  Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent
in the world population. Without diagnosis at the right time, NAFLD can lead to
non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis
and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver
fibrosis stage, which are usually evaluated from liver biopsies by
pathologists. In this work, we propose a novel method to automatically predict
NAS score and fibrosis stage from CT data that is non-invasive and inexpensive
to obtain compared with liver biopsy. We also present a method to combine the
information from CT and H\&E stained pathology data to improve the performance
of NAS score and fibrosis stage prediction, when both types of data are
available. This is of great value to assist the pathologists in computer-aided
diagnosis process. Experiments on a 30-patient dataset illustrate the
effectiveness of our method.


  We consider the non-linear thermoelastic plate equation in rectangular
domains $\Omega$. More precisely, $\Omega$ is considered to be given as the
Cartesian product of whole or half spaces and a cube. First the linearized
equation is treated as an abstract Cauchy problem in $L^p$-spaces. We take
advantage of the structure of $\Omega$ and apply operator-valued Fourier
multiplier results to infer an $\mathcal R$-bounded $\mathcal
H^\infty$-calculus. With the help of maximal $L^p$-regularity existence and
uniqueness of local real-analytic strong solutions together with analytic
dependency on the data is shown.


  Distributed massive MIMO (D-mMIMO) has been considered for future networks as
it holds the potential to offer superior capacity while enabling energy savings
in the network. A D-mMIMO system has multiple arrays. Optimizing the locations
of the arrays is essential for the energy efficiency of the system. In existing
works, array placement has been optimized mostly based on common channel
models, which rely on a given statistical distribution and Euclidean distance
between user locations and arrays. These models are justified if applied to
sufficiently large cells, where the statistical description of the channel is
expected to fit its empirical condition. However, with the advent of small
cells, this is no longer the case. The channel propagation condition becomes
highly environment-specific. This paper investigates array placement
optimization with different ways of modeling the propagation conditions taking
the environment information (e.g., buildings) into account. We capture the
environment information via a graph. Two shortest path-based propagation models
are introduced based on the graph. We validate the performance of the models
with a ray-tracing simulator considering different signal coverage levels. The
simulation results demonstrate that higher energy efficiency is achieved by
using the array placements found via the proposed models compared to a
Euclidean distance-based propagation model in small cells. The average power
saving at 96% signal coverage, for example, reaches more than 5 dB.


  Chandra X-ray Observatory observations of the powerful, peculiar radio galaxy
3C123 have resulted in an X-ray detection of the bright eastern hotspot, with a
1-keV flux density of ~ 5 nJy. The X-ray flux and spectrum of the hotspot are
consistent with the X-rays being inverse-Compton scattering of radio
synchrotron photons by the population of electrons responsible for the radio
emission (`synchrotron self-Compton emission') if the magnetic fields in the
hotspot are close to their equipartition values. 3C123 is thus the third radio
galaxy to show X-ray emission from a hotspot which is consistent with being in
equipartition. Chandra also detects emission from a moderately rich cluster
surrounding 3C123, with Lx (2-10 keV) = 2 x 10^44 ergs/s and kT ~ 5 keV, and
absorbed emission from the active nucleus, with an inferred intrinsic column
density of 1.7 x 10^22 cm^-2 and an intrinsic 2-10 keV luminosity of 10^44
ergs/s.


  NectarCAM is a camera developed to detect Cherenkov light between 80 GeV and
30 TeV. It will equip the medium-sized telescopes (MST) of the Cherenkov
Telescope Array Observatory (CTAO). The camera comprises 265 modules, covering
a field of view of 8 degrees. Each module consists of 7 photomultiplier Tubes
(PMTs) equipped with light guides and a front-end board performing the data
capture. NectarCAM is based on the NECTAr chip, which combines a switch
capacitor array sampling at 1GHz and a 12-bit Analog to Digital Converter
(ADC). The NectarCAM camera is currently under integration in CEA Paris-Saclay
(France). In this contribution, I focus on the ongoing performance tests for
its characterization and calibration before deployment on the CTAO North site.


  Aim of this paper is to define a new type of cohomology for multiplicative
Hom-Leibniz algebras which controls deformations of Hom-Leibniz algebra
structure. The cohomology and the associated deformation theory for Hom-Leibniz
algebras as developed here are also extended to equvariant context, under the
presence of finite group actions on Hom-Leibniz algebras.


  In this article, we focus on the critical tasks of plant protection in arable
farms, addressing a modern challenge in agriculture: integrating ecological
considerations into the operational strategy of precision weeding robots like
\bbot. This article presents the recent advancements in weed management
algorithms and the real-world performance of \bbot\ at the University of Bonn's
Klein-Altendorf campus. We present a novel Rolling-view observation model for
the BonnBot-Is weed monitoring section which leads to an average absolute
weeding performance enhancement of $3.4\%$. Furthermore, for the first time, we
show how precision weeding robots could consider bio-diversity-aware concerns
in challenging weeding scenarios. We carried out comprehensive weeding
experiments in sugar-beet fields, covering both weed-only and mixed crop-weed
situations, and introduced a new dataset compatible with precision weeding. Our
real-field experiments revealed that our weeding approach is capable of
handling diverse weed distributions, with a minimal loss of only $11.66\%$
attributable to intervention planning and $14.7\%$ to vision system limitations
highlighting required improvements of the vision system.


  We present the findings of our weak lensing study of a sample of 116 CNOC2
galaxy groups. The lensing signal is used to estimate the mass-to-light ratio
of these galaxy groups. The best fit isothermal sphere model to our lensing
data has an Einstein radius of 0.88"+/-0.12", which corresponds to a
shear-weighted velocity dispersion of 245+/-18 km/s. The mean mass-to-light
ratio within 1 h^-1 Mpc is 185+/-28 h times solar in the B-band and is
independent of radius from the group center.
  The signal-to-noise ratio of the shear measurement is sufficient to split the
sample into subsets of "poor" and "rich" galaxy groups. The poor galaxy groups
were found to have an average velocity dispersion of 193+/-38 km/s and a
mass-to-light ratio of 134+/-26 h times solar in the B-band, while the rich
galaxy groups have a velocity dispersion of 270+/-39 km/s and a mass-to-light
ratio of 278+/-42 h times solar in the B-band, similar to the mass-to-light
ratio of clusters. This steep increase in the mass-to-light ratio as a function
of mass, suggests that the mass scale of ~10^13 solar masses is where the
transition between the actively star-forming field environment and the
passively-evolving cluster environment occurs. This is the first such detection
from weak lensing.


  By increasing model parameters but activating them sparsely when performing a
task, the use of Mixture-of-Experts (MoE) architecture significantly improves
the performance of Large Language Models (LLMs) without increasing the
inference cost. However, the memory consumption due to the growing number of
experts presents a challenge to the deployment of these models in many real
world settings. Our empirical study reveals that some experts encode redundant
knowledge during pre-training. We thus propose a method of grouping and pruning
similar experts to improve model's parameter efficiency. We validate the
effectiveness of our method by pruning two state-of-the-art MoE models,
Mixtral-8x7B and Mixtral-8x22B. Evaluation shows that our method outperforms
other model pruning methods on a range of natural language tasks. To facilitate
future research, we will release our code and the pruned MoE models.


  We investigate the anti-adiabatic limit of an anti-ferromagnetic S=1/2
Heisenberg chain coupled to Einstein phonons. The flow equation method is used
to decouple the spin and the phonon part of the Hamiltonian. In the effective
spin model long range spin-spin interactions are generated. We determine the
phase transition from a gapless state to a gapped (dimerised) phase, which
occurs at a non-zero value of the spin-phonon coupling. In the effective phonon
sector a phonon hardening is observed.


  The proliferation of Bluetooth mobile device communications into all aspects
of modern society raises security questions by both academicians and
practitioners. This environment prompted an investigation into the real-world
use of Bluetooth protocols along with an analysis of documented security
attacks. The experiment discussed in this paper collected data for one week in
a local coffee shop. The data collection took about an hour each day and
identified 478 distinct devices. The contribution of this research is two-fold.
First, it provides insight into real-world Bluetooth protocols that are being
utilized by the general public. Second, it provides foundational research that
is necessary for future Bluetooth penetration testing research.


  We use an algebraic method to prove a degree version of the celebrated Erd\H
os-Ko-Rado theorem: given $n>2k$, every intersecting $k$-uniform hypergraph $H$
on $n$ vertices contains a vertex that lies on at most $\binom{n-2}{k-2}$
edges. This result can be viewed as a special case of the degree version of a
well-known conjecture of Erd\H{o}s on hypergraph matchings. Improving the work
of Bollob\'as, Daykin, and Erd\H os from 1976, we show that given integers $n,
k, s$ with $n\ge 3k^2 s$, every $k$-uniform hypergraph $H$ on $n$ vertices with
minimum vertex degree greater than $\binom{n-1}{k-1}-\binom{n-s}{k-1}$ contains
$s$ disjoint edges.


  The decay constants of the charmed heavy pseudoscalar D and D_s mesons are
revisited within a recently developed novel approach to dispersive QCD sum
rules which relies on an unprejudiced implementation of quark-hadron duality.
The proposed modification of the conventional sum-rule techniques is assessed
by applying our prescriptions to quantum mechanics, where exact solutions may
be easily obtained by simply solving the Schr\"odinger equation. The very
striking similarity of the extraction procedures of bound-state parameters in
potential models and in QCD gives us great confidence in the reliability of our
improvements of the sum-rule formalism and their applicability to hadron
phenomenology. The implications of one's chosen definition of the heavy-quark
masses are scrutinized and the MS-bar quark-mass scheme is identified as the
optimal choice for our purposes. Our ideas turn out to reconcile QCD sum-rule
predictions for the charmed-meson decay constants (which before tended to be
markedly too low) with the findings of both lattice QCD and experiment.


  We study a minimal extension of a recently proposed modification of general
relativity that draws on concepts from topological field theory to resolve
aspects of the cosmological constant problem. In the original model, the field
content of general relativity was augmented to include a gauge field and an
adjoint-valued two-form without modifying the classical gravitational dynamics.
Here we include a kinetic term for the gauge field which sources fluctuations
of the cosmological constant. We then study spherically symmetric black holes
and a simple homogeneous, isotropic cosmological model predicted by the
extended theory. For the black hole case, we observe deviations near the event
horizon as well as a ``charge"-like behavior induced by the gauge field. In the
cosmological case, $\dot{H}$ is always positive and some solutions asymptote to
a constant $H$.


  The exact expressions for the characteristics of synchrotron radiation of
charged particles in the first excited state are obtained in analytical form
using quantum theory methods. We performed a detailed analysis of the angular
distribution structure of radiation power and its polarization for particles
with spin 0 and 1/2. It is shown that the exact quantum calculations lead to
results that differ substantially from the predictions of classical theory.


  We present Multiple Scenario Verifiable Reinforcement Learning via Policy
Extraction (MSVIPER), a new method for policy distillation to decision trees
for improved robot navigation. MSVIPER learns an "expert" policy using any
Reinforcement Learning (RL) technique involving learning a state-action mapping
and then uses imitation learning to learn a decision-tree policy from it. We
demonstrate that MSVIPER results in efficient decision trees and can accurately
mimic the behavior of the expert policy. Moreover, we present efficient policy
distillation and tree-modification techniques that take advantage of the
decision tree structure to allow improvements to a policy without retraining.
We use our approach to improve the performance of RL-based robot navigation
algorithms for indoor and outdoor scenes. We demonstrate the benefits in terms
of reduced freezing and oscillation behaviors (by up to 95\% reduction) for
mobile robots navigating among dynamic obstacles and reduced vibrations and
oscillation (by up to 17\%) for outdoor robot navigation on complex, uneven
terrains.


  We calculate the quantum discord between two free modes of a scalar field
which start in a maximally entangled state and then undergo a relative,
constant acceleration. In a regime where there is no distillable entanglement
due to the Unruh effect, we show that there is a finite amount of quantum
discord, which is a measure of purely quantum correlations in a state, over and
above quantum entanglement. Even in the limit of infinite acceleration of the
observer detecting one of the modes, we provide evidence for a non-zero amount
of purely quantum correlations, which might be exploited to gain non-trivial
quantum advantages.


  The 7Be(p,gamma)8B reaction is one of the major source of uncertainties in
estimating the 8B solar neutrino flux and is critical for undertsanding the
Solar Neutrino Problem and neutrinos. The main source of uncertainty is the
existence of conflicting data with different absolute normalization. Attempts
to measure this reaction rate with 7Be beams are under way by the UConn-LLN
collaboration, and we discuss a newly emerging method to extract this cross
section from the Coulomb dissociation of the radioactive beam of 8B. We discuss
some of the issues relevant for this study including the question of the E2
contribution to the Coulomb dissociation process which was measured to be
small. The Coulomb dissociation appears to provide a viable alternative method
for measuring the 7Be(p,gamma)8B reaction rate, with a weighted average of the
RIKEN1, RIKEN2 and GSI1 published results of S17(0) = 19.4 +/- 1.3 eV-b.


  In distributed optimization and machine learning, multiple nodes coordinate
to solve large problems. To do this, the nodes need to compress important
algorithm information to bits so that it can be communicated over a digital
channel. The communication time of these algorithms follows a complex interplay
between a) the algorithm's convergence properties, b) the compression scheme,
and c) the transmission rate offered by the digital channel. We explore these
relationships for a general class of linearly convergent distributed
algorithms. In particular, we illustrate how to design quantizers for these
algorithms that compress the communicated information to a few bits while still
preserving the linear convergence. Moreover, we characterize the communication
time of these algorithms as a function of the available transmission rate. We
illustrate our results on learning algorithms using different communication
structures, such as decentralized algorithms where a single master coordinates
information from many workers and fully distributed algorithms where only
neighbours in a communication graph can communicate. We conclude that a
co-design of machine learning and communication protocols are mandatory to
flourish machine learning over networks.


  The authors of the paper "The third-order perturbed Korteweg-de Vries
equation for shallow water waves with a non-flat bottom" [1] claim that they
have derived the full third order perturbed KdV equation for the case of uneven
bottom. We show that the authors' derivation is not consistent due to the fact
that they took into account only some of the third order corrections but not
all of them. Moreover, we show that a consistent third order perturbed
Korteweg-de Vries equation for shallow water waves with a non-flat bottom
cannot be derived for a general form of bottom function.


  The global SARS-CoV-2 pandemic suggests a novel type of disease spread
dynamics. WHO states that there is currently no evidence that people who have
recovered from COVID-19 and have antibodies are immune from a second infection
[WHO]. Conventional mathematical models consider cases for which a recovered
individual either becomes susceptible again or develops an immunity. Here, we
study the case where infected agents recover and only develop immunity if they
are continuously infected for some time. Otherwise, they become susceptible
again. We show that field theory bounds the peak of the infectious rate.
Consequently, the theory's phases characterise the disease dynamics: (i) a
pandemic phase and (ii) a response regime. The model excellently describes the
epidemic spread of the SARS-CoV-2 outbreak in the city of Wuhan, China. We find
that only 30% of the recovered agents have developed an immunity. We anticipate
our paper to influence the decision making upon balancing the economic impact
and the pandemic impact on society. As long as disease controlling measures
keep the disease dynamics in the "response regime", a pandemic escalation
('second wave') is ruled out.


  In this work, we propose a new nonparametric approach for reconstructing a
function from observational data using an Artificial Neural Network (ANN),
which has no assumptions about the data and is a completely data-driven
approach. We test the ANN method by reconstructing functions of the Hubble
parameter measurements $H(z)$ and the distance-redshift relation $D_L(z)$ of
Type Ia supernovae. We find that both $H(z)$ and $D_L(z)$ can be reconstructed
with high accuracy. Furthermore, we estimate cosmological parameters using the
reconstructed functions of $H(z)$ and $D_L(z)$ and find the results are
consistent with those obtained using the observational data directly.
Therefore, we propose that the function reconstructed by ANN can represent the
actual distribution of observational data and can be used for parameter
estimation in further cosmological research. In addition, we present a new
strategy for training and evaluating the neural network, and a code for
reconstructing functions using ANN has been developed and is available for
download.


  We studied the spin-flip excitations of a double-decker nickelocene molecule
(Nc) adsorbed on Cu(100) by means of inelastic tunneling spectroscopy (IETS),
X-ray magnetic circular dichroism (XMCD) and density functional theory
calculations (DFT). The results show that the molecule preserves its magnetic
moment and magnetic anisotropy not only on Cu(100), but also in different
metallic environments including the tip apex. Taking advantage of the efficient
spin-flip excitation of this molecule, we show how such a molecular
functionalized tip boosts the inelastic signal of a surface supported Nc by
almost one order of magnitude thanks to a double spin-excitation process.


  In this review we discuss currents in celestial CFT and the consistency of
their naive symmetry algebras. In particular we study in detail the Jacobi
identity and the double residue condition for soft insertions, hard momentum
space insertions, and hard celestial insertions. In the latter case we
introduce the notion of a "hard current" in CFT and work through examples in
the 2D critical Ising model. The current algebra of hard insertions in pure
Einstein gravity is a slight conceptual generalization of the familiar
$w_{1+\infty}$-wedge current algebra. We also review branch cut terms in the
celestial OPE, which indicate new primary content and were previously missed
until recently. We work through an explicit toy example illustrating the
mechanism by which such branch cut terms can arise. These branch cut terms
prevent a symmetry interpretation but are fully compatible with a consistent
OPE.


  The blue-shifted broad absorption lines (BAL) or troughs are observed in
Active Galactic Nuclei (AGNs) when our line of sight is intercepted by a high
speed outflow (wind), likely originating in the accretion disc. The outflow or
wind can shed light on the internal structure obscured by the AGN torus.
Recently, it has been shown that this outflow is rotating in the BAL quasar PG
1700+518, further supporting the accretion disc origin of the wind. With the
purpose of giving independent constraints on the wind geometry, we performed
high-resolution European VLBI Network (EVN) observations at 1.6 GHz in 2010.
Combining the VLBI (Very Long Baseline Interferometry) results with the Very
Large Array (VLA) archival data at 8.4 GHz, we present its jet structure on
scales of parsec (pc) to kiloparsec (kpc) for the first time. The source shows
two distinct jet features in East-West direction with a separation of around 4
kpc. The Eastern feature, which has so far been assumed to hide the core, is a
kpc-scale hot spot, which is completely resolved out in the EVN image. In the
western jet feature, we find a compact jet component, which pinpoints the
position of the central black hole in the galaxy. Jet components on both sides
of the core are additionally detected in the Northwest-Southeast direction, and
they show a symmetric morphology on scale of <1 kpc. This two-sided jet feature
is not common in the known BAL quasars and indicates that the jet axis is far
away from the line of sight. Furthermore, it is nearly parallel to the
scattering plane revealed earlier by optical polarimetry. By analogy to
polar-scattered Seyfert 1 galaxies, we conclude that the jet likely has a
viewing angle around 45 degree. The analogy is further supported by the recent
report of significant cold absorption in the soft X-rays, a nearly unique
feature to polar-scattered Seyfert galaxies.


  We present polarisation data for 180 extragalactic sources extracted from the
Australia Telescope 20 GHz (AT20G) survey catalog, and observed with the
Australia Telescope Compact Array during a dedicated, high sensitivity run. For
the sake of completeness we extracted the polarisation information for 7
extended sources from the 9-yr WMAP coadded maps at 23 GHz. The full sample of
187 sources constitutes a 99% complete sample of extragalactic sources brighter
than S(20 GHz)=500mJy at the selection epoch with declination <-30 deg. The
sample has a 91.4% detection rate in polarisation at 20GHz (94% if considering
the sub-sample of point like sources). We have measurements also at 4.8 and 8.6
GHz within 1 month of the 20GHz observations for 172 sources to reconstruct the
spectral properties of the sample in total intensity and in polarisation: 143
of them have a polarisation detection at all three frequencies. We find that
there is no statistically significant evidence of a relationship either between
the fraction of polarisation and frequency or between the fraction of
polarisation and the total intensity flux density. This indicates that Faraday
depolarisation is not very important above 4.8 GHz and that the magnetic field
is not substantially more ordered in the regions dominating the emission at
higher frequencies (up to 20 GHz). We estimate the distribution of the
polarisation fraction and the polarised flux density source counts at 20GHz.


  Event rate of long Gamma-Ray Bursts (GRBs) is expected to be an useful tracer
of the cosmic star-formation history. For this purpose, it is necessary to
understand what kind of star formations/galaxies are traced by GRBs. Here we
report rest-frame far-infrared (FIR) continuum detections of GRB 070521 and
080207 host galaxies at $z\sim2$ with ALMA band 8 and 9. The FIR photometries
provide the reliable star-formation rates (SFRs), because FIR emission is free
from dust extinction and possible radio contamination from long-lived
afterglows of GRBs. The spectral energy distribution fittings indicate
49.85$^{+72.33}_{-2.86}$ and 123.4$^{+25.19}_{-21.78}$ $M_{\odot}$ yr$^{-1}$
for the 070521 and 080207 hosts, respectively. The derived SFRs place them on
the \lq \lq main sequence\rq \rq\ of normal star-forming galaxies at $z\sim2$.
The derived SFRs are significantly lower than that of radio observations. It is
inferred that the observed radio fluxes in a previous study are contaminated by
the afterglows. ALMA marginally detected [C~{\sc ii}]\,158\,$\mu$m emission
line from the GRB 080207 host galaxy with S/N $\sim$ 4. This is the first
detection of [C~{\sc ii}]\,158\,$\mu$m of a GRB host at $z>2$, and the second
detection among known GRBs. The luminosity ratio of [C~{\sc~ii}]\,158$\mu$m to
FIR is 7.5$\times 10^{-4}$, which is one of the smallest values among galaxies
at $z\sim 1-2$ with the same FIR luminosity. The \lq \lq [C~{\sc ii}]
deficit\rq \rq\ could be a new physical property to characterise GRB hosts at
$z\sim1-2$. Possible parameters controlling the deficit include the
metallicity, initial mass function, and gas density.


  Quantum correlation provides a promising measure beyond entanglement. Here,
we propose a necessary and sufficient condition for nonzero quantum correlation
in continuous variable systems, which is simple and easy to perform in terms of
a marker $Q_r$. In order to get this condition, we introduce
continuous-variable local orthogonal bases of the operator space, which are
generalized from the orthogonal basis sets in local operator space for discrete
variables. Based on this, we obtain the marker $Q_r$ for all bipartite
continuous variable states, and provide several examples including two-mode
Gaussian and non-Gaussian states. Our result may provide a candidate for
quantum correlation measures, and can be measured by designed quantum circuits.


  Automated code completion, aiming at generating subsequent tokens from
unfinished code, has been significantly benefited from recent progress in
pre-trained Large Language Models (LLMs). However, these models often suffer
from coherence issues and hallucinations when dealing with complex code logic
or extrapolating beyond their training data. Existing Retrieval Augmented
Generation (RAG) techniques partially address these issues by retrieving
relevant code with a separate encoding model where the retrieved snippet serves
as contextual reference for code completion. However, their retrieval scope is
subject to a singular perspective defined by the encoding model, which largely
overlooks the complexity and diversity inherent in code semantics. To address
this limitation, we propose ProCC, a code completion framework leveraging
prompt engineering and the contextual multi-armed bandits algorithm to flexibly
incorporate and adapt to multiple perspectives of code. ProCC first employs a
prompt-based multi-retriever system which crafts prompt templates to elicit LLM
knowledge to understand code semantics with multiple retrieval perspectives.
Then, it adopts the adaptive retrieval selection algorithm to incorporate code
similarity into the decision-making process to determine the most suitable
retrieval perspective for the LLM to complete the code. Experimental results
demonstrate that ProCC outperforms state-of-the-art code completion technique
by 8.6% on our collected open-source benchmark suite and 10.1% on the
private-domain benchmark suite collected from a billion-user e-commerce company
in terms of Exact Match. ProCC also allows augmenting fine-tuned techniques in
a plug-and-play manner, yielding 5.6% improvement over our studied fine-tuned
model.


  We study the flavour breaking effects appearing in the Wilson twisted mass
formulation of lattice QCD. In this quenched study, we focus on the mass
splitting between the neutral and the charged pion, determining the neutral
pion mass with a stochastic noise method to evaluate the disconnected
contributions. We find that these disconnected contributions are significant.
Using the Osterwalder-Seiler interpretation of the connected piece of the
neutral pion correlator, we compute the corresponding neutral pion mass to
study with more precision the scaling behaviour of the mass splitting.


  Job security can never be taken for granted, especially in times of rapid,
widespread and unexpected social and economic change. These changes can force
workers to transition to new jobs. This may be because new technologies emerge
or production is moved abroad. Perhaps it is a global crisis, such as COVID-19,
which shutters industries and displaces labor en masse. Regardless of the
impetus, people are faced with the challenge of moving between jobs to find new
work. Successful transitions typically occur when workers leverage their
existing skills in the new occupation. Here, we propose a novel method to
measure the similarity between occupations using their underlying skills. We
then build a recommender system for identifying optimal transition pathways
between occupations using job advertisements (ads) data and a longitudinal
household survey. Our results show that not only can we accurately predict
occupational transitions (Accuracy = 76%), but we account for the asymmetric
difficulties of moving between jobs (it is easier to move in one direction than
the other). We also build an early warning indicator for new technology
adoption (showcasing Artificial Intelligence), a major driver of rising job
transitions. By using real-time data, our systems can respond to labor demand
shifts as they occur (such as those caused by COVID-19). They can be leveraged
by policy-makers, educators, and job seekers who are forced to confront the
often distressing challenges of finding new jobs.


  We consider the asymptotic behavior of small global-in-time solutions to a 1D
Klein-Gordon equation with a spatially localized, variable coefficient
quadratic nonlinearity and a non-generic linear potential. The purpose of this
work is to continue the investigation of the occurrence of a novel modified
scattering behavior of the solutions that involves a logarithmic slow-down of
the decay rate along certain rays. This phenomenon is ultimately caused by the
threshold resonance of the linear Klein-Gordon operator. It was previously
uncovered for the special case of the zero potential in [51]. The Klein-Gordon
model considered in this paper is motivated by the asymptotic stability problem
for kink solutions arising in classical scalar field theories on the real line.


  Let $(\mathcal{C},\mathbb{E},\mathfrak{s})$ be an extriangulated category
with a proper class $\xi$ of $\mathbb{E}$-triangles. The authors introduced and
studied $\xi$-$\mathcal{G}$projective and $\xi$-$\mathcal{G}$injective in
\cite{HZZ}. In this paper, we discuss Gorenstein homological dimensions for
extriangulated categories. More precisely, we first give some characterizations
of $\xi$-$\mathcal{G}$projective dimension by using derived functors on
$\mathcal{C}$. Second, let $\mathcal{P}(\xi)$ (resp. $\mathcal{I}(\xi)$) be a
generating (resp. cogenerating) subcategory of $\mathcal{C}$. We show that the
following equality holds under some assumptions:
$$\sup\{\xi\textrm{-}\mathcal{G}{\rm pd}M \ | \ \textrm{for} \ \textrm{any} \
M\in{\mathcal{C}}\}=\sup\{\xi\textrm{-}\mathcal{G}{\rm id}M \ | \ \textrm{for}
\ \textrm{any} \ M\in{\mathcal{C}}\},$$ where $\xi\textrm{-}\mathcal{G}{\rm
pd}M$ (resp. $\xi\textrm{-}\mathcal{G}{\rm id}M$) denotes
$\xi$-$\mathcal{G}$projective (resp. $\xi$-$\mathcal{G}$injective) dimension of
$M$. As an application, our main results generalize their work by Bennis-Mahdou
and Ren-Liu. Moreover, our proof is not far from the usual module or
triangulated case.


  We develop a set of tools for the asymptotic analysis of minimizers of the
anisotropic Ginzburg-Landau functional among the admissible competitors with
Dirichlet boundary datum of negative degree.


  A current-controlled negative differential resistance has been revealed in
the I-V characteristics of single crystal La$_{2}$CuO$_{4+\delta}$ in the low
temperature region. The non-linear behavior of conductivity is accompanied by a
transition from positive to negative magnetoresistance when the current is
growing. Possible reasons for the effect observed are discussed.


  The purpose of these notes is to provide a pedagogical introduction to the
concept of renormalization in atomic physics. We study quantum dynamics of a
model of a nonrelativistic single electron atom coupled to the quantum
radiation field in the dipole approximation. An interaction between the
electron and the radiation field is regularized, using a suitable cutoff
prescription which eliminates the coupling of the electron to the highly
ultraviolet (UV) field modes. Subsequently, we analyze the corresponding
Heisenberg picture equations of motion, and focus on behavior of physical
observables in the limit of a pointlike electron (i.e., in the limit when the
above mentioned UV cutoff is gradually lifted to infinity). We identify the
radiation reaction force acting on the electron, and show that this force
actually diverges towards infinity when gradually removing the UV cutoff. Such
an analysis leads ultimately to an unique renormalization prescription for the
electron mass, as well as to determination of the Abraham-Lorentz force acting
on the electron. Dependence of the just sketched renormalization procedure upon
the spatial dimension is highlighted. Finally, we present an example
calculation of the atomic level shifts by means of the renormalized
perturbation theory, and formulate also the renormalized mean field theory.


  The effects of plasma ion motion in self-modulated plasma based accelerators
is examined. An analytical model describing ion motion in the narrow beam limit
is developed, and confirmed through multi-dimensional particle-in-cell
simulations. It is shown that the ion motion can lead to the early saturation
of the self-modulation instability, and to the suppression of the accelerating
gradients. This can reduce the total energy that can be transformed into
kinetic energy of accelerated particles. For the parameters of future
proton-driven plasma accelerator experiments, the ion dynamics can have a
strong impact. Possible methods to mitigate the effects of the ion motion in
future experiments are demonstrated.


  In this work we present a three step procedure for generating a closed form
expression of the Green's function on both closed and open finite quantum
graphs with general self-adjoint matching conditions. We first generalize and
simplify the approach by Barra and Gaspard [Barra F and Gaspard P 2001, Phys.
Rev. E {\bf 65}, 016205] and then discuss the validity of the explicit
expressions. For compact graphs, we show that the explicit expression is
equivalent to the spectral decomposition as a sum over poles at the discrete
energy eigenvalues with residues that contain projector kernel onto the
corresponding eigenstate. The derivation of the Green's function is based on
the scattering approach, in which stationary solutions are constructed by
treating each vertex or subgraph as a scattering site described by a scattering
matrix. The latter can then be given in a simple closed form from which the
Green's function is derived. The relevant scattering matrices contain inverse
operators which are not well defined for wave numbers at which bound states in
the continuum exists. It is shown that the singularities in the scattering
matrix related to these bound states or perfect scars can be regularised.
Green's functions or scattering matrices can then be expressed as a sum of a
regular and a singular part where the singular part contains the projection
kernel onto the perfect scar.


  For a modified Randall-Sundrum model [Phys. Rev. D 88 (2013) 025048], the
graviton equations are derived and the mass spectrum found. The latter includes
a massless graviton and a continuum mass with a gap. There is no negative
mass-squared in the spectrum, so the model is stable. The gravitational
Newtonian limit is obtained with an exponentially suppressed modification from
extra dimension.


  This paper makes several important contributions to the literature about
nonparametric instrumental variables (NPIV) estimation and inference on a
structural function $h_0$ and its functionals. First, we derive sup-norm
convergence rates for computationally simple sieve NPIV (series 2SLS)
estimators of $h_0$ and its derivatives. Second, we derive a lower bound that
describes the best possible (minimax) sup-norm rates of estimating $h_0$ and
its derivatives, and show that the sieve NPIV estimator can attain the minimax
rates when $h_0$ is approximated via a spline or wavelet sieve. Our optimal
sup-norm rates surprisingly coincide with the optimal root-mean-squared rates
for severely ill-posed problems, and are only a logarithmic factor slower than
the optimal root-mean-squared rates for mildly ill-posed problems. Third, we
use our sup-norm rates to establish the uniform Gaussian process strong
approximations and the score bootstrap uniform confidence bands (UCBs) for
collections of nonlinear functionals of $h_0$ under primitive conditions,
allowing for mildly and severely ill-posed problems. Fourth, as applications,
we obtain the first asymptotic pointwise and uniform inference results for
plug-in sieve t-statistics of exact consumer surplus (CS) and deadweight loss
(DL) welfare functionals under low-level conditions when demand is estimated
via sieve NPIV. Empiricists could read our real data application of UCBs for
exact CS and DL functionals of gasoline demand that reveals interesting
patterns and is applicable to other markets.


  The antifield formalism adapted in the exact renormalization group is found
to be useful for describing a system with some symmetry, especially the gauge
symmetry. In the formalism, the vanishing of the quantum master operator
implies the presence of a symmetry. The QM operator satisfies a simple
algebraic relation that will be shown to be related to the Wess-Zumino
condition for anomalies. We also explain how an anomaly contributes to the QM
operator.


  Semi-supervised semantic segmentation methods leverage unlabeled data by
pseudo-labeling them. Thus the success of these methods hinges on the
reliablility of the pseudo-labels. Existing methods mostly choose
high-confidence pixels in an effort to avoid erroneous pseudo-labels. However,
high confidence does not guarantee correct pseudo-labels especially in the
initial training iterations. In this paper, we propose a novel approach to
reliably learn from pseudo-labels. First, we unify the predictions from a
trained object detector and a semantic segmentation model to identify reliable
pseudo-label pixels. Second, we assign different learning weights to
pseudo-labeled pixels to avoid noisy training signals. To determine these
weights, we first use the reliable pseudo-label pixels identified from the
first step and labeled pixels to construct a prototype for each class. Then,
the per-pixel weight is the structural similarity between the pixel and the
prototype measured via rank-statistics similarity. This metric is robust to
noise, making it better suited for comparing features from unlabeled images,
particularly in the initial training phases where wrong pseudo labels are prone
to occur. We show that our method can be easily integrated into four
semi-supervised semantic segmentation frameworks, and improves them in both
Cityscapes and Pascal VOC datasets.


  An accurate description of the low-energy electronic bands in twisted bilayer
graphene (tBLG) is of great interest due to their relation to correlated
electron phases, such as superconductivity and Mott-insulator behavior at
half-filling. The paradigmatic model of Bistritzer and MacDonald [PNAS 108,
12233 (2011)], based on the moir\'e pattern formed by tBLG, predicts the
existence of "magic angles" at which the Fermi velocity of the low-energy bands
goes to zero, and the bands themselves become dispersionless. Here, we
reexamine the low-energy bands of tBLG from the ab initio electronic structure
perspective, motivated by features related to the atomic relaxation in the
moir\'e pattern, namely circular regions of AA stacking, triangular regions of
AB/BA stacking and domain walls separating the latter. We find that the bands
are never perfectly flat and the Fermi velocity never vanishes, but rather a
"magic range" exists where the lower band becomes extremely flat and the Fermi
velocity attains a non-zero minimum value. We propose a simple $(2+2)$-band
model, comprised of two different pairs of orbitals, both on a honeycomb
lattice: the first pair represents the low-energy bands with high localization
at the AA sites, while the second pair represents highly dispersive bands
associated with domain-wall states. This model gives an accurate description of
the low-energy bands with few (13) parameters which are physically motivated
and vary smoothly in the magic range. In addition, we derive an effective
two-band hamiltonian which also gives an accurate description of the low-energy
bands. This minimal two-band model affords a connection to a Hubbard-like
description of the occupancy of sub-bands and can be used a basis for exploring
correlated states.


  The reduced BCS Hamiltonian for a metallic grain with a finite number of
electrons is considered. The crossover between the ultrasmall regime, in which
the level spacing, $d$, is larger than the bulk superconducting gap, $\Delta$,
and the small regime, where $\Delta \gtrsim d$, is investigated analytically
and numerically. The condensation energy, spin magnetization and tunneling peak
spectrum are calculated analytically in the ultrasmall regime, using an
approximation controlled by $1/\ln N$ as small parameter, where $N$ is the
number of interacting electron pairs. The condensation energy in this regime is
perturbative in the coupling constant $\lambda$, and is proportional to $d N
\lambda^2 = \lambda^2 \omega_D$. We find that also in a large regime with
$\Delta>d$, in which pairing correlations are already rather well developed,
the perturbative part of the condensation energy is larger than the singular,
BCS, part. The condition for the condensation energy to be well approximated by
the BCS result is found to be roughly $\Delta > \sqrt{d \omega_D}$. We show how
the condensation energy can, in principle, be extracted from a measurement of
the spin magnetization curve, and find a re-entrant susceptibility at zero
temperature as a function of magnetic field, which can serve as a sensitive
probe for the existence of superconducting correlations in ultrasmall grains.
Numerical results are presented which suggest that in the large $N$ limit the
1/N correction to the BCS result for the condensation energy is larger than
$\Delta$.


  We propose a new stochastic method SAPD+ for solving nonconvex-concave
minimax problems of the form $\min\max \mathcal{L}(x,y) = f(x)+\Phi(x,y)-g(y)$,
where $f,g$ are closed convex and $\Phi(x,y)$ is a smooth function that is
weakly convex in $x$, (strongly) concave in $y$. For both strongly concave and
merely concave settings, SAPD+ achieves the best known oracle complexities of
$\mathcal{O}(L\kappa_y\epsilon^{-4})$ and $\mathcal{O}(L^3\epsilon^{-6})$,
respectively, without assuming compactness of the problem domain, where
$\kappa_y$ is the condition number and $L$ is the Lipschitz constant. We also
propose SAPD+ with variance reduction, which enjoys the best known oracle
complexity of $\mathcal{O}(L\kappa_y^2\epsilon^{-3})$ for weakly
convex-strongly concave setting. We demonstrate the efficiency of SAPD+ on a
distributionally robust learning problem with a weakly convex cost and also on
a multi-class classification problem in deep learning.


  We establish the general machinery of string topology for differentiable
stacks. This machinery allows us to treat on an equal footing free loops in
stacks and hidden loops. In particular, we give a good notion of a free loop
stack, and of a mapping stack $\map(Y,\XX)$, where $Y$ is a compact space and
$\XX$ a topological stack, which is functorial both in $\XX$ and $Y$ and
behaves well enough with respect to pushouts. We also construct a bivariant (in
the sense of Fulton and MacPherson) theory for topological stacks: it gives us
a flexible theory of Gysin maps which are automatically compatible with
pullback, pushforward and products. Further we prove an excess formula in this
context. We introduce oriented stacks, generalizing oriented manifolds, which
are stacks on which we can do string topology. We prove that the homology of
the free loop stack of an oriented stack and the homology of hidden loops
(sometimes called ghost loops) are a Frobenius algebra which are related by a
natural morphism of Frobenius algebras. We also prove that the homology of free
loop stack has a natural structure of a BV-algebra, which together with the
Frobenius structure fits into an homological conformal field theories with
closed positive boundaries. Using our general machinery, we construct an
intersection pairing for (non necessarily compact) almost complex orbifolds
which is in the same relation to the intersection pairing for manifolds as
Chen-Ruan orbifold cup-product is to ordinary cup-product of manifolds. We show
that the hidden loop product of almost complex is isomorphic to the orbifold
intersection pairing twisted by a canonical class. Finally we gave some
examples including the case of the classifying stacks $[*/G]$ of a compact Lie
group.


  We have developed an image restoration technique based on the Richardson-Lucy
algorithm optimized for GLAST-LAT image analysis. Our algorithm is original
since it utilizes the PSF (point spread function) that is calculated for each
event. This is critical for EGRET and GLAST-LAT image analysis since the PSF
depends on the energy and angle of incident gamma-rays and varies by more than
one order of magnitude. EGRET and GLAST-LAT image analysis also faces Poisson
noise due to low photon statistics. Our technique incorporates wavelet
filtering to minimize noise effects. We present studies of EGRET sources using
this novel image restoration technique for possible identification of extended
gamma-ray sources.


  By applying the WKB (Wentzel, Kramers, Brillouin) approximation, we analyse
the interaction of the electric quadrupole moment of a neutral particle with
radial electric fields produced by a non-uniform electric charge densities.
Then, we show particular cases of this interaction where a discrete spectrum of
energy can be achieved for the s-waves of the neutral particle.


  Rapidly advancing AR technologies are in a unique position to directly
mediate between the human brain and the physical world. Though this tight
coupling presents tremendous opportunities for human augmentation, it also
presents new risks due to potential adversaries, including AR applications or
devices themselves, as well as bugs or accidents. In this paper, we begin
exploring potential risks to the human brain from augmented reality. Our
initial focus is on sensory and perceptual risks (e.g., accidentally or
maliciously induced visual adaptations, motion-induced blindness, and
photosensitive epilepsy), but similar risks may span both lower- and
higher-level human brain functions, including cognition, memory, and
decision-making. Though they have not yet manifested in practice in
early-generation AR technologies, we believe that such risks are uniquely
dangerous in AR due to the richness and depth with which it interacts with a
user's experience of the physical world. We propose a framework, based in
computer security threat modeling, to conceptually and experimentally evaluate
such risks. The ultimate goal of our work is to aid AR technology developers,
researchers, and neuroscientists to consider these issues before AR
technologies are widely deployed and become targets for real adversaries. By
considering and addressing these issues now, we can help ensure that future AR
technologies can meet their full, positive potential.


  Rare-earth (RE) kagome-lattice magnets offer an excellent platform to
discover the novel magnetic phase as well as quantum phase transition tuned by
non-thermal control parameters, while the experimental realizations remain
largely unexplored. Here, we report the discovery of magnetic-field (B)-induced
anisotropic quantum phase transition in a distorted kagome antiferromagnet
Nd3BWO9 with TN~0.32 K. The isothermal magnetizations at 0.05 K exhibit the
spin-flop like metamagnetic crossover behaviors with different fractional
magnetization anomalies for B perpendicular (B // c-axis) and parallel (B //
a*-axis) to the kagome plane, respectively. In combination with the
thermodynamic measurements, the field-temperature (B-T) phase diagrams for both
field directions are constructed and that reveal the existence of several
field-induced magnetic states. Along the c-axis, a proximate quantum bicritical
point is observed near the metamagnetic crossover, which separates the
low-field antiferromagnetic (AFM) phase and the intermediate AFM phase. While,
for B // a*, another intermediate magnetic phase (IAFM2) appears between the
low-field AFM phase and intermediate AFM (IAFM1) phase, giving rise to a
tetracritical point. These results support the anisotropic field-induced
metamagnetic quantum criticalities in Nd3BWO9, making it as a rare kagome
antiferromagnet to investigate the quantum multi-criticality driven by spin
frustration.


  We propose a method to construct numerical solutions of parabolic equations
on the unit sphere. The time discretization uses Laplace transforms and
quadrature. The spatial approximation of the solution employs radial basis
functions restricted to the sphere. The method allows us to construct high
accuracy numerical solutions in parallel. We establish $L_2$ error estimates
for smooth and nonsmooth initial data, and describe some numerical experiments.


  The evolution of halos consisting of weakly self-interacting dark matter
particles is summarized. The halos initially contain a central density cusp as
predicted by cosmological models. Weak self-interaction leads to the formation
of an isothermal, low-density core which is in agreement with published data on
the rotation curves of dwarf galaxies. However, subsequently, core collapse
leads to an even steeper central density cusp. In order to explain the observed
dark matter rotation curves by weak interaction, the cross section must be in a
narrow range around sigma/mp= 0.1 cm*cm/g.


  We present predictions for Higgs production via gluon fusion with a p_T veto
on jets and with the resummation of jet-veto logarithms at NNLL'+$NNLO order.
These results incorporate explicit O(alphas^2) calculations of soft and beam
functions, which include the dominant dependence on the jet radius R. In
particular the NNLL' order accounts for the correct boundary conditions for the
N3LL resummation, for which the only unknown ingredients are higher-order
anomalous dimensions. We use scale variations in a factorization theorem in
both rapidity and virtuality space to estimate the perturbative uncertainties,
accounting for both higher fixed-order corrections as well as higher-order
towers of jet-p_T logarithms. This formalism also predicts the correlations in
the theory uncertainty between the exclusive 0-jet and inclusive 1-jet bins. At
the values of R used experimentally, there are important corrections due to jet
algorithm clustering that include logarithms of R. Although we do not sum
logarithms of R, we do include an explicit contribution in our uncertainty
estimate to account for higher-order jet clustering logarithms. Precision
predictions for this H+0-jet cross section and its theoretical uncertainty are
an integral part of Higgs analyses that employ jet binning.


  Multiplicity fluctuations play a crucial role in relativistic heavy-ion
collisions. In this work, we explore how the multiplicity fluctuations can be
effectively suppressed in the measurement of particle correlations. In
particular, through proper normalization, particle correlations can be
evaluated in a manner irrelevant to multiplicity. When the multiplicity
fluctuations are adequately extracted, Monte Carlo simulations show that the
remaining correlations possess distinct features buried in the otherwise
overwhelming fluctuations. Moreover, we argue that such a normalization scheme
naturally agrees with the multi-particle correlator, which can be evaluated
using the Q-vectors. The implications of the present study in the data analysis
are also addressed.


  We investigate the collective behavior of a system of social agents subject
to the competition between two mass media influences considered as external
fields. We study under what conditions either of two mass media with different
intensities can impose its message to the majority. In addition to a collective
state dominated by the stronger mass media and a disordered phase, we
characterize two nontrivial effects as the parameters of the system are varied:
(i) the appearance of a majority sharing the state of the weaker mass media,
and (ii) the emergence of an alternative ordering in a state different from
those of either media. We explore the dependence of both phenomena on the
topology of the network of interactions. We show that the presence of
long-range interactions rather than random connections is essential for the
occurrence of both effects. The model can be extended to include multiple mass
media and we illustrate it by considering three mass media fields acting on the
system. Nontrivial collective behaviors persist for some ranges of parameters:
the weakest mass media can convince the majority, and the system can
spontaneously order against all applied fields.


  This paper studies the problem of enforcing safety of a stochastic dynamical
system over a finite-time horizon. We use stochastic control barrier functions
as a means to quantify the probability that a system exits a given safe region
of the state space in finite time. A barrier certificate condition that bounds
the expected value of the barrier function over the time horizon is recast as a
sum-of-squares optimization problem for efficient numerical computation. Unlike
prior works, the proposed certificate condition includes a state-dependent
upper bound on the evolution of the expectation. We present formulations for
both continuous-time and discrete-time systems. Moreover, for systems for which
the drift dynamics are affine-in-control, we propose a method for synthesizing
polynomial state feedback controllers that achieve a specified probability of
safety. Several case studies are presented which benchmark and illustrate the
performance of our verification and control method in the continuous-time and
discrete-time domains.


  We introduce a general non-parametric independence test between
right-censored survival times and covariates, which may be multivariate. Our
test statistic has a dual interpretation, first in terms of the supremum of a
potentially infinite collection of weight-indexed log-rank tests, with weight
functions belonging to a reproducing kernel Hilbert space (RKHS) of functions;
and second, as the norm of the difference of embeddings of certain finite
measures into the RKHS, similar to the Hilbert-Schmidt Independence Criterion
(HSIC) test-statistic. We study the asymptotic properties of the test, finding
sufficient conditions to ensure our test correctly rejects the null hypothesis
under any alternative. The test statistic can be computed straightforwardly,
and the rejection threshold is obtained via an asymptotically consistent Wild
Bootstrap procedure. Extensive investigations on both simulated and real data
suggest that our testing procedure generally performs better than competing
approaches in detecting complex non-linear dependence.


  In terms of artificial intelligence, there are several security and privacy
deficiencies in the traditional centralized training methods of machine
learning models by a server. To address this limitation, federated learning
(FL) has been proposed and is known for breaking down ``data silos" and
protecting the privacy of users. However, FL has not yet gained popularity in
the industry, mainly due to its security, privacy, and high cost of
communication. For the purpose of advancing the research in this field,
building a robust FL system, and realizing the wide application of FL, this
paper sorts out the possible attacks and corresponding defenses of the current
FL system systematically. Firstly, this paper briefly introduces the basic
workflow of FL and related knowledge of attacks and defenses. It reviews a
great deal of research about privacy theft and malicious attacks that have been
studied in recent years. Most importantly, in view of the current three
classification criteria, namely the three stages of machine learning, the three
different roles in federated learning, and the CIA (Confidentiality, Integrity,
and Availability) guidelines on privacy protection, we divide attack approaches
into two categories according to the training stage and the prediction stage in
machine learning. Furthermore, we also identify the CIA property violated for
each attack method and potential attack role. Various defense mechanisms are
then analyzed separately from the level of privacy and security. Finally, we
summarize the possible challenges in the application of FL from the aspect of
attacks and defenses and discuss the future development direction of FL
systems. In this way, the designed FL system has the ability to resist
different attacks and is more secure and stable.


  We explore bosonic strings and Type II superstrings in the simplest time
dependent backgrounds, namely orbifolds of Minkowski space by time reversal and
some spatial reflections. We show that there are no negative norm physical
excitations. However, the contributions of negative norm virtual states to
quantum loops do not cancel, showing that a ghost-free gauge cannot be chosen.
The spectrum includes a twisted sector, with strings confined to a ``conical''
singularity which is localized in time. Since these localized strings are not
visible to asymptotic observers, interesting issues arise regarding unitarity
of the S-matrix for scattering of propagating states. The partition function of
our model is modular invariant, and for the superstring, the zero momentum
dilaton tadpole vanishes. Many of the issues we study will be generic to
time-dependent cosmological backgrounds with singularities localized in time,
and we derive some general lessons about quantizing strings on such spaces.


  Coherent superpositions are one of the hallmarks of quantum mechanics and are
vital for any quantum mechanical device to outperform the classically
achievable. Generically, superpositions are verified in interference
experiments, but despite their longstanding central role we know very little
about how to extract the number of coherently superposed amplitudes from a
general interference pattern. A fundamental issue is that performing a
phase-sensitive measurement is as challenging as creating a coherent
superposition, so that assuming a perfectly implemented measurement for
verification of quantum coherence is hard to justify. In order to overcome this
issue, we construct a coherence certifier derived from simple statistical
properties of an interference pattern, such that any imperfection in the
measurement can never over-estimate the number of coherently superposed
amplitudes. We numerically test how robust this measure is to under-estimating
the coherence in the case of imperfect state preparation or measurement, and
find it to be very resilient in both cases.


  We report the nonequilibrium dynamical phase transition (NDPT) appearing in a
kinetic Ising spin system (ISS) subject to the joint application of a
deterministic external field and the stochastic mutually correlated noises
simultaneously. A time-dependent Ginzburg-Landau stochastic differential
equation, including an oscillating modulation and the correlated multiplicative
and additive white noises, was addressed and the numerical solution to the
relevant Fokker-Planck equation was presented on the basis of an average-period
approach of driven field. The correlated white noises and the deterministic
modulation induce a kind of dynamic symmetry-breaking order, analogous to the
stochastic resonance in trend, in the kinetic ISS, and the reentrant transition
has been observed between the dynamic disorder and order phases when the
intensities of multiplicative and additive noises were changing. The
dependencies of a dynamic order parameter Q upon the intensities of additive
noise A and multiplicative noise M, the correlation lmda between two noises,
and the amplitude of applied external field h were investigated quantitatively
and visualized vividly. A brief discussion was given to outline the underlying
mechanism of the NDPT in a kinetic ISS driven by an external force and
correlated noises.
  Keywords: Ising spin system, nonequilibrium dynamical phase transition,
stochastic resonance, correlated noises, TDGL model.
  PACS: 75.10.Hk, 64.60.Ht, 05.10.Gg, 76.20.+q


  Spintronic devices are a promising beyond-CMOS device option thanks to their
energy efficiency and compatibility with CMOS. To accurately capture their
multi-physics dynamics, a rigorous treatment of both spin and charge and their
inter-conversion is required. Here we present physics-based device models based
on 4x4 matrices for the spin-orbit coupling part of the magneto-electric
spin-orbit (MESO) device. Also, a more rigorous physics model of ferroelectric
and magnetoelectric switching of ferromagnets, based on Landau-Lifshitz-Gilbert
(LLG) and Landau-Khalatnikov (LK) equations, is presented. With the combined
model implemented in a SPICE circuit simulator environment, simulation results
were obtained which show feasibility of MESO implementation and functional
operation of buffers, oscillators, and majority gates.


  On the level-1 Fock space modules of the algebra $U_q(\hat{sl_n})$ we define
a level-0 action $U_0$ of the $U_q(\hat{sl_n})$, and an action of an abelian
algebra of conserved Hamiltonians commuting with the $U_0$. An irreducible
decomposition of the Fock space with respect to the level-0 action is derived
by constructing a base of the Fock space in terms of the Non-symmetric
Macdonald Polynomials.


  The paper presents results from kinetic Monte Carlo simulations of kinetic
surface roughening using an important and experimentally relevant model of
epitaxial growth -- the solid-on-solid model with Arrhenius dynamics. A
restriction on diffusing adatoms is included allowing hopping down only at
steps of height one monolayer in order to avoid possibly unrealistic events of
jumping from arbitrarily high steps. Simulation results and precise analytic
expressions representing the time evolution of surface roughness do not depend
on the substrate size and clearly put forward the conclusion that for any basic
set of parameters the model approaches in asymptotic limit the usual random
deposition process with growth exponent $\beta=1/2$. At high temperatures, it
is preceded by a long transient regime characterized by a smooth surface
covered with porous pillars and described by a power law with $\beta=3/4$.


  In this article, we proposethe SISO-OFDM and MISO-OFDM based IDET systems,
which are the counterparts of our optimal wideband waveforming strategy in [1].
The fair-throughput and the sum-throughput optimisation problems are
formulated. By exploiting the FP based alternating algorithm, we obtain the
optimal fair-throughput and sum-throughput for both the SISO-OFDM and
MISO-OFDM.


  We study theoretically the resistance at the interface between the two planar
systems with different lattice constants a and b. The resistance and the effect
of the magnetic field depends sensitively on the ratio a/b. The size of the
enlarged unit cell $\lambda = n_Aa = n_Bb$ ($n_A$, $n_B$: integers) is the
crucial quantity, and the magnetic flux penetrating this enlarged unit cell
determines the oscillation of the resistance. Therefore, the magnetoresistance
is very much enhanced at (nearly) incommensurate relation between a and b.


  In this note we prove that a molecule $d(x,y)^{-1}(\delta(x)-\delta(y))$ is
an exposed point of the unit ball of a Lispchitz free space $\mathcal F(M)$ if
and only if the metric segment $[x,y]=\{z \in M \; : \; d(x,y)=d(z,x)+d(z,y)
\}$ is reduced to $\{x,y\}$. This is based on a recent result due to Aliaga and
Perneck\'a which states that the class of Lipschitz free spaces over closed
subsets of M is closed under arbitrary intersections when M has finite
diameter.


  Belk and Connelly introduced the realizable dimension $\textrm{rd}(G)$ of a
finite graph $G$, which is the minimum nonnegative integer $d$ such that every
framework $(G,p)$ in any dimension admits a framework in $\mathbb{R}^d$ with
the same edge lengths. They characterized finite graphs with realizable
dimension at most $1$, $2$, or $3$ in terms of forbidden minors. In this paper,
we consider periodic frameworks and extend the notion to $\mathbb{Z}$-symmetric
graphs. We give a forbidden minor characterization of $\mathbb{Z}$-symmetric
graphs with realizable dimension at most $1$ or $2$, and show that the
characterization can be checked in polynomial time for given quotient
$\mathbb{Z}$-labelled graphs.


  In the design of laser plasma electron injectors for multi-stage laser driven
wakefield accelerators, the control of plasma density is a key element to
stabilize the acceleration process. A cell with variable parameters is used to
confine the gas and tailor the density profile. The gas filling process was
characterized both experimentally and by fluid simulations. Results show a good
agreement between experiments and simulations. Simulations were also used to
study the effect of each of the gas cell parameters on the density distribution
and show the possibility to finely control the density profile.


  We address the problem of antimicrobial peptides that create pores in lipid
bilayers, focusing on voltage-temperature dependence of pore opening. Two novel
experiments (voltage-clamp with alamethicin as an emblematic representative of
these peptides and neutron reflectivity of lipid-monolayer at solid/water
interface under electric field) serve to revise the only current theoretical
model (H. W. Huang et al.Phys. Rev. Lett., 92, 198304 (2004)). We introduce a
general contribution of the electric field as being responsible for an
unbalanced tension of the two bilayer leaflets and we claim that the main
entropy cost of one pore opening is due to the corresponding "excluded-area"
for lipid translation.


  This paper concerns the problem of integrability of non closed distributions
on Banach manifolds. We introduce the notion of weak distribution and we look
for conditions under which these distributions admit weak integral
submanifolds. We give some applications to Banach Lie algebroid and Banach
Lie-Poisson manifold. The main results of this paper generalize the works
presented in [ChSt], [Nu] and [Gl].


  Mirror play (MP) is a well-accepted primal-dual multi-agent learning
algorithm where all agents simultaneously implement mirror descent in a
distributed fashion. The advantage of MP over vanilla gradient play lies in its
usage of mirror maps that better exploit the geometry of decision domains.
Despite extensive literature dedicated to the asymptotic convergence of MP to
equilibrium, the understanding of the finite-time behavior of MP before
reaching equilibrium is still rudimentary. To facilitate the study of MP's
non-equilibrium performance, this work establishes an equivalence between MP's
finite-time primal-dual path (mirror path) in monotone games and the
closed-loop Nash equilibrium path of a finite-horizon differential game,
referred to as mirror differential game (MDG). Our construction of MDG rests on
the Brezis-Ekeland variational principle, and the stage cost functional for MDG
is Fenchel coupling between MP's iterates and associated gradient updates. The
variational interpretation of mirror path in static games as the equilibrium
path in MDG holds in deterministic and stochastic cases. Such a variational
interpretation translates the non-equilibrium studies of learning dynamics into
a more tractable equilibrium analysis of dynamic games, as demonstrated in a
case study on the Cournot game, where MP dynamics corresponds to a linear
quadratic game.


  We provide a partially affirmative answer to the following question on
robustness of polynomial stability with respect to sampling: ``Suppose that a
continuous-time state-feedback controller achieves the polynomial stability of
the infinite-dimensional linear system. We apply an idealized sampler and a
zero-order hold to a feedback loop around the controller. Then, is the
sampled-data system strongly stable for all sufficiently small sampling
periods? Furthermore, is the polynomial decay of the continuous-time system
transferred to the sampled-data system under sufficiently fast sampling?'' The
generator of the open-loop system is assumed to be a Riesz-spectral operator
whose eigenvalues are not on the imaginary axis but may approach it
asymptotically. We provide conditions for strong stability to be preserved
under fast sampling. Moreover, we estimate the decay rate of the state of the
sampled-data system with a smooth initial state and a sufficiently small
sampling period.


  Let $X$ be a smooth projective variety of dimension $5$ and $L$ be an ample
line bundle on $X$ such that $L^5>7^5$ and $L^d\cdot Z\geq 7^d$ for any
subvariety $Z$ of dimension $1\leq d\leq 4$. We show that
$\mathcal{O}_X(K_X+L)$ is globally generated.


  Phosphorene is a promising single elemental two-dimensional layered
semiconductor with huge potential for future nanoelectronics and spintronics
applications. In this work, we investigated the effect of an organic molecule
(benzene) in the close proximity of a Phosphorene nanoribbon. Our extensive
calculations reveal that the semiconducting nature of Phosphorene stays
unaffected as a result of the molecular adsorption while the transport
properties go through drastic changes. Under the influence of dopant atoms and
external strain, colossal changes in the conductivity is observed with a
maximum enhancement > 1500% which has not been observed earlier. This effect is
pretty robust against the (i) variation of system size, (ii) type, location and
concentration of dopants and (iii) nature and magnitude of the external strain.
Furthermore, we demonstrated how a gate voltage can be used to fine tune the
enhanced conductivity response in a Field-effect transistor (FET) structure.
Our results provide new direction for Phosphorene based nanoelectronics in
applications like sensing, switching where a higher level of conduction can
offer better resolution, higher ON/OFF ratio and superior energy efficiency.


  Buffering architectures and policies for their efficient management
constitute one of the core ingredients of a network architecture. In this work
we introduce a new specification language, BASEL, that allows to express
virtual buffering architectures and management policies representing a variety
of economic models. BASEL does not require the user to implement policies in a
high-level language; rather, the entire buffering architecture and its policy
are reduced to several comparators and simple functions. We show examples of
buffering architectures in BASEL and demonstrate empirically the impact of
various settings on performance.


  We studied the nematic isotropic phase transition by applying the functional
renormalization group to the Landau-de Gennes model. We derived the flow
equations for the effective potential as well as the cubic and quartic
"couplings" and the anomalous dimension. We then solved the coupled flow
equations on a grid using Newton Raphson method. A first order phase transition
is observed. We also investigated the nematic isotropic puzzle (the NI puzzle)
in this paper. We obtained the NI transition temperature difference
${T_c-T^*}=5.85K$ with sizable improvement over previous results.


  We present the Bio-Silicon Intelligence System (BSIS), an innovative hybrid
platform that integrates biological neural networks with silicon-based
computing. The BSIS, a Physics-Informed Hybrid Hierarchical Reinforcement
Learning State Machine, employs carbon nanotube-coated electrodes to interface
rat brains with computational systems, enabling high-fidelity neural
interfacing and bidirectional communication through self-organizing systems in
both biological and silicon forms. Our system leverages both analogue and
digital AI theory, incorporating concepts from computational theory, chaos
theory, dynamical systems theory, physics, and quantum mechanics. Additionally,
the BSIS replicates the neuronal dynamics typical of intelligent brain tissue,
employing nonlinear operations underlying learning and information storage.
Neural signals are read through the FreeEEG32 board and BrainFlow software,
then features are extracted and mapped to game actions by tracking feature
changes in continuous data. Metadata is encoded into both analogue and digital
brain stimulation signals at the microvolt level using our proprietary software
and hardware. The system employs a dual signaling approach for training the rat
brain, incorporating a reward solution and sound as well as human-inaudible
distress sounds. This paper details the design, theory, functionality, and
technical specifications of the BSIS, highlighting its interdisciplinary
approach and advanced technological integration.


  We consider the link and three-manifold invariants in [DGGPR], which are
defined in terms of certain non-semisimple finite ribbon categories
$\mathcal{C}$ together with a choice of tensor ideal and modified trace. If the
ideal is all of $\mathcal{C}$, these invariants agree with those defined by
Lyubashenko in the 90's. We show that in that case the invariants depend on the
objects labelling the link only through their simple composition factors, so
that in order to detect non-trivial extensions one needs to pass to proper
ideals. We compute examples of link and three-manifold invariants for
$\mathcal{C}$ being the category of $N$ pairs of symplectic fermions. Using a
quasi-Hopf algebra realisation of $\mathcal{C}$, we find that the
Lyubashenko-invariant of a lens space is equal to the order of its first
homology group to the power $N$, a relation we conjecture to hold for all
rational homology spheres. For $N \ge 2$, $\mathcal{C}$ allows for tensor
ideals $\mathcal{I}$ with a modified trace which are different from all of
$\mathcal{C}$ and from the projective ideal. Using the theory of pull-back
traces and symmetrised cointegrals, we show that the link invariant obtained
from $\mathcal{I}$ can distinguish a continuum of indecomposable but reducible
objects which all have the same composition series.


  We study both the cosmological self-tuning and the local predictions (inside
the Solar system) of the most general shift-symmetric beyond Horndeski theory.
We first show that the cosmological self-tuning is generic in this class of
theories: By adjusting a mass parameter entering the action, a large bare
cosmological constant can be effectively reduced to a small observed one.
Requiring then that the metric should be close enough to the Schwarzschild
solution in the Solar system, to pass the experimental tests of general
relativity, and taking into account the renormalization of Newton's constant,
we select a subclass of models which presents all desired properties: It is
able to screen a big vacuum energy density, while predicting an exact
Schwarzschild-de Sitter solution around a static and spherically symmetric
source. As a by-product of our study, we identify a general subclass of beyond
Horndeski theory for which regular self-tuning black hole solutions exist, in
presence of a time-dependent scalar field. We discuss possible future
development of the present work.


  Using accurate numerical relativity simulations of (nonspinning) black-hole
binaries with mass ratios 1:1, 2:1 and 3:1 we compute the gauge invariant
relation between the (reduced) binding energy $E$ and the (reduced) angular
momentum $j$ of the system. We show that the relation $E(j)$ is an accurate
diagnostic of the dynamics of a black-hole binary in a highly relativistic
regime. By comparing the numerical-relativity $E^{\rm NR} (j)$ curve with the
predictions of several analytic approximation schemes, we find that, while the
usual, non-resummed post-Newtonian-expanded $E^{\rm PN} (j)$ relation exhibits
large and growing deviations from $E^{\rm NR} (j)$, the prediction of the
effective one-body formalism, based purely on known analytical results (without
any calibration to numerical relativity), agrees strikingly well with the
numerical-relativity results.


  The future fibre optic communications network will rely on photons as
carriers of information, which may be stored in intensity, polarization or
phase of light. However, processing of such optical information usually relies
on electronics. Aiming to avoid the conversion between optical and electronic
signals, modulation of light with light based on optical nonlinearity has
become a major research field, but real integrated all-optical systems face
thermal management and energy challenges. On the other hand, it has recently
been demonstrated that the interaction of two coherent light beams on a thin,
lossy, linear material can lead to large and ultrafast intensity modulation at
arbitrarily low power resulting from coherent absorption. Here we demonstrate
that birefringence and optical activity (linear and circular birefringence and
dichroism) of functional materials can be coherently controlled by placing a
thin material slab into a standing wave formed by the signal and control waves.
Efficient control of the polarization azimuth and ellipticity of the signal
wave with the coherent control wave has been demonstrated in proof-of-principle
experiments in different chiral and anisotropic microwave metamaterials.


  In this paper we show that a pseudo-Nambu-Goldstone boson (PNGB) quintessence
of spontaneous symmetry breaking (SSB) is responsible for an epoch of the late
time cosmic acceleration. We suggest that an N=2 PNGB quintessence with SSB can
give rise to the fast-roll cosmic acceleration in evolution of the Universe. In
the N=2 PNGB quintessence model, the standard slow-roll condition in which the
absolute value of the tachyonic mass squared $|m_q^2|$ of this model is much
less than the square of the Hubble constant $H^2$, is broken down to the
fast-roll condition, $|m_q^2|=O(H^2)$. However, it is shown that the
fast-rolling associated with the N=2 PNGB quintessence of SSB can be led to the
epoch of the late time cosmic acceleration. Indeed, in our PNGB model, this
epoch can be quite long lasting since the mass of the N=2 PNGB quintessence
field is extremely small as $m_q \approx 10^{-33} \rm{eV}$.


  Some experimental studies of $\eta$ production and $\eta$ interactions
performed or presently under way by the GEM collaboration at COSY J\"{u}lich
are reviewed.


  We have exactly solved the relaxational dynamics of a model protein which
possesses a kinetically perfect funnel-like energy landscape. We find that the
dependence of the relaxation time, $\tau$, on the density of states (DOS) and
the energy level spacing distributions of the model displays several main types
of behavior depending on the temperature $T$. This allows us to identify
possible generic features of the relaxation. For some ranges of $T$, $\tau$ is
insensitive to the density of states; for intermediate values of $T$ it depends
on the energy level spacing distribution rather than on the DOS directly, and
it becomes gradually more dependent on DOS with increasing temperature;
finally, the relaxation can also be determined exclusively by the presence of a
deep gap in the energy spectrum rather than by the detailed features of the
density of states. We found that the behavior of $\tau$ crucially depends on
the degeneracy of the energy spectrum. For the special case of exponentially
increasing degeneracy, we were able to identify a characteristic temperature
which roughly separates the relaxational regimes controlled by energetics and
by entropy, respectively. Finally, the validity of our theory is discussed when
roughness of energy landscape is added.


  When magnetic skyrmions move under spin orbit torque in magnetic nanowires,
they experience a skyrmion Hall effect, which pushes them towards the nanowire
edge where they risk being annihilated; this puts an upper limit on how fast
they can be driven. However, the same magnetic multilayer harboring skyrmions
can sustain a N\'eel-type strip domain wall along the nanowire length,
potentially keeping the skyrmions separated from the edge. Here we study the
interplay between current driven skyrmions and domain walls and find that they
increase the annihilation current and allow the skyrmions to move faster. Based
on the Thiele formalism, we confirm that the emergent longitudinal repulsive
force and the modified energy landscape linked to the domain wall are
responsible for the enhanced skyrmion motion. Furthermore, we identify that the
longitudinal repulsive force emerges because of the broken axisymmetry in the
local magnetization in front of the skyrmion. Our study uncovers key aspects in
the interplay between two topological magnetic textures from different homotopy
groups and may inspire new device concepts.


  We present a model for typical charged defects in weakly doped Y1-xCaxVO3
perovskites and study how they influence the magnetic and orbital order.
Starting from a multiband Hubbard model, we show that the charge carriers
introduced by doping are bound to the Ca defects with large binding energy of
about 1 eV at small doping, and give rise to the in-gap absorption band
observed in the optical spectroscopy. The central position of a generic Ca
defect with eight equidistant vanadium neighbors implies a partly filled defect
band and permits activated transport due to Coulomb disorder. We explore the
effect of bound charge carriers on the dynamics of the (yz,zx) orbital and spin
degrees of freedom in the context of a spin-orbital t-J model. After deriving
the superexchange interactions around the doped hole, we show that the
transition from G-type to C-type antiferromagnetic (AF) order is triggered by
the kinetic energy of doped holes via the double-exchange mechanism. The defect
states lead to local modification of orbital correlations within ferromagnetic
chains along the c axis; some of them contain hole defects, while the
charge-orbital coupling suppresses locally (yz,zx) orbital fluctuations in the
others. Thereby, Ca defects provide a physical mechanism for spin-orbital
dimerization along the ferromagnetic bonds, suggesting that, in the C-AF phase
of weakly doped Y1-xCaxVO3, dimerization increases with doping.


  The measurement of oscillations excited by surface convection is a powerful
method to study the structure and evolution of cool stars. CoRoT and Kepler
have initiated a revolution in asteroseismology by detecting oscillations in
thousands of stars from the main sequence to the red-giant branch, including a
large number of exoplanet host stars. In this contribution I will review recent
asteroseismic results, focusing in particular on the internal rotation of red
giant stars and the impact of asteroseismology on the characterization of
exoplanets.


  Assessment of classification techniques on predicting success or failure of
Software reusability


  This paper explores applying the wav2vec2 framework to speaker recognition
instead of speech recognition. We study the effectiveness of the pre-trained
weights on the speaker recognition task, and how to pool the wav2vec2 output
sequence into a fixed-length speaker embedding. To adapt the framework to
speaker recognition, we propose a single-utterance classification variant with
CE or AAM softmax loss, and an utterance-pair classification variant with BCE
loss. Our best performing variant, w2v2-aam, achieves a 1.88% EER on the
extended voxceleb1 test set compared to 1.69% EER with an ECAPA-TDNN baseline.
Code is available at https://github.com/nikvaessen/w2v2-speaker.


  Spin chains can be used to describe a wide range of platforms for quantum
computation and quantum information. They enable the understanding,
demonstration, and modeling of numerous useful phenomena, such as high fidelity
transfer of quantum states, creation and distribution of entanglement, and
creation of resources for measurement-based quantum processing. In this paper,
a more complex spin system, a 2D spin network (SN) engineered by applying
suitable unitaries to two uncoupled spin chains, is studied. Considering only
the single-excitation subspace of the SN, it is demonstrated that the system
can be operated as a router, directing information through the SN. It is also
shown that it can serve to generate maximally entangled states between two
sites. Furthermore, it is illustrated that this SN system can be used as a
sensor device able to determine an unknown phase applied to a system spin. A
detailed modeling investigation of the effects of static disorder in the system
shows that this system is robust against different types of disorder.


  After Aceh's quake many earthquakes have struck Indonesia alternately and
even other disasters have been a threat for every citizen in this country.
Actually an everyday occurrence on earth and more than 3 million earthquakes
occur every year, about 8,000 a day, or one every 11 seconds in Indonesia there
are 5 to 30 quakes prediction everyday. Government's responsibility to protect
the citizen has been done by making National body of disaster management.
Preparing, saving and distribution logistic become National body of disaster
management's responsibility to build information management. Many law's
products have been produced as a government's responsibility to give secure
life for the citizen. We can not prevent them totally, we have to learn to live
with them and need to be prepared all the time, need to learn how to mitigate
risk of losses in such events by managing crisis and emergencies correctly.
After disaster happens respond must be rapidly and at an optimal level to save
lives and help to victims. DSS is information technology environment which can
be used to help human in order to learn from past earthquake, record it, learn
and plan for future mitigation and hope will reduce the disaster risk in the
future. Using web technology for DSS will give value added where not only make
a strategic decision for the decision maker, but for others who need national
earthquake information like citizen, scholars, researches and people around the
world.


  This work reports on two related investigations of stochastic simulations
which are widely used to study biodiversity and other related issues. We first
deal with the behavior of the Hamming distance under the increase of the number
of species and the size of the lattice, and then investigate how the mobility
of the species contributes to jeopardize biodiversity. The investigations are
based on the standard rules of reproduction, mobility and predation or
competition, which are described by specific rules, guided by generalization of
the rock-paper-scissors game, valid in the case of three species. The results
on the Hamming distance indicate that it engenders universal behavior,
independently of the number of species and the size of the square lattice. The
results on the mobility confirm the prediction that it may destroy diversity,
if it is increased to higher and higher values.


  Metal-organic species can be designed to self-assemble in large-scale,
atomically defined, supramolecular architectures. Hybrid quantum wells, where
inorganic two-dimensional (2D) planes are separated by organic ligands, are a
particular example. The ligands effectively provide an intralayer confinement
for charge carriers resulting in a 2D electronic structure, even in
multilayered assemblies. Air-stable metal organic chalcogenides hybrid quantum
wells have recently been found to host tightly bound 2D excitons with strong
optical anisotropy in a bulk matrix. Here, we investigate the excited carrier
dynamics in the prototypical metal organic chalcogenide [AgSePh], disentangling
three excitonic resonances by low temperature transient absorption
spectroscopy. Our analysis suggests a complex relaxation cascade comprising
ultrafast screening and renormalization, inter-exciton relaxation, and
self-trapping of excitons within few picoseconds. The ps-decay provided by the
self-trapping mechanism may be leveraged to unlock the material's potential for
ultrafast optoelectronic applications.


  We have measured the half-life of $^{14}$O, a superallowed $(0^{+} \to
0^{+})$ $\beta$ decay isotope. The $^{14}$O was produced by the
$^{12}$C($^{3}$He,n)$^{14}$O reaction using a carbon aerogel target. A
low-energy ion beam of $^{14}$O was mass separated and implanted in a thin
beryllium foil. The beta particles were counted with plastic scintillator
detectors. We find $t_{1/2} = 70.696\pm 0.052$ s. This result is $1.5\sigma$
higher than an average value from six earlier experiments, but agrees more
closely with the most recent previous measurement.


  Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data
processing and content sharing among vehicles and terrestrial edge computing
(TEC) infrastructures (e.g., 5G base stations and roadside units) with little
or no human intervention, plays a key role in the intelligent transportation
systems. However, EC-IoV is heavily dependent on the connections and
interactions between vehicles and TEC infrastructures, thus will break down in
some remote areas where TEC infrastructures are unavailable (e.g., desert,
isolated islands and disaster-stricken areas). Driven by the ubiquitous
connections and global-area coverage, space-air-ground integrated networks
(SAGINs) efficiently support seamless coverage and efficient resource
management, represent the next frontier for edge computing. In light of this,
we first review the state-of-the-art edge computing research for SAGINs in this
article. After discussing several existing orbital and aerial edge computing
architectures, we propose a framework of edge computing-enabled
space-air-ground integrated networks (EC-SAGINs) to support various IoV
services for the vehicles in remote areas. The main objective of the framework
is to minimize the task completion time and satellite resource usage. To this
end, a pre-classification scheme is presented to reduce the size of action
space, and a deep imitation learning (DIL) driven offloading and caching
algorithm is proposed to achieve real-time decision making. Simulation results
show the effectiveness of our proposed scheme. At last, we also discuss some
technology challenges and future directions.


  This work introduces a neuro-symbolic agent that combines deep reinforcement
learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e.,
never-seen-before, generalisation of formally specified instructions. In
particular, we present a neuro-symbolic framework where a symbolic module
transforms TL specifications into a form that helps the training of a DRL agent
targeting generalisation, while a neural module learns systematically to solve
the given tasks. We study the emergence of systematic learning in different
settings and find that the architecture of the convolutional layers is key when
generalising to new instructions. We also provide evidence that systematic
learning can emerge with abstract operators such as negation when learning from
a few training examples, which previous research have struggled with.


  Over the last few years, ReS2 has generated a myriad of unattended queries
regarding its structure, the concomitant thickness dependent electronic
properties and apparently contrasting experimental optical response. In this
work, with elaborate first-principles investigations, using density functional
theory (DFT) and time-dependent DFT (TDDFT), we identify the structure of ReS2,
which is capable of reproducing and analyzing the layer-dependent optical
response. The theoretical results are further validated by an in-depth
structural, chemical, optical and optoelectronic analysis of the large-area
ReS2 thin films, grown by chemical vapor deposition (CVD) process. Micro-Raman
(MR), X-ray photoelectron spectroscopy (XPS), cross-sectional transmission
electron microscopy (TEM) and energy-dispersive X-ray analysis (EDAX) have
enabled the optimization of the uniform growth of the CVD films. The
correlation between the layer-dependent optical and electronic properties of
the excited states was established by static photoluminescence (PL) and
transient absorption (TA) measurements. Sulfur vacancy-induced localized
mid-gap states render a significantly long life-time of the excitons in these
films. The ionic gel top-gated photo-detectors, fabricated from the as-prepared
CVD films, exhibit a large photo-response of ~ 5 A/W and a remarkable
detectivity of ~ 1011 Jones. The outcome of the present work will be useful to
promote the application of vertically grown large-area films in the field of
optics and opto-electronics.


  3D imaging modalities are becoming increasingly popular and relevant in
retinal imaging owing to their effectiveness in highlighting structures in
sub-retinal layers. OCT is one such modality which has great importance in the
context of analysis of cystoid structures in subretinal layers. Signal to noise
ratio(SNR) of the images obtained from OCT is less and hence automated and
accurate determination of cystoid structures from OCT is a challenging task. We
propose an automated method for detecting/segmenting cysts in 3D OCT volumes.
The proposed method is biologically inspired and fast aided by the domain
knowledge about the cystoid structures. An ensemble learning methodRandom
forests is learnt for classification of detected region into cyst region. The
method achieves detection and segmentation in a unified setting. We believe the
proposed approach with further improvements can be a promising starting point
for more robust approach. This method is validated against the training set
achieves a mean dice coefficient of 0.3893 with a standard deviation of 0.2987


  We study a general class of entropy-regularized multi-variate LQG mean field
games (MFGs) in continuous time with $K$ distinct sub-population of agents. We
extend the notion of actions to action distributions (exploratory actions), and
explicitly derive the optimal action distributions for individual agents in the
limiting MFG. We demonstrate that the optimal set of action distributions
yields an $\epsilon$-Nash equilibrium for the finite-population
entropy-regularized MFG. Furthermore, we compare the resulting solutions with
those of classical LQG MFGs and establish the equivalence of their existence.


  The purpose of the automatic dependent surveillance broadcast (ADS-B)
technology is to serve as a replacement for the current radar-based, air
traffic control systems. Despite the considerable time and resources devoted to
designing and developing the system, the ADS-B is well known for its lack of
security mechanisms. Attempts to address these security vulnerabilities have
been made in previous studies by modifying the protocol's current architecture
or by using additional hardware components. These solutions, however, are
considered impractical because of 1) the complex regulatory process involving
avionic systems, 2) the high costs of using hardware components, and 3) the
fact that the ADS-B system itself is already deployed in most aircraft and
ground stations around the world. In this paper, we propose VizADS-B, an
alternative software-based security solution for detecting anomalous ADS-B
messages, which does not require any alteration of the current ADS-B
architecture or the addition of sensors. According to the proposed method, the
information obtained from all aircraft within a specific geographical area is
aggregated and represented as a stream of images. Then, a convolutional LSTM
encoder-decoder model is used for analyzing and detecting anomalies in the
sequences of images. In addition, we propose an explainability technique,
designed specifically for convolutional LSTM encoder-decoder models, which is
used for providing operative information to the pilot as a visual indicator of
a detected anomaly, thus allowing the pilot to make relevant decisions. We
evaluated our proposed method on five datasets by injecting and subsequently
identifying five different attacks. Our experiments demonstrate that most of
the attacks can be detected based on spatio-temporal anomaly detection
approach.


  We investigate a possibility to describe the non-Debye relaxation processes
using the Volterra-type equations with kernels given by the Prabhakar functions
with the upper parameter $\nu$ being negative. Proposed integro-differential
equations mimic the fading memory effects and are explicitly solved using the
umbral calculus and the Laplace transform methods. Both approaches lead to the
same results valid for admissible domain of the parameters $\alpha$, $\mu$ and
$\nu$ characterizing the Prabhakar function. For the special case $\alpha\in
(0,1]$, $\mu=0$ and $\nu=-1$ we recover the Cole-Cole model, in general having
a residual polarization. We also show that our scheme gives results equivalent
to those obtained using the stochastic approach to relaxation phenomena merged
with integral equations involving kernels given by the Prabhakar functions with
the positive upper parameter.