  Chalcogen vacancies in the semiconducting monolayer transition-metal
dichalcogenides (TMDs) have frequently been invoked to explain a wide range of
phenomena, including both unintentional p-type and n-type conductivity, as well
as sub-band gap defect levels measured via tunneling or optical spectroscopy.
These conflicting interpretations of the deep versus shallow nature of the
chalcogen vacancies are due in part to shortcomings in prior first-principles
calculations of defects in the semiconducting two-dimensional (2D) TMDs that
have been used to explain experimental observations. Here we report results of
hybrid density functional calculations for the chalcogen vacancy in a series of
monolayer TMDs, correctly referencing the thermodynamic charge transition
levels to the fundamental band gap (as opposed to the optical band gap). We
find that the chalcogen vacancies are deep acceptors and cannot lead to n-type
or p-type conductivity. Both the (0/$-1$) and ($-$1/$-$2) transition levels
occur in the gap, leading to paramagnetic charge states S=1/2 and S=1,
respectively, in a collinear-spin representation. We discuss trends in terms of
the band alignments between the TMDs, which can serve as a guide to future
experimental studies of vacancy behavior.


  We study ergodic properties of a family of traffic maps acting in the space
of bi-infinite sequences of real numbers. The corresponding dynamics mimics the
motion of vehicles in a simple traffic flow, which explains the name. Using
connections to topological Markov chains we obtain nontrivial invariant
measures, prove their stochastic stability, and calculate the topological
entropy. Technically these results in the deterministic setting are related to
the construction of measures of maximal entropy via measures uniformly
distributed on periodic points of a given period, while in the random setting
we directly construct (spatially) Markov invariant measures. In distinction to
conventional results the limiting measures in non-lattice case are non-ergodic.
Average velocity of individual ``vehicles'' as a function of their density and
its stochastic stability is studied as well.


  Clathrin mediated endocytosis (CME) is an ubiquitous cellular pathway that
regulates central aspects of cell physiology such as nutrient uptake,
modulation of signal transduction, synaptic transmission and membrane
turn-over. Endocytic vesicle formation depends on the timed production of
specific phosphoinositides and their interactions with various endocytic
proteins. Recently, it has been found that
phosphatidylinositol-3,4-bisphosphate (PI(3,4)P2) produced by the class II
phosphatidylinositol 3-kinase C2alpha plays a key role in the recruitment of
the PX-BAR domain protein SNX9, which is proposed to play a role in the
constriction of the endocytic vesicle neck [Posor et al, Nature 499, p233
(2013)]. Interestingly, SNX9 and its close paralog SNX18 are not fully specific
to PI(3,4)P2 but can also bind other phospholipids, in particular to PI(4,5)P2,
an abundant plasma membrane lipid required for the recruitment of many
endocytic proteins. In order to understand the dynamical interplay between
phospholipids and endocytic proteins, we developed a computational model of the
temporal changes in the population of the phosphoinositide-associated endocytic
proteins and their spatial distribution at a clathrin-coated pit (CCP). The
model resolves single molecules in time and space, and incorporates the complex
interplay of proteins and lipids, as well as their movement within the CCP. We
find that the comparably small differences in lipid binding affinities of
endocytic proteins are amplified by competition among them, allowing for the
selective enrichment of SNX9 at late stage CCPs as a result of timed PI(3,4)P2
production.


  Extensive analyses of statistical strong gravitational lensing are performed
based on the final Cosmic Lens All Sky Survey (CLASS) well-defined statistical
sample of flat spectrum radio sources and current estimates of galaxy
luminosity functions per morphological type. The analyses are done under the
assumption that galactic lenses are well-approximated by singular isothermal
ellipsoids and early-type galaxies evolved passively since redshift $z \sim 1$.
Depending on how the late-type galaxy population is treated (i.e., whether its
characteristic velocity dispersion is constrained or not), we find for a flat
universe with a cosmological constant that the present matter fraction of the
present critical density $\Omega_{\rm m} = 0.31^{+0.27}_{-0.14}$ (68%) for the
unconstrained case or $0.40^{+0.28}_{-0.16}$ (68%) for the constrained case,
with an additional systematic uncertainty of $\approx 0.11$ arising from the
present uncertainty in the distribution of CLASS sources in redshift and flux
density. For a flat universe with a constant equation of state for dark energy
w = $p_x$(pressure)/$\rho_x$(energy density), we find that $w <
-0.55^{+0.18}_{-0.11}$ (68%) for the unconstrained case or $w <
-0.41^{+0.28}_{-0.16}$ (68%) for the constrained case. For the equal
frequencies of oblates and prolates, we find that $\sigma_{*}^{(e)} =
198^{+22}_{-18}$ km s$^{-1}$ (68%) for a `steep' $\alpha^{(e)}=-1$ or
$\sigma_{*}^{(e)} = 181^{+18}_{-15}$ km s$^{-1}$ (68%) for a `shallow'
$\alpha^{(e)}=-0.54$. Finally, from the relative frequencies of doubly-imaged
sources and quadruply-imaged sources, we find that a mean projected mass
ellipticity of early-type galaxies $\bar{\epsilon}_{\rm mass} = 0.42$ with a
68% lower limit of 0.28. (Abridged)


  In this paper, we consider massive charged Dirac fields propagating in the
exterior region of de Sitter-Reissner-Nordstr\"om black holes.We show that the
parameters of such black holes are uniquely determined by the partial knowledge
of the corresponding scattering operator $S(\lambda)$ at a fixed energy
$\lambda$.More precisely, we consider the partial wave scattering operators
$S(\lambda,n)$ (here $\lambda \in \mathbb{R}$ is the energy and $n \in
\mathbb{N}^{\star}$ denotes the angular momentum) defined as the restrictions
of the full scattering operator on a well chosen basis of spin-weighted
spherical harmonics.We prove that the knowledge of the scattering operators
$S(\lambda,n)$, for all $n \in \mathcal{L}$, where $\mathcal{L}$ is a subset of
$\mathbb{N}^{\star}$ that satisfies the M\"untz condition $\sum\_{n \in
\mathcal{L}} \frac{1}{n} = + \infty$, allows to recover the mass, the charge
and the cosmological constant of a dS-RN black hole.The main tool consists in
the complexification of the angular momentum $n$ and in studying the analytic
properties of the "unphysical" corresponding data in the complex variable $z$.


  This study examines a human-based approach for knowledge retention that is
evolving through various knowledge sharing channels in a low-technology
environment with a strong emphasis on social networks in a loosely-coupled
inter-organisational government-industry collaboration focused on regional
sustainability. Using social network analysis combined with interview and
observational analysis, our results show that a combination of close-knit
community ties and group interaction promote the development of strong personal
networks that provide continued access to group memory to retain the groups
knowledge.


  Object detection and tracking in videos represent essential and
computationally demanding building blocks for current and future visual
perception systems. In order to reduce the efficiency gap between available
methods and computational requirements of real-world applications, we propose
to re-think one of the most successful methods for image object detection,
Faster R-CNN, and extend it to the video domain. Specifically, we extend the
detection framework to learn instance-level embeddings which prove beneficial
for data association and re-identification purposes. Focusing on the
computational aspects of detection and tracking, our proposed method reaches a
very high computational efficiency necessary for relevant applications, while
still managing to compete with recent and state-of-the-art methods as shown in
the experiments we conduct on standard object tracking benchmarks


  This paper addresses particular eigenvalue problems within the context of two
quaternionic function theories. More precisely, we study two concrete classes
of quaternionic eigenvalue problems, the first one for the slice derivative
operator in the class of quaternionic slice-regular functions and the second
one for the Cauchy-Riemann-Fueter operator in the class of axially monogenic
functions. The two problems are related to each other by the four-dimensional
Laplace operator and Fueter's Theorem. As an application of a particular case
of second order eigenvalue problems, we obtain a representation of axially
monogenic solutions for time-harmonic Helmholtz and stationary Klein-Gordon
equations.


  In a tight-binding model of AA-stacked bilayer graphene, it is demonstrated
that a bound defect state within the region of continuous spectrum can exist
stably with respect to variations in the strength of a perpendicular magnetic
field. This is accomplished by creating a defect that is compatible with the
interlayer coupling, thereby shielding the bound state from the effects of the
continuous spectrum, which varies erratically in a pattern known as the
Hofstadter butterfly.


  We present the Anti-Lambda hyperon global polarization in Au+Au collisions at
sqrt(s_NN) = 62 GeV and 200 GeV measured with the STAR detector at RHIC. The
observed Anti-Lambda hyperon global polarization is consistent with zero, what
is in agreement with recent measurements of Lambda global polarization, as well
as phi(1020) and K*0(892) vector mesons spin alignment with respect to the
reaction plane. The possible dependence of the global polarization on relative
azimuthal angle between the orbital momentum of the system and the hyperon
3-momentum is discussed. The corresponding systematic uncertainty due to
detector acceptance is found to be less than 20%.


  The spectrum of linearized excitations of the Type IIB SUGRA on AdS5xS5
contains both unitary and non-unitary representations. Among the non-unitary,
some are finite-dimensional. We explicitly construct the pure spinor vertex
operators for a family of such finite-dimensional representations. The
construction can also be applied to infinite-dimensional representations,
including unitary, although it becomes in this case somewhat less explicit.


  Structural strain severely impacts material properties such as the linear and
non-linear optical response. Moreover, strain plays a key role, e.g., in the
physics of ferroelectrics and in particular of their domain walls. $\mu$-Raman
spectroscopy is a well-suited technique for the investigation of such strain
effects, as it allows to measure the lattice dynamics locally. However,
quantifying and reconstructing strain fields from Raman maps requires knowledge
on the strain dependence of phonon frequencies. In this work, we have analyzed
both theoretically and experimentally the phonon frequencies in the widely used
ferroelectrics lithium niobate and lithium tantalate as a function of uniaxial
strain via density functional theory and $\mu$-Raman spectroscopy. Overall, we
find a good agreement between our $ab$ $initio$ models and the experimental
data performed with a stress cell. The majority of phonons show an increase in
frequency under compressive strain, while the opposite is observed for tensile
strains. Moreover, for E-type phonons, we observe the lifting of degeneracy
already at moderate strain fields (i.e. at $\pm0.2~\%$) along the x and y
directions. This work hence allows for the systematic analysis of 3D strains in
modern-type bulk and thin-film devices assembled from lithium niobate and
tantalate.


  We give an alternate derivation of Weldon's formula for combining products of
factors with non identical analytic behavior. While such a formula would appear
to be useful in finite temperature calculations, we give an example of a zero
temperature calculation, namely, the degenerate electron gas, to justify the
result.


  In complex Langevin simulations, the insufficient decay of the probability
density near infinity leads to boundary terms that spoil the formal argument
for correctness. We present a formulation of this term that is cheaply
measurable in lattice models, and in principle allows also the direct
estimation of the systematic error of the CL method. Results for a toy model,
3d XY model and HDQCD are presented.


  This paper presents a new method for unsupervised segmentation of complex
activities from video into multiple steps, or sub-activities, without any
textual input. We propose an iterative discriminative-generative approach which
alternates between discriminatively learning the appearance of sub-activities
from the videos' visual features to sub-activity labels and generatively
modelling the temporal structure of sub-activities using a Generalized Mallows
Model. In addition, we introduce a model for background to account for frames
unrelated to the actual activities. Our approach is validated on the
challenging Breakfast Actions and Inria Instructional Videos datasets and
outperforms both unsupervised and weakly-supervised state of the art.


  For the ring R of integers of a ramified extension of the field of p-adic
numbers and a cyclic group G of prime order p we study the extensions of the
additive groups of R-representations modules of G by the group G.


  Event-based video reconstruction has garnered increasing attention due to its
advantages, such as high dynamic range and rapid motion capture capabilities.
However, current methods often prioritize the extraction of temporal
information from continuous event flow, leading to an overemphasis on
low-frequency texture features in the scene, resulting in over-smoothing and
blurry artifacts. Addressing this challenge necessitates the integration of
conditional information, encompassing temporal features, low-frequency texture,
and high-frequency events, to guide the Denoising Diffusion Probabilistic Model
(DDPM) in producing accurate and natural outputs. To tackle this issue, we
introduce a novel approach, the Temporal Residual Guided Diffusion Framework,
which effectively leverages both temporal and frequency-based event priors. Our
framework incorporates three key conditioning modules: a pre-trained
low-frequency intensity estimation module, a temporal recurrent encoder module,
and an attention-based high-frequency prior enhancement module. In order to
capture temporal scene variations from the events at the current moment, we
employ a temporal-domain residual image as the target for the diffusion model.
Through the combination of these three conditioning paths and the temporal
residual framework, our framework excels in reconstructing high-quality videos
from event flow, mitigating issues such as artifacts and over-smoothing
commonly observed in previous approaches. Extensive experiments conducted on
multiple benchmark datasets validate the superior performance of our framework
compared to prior event-based reconstruction methods.


  We consider two models of interacting DNA molecules: \textit{First} is (four
parametric) bubble coalescence model in interacting DNAs (shortly: BCI-DNA).
\textit{Second} is (three parametric) bubble coalescence model in a condensed
DNA molecules (shortly BCC-DNA). To study bubble coalescence thermodynamics of
BCI-DNA and BCC-DNA models we use methods of statistical physics. Namely, we
define Hamiltonian of each model and give their translation-invariant Gibbs
measures (TIGMs). For the first model we find parameters such that
corresponding Hamiltonian has up to three TIGMs (three phases of system)
biologically meaning existence of three states: ``No bubble coalescence'',
``Dominated soft zone'', ``Bubble coalescence''. For the second model we show
that for any (admissible) parameters this model has unique TIGM. This is a
state where ``No bubble coalescence'' phase dominates.


  Alzheimer's disease (AD) is the most common age-related dementia. Mild
cognitive impairment (MCI) is the early stage of cognitive decline before AD.
It is crucial to predict the MCI-to-AD conversion for precise management, which
remains challenging due to the diversity of patients. Previous evidence shows
that the brain network generated from diffusion MRI promises to classify
dementia using deep learning. However, the limited availability of diffusion
MRI challenges the model training. In this study, we develop a self-supervised
contrastive learning approach to generate structural brain networks from
routine anatomical MRI under the guidance of diffusion MRI. The generated brain
networks are applied to train a learning framework for predicting the MCI-to-AD
conversion. Instead of directly modelling the AD brain networks, we train a
graph encoder and a variational autoencoder to model the healthy ageing
trajectories from brain networks of healthy controls. To predict the MCI-to-AD
conversion, we further design a recurrent neural networks based approach to
model the longitudinal deviation of patients' brain networks from the healthy
ageing trajectory. Numerical results show that the proposed methods outperform
the benchmarks in the prediction task. We also visualize the model
interpretation to explain the prediction and identify abnormal changes of white
matter tracts.


  We investigate the large scale evolution of a relativistic magnetic
reconnection in an electron-positron pair plasma by a relativistic two-fluid
magnetohydrodynamic (MHD) code. We introduce an inter-species friction force as
an effective resistivity to dissipate magnetic fields. We demonstrate that
magnetic reconnection successfully occurs in our two-fluid system, and that it
involves Petschek-type bifurcated current layers in later stage. We further
observe a quasi-steady evolution thanks to an open boundary condition, and find
that the Petschek-type structure is stable over the long time period.
Simulation results and theoretical analyses exhibit that the Petschek outflow
channel becomes narrower when the reconnection inflow contains more magnetic
energy, as previously claimed. Meanwhile, we find that the reconnection rate
goes up to ~1 in extreme cases, which is faster than previously thought. The
role of the resistivity, implications for reconnection models in the
magnetically dominated limit, and relevance to kinetic reconnection works are
discussed.


  We present first results from a LOFAR census of non-recycled pulsars. The
census includes almost all such pulsars known (194 sources) at declinations
${\rm Dec}> 8^\circ$ and Galactic latitudes $|{\rm Gb}| > 3^\circ$, regardless
of their expected flux densities and scattering times. Each pulsar was observed
for $\geq 20$ minutes in the contiguous frequency range of 110--188 MHz.
Full-Stokes data were recorded. We present the dispersion measures, flux
densities, and calibrated total intensity profiles for the 158 pulsars detected
in the sample. The median uncertainty in census dispersion measures ($1.5
\times 10^{-3}$ pc cm$^{-3}$) is ten times smaller, on average, than in the
ATNF pulsar catalogue. We combined census flux densities with those in the
literature and fitted the resulting broadband spectra with single or broken
power-law functions. For 48 census pulsars such fits are being published for
the first time. Typically, the choice between single and broken power-laws, as
well as the location of the spectral break, were highly influenced by the
spectral coverage of the available flux density measurements. In particular,
the inclusion of measurements below 100 MHz appears essential for investigating
the low-frequency turnover in the spectra for most of the census pulsars. For
several pulsars, we compared the spectral indices from different works and
found the typical spread of values to be within 0.5--1.5, suggesting a
prevailing underestimation of spectral index errors in the literature. The
census observations yielded some unexpected individual source results, as we
describe in the paper. Lastly, we will provide this unique sample of wide-band,
low-frequency pulse profiles via the European Pulsar Network Database.


  In this paper,we have found the equations of motion of Generalized
Photogravitational restricted three body problem with Poynting-Robertson drag.
The problem is generalized in the sense that smaller primary is supposed to be
an oblate spheroid. The bigger primary is considered as radiating. The
equations of motion are affected by radiation pressure force oblateness and P-R
drag. We have located triangular equilibrium points in our problem. All
classical results involving photogravitational and oblateness in restricted
three body problem may be verified from this result.


  Prediction of new drug-target interactions is extremely important as it can
lead the researchers to find new uses for old drugs and to realize the
therapeutic profiles or side effects thereof. However, experimental prediction
of drug-target interactions is expensive and time-consuming. As a result,
computational methods for prediction of new drug-target interactions have
gained much interest in recent times. We present iDTI-ESBoost, a prediction
model for identification of drug-target interactions using evolutionary and
structural features. Our proposed method uses a novel balancing technique and a
boosting technique for the binary classification problem of drug-target
interaction. On four benchmark datasets taken from a gold standard data,
iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under
Receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms
the latest and the best-performing method in the literature to-date in terms of
area under precision recall (auPR) curve. This is significant as auPR curves
are argued to be more appropriate as a metric for comparison for imbalanced
datasets, like the one studied in this research. In the sequel, our experiments
establish the effectiveness of the classifier, balancing methods and the novel
features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction
method that has for the first time exploited the structural features along with
the evolutionary features to predict drug-protein interactions. We believe the
excellent performance of iDTI-ESBoost both in terms of auROC and auPR would
motivate the researchers and practitioners to use it to predict drug-target
interactions. To facilitate that, iDTI-ESBoost is readily available for use at:
http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/


  The Belinski-Khalatnikov-Lifshitz (BKL) conjecture predicts a chaotic
alternation of Kasner epochs in the evolution of generic classical spacetimes
towards a spacelike singularity. As a first step towards understanding the full
quantum BKL scenario, we analyze a vacuum Bianchi II model with local
rotational symmetry, which presents just one Kasner transition. During the
Kasner epochs, the quantum state is coherent and it is thus characterized by
constant values of the different quantum fluctuations, correlations and
higher-order moments. By computing the constants of motion of the system we
provide, for any peaked semiclassical state, the explicit analytical transition
rules that relate the parametrization of the asymptotic coherent state before
and after the transition. In particular, we obtain the modification of the
transition rules for the classical variables due to quantum back-reaction
effects. This analysis is performed by considering a high-order truncation in
moments (the full computations are performed up to fifth-order, which
corresponds to neglecting terms of an order $\hbar^3$), providing a solid
estimate about the quantum modifications to the classical model. Finally, in
order to understand the dynamics of the state during the transition, we perform
some numerical simulations for an initial Gaussian state, that show that the
initial and final equilibrium values of the quantum variables are connected by
strong and rapid oscillations.


  In two previous papers we have analyzed the C-metric in a background with a
cosmological constant, namely the de Sitter (dS) C-metric, and the anti-de
Sitter (AdS) C-metric, following the work of Kinnersley and Walker for the flat
C-metric. These exact solutions describe a pair of accelerated black holes in
the flat or cosmological constant background, with the acceleration A being
provided by a strut in-between that pushes away the two black holes. In this
paper we analyze the extremal limits of the C-metric in a background with
generic cosmological constant. We follow a procedure first introduced by
Ginsparg and Perry in which the Nariai solution, a spacetime which is the
direct topological product of the 2-dimensional dS and a 2-sphere, is generated
from the four-dimensional dS-Schwarzschild solution by taking an appropriate
limit, where the black hole event horizon approaches the cosmological horizon.
Similarly, one can generate the Bertotti-Robinson metric from the
Reissner-Nordstrom metric by taking the limit of the Cauchy horizon going into
the event horizon of the black hole, as well as the anti-Nariai by taking an
appropriate solution and limit. Using these methods we generate the C-metric
counterparts of the Nariai, Bertotti-Robinson and anti-Nariai solutions, among
others. One expects that the solutions found in this paper are unstable and
decay into a slightly non-extreme black hole pair accelerated by a strut or by
strings. Moreover, the Euclidean version of these solutions mediate the quantum
process of black hole pair creation, that accompanies the decay of the dS and
AdS spaces.


  We exhibit some simple gadgets useful in designing shallow parallel circuits
for quantum algorithms. We prove that any quantum circuit composed entirely of
controlled-not gates or of diagonal gates can be parallelized to logarithmic
depth, while circuits composed of both cannot. Finally, while we note the
Quantum Fourier Transform can be parallelized to linear depth, we exhibit a
simple quantum circuit related to it that we believe cannot be parallelized to
less than linear depth, and therefore might be used to prove that QNC < QP.


  We generalize the well-known inequality that the limit of the $L^p$ norm of a
function as $p\rightarrow\infty$ is the $L^\infty$ norm to the scale of Orlicz
spaces.


  In this work we develop a theoretical framework for the localization of flow
in the steadily flowing regime of sheared disordered solids with inertial
dynamics on a microscopic scale. To this aim we perform rheology studies at
fixed shear rate on a 3D model of dense disordered solid. Our particle based
simulations reveal the existence of heterogeneous shear-profiles in the
stationary flow under homogeneous driving conditions. To rationalize this
result, we propose a continuum model that couples the dynamics of the local
flow to the evolution of a kinetic temperature field. A linear stability
analysis of this theory predicts the minimum system size necessary for the flow
instability to develop. This prediction as well as the velocity profiles
obtained from this continuum model are in good agreement with the results from
the particle based simulations.


  In the present work the generation of zonal flows in collisionless trapped
electron mode (TEM) turbulence is studied analytically. A reduced model for TEM
turbulence is utilized based on an advanced fluid model for reactive drift
waves. An analytical expression for the zonal flow growth rate is derived and
compared with the linear TEM growth, and its scaling with plasma parameters is
examined for typical tokamak parameter values.


  The literature on void superlattice formation observed under irradation of
metals and insulators is analyzed with a special attention to the
self-organization; a general scenario of this process is discussed. A simple
relation for the superlattice parameter as a function of the dose rate and
temperature is suggested, in a good agreement with existing experimental data.
Special attention is paid to analysis of the halogen gas void formation in an
electron-irradiated CaF2


  Apart from the known weak gravitational lensing effect, the cosmic
magnification acquires relativistic corrections owing to Doppler, integrated
Sachs-Wolfe, time-delay and other (local) gravitational potential effects,
respectively. These corrections grow on very large scales and high redshifts z,
which will be the reach of forthcoming surveys. In this work, these
relativistic corrections are investigated in the magnification angular power
spectrum, using both (standard) noninteracting dark energy (DE), and
interacting DE (IDE). It is found that for noninteracting DE, the relativistic
corrections can boost the magnification large-scale power by ~ 40% at z = 3,
and increases at lower z. It is also found that the IDE effect is sensitive to
the relativistic corrections in the magnification power spectrum, particularly
at low z---which will be crucial for constraints on IDE. Moreover, the results
show that if relativistic corrections are not taken into account, this may lead
to an incorrect estimate of the large-scale imprint of IDE in the cosmic
magnification; including the relativistic corrections can enhance the true
potential of the cosmic magnification as a cosmological probe.


  We solve the problem of determining the fundamental degrees of freedom
underlying a generalized K\"ahler structure of symplectic type. For a usual
K\"ahler structure, it is well-known that the geometry is determined by a
complex structure, a K\"ahler class, and the choice of a positive $(1,1)$-form
in this class, which depends locally on only a single real-valued function: the
K\"ahler potential. Such a description for generalized K\"ahler geometry has
been sought since it was discovered in 1984. We show that a generalized
K\"ahler structure of symplectic type is determined by a pair of holomorphic
Poisson manifolds, a holomorphic symplectic Morita equivalence between them,
and the choice of a positive Lagrangian brane bisection, which depends locally
on only a single real-valued function, which we call the generalized K\"ahler
potential. Our solution draws upon, and specializes to, the many results in the
physics literature which solve the problem under the assumption (which we do
not make) that the Poisson structures involved have constant rank. To solve the
problem we make use of, and generalize, two main tools: the first is the notion
of symplectic Morita equivalence, developed by Weinstein and Xu to study
Poisson manifolds; the second is Donaldson's interpretation of a K\"ahler
metric as a real Lagrangian submanifold in a deformation of the holomorphic
cotangent bundle.


  Coupling frequently enhances noise-induced coherence and synchronization in
interacting nonlinear systems, but it does so separately. In principle
collective stochastic coherence and synchronizability are incompatible
phenomena, since strongly synchronized elements behave identically and thus
their response to noise is indistinguishable to that of a single element.
Therefore one can expect systems that synchronize well to have a poor
collective response to noise. Here we show that, in spite of this apparent
conflict, a certain coupling architecture is able to reconcile the two
properties. Specifically, our results reveal that weighted scale-free networks
of diffusively coupled excitable elements exhibit both high synchronizability
of their subthreshold dynamics and a good collective response to noise of their
pulsed dynamics. This is established by comparing the behavior of this system
to that of random, regular, and unweighted scale-free networks. We attribute
the optimal response of weighted scale-free networks to the link homogeneity
(with respect to node degree) provided by the weighting procedure, which
balances the degree heterogeneity typical of SFNs.


  This report summarises the results of the four-fermion working group of the
LEP2-MC workshop, held at CERN from 1999 to 2000. Recent developments in the
calculation of four-fermion processes in electron-positron collisions at LEP-2
centre-of-mass energies are presented, concentrating on predictions for four
main reactions: W-pair production, visible photons in four-fermion events,
single-W production and Z-pair production. Based on a comparison of results
derived within different approaches, theoretical uncertainties on these
predictions are established.


  The Golden (British Columbia, Canada) meteorite fall occurred on Oct 4, 2021
at 0534 UT with the first recovered fragment (1.3 kg) landing on an occupied
bed. The meteorite is an unbrecciated, low-shock (S2) ordinary chondrite of
intermediate composition, typed as an L/LL5. From noble gas measurements the
cosmic ray exposure age is 25 Ma while gas retention ages are all >2 Ga.
Short-lived radionuclides and noble gas measurements of the pre-atmospheric
size overlap with estimates from infrasound and lightcurve modelling producing
a preferred pre-atmospheric mass of 70-200 kg. The orbit of Golden has a high
inclination (23.5 degs) and is consistent with delivery from the inner main
belt. The highest probability (60%) of an origin is from the Hungaria group. We
propose that Golden may originate among the background S-type asteroids found
interspersed in the Hungaria region. The current collection of 18 L and LL
chondrite orbits shows a strong preference for origins in the inner main belt,
suggesting multiple parent bodies may be required to explain the diversity in
CRE ages and shock states.


  In this paper, we investigate the photon sphere, shadow radius and
quasinormal modes of a four-dimensional charged Einstein-Gauss-Bonnet black
hole. The perturbation of a massless scalar field in the black hole's
background is adopted. The quasinormal modes are gotten by the $6th$ order WKB
approximation approach and shadow radius, respectively. When the value of the
Gauss-Bonnet coupling constant increase, the values of the real parts of the
quasinormal modes increase and those of the imaginary parts decrease. The
coincidence degrees of quasinormal modes derived by the two approaches
increases with the increase of the values of the Gauss-Bonnet coupling constant
and multiple number. It shows the correspondence between the shadow and test
field in the four-dimensional Einstein-Gauss-Bonnet-Maxwell gravity. The radii
of the photon sphere and shadow increase with the decrease of the Gauss-Bonnet
coupling constant.


  MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information that needs to be protected.
Whereas MRI metadata is easily sanitized, MRI image data is a privacy risk
because it contains information to render highly-realistic 3D visualizations of
a patient's head, enabling malicious actors to possibly identify the subject by
cross-referencing a database. Data anonymization and de-identification is
concerned with ensuring the privacy and confidentiality of individuals'
personal information. Traditional MRI de-identification methods remove
privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This comes at
the expense of introducing a domain shift that can throw off downstream
analyses. In this work, we propose CP-MAE, a model that de-identifies the face
by remodeling it (e.g. changing the face) rather than by removing parts using
masked autoencoders. CP-MAE outperforms all previous approaches in terms of
downstream task performance as well as de-identification. With our method we
are able to synthesize high-fidelity scans of resolution up to $256^3$ --
compared to $128^3$ with previous approaches -- which constitutes an eight-fold
increase in the number of voxels.


  Existing language model compression methods mostly use a simple L2 loss to
distill knowledge in the intermediate representations of a large BERT model to
a smaller one. Although widely used, this objective by design assumes that all
the dimensions of hidden representations are independent, failing to capture
important structural knowledge in the intermediate layers of the teacher
network. To achieve better distillation efficacy, we propose Contrastive
Distillation on Intermediate Representations (CoDIR), a principled knowledge
distillation framework where the student is trained to distill knowledge
through intermediate layers of the teacher via a contrastive objective. By
learning to distinguish positive sample from a large set of negative samples,
CoDIR facilitates the student's exploitation of rich information in teacher's
hidden layers. CoDIR can be readily applied to compress large-scale language
models in both pre-training and finetuning stages, and achieves superb
performance on the GLUE benchmark, outperforming state-of-the-art compression
methods.


  The concept of gyrogroups, with a weaker algebraic structure without
associative law, was introduced under the background of $c$-ball of
relativistically admissible velocities with Einstein velocity addition. A
topological gyrogroup is just a gyrogroup endowed with a compatible topology
such that the multiplication is jointly continuous and the inverse is
continuous. This concept is a good generalization of a topological group. In
this paper, we are going to establish that for a locally compact admissible
$L$-subgyrogroup $H$ of a strongly topological gyrogroup $G$, the natural
quotient mapping $\pi$ from $G$ onto the quotient space $G/H$ has some nice
local properties, such as, local compactness, local pseudocompactness, local
paracompactness, etc. Finally, we prove that each locally paracompact strongly
topological gyrogroup is paracompact.


  Let {\rho} the density matrix of a mixed Gaussian state. Assuming that one of
the Robertson--Schr\"odinger uncertainty inequalities is saturated by {\rho},
e.g.
({\Delta}^{{\rho}}X_1)^2({\Delta}^{{\rho}}P_1)^2={\Delta}^{{\rho}}(X_1,P_1)^2+(1/4)\hbar^2,
we show that there exists a unique pure Gaussian state whose Wigner
distribution is dominated by that of {\rho} and having the same variances and
covariance {\Delta}^{{\rho}}X_1,{\Delta}^{{\rho}}P_1, and
{\Delta}^{{\rho}}(X_1,P_1) as {\rho}. This property can be viewed as an
analytic version of Gromov's non-squeezing theorem in the linear case, which
implies that the intersection of a symplectic ball by a single plane of
conjugate coordinates determines the radius of this ball.


  Deep learning-based scene text detection can achieve preferable performance,
powered with sufficient labeled training data. However, manual labeling is time
consuming and laborious. At the extreme, the corresponding annotated data are
unavailable. Exploiting synthetic data is a very promising solution except for
domain distribution mismatches between synthetic datasets and real datasets. To
address the severe domain distribution mismatch, we propose a synthetic-to-real
domain adaptation method for scene text detection, which transfers knowledge
from synthetic data (source domain) to real data (target domain). In this
paper, a text self-training (TST) method and adversarial text instance
alignment (ATA) for domain adaptive scene text detection are introduced. ATA
helps the network learn domain-invariant features by training a domain
classifier in an adversarial manner. TST diminishes the adverse effects of
false positives~(FPs) and false negatives~(FNs) from inaccurate pseudo-labels.
Two components have positive effects on improving the performance of scene text
detectors when adapting from synthetic-to-real scenes. We evaluate the proposed
method by transferring from SynthText, VISD to ICDAR2015, ICDAR2013. The
results demonstrate the effectiveness of the proposed method with up to 10%
improvement, which has important exploration significance for domain adaptive
scene text detection. Code is available at
https://github.com/weijiawu/SyntoReal_STD


  The main results of this paper provide an Efficient Polynomial-Time
Approximation Scheme (EPTAS) for approximating the genus (and non-orientable
genus) of dense graphs. By dense we mean that $|E(G)|\ge \alpha |V(G)|^2$ for
some fixed $\alpha>0$. While a constant factor approximation is trivial for
this class of graphs, approximations with factor arbitrarily close to 1 need a
sophisticated algorithm and complicated mathematical justification. More
precisely, we provide an algorithm that for a given (dense) graph $G$ of order
$n$ and given $\varepsilon>0$, returns an integer $g$ such that $G$ has an
embedding into a surface of genus $g$, and this is $\varepsilon$-close to a
minimum genus embedding in the sense that the minimum genus $\mathsf{g}(G)$ of
$G$ satisfies: $\mathsf{g}(G)\le g\le (1+\varepsilon)\mathsf{g}(G)$. The
running time of the algorithm is $O(f(\varepsilon)\,n^2)$, where $f(\cdot)$ is
an explicit function. Next, we extend this algorithm to also output an
embedding (rotation system) whose genus is $g$. This second algorithm is an
Efficient Polynomial-time Randomized Approximation Scheme (EPRAS) and runs in
time $O(f_1(\varepsilon)\,n^2)$.


  A simple methodology for evaluating the dynamic-range of gas avalanche
detectors in the laboratory is presented and discussed. It comprises two tools:
a charge injector of tunable gain which transfers radiation-induced amplified
electron swarms to the investigated detector to mimic events with well defined
primary-ionization spectra, and a systematic approach for measuring the
detector's discharge probability. The methodology, applicable to a broad range
of detectors, is applied here for instability studies in various single-stage
THGEM and THGEM-WELL structures. The results indicate upon a somewhat larger
attainable dynamic range in a single-stage THGEM operated with additional
multiplication in the induction gap.


  A new mechanism to form a magnetic pressure supported, high temperature
corona above the photosphere of an accretion disk is explored using three
dimensional radiation magneto-hydrodynamic (MHD) simulations. The thermal
properties of the disk are calculated self-consistently by balancing radiative
cooling through the surfaces of the disk with heating due to dissipation of
turbulence driven by magneto-rotational instability (MRI). As has been noted in
previous work, we find the dissipation rate per unit mass increases
dramatically with height above the mid-plane, in stark contrast to the
alpha-disk model which assumes this quantity is a constant. Thus, we find that
in simulations with a low surface density (and therefore a shallow
photosphere), the fraction of energy dissipated above the photosphere is
significant (about 3.4% in our lowest surface density model), and this fraction
increases as surface density decreases. When a significant fraction of the
accretion energy is dissipated in the optically thin photosphere, the gas
temperature increases substantially and a high temperature, magnetic pressure
supported corona is formed. The volume-averaged temperature in the disk corona
is more than 10 times larger than at the disk mid-plane. Moreover, gas
temperature in the corona is strongly anti-correlated with gas density, which
implies the corona formed by MRI turbulence is patchy. This mechanism to form
an accretion disk corona may help explain the observed relation between the
spectral index and luminosity from AGNs, and the soft X-ray excess from some
AGNs. It may also be relevant to spectral state changes in X-ray binaries.


  (Abridged) We consider the properties of protoplanetary discs that are
undergoing inside-out clearing by photoevaporation. In particular, we aim to
characterise the conditions under which a protoplanetary disc may undergo
`thermal sweeping', a rapid (< 1e4 years) disc destruction mechanism proposed
to occur when a clearing disc reaches sufficiently low surface density at its
inner edge and where the disc is unstable to runaway penetration by the X-rays.
We use a large suite of 1D radiation-hydrodynamic simulations to probe the
observable parameter space, which is unfeasible in higher dimensions. These
models allow us to determine the surface density at which thermal sweeping will
take over the disc's evolution and to evaluate this critical surface density as
a function of X-ray luminosity, stellar mass and inner hole radius. We find
that this critical surface density scales linearly with X-ray luminosity,
increases with inner hole radius and decreases with stellar mass and we develop
an analytic model that reproduces these results.
  This surface density criterion is then used to determine the evolutionary
state of protoplanetary discs at the point that they become unstable to
destruction by thermal sweeping. We find that transition discs created by
photoevaporation will undergo thermal sweeping when their inner holes reach
20-40 AU, implying that transition discs with large holes and no accretion
(which were previously a predicted outcome of the later stages of all flavours
of photoevaporation model) will not form.
  We emphasise that the surface density criteria that we have developed apply
to all situations where the disc develops an inner hole that is optically thin
to X-rays. It thus applies not only to the case of holes originally created by
photoevaporation but also to holes formed, for example, by the tidal influence
of planets.


  Rare kaon decays offer a powerful tool for investigating new physics in $s\to
d$ transitions. Currently, many of the interesting decay modes are either
measured with rather large uncertainties compared to their theoretical
predictions or have not yet been observed. The future HIKE programme at CERN
will provide unprecedented sensitivity to rare kaon decays, allowing for strong
constraints on new physics scenarios with lepton flavour universality
violation. We present the overall picture that emerges from a study of the
different decay modes with a global analysis considering projections based on
the HIKE programme, both with and without KOTO-II future measurements. We also
highlight the most relevant decays and identify that in addition to the "golden
channel", $K^+\to\pi^+\nu\bar\nu$, the rare $K_L\to\pi^0 \ell\bar\ell$ decay,
especially in the electron sector offers strong constraints on short-distance
physics.


  The multiplicity dependence of the production of the charm mesons in p$-$p
collisions at $\sqrt{s} = 7$ TeV and 13 TeV as measured by ALICE experiment has
been investigated using Pythia 8 event generator by studying the effect of
various processes at partonic level such as the effect of different modes of
color reconnections and rope hadronization. The relative yields ($\rm
Yield/\langle Yield \rangle$) of D-mesons and $J/\psi$ as a function of
relative charged particle multiplicity for various transverse momentum
($p\textsubscript{T}$) ranges as measured by the ALICE experiment are in
reasonable agreement with the estimations of Pythia 8 model within the
framework of microscopic processes. The relative yields of B mesons for various
$p\textsubscript{T}$ intervals ($1 < p_{T} < 20$ GeV/$c$) have also been
predicted in p$-$p collisions at $\sqrt{s} = 7$ TeV and $\sqrt{s} = 13$ TeV.


  By means of an experimental dataset, we use deep learning to implement an RGB
extrapolation of emotions associated to color, and do a mathematical study of
the results obtained through this neural network. In particular, we see that
males typically associate a given emotion with darker colors while females with
brighter colors. A similar trend was observed with older people and
associations to lighter colors. Moreover, through our classification matrix, we
identify which colors have weak associations to emotions and which colors are
typically confused with other colors.


  Let $A$ be a finite-dimensional local algebra over an algebraically closed
field, let $J$ be the radical of $A.$ The modules we are interested in are the
finitely generated left $A$-modules. Projective modules are always reflexive,
and an algebra is self-injective iff all modules are reflexive. We discuss the
existence of non-projective reflexive module in case $A$ is not self-injective.
We assume that $A$ is short (this means that $J^3 = 0$). In a joint paper with
Zhang Pu, it has been shown that 6 is the smallest possible dimension of $A$
that can occur and that in this case the following conditions have to be
satisfied: $J^2$ is both the left socle and the right socle of $A$ and there is
no uniform ideal of length 3. The present paper is devoted to show the
converse.


  We compute the Hochschild homology and cohomology, and cyclic homology, of
almost Calabi-Yau algebras for SU(3) ADE graphs. These almost Calabi-Yau
algebras are a higher rank analogue of the pre-projective algebras for Dynkin
diagrams, which are SU(2)-related constructions. The Hochschild (co)homology
and cyclic homology of A can be regarded as invariants for the braided
subfactors associated to the SU(3) modular invariants.


  Point set registration is the process of finding the best alignment between
two point sets, and it is a common task in different domains, especially in the
automotive and mobile robotics domains. Lots of approaches are proposed in the
literature, where the iterative closest point ICP is a well-known approach in
this vein, which builds an explicit correspondence between both point sets to
achieve the registration task. However, this work is interested in achieving
the registration without building any explicit correspondence between both
point sets, following a probabilistic framework.
  The most critical task in point set registration is how to elaborate the cost
function, which measures the distance between both point sets. The
probabilistic framework includes two possible ways to build the cost function:
The summing and the likelihood. The main focus of this work is to analyze and
compare the behavior of both approaches. Therefore, a 1D synthetic scenario is
used to build the cost function step by step, besides the estimation error.
Finally, this work uses two data sets for evaluation: A 2D synthetic data set
and a real data set. The evaluation process compares and analyzes the
estimation error and estimated uncertainty. Thus, two different methods are
used in the evaluation process: The normalized estimation error squared NEES
and noncredibility index NCI. A 77 GHz automotive Doppler radar provides the
real data set, and in the real evaluation, we evaluate the ego-motion
estimation of a robot as an application for the registration.


  Let $R$ be a commutative Noetherian ring and $M$ be a finitely generated
$R$-module. Considering the new concept of linkage of ideals over a module, we
study associated prime ideals, cofiniteness and Artinianness of local
cohomology modules of $M$ with respect to some linked ideals over it.


  We explicate a procedure to solve general linear differential equations,
which connects the desired solutions to monomials x^m of an appropriate degree
m. In the process the underlying symmetry of the equations under study, as well
as that of the solutions are made transparent. We demonstrate the efficacy of
the method by showing the common structure of the solution space of a wide
variety of differential equations viz. Hermite, Laguerre, Jocobi, Bessel and
hypergeometric etc. We also illustrate the use of the procedure to develop
approximate solutions, as well as in finding solutions of many particle
interacting systems.


  Prompt-based tuning for pre-trained language models (PLMs) has shown its
effectiveness in few-shot learning. Typically, prompt-based tuning wraps the
input text into a cloze question. To make predictions, the model maps the
output words to labels via a verbalizer, which is either manually designed or
automatically built. However, manual verbalizers heavily depend on
domain-specific prior knowledge and human efforts, while finding appropriate
label words automatically still remains challenging.In this work, we propose
the prototypical verbalizer (ProtoVerb) which is built directly from training
data. Specifically, ProtoVerb learns prototype vectors as verbalizers by
contrastive learning. In this way, the prototypes summarize training instances
and are able to enclose rich class-level semantics. We conduct experiments on
both topic classification and entity typing tasks, and the results demonstrate
that ProtoVerb significantly outperforms current automatic verbalizers,
especially when training data is extremely scarce. More surprisingly, ProtoVerb
consistently boosts prompt-based tuning even on untuned PLMs, indicating an
elegant non-tuning way to utilize PLMs. Our codes are avaliable at
https://github.com/thunlp/OpenPrompt.


  In this paper we provide quantitative Bloom type estimates for iterated
commutators of fractional integrals improving and extending results from a work
of Holmes, Rahm and Spencer. We give new proofs for those inequalities relying
upon a new sparse domination that we provide as well in this paper and also in
techniques developed in a recent paper due to Lerner, Ombrosi and the third
author. We extend as well the necessity established in the work of Holmes, Rahm
and Spencer to iterated commutators providing a new proof. As a consequence of
the preceding results we recover the one weight estimates in works of
Cruz-Uribe and Moen and B\'enyi, Martell, Moen, Stachura, Torres and establish
the sharpness in the iterated case. Our result provides as well a new
characterization of the BMO space.


  In this paper, we consider the Yamabe equation on a complete noncompact
Riemannian manifold and find some geometric conditions on the manifold such
that the Yamabe problem admits a bounded positive solution.


  The large--scale structure (LSS) in the Universe comprises a complicated
filamentary network of matter. We study this network using a high--resolution
simulation of structure formation of a $\Lambda$ Cold Dark Matter cosmology. We
investigate the distribution of matter between neighbouring large haloes whose
masses are comparable to massive clusters of galaxies. We identify a total of
228 filaments between neighbouring clusters. Roughly half of the filaments are
either warped or lie off the cluster--cluster axis. We find that straight
filaments on the average are shorter than warped ones. More massive clusters
are connected to more filaments than less massive ones on average. This finding
indicates that the most massive clusters form at the intersections of the
filamentary backbone of LSS. For straight filaments, we compute mass profiles.
Radial profiles show a fairly well--defined radius, $r_s$, beyond which the
profiles follow an $r^{-2}$ power law fairly closely. For the majority of
filaments, $r_s$ lies between 1.5 $h^{-1}$ Mpc and 2.0 $h^{-1}$ Mpc. The
enclosed overdensity inside $r_s$ varies between a few times up to 25 times
mean density, independent of the length of the filaments. Along the filaments'
axes, material is not distributed uniformly. Towards the clusters, the density
rises, indicating the presence of the cluster infall regions. In addition, we
also find some sheet--like connections between clusters. In roughly a fifth of
all cluster--cluster connections where we could not identify a filament or
sheet, projection effects lead to filamentary structures in the projected mass
distribution. (abridged)


  Public security vulnerability reports (e.g., CVE reports) play an important
role in the maintenance of computer and network systems. Security companies and
administrators rely on information from these reports to prioritize tasks on
developing and deploying patches to their customers. Since these reports are
unstructured texts, automatic information extraction (IE) can help scale up the
processing by converting the unstructured reports to structured forms, e.g.,
software names and versions and vulnerability types. Existing works on
automated IE for security vulnerability reports often rely on a large number of
labeled training samples. However, creating massive labeled training set is
both expensive and time consuming. In this work, for the first time, we propose
to investigate this problem where only a small number of labeled training
samples are available. In particular, we investigate the performance of
fine-tuning several state-of-the-art pre-trained language models on our small
training dataset. The results show that with pre-trained language models and
carefully tuned hyperparameters, we have reached or slightly outperformed the
state-of-the-art system on this task. Consistent with previous two-step process
of first fine-tuning on main category and then transfer learning to others as
in [7], if otherwise following our proposed approach, the number of required
labeled samples substantially decrease in both stages: 90% reduction in
fine-tuning from 5758 to 576,and 88.8% reduction in transfer learning with 64
labeled samples per category. Our experiments thus demonstrate the
effectiveness of few-sample learning on NER for security vulnerability report.
This result opens up multiple research opportunities for few-sample learning
for security vulnerability reports, which is discussed in the paper. Code:
https://github.com/guanqun-yang/FewVulnerability.


  The enrichment in s-process elements of barium stars is known to be due to
pollution by mass transfer from a companion formerly on the thermally-pulsing
asymptotic giant branch (AGB), now a carbon-oxygen white-dwarf (WD). This paper
investigates the relationship between the s-process enrichment in the barium
star and the mass of its WD companion. It is expected that helium WDs, which
have masses smaller than about 0.5 Msun and never reached the AGB phase, should
not pollute with s-process elements their giant companion, which should thus
never turn into a barium star. Spectra with a resolution of R ~ 86000 were
obtained with the HERMES spectrograph on the 1.2-m Mercator telescope for a
sample of 11 binary systems involving WD companions of various masses. We use
standard 1D LTE MARCS model atmospheres coupled with the Turbospectrum
radiative-transfer code to derive the atmospheric parameters using equivalent
widths of FeI and Fe II lines. The abundances of s-process elements for the
entire sample of 11 binary stars were derived homogeneously. The sample
encompasses all levels of overabundances: from solar [s/Fe]=0 to 1.5 dex in the
2 binary systems with S-star primaries (for which dedicated MARCS model
atmospheres were used). The primary components of binary systems with a WD more
massive than 0.5 Msun are enriched in s-process elements. We also found a trend
of increasing [s/Fe] with [C/Fe] or [(C+N)/Fe]. Our results conform to the
expectation that binary systems with WD companions less massive than 0.5 Msun
do not host barium stars.


  The $k$-means algorithm is one of the most popular and critical techniques in
data mining and machine learning, and it has achieved significant success in
numerous science and engineering domains. Computing $k$-means to a global
optimum is NP-hard in Euclidean space, yet there are a variety of efficient
heuristic algorithms, such as Lloyd's algorithm, that converge to a local
optimum with superpolynomial complexity in the worst case. Motivated by the
emergence and prominence of mixed precision capabilities in hardware, a current
trend is to develop low and mixed precision variants of algorithms in order to
improve the runtime and energy consumption. In this paper we study the
numerical stability of Lloyd's $k$-means algorithm, and, in particular, we
confirm the stability of the widely used distance computation formula. We
propose a mixed-precision framework for $k$-means computation and investigate
the effects of low-precision distance computation within the framework. Through
extensive simulations on various data clustering and image segmentation tasks,
we verify the applicability and robustness of the mixed precision $k$-means
method. We find that, in $k$-means computation, normalized data is more
tolerant to the reduction of precision in the distance computation, while for
nonnormalized data more care is needed in the use of reduced precision, mainly
to avoid overflow. Our study demonstrates the potential for the use of mixed
precision to accelerate the $k$-means computation and offers some insights into
other distance-based machine learning methods.


  The electrostatic perturbations in an unmagnetized, non-isothermal
($T_i{\ll}T_e$) electron-ion plasma shear flow are considered. New physical
effects, arising due to the non-normality of linear dynamics, are described. It
is shown that the velocity shear induces the extraction of the mean flow energy
by the acoustic perturbations (ion-sound waves). The influence of the medium
dispersion, rising due to the violation of quasineutrality for perturbations,
is examined. It is shown that in the course of the evolution ion-sound waves
turn into ion plasma oscillations. New class of nonperiodic, electrostatic
perturbations (with vortical motion of ion component), characterized by the
intense energy exchange with the mean flow, is also described.


  Egocentric vision holds great promises for increasing access to visual
information and improving the quality of life for people with visual
impairments, with object recognition being one of the daily challenges for this
population. While we strive to improve recognition performance, it remains
difficult to identify which object is of interest to the user; the object may
not even be included in the frame due to challenges in camera aiming without
visual feedback. Also, gaze information, commonly used to infer the area of
interest in egocentric vision, is often not dependable. However, blind users
often tend to include their hand either interacting with the object that they
wish to recognize or simply placing it in proximity for better camera aiming.
We propose localization models that leverage the presence of the hand as the
contextual information for priming the center area of the object of interest.
In our approach, hand segmentation is fed to either the entire localization
network or its last convolutional layers. Using egocentric datasets from
sighted and blind individuals, we show that the hand-priming achieves higher
precision than other approaches, such as fine-tuning, multi-class, and
multi-task learning, which also encode hand-object interactions in
localization.


  Equations of motion with delays naturally emerge in the analysis of complex
biological control systems which are organized around biochemically mediated
feedback interactions. We study the properties of a Mackey-Glass-type nonlinear
map with delay -- the deterministic part of the stochastic cerebral blood flow
map (CBFM) recently introduced to elucidate the scaling properties of cerebral
hemodynamics. We point out the existence of deterministic and nondeterministic
subspaces in the reconstructed phase space of delay-difference equations and
discuss the problem of detection of nonlinearities in time series generated by
such dynamical systems.


  Due to the more complicated population dynamics of the NSGA-II, none of the
existing runtime guarantees for this algorithm is accompanied by a non-trivial
lower bound. Via a first mathematical understanding of the population dynamics
of the NSGA-II, that is, by estimating the expected number of individuals
having a certain objective value, we prove that the NSGA-II with suitable
population size needs $\Omega(Nn\log n)$ function evaluations to find the
Pareto front of the OneMinMax problem and $\Omega(Nn^k)$ evaluations on the
OneJumpZeroJump problem with jump size $k$. These bounds are asymptotically
tight (that is, they match previously shown upper bounds) and show that the
NSGA-II here does not even in terms of the parallel runtime (number of
iterations) profit from larger population sizes. For the OneJumpZeroJump
problem and when the same sorting is used for the computation of the crowding
distance contributions of the two objectives, we even obtain a runtime estimate
that is tight including the leading constant.


  Our goal is to enable machine learning systems to be trained interactively.
This requires models that perform well and train quickly, without large amounts
of hand-labeled data. We take a step forward in this direction by borrowing
from weak supervision (WS), wherein models can be trained with noisy sources of
signal instead of hand-labeled data. But WS relies on training downstream deep
networks to extrapolate to unseen data points, which can take hours or days.
Pre-trained embeddings can remove this requirement. We do not use the
embeddings as features as in transfer learning (TL), which requires fine-tuning
for high performance, but instead use them to define a distance function on the
data and extend WS source votes to nearby points. Theoretically, we provide a
series of results studying how performance scales with changes in source
coverage, source accuracy, and the Lipschitzness of label distributions in the
embedding space, and compare this rate to standard WS without extension and TL
without fine-tuning. On six benchmark NLP and video tasks, our method
outperforms WS without extension by 4.1 points, TL without fine-tuning by 12.8
points, and traditionally-supervised deep networks by 13.1 points, and comes
within 0.7 points of state-of-the-art weakly-supervised deep networks-all while
training in less than half a second.


  The standard model weak interactions can be described by four-fermion V-A
operators at low energies. New physics at the TeV scale can, however, generate
the other Lorentz structures. In this talk, we review the constraints on such
interactions from nuclear and hadronic decays, as well as from collider
searches. Currently the most stringent bounds come from the analysis of the 0+
to 0+ nuclear and the pi to e nu gamma radiative pion decays. In the near
future, the ultracold neutron beta decay experiments and the direct LHC
measurements will compete in setting the most stringent bounds, provided,
however, that the neutron-to-proton non-perturbative transition matrix elements
can be calculated to a level of 10-20% accuracy.


  We consider Nahm's equations on a bounded open interval with first order
poles at the ends. By imposing further boundary conditions, extended moduli
spaces are identified with spaces of rational maps from
$\mathbb{C}\mathrm{P}^1$ to $\mathbb{C}\mathrm{P}^n$. Having the residues at
one end define sums of irreducible representations of $\mathfrak{su}(2)$, the
dimensions of those summands correspond to holomorphic charge on the rational
map side. Technical difficulties arise due to half powers in the boundary
conditions. Further, a symplectomorphic identification with moduli spaces
corresponding to spaces of rational maps into complete flag varieties is given.


  We provide a geometric solution to the problem of optimal relay positioning
to maximize the multicast rate for low-SNR networks. The networks we consider,
consist of a single source, multiple receivers and the only intermediate and
locatable node as the relay. We construct network the hypergraph of the system
nodes from the underlying information theoretic model of low-SNR regime that
operates using superposition coding and FDMA in conjunction (which we call the
"achievable hypergraph model"). We make the following contributions. 1) We show
that the problem of optimal relay positioning maximizing the multicast rate can
be completely decoupled from the flow optimization by noticing and exploiting
geometric properties of multicast flow. 2) All the flow maximizing the
multicast rate is sent over at most two paths, in succession. The relay
position is dependent only on one path (out of the two), irrespective of the
number of receiver nodes in the system. Subsequently, we propose simple and
efficient geometric algorithms to compute the optimal relay position. 3)
Finally, we show that in our model at the optimal relay position, the
difference between the maximized multicast rate and the cut-set bound is
minimum. We solve the problem for all (Ps,Pr) pairs of source and relay
transmit powers and the path loss exponent \alpha greater than 2.


  We deal with connected $k$-regular multigraphs of order $n$ that has only
three distinct eigenvalues. In this paper, we study the largest possible number
of vertices of such a graph for given $k$. For $k=2,3,7$, the Moore graphs are
largest. For $k\ne 2,3,7,57$, we show an upper bound $n\leq k^2-k+1$, with
equality if and only if there exists a finite projective plane of order $k-1$
that admits a polarity.


  Contending hate speech in social media is one of the most challenging social
problems of our time. There are various types of anti-social behavior in social
media. Foremost of them is aggressive behavior, which is causing many social
issues such as affecting the social lives and mental health of social media
users. In this paper, we propose an end-to-end ensemble-based architecture to
automatically identify and classify aggressive tweets. Tweets are classified
into three categories - Covertly Aggressive, Overtly Aggressive, and
Non-Aggressive. The proposed architecture is an ensemble of smaller subnetworks
that are able to characterize the feature embeddings effectively. We
demonstrate qualitatively that each of the smaller subnetworks is able to learn
unique features. Our best model is an ensemble of Capsule Networks and results
in a 65.2% F1 score on the Facebook test set, which results in a performance
gain of 0.95% over the TRAC-2018 winners. The code and the model weights are
publicly available at
https://github.com/parthpatwa/Hater-O-Genius-Aggression-Classification-using-Capsule-Networks.


  We analyse the testability of the type II radiative seesaw in which neutrino
mass and dark matter (DM) are related at one-loop level. Under the constraints
from DM relic density, direct and indirect detection, and invisible Higgs
decays, we find three possible regions of DM mass $M_{s_1}$ that can survive
the present and even the future experiments: (1) the Higgs resonance region
with $M_{s_1}\sim M_h/2$, (2) the Higgs region with $M_{s_1}\sim M_h$, and (3)
the coannihilation region with $M_{s_2}\sim M_{s_1}$. Here $s_{1,2}$ are two
scalar singlets with the lighter $s_1$ being the DM candidate. Based on DM
properties and direct collider constraints, we choose three benchmark points to
illustrate the testability of this model at LHC. We perform a detailed
simulation of the four-lepton and tri-lepton signatures at 13 (14) TeV LHC.
While both signatures are found to be promising at all benchmark points, the
tri-lepton one is even better: it is possible to reach the $5\sigma$
significance with an integrated luminosity of 100/fb.


  Wireless Sensor Networks (WSNs) have recently attracted greater attention
worldwide due to their practicality in monitoring, communicating, and reporting
specific physical phenomena. The data collected by WSNs is often inaccurate as
a result of unavoidable environmental factors, which may include noise, signal
weakness, or intrusion attacks depending on the specific situation. Sending
high-noise data has negative effects not just on data accuracy and network
reliability, but also regarding the decision-making processes in the base
station. Anomaly detection, or outlier detection, is the process of detecting
noisy data amidst the contexts thus described. The literature contains
relatively few noise detection techniques in the context of WSNs, particularly
for outlier-detection algorithms applying time series analysis, which considers
the effective neighbors to ensure a global-collaborative detection. Hence, the
research presented in this paper is intended to design and implement a global
outlier-detection approach, which allows us to find and select appropriate
neighbors to ensure an adaptive collaborative detection based on time-series
analysis and entropy techniques. The proposed approach applies a random forest
algorithm for identifying the best results. To measure the effectiveness and
efficiency of the proposed approach, a comprehensive and real scenario provided
by the Intel Berkeley Research lab has been simulated. Noisy data have been
injected into the collected data randomly. The results obtained from the
experiment then conducted experimentation demonstrate that our approach can
detect anomalies with up to 99% accuracy.


  We prove that the double covers of the alternating and symmetric groups are
determined by their complex group algebras. To be more precise, let $n\geq 5$
be an integer, $G$ a finite group, and let $\AAA$ and $\SSS^\pm$ denote the
double covers of $\Al_n$ and $\Sy_n$, respectively. We prove that $\CC G\cong
\CC \AAA$ if and only if $G\cong \AAA$, and $\CC G\cong \CC
\SSS^+\cong\CC\SSS^-$ if and only if $G\cong \SSS^+$ or $\SSS^-$. This in
particular completes the proof of a conjecture proposed by the second and
fourth authors that every finite quasi-simple group is determined uniquely up
to isomorphism by the structure of its complex group algebra. The known results
on prime power degrees and relatively small degrees of irreducible (linear and
projective) representations of the symmetric and alternating groups together
with the classification of finite simple groups play an essential role in the
proofs.


  The conversational recommender systems (CRSs) have received extensive
attention in recent years. However, most of the existing works focus on various
deep learning models, which are largely limited by the requirement of
large-scale human-annotated datasets. Such methods are not able to deal with
the cold-start scenarios in industrial products. To alleviate the problem, we
propose FORCE, a Framework Of Rule-based Conversational Recommender system that
helps developers to quickly build CRS bots by simple configuration. We conduct
experiments on two datasets in different languages and domains to verify its
effectiveness and usability.


  Having its origin in theoretical computer science, the
Kannan-Lov\'asz-Simonovits (KLS) conjecture is one of the major open problems
in asymptotic convex geometry and high-dimensional probability theory today. In
this work, we establish a new connection between this conjecture and the study
of large and moderate deviations for isotropic log-concave random vectors,
thereby providing a novel possibility to tackle the conjecture. We then study
the moderate deviations for the Euclidean norm of random orthogonally projected
random vectors in an $\ell_p^n$-ball. This leads to a number of interesting
observations: (A) the $\ell_1^n$-ball is critical for the new approach; (B) for
$p\geq 2$ the rate function in the moderate deviations principle undergoes a
phase transition, depending on whether the scaling is below the square-root of
the subspace dimensions or comparable; (C) for $1\leq p<2$ and comparable
subspace dimensions, the rate function again displays a phase transition
depending on its growth relative to $n^{p/2}$.


  In this note, we extend the notion of a Monge hypersurface from its roots in
semi-Euclidean space to more general spaces. For the degenerate case, the
geometry of these structures is studied using the Bejancu-Duggal method of
screen distributions.


  In this paper, the criterion used to determine whether a density perturbation
will collapse to form a primordial black hole (PBH) is re-examined, in respect
of its use to determine the abundance of PBHs. There is particular focus on
which parameter to use, the time at which the abundance should be calculated,
and the use of different smoothing functions. It is concluded that, with the
tools currently available, the smoothed density contrast should be used rather
than the peak value, and should be calculated from the time-independent
component of the density contrast in the super-horizon limit (long before
perturbations enter the horizon) rather than at horizon crossing. For the first
time the effect of the choice of smoothing function upon the formation
criterion is calculated, and, for a given abundance of PBHs, it is found that
the uncertainty in the amplitude of the power spectrum due to this is
$\mathcal{O}(10\%)$, an order of magnitude smaller than previous calculations
suggest. The relation between the formation criterion stated in terms of the
density contrast and the curvature perturbation $\mathcal{R}$ is also
discussed.


  We expose the basics of the Fedosov quantization procedure, placed in the
general framework of symplectic ringed spaces. This framework also includes
some Poisson manifolds with non regular Poisson structures, presymplectic
manifolds, complex analytic symplectic manifolds, etc.


  A class of random graphs is introduced and studied. The graphs are
constructed in an algorithmic way from five motifs which were found in [Milo
R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D., Alon U., Science,
2002, 298, 824-827]. The construction scheme resembles that used in [Hinczewski
M., A. Nihat Berker, Phys. Rev. E, 2006, 73, 066126], according to which the
short-range bonds are non-random, whereas the long-range bonds appear
independently with the same probability. A number of structural properties of
the graphs have been described, among which there are degree distributions,
clustering, amenability, small-world property. For one of the motifs, the
critical point of the Ising model defined on the corresponding graph has been
studied.


  The "monstrous proposal" of the first author is that the quotient of a
certain 13-dimensional complex hyperbolic braid group, by the relations that
its natural generators have order 2, is the bimonster" (M x M)semidirect Z/2.
Here M is the monster simple group. We prove that this quotient is either the
bimonster or Z/2. In the process, we give new information about the isomorphism
found by Deligne-Mostow, between the moduli space of 12-tuples in CP1 and a
quotient of the complex 9-ball. Namely, we identify which loops in the 9-ball
quotient correspond to the standard braid generators.


  In recent years, artificial intelligence has played an important role in
medicine and disease diagnosis, with many applications to be mentioned, one of
which is Medical Visual Question Answering (MedVQA). By combining computer
vision and natural language processing, MedVQA systems can assist experts in
extracting relevant information from medical image based on a given question
and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023
challenge carried out visual question answering task in the gastrointestinal
domain, which includes gastroscopy and colonoscopy images. Our team approached
Task 1 of the challenge by proposing a multimodal learning method with image
enhancement to improve the VQA performance on gastrointestinal images. The
multimodal architecture is set up with BERT encoder and different pre-trained
vision models based on convolutional neural network (CNN) and Transformer
architecture for features extraction from question and endoscopy image. The
result of this study highlights the dominance of Transformer-based vision
models over the CNNs and demonstrates the effectiveness of the image
enhancement process, with six out of the eight vision models achieving better
F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image
enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the
development test set, while also producing good result on the private test set
with accuracy of 82.01%.


  We considered the nonequlibrium spin dipoles induced around spin independent
elastic scatterers by the intrinsic spin-Hall effect associated with the Rashba
spin-orbit coupling. The normal to 2DEG spin polarization has been calculated
in the diffusion range around the scatterer. We found that although around each
impurity this polarization is finite, the corresponding macroscopic spin
density, obtained via averaging of individual spin dipole distributions over
impurity positions is zero in the bulk. At the same time, the spin density is
finite near the boundary of 2DEG, except for a special case of a hard wall
boundary. The boundary value of the spin polarization can be associated with
the interface spin-Hall resistance determining the additional energy
dissipation due to spin accumulation.


  We introduce and study configuration schemes, which are obtained by
``glueing'' usual schemes along closed embeddings. The category of coherent
sheaves on a configuration scheme is investigated. Smooth configuration schemes
provide alternative ``resolutions of singularities'' of usual singular schemes.
We consider in detail the case when the singular scheme is a union of
hyperplanes in a projective space.


  Higher derivative couplings of hypermultiplets to $6D, N=(1,0)$ supergravity
are obtained from dimensional reduction of 10D heterotic supergravity that
includes order $\alpha'$ higher derivative corrections. Reduction on $T^4$ is
followed by a consistent truncation. In the resulting action the hyperscalar
fields parametrize the coset $SO(4,4)/(SO(4)\times SO(4))$. While the $SO(4,4)$
symmetry is ensured by Sen's construction based on string field theory, its
emergence at the field theory level is a nontrivial phenomenon. A number of
field redefinitions in the hypermultiplet sector are required to remove several
terms that break the $SO(4)\times SO(4)$ down to its $SO(4)$ diagonal subgroup
in the action and the supersymmetry transformation rules. Working with the
Lorentz Chern-Simons term modified 3-form field strength, where the spin
connection has the 3-form field strength as torsion, is shown to simplify
considerably the dimensional reduction.


  The Rabin public-key cryptosystem is revisited with a focus on the problem of
identifying the encrypted message unambiguously for any pair of primes. In
particular, a deterministic scheme using quartic reciprocity is described that
works for primes congruent 5 modulo 8, a case that was still open. Both
theoretical and practical solutions are presented. The Rabin signature is also
reconsidered and a deterministic padding mechanism is proposed.


  Little research into tactile feet has been done for walking robots despite
the benefits such feedback could give when walking on uneven terrain. This
paper describes the development of a simple, robust and inexpensive tactile
foot for legged robots based on a high-resolution biomimetic TacTip tactile
sensor. Several design improvements were made to facilitate tactile sensing
while walking, including the use of phosphorescent markers to remove the need
for internal LED lighting. The usefulness of the foot is verified on a
quadrupedal robot performing a beam walking task and it is found the sensor
prevents the robot falling off the beam. Further, this capability also enables
the robot to walk along the edge of a curved table. This tactile foot design
can be easily modified for use with any legged robot, including much larger
walking robots, enabling stable walking in challenging terrain.


  With advanced image journaling tools, one can easily alter the semantic
meaning of an image by exploiting certain manipulation techniques such as
copy-clone, object splicing, and removal, which mislead the viewers. In
contrast, the identification of these manipulations becomes a very challenging
task as manipulated regions are not visually apparent. This paper proposes a
high-confidence manipulation localization architecture which utilizes
resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder
network to segment out manipulated regions from non-manipulated ones.
Resampling features are used to capture artifacts like JPEG quality loss,
upsampling, downsampling, rotation, and shearing. The proposed network exploits
larger receptive fields (spatial maps) and frequency domain correlation to
analyze the discriminative characteristics between manipulated and
non-manipulated regions by incorporating encoder and LSTM network. Finally,
decoder network learns the mapping from low-resolution feature maps to
pixel-wise predictions for image tamper localization. With predicted mask
provided by final layer (softmax) of the proposed architecture, end-to-end
training is performed to learn the network parameters through back-propagation
using ground-truth masks. Furthermore, a large image splicing dataset is
introduced to guide the training process. The proposed method is capable of
localizing image manipulations at pixel level with high precision, which is
demonstrated through rigorous experimentation on three diverse datasets.


  The nature of the interaction between ultracold atoms with a large orbital
and spin angular momentum has attracted considerable attention. It was
suggested that such interactions can lead to the realization of exotic states
of highly correlated matter. Here, we report on a theoretical study of the
competing anisotropic dispersion, magnetic dipole-dipole, and electric
quadrupole-quadrupole forces between two dysprosium atoms. Each dysprosium atom
has an orbital angular momentum L=6 and magnetic moment $\mu=10\mu_B$. We show
that the dispersion coefficients of the ground state adiabatic potentials lie
between 1865 a.u. and 1890 a.u., creating a non-negligible anisotropy with a
spread of 25 a.u. and that the electric quadrupole-quadrupole interaction is
weak compared to the other interactions. We also find that for interatomic
separations $R< 50\,a_0$ both the anisotropic dispersion and magnetic
dipole-dipole potential are larger than the atomic Zeeman splittings for
external magnetic fields of order 10 G to 100 G. At these separations spin
exchange can occur. We finish by describing two scattering models for inelastic
spin exchange. A universal scattering theory is used to model loss due to the
anisotropy in the dispersion and a distorted-wave-Born theory is used to model
losses from the magnetic dipole-dipole interaction for the $^{164}$Dy isotope.
These models find loss rates that are the same order of magnitude as the
experimental value.


  Diagnostic ultrasound is a versatile and practical tool in the abdomen, and
is particularly vital toward the detection and mitigation of early-stage
non-alcoholic fatty liver disease (NAFLD). However, its performance in those
with obesity -- who are at increased risk for NAFLD -- is degraded due to
distortions of the ultrasound as it traverses thicker, acoustically
heterogeneous body walls (aberration). Many aberration correction methods for
ultrasound require measures of channel data relationships. Simpler, bulk speed
of sound optimizations based on the image itself have demonstrated empirical
efficacy, but their analytical limitations have not been evaluated. Herein, we
assess analytically the bounds of a single, optimal speed of sound correction
in receive beamforming to correct aberration, and improve the resulting images.
Additionally, we propose an objective metric on the post-sum B-mode image to
identify this speed of sound, and validate this technique through in virto
phantom experiments and in vivo abdominal ultrasound data collection with
physical aberrating layers. We find that a bulk correction may approximate the
aberration profile for layers of relevant thicknesses (1 to 3 cm) and speeds of
sound (1400 to 1500 m/s). Additionally, through in vitro experiments, we show
significant improvement in resolution (average point target width reduced by 60
%) and improved boundary delineation in vivo with bulk speed of sound
correction determined automatically from the beamformed images. Together, our
results demonstrate the utility of simple, efficient bulk speed of sound
correction to improve the quality of diagnostic liver images.


  Anatomic connections between brain areas affect information flow between
neuronal circuits and the synchronization of neuronal activity. However, such
structural connectivity does not coincide with effective connectivity, related
to the more elusive question "Which areas cause the present activity of which
others?". Effective connectivity is directed and depends flexibly on contexts
and tasks. Here we show that a dynamic effective connectivity can emerge from
transitions in the collective organization of coherent neural activity.
Integrating simulation and semi-analytic approaches, we study mesoscale network
motifs of interacting cortical areas, modeled as large random networks of
spiking neurons or as simple rate units. Through a causal analysis of
time-series of model neural activity, we show that different dynamical states
generated by a same structural connectivity motif correspond to distinct
effective connectivity motifs. Such effective motifs can display a dominant
directionality, due to spontaneous symmetry breaking and effective entrainment
between local brain rhythms, although all connections in the considered
structural motifs are reciprocal [...] Finally, we analyze how the information
encoded in spiking patterns of a local neuronal population is propagated across
a fixed structural connectivity motif, demonstrating that changes in the active
effective connectivity regulate both the efficiency and the directionality of
information transfer [...] Going beyond these early proposals, we advance here
that dynamic interactions between brain rhythms provide as well the basis for
the self-organized control of this "communication-through-coherence", making
thus possible a fast "on-demand" reconfiguration of global information routing
modalities.


  Gibbs sampling is a Markov Chain Monte Carlo sampling technique that
iteratively samples variables from their conditional distributions. There are
two common scan orders for the variables: random scan and systematic scan. Due
to the benefits of locality in hardware, systematic scan is commonly used, even
though most statistical guarantees are only for random scan. While it has been
conjectured that the mixing times of random scan and systematic scan do not
differ by more than a logarithmic factor, we show by counterexample that this
is not the case, and we prove that that the mixing times do not differ by more
than a polynomial factor under mild conditions. To prove these relative bounds,
we introduce a method of augmenting the state space to study systematic scan
using conductance.


  Mobile energy storage devices (MESDs) operate as medium- or large-sized
batteries that can be loaded onto electric trucks and connected to charging
stations to provide various ancillary services for distribution grids. This
paper proposes a new strategy for MESD operation, in which their power outputs
and paths are co-optimally scheduled to minimize the total energy loss in both
power and transportation networks. The distances moved by MESDs and time at
different locations are modeled using a set of linear equations, considering
the time-varying traffic flow. The linear transit model is integrated with
linearized constraints to support the reliable operation of the distribution
grid. In particular, an optimal scheduling problem is formulated considering
the maximum limits on incremental variations in bus voltages and line power
flows for active and reactive power outputs of MESDs. A mixed-integer linear
programming solver can be readily applied to the optimization problem, ensuring
the global optimality of the solution. Simulation case studies are carried out
under various power and transportation network conditions. The results of these
case studies confirm that the proposed strategy using MESDs is effective in
reducing total energy losses, compared to conventional methods using stationary
batteries and plug-in electric vehicles.


  This paper has been withdrawn. I belatedly found that the alignment I saw in
galaxy cluster axes was bogus. It turns out that it is due to a well-known
effect called the "Fingers of God" that stretches out the redshifts of galaxies
in a cluster due to their motion within the cluster. This would not cause an
overall bias if the SDSS survey were complete, but there is no coverage toward
right ascensions near 90 degrees or 270 deg. Thus the apparent alignment
appears along 0 -- 180 deg.


  Significant numbers of high-velocity HI clouds (HVCs) have now been detected
in H-alpha, with a subset seen in low ionization lines (e.g. [NII]). It was
originally hoped that the observed H-alpha strength would provide a distance
constraint to individual clouds. This idea requires that a useful fraction
(f_esc > 1%) of ionizing photons escape the Galaxy, and that the halo ionizing
field is relatively smooth, as we discuss. Most HVCs which are known to be
close to the Sun are H-alpha; the brightest clouds also show enhanced [NII]
emission, in contrast to the Magellanic Stream where the low ionization
emission lines are always weak compared to H-alpha. But an acute complication
for H-alpha distances is the apparent H-alpha brightness of the Magellanic
Stream along several sight lines. To account for this, we present three
possible configurations for the Magellanic Stream and propose a follow-up
experiment. If we normalize the distances to local HVCs, some HVCs appear to be
scattered throughout the Galactic halo on scales of tens of kiloparsecs.


  We improve the concept of our previous paper "Dirac type tensor equations
with nonabelian gauge symmetries on pseudo-Riemannian space" and present a new
compact formula for the tensor $B_\mu$.


  The infrared dark clouds (IRDCs) G11.11$-$0.12 and G28.34$+$0.06 are two of
the best-studied IRDCs in our Galaxy. These two clouds host clumps at different
stages of evolution, including a massive dense clump in both clouds that is
dark even at 70 and 100$\mu$m. Such seemingly quiescent massive dense clumps
have been speculated to harbor cores that are precursors of high-mass stars and
clusters. We observed these two "prestellar" regions at 1mm with the
Submillimeter Array (SMA) with the aim of characterizing the nature of such
cores. We show that the clumps fragment into several low- to high-mass cores
within the filamentary structure of the enveloping cloud. However, while the
overall physical properties of the clump may indicate a starless phase, we find
that both regions host multiple outflows. The most massive core though 70
$\mu$m dark in both clumps is clearly associated with compact outflows. Such
low-luminosity, massive cores are potentially the earliest stage in the
evolution of a massive protostar. We also identify several outflow features
distributed in the large environment around the most massive core. We infer
that these outflows are being powered by young, low-mass protostars whose core
mass is below our detection limit. These findings suggest that low-mass
protostars have already formed or are coevally formed at the earliest phase of
high-mass star formation.


  We present optical UBVRI photometry and medium resolution spectroscopy of the
type Ib supernova iPTF13bvn, spanning a phase of $\sim$ $-13\,$d to $+71\,$d
with respect to $B$-band maximum. The post maximum decline rates indicate a
fast decline with $\Delta m_{15}(B) = 1.82$. Correcting for a galactic
extinction $E(B-V){\rm_{MW}}=0.045$ and host galaxy extinction of
$E(B-V){\rm_{host}}=0.17$, the absolute $V$-band magnitude peaks at
M$_V=-17.23\, \pm \,0.20$. The bolometric light curve indicates that $\sim
0.09$ M$_{\odot}$ of $^{56}$Ni was synthesized in the explosion. The earliest
spectrum ($-13$d) shows the presence of He~{\sc i} 5876 \AA\ feature at a
velocity of $\sim$15000 km s$^{-1}$, which falls rapidly by the time the SN
approaches the epoch of B-band maximum. The photospheric velocity near maximum
light, as indicated by the Fe~{\sc ii} 5169~\AA\ feature, is $\sim 9000$ km
s$^{-1}$. The estimate for the $^{56}$Ni mass, together with the estimates for
the ejected mass ($M_{\rm{ej}}$) and kinetic energy of the explosion
($E_{\rm{k}}$) indicate that iPTF13bvn is a low luminosity type Ib supernova,
with a lower than average ejected mass and kinetic energy. Our results suggest
that the progenitor of iPTF13bvn is inconsistent with a single Wolf-Rayet star.


  We study the problem of learning disentangled representations for data across
multiple domains and its applications in human retargeting. Our goal is to map
an input image to an identity-invariant latent representation that captures
intrinsic factors such as expressions and poses. To this end, we present a
novel multi-view learning approach that leverages various data sources such as
images, keypoints, and poses. Our model consists of multiple id-conditioned
VAEs for different views of the data. During training, we encourage the latent
embeddings to be consistent across these views. Our observation is that
auxiliary data like keypoints and poses contain critical, id-agnostic semantic
information, and it is easier to train a disentangling CVAE on these simpler
views to separate such semantics from other id-specific attributes. We show
that training multi-view CVAEs and encourage latent-consistency guides the
image encoding to preserve the semantics of expressions and poses, leading to
improved disentangled representations and better human retargeting results.


  We prove the unitary equivalence of the inverse of the Krein--von Neumann
extension (on the orthogonal complement of its kernel) of a densely defined,
closed, strictly positive operator, $S\geq \epsilon I_{\mathcal{H}}$ for some
$\epsilon >0$ in a Hilbert space $\mathcal{H}$ to an abstract buckling problem
operator.
  In the concrete case where $S=\bar{-\Delta|_{C_0^\infty(\Omega)}}$ in
$L^2(\Omega; d^n x)$ for $\Omega\subset\mathbb{R}^n$ an open, bounded (and
sufficiently regular) domain, this recovers, as a particular case of a general
result due to G. Grubb, that the eigenvalue problem for the Krein Laplacian
$S_K$ (i.e., the Krein--von Neumann extension of $S$), \[ S_K v = \lambda v,
\quad \lambda \neq 0, \] is in one-to-one correspondence with the problem of
{\em the buckling of a clamped plate}, \[ (-\Delta)^2u=\lambda (-\Delta) u
\text{in} \Omega, \quad \lambda \neq 0, \quad u\in H_0^2(\Omega), \] where $u$
and $v$ are related via the pair of formulas \[ u = S_F^{-1} (-\Delta) v, \quad
v = \lambda^{-1}(-\Delta) u, \] with $S_F$ the Friedrichs extension of $S$.
  This establishes the Krein extension as a natural object in elasticity theory
(in analogy to the Friedrichs extension, which found natural applications in
quantum mechanics, elasticity, etc.).


  This paper presents exact Semi-Definite Program (SDP) reformulations for
infinite-dimensional moment optimization problems involving a new class of
piecewise Sum-of-Squares (SOS)-convex functions and projected spectrahedral
support sets. These reformulations show that solving a single SDP finds the
optimal value and an optimal probability measure of the original moment
problem. This is done by establishing an SOS representation for the
non-negativity of a piecewise SOS-convex function over a projected
spectrahedron. Finally, as an application and a proof-of-concept illustration,
the paper presents numerical results for the Newsvendor and revenue
maximization problems with higher-order moments by solving their equivalent SDP
reformulations. These reformulations promise a flexible and efficient approach
to solving these models. The main novelty of the present work in relation to
the recent research lies in finding the solution to moment problems, for the
first time, with piecewise SOS-convex functions from their numerically
tractable exact SDP reformulations.