  We introduce the negative binomial states with negative binomial distribution
as their photon number distribution. They reduce to the ordinary coherent
states and Susskind-Glogower phase states in different limits. The ladder and
displacement operator formalisms are found and they are essentially the
Perelomov's su(1,1) coherent states via its Holstein-Primakoff realisation.
These states exhibit strong squeezing effect and they obey the super-Poissonian
statistics. A method to generate these states is proposed.


  About twenty years ago we wrote a paper, "Boolean Functions whose Fourier
Transform is Concentrated on the First Two Levels", \cite{FKN}. In it we
offered several proofs of the statement that Boolean functions
$f(x_1,x_2,\dots,x_n)$, whose Fourier coefficients are concentrated on the
lowest two levels are close to a constant function or to a function of the form
$f=x_k$ or $f=1-x_k$. Returning to the paper lately, we noticed that the
presentation of the first proof is rather cumbersome, and includes several
typos. In this note we rewrite that proof, as a service to the public.


  We propose a three terminal heat engine based on semiconductor superlattices
for energy harvesting. The periodicity of the superlattice structure creates an
energy miniband, giving an energy window for allowed electron transport. We
find that this device delivers a large power, nearly twice than the heat engine
based on quantum wells, with a small reduction of efficiency. This engine also
works as a refrigerator in a different regime of the system's parameters. The
thermoelectric performance of the refrigerator is analyzed, including the
cooling power and coefficient of performance in the optimized condition. We
also calculate phonon heat current through the system, and explore the
reduction of phonon heat current compared to the bulk material. The direct
phonon heat current is negligible at low temperatures, but dominates over the
electronic at room temperature and we discuss ways to reduce it.


  As the world continues to face the challenges of climate change, it is
crucial to consider the environmental impact of the technologies we use. In
this study, we investigate the performance and computational carbon emissions
of various transfer learning models for garbage classification. We examine the
MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models.
Our findings indicate that the EfficientNetV2 family achieves the highest
accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model
requires more time and produces higher carbon emissions. ResNet50 outperforms
ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger
carbon footprint. We conclude that EfficientNetV2S is the most sustainable and
accurate model with 96.41% accuracy. Our research highlights the significance
of considering the ecological impact of machine learning models in garbage
classification.


  We construct ribbon surfaces of Euler characteristic one for several infinite
families of alternating 3-braid closures. We also use a twisted Alexander
polynomial obstruction to conclude the classification of smoothly slice knots
which are closures of alternating 3-braids with up to 20 crossings.


  Answering a question of Dan Haran and generalizing some results of
Aschbacher-Guralnick and Suzuki, we prove that given a set of primes pi, any
finite group can be generated by a pi-subgroup and a pi'-subgroup. This gives a
free product description of a countably generated free profinite group.


  The purpose of this article is to carry out a power-spectrum analysis (based
on likelihood methods) of the Super-Kamiokande 5-day dataset that takes account
of the asymmetry in the error estimates. Whereas the likelihood analysis
involves a linear optimization procedure for symmetrical error estimates, it
involves a nonlinear optimization procedure for asymmetrical error estimates.
  We find that for most frequencies there is little difference between the
power spectra derived from analyses of symmetrized error estimates and from
asymmetrical error estimates. However, this proves not to be the case for the
principal peak in the power spectra, which is found at 9.43 yr-1. A likelihood
analysis which allows for a "floating offset" and takes account of the start
time and end time of each bin and of the flux estimate and the symmetrized
error estimate leads to a power of 11.24 for this peak. A Monte Carlo analysis
shows that there is a chance of only 1% of finding a peak this big or bigger in
the frequency band 1 - 36 yr-1 (the widest band that avoids artificial peaks).
On the other hand, an analysis that takes account of the error asymmetry leads
to a peak with power 13.24 at that frequency. A Monte Carlo analysis shows that
there is a chance of only 0.1% of finding a peak this big or bigger in that
frequency band 1 - 36 yr-1. From this perspective, power spectrum analysis that
takes account of asymmetry of the error estimates gives evidence for
variability that is significant at the 99.9% level.
  We comment briefly on an apparent discrepancy between power spectrum analyses
of the Super-Kamiokande and SNO solar neutrino experiments.


  The deep imaging of the field of an observed lensing event by a cosmic string
reveals many additional lensing events. We study the statistics of such string
lensing. We derive explicit expressions for the distributions of image
separations of lensing by a cosmic string and point out that they are quite
sensitive to parameters which characterize the cosmic string, such as the
redshift and tension of the cosmic string. Thus the statistics of string
lensing events add new important information on the cosmic string which cannot
be obtained from the detailed investigation of one lensing event.


  Single spin asymmetries in semi-inclusive deep-inelastic scattering off
transversely polarized nucleon targets have been under intense experimental
investigation over the past few years. They provide new insights into QCD and
the nucleon structure. For instance, they allow the determination of the third
yet-unknown leading-twist quark distribution function $\Delta_{T}q(x)$, the
transversity distribution. Additionally, they give insight into the parton
transverse momentum distribution and angular momentum.
  The measurement of transverse spin effects in semi-inclusive deep-inelastic
scattering is an important part of the COMPASS physics program. In the years
2002-2004 data were collected scattering a 160 GeV muon beam on a transversely
polarized deuteron target. In 2007, additional data were collected on a
transversely polarized proton target. New results from the analysis of the
proton data will be presented. A different but not less important insight into
the nucleon structure might be given by the Sivers asymmetry. This angular
dependence of the cross-section arises from an intrinsic asymmetry in the
parton transverse momentum distribution. The Sivers function is tightly related
to the total angular momentum carried by the quarks in the nucleon. New COMPASS
results for the Sivers asymmetry of the proton will be shown.


  We study theoretically and experimentally the quantification of non-Gaussian
distributions via non-destructive measurements. Using the theory of cumulants,
their unbiased estimators, and the uncertainties of these estimators, we
describe a quantification which is simultaneously efficient, unbiased by
measurement noise, and suitable for hypothesis tests, e.g., to detect
non-classical states. The theory is applied to cold $^{87}$Rb spin ensembles
prepared in non-gaussian states by optical pumping and measured by
non-destructive Faraday rotation probing. We find an optimal use of measurement
resources under realistic conditions, e.g., in atomic ensemble quantum
memories.


  We derive a shortcut stationary metric formula for generating imperfect fluid
rotating solutions, in Boyer-Lindquist coordinates, from spherically symmetric
static ones. We explore the properties of the curvature scalar and
stress-energy tensor for all types of rotating regular solutions we can
generate without restricting ourselves to specific examples of regular
solutions (regular black holes or wormholes). We show through examples how it
is generally possible to generate an imperfect fluid regular rotating solution
via radial coordinate transformations. We derive rotating wormholes that are
modeled as imperfect fluids and discuss their physical properties that are
independent on the way the stress-energy tensor is interpreted. A solution
modeling an imperfect fluid rotating loop black hole is briefly discussed. We
then specialize to the recently discussed stable exotic dust Ellis wormhole
emerged in a source-free radial electric or magnetic field, generate its,
conjecturally stable, rotating counterpart which turns out to be an exotic
imperfect fluid wormhole and determine the stress-energy tensor of both the
imperfect fluid and the electric or magnetic field.


  We consider theoretical models of emission of TeV photons by Cyg X-1 during a
flare discovered by the MAGIC detector. We study acceleration of electrons to
energies sufficient for TeV emission, and find the emission site is allowed to
be close to the black hole. We then consider pair absorption in the photon
field of the central X-ray source and a surrounding accretion disc, and find
its optical depth is <1, allowing emission close to the black hole. On the
other hand, the optical depth in the stellar field is about 10 at 1 TeV.
However, the optical depth drops with increasing energy, allowing a model with
the initial energy of >3 TeV, in which photons travel far away from the star,
initiating a spatially extended pair cascade. This qualitatively explains the
observed TeV spectrum, though still not its exact shape.


  In this article, we illustrate how the Coulomb field of a highly relativistic
electron beam can be shaped into a broadband pulse suitable for driving
ultrafast and strong-field physics. In contrast to a solid-state laser, the
Coulomb field creates a pulse which can be intrinsically synchronized with an
x-ray free electron laser (XFEL), can have a cutoff frequency which is broadly
tunable from THz to EUV, and which acts on target systems as a "half-cycle"
impulse. Explicit examples are presented to emphasize how the unique features
of this excitation can be a tool for novel science at XFEL facilities like the
LCLS.


  The Ring Loading Problem is an optimal routing problem arising in the
planning of optical communication networks which use bidirectional SONET rings.
In mathematical terms, it is an unsplittable multicommodity flow problem on
undirected ring networks. We prove that any split routing solution to the Ring
Loading Problem can be turned into an unsplittable solution while increasing
the load on any edge of the ring by no more than +(19/14)D, where D is the
maximum demand value. This improves upon a classical result of Schrijver,
Seymour, and Winkler (1998) who obtained a slightly larger bound of +(3/2)D. We
also present an improved lower bound of +1.1 D (previously +1.01 D) on the best
possible bound and disprove a famous long-standing conjecture of Schrijver et
al. in this context.


  Conditional GANs are at the forefront of natural image synthesis. The main
drawback of such models is the necessity for labeled data. In this work we
exploit two popular unsupervised learning techniques, adversarial training and
self-supervision, and take a step towards bridging the gap between conditional
and unconditional GANs. In particular, we allow the networks to collaborate on
the task of representation learning, while being adversarial with respect to
the classic GAN game. The role of self-supervision is to encourage the
discriminator to learn meaningful feature representations which are not
forgotten during training. We test empirically both the quality of the learned
image representations, and the quality of the synthesized images. Under the
same conditions, the self-supervised GAN attains a similar performance to
state-of-the-art conditional counterparts. Finally, we show that this approach
to fully unsupervised learning can be scaled to attain an FID of 23.4 on
unconditional ImageNet generation.


  Recent statistical approaches have improved the robustness and scalability of
spoken dialogue systems. However, despite recent progress in domain adaptation,
their reliance on in-domain data still limits their cross-domain scalability.
In this paper, we argue that this problem can be addressed by extending current
models to reflect and exploit the multi-dimensional nature of human dialogue.
We present our multi-dimensional, statistical dialogue management framework, in
which transferable conversational skills can be learnt by separating out
domain-independent dimensions of communication and using multi-agent
reinforcement learning. Our initial experiments with a simulated user show that
we can speed up the learning process by transferring learnt policies.


  Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within
biological pathways, the incorporation of prior pathways information into a
statistical model is expected to increase the power to detect true associations
in a genetic association study. Most existing pathways-based methods rely on
marginal SNP statistics and do not fully exploit the dependence patterns among
SNPs within pathways. We use a sparse regression model, with SNPs grouped into
pathways, to identify causal pathways associated with a quantitative trait.
Notable features of our "pathways group lasso with adaptive weights" (P-GLAW)
algorithm include the incorporation of all pathways in a single regression
model, an adaptive pathway weighting procedure that accounts for factors
biasing pathway selection, and the use of a bootstrap sampling procedure for
the ranking of important pathways. P-GLAW takes account of the presence of
overlapping pathways and uses a novel combination of techniques to optimise
model estimation, making it fast to run, even on whole genome datasets. In a
comparison study with an alternative pathways method based on univariate SNP
statistics, our method demonstrates high sensitivity and specificity for the
detection of important pathways, showing the greatest relative gains in
performance where marginal SNP effect sizes are small.


  We study $\infty$-modulus on general metric spaces and establish its relation
to shortest lengths of paths. This connection was already known for modulus on
graphs, but the formulation in metric measure spaces requires more attention to
exceptional families. We use this to define a metric that we call the essential
metric, and show how this recovers a metric that had already been advanced in
the literature by De Cecco and Palmieri.


  In the context of the ClearMind project, we measured the scintillating
properties, as induced from from gamma ray interactions, of today available
PbWO4 crystal. We measured scintillation s yields and time constants by
measuring the signal shape measured on a fast photo-multiplier and
deconvoluting it from the instrumental effects. For the doped crystals at room
temperature, we measured a fast scintillation component, with time constants of
2 ns, 55 percent of the total light yield, and a slow component of 6 ns. We
observe a significant increase of the light yield for the slow component when
the temperature decreases and simultaneous increase of the time constants, but
no increase in the fast component light yield. Our measurements reproduce the
main qualitative features of PbWO4 crystals quoted in the literature.
Quantitatively though, we measured significantly shorter time constants and
larger light yields. This is explained by a rigorous treatment of the
instrumental contributions in the measurements. Results are discussed and
prospect for future developments, tailored for the ClearMind project, are
presented.


  We consider a class of optimal control problems of stochastic delay
differential equations (SDDE) that arise in connection with optimal advertising
under uncertainty for the introduction of a new product to the market,
generalizing classical work of Nerlove and Arrow (1962). In particular, we deal
with controlled SDDE where the delay enters both the state and the control.
Following ideas of Vinter and Kwong (1981) (which however hold only in the
deterministic case), we reformulate the problem as an infinite dimensional
stochastic control problem to which we associate, through the dynamic
programming principle, a second order Hamilton-Jacobi-Bellman equation. We show
a verification theorem and we exhibit some simple cases where such equation
admits an explicit smooth solution, allowing us to construct optimal feedback
controls.


  Weak gravitational lensing can cause displacements, magnification, rotation
and shearing of the images of distant galaxies. Most studies focus on the shear
and magnification effects since they are more easily observed. In this paper we
focus on the effect of lensing displacements on wide field images. Galaxies at
redshifts 0.5--1 are typically displaced by 1 arcminute, and the displacements
are coherent over degree-size patches. However the displacement effect is
redshift-dependent, so there is a visible relative shift between galaxies at
different redshifts, even if they are close on the sky. We show that the
reconstruction of the original galaxy position is now feasible with lensing
surveys that cover many hundreds of square degrees. We test with simulations
two approaches to "delensing": one uses shear measurements and the other uses
the foreground galaxy distribution as a proxy for the mass. We also estimate
the effect of foreground deflections on galaxy-galaxy lensing measurements and
find it is relevant only for LSST and Euclid-era surveys.


  We present a non-minimal renormalizable SUSY $SU(5)$~model, with extended
Higgs sector and right-handed neutrinos, where the flavour sector exhibits a
$Q_{6}$ flavour symmetry. We analysed the simplest version of this model, in
which R-parity is conserved and the right-handed neutrino masses in the flavour
doublet are considered with and without degeneration. We find the generic form
of the mass matrices both in the quark and lepton sectors. We reproduce,
according to current data, the mixing in the CKM matrix. In the leptonic
sector, in the general case where the right-handed neutrino masses are not
degenerate, we find that the values for the solar, atmospheric, and reactor
mixing angles are in very good agreement with the experimental data, both for a
normal and an inverted hierarchy. In the particular case where the right handed
neutrinos masses are degenerate, the model predicts a strong inverted hierarchy
spectrum and a sum rule among the neutrino masses. In this case the atmospheric
and solar angles are in very good agreement with experimental data, and the
reactor one is different from zero, albeit too small ($\theta^{\ell^{th}}_{13}
\sim 3.38 $). This value constitues a lower bound for $\theta_{13}$ in the
general case. We also find the range of values for the neutrino masses in each
case.


  We review the evolution of the interstellar medium in disc galaxies, and
show, both analytically and by numerical 3D hydrodynamic simulations, that the
disc-halo connection is an essential ingredient in understanding the evolution
of star forming galaxies. Depending on the star formation rate of the
underlying gaseous disc, a galactic fountain is established. If the star
formation rate is sufficiently high and/or cosmic rays are well coupled to the
thermal plasma, a galactic wind will be formed and lead to a secular mass loss
of the galaxy. Such a wind leaves a unique imprint on the soft X-ray spectra in
edge-on galaxies, with delayed recombination being one of its distinctive
features. We argue that synthetic spectra, obtained from self-consistent
dynamical and thermal modelling of a galactic outflow, should be treated on an
equal footing as observed spectra. We show that it is thus possible to
successfully fit the spectrum of the starburst galaxy NGC 3079.


  Quantum Mechanical weak values are an interference effect measured by the
cross-Wigner transform W({\phi},{\psi}) of the post-and preselected states,
leading to a complex quasi-distribution {\rho}_{{\phi},{\psi}}(x,p) on phase
space. We show that the knowledge of {\rho}_{{\phi},{\psi}}(z) and of one of
the two functions {\phi},{\psi} unambiguously determines the other, thus
generalizing a recent reconstruction result of Lundeen and his collaborators.


  Speckle is the spatial fluctuation of irradiance seen when coherent light is
reflected from a rough surface. It is due to light reflected from the surface's
many nooks and crannies accumulating vastly-discrepant time delays, spanning
much more than an optical period, en route to an observation point. Although
speckle with continuous-wave (cw) illumination is well understood, the emerging
interest in non-line-of-sight (NLoS) imaging using coherent light has created
the need to understand the higher-order speckle that results from multiple
rough-surface reflections, viz., speckled speckle and speckled speckled
speckle. Moreover, the recent introduction of phasor-field
($\mathcal{P}$-field) NLoS imaging---which relies on amplitude-modulated
coherent illumination---requires pushing beyond cw scenarios for speckle and
higher-order speckle. In this paper, we take first steps in addressing the
foregoing needs using a three-diffuser transmissive geometry that is a proxy
for three-bounce NLoS imaging. In the small-diffusers limit, we show that the
irradiance variances of cw and modulated $n$th-order speckle coincide and are
$(2^n-1)$-times those of ordinary (first-order) speckle. The more important
case for NLoS imaging, however, involves extended diffuse reflectors. For our
transmissive geometry with extended diffusers, we treat third-order cw speckle
and first-order modulated speckle. Our results there imply that speckle is
unlikely to impede successful operation of coherent-illumination cw imagers,
and they suggest that the same might be true for $\mathcal{P}$-field imagers.


  In this paper, we generalize the notion of $\lambda$-generalized contractions
introduced by \'Ciri\'c from metric to uniform spaces endowed with a graph and
discuss on the existence and uniqueness of fixed points for this type of
contractions using the basic entourages.


  In this paper, we present a kernel subspace clustering method that can handle
non-linear models. In contrast to recent kernel subspace clustering methods
which use predefined kernels, we propose to learn a low-rank kernel matrix,
with which mapped data in feature space are not only low-rank but also
self-expressive. In this manner, the low-dimensional subspace structures of the
(implicitly) mapped data are retained and manifested in the high-dimensional
feature space. We evaluate the proposed method extensively on both motion
segmentation and image clustering benchmarks, and obtain superior results,
outperforming the kernel subspace clustering method that uses standard
kernels[Patel 2014] and other state-of-the-art linear subspace clustering
methods.


  (Abridged) We examine the power source of 41 local Ultraluminous Infrared
Galaxies using archival infrared and optical photometry. We fit the observed
Spectral Energy Distributions (SEDs) with starburst and AGN components; each
component being drawn from a family of templates. We find all of the sample
require a starburst, whereas only half require an AGN. In 90% of the sample the
starburst provides over half the IR emission, with a mean fractional luminosity
of 82%. When combined with other galaxy samples we find that starburst and AGN
luminosities correlate over 6 decades in IR luminosity suggesting that a common
factor governs both luminosities, plausibly the gas masses in the nuclear
regions. We find that the mid-IR 7.7 micron line-continuum ratio is no
indication of the starburst luminosity, or the fractional AGN luminosity, and
therefore that this ratio is not a reliable diagnostic of the power source in
ULIRGs. We propose that the scatter in the radio-IR correlation in ULIRGs is
due to a skewed starburst IMF and/or relic relativistic electrons from a
previous starburst, rather than contamination from an obscured AGN. We show
that most ULIRGs undergo multiple starbursts during their lifetime, and by
inference that mergers between more than two galaxies may be common amongst
ULIRGs. Our results support the evolutionary model for ULIRGs proposed by
Farrah et al 2001, where they can follow many different evolutionary paths of
starburst and AGN activity in transforming merging spiral galaxies into
elliptical galaxies, but that most do not go through an optical QSO phase. The
lower level of AGN activity in our local sample than in z~1 HLIRGs implies that
the two samples are distinct populations. We postulate that different galaxy
formation processes at high-z are responsible for this difference.


  Recently, large-scale visual language pre-trained (VLP) models have
demonstrated impressive performance across various downstream tasks. Motivated
by these advancements, pioneering efforts have emerged in multi-label image
recognition with missing labels, leveraging VLP prompt-tuning technology.
However, they usually cannot match text and vision features well, due to
complicated semantics gaps and missing labels in a multi-label image. To tackle
this challenge, we propose \textbf{T}ext-\textbf{R}egion \textbf{M}atching for
optimizing \textbf{M}ulti-\textbf{L}abel prompt tuning, namely TRM-ML, a novel
method for enhancing meaningful cross-modal matching. Compared to existing
methods, we advocate exploring the information of category-aware regions rather
than the entire image or pixels, which contributes to bridging the semantic gap
between textual and visual representations in a one-to-one matching manner.
Concurrently, we further introduce multimodal contrastive learning to narrow
the semantic gap between textual and visual modalities and establish
intra-class and inter-class relationships. Additionally, to deal with missing
labels, we propose a multimodal category prototype that leverages intra- and
inter-category semantic relationships to estimate unknown labels, facilitating
pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC,
Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that
our proposed framework outperforms the state-of-the-art methods by a
significant margin. Our code is available
here\href{https://github.com/yu-gi-oh-leilei/TRM-ML}{\raisebox{-1pt}{\faGithub}}.


  With the discovery of a modest size for the mixing angle $\theta_{13} \sim
9^\circ$ by the Daya Bay collaboration at $>$5 sigma (\cite{dayabay}) the
science of neutrino oscillations has shifted to explicit demonstration of CP
violation and precision determination of the CP phase in the 3-flavor
framework. Any additional contributions from new physics to the oscillation
channel $\nu_\mu \to \nu_e$ could be uncovered by multiple constraints in the
($\theta_{13}, \delta_{CP}$) parameter space. In long-baseline experiments such
constraints will require examination of the oscillation strength at higher
$L/E$ where the effects of CP violation will be large. For the fixed baseline
of 1300 km for the Long-Baseline Neutrino Experiment (LBNE, Fermilab to
Homestake), it will be important to examine oscillations at low energies
($<1.5$ GeV) with good statistics, low backgrounds, and excellent energy
resolution. The accelerator upgrades in the Project-X era have the potential to
offer the beams of the needed intensity and quality for this advanced science
program. In this paper we examine the event rates for high intensity, low
energy running of Project-X and the Fermilab Main Injector complex, and the
precision in the ($\theta_{13}, \delta_{CP}$) space. In this paper we have
examined the baseline distance of 1300 km in detail, however we point out that
much longer distances such as 2500 km should also be exmained with a beam from
FNAL in light of the new understanding of the neutrino mixing.


  We study the properties of the Higgs boson sector in the MSSM, putting
special emphasis on radiative effects which can affect the discovery potential
of the LHC, Tevatron and/or LEP colliders. We concentrate on the V b b-bar
channel, with V=Z or W, and on the channels with diphoton final states, which
are the dominant ones for the search for a light Standard Model Higgs boson at
LEP/Tevatron and LHC, respectively. By analyzing the regions of parameter space
for which the searches in at least one of these colliders can be particularly
difficult, we demonstrate the complementarity of these three colliders in the
search for a light Higgs boson which couples in a relevant way to the W and Z
gauge bosons (and hence plays a relevant role in the mechanism of electroweak
symmetry breaking).


  The emergence of network technologies and the appearance of new varied
applications in terms of services and resources, has created new security
problems for which existing solutions and mechanisms are inadequate, especially
problems of identification and authentication. In a highly distributed and
pervasive system, a uniform and centralized security management is not an
option. It then becomes necessary to give more autonomy to security systems by
providing them with mechanisms that allows a dynamic and flexible cooperation
and collaboration between the actors in the system.


  The energetic beam of (spin and isospin zero) $\alpha$-particles remains a
very efficient probe for the nuclear isoscalar giant resonances. In the present
work, a microscopic folding model study of the isoscalar giant resonances in
$^{208}$Pb induced by inelastic \aPb scattering at $E_{\rm lab}=240$ and 386
MeV has been performed using the (complex) CDM3Y6 interaction and nuclear
transition densities given by both the collective model and Random Phase
Approximation (RPA) approach. The fractions of energy weighted sum rule around
the main peaks of the isoscalar monopole, dipole and quadrupole giant
resonances were probed in the Distorted Wave Born Approximation analysis of
inelastic \aPb scattering using the double-folded form factors given by
different choices of the nuclear transition densities. The energy distribution
of the $E0, E1$ and $E2$ strengths given by the multipole decomposition
{analyses} of the \aap data under study are compared with those predicted by
the RPA calculation.


  In the cosmological paradigm, cold dark matter (DM) dominates the mass
content of the Universe and is present at every scale. Candidates for DM
include many extensions of the Standard Model with weakly interacting massive
particles (WIMPs) in the mass range from ~10 GeV to greater than 10 TeV. The
self-annihilation or decay of WIMPs in astrophysical regions of high DM density
can produce secondary particles including very-high-energy (VHE; E>100 GeV)
gamma rays. VERITAS, an array of atmospheric Cherenkov telescopes, sensitive to
VHE gamma rays in the 85 GeV-30 TeV energy range, has been utilized for
indirect DM searches. Astrophysical candidates for indirect DM detection by
VERITAS are dwarf spheroidal galaxies (dSphs) of the Local Group and the
Galactic Center, among others. This is a report on the current status of
VERITAS observations of dark matter targets and the results of a combined
search of four dSphs.


  Self-supervised learning has brought about a revolutionary paradigm shift in
various computing domains, including NLP, vision, and biology. Recent
approaches involve pre-training transformer models on vast amounts of unlabeled
data, serving as a starting point for efficiently solving downstream tasks. In
reinforcement learning, researchers have recently adapted these approaches,
developing models pre-trained on expert trajectories. This advancement enables
the models to tackle a broad spectrum of tasks, ranging from robotics to
recommendation systems. However, existing methods mostly rely on intricate
pre-training objectives tailored to specific downstream applications. This
paper conducts a comprehensive investigation of models, referred to as
pre-trained action-state transformer agents (PASTA). Our study covers a unified
methodology and covers an extensive set of general downstream tasks including
behavioral cloning, offline RL, sensor failure robustness, and dynamics change
adaptation. Our objective is to systematically compare various design choices
and offer valuable insights that will aid practitioners in developing robust
models. Key highlights of our study include tokenization at the component level
for actions and states, the use of fundamental pre-training objectives such as
next token prediction or masked language modeling, simultaneous training of
models across multiple domains, and the application of various fine-tuning
strategies. In this study, the developed models contain fewer than 7 million
parameters allowing a broad community to use these models and reproduce our
experiments. We hope that this study will encourage further research into the
use of transformers with first principle design choices to represent RL
trajectories and contribute to robust policy learning.


  In this work we explore the applicability of a special gluon mass generating
mechanism in the context of the linear covariant gauges. In particular, the
implementation of the Schwinger mechanism in pure Yang-Mills theories hinges
crucially on the inclusion of massless bound-state excitations in the
fundamental nonperturbative vertices of the theory. The dynamical formation of
such excitations is controlled by a homogeneous linear Bethe-Salpeter equation,
whose nontrivial solutions have been studied only in the Landau gauge. Here,
the form of this integral equation is derived for general values of the
gauge-fixing parameter, under a number of simplifying assumptions that reduce
the degree of technical complexity. The kernel of this equation consists of
fully-dressed gluon propagators, for which recent lattice data are used as
input, and of three-gluon vertices dressed by a single form factor, which is
modelled by means of certain physically motivated Ans\"atze. The
gauge-dependent terms contributing to this kernel impose considerable
restrictions on the infrared behavior of the vertex form factor; specifically,
only infrared finite Ans\"atze are compatible with the existence of nontrivial
solutions. When such Ans\"atze are employed, the numerical study of the
integral equation reveals a continuity in the type of solutions as one varies
the gauge-fixing parameter, indicating a smooth departure from the Landau
gauge. Instead, the logarithmically divergent form factor displaying the
characteristic "zero crossing", while perfectly consistent in the Landau gauge,
has to undergo a dramatic qualitative transformation away from it, in order to
yield acceptable solutions. The possible implications of these results are
briefly discussed.


  For every non-elementary hyperbolic group, we show that for every random walk
with finitely supported admissible step distribution, the associated entropy
equals the drift times the logarithmic volume growth if and only if the
corresponding harmonic measure is comparable with Hausdorfff measure on the
boundary. Moreover, we introduce one parameter family of probability measures
which interpolates a Patterson-Sullivan measure and the harmonic measure, and
establish a formula of Hausdorff spectrum (multifractal spectrum) of the
harmonic measure. We also give some finitary versions of dimensional properties
of the harmonic measure.


  We study the photoluminescence spectrum of a low density ($\nu <1$)
two-dimensional electron gas at high magnetic fields and low temperatures. We
find that the spectrum in the fractional quantum Hall regime can be understood
in terms of singlet and triplet charged-excitons. We show that these spectral
lines are sensitive probes for the electrons compressibility. We identify the
dark triplet charged-exciton and show that it is visible at the spectrum at
$T<2$ K. We find that its binding energy scales like $e^{2}/l $, where $l$ is
the magnetic length, and it crosses the singlet slightly above 15 T.


  Gamow-Teller (GT) transitions play an important and consequential role in
many astrophysical phenomena. These include, but are not limited to, electron
and positron capture rates which determine the fate of massive stars and play
an intricate role in the dynamics of core collapse. These $GT_{\pm}$
transitions rates are the significant inputs in the description of supernova
explosions. $GT_{\pm}$ strength function values are sensitive to the $^{56}Ni$
core excitation in the middle \textit{pf}-shell region and to the size of the
model space as well. We used the pn-QRPA theory for extracting the GT strength
for ground and excited states of $^{56}Ni$. We then used these GT strength
distributions to calculate the electron \textit{and} positron capture rates
which show differences with the earlier calculations. One curious finding of
this paper is our enhanced electron capture rates on $^{56}Ni$ at presupernova
temperatures. These differences need to be taken into account for the modeling
of the early stages of Type II supernova evolution.


  For a compact subset K of the plane and a point x, we define the visible part
of K from x to be the set K_x={u\in K : [x,u]\cap K={u}}. (Here [x,u] denotes
the closed line segment joining x to u.)
  In this paper, we use energies to show that if K is a compact connected set
of Hausdorff dimension larger than one, then for (Lebesgue) almost every point
x in the plane, the Hausdorff dimension of K_x is strictly less than the
Hausdorff dimension of K. In fact, for almost every x, dim(K_x)\leq
{1/2}+\sqrt{dim(K)-{3/4}}. We also give an estimate of the Hausdorff dimension
of those points where the visible set has dimension larger than
s+{1/2}+\sqrt{dim(K)-{3/4}}, for s>0.


  We describe the limit (for two topologies) of large uniform random square
permutations, i.e., permutations where every point is a record. The starting
point for all our results is a sampling procedure for asymptotically uniform
square permutations. Building on that, we first describe the global behavior by
showing that these permutations have a permuton limit which can be described by
a random rectangle. We also explore fluctuations about this random rectangle,
which we can describe through coupled Brownian motions. Second, we consider the
limiting behavior of the neighborhood of a point in the permutation through
local limits. As a byproduct, we also determine the random limits of the
proportion of occurrences (and consecutive occurrences) of any given pattern in
a uniform random square permutation.


  Automatically classifying cough sounds is one of the most critical tasks for
the diagnosis and treatment of respiratory diseases. However, collecting a huge
amount of labeled cough dataset is challenging mainly due to high laborious
expenses, data scarcity, and privacy concerns. In this work, our aim is to
develop a framework that can effectively perform cough classification even in
situations when enormous cough data is not available, while also addressing
privacy concerns. Specifically, we formulate a new problem to tackle these
challenges and adopt few-shot learning and federated learning to design a novel
framework, termed F2LCough, for solving the newly formulated problem. We
illustrate the superiority of our method compared with other approaches on
COVID-19 Thermal Face & Cough dataset, in which F2LCough achieves an average
F1-Score of 86%. Our results show the feasibility of few-shot learning combined
with federated learning to build a classification model of cough sounds. This
new methodology is able to classify cough sounds in data-scarce situations and
maintain privacy properties. The outcomes of this work can be a fundamental
framework for building support systems for the detection and diagnosis of
cough-related diseases.


  The fundamental guided dispersion characteristics of guided light in a
subwavelength dielectric slit channel embedded by two different plasmonic
metals are investigated when varying the gap width. As a result, an overall and
salient picture of the guided dispersion characteristics is obtained over a
wide spectrum range below and above the plasma frequencies of the two different
plasmonic metals, which is important preliminary information for analyzing this
type of subwavelength waveguide. In particular, the effects of using two
different metals on the guided mode dispersions are emphasized in comparison
with the effects of using the same plasmonic metal cladding.


  Many modern tech companies, such as Google, Uber, and Didi, utilize online
experiments (also known as A/B testing) to evaluate new policies against
existing ones. While most studies concentrate on average treatment effects,
situations with skewed and heavy-tailed outcome distributions may benefit from
alternative criteria, such as quantiles. However, assessing dynamic quantile
treatment effects (QTE) remains a challenge, particularly when dealing with
data from ride-sourcing platforms that involve sequential decision-making
across time and space. In this paper, we establish a formal framework to
calculate QTE conditional on characteristics independent of the treatment.
Under specific model assumptions, we demonstrate that the dynamic conditional
QTE (CQTE) equals the sum of individual CQTEs across time, even though the
conditional quantile of cumulative rewards may not necessarily equate to the
sum of conditional quantiles of individual rewards. This crucial insight
significantly streamlines the estimation and inference processes for our target
causal estimand. We then introduce two varying coefficient decision process
(VCDP) models and devise an innovative method to test the dynamic CQTE.
Moreover, we expand our approach to accommodate data from spatiotemporal
dependent experiments and examine both conditional quantile direct and indirect
effects. To showcase the practical utility of our method, we apply it to three
real-world datasets from a ride-sourcing platform. Theoretical findings and
comprehensive simulation studies further substantiate our proposal.


  Typical applications of the mobile ad-hoc network, MANET, are in disaster
recovery operations which have to respect time constraint needs. Since MANET is
affected by limited resources such as power constraints, it is a challenge to
respect the deadline of a real-time data. This paper proposes the Energy and
Delay aware based on Dynamic Source Routing protocol, ED-DSR. ED-DSR
efficiently utilizes the network resources such as the intermediate mobile
nodes energy and load. It ensures both timeliness and energy efficiency by
avoiding low-power and overloaded intermediate mobile nodes. Through
simulations, we compare our proposed routing protocol with the basic routing
protocol Dynamic Source Routing, DSR. Weighting factors are introduced to
improve the route selection. Simulation results, using the NS-2 simulator, show
that the proposed protocol prolongs the network lifetime (up to 66%), increases
the volume of packets delivered while meeting the data flows real-time
constraints and shortens the endto- end delay.


  Sentence function is an important linguistic feature referring to a user's
purpose in uttering a specific sentence. The use of sentence function has shown
promising results to improve the performance of conversation models. However,
there is no large conversation dataset annotated with sentence functions. In
this work, we collect a new Short-Text Conversation dataset with manually
annotated SEntence FUNctions (STC-Sefun). Classification models are trained on
this dataset to (i) recognize the sentence function of new data in a large
corpus of short-text conversations; (ii) estimate a proper sentence function of
the response given a test query. We later train conversation models conditioned
on the sentence functions, including information retrieval-based and neural
generative models. Experimental results demonstrate that the use of sentence
functions can help improve the quality of the returned responses.


  With the aim of describing bound and continuum states for diatomic molecules,
we develop and implement a spectral method that makes use of Generalized
Sturmian Functions (GSF) in prolate spheroidal coordinates. In order to master
all computational issues, we apply here the method to one--electron molecular
ions and compare it with benchmark data for both ground and excited states. We
actually propose two different computational schemes to solve the two coupled
differential equations.
  The first one is an iterative 1d procedure in which one solves alternately
the angular and the radial equations, the latter yielding the state energy. The
second, named direct $2d$ method, consists in representing the Hamiltonian
matrix in a two--dimensional GSF basis set, and its further diagonalization.
Both spectral schemes are timewise computationally efficient since the basis
elements are such that no derivatives have to be calculated numerically.
Moreover, very accurate results are obtained with minimal basis sets. This is
related on one side to the use of the natural coordinate system and, on the
other, to the intrinsic good property of all GSF basis elements that are
constructed as to obey appropriate physical boundary conditions. The present
implementation for bound states paves the way for the study of continuum states
involved in ionization of one or two--electron diatomic targets.


  This paper is devoted to the numerical approximation of the
spatially-extended FitzHugh-Nagumo transport equation with strong local
interactions based on a particle method. In this regime, the time step can be
subject to stability constraints related to the interaction kernel. To avoid
this limitation, our approach is based on higher-order implicit-explicit
numerical schemes. Thus, when the magnitude of the interactions becomes large,
this method provides a consistent discretization of the macroscopic
reaction-diffusion FitzHugh-Nagumo system. We carry out some theoretical proofs
and perform several numerical experiments that establish a solid validation of
the method and its underlying concepts.


  High-dimensional quantum states have already settled their advantages in
different quantum technology applications. However, their reliable transmission
in fiber links remains an open challenge that must be addressed to boost their
application, e.g. in the future quantum internet. Here, we prove how path
encoded high-dimensional quantum states can be reliably transmitted over a 2 km
long multicore fiber, taking advantage of a phase-locked loop system
guaranteeing a stable interferometric detection.


  We present a versatile family of model galactic outflows including
non-uniform mass and energy source distributions, a gravitational potential
from an extended mass source, and radiative losses. The model easily produces
steady-state wind solutions for a range of mass-loading factors, energy-loading
factors, galaxy mass and galaxy radius. We find that, with radiative losses
included, highly mass-loaded winds must be driven at high central temperatures,
whereas low mass-loaded winds can be driven at low temperatures just above the
peak of the cooling curve, meaning radiative losses can drastically affect the
wind solution even for low mass-loading factors. By including radiative losses,
we are able to show that subsonic flows can be ignored as a possible mechanism
for expelling mass and energy from a galaxy compared to the more efficient
transonic solutions. Specifically, the transonic solutions with low
mass-loading and high energy-loading are the most efficient. Our model also
produces low-temperature, high-velocity winds that could explain the prevalence
of low-temperature material in observed outflows. Finally, we show that our
model, unlike the well-known Chevalier & Clegg (1985) model, can reproduce the
observed linear relationship between wind X-ray luminosity and star formation
rate (SFR) over a large range of SFR from $1-1000$ M$_{\odot}$/yr assuming the
wind mass-loading factor is higher for low-mass, and hence, low-SFR galaxies.
We also constrain the allowed mass-loading factors that can fit the observed
X-ray luminosity vs. SFR trend, further suggesting an inverse relationship
between mass-loading and SFR as explored in advanced numerical simulations.


  This pedagogical note revisits the concept of electromagnetic helicity in
classical systems. In particular, magnetic helicity and its role in mean field
dynamo theories is briefly discussed highlighting the major mathematical
inconsistency in most of these theories---violation of magnetic helicity
conservation. A short review of kinematic dynamo theory and its classic
theorems is also presented in the Appendix.


  We prove that for a hard core interaction the ground state spin polarization
in the low Zeeman energy limit is given by $P=2/\nu-5$ for filling fractions in
the range $ 1/3 \leq\nu\leq 2/5 $. The same result holds for a Coulomb
potential except for marginally small magnetic fields. At the magnetic fields
$B<20T$ unpolarized quasielectrons can manifest themselves by a characteristic
peak in the I-V characteristics for tunneling between two $\nu=1/3$
ferromagnets.


  The concept of derivative coordinate functions proved useful in the
formulation of analytic fractal functions to represent smooth symmetric binary
fractal trees [1]. In this paper we introduce a new geometry that defines the
fractal space around these fractal trees. We present the canonical and
degenerate form of this fractal space and extend the fractal geometrical space
to R3 specifically and Rn by a recurrence relation. We also discuss the usage
of such fractal geometry.


  Although the methods of calculation of power corrections in QCD sum rules are
well known, algebraic complexity rapidly grows with the increase of vacuum
condensates' dimensions. Currently, state-of-the-art calculations include
dimension 7 and 8 condensates. I summarize and extend algorithms of such
calculations. First, I present all the formulae necessary for application of
the systematic classification of bilinear quark condensates proposed earlier,
and extend this method to the case of gluon condensates. Then I apply these
systematic procedures to expansions of bilinear and noncollinear quark and
gluon condensates in local ones, and of noncollinear condensates in bilocal
ones. The formulae obtained can be used for calculation of correlators
involving nonlocal condensates, and for inventing consistent anzatzs for these
condensates. Finally, I briefly summarize the methods of calculation of heavy-
and light-quark currents' correlators. This paper is aimed both to present new
results on gluon and nonlocal condensates and to be a self-contained handbook
of formulae necessary for calculation of power corrections in QCD sum rules.


  A variety of methods have been applied to the architectural configuration and
learning or training of artificial deep neural networks (DNN). These methods
play a crucial role in the success or failure of the DNN for most problems and
applications. Evolutionary Algorithms (EAs) are gaining momentum as a
computationally feasible method for the automated optimisation of DNNs.
Neuroevolution is a term which describes these processes of automated
configuration and training of DNNs using EAs. However, the automatic design
and/or training of these modern neural networks through evolutionary algorithms
is computanalli expensive. Kimura's neutral theory of molecular evolution
states that the majority of evolutionary changes at molecular level are the
result of random fixation of selectively neutral mutations. A mutation from one
gene to another is neutral if it does not affect the phenotype. This work
discusses how neutrality, given certain conditions, can help to speed up the
training/design of deep neural networks.


  Finding an optimal key assignment (subject to given constraints) for a key
predistribution scheme in wireless sensor networks is a difficult task. Hence,
most of the practical schemes are based on probabilistic key assignment, which
leads to sub-optimal schemes requiring key storage linear in the total number
of nodes. A graph theoretic framework is introduced to study the fundamental
tradeoffs between key storage, average key path length (directly related to the
battery consumption) and resilience (to compromised nodes) of key
predistribution schemes for wireless sensor networks. Based on the proposed
framework, a lower bound on key storage is derived for a given average key path
length. An upper bound on the compromising probability is also given. This
framework also leads to the design of key assignment schemes with a storage
complexity of the same order as the lower bound.


  Recent results on structure functions and parton distribution functions at
HERA are presented. Results on the measurements of the structure functions F2,
FL and F3 will be covered, as well as the results of the most recent QCD fits
to determine the PDFs of the proton.


  We develop integral geometry for non-compactly causal symmetric spaces. We
define a complex horospherical transform and, for some cases, identify it with
a Cauchy type integral.


  This paper provides a mathematical framework based on the principle of
invariance to classify institutions in two paradigms according to the way in
which credit, debit and funding adjustments are calculated: accounting and
management perspectives. This conceptual classification helps to answer
questions such as: In which paradigm each institution sits (point of
situation)? Where is the market consensus and regulation pointing to (target
point)? What are the implications, pros and cons of switching perspective to
align with future consensus (design of a transition)? An improved solution of
the principle of invariance equations is presented to calculate these metrics
avoiding approximations and irrespective of the discounting curve used in Front
Office systems. The perspective is changed by appropriate selection of inputs
always using the same calculation engine. A description of balance sheet
financing is presented along with the justification of the funding curves used
for both perspectives.


  Peer-to-Peer (P2P) technology has been regarded as a promising way to help
Content Providers (CPs) cost-effectively distribute content. However, under the
traditional Internet pricing mechanism, the fact that most P2P traffic flows
among peers can dramatically decrease the profit of ISPs, who may take actions
against P2P and impede the progress of P2P technology. In this paper, we
develop a mathematical framework to analyze such economic issues. Inspired by
the idea from cooperative game theory, we propose a cooperative
profit-distribution model based on Nash Bargaining Solution (NBS), in which
eyeball ISPs and Peer-assisted CPs (PCPs) form two coalitions respectively and
then compute a fair Pareto point to determine profit distribution. Moreover, we
design a fair and feasible mechanism for profit distribution within each
coalition. We show that such a cooperative method not only guarantees the fair
profit distribution among network participators, but also helps to improve the
economic efficiency of the overall network system. To our knowledge, this is
the first work that systematically studies solutions for P2P caused unbalanced
profit distribution and gives a feasible cooperative method to increase and
fairly share profit.


  The aim of this article is to show the global existence of both martingale
and pathwise solutions of stochastic equations with a monotone operator, of the
Ladyzenskaya-Smagorinsky type, driven by a general Levy noise. The classical
approach based on using directly the Galerkin approximation is not valid.
Instead, our approach is based on using appropriate approximations for the
monotone operator, Galerkin approximations and on the theory of martingale
solutions.


  A finitary propositional logic can be given an algebraic reading in two
different ways: by translating formulas into equations and logical rules into
quasi-equations, or by translating logical rules directly into equations. The
former type of algebraic interpretation has been extensively studied and
underlies the theory of algebraization. Here we shall develop a systematic
theory of the latter type of algebraic interpretation. More precisely, we
consider a semantic form of this property which we call the equational
definability of compact filters (EDCF). Paralleling the well-studied hierarchy
of variants of the deduction--detachment theorem (DDT), this property also
comes in local, parametrized, and parametrized local variants. Our main results
give a semantic characterization of each of these variants of the EDCF in a
spirit similar to the existing characterizations of the DDT. While the EDCF
hierarchy and the DDT hierarchy coincide for algebraizable logics, part of the
interest of the EDCF stems from the fact it is often enjoyed even by logics
which are not well-behaved in terms of other existing classifications in
algebraic logic.


  We study the energy-critical focusing nonlinear Schr\"odinger equation with
an energy- subcritical perturbation. We show the existence of a ground state in
the four or higher dimensions. Moreover, we give a sufficient and necessary
condition for a solution to scatter, in the spirit of Kenig-Merle [16].


  With the of advent rich classification models and high computational power
visual recognition systems have found many operational applications.
Recognition in the real world poses multiple challenges that are not apparent
in controlled lab environments. The datasets are dynamic and novel categories
must be continuously detected and then added. At prediction time, a trained
system has to deal with myriad unseen categories. Operational systems require
minimum down time, even to learn. To handle these operational issues, we
present the problem of Open World recognition and formally define it. We prove
that thresholding sums of monotonically decreasing functions of distances in
linearly transformed feature space can balance "open space risk" and empirical
risk. Our theory extends existing algorithms for open world recognition. We
present a protocol for evaluation of open world recognition systems. We present
the Nearest Non-Outlier (NNO) algorithm which evolves model efficiently, adding
object categories incrementally while detecting outliers and managing open
space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to
validate the effectiveness of our method on large scale visual recognition
tasks. NNO consistently yields superior results on open world recognition.


  Cognitive diagnosis is a fundamental issue in intelligent education, which
aims to discover the proficiency level of students on specific knowledge
concepts. Existing approaches usually mine linear interactions of student
exercising process by manual-designed function (e.g., logistic function), which
is not sufficient for capturing complex relations between students and
exercises. In this paper, we propose a general Neural Cognitive Diagnosis
(NeuralCD) framework, which incorporates neural networks to learn the complex
exercising interactions, for getting both accurate and interpretable diagnosis
results. Specifically, we project students and exercises to factor vectors and
leverage multi neural layers for modeling their interactions, where the
monotonicity assumption is applied to ensure the interpretability of both
factors. Furthermore, we propose two implementations of NeuralCD by
specializing the required concepts of each exercise, i.e., the NeuralCDM with
traditional Q-matrix and the improved NeuralCDM+ exploring the rich text
content. Extensive experimental results on real-world datasets show the
effectiveness of NeuralCD framework with both accuracy and interpretability.


  An outlier is an event or observation that is defined as an unusual activity,
intrusion, or a suspicious data point that lies at an irregular distance from a
population. The definition of an outlier event, however, is subjective and
depends on the application and the domain (Energy, Health, Wireless Network,
etc.). It is important to detect outlier events as carefully as possible to
avoid infrastructure failures because anomalous events can cause minor to
severe damage to infrastructure. For instance, an attack on a cyber-physical
system such as a microgrid may initiate voltage or frequency instability,
thereby damaging a smart inverter which involves very expensive repairing.
Unusual activities in microgrids can be mechanical faults, behavior changes in
the system, human or instrument errors or a malicious attack. Accordingly, and
due to its variability, Outlier Detection (OD) is an ever-growing research
field. In this chapter, we discuss the progress of OD methods using AI
techniques. For that, the fundamental concepts of each OD model are introduced
via multiple categories. Broad range of OD methods are categorized into six
major categories: Statistical-based, Distance-based, Density-based,
Clustering-based, Learning-based, and Ensemble methods. For every category, we
discuss recent state-of-the-art approaches, their application areas, and
performances. After that, a brief discussion regarding the advantages,
disadvantages, and challenges of each technique is provided with
recommendations on future research directions. This survey aims to guide the
reader to better understand recent progress of OD methods for the assurance of
AI.


  High-fidelity quantum non-demolition qubit measurement is critical to error
correction and rapid qubit feedback in large-scale quantum computing.
High-fidelity readout requires passing a short and strong pulse through the
qubit's readout resonator, which is then processed by a sufficiently high
bandwidth, high saturation power, and quantum-limited amplifier. We have
developed a design pipeline that combines time-domain simulation of the
un-truncated device Hamiltonian, fabrication constraints, and maximization of
saturation power. We have realized an amplifier based on a modified NIST
tri-layer Nb fabrication suite which utilizes an array of 25 radio frequency
Superconducting QUantum Interference Devices (rf SQUIDs) embedded within a
low-Q resonator powered by a high-power voltage pump delivered via a diplexer
on the signal port. We show that, despite the intensity of the pump, the device
is quantum-efficient and capable of high-fidelity measurement limited by state
transitions in the transmon. We present experimental data demonstrating up to
-91.2 dBm input saturation power with 20 dB gain, up to 28 MHz instantaneous
bandwidth, and phase-preserving qubit measurements with 62% quantum efficiency.


  We theoretically investigate the temperature gradient-dependent or
unidirectional Seebeck effect (USE) in a magnetic/ nonmagnetic topological
insulator (TI) heterostructure with in-plane magnetization in terms of the
semiclassical electron dynamics and Fermi golden rule. The USE has a quantum
origin arising from the magnon asymmetric scattering of surface Dirac electrons
on TI. We discuss the USE in the heterostructures,
Cr$_{x}$(Bi$_{1-y}$Sb$_y$)$_{2-x}$Te$_{3}$/ (Bi$_{1-y}$Sb$_{y})_2$Te$_{3}$. The
USE exhibits $\cos\phi$-dependence (measured from $y$-direction) on the
orientation of magnetization. It's found that the sign of USE stays unchanged
when the system is transferred from $p$-doping to $n$-doping. The USE shows on
inverse-linearly temperature dependent at high temperature.


  An invertible field transformation is such that the old field variables
correspond one-to-one to the new variables. As such, one may think that two
systems that are related by an invertible transformation are physically
equivalent. However, if the transformation depends on field derivatives, the
equivalence between the two systems is nontrivial due to the appearance of
higher derivative terms in the equations of motion. To address this problem, we
prove the following theorem on the relation between an invertible
transformation and Euler-Lagrange equations: If the field transformation is
invertible, then any solution of the original set of Euler-Lagrange equations
is mapped to a solution of the new set of Euler-Lagrange equations, and vice
versa. We also present applications of the theorem to scalar-tensor theories.


  A generic half-BPS surface defect of ${\mathcal N}=4$ supersymmetric U$(N)$
Yang-Mills theory is described by a partition of $N = n_1 + \ldots + n_M$ and a
set of $4M$ continuous parameters. We show that such a defect can be realized
by $n_I$ stacks of fractional D3-branes in Type II B string theory on a
$\mathbb{Z}_M$ orbifold background in which the brane world-volume is partially
extended along the orbifold directions. In this set up we show that the $4M$
continuous parameters correspond to constant background values of certain
twisted closed string scalars of the orbifold. These results extend and
generalize what we have presented for the simple defects in a previous paper.


  We consider a two-dimensional dilute Bose gas above its superfluid transition
temperature. We show that the t-matrix approximation corresponds to the leading
set of diagrams in the dilute limit, provided the temperature is sufficiently
larger than the superfluid transition temperature. Within this approximation,
we give an explicit expression for the wave vector and frequency dependence of
the self-energy, and calculate the corrections to the chemical potential and
the effective mass arising from the interaction. We also argue that the
breakdown of the t-matrix approximation, which occurs upon lowering the
temperature, provides a simple criterion to estimate the superfluid critical
temperature for the two-dimensional dilute Bose gas. The critical temperature
identified by this criterion coincides with earlier results obtained by Popov
and by Fisher and Hohenberg using different methods. Extension of this
procedure to the three-dimensional case gives good agreement with recent Monte
Carlo data.


  While text-to-image (T2I) generative models have become ubiquitous, they do
not necessarily generate images that align with a given prompt. While previous
work has evaluated T2I alignment by proposing metrics, benchmarks, and
templates for collecting human judgements, the quality of these components is
not systematically measured. Human-rated prompt sets are generally small and
the reliability of the ratings -- and thereby the prompt set used to compare
models -- is not evaluated. We address this gap by performing an extensive
study evaluating auto-eval metrics and human templates. We provide three main
contributions: (1) We introduce a comprehensive skills-based benchmark that can
discriminate models across different human templates. This skills-based
benchmark categorises prompts into sub-skills, allowing a practitioner to
pinpoint not only which skills are challenging, but at what level of complexity
a skill becomes challenging. (2) We gather human ratings across four templates
and four T2I models for a total of >100K annotations. This allows us to
understand where differences arise due to inherent ambiguity in the prompt and
where they arise due to differences in metric and model quality. (3) Finally,
we introduce a new QA-based auto-eval metric that is better correlated with
human ratings than existing metrics for our new dataset, across different human
templates, and on TIFA160.


  Federated learning (FL), as a distributed machine learning paradigm, promotes
personal privacy by local data processing at each client. However, relying on a
centralized server for model aggregation, standard FL is vulnerable to server
malfunctions, untrustworthy server, and external attacks. To address this
issue, we propose a decentralized FL framework by integrating blockchain into
FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In
a round of the proposed BLADE-FL, each client broadcasts the trained model to
other clients, aggregates its own model with received ones, and then competes
to generate a block before its local training of the next round. We evaluate
the learning performance of BLADE-FL, and develop an upper bound on the global
loss function. Then we verify that this bound is convex with respect to the
number of overall aggregation rounds K, and optimize the computing resource
allocation for minimizing the upper bound. We also note that there is a
critical problem of training deficiency, caused by lazy clients who plagiarize
others' trained models and add artificial noises to disguise their cheating
behaviors. Focusing on this problem, we explore the impact of lazy clients on
the learning performance of BLADE-FL, and characterize the relationship among
the optimal K, the learning parameters, and the proportion of lazy clients.
Based on MNIST and Fashion-MNIST datasets, we show that the experimental
results are consistent with the analytical ones. To be specific, the gap
between the developed upper bound and experimental results is lower than 5%,
and the optimized K based on the upper bound can effectively minimize the loss
function.


  In [Phys. Rev. A 58, 1833 (1998)] a family of polynomial invariants which
separate the orbits of multi-qubit density operators $\rho$ under the action of
the local unitary group was presented. We consider this family of invariants
for the class of those $\rho$ which are the projection operators describing
stabilizer codes and give a complete translation of these invariants into the
binary framework in which stabilizer codes are usually described. Such an
investigation of local invariants of quantum codes is of natural importance in
quantum coding theory, since locally equivalent codes have the same
error-correcting capabilities and local invariants are powerful tools to
explore their structure. Moreover, the present result is relevant in the
context of multipartite entanglement and the development of the
measurement-based model of quantum computation known as the one-way quantum
computer.


  The rapid time variability of gamma-ray bursts implies the sources are very
compact, and the peak luminosities are so high that some matter must be ejected
at ultra-relativistic speeds. The very large Lorentz factors of the bulk flow
are also indicated by the very broad and hard spectra. It is natural to expect
that when the relativistic ejecta interact with the interstellar matter a
strong synchrotron radio emission is generated, as is the case with supernova
remnants and radio galaxies. We estimate that the strongest gamma-ray bursts
may be followed by radio transients with peak fluxes as high as 0.1 Jy. The
time of peak radio emission depends on the distance scale; it is less than a
minute if the bursts are in the galactic halo, and about a week if the bursts
are at cosmological distances.


  Proton acceleration by using a 620-TW, 18-J laser pulse of peak intensity of
$5\times 10^{21}$ W/cm$^{2}$ irradiating a disk target is examined using
three-dimensional particle-in-cell simulations. It is shown that protons are
accelerated efficiently to high energy for a "light" material in the first
layer of a double-layer target, because a strongly inhomogeneous expansion of
the first layer occurs by a Coulomb explosion within such a material. Moreover,
a large movement of the first layer for the accelerated protons is produced by
radiation-pressure-dominant acceleration. A time-varying electric potential
produced by this expanding and moving ion cloud accelerates protons
effectively. In addition, using the best material for the target, one can
generate a proton beam with an energy of 200 MeV and an energy spread of 2$%$.


  The technique of semidefinite programming (SDP) relaxation can be used to
obtain a nontrivial bound on the optimal value of a nonconvex quadratically
constrained quadratic program (QCQP). We explore concave quadratic inequalities
that hold for any vector in the integer lattice ${\bf Z}^n$, and show that
adding these inequalities to a mixed-integer nonconvex QCQP can improve the
SDP-based bound on the optimal value. This scheme is tested using several
numerical problem instances of the max-cut problem and the integer least
squares problem.


  In this work, we develop a neural network based model which leverages
dependency parsing to capture cross-positional dependencies and grammatical
structures. With the help of linguistic signals, sentence-level relations can
be correctly captured, thus improving news documents summarization performance.
Empirical studies demonstrate that this simple but effective method outperforms
existing works on the benchmark dataset. Extensive analyses examine different
settings and configurations of the proposed model which provide a good
reference to the community.


  If neutrino conversions within the Sun result in partial polarization of
initial solar neutrino fluxes, then a new opportunity arises to observe the
anti-\nu_e's in future neutrino experiments in the low energy region (such as
BOREXINO or HELLAZ) and thus to probe the Majorana nature of the neutrinos. The
\nu_e -> anti-\nu_e conversions may take place for low energy solar neutrinos
while being unobservable at the Kamiokande and Super-Kamiokande experiments.


  We show by means of an exact numerical approach that the momentum
distribution of a free expanding gas of hard-core bosons on a one-dimensional
lattice approaches to the one of noninteracting fermions, acquiring a Fermi
edge. Yet there is a power-law decay of the one-particle density matrix
$\rho_x\sim 1/\sqrt{x}$, as usual for hard-core bosons in the ground state,
which accounts for a large occupation of the lowest natural orbitals for all
expansion times. The fermionization of the momentum distribution function,
which is not observed in equilibrium, is analyzed in detail.


  The origin of the weak insulating behavior of the resistivity, i.e.
$\rho_{xx}\propto\ln(1/T)$, revealed when magnetic fields ($H$) suppress
superconductivity in underdoped cuprates has been a longtime mystery.
Surprisingly, the high-field behavior of the resistivity observed recently in
charge- and spin-stripe-ordered La-214 cuprates suggests a metallic, as opposed
to insulating, high-field normal state. Here we report the vanishing of the
Hall coefficient in this field-revealed normal state for all
$T<(2-6)T_{\mathrm{c}}^{0}$, where $T_{\mathrm{c}}^{0}$ is the zero-field
superconducting transition temperature. Our measurements demonstrate that this
is a robust fundamental property of the normal state of cuprates with
intertwined orders, exhibited in the previously unexplored regime of $T$ and
$H$. The behavior of the high-field Hall coefficient is fundamentally different
from that in other cuprates such as YBa$_2$Cu$_3$O$_{6+x}$ and
YBa$_2$Cu$_4$O$_{8}$, and may imply an approximate particle-hole symmetry that
is unique to stripe-ordered cuprates. Our results highlight the important role
of the competing orders in determining the normal state of cuprates.


  Let $M$ be a $\rm II_1$ factor and let $\mathcal{F}(M)$ denote the
fundamental group of $M$. In this article, we study the following property of
$M$: for arbitrary $\rm II_1$ factor $B$, we have $\mathcal{F}(M
\overline{\otimes} B)=\mathcal{F}(M)\mathcal{F}(B)$. We prove that for any
subgroup $G\leq \mathbb{R}^*_+$ which is realized as a fundamental group of a
$\rm II_1$ factor, there exists a $\rm II_1$ factor $M$ which satisfies this
property and whose fundamental group is $G$. Using this, we deduce that if $G,H
\leq \mathbb{R}^*_+$ are realized as fundamental groups of $\rm II_1$ factors
(with separable predual), then so are groups $G \cdot H$ and $G \cap H$.


  In this work, we propose a model of the gravitational collapse of dark matter
in the presence of quintessence or phantom-like scalar fields. Our treatment is
based on the principles of general relativity up to virialization. We have
chosen a spherical patch that starts to collapse gravitationally as it happens
in top-hat collapse. It is seen that although the dark matter sector collapses
the dark energy sector does keep a profile that is almost similar to the dark
energy profile for the background expanding Friedmann-Lemaitre-Robertson-Walker
(FLRW) universe for suitable model parameters. It is observed that in order to
formulate the problem in the general relativistic setting one has to abandon
the idea of a closed FLRW isolated collapsing patch. General relativity
requires an external generalized Vaidya spacetime to be matched with the
internal spherical patch whose dynamics is guided by the FLRW metric. It is
shown that almost all collapses are accompanied by some flux of matter and
radiation in the generalized Vaidya spacetime. Some of the spherical regions of
the universe are seen not to collapse but expand eternally, producing void-like
structures. Whether a spherical region will collapse or expand depends upon the
initial values of the system and other model parameters. As this work shows
that collapsing structures must emit some form of radiation, this may be taken
as an observational signature of our proposal.


  We follow the dynamics of nonlinear waves in two-dimensional disordered
lattices with tunable nonlinearity. In the absence of nonlinear terms Anderson
localization traps the packet in space. For the nonlinear case a destruction of
Anderson localization is found. The packet spreads subdiffusively, and its
second moment grows in time asymptotically as $t^\alpha$. We perform fine
statistical averaging and test theoretical predictions for $\alpha$. Along with
a precise confirmation of the predictions in [Chemical Physics \textbf{375},
548 (2010)], we also find potentially long lasting intermediate deviations due
to a growing number of surface resonances of the wave packet.


  We construct an effective Floquet lattice model for the triangular network
that emerges in interlayer-biased minimally twisted bilayer graphene and which
supports two chiral channels per link for a given valley and spin. We introduce
the Floquet scheme with the one-channel triangular network and subsequently
extend it to the two-channel case. From the bulk topological index (winding
number) and finite system calculations, we find that both cases host anomalous
Floquet insulators (AFIs) with a different gap-opening mechanism. In the
one-channel network, either time-reversal or in-plane inversion symmetry has to
be broken to open a gap. In contrast, in the two-channel network, interchannel
coupling can open a gap without breaking these symmetries yielding a valley AFI
with counterpropagating edge states. This phase is topologically trivial with
respect to the total winding number but robust in the absence of intervalley
scattering. Finally, we demonstrate the applicability of the Floquet model with
magnetotransport calculations.


  In-vivo tissue stiffness identification can be useful in pulmonary fibrosis
diagnostics and minimally invasive tumor identification, among many other
applications. In this work, we propose a palpation-based method for tissue
stiffness estimation that uses a sensorized beam buckled onto the surface of a
tissue. Fiber Bragg Gratings (FBGs) are used in our sensor as a
shape-estimation modality to get real-time beam shape, even while the device is
not visually monitored. A mechanical model is developed to predict the behavior
of a buckling beam and is validated using finite element analysis and bench-top
testing with phantom tissue samples (made of PDMS and PA-Gel). Bench-top
estimations were conducted and the results were compared with the actual
stiffness values. Mean RMSE and standard deviation (from the actual
stiffnesses) values of 413.86 KPa and 313.82 KPa were obtained. Estimations for
softer samples were relatively closer to the actual values. Ultimately, we used
the stiffness sensor within a mock concentric tube robot as a demonstration of
\textit{in-vivo} sensor feasibility. Bench-top trials with and without the
robot demonstrate the effectiveness of this unique sensing modality in
\textit{in-vivo} applications.


  The TeV extragalactic sky is dominated by blazars, radio-loud active galactic
nuclei with a relativistic jet pointing towards the Earth. Blazars show
variability that can be quite exceptional both in terms of flux (orders of
magnitude of brightening) and time (down to the minute timescale). This bright
flaring activity contains key information on the physics of particle
acceleration and photon production in the emitting region, as well as the
structure and physical properties of the jet itself. The TeV band is accessed
from the ground by Cherenkov telescopes that image the pair cascade triggered
by the interaction of the gamma ray with the Earth's atmosphere. The Cherenkov
Telescope Array (CTA) represents the upcoming generation of imaging atmospheric
Cherenkov telescopes, with a significantly higher sensitivity and larger energy
coverage with respect to current instruments. It will thus provide us with
unprecedented statistics on blazar light-curves and spectra. In this
contribution we present the results from realistic simulations of CTA
observations of bright blazar flares, taking as input state-of-the-art
numerical simulations of blazar emission models and including all relevant
observational constraints.


  The stability of a single Belavin-Polyakov (BP) skyrmion in isotropic
Heisenberg ferromagnet is studied. Such skyrmion is higher in energy than the
uniform ferromagnetic state and is thus metastable. Starting from the lattice
model in two spatial dimensions and using Maleyev-Dyson representation for spin
operators, we examine the effects of magnon-magnon interaction for two
quantities at $T=0$. First we discuss the self-energy corrections to magnon
energy. Second we analyze the two-particle Green's function and possible bound
states of two magnons. The simplicity of the model makes possible full analytic
treatment of all relevant processes. We found that the magnons remain
well-defined quasiparticles with a finite lifetime. The bound states of two
magnons are suppressed near the skyrmion, although they are not excluded far
away from it. A resonance for the magnons of dilational mode in the vicinity of
BP skyrmion is also found, which leads to a redistribution of the spectral
weight. We conclude that the BP skyrmion as the classical topological object is
not destroyed by quantum fluctuations.


  A theoretical, and potentially also practical, problem with stochastic
gradient descent is that trajectories may escape to infinity. In this note, we
investigate uniform boundedness properties of iterates and function values
along the trajectories of the stochastic gradient descent algorithm and its
important momentum variant. Under smoothness and $R$-dissipativity of the loss
function, we show that broad families of step-sizes, including the widely used
step-decay and cosine with (or without) restart step-sizes, result in uniformly
bounded iterates and function values. Several important applications that
satisfy these assumptions, including phase retrieval problems, Gaussian mixture
models, and some neural network classifiers, are discussed in detail. We
further extend the uniform boundedness of SGD and its momentum variant under
the generalized dissipativity for the functions whose tails grow slower than
quadratic functions. This includes some interesting applications, for example,
Bayesian logistic regression and logistic regression with $\ell_1$
regularization.


  We argue that lattice simulations of full QCD with varying quark mass are
best conducted at fixed lattice spacing rather than at fixed $\beta$. We
present techniques which enable this to be carried out effectively, namely the
tuning in bare parameter space and efficient stochastic estimation of the
fermion determinant. Results and tests of the method are presented. We discuss
other applications of such techniques.


  We discuss the statistical properties of index returns in a financial market
just after a major market crash. The observed non-stationary behavior of index
returns is characterized in terms of the exceedances over a given threshold.
This characterization is analogous to the Omori law originally observed in
geophysics. By performing numerical simulations and theoretical modelling, we
show that the nonlinear behavior observed in real market crashes cannot be
described by a GARCH(1,1) model. We also show that the time evolution of the
Value at Risk observed just after a major crash is described by a power-law
function lacking a typical scale.


  I review some recent developments in the field of X-ray pulsars: the
discovery of millisecond pulsations in the Low Mass Binary System SAX
J1808.4-3658, the large number of transient Be systems discovered in the
Magellanic Clouds and the enigmatic class of objects known as Anomalous X-ray
Pulsars.


  We discuss two distinct approaches, for distorting risk measures of sums of
dependent random variables, which preserve the property of coherence. The
first, based on distorted expectations, operates on the survival function of
the sum. The second, simultaneously applies the distortion on the survival
function of the sum and the dependence structure of risks, represented by
copulas. Our goal is to propose risk measures that take into account the
fluctuations of losses and possible correlations between risk components.


  In this paper, we consider a class of Mckean-Vlasov stochastic differential
equation with oblique reflection over an non-smooth time dependent domain. We
establish the existence and uniqueness results of this class, address the
propagation of chaos and prove a Fredlin-Wentzell type large deviations
principle (LDP). One of the main difficulties is raised by the setting of
non-smooth time dependent domain. To prove the LDP, a sufficient condition for
the weak convergence method, which is suitable for Mckean-Vlasov stochastic
differential equation, plays an important role.


  The Deep Underground Neutrino Experiment (DUNE) is a next-generation
long-baseline neutrino oscillation experiment consisting of a high-power,
broadband neutrino beam, a highly capable near detector located on site at
Fermilab, in Batavia, Illinois, and a massive liquid argon time projection
chamber (LArTPC) far detector located at the 4850L of Sanford Underground
Research Facility in Lead, South Dakota. The long-baseline physics sensitivity
calculations presented in the DUNE Physics TDR, and in a related physics paper,
rely upon simulation of the neutrino beam line, simulation of neutrino
interactions in the near and far detectors, fully automated event
reconstruction and neutrino classification, and detailed implementation of
systematic uncertainties. The purpose of this posting is to provide a
simplified summary of the simulations that went into this analysis to the
community, in order to facilitate phenomenological studies of long-baseline
oscillation at DUNE. Simulated neutrino flux files and a GLoBES configuration
describing the far detector reconstruction and selection performance are
included as ancillary files to this posting. A simple analysis using these
configurations in GLoBES produces sensitivity that is similar, but not
identical, to the official DUNE sensitivity. DUNE welcomes those interested in
performing phenomenological work as members of the collaboration, but also
recognizes the benefit of making these configurations readily available to the
wider community.


  A method based on the Fast Fourier Transform is proposed to obtain the
dispersion relation of acoustic waves in heterogeneous periodic media with
arbitrary microstructures. The microstructure is explicitly considered using a
voxelized Representative Volume Element (RVE). The dispersion diagram is
obtained solving an eigenvalue problem for Bloch waves in Fourier space. To
this aim, two linear operators representing stiffness and mass are defined
through the use of differential operators in Fourier space. The smallest
eigenvalues are obtained using the implicitly restarted Lanczos and the
subspace iteration methods, and the required inverse of the stiffness operator
is done using the conjugate gradient with a preconditioner. The method is used
to study the propagation of acoustic waves in elastic polycrystals, showing the
strong effect of crystal anistropy and polycrystaline texture on the
propagation. It is shown that the method combines the simplicity of classical
Fourier series analysis with the versatility of Finite Elements to account for
complex geometries proving an efficient and general approach which allows the
use of large RVEs in 3D.


  Peak/off-peak spreads on European electricity forward and spot markets are
eroding due to the ongoing nuclear phaseout in Germany and the steady growth in
photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage
forces hydropower producers to recover part of their original profitability on
the reserve markets. We propose a bi-layer stochastic programming framework for
the optimal operation of a fleet of interconnected hydropower plants that sells
energy on both the spot and the reserve markets. The outer layer (the planner's
problem) optimizes end-of-day reservoir filling levels over one year, whereas
the inner layer (the trader's problem) selects optimal hourly market bids
within each day. Using an information restriction whereby the planner
prescribes the end-of-day reservoir targets one day in advance, we prove that
the trader's problem simplifies from an infinite-dimensional stochastic program
with 25 stages to a finite two-stage stochastic program with only two
scenarios. Substituting this reformulation back into the outer layer and
approximating the reservoir targets by affine decision rules allows us to
simplify the planner's problem from an infinite-dimensional stochastic program
with 365 stages to a two-stage stochastic program that can conveniently be
solved via the sample average approximation. Numerical experiments based on a
cascade in the Salzburg region of Austria demonstrate the effectiveness of the
suggested framework.


  The Born's rule introduces intrinsic randomness to the outcomes of a
measurement performed on a quantum mechanical system. But, if the system is
prepared in the eigenstate of an observable then the measurement outcome of
that observable is completely predictable and hence there is no intrinsic
randomness. On the other hand, if two incompatible observables are measured
(either sequentially on a particle or simultaneously on two identical copies of
the particle) then uncertainty principle guarantees intrinsic randomness in the
subsequent outcomes independent of the preparation state of the system. In this
article we show that this is true not only in quantum mechanics but for any
no-signaling probabilistic theory. Also the minimum amount of intrinsic
randomness that can be guaranteed for arbitrarily prepared state of the system
is quantified by the amount of (un)certainty.


  As an example of the unification of gravitation and particle physics, an
exact solution of the five-dimensional field equations is studied which
describes waves in the classical Einstein vacuum. While the solution is
essentially 5D in nature, the waves exist in ordinary 3D space, and may provide
a way to test for an extra dimension.


  We discuss the detailed composition of 28 extremely metal-poor dwarfs, 22 of
which are from the Hamburg/ESO Survey, based on Keck Echelle spectra. Our
sample has a median [Fe/H] of -2.7 dex, extends to -3.5 dex, and is somewhat
less metal-poor than was expected from [Fe/H](HK,HES) determined from low
resolution spectra. Our analysis supports the existence of a sharp decline in
the distribution of halo stars with metallicity below [Fe/H] = -3.0 dex. So far
no additional turnoff stars with [Fe/H]}<-3.5 have been identified in our
follow up efforts. For the best observed elements between Mg and Ni, we find
that the abundance ratios appear to have reached a plateau, i.e. [X/Fe] is
approximately constant as a function of [Fe/H], except for Cr, Mn and Co, which
show trends of abundance ratios varying with [Fe/H]. These abundance ratios at
low metallicity correspond approximately to the yield expected from Type II SN
with a narrow range in mass and explosion parameters; high mass Type II SN
progenitors are required. The dispersion of [X/Fe] about this plateau level is
surprisingly small, and is still dominated by measurement errors rather than
intrinsic scatter. The dispersion in neutron-capture elements, and the
abundance trends for Cr, Mn and Co are consistent with previous studies of
evolved EMP stars. Two dwarfs in the sample are carbon stars, while two others
have significant C enhancements, all with C12/C13 ~ 7 and with C/N between 10
and 150. Three of these C-rich stars have large enhancements of the heavy
neutron capture elements, including lead, which implies a strong s-process
contribution, presumably from binary mass transfer; the fourth shows no excess
of Sr or Ba.


  Given unstructured text, Large Language Models (LLMs) are adept at answering
simple (single-hop) questions. However, as the complexity of the questions
increase, the performance of LLMs degrade. We believe this is due to the
overhead associated with understanding the complex question followed by
filtering and aggregating unstructured information in the raw text. Recent
methods try to reduce this burden by integrating structured knowledge triples
into the raw text, aiming to provide a structured overview that simplifies
information processing. However, this simplistic approach is query-agnostic and
the extracted facts are ambiguous as they lack context. To address these
drawbacks and to enable LLMs to answer complex (multi-hop) questions with ease,
we propose to use a knowledge graph (KG) that is context-aware and is distilled
to contain query-relevant information. The use of our compressed distilled KG
as input to the LLM results in our method utilizing up to $67\%$ fewer tokens
to represent the query relevant information present in the supporting
documents, compared to the state-of-the-art (SoTA) method. Our experiments show
consistent improvements over the SoTA across several metrics (EM, F1,
BERTScore, and Human Eval) on two popular benchmark datasets (HotpotQA and
MuSiQue).


  Let ${\mathcal C}= \bigcup_{i=1}^n C_i \subseteq \mathbb{P}^2$ be a
collection of smooth rational plane curves. We prove that the addition-deletion
operation used in the study of hyperplane arrangements has an extension which
works for a large class of arrangements of smooth rational curves, giving an
inductive tool for understanding the freeness of the module $\Omega^1({\mathcal
C})$ of logarithmic differential forms with pole along ${\mathcal C}$. We also
show that the analog of Terao's conjecture (freeness of $\Omega^1({\mathcal
C})$ is combinatorially determined if ${\mathcal C}$ is a union of lines) is
false in this setting.


  Phylogenetic analyses which include fossils or molecular sequences that are
sampled through time require models that allow one sample to be a direct
ancestor of another sample. As previously available phylogenetic inference
tools assume that all samples are tips, they do not allow for this possibility.
We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC)
algorithm to infer what we call sampled ancestor trees, that is, trees in which
sampled individuals can be direct ancestors of other sampled individuals. We
use a family of birth-death models where individuals may remain in the tree
process after the sampling, in particular we extend the birth-death skyline
model [Stadler et al, 2013] to sampled ancestor trees. This method allows the
detection of sampled ancestors as well as estimation of the probability that an
individual will be removed from the process when it is sampled. We show that
sampled ancestor birth-death models where all samples come from different time
points are non-identifiable and thus require one parameter to be known in order
to infer other parameters. We apply this method to epidemiological data, where
the possibility of sampled ancestors enables us to identify individuals that
infected other individuals after being sampled and to infer fundamental
epidemiological parameters. We also apply the method to infer divergence times
and diversification rates when fossils are included among the species samples,
so that fossilisation events are modelled as a part of the tree branching
process. Such modelling has many advantages as argued in literature. The
sampler is available as an open-source BEAST2 package
(https://github.com/gavryushkina/sampled-ancestors).


  The propagation of chirality across scales is a common but poorly understood
phenomenon in soft matter. In this work, we use computer simulations to study
chiral monolayer assemblies formed by hard rod-like colloidal particles in the
presence of non-adsorbing polymer and characterize the thermodynamic driving
forces responsible for the twisting. Simulations show that straight (achiral)
rods assemble into monolayers with a spontaneous twist that is either left- or
right-handed, while helical (chiral) rods lead to assemblies with preferential
chiral features that depend on their handedness and curliness. The onset of
chirality in these monolayers can be traced back to small clusters formed at
the initial stage of the self-assembly. In these microscopic monolayers,
entropy drives twisting in ways that differ from the assumptions on which
existing continuum theory is built. Depending on the geometry of the
constituent rods, the preferred chiral twist can be driven by entropy gain of
the polymers, or of the rods, or both. In addition, the variation of the
polymer entropy with twist depends on changes in both the surface area and the
volume of the monolayer. Rod fluctuations perpendicular to the monolayer also
play an important role in stabilising the twisting.


  We develop a formalism to study non-local higher-dimensional effects in
braneworld scenarios from a four-dimensional effective theory point of view and
check it against the well-known Garriga-Tanaka result in the appropriate limit.
We then use this formalism to study the spectrum of density perturbations
during inflation as seen from the lower-dimensional effective theory. In
particular, we find that the gravitational potential is greatly enhanced at
short wavelengths. The consequences to the curvature perturbations are
nonetheless very weak and will lead to no characteristic signatures on the
power spectrum.