict the junction
temperature. In this paper, we used the phonon Monte Carlo (MC) method with the
phonon dispersion of various typical WBG semiconductors, including GaN, SiC,
AlN, and \ce{\beta-Ga_2O_3}, to investigate the thermal spreading resistance in
a ballistic-diffusive regime. It was found that when compared with Fourier's
law-based predictions, the increase in the thermal resistance caused by
ballistic effects was strongly related to different phonon dispersions. Based
on the model deduced under the gray-medium approximation and the results of
dispersion MC, we obtained a thermal resistance model that can well address the
issues of thermal spreading and ballistic effects, and the influences of phonon
dispersion. The model can be easily coupled with FEM based thermal analysis and
applied to different materials. This paper can provide a clearer understanding
of the influences of phonon dispersion on the thermal transport process, and it
can be useful for the prediction of junction temperatures and the development
of thermal management strategies for WBG semiconductor devices.


  In this work, we investigate the heat exchange between two quantum systems
whose initial equilibrium states are described by the generalized Gibbs
ensemble. First, we generalize the fluctuation relations for heat exchange
discovered by Jarzynski and W\'ojcik to quantum systems prepared in the
equilibrium states described by the generalized Gibbs ensemble at different
generalized temperatures. Second, we extend the connections between heat
exchange and R\'enyi divergences to quantum systems with very general initial
conditions.These relations are applicable for quantum systems with conserved
quantities and are universally valid for quantum systems in the integrable and
chaotic regimes.


  Relations between static and dynamic viscoelastic responses in gels can be
very elucidating and may provide useful tools to study the behavior of
bio-materials such as protein hydrogels. An important example comes from the
viscoelasticity of semisolid gel-like materials, which is characterized by two
regimes: a low-frequency regime where the storage modulus $G^{\prime}(\omega)$
displays a constant value $G_{\text{eq}}$, and a high-frequency power-law
stiffening regime, where $G^{\prime}(\omega) \sim \omega^{n}$. Recently, by
considering Monte Carlo simulations to study the formation of peptides
networks, we found an intriguing and somewhat related power-law relationship
between the plateau modulus and the threshold frequency, i.e. $G_{\text{eq}}
\sim ( \omega^{*} )^{\Delta}$ with $\Delta = 2/3$. Here we present a simple
theoretical approach to describe that relationship and test its validity by
using experimental data from a $\beta$-lactoglobulin gel. We show that our
approach can be used even in the coarsening regime where the fractal model
fails. Remarkably, the very same exponent $\Delta$ is found to describe the
experimental data.


  While the complexity of translating future linear temporal logic (LTL) into
automata on infinite words is well-understood, the size increase involved in
turning automata back to LTL is not. In particular, there is no known
elementary bound on the complexity of translating deterministic
$\omega$-regular automata to LTL. Our first contribution consists of tight
bounds for LTL over a unary alphabet: alternating, nondeterministic and
deterministic automata can be exactly exponentially, quadratically and linearly
more succinct, respectively, than any equivalent LTL formula. Our main
contribution consists of a translation of general counter-free deterministic
$\omega$-regular automata into LTL formulas of double exponential
temporal-nesting depth and triple exponential length, using an intermediate
Krohn-Rhodes cascade decomposition of the automaton. To our knowledge, this is
the first elementary bound on this translation. Furthermore, our translation
preserves the acceptance condition of the automaton in the sense that it turns
a looping, weak, B\"uchi, coB\"uchi or Muller automaton into a formula that
belongs to the matching class of the syntactic future hierarchy. In particular,
it can be used to translate an LTL formula recognising a safety language to a
formula belonging to the safety fragment of LTL (over both finite and infinite
words).


  Our aim is to generalize the result that two generic complex line
arrangements are equivalent. In fact for a line arrangement A we associate its
defining polynomial, the product of a_ix+b_iy+c_i, so that A = (f=0). We prove
that the defining polynomials of two generic line arrangements are, up to a
small deformation, topologically equivalent. In higher dimension the related
result is that within a family of equivalent hyperplane arrangements the
defining polynomials are topologically equivalent.


  We study string field theory (third quantization) of the two-dimensional
model of quantum geometry called generalized CDT ("causal dynamical
triangulations"). Like in standard non-critical string theory the so-called
string field Hamiltonian of generalized CDT can be associated with W-algebra
generators through the string mode expansion. This allows us to define an
"absolute" vacuum. "Physical" vacua appear as coherent states created by vertex
operators acting on the absolute vacuum. Each coherent state corresponds to
specific values of the coupling constants of generalized CDT. The cosmological
"time" only exists relatively to a given "physical" vacuum and comes into
existence before space, which is created because the "physical" vacuum is
unstable. Thus each CDT "universe" is created as a "Big Bang" from the absolute
vacuum, its time evolution is governed by the CDT string field Hamiltonian with
given coupling constants, and one can imagine interactions between CDT
universes with different coupling constants ("fourth quantization")


  We first show that the Traveling Salesman Problem in an n-vertex graph with
average degree bounded by d can be solved in O*(2^{(1-\eps_d)n}) time and
exponential space for a constant \eps_d depending only on d, where the
O*-notation suppresses factors polynomial in the input size. Thus, we
generalize the recent results of Bjorklund et al. [TALG 2012] on graphs of
bounded degree.
  Then, we move to the problem of counting perfect matchings in a graph. We
first present a simple algorithm for counting perfect matchings in an n-vertex
graph in O*(2^{n/2}) time and polynomial space; our algorithm matches the
complexity bounds of the algorithm of Bjorklund [SODA 2012], but relies on
inclusion-exclusion principle instead of algebraic transformations. Building
upon this result, we show that the number of perfect matchings in an n-vertex
graph with average degree bounded by d can be computed in
O*(2^{(1-\eps_{2d})n/2}) time and exponential space, where \eps_{2d} is the
constant obtained by us for the Traveling Salesman Problem in graphs of average
degree at most 2d.
  Moreover we obtain a simple algorithm that counts the number of perfect
matchings in an n-vertex bipartite graph of average degree at most d in
O*(2^{(1-1/(3.55d))n/2}) time, improving and simplifying the recent result of
Izumi and Wadayama [FOCS 2012].


  The universality hypothesis for quark and lepton mixing matrices (CKM and
MNS) is further developed. This hypothesis explains why the CKM is almost
diagonal whereas the MNS is almost maximally mixed. If this hypothesis is true,
the Dirac CP violating phase of the MNS mixing matrix is bounded around $\pi$
or 0. Quark-lepton mass matrices which realizes this hypothesis are
constructed, showing simple power law relations among mass matrices for
up-type, down-type quarks and neutrinos.


  We confront the predicted gyroscopic precession (in particular the geodetic
precession) from metric $f(R)$ theory with the data provided by the mission,
Gravity Probe--B. We find the constraint, $|a_2| < 1.33\times 10^{12}
\mathrm{m}^2$, where $a_2$ is the coefficient assessing the strength of the
lowest order correction to the Einstein--Hilbert action for a metric $f(R)$
theory with $f$ analytic. This constraint improves over astrophysical bounds
provided by massive black holes and planetary precession which are
$|a_2|\gtrsim 10^{17} \mathrm{m}^2$ and $|a_2|\lesssim 1.2\times10^{18}
\mathrm{m}^2$ respectively and it is complementary to the stringent ones
provided by lab based experiments, like the E\"ot--Wash experiment. We also
investigate the modification of our result for gyroscopic precession if the
oblateness of Earth is taken into account by considering the quadrupole moment
of Earth. Finally, we provide a generalisation of our result for the gyroscopic
precession in the context of Brans--Dicke theories with a potential (recovering
the previously derived results in the appropriate limits).


  Support vector machines (SVMs) naturally embody sparseness due to their use
of hinge loss functions. However, SVMs can not directly estimate conditional
class probabilities. In this paper we propose and study a family of coherence
functions, which are convex and differentiable, as surrogates of the hinge
function. The coherence function is derived by using the maximum-entropy
principle and is characterized by a temperature parameter. It bridges the hinge
function and the logit function in logistic regression. The limit of the
coherence function at zero temperature corresponds to the hinge function, and
the limit of the minimizer of its expected error is the minimizer of the
expected error of the hinge loss. We refer to the use of the coherence function
in large-margin classification as C-learning, and we present efficient
coordinate descent algorithms for the training of regularized ${\cal
C}$-learning models.


  The change of the transverse spin asymmetry in the scattering of
highly-relativistic spin-polarized electrons from nuclei by vacuum polarization
effects is calculated within the Uehling approximation. Results are provided
for 1 - 100 MeV electrons scattering from 31P, 64Zn, 152Sm, 197Au and 207,208Pb
at backward angles.


  A number of studies based on data collected by the $\textit{Hubble Space
Telescope}$ ($\textit{HST}$) GO-13297 program "HST Legacy Survey of Galactic
Globular Clusters: Shedding UV Light on Their Populations and Formation" have
investigated the photometric properties of a large sample of Galactic globular
clusters and revolutionized our understanding of their stellar populations. In
this paper, we expand previous studies by focusing our attention on the stellar
clusters' internal kinematics. We computed proper motions for stars in 56
globular and one open clusters by combining the GO-13297 images with archival
$\textit{HST}$ data. The astro-photometric catalogs released with this paper
represent the most complete and homogeneous collection of proper motions of
stars in the cores of stellar clusters to date, and expand the information
provided by the current (and future) $\textit{Gaia}$ data releases to much
fainter stars and into the crowded central regions. We also census the general
kinematic properties of stellar clusters by computing the velocity-dispersion
and anisotropy radial profiles of their bright members. We study the dependence
on concentration and relaxation time, and derive dynamical distances. Finally,
we present an in-depth kinematic analysis of the globular cluster NGC 5904.


  This paper presents a new approach to the recovery of a spectrally sparse
signal (SSS) from partially observed entries, focusing on challenges posed by
large-scale data and heavy noise environments. The SSS reconstruction can be
formulated as a non-convex low-rank Hankel recovery problem. Traditional
formulations for SSS recovery often suffer from reconstruction inaccuracies due
to unequally weighted norms and over-relaxation of the Hankel structure in
noisy conditions. Moreover, a critical limitation of standard proximal gradient
(PG) methods for solving the optimization problem is their slow convergence. We
overcome this by introducing a more accurate formulation and a Low-rank
Projected Proximal Gradient (LPPG) method, designed to efficiently converge to
stationary points through a two-step process. The first step involves a
modified PG approach, allowing for a constant step size independent of signal
size, which significantly accelerates the gradient descent phase. The second
step employs a subspace projection strategy, optimizing within a low-rank
matrix space to further decrease the objective function. Both steps of the LPPG
method are meticulously tailored to exploit the intrinsic low-rank and Hankel
structures of the problem, thereby enhancing computational efficiency. Our
numerical simulations reveal a substantial improvement in both the efficiency
and recovery accuracy of the LPPG method compared to existing benchmark
algorithms. This performance gain is particularly pronounced in scenarios with
significant noise, demonstrating the method's robustness and applicability to
large-scale SSS recovery tasks.


  We examine e+e- --> Ds^-D_s^{*+} and Ds^{*-}Ds^{+} interactions at 4170 MeV
using the CLEO-c detector in order to measure the decay constant fDs with good
precision. Previously our measurements were substantially higher than the most
precise lattice based QCD calculation of (241 +/- 3) MeV. Here we use the D_s^+
--> ell^+ nu channel, where the ell^+ designates either a mu^+ or a tau^+, when
the tau^+ --> pi^+ anti-nu. Analyzing both modes independently, we determine
B(D_s^+ --> mu^+ nu)= 0.565 +/- 0.045 +/- 0.017)%, and B(D_s^+ --> mu^+ nu)=
(6.42 +/- 0.81 +/- 0.18)%. We also analyze them simultaneously to find an
effective value of B^{eff}(D_s^+ --> mu^+ nu)= (0.591 +/- 0.037 +/- 0.018)% and
fDs=(263.3 +/- 8.2 +/- 3.9) MeV. Combining with the CLEO-c value determined
independently using D_s^+ --> tau^+ nu, tau^+ --> e^+ nu anti-nu decays, we
extract fDs=(259.5 +/- 6.6 +/- 3.1) MeV. Combining with our previous
determination of B(D^+ --> mu^+ nu), we extract the ratio fDs/fD+=1.26 +/- 0.06
+/- 0.02. No evidence is found for a CP asymmetry between Gamma(D_s^+ -->
mu^+\nu) and \Gamma(D_s^- --> mu^- nu); specifically the fractional difference
in rates is measured to be (4.8 +/- 6.1)%. Finally, we find B(D_s^+ --> e^+ nu)
< 1.2x10^{-4} at 90% confidence level.


  The gravitational wave (GW) signal from binary black hole (BBH) mergers is a
promising probe of Population III (Pop III) stars. To fully unleash the power
of the GW probe, one important step is to understand the relative importance
and features of different BBH evolution channels. We model two channels,
isolated binary stellar evolution (IBSE) and nuclear star cluster-dynamical
hardening (NSC-DH), in one theoretical framework based on the semi-analytical
code A-SLOTH, under various assumptions on Pop III initial mass function (IMF),
initial binary statistics and high-$z$ nuclear star clusters (NSCs). The NSC-DH
channel contributes $\sim 8-95\%$ of Pop III BBH mergers across cosmic history,
with higher contributions achieved by initially wider binary stars, more
top-heavy IMFs, and more abundant high-$z$ NSCs. The dimensionless stochastic
GW background (SGWB) produced by Pop III BBH mergers has peak values
$\Omega^{\rm peak}_{\rm GW}\sim 10^{-11}-8\times 10^{-11}$ around
observer-frame frequencies $\nu\sim 10-100\ \rm Hz$. The Pop III contribution
can be a non-negligible ($\sim 2-32\%$) component in the total SGWB at
$\nu\lesssim 10\ \rm Hz$. The estimated detection rates of Pop III BBH mergers
by the Einstein Telescope are $\sim 6-230\ \rm yr^{-1}$ and $\sim 30-1230\ \rm
yr^{-1}$ for the NSC-DH and IBSE channels, respectively. Pop III BBH mergers in
NSCs are more massive than those from IBSE, so they dominate the Pop III SGWB
below $20$ Hz in most cases. Besides, the detection rate of Pop III BBH mergers
involving at least one intermediate-mass BH above $100\ \rm M_\odot$ by the
Einstein Telescope is $\sim 0.5-200\ \rm yr^{-1}$ in NSCs but remains below
$0.1\ \rm yr^{-1}$ for IBSE.


  Modern physics is largely devoted to study conservation laws, such as charge,
energy, linear momentum or angular momentum, because they give us information
about the symmetries of our universe. Here, we propose to add the relationship
between electromagnetic duality and helicity to the toolkit. Generalized
electromagnetic duality symmetry, broken in the microscopic Maxwell's equations
by the empirical absence of magnetic charges, can be restored for the
macroscopic Maxwell's equations. The restoration of this symmetry is shown to
be independent of the geometry of the problem. These results provide a simple
and powerful tool for the study of light-matter interactions within the
framework of symmetries and conservation laws. We apply such framework to the
experimental investigation of helicity transformations in cylindrical
nanoapertures, and we find that the transformation is significantly enhanced by
the coupling to surface modes, where electromagnetic duality is strongly
broken.


  Federated learning (FL) based magnetic resonance (MR) image reconstruction
can facilitate learning valuable priors from multi-site institutions without
violating patient's privacy for accelerating MR imaging. However, existing
methods rely on fully sampled data for collaborative training of the model. The
client that only possesses undersampled data can neither participate in FL nor
benefit from other clients. Furthermore, heterogeneous data distributions
hinder FL from training an effective deep learning reconstruction model and
thus cause performance degradation. To address these issues, we propose a
Self-Supervised Federated Learning method (SSFedMRI). SSFedMRI explores the
physics-based contrastive reconstruction networks in each client to realize
cross-site collaborative training in the absence of fully sampled data.
Furthermore, a personalized soft update scheme is designed to simultaneously
capture the global shared representations among different centers and maintain
the specific data distribution of each client. The proposed method is evaluated
on four datasets and compared to the latest state-of-the-art approaches.
Experimental results demonstrate that SSFedMRI possesses strong capability in
reconstructing accurate MR images both visually and quantitatively on both
in-distribution and out-of-distribution datasets.


  To calibrate the relationship between Ne3O2 (Ne3O2 =
log(\neiii$\lambda3869$/\oii$\lambda3727$)) and oxygen abundances, we present a
sample of $\sim$3000 $\hii$ galaxies from the Sloan Digital Sky Survey (SDSS)
data release four. They are associated with a sample from the literature
intended to enlarge the oxygen abundance region. We calculated the electron
temperatures ($T_e$) of 210 galaxies in the SDSS sample with the direct method,
and $T_e$ of the other 2960 galaxies in SDSS sample calculated with an
empirical method. Then, we use a linear least-square fitting to calibrate the
Ne3O2 oxygen abundance indicator. It is found that the Ne3O2 estimator follows
a linear relation with \zoh\ that holds for the whole abundance range covered
by the sample, from approximately 7.0 to 9.0. The best linear relationship
between the Ne3O2 and the oxygen abundance is calibrated. The dispersion
between oxygen abundance and Ne3O2 index in the metal rich galaxies may come
partly from the moderate depletion of oxygen onto grains. The $Ne3O2$ method
has the virtue of being single-valued and not affected by internal reddening.
As a result, the $Ne3O2$ method can be a good metallicity indicator in the \hii
regions and \hii galaxies, especially in high-redshift galaxies.


  Warm absorbers are an important new probe of the central regions of active
galaxies. So far, they revealed their existence mainly in the soft X-ray
spectral region. In observing and modeling this component, we can learn a lot
about the nature of the warm absorber itself, its relation to other components
of the active nucleus, and the intrinsic AGN X-ray spectral shape.
  Here, we briefly review the basic X-ray spectral features of warm absorbers
(dust-free WAs, dusty WAs, peculiar ~1.1 keV absorption, emission and
reflection components) and then discuss these in more detail based on analyses
of individual objects observed with the X-ray satellite ROSAT. The importance
of XMM in improving our knowledge of the warm material is discussed.


  Recently, stochastic, as opposed to deterministic, parameterizations are
being investigated to model the effects of unresolved subgrid scales (SGS) in
large eddy simulations (LES) of geophysical flows. We analyse such a stochastic
approach in the barotropic vorticity equation to show that (i) if the
stochastic parameterization approximates the actual SGS stresses, then the
solution of the stochastic LES approximates the "true" solution at appropriate
scale sizes; and that (ii) when the filter scale size approaches zero, the
solution of the stochastic LES approaches the true solution.


  Traditional black-hole binary puncture initial data is conformally flat. This
unphysical assumption is coupled with a lack of radiation signature from the
binary's past life. As a result, waveforms extracted from evolutions of this
data display an abrupt jump. In Kelly et al. [Class.Quant.Grav.27:114005,2010],
a new binary black-hole initial data with radiation contents derived in the
post-Newtonian (PN) calculation was adapted to puncture evolutions in numerical
relativity. This data satisfies the constraint equations to the 2.5PN order,
and contains a transverse-traceless "wavy" metric contribution, violating the
standard assumption of conformal flatness. Although the evolution contained
less spurious radiation, there were undesired features; the unphysical horizon
mass loss and the large initial orbital eccentricity. Introducing a hybrid
approach to the initial data evaluation, we significantly reduce these
undesired features.


  Interior design is crucial in creating aesthetically pleasing and functional
indoor spaces. However, developing and editing interior design concepts
requires significant time and expertise. We propose Virtual Interior DESign
(VIDES) system in response to this challenge. Leveraging cutting-edge
technology in generative AI, our system can assist users in generating and
editing indoor scene concepts quickly, given user text description and visual
guidance. Using both visual guidance and language as the conditional inputs
significantly enhances the accuracy and coherence of the generated scenes,
resulting in visually appealing designs. Through extensive experimentation, we
demonstrate the effectiveness of VIDES in developing new indoor concepts,
changing indoor styles, and replacing and removing interior objects. The system
successfully captures the essence of users' descriptions while providing
flexibility for customization. Consequently, this system can potentially reduce
the entry barrier for indoor design, making it more accessible to users with
limited technical skills and reducing the time required to create high-quality
images. Individuals who have a background in design can now easily communicate
their ideas visually and effectively present their design concepts.
https://sites.google.com/view/ltnghia/research/VIDES