  We consider the neutral triple gauge boson production process in the context
of large extra dimensions (LED) model including the Kaluza-Klein (KK) excited
gravitons at future linear colliders, say ILC(CLIC). We consider
$\gamma\gamma\gamma, \gamma\gamma Z, \gamma Z Z$ and $ZZZ$ production
processes, and analyse their impacts on both the total cross section and some
key distributions. These processes are important for new physics searches at
linear colliders. Our results show that KK graviton exchange has the most
significant effect on \eerzz among the four processes with relatively small
$M_S$, while it has the largest effect on \eerrr with larger $M_S$. By using
the neutral triple gauge boson production we could set the discovery limit on
the fundamental Plank scale $M_S$ up to around 6-9 TeV for $\delta$ = 4 at the
3 TeV CLIC.


  The Biedenharn type relativistic wavefunctions are considered on the group
manifold of the Poincar\'{e} group. It is shown that the wavefunctions can be
factorized on the group manifold into translation group and Lorentz group
parts. A Lagrangian formalism and field equations for such factorizations are
given. Parametrizations of the functions obtained are studied in terms of a
ten-parameter set of the Poincar\'{e} group. An explicit construction of the
wavefunction for the spin 1/2 is given. A relation of the proposed description
with the quantum field theory and harmonic analysis on the Poincar\'{e} group
is discussed.


  Violation of Bell's inequality has been the mainspring for secure key
generation in an entanglement assisted Quantum Key Distribution(QKD) protocol.
Various contributions have relied on the violation of appropriate Bell
inequalities to build an appropriate QKD protocol. Residing between Bell
nonlocality and entanglement, there exists a hybrid trait of correlations,
namely correlations exhibited through the violation of steering inequalities.
However, such correlations have not been put to use in QKD protocols as much as
their stronger counterpart, the Bell violations. In the present work, we show
that the violations of the CJWR(E.G.Cavalcanti,S.J. Jones,H.M Wiseman and M.D.
Reid, Phys.Rev.A 80,032112(2009))steering inequalities can act as key
ingredients in an entanglement assisted QKD protocol. We work with arbitrary
two qubit entangled states, characterize them in accordance with their utility
in such protocols. The characterization is based on the quantum bit error rate
and violation of a CJWR inequality. Furthermore, we show that subsequent
applications of local filtering operations on initially entangled states
exhibiting non violation, lead to violations necessary for the successful
implementation of the protocol. An additional vindication of our protocol is
provided by the use of absolutely Bell-CHSH local states, states which remain
Bell-CHSH local even under global unitary operations.


  We calibrate here cosmological radiative transfer simulation with ATON/RAMSES
with a range of measurements of the Lyman alpha opacity from QSO absorption
spectra. We find the Lyman alpha opacity to be very sensitive to the exact
timing of hydrogen reionisation. Models reproducing the measured evolution of
the mean photoionisation rate and average mean free path reach overlap at z ~ 7
and predict an accelerated evolution of the Lyman alpha opacity at z > 6
consistent with the rapidly evolving luminosity function of Lyman alpha
emitters in this redshift range. Similar to "optically thin" simulations our
full radiative transfer simulations fail, however, to reproduce the
high-opacity tail of the Lyman alpha opacity PDF at z > 5. We argue that this
is due to spatial UV fluctuations in the post-overlap phase of reionisation on
substantially larger scales than predicted by our source model, where the
ionising emissivity is dominated by large numbers of sub-L* galaxies. We
further argue that this suggests a significant contribution to the ionising UV
background by much rarer bright sources at high redshift.


  Using high resolution, high signal-to-noise ultraviolet spectra of the z =
0.9754 quasar PG1148+549 obtained with the Cosmic Origins Spectrograph (COS) on
the Hubble Space Telescope, we study the physical conditions and abundances of
NeVIII+OVI absorption line systems at z(abs) =0.68381, 0.70152, 0.72478. In
addition to NeVIII and OVI, absorption lines from multiple ionization stages of
oxygen (OII, OIII, OIV) are detected and are well-aligned with the more highly
ionized species. We show that these absorbers are multiphase systems including
hot gas (T ~ 10^{5.7} K) that produces NeVIII and OVI, and the gas metallicity
of the cool phase ranges from Z = 0.3 Z_{solar} to supersolar. The cool
(~10^{4} K) phases have densities n_{H} ~ 10^{-4} cm^{-3} and small sizes (<
4kpc); these cool clouds are likely to expand and dissipate, and the NeVIII may
be within a transition layer between the cool gas and a surrounding, much
hotter medium. The NeVIII redshift density, dN/dz = 7^{+7}_{-3}, requires a
large number of these clouds for every L > 0.1L* galaxy and a large effective
absorption cross section (>~ 100 kpc), and indeed, we find a star forming ~L*
galaxy at the redshift of the z(abs)=0.72478 system, at an impact parameter of
217 kpc. Multiphase absorbers like these NeVIII systems are likely to be an
important reservoir of baryons and metals in the circumgalactic media of
galaxies.


  Mandelbrot introduced the concept of fractals to describe the non-Euclidean
shape of many aspects of the natural world. In the time series context he
proposed the use of fractional Brownian motion (fBm) to model non-negligible
temporal persistence, the "Joseph Effect"; and Levy flights to quantify large
discontinuities, the "Noah Effect". In space physics, both effects are
manifested in the intermittency and long-range correlation which are by now
well-established features of geomagnetic indices and their solar wind drivers.
In order to capture and quantify the Noah and Joseph effects in one compact
model we propose the application of the "bridging" fractional Levy motion (fLm)
to space physics. We perform an initial evaluation of some previous scaling
results in this paradigm, and show how fLm can model the previously observed
exponents. We suggest some new directions for the future.


  In a recent paper, Ramos and Tilli proved certain sharp inequality for
analytic functions in subdomains of the unit disk. We will generalize their
main inequality for derivatives of functions from Bergman space with respect to
two diferent measures. Some connections with an analog for the Fock spaces,
earlier investigated in Kalaj, will also be discussed.


  Near-field microwave microscopy has created the opportunity for a new class
of electrodynamics experiments of materials. Freed from the constraints of
traditional microwave optics, experiments can be carried out at high spatial
resolution over a broad frequency range. In addition, the measurements can be
done quantitatively so that images of microwave materials properties can be
created. We review the five major types of near-field microwave microscopes and
discuss our own form of microscopy in detail. Quantitative images of microwave
sheet resistance, dielectric constant, and dielectric tunability are presented
and discussed. Future prospects for near-field measurements of microwave
electrodynamic properties are also presented.


  A Baker-Nunn Camera (BNC), originally installed at the Real Instituto y
Observatorio de la Armada (ROA) in 1958, was refurbished and robotized. The new
facility, called Telescope Fabra ROA Montsec (TFRM), was installed at the
Observatori Astron\`omic del Montsec (OAdM).
  The process of refurbishment is described in detail. Most of the steps of the
refurbishment project were accomplished by purchasing commercial components,
which involve little posterior engineering assembling work. The TFRM is a 0.5m
aperture f/0.96 optically modified BNC, which offers a unique combination of
instrumental specifications: fully robotic and remote operation, wide-field of
view (4.4 deg x 4.4 deg), moderate limiting magnitude (V~19.5mag), ability of
tracking at arbitrary right ascension and declination rates, as well as opening
and closing CCD shutter at will during an exposure.
  Nearly all kind of image survey programs can benefit from those
specifications. Apart from other less time consuming programs, since the
beginning of science TFRM operations we have been conducting two specific and
distinct surveys: super-Earths transiting around M-type dwarfs stars, and
geostationary debris in the context of Space Situational Awareness / Space
Surveillance and Tracking (SSA/SST) programs. Preliminary results for both
cases will be shown.


  We explicitly apply localization results to study the interpolation between
three and two dimensional mirror symmetry for Abelian gauge theories with four
supercharges. We first use the ellipsoid S_b^3 partition functions to verify
the mirror symmetry between a pair of general three dimensional N=2 Abelian
Chern-Simons quiver gauge theories. These expressions readily factorize into
holomorphic blocks and their anti-holomorphic copies, so we can also obtain the
partition functions on S^1 x S^2 via fusion procedure. We then demonstrate S^1
x S^2 partition functions for the three dimensional Abelian gauge theories can
be dimensionally reduced to the S^2 partition functions of N=(2,2) GLSM and
Landau-Ginzburg model for the corresponding two dimensional mirror pair, as
anticipated previously in \cite{Aganagic:2001uw}. We also comment on the
analogous interpolation for the non-Abelian gauge theories and compute the
K-theory vortex partition function for a simple limit to verify the prediction
from holomorphic block.


  We explore the idea of restoring the full diffeomorphism (Diff) invariance in
theories with only transverse diffeomorphisms (TDiff) by the introduction of
additional fields. In particular, we consider in detail the case of a TDiff
invariant scalar field and how Diff symmetry can be restored preserving
locality by introducing an additional vector field. We reobtain the
corresponding dynamics and energy-momentum tensor from the covariantized action
and analyze the potential and kinetic domination regimes. For the former, the
theory describes a cosmological constant-type behaviour, while for the latter
we show that the theory can describe an adiabatic perfect fluid whose equation
of state and speed of sound is obtained in a straightforward way. Furthermore,
the reformulation with the full symmetry allows us to analyze the gravitational
properties of the theory beyond those particular regimes. In particular, we
find the general expression for the effective speed of sound of the
non-adiabatic perfect fluid, which provides us with physically reasonable
conditions that should be satisfied by the coupling functions. Finally, we
investigate the particular models leading to an adiabatic fluid.


  We propose a method for pricing American options whose pay-off depends on the
moving average of the underlying asset price. The method uses a finite
dimensional approximation of the infinite-dimensional dynamics of the moving
average process based on a truncated Laguerre series expansion. The resulting
problem is a finite-dimensional optimal stopping problem, which we propose to
solve with a least squares Monte Carlo approach. We analyze the theoretical
convergence rate of our method and present numerical results in the
Black-Scholes framework.


  The spin-boson model is analytically studied using displaced Fock states
(DFS) without discretization of the continuum bath. In the orthogonal displaced
Fock basis, the ground-state wavefunction can be systematically improved in a
controllable way. Interestingly, the zeroth-order DFS reproduces exactly the
well known Silbey-Harris results. In the framework of the second-order DFS, the
magnetization and the entanglement entropy are exactly calculated. It is found
that the magnetic critical exponent $\beta$ is converged to $0.5$ in the whole
sub-Ohmic bath regime $0<s<1$, compared with that by the exactly solvable
generalized Silbey-Harris ansatz. It is strongly suggested that the system with
sub-Ohmic bath is always above its upper critical dimension, in sharp contrast
with the previous findings. This is the first evidence of the violation of the
quantum-classical Mapping for $% 1/2<s<1$.


  Entanglement plays a central role in quantum information processing,
indicating the non-local correlation of quantum matters. However, few effective
ways are known to detect the amount of entanglement of an unknown quantum
state. In this work, we propose a scheme to estimate the entanglement
negativity of any bi-partition of a composite system. The proposed scheme is
based on the random unitary evolution and local measurements on the single-copy
quantum states, which is more practical compared with former methods based on
collective measurements on many copies of the identical state. Meanwhile, we
generalize the scheme to quantify the total multi-partite correlation. We
demonstrate the efficiency of the scheme with theoretical statistical analysis
and numerical simulations. The proposed scheme is quite suitable for
state-of-the-art quantum platforms, which can serve as not only a useful
benchmarking tool to advance the quantum technology, but also a probe to study
fundamental quantum physics, such as the entanglement dynamics.


  The close link between dissipativity and optimal control is already apparent
in Jan C. Willems' first papers on the subject. In recent years, research on
this link has been revived with a particular focus on nonlinear problems and
applications in model predictive control (MPC). This paper surveys these recent
developments and some of Willems' and other authors' earlier results.


  Traffic signal control is an important problem in urban mobility with a
significant potential of economic and environmental impact. While there is a
growing interest in Reinforcement Learning (RL) for traffic signal control, the
work so far has focussed on learning through simulations which could lead to
inaccuracies due to simplifying assumptions. Instead, real experience data on
traffic is available and could be exploited at minimal costs. Recent progress
in offline or batch RL has enabled just that. Model-based offline RL methods,
in particular, have been shown to generalize from the experience data much
better than others.
  We build a model-based learning framework which infers a Markov Decision
Process (MDP) from a dataset collected using a cyclic traffic signal control
policy that is both commonplace and easy to gather. The MDP is built with
pessimistic costs to manage out-of-distribution scenarios using an adaptive
shaping of rewards which is shown to provide better regularization compared to
the prior related work in addition to being PAC-optimal. Our model is evaluated
on a complex signalized roundabout showing that it is possible to build highly
performant traffic control policies in a data efficient manner.


  We consider a spatially extended mean-field model of a FitzHugh-Nagumo neural
network, with a rescaled interaction kernel. Our main purpose is to prove that
its asymptotic limit in the regime of strong local interactions converges
toward a system of reaction-diffusion equations taking account for theaverage
quantities of the network. Our approach is based on a modulated energy
argument, to compare the macroscopic quantities computed from the solution of
the transport equation, and the solution of the limit system. The main
difficulty, compared to the literature, lies in the need of regularity in space
of the solutions of the limit system and a careful control of an internal
nonlocal dissipation.


  In this work we use the holographic softwall AdS/QCD model with anomalous
dimension contributions coming from two different QCD beta functions to
calculate the masses of higher spin glueball states for both even and odd spins
and its respective Regge trajectories, related to the pomeron and the odderon,
respectively. We further investigate this model taking into account dynamical
corrections due to a dilaton potential consistent with Einstein equations in 5
dimensions. The results found in this work for the Regge trajectories within
the anomalous softwall model with dynamical corrections are consistent with
those presented in the literature.


  The evolution of novae and Cataclysmic Variables (CVs) is driven by changes
in the binary orbital periods. In a direct and critical test for various
evolution models and their physical mechanisms, I measure the sudden changes in
the period ($\Delta P$) across 14 nova eruptions and I measure the steady
period change during quiescence ($\dot{P}$) for 20 inter-eruption intervals.
The standard theory for $\Delta P$ is dominated by the mechanism of mass loss,
and this fails completely for the five novae with negative values, and it fails
to permit the $\Delta P$ for U Sco eruptions to change by one
order-of-magnitude from eruption-to-eruption. The Hibernation Model of
evolution is refuted because all the $\Delta P$ measures are orders of
magnitude too small to cause any significant drop in accretion luminosity, and
indeed, near half of the nova have negative $\Delta P$ as the opposite of the
required mechanism for any hibernation state. As for the Magnetic Braking
Model, this fails by many orders-of-magnitude in its predictions of the
required $\dot{P}$ for 9-out-of-13 novae. The observed $\dot{P}$ values
scatter, both positively and negatively, over a range of $\pm$10$^{-9}$, while
the predicted values are from $-$10$^{-13}$ to $-$10$^{-11}$. This huge scatter
is not possible with standard theory, and there must be some currently-unknown
mechanism to be added in, with this new mechanism 100--10000$\times$ larger in
effect than the current theory allows. In all, these failed predictions
demonstrate that nova systems must have unknown physical mechanisms for both
$\Delta P$ and $\dot{P}$ that dominate over all other effects.


  The knowledge of all the electron swarm parameters as a function of the
reduced electric field is indispensable for the fluid modeling of electrical
discharge at low pressure. In this paper we present the evolution of the swarm
parameters as a function of the reduced electric field in the He/H2 and Ne/H2
mixtures. For this we use a BE solver specially designed for this purpose,
freely available under the name BOLSIG+, which is more general and easier to
use than most other BE available solvers. The solver provides a stable solution
state of the BE for the electrons in a uniform electric field, it is based on
the conventional two-term approximation. The obtained results are validated
through a comparison to experimental swarm parameters already exist in the
literature.


  Knowledge of the shape of the mass spectrum of compact objects can be used to
help break the degeneracy between the mass and redshift of the gravitational
wave (GW) sources, and thus can be used to infer cosmological parameters in the
absence of redshift measurements obtained from electromagnetic observations. In
this paper, we study extensively different aspects of this approach, including
its computational limits and achievable accuracy. We focus on ground-based
detectors with current and future sensitivities, we first perform the analysis
of an extensive set of simulated data with a hierarchical Bayesian scheme
inferring population and cosmological parameters. We consider a population
model (power-law plus Gaussian) which exhibits characteristic scales (extremes
of the mass spectrum, presence of an accumulation point) that allows an
indirect estimate of the source redshift. Our analysis of this catalog
highlights and quantifies the tight interplay between source population and
cosmological parameters, as well as the influence of initial assumptions
(whether formulated on the source or cosmological parameters). We then validate
our results by an "end-to-end" analysis using simulated GW data and posterior
samples generated from Bayesian samplers used for GW parameter estimation, thus
mirroring the analysis chain used for observational data for the first time in
literature. Our results then lead us to re-examine the estimation of $H_0$
obtained with GWTC-1, and we show explicitly how population assumptions impact
the final $H_0$ result. Our results underline the importance of inferring
population and cosmological parameters jointly (and not separately as is often
assumed). The only exception, as we discuss, is if an electromagnetic
counterpart was to be observed for all the BBH events: then the population
assumptions have less impact on the estimation of cosmological parameters.


  In Nature, the primary goal of any network is to survive. This is less
obvious for engineering networks (electric power, gas, water, transportation
systems etc.) that are expected to operate under normal conditions most of
time. As a result, the ability of a network to withstand massive sudden damage
caused by adverse events (or survivability) has not been among traditional
goals in the network design. Reality, however, calls for the adjustment of
design priorities. As modern networks develop toward increasing their size,
complexity, and integration, the likelihood of adverse events increases too due
to technological development, climate change, and activities in the political
arena among other factors. Under such circumstances, a network failure has an
unprecedented effect on lives and economy. To mitigate the impact of adverse
events on the network operability, the survivability analysis must be conducted
at the early stage of the network design. Such analysis requires the
development of new analytical and computational tools. Computational analysis
of the network survivability is the exponential time problem at least. The
current paper describes a new algorithm, in which the reduction of the
computational complexity is achieved by mapping an initial network topology
with multiple sources and sinks onto a set of simpler smaller topologies with
multiple sources and a single sink. Steps for further reducing the time and
space expenses of computations are also discussed.


  Let $(X, r)$ be any set-theoretical non-degenerate solution of the
Yang-Baxter equation and $(X, \tilde r)$ be the derived solution of $(X, r)$.
As for any braided vector space $(W_{X, r}, c)$ associated to $(X, r)$, is it
possible to find some braided vector space $(W_{X, \tilde r}, \tilde c)$ which
is t-equivalent to $(W_{X, r}, c)$? In case that $(X, r)$ is a near-rack
solution, we give a sufficient condition to make an affirmative answer to the
question. Examples of t-equivalence are constructed, hence finite dimensional
Nichols algebras are obtained. In particular, all finite dimensional Nichols
algebras associated to involutive near-rack solutions are classified.


  We discuss Big Bang Nucleosynthesis constraints on maximal
$\nu_\mu\leftrightarrow\nu_s$ mixing. Vacuum $\nu_\mu\leftrightarrow\nu_s$
oscillation has been proposed as one possible explanation of the Super
Kamiokande atmospheric neutrino data. Based on the most recent primordial
abundance measurements, we find that the effective number of neutrino species
for Big Bang Nucleosynthesis (BBN) is $N_\nu\la 3.3$. Assuming that all three
active neutrinos are light (with masses $\ll 1$ MeV), we examine BBN
constraints on $\nu_\mu\leftrightarrow\nu_s$ mixing in two scenarios: (1) a
negligible lepton asymmetry (the standard picture); (2) the presence of a large
lepton asymmetry which has resulted from an amplification by
$\nu_\tau\leftrightarrow\nu_{s'}$ mixing ($\nu_{s'}$ being $\nu_s$ or another
sterile neutrino species). The latter scenario has been proposed recently to
reconcile the BBN constraints and large-angle $\nu_\mu\leftrightarrow\nu_s$
mixing. We find that the large-angle $\nu_\mu\leftrightarrow\nu_s$ mixing in
the first scenario, which would yield $N_\nu\approx 4$, is ruled out as an
explanation of the Super Kamiokande data. It is conceivably possible for the
$\nu_\mu\leftrightarrow\nu_s$ solution to evade BBN bounds in the second
scenario, but only if 200 eV$^2\la m^2_{\nu_\tau}-m^2_{\nu_{s'}}\la 10^4$
eV$^2$ is satisfied, and if $\nu_\tau$ decays non-radiatively with a lifetime
$\la 10^3$ years. This mass-squared difference implies 15 eV$\la
m_{\nu_\tau}\la 100$ eV if $\nu_{s'}$ is much lighter than $\nu_\tau$. We
conclude that maximal (or near maximal) $\nu_\mu\leftrightarrow\nu_\tau$ mixing
is a more likely explanation of the Super Kamiokande data.


  The charge radii and the decay rates of the pion and kaons are calculated,
using the relativistic equation of motion with a linear potential. Those
physical quantities are quite well explained with the current quark masses in
the case of the pion. It is found that the Van Royen-Weisskopf paradox can be
cleared out only in the linear potential model by considering the color degree
of freedom of the quark in the meson. The physical quantities for the kaon are
not so satisfactory that it should be required to reconsider the
Cabibbo-Kobayashi-Maskawa matrix element in the kaon decay.


  Data and data sources have become increasingly essential in recent decades.
Scientists and researchers require more data to deploy AI approaches as the
field continues to improve. In recent years, the rapid technological
advancements have had a significant impact on human existence. One major field
for collecting data is satellite technology. With the fast development of
various satellite sensor equipment, synthetic aperture radar (SAR) images have
become an important source of data for a variety of research subjects,
including environmental studies, urban studies, coastal extraction, water
sources, etc. Change detection and coastline detection are both achieved using
SAR pictures. However, speckle noise is a major problem in SAR imaging. Several
solutions have been offered to address this issue. One solution is to expose
SAR images to spatial fuzzy clustering. Another solution is to separate speech.
This study utilises the spatial function to overcome speckle noise and cluster
the SAR images with the highest achieved accuracy. The spatial function is
proposed in this work since the likelihood of data falling into one cluster is
what this function is all about. When the spatial function is employed to
cluster data in fuzzy logic, the clustering outcomes improve. The proposed
clustering technique is us


  We derive a complete pure de Sitter supergravity action with non-linearly
realized supersymmetry and its rigid limit, the Volkov-Akulov action, from the
corresponding models with linear supersymmetry, by computing the path integral
in the limit of infinite sgoldstino mass. In this, we use a non-Gaussian
functional integration formula that was recently discovered in a derivation of
de Sitter supergravity from the superconformal theory. We also present explicit
examples of pure dS supergravity and the case with one matter multiplet. These
two simple examples serve as a test and a demo of the universal action formula
valid for de Sitter supergravities with general matter coupling.


  The use of propaganda has spiked on mainstream and social media, aiming to
manipulate or mislead users. While efforts to automatically detect propaganda
techniques in textual, visual, or multimodal content have increased, most of
them primarily focus on English content. The majority of the recent initiatives
targeting medium to low-resource languages produced relatively small annotated
datasets, with a skewed distribution, posing challenges for the development of
sophisticated propaganda detection models. To address this challenge, we
carefully develop the largest propaganda dataset to date, ArPro, comprised of
8K paragraphs from newspaper articles, labeled at the text span level following
a taxonomy of 23 propagandistic techniques. Furthermore, our work offers the
first attempt to understand the performance of large language models (LLMs),
using GPT-4, for fine-grained propaganda detection from text. Results showed
that GPT-4's performance degrades as the task moves from simply classifying a
paragraph as propagandistic or not, to the fine-grained task of detecting
propaganda techniques and their manifestation in text. Compared to models
fine-tuned on the dataset for propaganda detection at different classification
granularities, GPT-4 is still far behind. Finally, we evaluate GPT-4 on a
dataset consisting of six other languages for span detection, and results
suggest that the model struggles with the task across languages. Our dataset
and resources will be released to the community.


  Let $Q$ be a local ring with maximal ideal $\mathfrak{n}$ and let $f,g\in
\mathfrak{n}\smallsetminus\mathfrak{n}^2$ with $fg=0$. When $M$ is a finite
$Q$-module with $fM=0$, we show that a minimal free resolution of $M$ over $Q$
has a differential graded module structure over the differential graded algebra
$Q\langle y,t\mid \partial(y)=f, \partial(t)=gy\rangle$. When $(f,g)$ is a pair
of exact zero divisors, we use this structure to describe a minimal free
resolution of $M$ over $Q/(f)$.


  We review some aspects of spin physics where QCD instantons play an important
role. In particular, their large contributions in semi-inclusive deep-inelastic
scattering and polarized proton on proton scattering. We also review their
possible contribution in the $\mathcal{P}$-odd pion azimuthal charge
correlations in peripheral $AA$ scattering at collider energies.


  Some recent, long-term numerical simulations of binary neutron star mergers
have shown that the long-lived remnants produced in such mergers might be
affected by convective instabilities. Those would trigger the excitation of
inertial modes, providing a potential method to improve our understanding of
the rotational and thermal properties of neutron stars through the analysis of
the modes' imprint in the late post-merger gravitational-wave signal. In this
paper we assess the detectability of those modes by injecting numerically
generated post-merger waveforms into colored Gaussian noise of
second-generation and future detectors. Signals are recovered using BayesWave,
a Bayesian data-analysis algorithm that reconstructs them through a
morphology-independent approach using series of sine-Gaussian wavelets. Our
study reveals that current interferometers (i.e. the Handford-Livingston-Virgo
network) recover the peak frequency of inertial modes only if the merger occurs
at distances of up to 1 Mpc. For future detectors such as the Einstein
Telescope, the range of detection increases by about a factor 10.


  We review the recent studies of tachyon condensation in string field theory.
After introducing the open string field theory both for bosonic string and for
superstring, we use them to examine the conjecture that the unstable
configurations of the D-brane will decay into the `closed string vacuum'
through the tachyon condensation. And we describe the attemps to construct a
lower dimensional bosonic D-brane as an unstable lump solution of the string
field equation. We obtain exact results from another formulation, background
independent open string field theory. We also discuss some other topics which
are related to tachyon condensation in string theory, such as the construction
of a D-brane as a noncommutative soliton and some field theory models. This
paper is based on my master's thesis submitted to Department of Physics,
Faculty of Science, University of Tokyo on January 2001.


  The advancement of Large Language Models (LLMs) has remarkably pushed the
boundaries towards artificial general intelligence (AGI), with their
exceptional ability on understanding diverse types of information, including
but not limited to images and audio. Despite this progress, a critical gap
remains in empowering LLMs to proficiently understand and reason on graph data.
Recent studies underscore LLMs' underwhelming performance on fundamental graph
reasoning tasks. In this paper, we endeavor to unearth the obstacles that
impede LLMs in graph reasoning, pinpointing the common practice of converting
graphs into natural language descriptions (Graph2Text) as a fundamental
bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering
end-to-end approach that synergistically integrates graph learning models with
LLMs. This synergy equips LLMs with the ability to proficiently interpret and
reason on graph data, harnessing the superior expressive power of graph
learning models. Our empirical evaluations across four fundamental graph
reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a
substantial average accuracy enhancement of 54.44%, alongside a noteworthy
context reduction of 96.45% across various graph reasoning tasks.


  We investigate the $2\,^3\!S_1$--$2\,^3\!P_J$ ($J = 0, 1, 2$) transitions in
$\rm^6Li^+$ using the optical Ramsey technique and achieve the most precise
values of the hyperfine splittings of the $2\,^3\!S_1$ and $2\,^3\!P_J$ states,
with smallest uncertainty of about 10~kHz. The present results reduce the
uncertainties of previous experiments by a factor of 5 for the $2\,^3\!S_1$
state and a factor of 50 for the $2\,^3\!P_J$ states, and are in better
agreement with theoretical values. Combining our measured hyperfine intervals
of the $2\,^3\!S_1$ state with the latest quantum electrodynamic (QED)
calculations, the improved Zemach radius of the $\rm^6Li$ nucleus is determined
to be 2.44(2)~fm, with the uncertainty entirely due to the uncalculated QED
effects of order $m\alpha^7$. The result is in sharp disagreement with the
value 3.71(16) fm determined from simple models of the nuclear charge and
magnetization distribution. We call for a more definitive nuclear physics value
of the $\rm^6Li$ Zemach radius.


  Let $G$ be a simple graph. In 1986, Herbert Wilf asked what kind of graphs
have an eigenvector with entries formed only by $\pm 1$? In this paper, we
answer this question for the adjacency, Laplacian and signless Laplacian matrix
of a graph. Besides, we generalize the concept of an exact graph to the
adjacency and signless Laplacian matrices. Infinity families of exact graphs
for all those matrices are presented.


  It is shown that the topological discrete sine-Gordon system introduced by
Speight and Ward models the dynamics of an infinite uniform chain of electric
dipoles constrained to rotate in a plane containing the chain. Such a chain
admits a novel type of static kink solution which may occupy any position
relative to the spatial lattice and experiences no Peierls-Nabarro barrier.
Consequently the dynamics of a single kink is highly continuum like, despite
the strongly discrete nature of the model. Static multikinks and kink-antikink
pairs are constructed, and it is shown that all such static solutions are
unstable. Exact propagating kinks are sought numerically using the
pseudo-spectral method, but it is found that none exist, except, perhaps, at
very low speed.


  We propose that a single, spontaneously broken, U(1) gauge symmetry may be
responsible for suppressing both the first two generation Yukawa couplings, and
also, in a correlated manner, parameters in the dynamical supersymmetry (SUSY)
breaking sector by the mechanism of retrofitting. In the dynamical SUSY
breaking sector, these small parameters are typically required in order to
introduce R-symmetry breaking in a controlled manner and obtain
phenomenologically viable meta-stable vacua. The heavy U(1) multiplet mediates
a dominant contribution to the first two generation MSSM sfermion soft masses,
while gauge mediation provides a parametrically suppressed soft term
contribution to the stop and most other states, so realising a natural SUSY
spectrum in a fashion consistent with SUSY unification. In explicit models the
spectra obtained can be such that current LHC limits are evaded, and
predictions of flavour changing processes are consistent with observation. We
examine both implementations with low scale mediation, and string-motivated
examples where the U(1) is anomalous before the inclusion of a generalised
Green-Schwarz mechanism.


  Mechanical frequency combs are poised to bring the applications and utility
of optical frequency combs into the mechanical domain. So far, their main
challenge has been strict requirements on drive frequencies and power, which
complicate operation. We demonstrate a straightforward mechanism to create a
frequency comb consisting of mechanical overtones (integer multiples) of a
single eigenfrequency, by monolithically integrating a suspended dielectric
membrane with a counter-propagating optical trap. The periodic optical field
modulates the dielectrophoretic force on the membrane at the overtones of a
membrane's motion. These overtones share a fixed frequency and phase relation,
and constitute a mechanical frequency comb. The periodic optical field also
creates an optothermal parametric drive that requires no additional power or
external frequency reference. This combination of effects results in an
easy-to-use mechanical frequency comb platform that requires no precise
alignment, no additional feedback or control electronics, and only uses a
single, mW continuous wave laser beam. This highlights the overtone frequency
comb as the straightforward future for applications in sensing, metrology and
quantum acoustics.


  Effects of the baroclinic torque on wave propagation normally neglected under
the Boussinesq approximation is investigated here, with a special focus on the
associated consequences for the mechanistic interpretation of shear instability
arising from the interaction between a pair of vorticity-propagating waves. To
illustrate and elucidate the physical effects that modify wave propagation, we
consider three examples of increasing complexity: wave propagation supported by
a uniform background flow; wave propagation supported on a piecewise-linear
basic state possessing one jump; and an instability problem of a
piecewise-linear basic state possessing two jumps, which supports the
possibility of shear instability. We find that the non-Boussinesq effects
introduces a preference for the direction of wave propagation that depends on
the sign of the shear in the region where waves are supported. This in turn
affects phase-locking of waves that is crucial for the mechanistic
interpretation for shear instability, and is seen here to have an inherent
tendency for stabilisation.


  This paper introduces SATformer, a novel Transformer-based approach for the
Boolean Satisfiability (SAT) problem. Rather than solving the problem directly,
SATformer approaches the problem from the opposite direction by focusing on
unsatisfiability. Specifically, it models clause interactions to identify any
unsatisfiable sub-problems. Using a graph neural network, we convert clauses
into clause embeddings and employ a hierarchical Transformer-based model to
understand clause correlation. SATformer is trained through a multi-task
learning approach, using the single-bit satisfiability result and the minimal
unsatisfiable core (MUC) for UNSAT problems as clause supervision. As an
end-to-end learning-based satisfiability classifier, the performance of
SATformer surpasses that of NeuroSAT significantly. Furthermore, we integrate
the clause predictions made by SATformer into modern heuristic-based SAT
solvers and validate our approach with a logic equivalence checking task.
Experimental results show that our SATformer can decrease the runtime of
existing solvers by an average of 21.33%.


  A relation between the centripetal acceleration g_obs in galaxies and the
gravity due to the baryon distribution g_bar has been found by McGaugh (2016)
and Lelli et al.(2017). It also summarizes properties such as the Tully-Fisher
and Faber-Jackson relations. Below about g_bar= 10E-10 m s^(-2), the observed
relation deviates from the 1:1 line, g_obs being much larger than g_bar. The
acceleration relation is followed by late and early type galaxies, and also by
the dwarf spheroidals where the deviations from the 1:1 line are the largest
ones. These deviations are currently attributed to dark matter.
  We show that the scale invariant theory, with the assumption of the scale
invariance of the empty space, correctly predicts the observed deviations in
the acceleration relation. The large deviations (up to a factor 400) and the
flattening of the acceleration relation observed for the dwarf spheroidal
galaxies are also well described. The presence of dark matter is no longer
necessary in the scale invariant context, which also accounts why dark matter
usually appears to dominate in galactic regions with low baryonic gravities.


  The Total Least Squares solution of an overdetermined, approximate linear
equation $Ax \approx b$ minimizes a nonlinear function which characterizes the
backward error. We show that a globally convergent variant of the Gauss--Newton
iteration can be tailored to compute that solution. At each iteration, the
proposed method requires the solution of an ordinary least squares problem
where the matrix $A$ is perturbed by a rank-one term.


  We revisit the color-magnitude relation (CMR) of the Virgo cluster early-type
galaxies in order to explore its alleged non-linearity. To this aim, we
reanalyze the relation already published from data obtained within the ACS
Virgo Cluster Survey of the Hubble Space Telescope, and perform our own
photometry and analysis of the images of the 100 early-type galaxies observed
as part of this survey. In addition, we compare our results with those reported
in the literature from data of the Sloan Digital Sky Survey. We have found that
when the brightest galaxies and untypical systems are excluded from the sample,
a linear relation arises in agreement with what is observed in other groups and
clusters. The central regions of the brightest galaxies also follow this
relation. In addition, we notice that Virgo contains at least four compact
elliptical galaxies besides the well known object VCC 1297 (NGC 4486B). Their
locations in the <mueff>-luminosity diagram define a different trend to that
followed by normal early-type dwarf galaxies, setting an upper limit in
effective surface brightness and a lower limit in effective radius for their
luminosities. Based on the distribution of different galaxy sub-samples in the
color-magnitude and <mueff>-luminosity diagrams we draw some conclusions on
their formation and the history of their evolution.


  Research interest in rapid structured-light imaging has grown increasingly
for the modeling of moving objects, and a number of methods have been suggested
for the range capture in a single video frame. The imaging area of a 3D object
using a single projector is restricted since the structured light is projected
only onto a limited area of the object surface. Employing additional projectors
to broaden the imaging area is a challenging problem since simultaneous
projection of multiple patterns results in their superposition in the
light-intersected areas and the recognition of original patterns is by no means
trivial. This paper presents a novel method of multi-projector color
structured-light vision based on projector-camera triangulation. By analyzing
the behavior of superposed-light colors in a chromaticity domain, we show that
the original light colors cannot be properly extracted by the conventional
direct estimation. We disambiguate multiple projectors by multiplexing the
orientations of projector patterns so that the superposed patterns can be
separated by explicit derivative computations. Experimental studies are carried
out to demonstrate the validity of the presented method. The proposed method
increases the efficiency of range acquisition compared to conventional active
stereo using multiple projectors.


  Topological Surface States (TSS) represent new types of two dimensional
electron systems with novel and unprecedented properties distinct from any
quantum Hall-like or spin-Hall effects. Their Z$_2$ topological order can be
realized at room temperatures without magnetic fields and they can be turned
into magnets, exotic superconductors or Kondo insulators leading to worldwide
interest and activity in the topic. We review the basic concepts defining such
topological matter and the experimental discovery via the key experimental
probe that revealed the Z$_2$ topological order in the bulk of these spin-orbit
interaction dominated insulators for the first time. This review focuses on the
key results that demonstrated the fundamental Z$_2$ topological properties such
as spin-momentum locking, non-trivial Berry's phases, mirror Chern number,
absence of backscattering, bulk-boundary correspondence (topology), protection
by time-reversal and other discrete (mirror) symmetries and their remarkable
persistence up to the room temperature elaborating on results first briefly
discussed in an early review by M.Z. Hasan and C.L. Kane [Rev. of Mod. Phys.,
82, 3045 (2010)]. Additionally, key results on broken symmetry phases such as
quantum magnetism and superconductivity induced in topological materials are
briefly discussed. Topological insulators beyond the Z$_2$ classification such
as Topological Crystalline Insulators (TCI) are discussed based on their spin
properties (mirror Chern invariants). The experimental methodologies detailed
here have been used in most of the subsequent studies of Z$_2$ topological
physics in almost all bulk topological insulator materials to this date.


  Prior research notes that BERT's computational cost grows quadratically with
sequence length thus leading to longer training times, higher GPU memory
constraints and carbon emissions. While recent work seeks to address these
scalability issues at pre-training, these issues are also prominent in
fine-tuning especially for long sequence tasks like document classification.
Our work thus focuses on optimizing the computational cost of fine-tuning for
document classification. We achieve this by complementary learning of both
topic and language models in a unified framework, named TopicBERT. This
significantly reduces the number of self-attention operations - a main
performance bottleneck. Consequently, our model achieves a 1.4x ($\sim40\%$)
speedup with $\sim40\%$ reduction in $CO_2$ emission while retaining $99.9\%$
performance over 5 datasets.


  We re-examine a genuine power of weak lensing bispectrum tomography for
constraining cosmological parameters, when combined with the power spectrum
tomography, based on the Fisher information matrix formalism. To account for
the full information at two- and three-point levels, we include all the power
spectrum and bispectrum information built from all-available combinations of
tomographic redshift bins, multipole bins and different triangle configurations
over a range of angular scales (up to lmax=2000 as our fiducial choice). For
the parameter forecast, we use the halo model approach in Kayo, Takada & Jain
(2013) to model the non-Gaussian error covariances as well as the
cross-covariance between the power spectrum and the bispectrum, including the
halo sample variance or the nonlinear version of beat-coupling. We find that
adding the bispectrum information leads to about 60% improvement in the dark
energy figure-of-merit compared to the lensing power spectrum tomography alone,
for three redshift-bin tomography and a Subaru-type survey probing galaxies at
typical redshift of zs~1. The improvement is equivalent to a 1.6 larger survey
area. Thus our results show that the bispectrum or more generally any
three-point correlation based statistics carries complementary information on
cosmological parameters to the power spectrum. However, the improvement is
modest compared to the previous claim derived using the Gaussian error
assumption, and therefore our results imply less additional information in even
higher-order moments such as the four-point correlation function.


  The purpose of this article is to newly define the $p$-adic polylogarithm as
an equivariant class in the cohomology of a certain infinite disjoint union of
algebraic tori associated to a totally real field. We will then express the
special values of $p$-adic $L$-functions interpolating nonpositive values of
Hecke $L$-functions of the totally real field in terms of special values of
these $p$-adic polylogarithms.


  Phylogenetic invariants are certain polynomials in the joint probability
distribution of a Markov model on a phylogenetic tree. Such polynomials are of
theoretical interest in the field of algebraic statistics and they are also of
practical interest--they can be used to construct phylogenetic trees. This
paper is a self-contained introduction to the algebraic, statistical, and
computational challenges involved in the practical use of phylogenetic
invariants. We survey the relevant literature and provide some partial answers
and many open problems.


  The Poisson-Boltzmann (PB) implicit solvent model is a popular framework for
studying the electrostatics of biomolecules immersed in water with dissolved
salt. In this model the dielectric interface between the biomolecule and
solvent is often taken to be the molecular surface or solvent-excluded surface
(SES), and the quality of the SES triangulation is critical in boundary element
simulations of the PB model. In this work we compare the MSMS and NanoShaper
surface triangulation codes for a set of 38 biomolecules. While MSMS produces
triangles of exceedingly small area and large aspect ratio, the two codes yield
comparable values for the SES surface area and electrostatic solvation energy,
where the latter calculations were performed using the treecode-accelerated
boundary integral (TABI) PB solver. However we found that NanoShaper is more
efficient and reliable than MSMS, especially when parameters are set to produce
highly resolved triangulations.


  We study spatial pattern formation and energy localization in the dynamics of
an anharmonic chain with quadratic and quartic intersite potential subject to
an optical, sinusoidally oscillating field and a weak damping. The
zone-boundary mode is stable and locked to the driving field below a critical
forcing that we determine analytically using an approximate model which
describes mode interactions. Above such a forcing, a standing modulated wave
forms for driving frequencies below the band-edge, while a ``multibreather''
state develops at higher frequencies. Of the former, we give an explicit
approximate analytical expression which compares well with numerical data. At
higher forcing space-time chaotic patterns are observed.


  Recent years have seen growing interest among both researchers and
practitioners in user-engaged approaches to algorithm auditing, which directly
engage users in detecting problematic behaviors in algorithmic systems.
However, we know little about industry practitioners' current practices and
challenges around user-engaged auditing, nor what opportunities exist for them
to better leverage such approaches in practice. To investigate, we conducted a
series of interviews and iterative co-design activities with practitioners who
employ user-engaged auditing approaches in their work. Our findings reveal
several challenges practitioners face in appropriately recruiting and
incentivizing user auditors, scaffolding user audits, and deriving actionable
insights from user-engaged audit reports. Furthermore, practitioners shared
organizational obstacles to user-engaged auditing, surfacing a complex
relationship between practitioners and user auditors. Based on these findings,
we discuss opportunities for future HCI research to help realize the potential
(and the mitigate risks) of user-engaged auditing in industry practice.


  Predictive Physics has been historically based upon the development of
mathematical models that describe the evolution of a system under certain
external stimuli and constraints. The structure of such mathematical models
relies on a set of hysical hypotheses that are assumed to be fulfilled by the
system within a certain range of environmental conditions. A new perspective is
now raising that uses physical knowledge to inform the data prediction
capability of artificial neural networks. A particular extension of this
data-driven approach is Physically-Guided Neural Networks with Internal
Variables (PGNNIV): universal physical laws are used as constraints in the
neural network, in such a way that some neuron values can be interpreted as
internal state variables of the system. This endows the network with unraveling
capacity, as well as better predictive properties such as faster convergence,
fewer data needs and additional noise filtering. Besides, only observable data
are used to train the network, and the internal state equations may be
extracted as a result of the training processes, so there is no need to make
explicit the particular structure of the internal state model. We extend this
new methodology to continuum physical problems, showing again its predictive
and explanatory capacities when only using measurable values in the training
set. We show that the mathematical operators developed for image analysis in
deep learning approaches can be used and extended to consider standard
functional operators in continuum Physics, thus establishing a common framework
for both. The methodology presented demonstrates its ability to discover the
internal constitutive state equation for some problems, including heterogeneous
and nonlinear features, while maintaining its predictive ability for the whole
dataset coverage, with the cost of a single evaluation.


  We study the connection of matter density and its tracers from the PDF
perspective. One aspect of this connection is the conditional expectation value
$\langle \delta_{\mathrm{tracer}}|\delta_m\rangle$ when averaging both tracer
and matter density over some scale. We present a new way to incorporate a
Lagrangian bias expansion of this expectation value into standard frameworks
for modelling the PDF of density fluctuations and counts-in-cells statistics.
Using N-body simulations and mock galaxy catalogs we confirm the accuracy of
this expansion and compare it to the more commonly used Eulerian
parametrization. For halos hosting typical luminous red galaxies, the
Lagrangian model provides a significantly better description of $\langle
\delta_{\mathrm{tracer}}|\delta_m\rangle$ at second order in perturbations. A
second aspect of the matter-tracer connection is shot-noise, \ie the scatter of
tracer density around $\langle \delta_{\mathrm{tracer}}|\delta_m\rangle$. It is
well known that this noise can be significantly non-Poissonian and we validate
the performance of a more general, two-parameter shot-noise model for different
tracers and simulations. Both parts of our analysis are meant to pave the way
for forthcoming applications to survey data.


  In ObjectGoal navigation (ObjectNav), agents must locate specific objects
within unseen environments, requiring effective observation, prediction, and
navigation capabilities. This study found that traditional methods looking only
for prediction accuracy often compromise on computational efficiency. To
address this, we introduce "Skip-SCAR," a modular framework that enhances
efficiency by leveraging sparsity and adaptive skips. The SparseConv-Augmented
ResNet (SCAR) at the core of our approach uses sparse and dense feature
processing in parallel, optimizing both the computation and memory footprint.
Our adaptive skip technique further reduces computational demands by
selectively bypassing unnecessary semantic segmentation steps based on
environmental constancy. Tested on the HM3D ObjectNav datasets, Skip-SCAR not
only minimizes resource use but also sets new performance benchmarks,
demonstrating a robust method for improving efficiency and accuracy in robotic
navigation tasks.


  In this paper, we investigate the entropy of the free gravitational field for
a given epoch for some well-known isotropic and anisotropic cosmologies. We use
the definition of gravitational entropy proposed by Clifton, Ellis, and
Tavakol, where the 2-index square root of the 4-index Bel-Robinson tensor is
taken to be the energy momentum tensor for the free gravity. We examine
whether, in the vicinity of the initial singularity, the ratio of the energy
density of free gravity to that of matter density goes to zero, validating
Penrose's conjecture on Weyl curvature. Whenever this is true, the
gravitational entropy increases monotonically with time, leading to structure
formation. For the models considered by us, we identify the conditions for
which the Weyl curvature hypothesis is valid, and the assumptions under which
it is validated, or otherwise.


  Overlay maps of science are global base maps over which subsets of
publications can be projected. Such maps can be used to monitor, explore, and
study research through its publication output. Most maps of science, including
overlay maps, are flat in the sense that they visualize research fields at one
single level. Such maps generally fail to provide both overview and detail
about the research being analyzed. The aim of this study is to improve overlay
maps of science to provide both features in a single visualization. I created a
map based on a hierarchical classification of publications, including broad
disciplines for overview and more granular levels to incorporate detailed
information. The classification was obtained by clustering articles in a
citation network of about 17 million publication records in PubMed from 1995
onwards. The map emphasizes the hierarchical structure of the classification by
visualizing both disciplines and the underlying specialties. To show how the
visualization methodology can help getting both overview of research and
detailed information about its topical structure, I projected two overlay maps
onto the base map: (1) open access publishing and (2) coronavirus/Covid-19
research.


  In this paper we revisit $D_s \to f_0$ form factors from the light-cone sum
rules with the light meson light-cone distribution amplitudes. The main
motivation of this study is the differential decay width of $D_s \to
\left[\pi\pi \right]_{\rm S} e \nu_e$ measured recently by BESIII collaboration
and the $D_s \to f_0$ form factor extracted under the intermediate resonant
model. Our result of the differential width of $D_s^+ \to f_0 (\to \left[
\pi\pi \right]_{\rm S}) e^+ \nu_e$ decay obtained under the narrow width
approximation is a litter bit lower than the data, the result obtained under
the resonant Flatt\'e model is in consistent with the data while shows a litter
bit larger, indicating a sizable mixing $\sim 20\degree$ between ${\bar s}s$
and ${\bar u}u+{\bar d}d$ of $f_0$. In order to obtain a model independent
prediction, we suggest to calculate $D_s \to \left[ \pi\pi \right]_{\rm S}$
form factors with the isoscalar scalar dipion light-cone distribution
amplitudes. Our calculation of $D_s \to \left[ \pi\pi \right]_{\rm S}$ form
factors is carried out at the leading twist level due to the finite knowledge
of dipion system, the result of differential width shows a moderate evolution
in contrast to that obtained from the narrow width approximation and the
Flatt\'e model, revealing a bright prospect to study the four-body leptonic
decays of heavy mesons with the dimeson light-cone distribution amplitudes.


  Strongly turbulent Taylor-Couette flow with independently rotating inner and
outer cylinders with a radius ratio of \eta = 0.716 is experimentally studied.
From global torque measurements, we analyse the dimensionless angular velocity
flux Nu_\omega(Ta, a) as a function of the Taylor number Ta and the angular
velocity ratio a = -\omega_o/\omega_i in the large-Taylor-number regime 10^{11}
\lesssim Ta \lesssim 10^{13}. We analyse the data with the common power-law
ansatz for the dimensionless angular velocity transport flux Nu_\omega(Ta, a) =
f(a)Ta^\gamma, with an amplitude f(a) and an exponent \gamma. The data are
consistent with one effective exponent \gamma = 0.39\pm0.03 for all a. The
amplitude of the angular velocity flux f(a) = Nu_\omega(Ta, a)/Ta^0.39 is
measured to be maximal at slight counter-rotation, namely at an angular
velocity ratio of a_opt = 0.33\pm0.04. This value is theoretically interpreted
as the result of a competition between the destabilizing inner cylinder
rotation and the stabilizing but shear-enhancing outer cylinder
counter-rotation. With the help of laser Doppler anemometry, we provide angular
velocity profiles and identify the radial position r_n of the neutral line.
While for moderate counter-rotation -0.40 \omega_i \lesssim \omega_o < 0, the
neutral line still remains close to the outer cylinder and the probability
distribution function (p.d.f.) of the bulk angular velocity is observed to be
monomodal. For stronger counter-rotation the neutral line is pushed inwards
towards the inner cylinder; in this regime the p.d.f. of the bulk angular
velocity becomes bimodal, reflecting intermittent bursts of turbulent
structures beyond the neutral line into the outer flow domain, which otherwise
is stabilized by the counter-rotating outer cylinder. Finally, a hypothesis is
offered allowing a unifying view for all these various results.


  We propose a method to generate a real-energy flat band in a two-dimensional
(2D) non-Hermitian Lieb lattice. The coincidence of the flat band eigenstate in
both real and momentum spaces is essential for the proposed flat band, which is
flexible at the appropriate match between the synthetic magnetic flux and
non-Hermiticity. The proposed method is not limited to the 2D non-Hermitian
Lieb lattice, and is applied to the 2D non-Hermitian Tasaki's decorated square
lattice, dice lattice, and kagome lattice. Our findings make a step forward for
the flat band engineering in 2D non-Hermitian optical lattices.


  We study a statistical method to estimate the optimal value, and the
optimality gap of a given solution for stochastic optimization as an assessment
of the solution quality. Our approach is based on bootstrap aggregating, or
bagging, resampled sample average approximation (SAA). We show how this
approach leads to valid statistical confidence bounds for non-smooth
optimization. We also demonstrate its statistical efficiency and stability that
are especially desirable in limited-data situations, and compare these
properties with some existing methods. We present our theory that views SAA as
a kernel in an infinite-order symmetric statistic, which can be approximated
via bagging. We substantiate our theoretical findings with numerical results.


  From the 16th to the 20th century, Western mathematics had a dramatic impact
in China. Firstly they triggered lexical developments, e.g. in the field of
geometry. Later, in the 19the century, even though Li Shanlan had invented his
own system of algebraic notations, however Western ones were enventually
adopted.


  In this paper, we study the boundedness of the Hilbert transformation in
Lorentz function spaces, thereby complementing classical results of Boyd. We
also characterize the optimal range of a triangular truncation operator in
Schatten-Lorentz ideals. These results further entail sharp commutator
estimates and applications to operator Lipschitz functions in Schatten-Lorentz
ideals.


  We describe the basic assumptions and key results of loop quantum gravity,
which is a background independent approach to quantum gravity. The emphasis is
on the basic physical principles and how one deduces predictions from them, at
a level suitable for physicsts in other areas such as string theory, cosmology,
particle physics, astrophysics and condensed matter physics. No details are
given, but references are provided to guide the interested reader to the
literature. The present state of knowledge is summarized in a list of 35 key
results on topics including the hamiltonian and path integral quantizations,
coupling to matter, extensions to supergravity and higher dimensional theories,
as well as applications to black holes, cosmology and Plank scale
phenomenology. We describe the near term prospects for observational tests of
quantum theories of gravity and the expectations that loop quantum gravity may
provide predictions for their outcomes. Finally, we provide answers to
frequently asked questions and a list of key open problems.


  The primordial black hole (PBH) formation is studied in light of the
inflating curvaton. The typical scale of the PBH formation is determined by
curvaton inflation, which may generate PBH with $10^{14}\mathrm{g}\le
M_\mathrm{PBH}\le 10^{38}\mathrm{g}$ when curvaton inflation gives the number
of e-foldings $5\leq N_2\leq 38$. The non-Gaussianity of the inflating curvaton
does not prevent the PBH formation.


  Comprehending natural language instructions is a charming property for both
2D and 3D layout synthesis systems. Existing methods implicitly model object
joint distributions and express object relations, hindering generation's
controllability. We introduce InstructLayout, a novel generative framework that
integrates a semantic graph prior and a layout decoder to improve
controllability and fidelity for 2D and 3D layout synthesis. The proposed
semantic graph prior learns layout appearances and object distributions
simultaneously, demonstrating versatility across various downstream tasks in a
zero-shot manner. To facilitate the benchmarking for text-driven 2D and 3D
scene synthesis, we respectively curate two high-quality datasets of
layout-instruction pairs from public Internet resources with large language and
multimodal models. Extensive experimental results reveal that the proposed
method outperforms existing state-of-the-art approaches by a large margin in
both 2D and 3D layout synthesis tasks. Thorough ablation studies confirm the
efficacy of crucial design components.


  Massive graphs, such as online social networks and communication networks,
have become common today. To efficiently analyze such large graphs, many
distributed graph computing systems have been developed. These systems employ
the "think like a vertex" programming paradigm, where a program proceeds in
iterations and at each iteration, vertices exchange messages with each other.
However, using Pregel's simple message passing mechanism, some vertices may
send/receive significantly more messages than others due to either the high
degree of these vertices or the logic of the algorithm used. This forms the
communication bottleneck and leads to imbalanced workload among machines in the
cluster. In this paper, we propose two effective message reduction techniques:
(1)vertex mirroring with message combining, and (2)an additional
request-respond API. These techniques not only reduce the total number of
messages exchanged through the network, but also bound the number of messages
sent/received by any single vertex. We theoretically analyze the effectiveness
of our techniques, and implement them on top of our open-source Pregel
implementation called Pregel+. Our experiments on various large real graphs
demonstrate that our message reduction techniques significantly improve the
performance of distributed graph computation.


  We consider QCD radiative corrections to WW pair production in hadron
collisions. We perform a calculation that consistently combines next-to-leading
order predictions with soft-gluon resummation valid at small transverse momenta
ptWW of the WW pair. We present results for the ptWW distribution at the LHC up
to (almost) next-to-next-to-leading logarithmic accuracy, and study the effect
of resummation on the charged-lepton distributions. Soft-gluon effects are
typically mild, but they can be strongly enhanced when hard cuts are applied.
The relevant distributions are generally well described by the MC@NLO event
generator.


  We introduce a method for extracting spectral information from
energy-resolved light curves folded at the neutron star spin period (known as
pulse profiles) in accreting X-ray binaries. Spectra of these sources are
sometimes characterized by features superimposed on a smooth continuum, such as
iron emission lines and cyclotron resonant scattering features. We address here
the question on how to derive quantitative constraints on such features from
energy-dependent changes in the pulse profiles. We developed a robust method
for determining in each energy-selected bin the value of the pulsed fraction
using the fast Fourier transform opportunely truncated at the number of
harmonics needed to satisfactorily describe the actual profile. We determined
the uncertainty on this value by sampling through Monte Carlo simulations a
total of 1000 faked profiles. We rebinned the energy-resolved pulse profiles to
have a constant minimum signal-to-noise ratio throughout the whole energy band.
Finally we characterize the dependence of the energy-resolved pulsed fraction
using a phenomenological polynomial model and search for features corresponding
to spectral signatures of iron emission or cyclotron lines using Gaussian line
profiles. We apply our method to a representative sample of NuSTAR observations
of well-known accreting X-ray pulsars. We show that, with this method, it is
possible to characterize the pulsed fraction spectra, and to constrain the
position and widths of such features with a precision comparable with the
spectral results. We also explore how harmonic decomposition, correlation, and
lag spectra might be used as additional probes for detection and
characterization of such features.


  Sorting is fundamental and ubiquitous in modern computing systems. Hardware
sorting systems are built based on comparison operations with Von Neumann
architecture, but their performance are limited by the bandwidth between memory
and comparison units and the performance of complementary
metal-oxide-semiconductor (CMOS) based circuitry. Sort-in-memory (SIM) based on
emerging memristors is desired but not yet available due to comparison
operations that are challenging to be implemented within memristive memory.
Here we report fast and reconfigurable SIM system enabled by digit read (DR) on
1-transistor-1-resistor (1T1R) memristor arrays. We develop DR tree node
skipping (TNS) that support variable data quantity and data types, and extend
TNS with multi-bank, bit-slice and multi-level strategies to enable cross-array
TNS (CA-TNS) for practical adoptions. Experimented on benchmark sorting
datasets, our memristor-enabled SIM system presents up to 3.32x~7.70x speedup,
6.23x~183.5x energy efficiency improvement and 2.23x~7.43x area reduction
compared with state-of-the-art sorting systems. We apply such SIM system for
shortest path search with Dijkstra's algorithm and neural network inference
with in-situ pruning, demonstrating the capability in solving practical sorting
tasks and the compatibility in integrating with other compute-in-memory (CIM)
schemes. The comparison-free TNS/CA-TNS SIM enabled by memristors pushes
sorting into a new paradigm of sort-in-memory for next-generation sorting
systems.


  Choosing a suitable doping level of channel relevant to channel diameter is
considered for determining the carbon nanotube field effect transistors'
performance which seem to be the best substitute of current transistor
technology. For low diameter values of channel the ratio of on/off current
declines by increasing the doping level. But for higher diameter values there
is an optimum point of doping level in obtaining the highest on/off current
ratio. For further verification, the variations of performance are justified by
electron distribution function's changes on energy band diagram of these
devices. The results are compared at two different gate fields.


  The observation of reduced rotational inertia in a cell containing solid
helium 4 has been interpreted as evidence for superfluidity of the solid. An
alternative explanation is slippage of the solid at the container wall due to
grain boundary premelting between the solid and dense adsorbed layers at the
container wall. We calculate the range of film thickness and the viscous drag,
and find that the slippage can account for the observations.


  There has been a recent surge in single-step adversarial training as it shows
robustness and efficiency. However, a phenomenon referred to as ``catastrophic
overfitting" has been observed, which is prevalent in single-step defenses and
may frustrate attempts to use FGSM adversarial training. To address this issue,
we propose a novel method, Stable and Efficient Adversarial Training (SEAT),
which mitigates catastrophic overfitting by harnessing on local properties that
distinguish a robust model from that of a catastrophic overfitted model. The
proposed SEAT has strong theoretical justifications, in that minimizing the
SEAT loss can be shown to favour smooth empirical risk, thereby leading to
robustness. Experimental results demonstrate that the proposed method
successfully mitigates catastrophic overfitting, yielding superior performance
amongst efficient defenses. Our single-step method can reach 51% robust
accuracy for CIFAR-10 with $l_\infty$ perturbations of radius $8/255$ under a
strong PGD-50 attack, matching the performance of a 10-step iterative
adversarial training at merely 3% computational cost.


  It has long been conjectured that the Euclidean Schwarzschild and Euclidean
Kerr instantons are the only non-trivial asymptotically flat (AF) gravitational
instantons. In this letter, we show that this conjecture is false by explicitly
constructing a new two-parameter AF gravitational instanton with a U(1)xU(1)
isometry group, using the inverse-scattering method. It has Euler number \chi=3
and Hirzebruch signature \tau=1, and its global topology is CP^2 with a circle
S^1 removed appropriately. Various other properties of this gravitational
instanton are also discussed.


  Recent work has discussed the importance of multiplicative closure for the
Markov models used in phylogenetics. For continuous-time Markov chains, a
sufficient condition for multiplicative closure of a model class is ensured by
demanding that the set of rate-matrices belonging to the model class form a Lie
algebra. It is the case that some well-known Markov models do form Lie algebras
and we refer to such models as "Lie Markov models". However it is also the case
that some other well-known Markov models unequivocally do not form Lie
algebras. In this paper, we will discuss how to generate Lie Markov models by
demanding that the models have certain symmetries under nucleotide
permutations. We show that the Lie Markov models include, and hence provide a
unifying concept for, "group-based" and "equivariant" models. For each of two,
three and four character states, the full list of Lie Markov models with
maximal symmetry is presented and shown to include interesting examples that
are neither group-based nor equivariant. We also argue that our scheme is
pleasing in the context of applied phylogenetics, as, for a given symmetry of
nucleotide substitution, it provides a natural hierarchy of models with
increasing number of parameters.


  Phase-Rectified Signal Averaging (PRSA) was shown to be a powerful tool for
the study of quasi-periodic oscillations and nonlinear effects in
non-stationary signals. Here we present a bivariate PRSA technique for the
study of the inter-relationship between two simultaneous data recordings. Its
performance is compared with traditional cross-correlation analysis, which,
however, does not work well for non-stationary data and cannot distinguish the
coupling directions in complex nonlinear situations. We show that bivariate
PRSA allows the analysis of events in one signal at times where the other
signal is in a certain phase or state; it is stable in the presence of noise
and impassible to non-stationarities.


  In this paper we investigate the nature and distribution of large neutral
regions during the late epoch of reionization. In the "bubble model" of
reionization, the mass distribution of large ionized regions ("bubbles") during
the early stage of reionization is obtained by using the excursion set model,
where the ionization of a region corresponds to the first up-crossing of a
barrier by random trajectories. We generalize this idea, and develop a method
to predict the distribution of large scale neutral regions during the late
stage of reionization, taking into account the ionizing background after the
percolation of HII regions. The large scale neutral regions which we call
"neutral islands" are not individual galaxies or minihalos, but larger regions
where fewer galaxies formed and hence ionized later, and they are identified in
the excursion set model with the first down-crossings of the island barrier.
Assuming that the consumption rate of ionizing background photons is
proportional to the surface area of the neutral islands, we obtained the size
distribution of the neutral islands. We also take the "bubbles-in-island"
effect into account by considering the conditional probability of up-crossing a
bubble barrier after down-crossing the island barrier. We find that this effect
is very important. An additional barrier is set to avoid islands being
percolated through. We find that there is a characteristic scale for the
neutral islands, while the small islands are rapidly swallowed up by the
ionizing background, this characteristic scale does not change much as the
reionization proceeds.


  We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid
English information from Text Tables and Infoboxes, a hybrid question-answering
(QA) pipeline that utilizes information from heterogeneous knowledge sources,
including knowledge base, text, tables, and infoboxes. Our LLM-augmented
approach achieves state-of-the-art performance on the Compmix dataset, the most
comprehensive heterogeneous open-domain QA dataset, with 56.5% exact match (EM)
rate. More importantly, manual analysis on a sample of the dataset suggests
that SPAGHETTI is more than 90% accurate, indicating that EM is no longer
suitable for assessing the capabilities of QA systems today.


  Deep generative models parameterized by neural networks have recently
achieved state-of-the-art performance in unsupervised and semi-supervised
learning. We extend deep generative models with auxiliary variables which
improves the variational approximation. The auxiliary variables leave the
generative model unchanged but make the variational distribution more
expressive. Inspired by the structure of the auxiliary variable we also propose
a model with two stochastic layers and skip connections. Our findings suggest
that more expressive and properly specified deep generative models converge
faster with better results. We show state-of-the-art performance within
semi-supervised learning on MNIST, SVHN and NORB datasets.


  Accurate seismic velocity estimations are vital to understanding Earth's
subsurface structures, assessing natural resources, and evaluating seismic
hazards. Machine learning-based inversion algorithms have shown promising
performance in regional (i.e., for exploration) and global velocity estimation,
while their effectiveness hinges on access to large and diverse training
datasets whose distributions generally cover the target solutions.
Additionally, enhancing the precision and reliability of velocity estimation
also requires incorporating prior information, e.g., geological classes, well
logs, and subsurface structures, but current statistical or neural
network-based methods are not flexible enough to handle such multi-modal
information. To address both challenges, we propose to use conditional
generative diffusion models for seismic velocity synthesis, in which we readily
incorporate those priors. This approach enables the generation of seismic
velocities that closely match the expected target distribution, offering
datasets informed by both expert knowledge and measured data to support
training for data-driven geophysical methods. We demonstrate the flexibility
and effectiveness of our method through training diffusion models on the
OpenFWI dataset under various conditions, including class labels, well logs,
reflectivity images, as well as the combination of these priors. The
performance of the approach under out-of-distribution conditions further
underscores its generalization ability, showcasing its potential to provide
tailored priors for velocity inverse problems and create specific training
datasets for machine learning-based geophysical applications.


  We propose an approximation for the total ice nucleation rate J=J(T,R) in
supercooled water droplets as a function of both droplet radius R and
temperature T, taking account of both volume-based and surface-stimulated
nucleation modes. Its crucial idea is that, even in the surface-stimulated mode
crystal nuclei initially emerge (as sub-critical clusters) homogeneously in the
sub-surface layer, not "pseudo-heterogeneously" at the surface. This mode is
negligible in large droplets, but becomes increasingly important with
decreasing droplet size and is dominant in small droplets. The crossover
droplet radius for the transition of homogeneous ice nucleation from the
volume-based mode to the surface-stimulated mode nonmonotonically depends on T
from 233 K to 239.5 K, ranging there from about 2 microns to about 100 microns.
Using experimental data on ice nucleation rates in small droplets, we determine
that the ice-air interfacial tension of basal facets of ice crystals
monotonically increases from about 89 dyn/cm at T=234.8 K to about 91 dyn/cm at
T=236.2 K.


  It is said that the finding of the maximum oscillation in neutrino
oscillation by Super-Kamiokande is one of the major achievements of the SK. In
present paper, we examine the assumption made by Super-Kamiokande Collaboration
that the direction of the incident neutrino is approximately the same as that
of the produced lepton, which is the cornerstone in their L/E analysis and we
find this approximation does not hold even approximately. In the Part 2 of the
subsequent paper, we apply the results from Figures 12, 13 and 14 to L/E
analysis and conclude that one cannot obtain the maximum oscillation in L/E
analysis which shows strongly the oscillation pattern from the neutrino
oscillation.


  Mobile devices have access to personal, potentially sensitive data, and there
is a large number of mobile applications and third-party libraries that
transmit this information over the network to remote servers (including app
developer servers and third party servers). In this paper, we are interested in
better understanding of not just the extent of personally identifiable
information (PII) exposure, but also its context i.e., functionality of the
app, destination server, encryption used, etc.) and the risk perceived by
mobile users today. To that end we take two steps. First, we perform a
measurement study: we collect a new dataset via manual and automatic testing
and capture the exposure of 16 PII types from 400 most popular Android apps. We
analyze these exposures and provide insights into the extent and patterns of
mobile apps sharing PII, which can be later used for prediction and prevention.
Second, we perform a user study with 220 participants on Amazon Mechanical
Turk: we summarize the results of the measurement study in categories, present
them in a realistic context, and assess users' understanding, concern, and
willingness to take action. To the best of our knowledge, our user study is the
first to collect and analyze user input in such fine granularity and on actual
(not just potential or permitted) privacy exposures on mobile devices. Although
many users did not initially understand the full implications of their PII
being exposed, after being better informed through the study, they became
appreciative and interested in better privacy practices.


  Randomized pre-post designs, with outcomes measured at baseline and
follow-ups, have been commonly used to compare the clinical effectiveness of
two competing treatments. There are vast, but often conflicting, amount of
information in current literature about the best analytic methods for pre-post
design. It is challenging for applied researchers to make an informed choice.
We discuss six methods commonly used in literature: one way analysis of
variance (ANOVA), analysis of covariance main effect and interaction models on
post-treatment measurement (ANCOVA I and II), ANOVA on change score between
baseline and post-treatment measurements, repeated measures and constrained
repeated measures models (cRM) on baseline and post-treatment measurements as
joint outcomes. We review a number of study endpoints in pre-post designs and
identify the difference in post-treatment measurement as the common treatment
effect that all six methods target. We delineate the underlying differences and
links between these competing methods in homogeneous and heterogeneous study
population. We demonstrate that ANCOVA and cRM outperform other alternatives
because their treatment effect estimators have the smallest variances. cRM has
comparable performance to ANCOVA I main effect model in homogeneous scenario
and to ANCOVA II interaction model in heterogeneous scenario. In spite of that,
ANCOVA has several advantages over cRM, including treating baseline measurement
as covariate because it is not an outcome by definition, the convenience of
incorporating other baseline variables and handling complex heteroscedasticity
patterns in a linear regression framework.


  We introduce the H-type deviation $\delta({\mathbb G})$ of a step two Carnot
group ${\mathbb G}$, which measures the deviation of the group from the class
of Heisenberg-type groups. We show that $\delta({\mathbb G})=0$ if and only if
${\mathbb G}$ carries a vertical metric which endows it with the structure of
an H-type group. We compute the H-type deviation for several naturally
occurring families of step two groups. In addition, we provide analytic
expressions which are comparable to the H-type deviation. As a consequence, we
establish new analytic characterizations for the class of H-type groups. For
instance, denoting by $N(g)=(||x||_h^4+16||t||_v^2)^{1/4}$, $g=\exp(x+t)$, the
canonical Kaplan-type quasi-norm in a step two group ${\mathbb G}$ with taming
Riemannian metric $g_h\oplus g_v$, we show that ${\mathbb G}$ is H-type if and
only if $||\nabla_0 N(g)||_h^2=||x||_h^2/N(g)^2$ for all $g\ne 0$. Similarly,
we show that ${\mathbb G}$ is H-type if and only if $N^{2-Q}$ is ${\mathcal
L}$-harmonic in ${\mathbb G} \setminus \{0\}$. Here $\nabla_0$ denotes the
horizontal differential operator, ${\mathcal L}$ the canonical sub-Laplacian,
and $Q = \dim{\mathfrak v}_1+2\dim{\mathfrak v}_2$ the homogeneous dimension of
${\mathbb G}$, where ${\mathfrak v}_1\oplus{\mathfrak v}_2$ is the
stratification of the Lie algebra. It is well-known that H-type groups satisfy
both of these analytic conclusions. The new content of these results lies in
the converse directions. Motivation for this work comes from a longstanding
conjecture regarding polarizable Carnot groups. We formulate a quantitative
stability conjecture regarding the fundamental solution for the sub-Laplacian
on step two Carnot groups. Its validity would imply that all step two
polarizable groups admit an H-type group structure. We confirm this conjecture
for a sequence of anisotropic Heisenberg groups.


  In this White Paper for Nancy Grace Roman Space Telescope (Roman) science, we
propose the Roman Survey of the Earth Transit Zone (RoSETZ), a transit search
for rocky planets within the habitable zones (HZs) of stars located within the
Earth Transit Zone (ETZ). The ETZ holds special interest in the search for
extra-terrestrial intelligence (SETI) - observers on planets within the ETZ can
see Earth as a transiting planet. RoSETZ would augment the Roman Galactic Bulge
Time Domain Survey (GBTDS) as an additional field located $\sim 5$~degrees away
from other GBTDS fields. Our simulations show that RoSETZ alone can find from
120 to 630 Earth-sized HZ planets around K- and M-type hosts, with the range
reflecting different survey design assumptions. These yields are 5-20 times the
number currently known. Such a sample will transform our knowledge of
``Eta-Earth'' ($\eta_{\oplus}$) -- the occurrence of Earth-sized HZ planets --
and would be the first catalogue of exoplanets selected in a manner optimized
according to the Mutual Detectability targetted-SETI strategy. If it can be
accommodated alongside the existing GBTDS design, we favour a RoSETZ-Max design
that is observed for the duration of the GBTDS. If not, we show that a
slimmed-down RoSETZ-Lite design, occupying two GBTDS seasons, would not
significantly impact overall GBTDS exoplanet yields, even if time allocated to
it had to come from time allocations to other fields. We argue that the angular
separation of RoSETZ from other GBTDS fields permits self-calibration of
systematic uncertainties that would otherwise hamper exoplanet demographic
modelling of both microlensing and transit datasets. Other science possible
with RoSETZ data include studies of small solar system bodies and high
resolution 3D extinction mapping.


  This representative study of German search engine users (N=1,000) focuses on
the ability of users to distinguish between organic results and advertisements
on Google results pages. We combine questions about Google's business with
task-based studies in which users were asked to distinguish between ads and
organic results in screenshots of results pages. We find that only a small
percentage of users is able to reliably distinguish between ads and organic
results, and that user knowledge of Google's business model is very limited. We
conclude that ads are insufficiently labelled as such, and that many users may
click on ads assuming that they are selecting organic results.


  The dimmest and most numerous outlier of the Type Ia supernova population,
Type Iax events, is increasingly being found in the results of observational
campaigns. There is currently no single accepted model to describe these
events. This 2D study explores the viability of modeling Type Iax events as a
hybrid C/O/Ne white dwarf progenitor undergoing a deflagration using the
multi-physics software FLASH. This hybrid was created using the stellar
evolution code MESA, and its C-depleted core and mixed structure have
demonstrated lower yields than traditional C/O progenitors in previous
deflagration-to-detonation studies. To generate a sample, 30 "realizations" of
this simulation were performed, the only difference being the shape of the
initial matchhead used to start the deflagration. As consistent with earlier
work, these realizations produce the familiar hot dense bound remnant
surrounded by sparse ejecta. Our results indicate the majority of the star
remains unburned (~70%) and bound (>90%). Our realizations produce total ejecta
yields on the order of 10$^{-2}$ - 10$^{-1}$ solar masses, ejected $^{56}$Ni
yields on the order of 10$^{-4}$ - 10$^{-2}$ solar masses, and ejecta kinetic
energies on the order of 10$^{48}$ - 10$^{49}$ ergs. Compared to yields
inferred from recent observations of the dimmest Type Iax events - SN 2007qd,
SN 2008ha, SN 2010ae, SN 2019gsc, SN 2019muj, SN 2020kyg, and SN 2021fcg - our
simulation produces comparable $^{56}$Ni yields, but too-small total yields and
kinetic energies. Reignition of the remnant is also seen in some realizations.


  Distributed learning algorithms aim to leverage distributed and diverse data
stored at users' devices to learn a global phenomena by performing training
amongst participating devices and periodically aggregating their local models'
parameters into a global model. Federated learning is a promising paradigm that
allows for extending local training among the participant devices before
aggregating the parameters, offering better communication efficiency. However,
in the cases where the participants' data are strongly skewed (i.e., non-IID),
the local models can overfit local data, leading to low performing global
model. In this paper, we first show that a major cause of the performance drop
is the weighted distance between the distribution over classes on users'
devices and the global distribution. Then, to face this challenge, we leverage
the edge computing paradigm to design a hierarchical learning system that
performs Federated Gradient Descent on the user-edge layer and Federated
Averaging on the edge-cloud layer. In this hierarchical architecture, we
formalize and optimize this user-edge assignment problem such that edge-level
data distributions turn to be similar (i.e., close to IID), which enhances the
Federated Averaging performance. Our experiments on multiple real-world
datasets show that the proposed optimized assignment is tractable and leads to
faster convergence of models towards a better accuracy value.


  We propose a new method for detecting changes in Markov network structure
between two sets of samples. Instead of naively fitting two Markov network
models separately to the two data sets and figuring out their difference, we
\emph{directly} learn the network structure change by estimating the ratio of
Markov network models. This density-ratio formulation naturally allows us to
introduce sparsity in the network structure change, which highly contributes to
enhancing interpretability. Furthermore, computation of the normalization term,
which is a critical bottleneck of the naive approach, can be remarkably
mitigated. We also give the dual formulation of the optimization problem, which
further reduces the computation cost for large-scale Markov networks. Through
experiments, we demonstrate the usefulness of our method.


  We study the noncommutative Poincar\'e duality between the Poisson homology
and cohomology of unimodular Poisson algebras, and show that Kontsevich's
deformation quantization as well as Koszul duality preserve the corresponding
Poincar\'e duality. As a corollary, the Batalin-Vilkovisky algebra structures
that naturally arise in these cases are all isomorphic.


  Applications of Trigger Units (TU) can be found in almost all accelerators at
CERN. The requirements in terms of operating frequencies, configuration or
modes of operation change from one application to another, how-ever, in terms
of design requirements for the Trigger Unit, the operating frequency is
probably the most demanding one. In this work, we present an implementation of
a Trigger Unit almost fully embedded in the FPGA logic operating at a maximum
frequency of 1 GHz using the internal serializer/deserializer circuitry to
simplify the timing constraints of the design. This implementation allows easy
reconfiguration of the module and the development of new modes of operation,
which are described in this paper.


  In order to plan for failure recovery, the designers of cloud systems need to
understand how their system can potentially fail. Unfortunately, analyzing the
failure behavior of such systems can be very difficult and time-consuming, due
to the large volume of events, non-determinism, and reuse of third-party
components. To address these issues, we propose a novel approach that joins
fault injection with anomaly detection to identify the symptoms of failures. We
evaluated the proposed approach in the context of the OpenStack cloud computing
platform. We show that our model can significantly improve the accuracy of
failure analysis in terms of false positives and negatives, with a low
computational cost.


  This paper proposes a data-driven approach to detect the switching actions
and topology transitions in distribution networks. It is based on the real time
analysis of time-series voltages measurements. The analysis approach draws on
data from high-precision phasor measurement units ($\mu$PMUs or synchrophasors)
for distribution networks. The key fact is that time-series measurement data
taken from the distribution network has specific patterns representing state
transitions such as topology changes. The proposed algorithm is based on
comparison of actual voltage measurements with a library of signatures derived
from the possible topologies simulation. The IEEE 33-bus model is used for the
algorithm validation.


  The Dynkin algebras are the hereditary artin algebras of finite
representation type. The paper exhibits the number of support-tilting modules
for any Dynkin algebra. Since the support-tilting modules for a Dynkin algebra
correspond bijectively to the non-crossing partitions of the same type, the
calculations presented here may also be considered as a categorification of
results concerning non-crossing partitions.


  We study the quantization of the motion of a charged particle without spin
inside a flat box under a static electromagnetic field. Contrary to Landau's
solution with constant magnetic field transverse to the box, we found a non
separable variables solution for the wave function, and this fact remains when
static electric field is added. However, the Landau's Levels appear in all
cases.


  We study inclusive $\chi_{cJ}$ production with definite polarizations in
polarized proton-proton collisions at $\sqrt{s}$ = 200 GeV and 500 GeV at RHIC
by using non-relativistic QCD (NRQCD) color-octet mechanism. We present results
of rapidity distribution of $\chi_{c0}$, $\chi_{c1}$ and $\chi_{c2}$ production
with specific polarizations in polarized p-p collisions at RHIC within the
PHENIX detector acceptance range. We also present the corresponding results for
the spin asymmetries.


  In person re-identification (ReID), very recent researches have validated
pre-training the models on unlabelled person images is much better than on
ImageNet. However, these researches directly apply the existing self-supervised
learning (SSL) methods designed for image classification to ReID without any
adaption in the framework. These SSL methods match the outputs of local views
(e.g., red T-shirt, blue shorts) to those of the global views at the same time,
losing lots of details. In this paper, we propose a ReID-specific pre-training
method, Part-Aware Self-Supervised pre-training (PASS), which can generate
part-level features to offer fine-grained information and is more suitable for
ReID. PASS divides the images into several local areas, and the local views
randomly cropped from each area are assigned with a specific learnable [PART]
token. On the other hand, the [PART]s of all local areas are also appended to
the global views. PASS learns to match the output of the local views and global
views on the same [PART]. That is, the learned [PART] of the local views from a
local area is only matched with the corresponding [PART] learned from the
global views. As a result, each [PART] can focus on a specific local area of
the image and extracts fine-grained information of this area. Experiments show
PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on
various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves
92.2\%/90.2\%/88.5\% mAP accuracy on Market1501 for supervised/UDA/USL ReID.
Our codes are available at https://github.com/CASIA-IVA-Lab/PASS-reID.