  In frequency division duplex (FDD) massive MIMO systems, reliable downlink
channel estimation is essential for the subsequent data transmission but is
realized at the cost of massive pilot overhead due to hundreds of antennas at
base station (BS). In order to reduce the pilot overhead without compromising
the estimation, compressive sensing (CS) based methods have been widely applied
for channel estimation by exploiting the inherent sparse structure of massive
MIMO channel in angular domain. However, they still suffer from high complexity
during optimization process and the requirement of prior knowledge on sparsity
information. To overcome these challenges, this paper develops a novel hybrid
channel estimation framework by integrating the model-driven CS and data-driven
deep unrolling techniques. The proposed framework is composed of a coarse
estimation part and a fine correction part, which is implemented in a two-stage
manner to exploit both inter- and intra-frame sparsities of channels in angular
domain. Then, two estimation schemes are designed depending on whether priori
sparsity information is required, where the second scheme designs a new
thresholding function to eliminate such requirement. Numerical results are
provided to verify that our schemes can achieve high accuracy with low pilot
overhead and low complexity.


  Quality control (QC) of medical images is essential to ensure that downstream
analyses such as segmentation can be performed successfully. Currently, QC is
predominantly performed visually at significant time and operator cost. We aim
to automate the process by formulating a probabilistic network that estimates
uncertainty through a heteroscedastic noise model, hence providing a proxy
measure of task-specific image quality that is learnt directly from the data.
By augmenting the training data with different types of simulated k-space
artefacts, we propose a novel cascading CNN architecture based on a
student-teacher framework to decouple sources of uncertainty related to
different k-space augmentations in an entirely self-supervised manner. This
enables us to predict separate uncertainty quantities for the different types
of data degradation. While the uncertainty measures reflect the presence and
severity of image artefacts, the network also provides the segmentation
predictions given the quality of the data. We show models trained with
simulated artefacts provide informative measures of uncertainty on real-world
images and we validate our uncertainty predictions on problematic images
identified by human-raters.


  The effects of ferroic distortion and biaxial strain on the band gap and band
edges of SrTiO$_{3}$ (STO) are calculated using density functional theory and
many-body perturbation theory. Anisotropic strains are shown to reduce the gap
by breaking degeneracies at the band edges. Ferroic distortions are shown to
widen the gap by allowing new band edge orbital mixings. Compressive biaxial
strains raise band edge energies, while tensile strains lower them. To reduce
the STO gap, one must lower the symmetry from cubic while suppressing ferroic
distortions. Our calculations indicate that for engineered orientation of the
growth direction along [111], the STO gap can be controllably and considerably
reduced at room temperature.


  We use the symbol calculus for foliations developed in our previous paper to
derive a cohomological formula for the Connes-Chern character of the
semi-finite spectral triple. The same proof works for the Type I spectral
triple of Connes-Moscovici. The cohomology classes of the two Connes-Chern
characters induce the same map on the image of the maximal Baum-Connes map in
K-theory, thereby proving an Atiyah $L^2$ covering index theorem.


  We employ the density matrix renormalization group (DMRG) and the wave
function factorization method for the numerical solution of large scale nuclear
structure problems. The DMRG exhibits an improved convergence for problems with
realistic interactions due to the implementation of the finite algorithm. The
wave function factorization of fpg-shell nuclei yields rapidly converging
approximations that are at the present frontier for large-scale shell model
calculations.


  Using a spatially structured, optical pump pulse with a THz probe pulse, we
are able to determine spatial variations of the ultrafast THz photoconductivity
with sub-wavelength resolution (75 $\mu m \approx \lambda/5$ at 0.8 THz) in a
planar graphene sample. We compare our results to Raman spectroscopy and
correlate the existence of the spatial inhomogeneities between the two
measurements. We find a strong correlation with inhomogeneity in electron
density. This demonstrates the importance of eliminating inhomogeneities in
doping density during CVD growth and fabrication for photoconductive devices.


  We study the topological structure of the space of Volterra-type integral
operators on Fock spaces endowed with the operator norm. We proved that the
space has the same connected and path connected components which is the set of
all compact operators acting on the Fock spaces. We also obtained a
characterization of isolated points of the space of the operators and showed
that there exists no essentially isolated Volterra-type integral operator.


  Extensive observations of comet 260P (McNaught) were carried out between
August 2012 and January 2013. The images obtained were used to analyze the
inner coma morphology of the comet at resolutions ranging from 250 to about
1000 km/pixel. A deep investigation of the dust features in the inner coma
allowed us to identify only a single main active source on the nucleus of the
comet, at an estimated latitude of -50 deg +/-15 deg. A thorough analysis of
the appearance and of the motion of the morphological structures, supported by
graphic simulations of the geometrical conditions of the observations, allowed
us to determine a pole orientation located within a circular spot of a 15
deg-radius centered at RA=60 deg, Dec=0 deg. The rotation of the nucleus seems
to occur on a single axis and is not chaotic, furthermore no precession effects
could be estimated from our measurements. The comet's spin axis never reached
the plane of the sky from October 2012 to January 2013; during this period it
did not change its direction significantly (less than 30 deg), thus giving us
the opportunity to observe mainly structures such as bow-shaped jets departing
from the single active source located on the comet's nucleus. Only during the
months of August 2012 and January 2013 the polar axis was directed towards the
Earth at an angle of about 45 deg from the plane of the sky; this made it
possible to observe the development of faint structures like fragments of
shells or spirals. A possible rotation period of 0.340 +/-0.01 days was
estimated by means of differential photometric analysis.


  Research on success factors involved in the agile transformation process is
not conclusive and there is still need for guidelines to help in the
transformation process considering the organizational context (culture, values,
needs, reality and goals). The usage of success factors as a tool to help agile
adoption raises the following research question: What are the success factors
for an organization and their teams in preparation for the agile transformation
process? This research presents an assessment of the organizational environment
including the company's goals and the perception of the team members to provide
awareness of how the organization should prepare for the next steps in the
agile transformation. The findings show that a company based in Chicago, USA,
succeeded implementing customer involvement and self-organized teams but faces
challenges with measurement models and training. The main contribution of the
research is understand which success factors exist in their environment and how
they can be used during agile adoption.


  In order to explain the recently reported peak at $3.55$ keV in the galactic
x-ray spectrum, we propose a simple model. In this model, the Standard Model is
extended by including a neutral spin-$3/2$ vector-like fermion that transforms
like a singlet under SM gauge group. This $7.1$ keV spin-$3/2$ fermion is
considered to comprise a portion of the observed dark matter. Its decay into a
neutrino and a photon with decay life commensurate with the observed data, fits
the relic dark matter density and obeys the astrophysical constraints from the
supernova cooling.


  Quantum computing is quickly turning from a promise to a reality, witnessing
the launch of several cloud-based, general-purpose offerings, and IDEs.
Unfortunately, however, existing solutions typically implicitly assume intimate
knowledge about quantum computing concepts and operators. This paper introduces
Quantum Service-Oriented Computing (QSOC), including a model-driven methodology
to allow enterprise DevOps teams to compose, configure and operate enterprise
applications without intimate knowledge on the underlying quantum
infrastructure, advocating knowledge reuse, separation of concerns, resource
optimization, and mixed quantum- & conventional QSOC applications.


  We show the properness of the moduli stack of stable surfaces over
$\mathbb{Z}[1/30]$, assuming the locally-stable reduction conjecture for stable
surfaces. This relies on a local Kawamata--Viehweg vanishing theorem for for
3-dimensional log canonical singularities at closed point of characteristic $p
\neq 2, 3$ and $5$ which are not log canonical centres.


  This study presents a new approach to metaphorical paraphrase generation by
masking literal tokens of literal sentences and unmasking them with
metaphorical language models. Unlike similar studies, the proposed algorithm
does not only focus on verbs but also on nouns and adjectives. Despite the fact
that the transfer rate for the former is the highest (56%), the transfer of the
latter is feasible (24% and 31%). Human evaluation showed that our
system-generated metaphors are considered more creative and metaphorical than
human-generated ones while when using our transferred metaphors for data
augmentation improves the state of the art in metaphorical sentence
classification by 3% in F1.


  We show star images obtained with a miniature ``densified pupil imaging
interferometer'' also called a hyper-telescope. The formation of such images
violates a ``golden rule of imaging interferometers'' which appeared to forbid
the use of interferometric arrangements differing from a Fizeau interferometer.
These produce useless images when the sub-apertures spacing is much wider than
their size, owing to diffraction through the sub-apertures. The hyper-telescope
arrangement solves these problems opening the way towards multi-kilometer
imaging arrays in space. We experimentally obtain an intensity gain of 24 +- 3X
when a densified-pupil interferometer is compared to an equivalent Fizeau-type
interferometer and show images of the double star alpha Gem. The initial
results presented confirm the possibility of directly obtaining high resolution
and high dynamic range images in the recombined focal plane of a large
interferometer if enough elements are used.


  ALICE (A Large Ion Collider Experiment) is one of four major experiments at
the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is a
compute cluster, which reconstructs collisions as recorded by the ALICE
detector in real-time. It employs a custom online data-transport framework to
distribute data and workload among the compute nodes.
  ALICE employs subdetectors sensitive to environmental conditions such as
pressure and temperature, e.g. the Time Projection Chamber (TPC). A precise
reconstruction of particle trajectories requires the calibration of these
detectors. Performing the calibration in real time in the HLT improves the
online reconstructions and renders certain offline calibration steps obsolete
speeding up offline physics analysis. For LHC Run 3, starting in 2020 when data
reduction will rely on reconstructed data, online calibration becomes a
necessity. Reconstructed particle trajectories build the basis for the
calibration making a fast online-tracking mandatory. The main detectors used
for this purpose are the TPC and ITS (Inner Tracking System). Reconstructing
the trajectories in the TPC is the most compute-intense step.
  We present several improvements to the ALICE High Level Trigger developed to
facilitate online calibration. The main new development for online calibration
is a wrapper that can run ALICE offline analysis and calibration tasks inside
the HLT. On top of that, we have added asynchronous processing capabilities to
support long-running calibration tasks in the HLT framework, which runs
event-synchronously otherwise. In order to improve the resiliency, an isolated
process performs the asynchronous operations such that even a fatal error does
not disturb data taking. We have complemented the original loop-free HLT chain
with ZeroMQ data-transfer components. [...]


  We present an attempt to formulate an action for the worldvolume theory of a
single M5-brane, based on the splitting of the six worldvolume directions into
2+4, which breaks manifest Lorentz invariance from $SO(1,5)$ to $SO(1,1)\times
SO(4)$. To this end, an action for the free six--dimensional (2,0) chiral
tensor multiplet, and separately, a nonlinearly interacting chiral 2-form
action are constructed. By studying the Lagrangian formulation for the chiral
2-form with 2+4 splitting, it is suggested that, if exists, the modified
diffeomorphism of the theory on curved six--dimensional space--time is less
trivial than its 1+5 and 3+3 counterpart, thus hindering the coupling of the
chiral 2-form to the induced metric on the worldvolume of the M5-brane. We
discuss difficulties of further generalisation of the theory. Finally, in terms
of Hamiltonian analysis, we show that the naively gauge-fixed
failed-PST-covariantised Lagrangian has the correct number of degrees of
freedom, and satisfies the hyper--surface deformation algebra.


  Traditional spectroscopy, by its very nature, characterizes properties of
physical systems in the momentum and frequency domains. The most interesting
and potentially practically useful quantum many-body effects however emerge
from the deep composition of local, short-time correlations. Here, using
inelastic neutron scattering and methods of integrability, we experimentally
observe and theoretically describe a local, coherent, long-lived,
quasiperiodically oscillating magnetic state emerging out of the distillation
of propagating excitations following a local quantum quench in a Heisenberg
antiferromagnetic chain. This "quantum wake" displays similarities to Floquet
states, discrete time crystals and nonlinear Luttinger liquids.


  We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the
inbetweening transformations starting from an initial image to an equilibrium
image, which covers images varying from a greyscale type to a colorful type on
plane or manifold. The algorithm is based on Fokker-Planck dynamics on
manifold, which automatically cooperates positivity, unconditional stability,
mass conservation law, exponentially convergence and also the manifold
structure suggested by dataset. The thresholding scheme is adapted for the
sharp interface dynamics and is used to achieve the finite time convergence.
Using EDDA, three challenging examples, (I) facial aging process, (II)
coronavirus disease 2019 (COVID-19) invading/treatment process, and (III)
continental evolution process are conducted efficiently.


  We address the question of the quantitative relationship between
thermodynamic phase transitions and topological changes in the potential energy
manifold analyzing two classes of one dimensional models, the Burkhardt
solid-on-solid model and the Peyrard-Bishop model for DNA thermal denaturation,
both in the confining and non-confining version. These models, apparently, do
not fit [M.Kastner, Phys. Rev. Lett. 93, 150601 (2004)] in the general idea
that the phase transition is signaled by a topological discontinuity. We show
that in both models the phase transition energy v_c is actually non-coincident
with, and always higher than, the energy v_theta at which a topological change
appears. However, applying a procedure already successfully employed in other
cases as the mean field phi^4 model, i.e. introducing a map M(v)=v_s from
levels of the energy hypersurface V to the level of the stationary points
"visited" at temperature T, we find that M(v_c)=v_theta. This result enhances
the relevance of the underlying stationary points in determining the
thermodynamics of a system, and extends the validity of the topological
approach to the study of phase transition to the elusive one-dimensional
systems considered here.


  Large language models that are capable of zero or few-shot prompting
approaches have given rise to the new research area of prompt engineering.
Recent advances showed that for example Chain-of-Thought (CoT) prompts can
improve arithmetic or common sense tasks significantly. We explore how such
approaches fare with legal reasoning tasks and take the COLIEE entailment task
based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning
approaches. Our findings show that while CoT prompting and fine-tuning with
explanations approaches show improvements, the best results are produced by
prompts that are derived from specific legal reasoning techniques such as IRAC
(Issue, Rule, Application, Conclusion). Based on our experiments we improve the
2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best
system of 0.6789 accuracy with an accuracy of 0.7431.


  This paper presents a database of human faces for persons wearing spectacles.
The database consists of images of faces having significant variations with
respect to illumination, head pose, skin color, facial expressions and sizes,
and nature of spectacles. The database contains data of 60 subjects. This
database is expected to be a precious resource for the development and
evaluation of algorithms for face detection, eye detection, head tracking, eye
gaze tracking, etc., for subjects wearing spectacles. As such, this can be a
valuable contribution to the computer vision community.


  We report applications of analytical formalisms and Molecular Dynamics (MD)
simulations to the calculation of redox entropy of plastocyanin metalloprotein
in aqueous solution. The goal of our analysis is to establish critical
components of the theory required to describe polar solvation at the mesoscopic
scale. The analytical techniques include a microscopic formalism based on
structure factors of the solvent dipolar orientations and density and continuum
dielectric theories. The microscopic theory employs the atomistic structure of
the protein with force-field atomic charges and solvent structure factors
obtained from separate MD simulations of the homogeneous solvent. The MD
simulations provide linear response solvation free energies and reorganization
energies of electron transfer in the temperature range 280--310 K. We found
that continuum models universally underestimate solvation entropies, and a more
favorable agreement is reported between the microscopic calculations and MD
simulations. The analysis of simulations also suggests that difficulties of
extending standard formalisms to protein solvation are related to the
inhomogeneous structure of the solvation shell at the protein-water interface
combining islands of highly structured water around ionized residues along with
partial dewetting of hydrophobic patches. Quantitative theories of
electrostatic protein hydration need to incorporate realistic density profile
of water at the protein-water interface.


  Decay parameter of coherence and population inversion are calculated from the
master equation of a two level atom tunneling through a squeezed vacuum. Using
those parameters, the timescales for decoherence and zeno effect are calculated
in the weak measurement scheme. By comparing those timescales, a certain
condition has been found for sustainable coherent dynamics.


  Three related analyses of $\phi^4$ theory with $O(N)$ symmetry are presented.
In the first, we review the $O(N)$ model over the $p$-adic numbers and the
discrete renormalization group transformations which can be understood as spin
blocking in an ultrametric context. We demonstrate the existence of a
Wilson-Fisher fixed point using an $\epsilon$ expansion, and we show how to
obtain leading order results for the anomalous dimensions of low dimension
operators near the fixed point. Along the way, we note an important aspect of
ultrametric field theories, which is a non-renormalization theorem for kinetic
terms. In the second analysis, we employ large $N$ methods to establish
formulas for anomalous dimensions which are valid equally for field theories
over the $p$-adic numbers and field theories on $\mathbb{R}^n$. Results for
anomalous dimensions agree between the first and second analyses when they can
be meaningfully compared. In the third analysis, we consider higher derivative
versions of the $O(N)$ model on $\mathbb{R}^n$, the simplest of which has been
studied in connection with spatially modulated phases. Our general formula for
anomalous dimensions can still be applied. Analogies with two-derivative
theories hint at the existence of some interesting unconventional field
theories in four real Euclidean dimensions.


  This paper explores resonance-driven secular evolution between a bar and
dark-matter halo using N-body simulations. We make direct comparisons to our
analytic theory (Weinberg & Katz 2005) to demonstrate the great difficulty that
an N-body simulation has representing these dynamics for realistic astronomical
interactions. In a dark-matter halo, the bar's angular momentum is coupled to
the central density cusp (if present) by the Inner Lindblad Resonance. Owing to
this angular momentum transfer and self-consistent re-equilibration, strong
realistic bars WILL modify the cusp profile, lowering the central densities
within about 30% of the bar radius in a few bar orbits. Past results to the
contrary (Sellwood 2006, McMillan & Dehnen 2005) may be the result of weak bars
or numerical artifacts. The magnitude depends on many factors and we illustrate
the sensitivity of the response to the dark-matter profile, the bar shape and
mass, and the galaxy's evolutionary history. For example, if the bar length is
comparable to the size of a central dark-matter core, the bar may exchange
angular momentum without changing its pattern speed significantly. We emphasise
that this apparently simple example of secular evolution is remarkably subtle
in detail and conclude that an N-body exploration of any astronomical scenario
requires a deep investigation into the underlying dynamical mechanisms for that
particular problem to set the necessary requirements for the simulation
parameters and method (e.g. particle number and Poisson solver). Simply put,
N-body simulations do not divinely reveal truth and hence their results are not
infallible. They are unlikely to provide useful insight on their own,
particularly for the study of even more complex secular processes such as the
production of pseudo-bulges and disk heating.


  Correlation matrices of foreign exchange rate time series are investigated
for 60 world currencies. Minimal Spanning Tree (MST) graphs for the gold,
silver and platinum are presented. Inverse power like scaling is discussed for
these graphs as well as for four distinct currency groups (major, liquid, less
liquid and non-tradable). The worst scaling has been found for USD and related
currencies.


  The theory of Newton-Okounkov bodies attaches a convex body to a line bundle
on a variety equipped with flag of subvarieties. This convex body encodes the
asymptotic properties of sections of powers of the line bundle. In this paper,
we study Newton-Okounkov bodies for schemes defined over discrete valuation
rings. We give the basic properties and then focus on the case of toric schemes
and semistable curves. We provide a description of the Newton-Okounkov bodies
for semistable curves in terms of the Baker--Norine theory of linear systems on
graphs, finding a connection with tropical geometry. We do this by introducing
an intermediate object, the Newton-Okounkov linear system of a divisor on a
curve. We prove that it is equal to the set of effective elements of the real
Baker-Norine linear system of the specialization of that divisor on the dual
graph of the curve. As a bonus, we obtain an asymptotic algebraic geometric
description of the Baker-Norine linear system.


  Effective descriptions accounting for the evolution of quantum systems that
are acted on by a bath are desirable. As the number of bath degrees of freedom
increases and full quantum simulations turn out computationally prohibitive,
simpler models become essential to understand and gain an insight into the main
physical mechanisms involved in the system dynamics. In this regard,
vibrational decoherence of an I$_2$ diatomics is tackled here within the
framework of Markovian quantum state diffusion. The I$_2$ dynamics are analyzed
in terms of an effective decoherence rate, $\Lambda$, and the specific choice
of the initial state, in particular, Gaussian wave packets and two-state
superpositions. It is found that, for Markovian baths, the relevant quantity
regarding decoherence is the product of friction ($\eta$) and temperature
($T$); there is no distinction between varying one or the other. It is also
observed that decoherence becomes faster as the energy levels involved in the
system state correspond to higher eigenvalues. This effect is due to a
population redistribution during the dynamical process and an eventual
irreversible loss of the initial coherence. These results have been compared
with those available in the literature from more detailed semiclassical IVR
simulations, finding a good agreement.


  The core of many cryptocurrencies is the decentralised validation network
operating on proof-of-work technology. In these systems, validation is done by
so-called miners who can digitally sign blocks once they solve a
computationally-hard problem. Conventional wisdom generally considers this
protocol as secure and stable as miners are incentivised to follow the
behaviour of the majority. However, whether some strategic mining behaviours
occur in practice is still a major concern. In this paper we target this
question by focusing on a security threat: a selfish mining attack in which
malicious miners deviate from protocol by not immediately revealing their newly
mined blocks. We propose a statistical test to analyse each miner's behaviour
in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and
Bitcoin Cash. Our method is based on the realisation that selfish mining
behaviour will cause identifiable anomalies in the statistics of miner's
successive blocks discovery. Secondly, we apply heuristics-based address
clustering to improve the detectability of this kind of behaviour. We find a
marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a
lesser extent, in Ethereum. Finally, we extend our method to detect coordinated
selfish mining attacks, finding mining cartels in Monacoin where miners might
secretly share information about newly mined blocks in advance. Our analysis
contributes to the research on security in cryptocurrency systems by providing
the first empirical evidence that the aforementioned strategic mining
behaviours do take place in practice.


  Let $ \Omega \subset \mathbb{C}^m $ be an open, connected and bounded set and
$\mathcal{A}(\Omega)$ be a function algebra of holomorphic functions on
$\Omega$. Suppose that $\mathcal{M}_q$ is the quotient Hilbert module obtained
from a submodule of functions in a Hilbert module $\mathcal{M}$ vanishing to
order $k$ along a smooth irreducible complex analytic set
$\mathcal{Z}\subset\Omega$ of codimension at least $2$. In this article, we
prove that the compression of the multiplication operators onto $\mathcal{M}_q$
is homogeneous with respect to a suitable subgroup of the automorphism group
Aut$(\Omega)$ of $\Omega$ depending upon a subgroup $G$ of Aut$(\Omega)$
whenever the tuple of multiplication operators on $\mathcal{M}$ is homogeneous
with respect to $G$ and both $\mathcal{M}$ as well as $\mathcal{M}_q$ are in
the Cowen-Douglas class. We show that these compression of multiplication
operators might be reducible even if the tuple of multiplication operators on
$\mathcal{M}$ is irreducible by exhibiting a concrete example. Moreover, the
irreducible components of these reducible operators are identified as
Generalized Wilkins' operators.


  Unimolecular current rectifiers are fundamental building blocks in organic
electronics. Rectifying behavior has been identified in numerous organic
systems due to electron-hole asymmetries of orbital levels interfaced by a
metal electrode. As a consequence, the rectifying ratio (RR) determining the
diode efficiency remains fixed for a chosen molecule-metal interface. Here, we
present a mechanically tunable molecular diode exhibiting an exceptionally
large rectification ratio (>10^5) and reversible direction. The molecular
system comprises a 7-armchair graphene nanoribbon (GNR) doped with a single
unit of substitutional diboron within its structure, synthesized with atomic
precision on a gold substrate by on-surface synthesis. The diboron unit creates
half-populated in-gap bound states and splits the GNR frontier bands into two
segments, localizing the bound state in a double barrier configuration. By
suspending these GNRs freely between the tip of a low-temperature scanning
tunneling microscope and the substrate, we demonstrate unipolar hole transport
through the boron in-gap state's resonance. Strong current rectification is
observed, associated with the varying widths of the two barriers, which can be
tuned by altering the distance between tip and substrate. This study introduces
an innovative approach for the precise manipulation of molecular electronic
functionalities, opening new avenues for advanced applications in organic
electronics.


  The Beam Energy Scan program has been undertaken at the Relativistic Heavy
Ion Collider (RHIC) to search for the QCD critical point. The presence of the
critical point is expected to lead to non-monotonic behavior of several
quantities. Here we report the result of higher moments of net-charge
distributions for Au+Au collisions at $\sqrt{s_{NN}}$ = 39 GeV as measured by
the STAR experiment. The STAR results are compared with results from HIJING
event generator and Hadron Resonance (HRG) Models.


  We present an intuitive and scalable algorithm for the diagonalization of
complex symmetric matrices, which arise from the projection of
pseudo--Hermitian and complex scaled Hamiltonians onto a suitable basis set of
"trial" states. The algorithm diagonalizes complex and symmetric
(non--Hermitian) matrices and is easily implemented in modern computer
languages. It is based on generalized Householder transformations and relies on
iterative similarity transformations T -> T' = Q^T T Q, where Q is a complex
and orthogonal, but not unitary, matrix, i.e, Q^T equals Q^(-1) but Q^+ is
different from Q^(-1). We present numerical reference data to support the
scalability of the algorithm. We construct the generalized Householder
transformations from the notion that the conserved scalar product of
eigenstates Psi_n and Psi_m of a pseudo-Hermitian quantum mechanical
Hamiltonian can be reformulated in terms of the generalized indefinite inner
product [integral of the product Psi_n(x,t) Psi_m(x,t) over dx], where the
integrand is locally defined, and complex conjugation is avoided. A few example
calculations are described which illustrate the physical origin of the ideas
used in the construction of the algorithm.


  We propose an $E_8 \otimes E_8$ unification of the standard model with
pre-gravitation, on an exceptional Lie algebra-valued space. Each of the $E_8$
has in its branching an $SU(3)$ for space-time and an $SU(3)$ for three fermion
generations. The first $E_8$ further branches to the standard model $SU(3)_c
\otimes SU(2)_L \otimes U(1)_Y$ and describes the gauge bosons, Higgs and the
left chiral fermions of the standard model. The second $E_8$ further branches
into a right-handed counterpart (pre-gravitation) $SU(3)_{grav}\otimes SU(2)_R
\otimes U(1)_g$ of the standard model, and describes right chiral fermions, a
Higgs, and twelve gauge bosons associated with pre-gravitation, from which
general relativity is emergent. We account for 208 out of the 496 degrees of
freedom of $E_8 \otimes E_8$ and propose an interpretation for the remaining
288, motivated by the trace dynamics Lagrangian of our theory. We explain how
the two copies of $SU(3)_{spacetime}$ together give rise to a 6D spacetime with
signature $(3,3)$ which upon symmetry breaking gives rise to our 4D spacetime
and to a second 4D anti-spacetime with flipped signature.


  A microscopic approach is developed to determine the excitation energetics of
highly correlated quasi-particles in optically excited semiconductors based
entirely on a pair-correlation function input. For this purpose, the Wannier
equation is generalized to compute the energy per excited electron-hole pair of
a many-body state probed by a weak pair excitation. The scheme is verified for
the degenerate Fermi gas and incoherent excitons. In a certain range of
experimentally accessible parameters, a new stable quasi-particle state is
predicted which consists of four to six electron-hole pairs forming a liquid
droplet of fixed radius. The energetics and pair-correlation features of these
"quantum droplets" are analyzed.


  We present an extensive, systematic study of the Prisoner's Dilemma and
Snowdrift games on a square lattice under a synchronous, noiseless imitation
dynamics. We show that for both the occupancy of the network and the (random)
mobility of the agents there are intermediate values that may increase the
amount of cooperators in the system and new phases appear. We analytically
determine the transition lines between these phases and compare with the mean
field prediction and the observed behavior on a square lattice. We point out
which are the more relevant microscopic processes that entitle cooperators to
invade a population of defectors in the presence of mobility and discuss the
universality of these results.


  It is a classical result in matrix algebra that any square matrix over a
field can be conjugated to its transpose by a symmetric matrix. For $F$ a
non-Archimedean local field, Tupan used this to give an elementary proof that
transpose inverse takes each irreducible smooth representation of ${\rm
GL}_n(F)$ to its dual. We re-prove the matrix result and related observations
using module-theoretic arguments. In addition, we write down a generalization
that applies to central simple algebras with an involution of the first kind.
We use this generalization to extend Tupan's method of argument to ${\rm
GL}_n(D)$ for $D$ a quaternion division algebra over $F$.


  We derive a large deviation principle for the empirical currents of lattice
gas dynamics which combine a fast stirring mechanism (Symmetric Simple
Exclusion Process) and creation/annihilation mechanisms (Glauber dynamics).
Previous results on the density large deviations can be recovered from this
general large deviation principle. The contribution of external driving forces
due to reservoirs at the boundary of the system is also taken into account.


  Understanding neutrino oscillations in matter requires a non-trivial
diagonalization of the Hamiltonian. As the exact solution is very complicated,
many approximation schemes have been pursued. Here we show that one scheme,
systematically applying rotations to change to a better basis, converges
exponentially fast wherein the rate of convergence follows the Fibonacci
sequence. We find that the convergence rate of this procedure depends very
sensitively on the initial choices of the rotations as well as the mechanism of
selecting the pivots. We then apply this scheme for neutrino oscillations in
matter and discover that the optimal convergence rate is found using the
following simple strategy: first apply the vacuum (2-3) rotation and then use
the largest off-diagonal element as the pivot for each of the following
rotations. The Fibonacci convergence rate presented here may be extendable to
systems beyond neutrino oscillations.


  We present a combined study of thermal expansion and resistance fluctuation
spectroscopy measurements exploring the static and dynamic aspects of the
charge-glass formation in the quasi-two-dimensional organic conductors
$\theta$-(BEDT-TTF)$_2$$MM^\prime$(SCN)$_4$ with $M$ = Cs and $M^\prime$ =
Co,Zn. In these materials, the emergence of a novel charge-glass state so far
has been interpreted in purely electronic terms by considering the strong
frustration of the Coulomb interactions on a triangular lattice. Contrary to
this view, we provide comprehensive evidence for the involvement of a
\textit{structural} glass-like transition at $T_{\text{g}} \sim 90-100\,$K.
This glassy transition can be assigned to the freezing of structural
conformations of the ethylene endgroups in the donor molecule with an
activation energy of $E_{\rm{a}}\approx 0.32\,$eV, and the concomitant slowing
down of the charge carrier dynamics is well described by a model of
non-exponential kinetics. These findings discolse an important aspect of the
phase diagram and renders the current understanding of the charge-glass state
in the whole family of $\theta$-(BEDT-TTF)$_2MM^\prime$(SCN)$_4$ incomplete.
Our results suggest that the entanglement of slow structural and charge-cluster
dynamics due to the intimate coupling of lattice and electronic degrees of
freedom determine the charge-glass formation under geometric frustration.


  We report variational and diffusion quantum Monte Carlo calculations of the
binding energies of indirect trions and biexcitons in ideal two-dimensional
bilayer systems within the effective-mass approximation, and with a Coulomb
$1/r$ interaction between charge carriers. The critical layer separation at
which trions become unbound has been studied for various electron-hole mass
ratios, and found to be over an order of magnitude larger than the critical
layer separation for biexcitons.


  This paper extends the concept of generalized polarization tensors (GPTs),
which was previously defined for inclusions with homogeneous conductivities, to
inhomogeneous conductivity inclusions. We begin by giving two slightly
different but equivalent definitions of the GPTs for inhomogeneous inclusions.
We then show that, as in the homogeneous case, the GPTs are the basic building
blocks for the far-field expansion of the voltage in the presence of the
conductivity inclusion. Relating the GPTs to the Neumann-to-Dirichlet (NtD)
map, it follows that the full knowledge of the GPTs allows unique determination
of the conductivity distribution. Furthermore, we show important properties of
the the GPTs, such as symmetry and positivity, and derive bounds satisfied by
their harmonic sums. We also compute the sensitivity of the GPTs with respect
to changes in the conductivity distribution and propose an algorithm for
reconstructing conductivity distributions from their GPTs. This provides a new
strategy for solving the highly nonlinear and ill-posed inverse conductivity
problem. We demonstrate the viability of the proposed algorithm by preforming a
sensitivity analysis and giving some numerical examples.


  Major theories of military innovation focus on relatively narrow
technological developments, such as nuclear weapons or aircraft carriers.
Arguably the most profound military implications of technological change,
however, come from more fundamental advances arising from general purpose
technologies, such as the steam engine, electricity, and the computer. With few
exceptions, political scientists have not theorized about GPTs. Drawing from
the economics literature on GPTs, we distill several propositions on how and
when GPTs affect military affairs. We call these effects general-purpose
military transformations. In particular, we argue that the impacts of GMTs on
military effectiveness are broad, delayed, and shaped by indirect productivity
spillovers. Additionally, GMTs differentially advantage those militaries that
can draw from a robust industrial base in the GPT. To illustrate the
explanatory value of our theory, we conduct a case study of the military
consequences of electricity, the prototypical GPT. Finally, we apply our
findings to artificial intelligence, which will plausibly cause a profound
general-purpose military transformation.


  It was recently reported that a continuous electric current is a powerful
control parameter to trigger changes in the electronic structure and
metal-insulator transitions (MITs) in Ca2RuO4. However, the spatial evolution
of the MIT and the implications of the unavoidable Joule heating have not been
clarified yet, often hindered by the difficulty to asses the local sample
temperature. In this work, we perform infrared thermal imaging on
single-crystal Ca2RuO4 while controlling the MIT by electric current. The
change in emissivity at the phase transition allows us to monitor the gradual
formation and expansion of metallic phase upon increasing current. Our local
temperature measurements indicate that, within our experimental resolution, the
MIT always occurs at the same local transition temperatures, irrespectively if
driven by temperature or by current. Our results highlight the importance of
local heating, phase coexistence, and microscale inhomogeneity when studying
strongly correlated materials under the flow of electric current.


  Self-supervised representation learning has seen remarkable progress in the
last few years. More recently, contrastive instance learning has shown
impressive results compared to its supervised learning counterparts. However,
even with the ever increased interest in contrastive instance learning, it is
still largely unclear why these methods work so well. In this paper, we aim to
unravel some of the mysteries behind their success, which are the good
practices. Through an extensive empirical analysis, we hope to not only provide
insights but also lay out a set of best practices that led to the success of
recent work in self-supervised representation learning.


  We present a novel approach to learn representations for sentence-level
semantic similarity using conversational data. Our method trains an
unsupervised model to predict conversational input-response pairs. The
resulting sentence embeddings perform well on the semantic textual similarity
(STS) benchmark and SemEval 2017's Community Question Answering (CQA) question
similarity subtask. Performance is further improved by introducing multitask
training combining the conversational input-response prediction task and a
natural language inference task. Extensive experiments show the proposed model
achieves the best performance among all neural models on the STS benchmark and
is competitive with the state-of-the-art feature engineered and mixed systems
in both tasks.


  $\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard
Model or any of its extensions, allows to $\textit{i)}$ fit the model
parameters to a given set of experimental observables; $\textit{ii)}$ obtain
predictions for observables. $\texttt{HEPfit}$ can be used either in Monte
Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given
model, or as a library, to obtain predictions of observables for a given point
in the parameter space of the model, allowing $\texttt{HEPfit}$ to be used in
any statistical framework. In the present version, around a thousand
observables have been implemented in the Standard Model and in several new
physics scenarios. In this paper, we describe the general structure of the code
as well as models and observables implemented in the current release.


  We show that the Southern Crab (aka Hen2-104) presents an auspicious
opportunity to study the form and speed of the invisible winds that excavate
and shock the lobes of various types of bipolar nebulae associated with close
and highly evolved binary stars. A deep three-color image overlay of Hen2-104
reveals that the ionization state of its lobe edges, or "claws", increases
steadily from singly to doubly ionized values with increasing wall latitude.
This "reverse" ionization pattern is unique among planetary nebulae (and
similar objects) and incompatible with UV photoionization from a central
source. We show that the most self-consistent explanation for the ionization
pattern is shock ionization by a fast (~600 km s^-1) "tapered" stellar wind in
which the speed and momentum flux of the wind increase with equatorial
latitude. We present a hydrodynamic simulation that places the
latitude-dependent form, the knotty walls, and the reverse ionization of the
outer lobes of Hen2-104 into a unified context.


  Fascinating phenomena have been known to arise from the Dirac theory of
relativistic quantum mechanics, which describes high energy particles having
linear dispersion relations. Electrons in solids usually have non-relativistic
dispersion relations but their quantum excitations can mimic relativistic
effects. In topological insulators, electrons have both a linear dispersion
relation, the Dirac behavior, on the surface and a non-relativistic energy
dispersion in the bulk. Topological phases of matter have attracted much
interest, particularly broken-symmetry phases in topological insulator
materials. Here, we report by Nb doping that the topological insulator Bi2Se3
can be turned into a bulk type-II superconductor while the Dirac surface
dispersion in the normal state is preserved. A macroscopic magnetic ordering
appears below the superconducting critical temperature of 3.2 K indicating a
spontaneous spin rotation symmetry breaking of the Nb magnetic moments. Even
though such a magnetic order may appear at the edge of the superconductor, it
is mediated by superconductivity and presents a novel phase of matter which
gives rise to a zero-field Hall effect.


  This paper addresses the challenging task of reconstructing the poses of
multiple individuals engaged in close interactions, captured by multiple
calibrated cameras. The difficulty arises from the noisy or false 2D keypoint
detections due to inter-person occlusion, the heavy ambiguity in associating
keypoints to individuals due to the close interactions, and the scarcity of
training data as collecting and annotating motion data in crowded scenes is
resource-intensive. We introduce a novel system to address these challenges.
Our system integrates a learning-based pose estimation component and its
corresponding training and inference strategies. The pose estimation component
takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of
each individual using a 3D conditional volumetric network. As the network
doesn't need images as input, we can leverage known camera parameters from test
scenes and a large quantity of existing motion capture data to synthesize
massive training data that mimics the real data distribution in test scenes.
Extensive experiments demonstrate that our approach significantly surpasses
previous approaches in terms of pose accuracy and is generalizable across
various camera setups and population sizes. The code is available on our
project page: https://github.com/zju3dv/CloseMoCap.


  In this paper we investigate anomalous interactions of the Higgs boson with
heavy fermions, employing shapes of kinematic distributions. We study the
processes $pp \to t\bar{t} + H$, $b\bar{b} + H$, $tq+H$, and $pp \to
H\to\tau^+\tau^-$, and present applications of event generation, re-weighting
techniques for fast simulation of anomalous couplings, as well as matrix
element techniques for optimal sensitivity. We extend the MELA technique, which
proved to be a powerful matrix element tool for Higgs boson discovery and
characterization during Run I of the LHC, and implement all analysis tools in
the JHU generator framework. A next-to-leading order QCD description of the $pp
\to t\bar{t} + H$ process allows us to investigate the performance of MELA in
the presence of extra radiation. Finally, projections for LHC measurements
through the end of Run III are presented.


  A permutation graph is an intersection graph of segments lying between two
parallel lines. A Seidel complementation of a finite graph at one of it vertex
$v$ consists to complement the edges between the neighborhood and the
non-neighborhood of $v$. Two graphs are Seidel complement equivalent if one can
be obtained from the other by a successive application of Seidel
complementation.
  In this paper we introduce the new concept of Seidel complementation and
Seidel minor, we then show that this operation preserves cographs and the
structure of modular decomposition. The main contribution of this paper is to
provide a new and succinct characterization of permutation graphs i.e. A graph
is a permutation graph \Iff it does not contain the following graphs: $C_5$,
$C_7$, $XF_{6}^{2}$, $XF_{5}^{2n+3}$, $C_{2n}, n\geqslant6$ and their
complement as Seidel minor. In addition we provide a $O(n+m)$-time algorithm to
output one of the forbidden Seidel minor if the graph is not a permutation
graph.


  A recent paper (Neural Networks, {\bf 132} (2020), 253-268) introduces a
straightforward and simple kernel based approximation for manifold learning
that does not require the knowledge of anything about the manifold, except for
its dimension. In this paper, we examine how the pointwise error in
approximation using least squares optimization based on similarly localized
kernels depends upon the data characteristics and deteriorates as one goes away
from the training data. The theory is presented with an abstract localized
kernel, which can utilize any prior knowledge about the data being located on
an unknown sub-manifold of a known manifold.
  We demonstrate the performance of our approach using a publicly available
micro-Doppler data set, and investigate the use of different preprocessing
measures, kernels, and manifold dimensions. Specifically, it is shown that the
localized kernel introduced in the above mentioned paper when used with PCA
components leads to a near-competitive performance to deep neural networks, and
offers significant improvements in training speed and memory requirements. To
demonstrate the fact that our methods are agnostic to the domain knowledge, we
examine the classification problem in a simple video data set.


  We quantize the Brownian motion undergone by a free particle in the absence
of inertial force (the so-called large friction regime) as described by the
diffusion equation early found out by Einstein in 1905. Accordingly, we are
able to come up with a time-dependent attractive quantum force F(t) that acts
upon the Brownian free particle as a result of quantum-mechanical thermal
fluctuations of a heat bath consisting of a set of quantum harmonic oscillators
having the same oscillation frequency /omega in thermodynamic equilibrium at
temperature T. More specifically, at zero temperature we predict that the
zero-point force is given by F^((T=0)) (t)=-[\omega/(1+2\omegat)^(3/2)]
\sqrt(\gamma/2), where \gamma is the friction constant with dimensions of mass
per time and /eta the Planck constant divided by 2\pi. For evolution times
t~1/\omega, \omega~0^14 Hz, /gamma~10^(-10) kg/s, and /eta~10^(-34) m^2 kg/s,
we find out F^((T=0)) ~10^(-8) N, which exhibits the same magnitude order as
the Casimir electromagnetic quantum force, for instance. Thus, we reckon that
novel quantum effects arising from our concept of time-dependent thermal
quantum force F(t) may be borne out by some experimental set-up in
nanotechnology.


  We study a system of $N$ Bose atoms trapped by a symmetric harmonic
potential, interacting via weak central forces. Considering the ground state of
the rotating system as a function of the two conserved quantities, the total
angular momentum and its collective component, we develop an algebraic approach
to derive exact wave functions and energies of these ground states. We describe
a broad class of the interactions for which these results are valid. This
universality class is defined by simple integral condition on the potential.
Most of the potentials of practical interest which have pronounced repulsive
component belong to this universality class.


  We propose a novel regularization algorithm to train deep neural networks, in
which data at training time is severely biased. Since a neural network
efficiently learns data distribution, a network is likely to learn the bias
information to categorize input data. It leads to poor performance at test
time, if the bias is, in fact, irrelevant to the categorization. In this paper,
we formulate a regularization loss based on mutual information between feature
embedding and bias. Based on the idea of minimizing this mutual information, we
propose an iterative algorithm to unlearn the bias information. We employ an
additional network to predict the bias distribution and train the network
adversarially against the feature embedding network. At the end of learning,
the bias prediction network is not able to predict the bias not because it is
poorly trained, but because the feature embedding network successfully unlearns
the bias information. We also demonstrate quantitative and qualitative
experimental results which show that our algorithm effectively removes the bias
information from feature embedding.


  In this work, we consider a modification of the usual Branching Random Walk
(BRW), where we give certain independent and identically distributed (i.i.d.)
displacements to all the particles at the $n$-th generation, which may be
different from the driving increment distribution. This model was first
introduced by Bandyopadhyay and Ghosh (2021) and they termed it as Last Progeny
Modified Branching Random Walk (LPM-BRW). Under very minimal assumptions, we
derive the large deviation principle (LDP) for the right-most position of a
particle in generation $n$. As a byproduct, we also complete the LDP for the
classical model, which complements the earlier work by Gantert and
H\"{o}felsauer (2018).


  We focus on electronic theses and dissertations (ETDs), aiming to improve
access and expand their utility, since more than 6 million are publicly
available, and they constitute an important corpus to aid research and
education across disciplines. The corpus is growing as new born-digital
documents are included, and since millions of older theses and dissertations
have been converted to digital form to be disseminated electronically in
institutional repositories. In ETDs, as with other scholarly works, figures and
tables can communicate a large amount of information in a concise way. Although
methods have been proposed for extracting figures and tables from born-digital
PDFs, they do not work well with scanned ETDs. Considering this problem, our
assessment of state-of-the-art figure extraction systems is that the reason
they do not function well on scanned PDFs is that they have only been trained
on born-digital documents. To address this limitation, we present ScanBank, a
new dataset containing 10 thousand scanned page images, manually labeled by
humans as to the presence of the 3.3 thousand figures or tables found therein.
We use this dataset to train a deep neural network model based on YOLOv5 to
accurately extract figures and tables from scanned ETDs. We pose and answer
important research questions aimed at finding better methods for figure
extraction from scanned documents. One of those concerns the value for
training, of data augmentation techniques applied to born-digital documents
which are used to train models better suited for figure extraction from scanned
documents. To the best of our knowledge, ScanBank is the first manually
annotated dataset for figure and table extraction for scanned ETDs. A
YOLOv5-based model, trained on ScanBank, outperforms existing comparable
open-source and freely available baseline methods by a considerable margin.


  We present UBR and H$\alpha$ imaging of NGC 1620, a highly inclined spiral
galaxy that contains a large scale, arc-like feature of radius 3 kpc in its
outer disk at a distance of $\sim$ 11 kpc from the center. What is unusual
about this arc-like feature is its stellar nature and the presence of a
luminous star cluster at its center. The arc is fragmented into HII region
complexes and OB star clusters and shows two kinks in optical continuum light.
It spans an angle of 220$^{\circ}$ on our U image and a full, though
fragmented, circle on an unsharp masked R image. It is centered on a young star
cluster that is the most luminous clump in blue optical continuum light besides
the nucleus of the galaxy. This central star cluster has UBR colors and a
surface brightness similar to those of other HII regions, but is a relatively
weak H$\alpha$ emitter. It consists of at least three unresolved condensations
in optical continuum light. Its location at the center of the arc and its
prominence within the galaxy suggests that it has been the site of several
generations of supernova explosions that swept up the surrounding gas into a
supershell. When it attained a radius of $0.5-1$ kpc, this shell became
gravitationally unstable and formed the stars which now delineate the arc. The
constraints imposed by the survival of the expanding arc against random stellar
motions and the age of the stars in the arc yield a required energy input by a
minimum of 400 and a maximum of 6500 supernovae. In this scenario the asymmetry
in surface brightness of the arc reflects the radial gradient of the gas
density in the disk of NGC 1620, while the kinks reflect inhomogeneities in the
original gas distribution with respect to the central star cluster. The
supernova superbubble formed at least $5 \times 10^7$ yr ago so that, unless


  This paper addresses the Generalized Covariance Intersection (GCI) fusion
method for labeled random finite sets. We propose a joint label space for the
support of fused labeled random finite sets to represent the label association
between different agents, avoiding the label consistency condition for the
label-wise GCI fusion algorithm. Specifically, we devise the joint label space
by the direct product of all label spaces for each agent. Then we apply the GCI
fusion method to obtain the joint labeled multi-target density. The joint
labeled RFS is then marginalized into a general labeled RFS, providing that
each target is represented by a single Bernoulli component with a unique label.
The joint labeled GCI (JL-GCI) for fusing LMB RFSs from different agents is
demonstrated. We also propose the simplified JL-GCI method given the assumption
that targets are well-separated in the scenario. The simulation result presents
the effectiveness of label inconsistency and excellent performance in
challenging tracking scenarios.


  Let $\preceq$ be a preorder on a monoid $H$ and $s$ be an integer $\ge 2$.
The $\preceq$-height of an $x \in H$ is the sup of the integers $k \ge 1$ for
which there is a (strictly) $\preceq$-decreasing sequence $x_1,\ldots,x_k$ of
$\preceq$-non-units of $H$ with $x_1 = x$ (with $\sup\emptyset:=0$), where
$u\in H$ is a $\preceq$-unit if $u\preceq 1_H\preceq u$ and a
$\preceq$-non-unit otherwise. We say $H$ is $\preceq$-artinian if there exists
no $\preceq$-decreasing sequence $x_1,x_2,\ldots$ of elements of $H$; and
strongly $\preceq$-artinian if the $\preceq$-height of each element is finite.
  We establish that, if $H$ is $\preceq$-artinian, then each $\preceq$-non-unit
$x\in H$ factors through the $\preceq$-irreducibles of degree $s$, where a
$\preceq$-irreducible of degree $s$ is a $\preceq$-non-unit $a\in H$ that
cannot be written as a product of $s$ or fewer $\preceq$-non-units each of
which is (strictly) smaller than $a$ with respect to $\preceq$. In addition, we
show that, if $H$ is strongly $\preceq$-artinian, then $x$ factors through the
$\preceq$-quarks of $H$, where a $\preceq$-quark is a $\preceq$-min
$\preceq$-non-unit. In the process, we also obtain upper bounds for the length
of a shortest factorization of $x$ (into either $\preceq$-irreducible of degree
$s$ or $\preceq$-quarks) in terms of its $\preceq$-height.
  Next, we specialize these abstract results to the case in which $H$ is the
multiplicative submonoid of a ring $R$ formed by the zero divisors and the
identity $1_R$, and $\preceq$ is the preorder on $H$ defined by $a\preceq b$
iff $r_R(1_R-b)\subseteq r_R(1_R-a)$, where $r_R(\cdot)$ denotes a right
annihilator. We can thus recover and improve on classical theorems of J.A.
Erdos (1967), R.J.H. Dawlings (1981), and J. Fountain (1991) on idempotent
factorizations in the endomorphism ring of a free module of finite rank over a
skew field or a commutative DVD.


  A labeled gene tree topology that is more probable than the labeled gene tree
topology matching a species tree is called \textit{anomalous}. Species trees
that can generate such anomalous gene trees are said to be in the
\textit{anomaly zone}. Here, probabilities of \textit{unranked} and
\textit{ranked} gene tree topologies under the multispecies coalescent are
considered. A ranked tree depicts not only the topological relationship among
gene lineages, as an unranked tree does, but also the sequence in which the
lineages coalesce. In this article, we study how the parameters of a species
tree simulated under a constant rate birth-death process can affect the
probability that the species tree lies in the anomaly zone. We find that with
more than five taxa, it is possible for species trees have both AGTs and ARGTs.
The probability of being in either type of anomaly zones increases with more
taxa. The probability of AGTs also increases with higher speciation rates. We
observe that the probabilities of unranked anomaly zones are higher and grow
much faster than those of ranked anomaly zones as the speciation rate
increases. Our simulation shows that the most probable ranked gene tree is
likely to have the same unranked topology as the species tree. We design the
software {\it PRANC} which computes probabilities of ranked gene tree
topologies given a species tree under the coalescent model.


  Digital video broadcasting (DVB-C2) and other modern communication standards
increase diversity by means of a symbol-level interleaver that spans over
several codewords. De-interleaving at the receiver requires a large memory,
which has a significant impact on the implementation cost. In this paper, we
propose a technique that reduces the de-interleaver memory size. By quantizing
log-likelihood ratios with bit-specific quantizers and compressing the
quantized output, we can significantly reduce the memory size with a negligible
increase in computational complexity. Both the quantizer and compressor are
designed via a GMI-based maximization procedure. For a typical DVB-C2 scenario,
numerical results show that the proposed solution enables a memory saving up to
30%.


  Numerous works have analyzed biases in vision and pre-trained language models
individually - however, less attention has been paid to how these biases
interact in multimodal settings. This work extends text-based bias analysis
methods to investigate multimodal language models, and analyzes intra- and
inter-modality associations and biases learned by these models. Specifically,
we demonstrate that VL-BERT (Su et al., 2020) exhibits gender biases, often
preferring to reinforce a stereotype over faithfully describing the visual
scene. We demonstrate these findings on a controlled case-study and extend them
for a larger set of stereotypically gendered entities.


  The term, Big Data, has been authored to refer to the extensive heave of data
that can't be managed by traditional data handling methods or techniques. The
field of Big Data plays an indispensable role in various fields, such as
agriculture, banking, data mining, education, chemistry, finance, cloud
computing, marketing, health care stocks. Big data analytics is the method for
looking at big data to reveal hidden patterns, incomprehensible relationship
and other important data that can be utilize to resolve on enhanced decisions.
There has been a perpetually expanding interest for big data because of its
fast development and since it covers different areas of applications. Apache
Hadoop open source technology created in Java and keeps running on Linux
working framework was used. The primary commitment of this exploration is to
display an effective and free solution for big data application in a
distributed environment, with its advantages and indicating its easy use. Later
on, there emerge to be a required for an analytical review of new developments
in the big data technology. Healthcare is one of the best concerns of the
world. Big data in healthcare imply to electronic health data sets that are
identified with patient healthcare and prosperity. Data in the healthcare area
is developing past managing limit of the healthcare associations and is relied
upon to increment fundamentally in the coming years.


  As the Covid-19 pandemic grips the world, healthcare systems are being
reshaped, where the e-health concepts become more likely to be accepted.
Wearable devices often carry sensitive information from users which are exposed
to security and privacy risks. Moreover, users have always had the concern of
being counterfeited between the fabrication process and vendors' storage.
Hence, not only securing personal data is becoming a crucial obligation, but
also device verification is another challenge. To address biometrics
authentication and physically unclonable functions (PUFs) need to be put in
place to mitigate the security and privacy of the users. Among biometrics
modalities, Electrocardiogram (ECG) based biometric has become popular as it
can authenticate patients and monitor the patient's vital signs. However,
researchers have recently started to study the vulnerabilities of the ECG
biometric systems and tried to address the issues of spoofing. Moreover, most
of the wearable is enabled with CPU and memories. Thus, volatile memory-based
(NVM) PUF can be easily placed in the device to avoid counterfeit. However,
many research challenged the unclonability characteristics of PUFs. Thus, a
careful study on these attacks should be sufficient to address the need. In
this paper, our aim is to provide a comprehensive study on the state-of-the-art
developments papers based on biometrics enabled hardware security.


  We study the probing prospects of cosmic ray boosted dark matter (DM) in the
framework of simplified electron-philic dark photon model. Focusing on the dark
matter and dark photon masses around keV $\sim $ MeV scale, we consider the
bounds obtained from the XENON1T and Super-K experiments. The electron bound
state effects are treated carefully in calculating the XENON1T constraint. As
for the detection at neutrino detector where the energy threshold is relatively
higher, the large logarithmic effects induced by the scale hierarchy between
the masses and momentum transfer are considered by introducing the DM parton
distribution function (PDF). The logarithmic effects will reduce the electron
recoil rate for DM scattering in neutrino detectors. Moreover, we find the DUNE
and JUNO experiments provide high sensitivities for probing the dark photon
component in the DM PDF through the dark Compton process. We also check the
Bullet Cluster constraint on the DM self-scattering cross section.


  This paper identifies a problem in both the TLA+ specification and the
implementation of the Egalitarian Paxos protocol. It is related to how replicas
switch from one ballot to another when computing the dependencies of a command.
The problem may lead replicas to diverge and break the linearizability of the
replicated service.


  A comprehensive understanding of the organizational principles in the human
brain requires, among other factors, well-quantifiable descriptors of nerve
fiber architecture. Three-dimensional polarized light imaging (3D-PLI) is a
microscopic imaging technique that enables insights into the fine-grained
organization of myelinated nerve fibers with high resolution. Descriptors
characterizing the fiber architecture observed in 3D-PLI would enable
downstream analysis tasks such as multimodal correlation studies, clustering,
and mapping. However, best practices for observer-independent characterization
of fiber architecture in 3D-PLI are not yet available. To this end, we propose
the application of a fully data-driven approach to characterize nerve fiber
architecture in 3D-PLI images using self-supervised representation learning. We
introduce a 3D-Context Contrastive Learning (CL-3D) objective that utilizes the
spatial neighborhood of texture examples across histological brain sections of
a 3D reconstructed volume to sample positive pairs for contrastive learning. We
combine this sampling strategy with specifically designed image augmentations
to gain robustness to typical variations in 3D-PLI parameter maps. The approach
is demonstrated for the 3D reconstructed occipital lobe of a vervet monkey
brain. We show that extracted features are highly sensitive to different
configurations of nerve fibers, yet robust to variations between consecutive
brain sections arising from histological processing. We demonstrate their
practical applicability for retrieving clusters of homogeneous fiber
architecture and performing data mining for interactively selected templates of
specific components of fiber architecture such as U-fibers.


  Standard methods, such as sequential procedures based on Johansen's
(pseudo-)likelihood ratio (PLR) test, for determining the co-integration rank
of a vector autoregressive (VAR) system of variables integrated of order one
can be significantly affected, even asymptotically, by unconditional
heteroskedasticity (non-stationary volatility) in the data. Known solutions to
this problem include wild bootstrap implementations of the PLR test or the use
of an information criterion, such as the BIC, to select the co-integration
rank. Although asymptotically valid in the presence of heteroskedasticity,
these methods can display very low finite sample power under some patterns of
non-stationary volatility. In particular, they do not exploit potential
efficiency gains that could be realised in the presence of non-stationary
volatility by using adaptive inference methods. Under the assumption of a known
autoregressive lag length, Boswijk and Zu (2022) develop adaptive PLR test
based methods using a non-parameteric estimate of the covariance matrix
process. It is well-known, however, that selecting an incorrect lag length can
significantly impact on the efficacy of both information criteria and bootstrap
PLR tests to determine co-integration rank in finite samples. We show that
adaptive information criteria-based approaches can be used to estimate the
autoregressive lag order to use in connection with bootstrap adaptive PLR
tests, or to jointly determine the co-integration rank and the VAR lag length
and that in both cases they are weakly consistent for these parameters in the
presence of non-stationary volatility provided standard conditions hold on the
penalty term. Monte Carlo simulations are used to demonstrate the potential
gains from using adaptive methods and an empirical application to the U.S. term
structure is provided.


  The consensus is that an object with a large mass will not manifest quantum
behavior. Therefore, we expect that the quantumness of a time-dependent
effective mass (TDEM) will erase after a long time when the mass profile
continuously grows with time. However, the present article depicts that the
Wigner quasi-probability distribution (WQD) will manifest an entanglement
behavour forever for two spatially separated free TDEM. The time-dependent
Schr\"{o}dinger equation for a free particle with TDEM is solved with the help
of Lewis-Riesenfeld phase space invariant method. WQD for the system of two
identical TDEM with quadratically increasing mass profile shows that the
particles are never separated. In particular, their reminiscent is present at
the origin of the phase-space forever.


  The main aim of this paper is to introduced the operations "Union" and
"Intersection," and four operators $O_1, O_2, O_3, O_4 $ on interval-valued
hesitant fuzzy soft sets and discuss some of their properties.


  The low flux infrared imaging needs performant high wavelength detectors.
Quantum Well Infrared Photodetectors (QWIP), thanks to the maturity of GaAs,
the possibility to adjust the detected wavelength on a large range and to
realize large uniform matrix are good candidate for such applications. In order
to validate this interest, we have performed an electro-optic characterization
of a 15{\mu}m sample. These measurements have been used to simulate the
performance of a camera based on this QWIP and used in a low infrared photons
flux scenario. We predict that this QWIP would succeed. Nevertheless these
simulations also underline the detrimental role of the dark current. Thus we
have developed a simulation tool based on a hoping approach between localized
states, which provide us a better understanding of the transport in these
heterostructures. The code has in particular underlines the role plays by the
electron -ionized impurities interaction, which make the dark current very
sensitive to the doping profile. Using this tool we have designed new
structures, with optimized doping profile, in which the scattering rate has
been decreased by a factor two. Moreover we have identified a quantum origin to
the plateau shape of the I(V) curve. This code is more generally a useful
simulation tool for the transport in h\'et\'erostructures. The influence of
growth defects (non ideal interface and disorder) has been quantized and we
have performed the first evaluation of The R0A in a THz QCD. Finally non local
transport effects have been investigated. Saw teeth observation on the I(V)
curves have been modeled and their influence on the detectivty estimated.


  Korpelainen, Lozin, and Razgon conjectured that a hereditary property of
graphs which is well-quasi-ordered by the induced subgraph order and defined by
only finitely many minimal forbidden induced subgraphs is labelled
well-quasi-ordered, a notion stronger than that of $n$-well-quasi-order
introduced by Pouzet in the 1970s. We present a counterexample to this
conjecture. In fact, we exhibit a hereditary property of graphs which is
well-quasi-ordered by the induced subgraph order and defined by finitely many
minimal forbidden induced subgraphs yet is not $2$-well-quasi-ordered. This
counterexample is based on the widdershins spiral, which has received some
study in the area of permutation patterns.


  We are interested in image manipulation via natural language text -- a task
that is useful for multiple AI applications but requires complex reasoning over
multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning
(NSCL), which has been quite effective for the task of Visual Question
Answering (VQA), for the task of image manipulation. Our system referred to as
NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and
only requires weak supervision in the form of annotated data for VQA. NeuroSIM
parses an instruction into a symbolic program, based on a Domain Specific
Language (DSL) comprising of object attributes and manipulation operations,
that guides its execution. We create a new dataset for the task, and extensive
experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA
baselines that make use of supervised data for manipulation.


  The metal-semiconductor transition of peanut-shaped fullerene (C$_{60}$)
polymers is clarified by considering the electron-phonon coupling in the uneven
structure of the polymers. We established a theory that accounts for the
transition temperature $T_c$ reported in a recent experiment and also suggests
that $T_c$ is considerably lowered by electron doping or prolonged irradiation
during synthesis. The decrease in $T_c$ is an appealing phenomenon with regard
to realizing high-conductivity C$_{60}$-based nanowires even at low
temperatures.


  Recent advances in unsupervised learning for object detection, segmentation,
and tracking hold significant promise for applications in robotics. A common
approach is to frame these tasks as inference in probabilistic latent-variable
models. In this paper, however, we show that the current state-of-the-art
struggles with visually complex scenes such as typically encountered in robot
manipulation tasks. We propose APEX, a new latent-variable model which is able
to segment and track objects in more realistic scenes featuring objects that
vary widely in size and texture, including the robot arm itself. This is
achieved by a principled mask normalisation algorithm and a high-resolution
scene encoder. To evaluate our approach, we present results on the real-world
Sketchy dataset. This dataset, however, does not contain ground truth masks and
object IDs for a quantitative evaluation. We thus introduce the Panda Pushing
Dataset (P2D) which shows a Panda arm interacting with objects on a table in
simulation and which includes ground-truth segmentation masks and object IDs
for tracking. In both cases, APEX comprehensively outperforms the current
state-of-the-art in unsupervised object segmentation and tracking. We
demonstrate the efficacy of our segmentations for robot skill execution on an
object arrangement task, where we also achieve the best or comparable
performance among all the baselines.


  Let $k$ be an algebraically closed field and $A$ be a (left and right)
Noetherian associative $k$-algebra. Assume further that $A$ is either
positively graded or semiperfect (this includes the class of finite dimensional
$k$-algebras, and $k$-algebras that are finitely generated modules over a
Noetherian central Henselian ring). Let $e$ be a primitive idempotent of $A$,
which we assume is of degree $0$ if $A$ is positively graded. We consider the
idempotent subalgebra $\Gamma = (1-e)A(1-e)$ and $S_e$ the simple right
$A$-module $S_e = eA/e{\rm rad}A$, where ${\rm rad}A$ is the Jacobson radical
of $A$, or the graded Jacobson radical of $A$ if $A$ is positively graded. In
this paper, we relate the homological dimensions of $A$ and $\Gamma$, using the
homological properties of $S_e$. First, if $S_e$ has no self-extensions of any
degree, then the global dimension of $A$ is finite if and only if that of
$\Gamma$ is. On the other hand, if the global dimensions of both $A$ and
$\Gamma$ are finite, then $S_e$ cannot have self-extensions of degree greater
than one, provided $A/{\rm rad}A$ is finite dimensional.


  The outer automorphism group $\text{Out}(\Gamma;\mathscr A)$ of a group
$\Gamma$ relative to a free factor system $\mathscr A$ of $\Gamma$ acts by
isometries on the relative free splitting complex $\mathcal{FS}(\Gamma;\mathscr
A)$, which is Gromov hyperbolic. This is the first of a multipart study of
stable translation lengths of elements of this action, a quantitative
generalization of earlier work on $\text{Out}(F_n)$. In this first part we
state and prove the Two Over All Theorem, expressing a uniform exponential
growth property along Stallings fold paths in $\mathcal{FS}(\Gamma;\mathscr
A)$, and we give two applications of that theorem. First, the natural map from
the outer space $\mathscr O(\Gamma;\mathscr A)$ to the free splitting complex
$\mathcal{FS}(\Gamma;\mathscr A)$ is coarsely Lipschitz, with respect to the
log-Lipschitz semimetric on $\mathscr O(\Gamma;\mathscr A)$. Second, if $\phi
\in \text{Out}(\Gamma;\mathscr A)$ has a filling attracting lamination with
expansion factor $\lambda>1$ then the stable translation length of $\phi$ on
$\mathcal{FS}(\Gamma;\mathscr A)$ has an upper bounded of the form $B
\log(\lambda)$.


  We report magnetization, specific heat, and NMR investigations on YFe2Al10
over a wide range in temperature and magnetic field and zero field (NQR)
measurements. Magnetic susceptibility, specific heat and spin-lattice
relaxation rate divided by T (1/T1T) follow a weak power law (T^-0.4)
temperature dependence, which is a signature of critical fluctuations of Fe
moments. The value of the Sommerfeld-Wilson ratio and linear relation between
1/T1T and chi(T) suggest the existence of ferromagnetic correlations in this
system. No magnetic ordering down to 50 mK in Cp(T) and the unusual temperature
and field scaling of the bulk and NMR data are associated with a magnetic
instability which drives the system to quantum criticality. The magnetic
properties of the system are tuned by field wherein ferromagnetic fluctuations
are suppressed and a crossover from quantum critical to FL behavior is observed
with increasing magnetic field.


  Functional MRI (fMRI) is widely used to examine brain functionality by
detecting alteration in oxygenated blood flow that arises with brain activity.
This work aims to investigate the neurological variation of human brain
responses during viewing of images with varied complexity using fMRI time
series (TS) analysis. Publicly available BOLD5000 dataset is used for this
purpose which contains fMRI scans while viewing 5254 distinct images of diverse
categories, drawn from three standard computer vision datasets: COCO, Imagenet
and SUN. To understand vision, it is important to study how brain functions
while looking at images of diverse complexities. Our first study employs
classical machine learning and deep learning strategies to classify image
complexity-specific fMRI TS, represents instances when images from COCO,
Imagenet and SUN datasets are seen. The implementation of this classification
across visual datasets holds great significance, as it provides valuable
insights into the fluctuations in BOLD signals when perceiving images of
varying complexities. Subsequently, temporal semantic segmentation is also
performed on whole fMRI TS to segment these time instances. The obtained result
of this analysis has established a baseline in studying how differently human
brain functions while looking into images of diverse complexities. Therefore,
accurate identification and distinguishing of variations in BOLD signals from
fMRI TS data serves as a critical initial step in vision studies, providing
insightful explanations for how static images with diverse complexities are
perceived.


  The countably infinite number of Virasoro representations of the logarithmic
minimal model LM(p,p') can be reorganized into a finite number of
W-representations with respect to the extended Virasoro algebra symmetry W.
Using a lattice implementation of fusion, we recently determined the fusion
algebra of these representations and found that it closes, albeit without an
identity for p>1. Here, we provide a fusion-matrix realization of this fusion
algebra and identify a fusion ring isomorphic to it. We also consider various
extensions of it and quotients thereof, and introduce and analyze commutative
diagrams with morphisms between the involved fusion algebras and the
corresponding quotient polynomial fusion rings. One particular extension is
reminiscent of the fundamental fusion algebra of LM(p,p') and offers a natural
way of introducing the missing identity for p>1. Working out explicit fusion
matrices is facilitated by a further enlargement based on a pair of mutual
Moore-Penrose inverses intertwining between the W-fundamental and enlarged
fusion algebras.


  The 3D ultrasound (US) entrance inspires a multitude of automated prenatal
examinations. However, studies about the structuralized description of the
whole fetus in 3D US are still rare. In this paper, we propose to estimate the
3D pose of fetus in US volumes to facilitate its quantitative analyses in
global and local scales. Given the great challenges in 3D US, including the
high volume dimension, poor image quality, symmetric ambiguity in anatomical
structures and large variations of fetal pose, our contribution is three-fold.
(i) This is the first work about 3D pose estimation of fetus in the literature.
We aim to extract the skeleton of whole fetus and assign different
segments/joints with correct torso/limb labels. (ii) We propose a
self-supervised learning (SSL) framework to finetune the deep network to form
visually plausible pose predictions. Specifically, we leverage the
landmark-based registration to effectively encode case-adaptive anatomical
priors and generate evolving label proxy for supervision. (iii) To enable our
3D network perceive better contextual cues with higher resolution input under
limited computing resource, we further adopt the gradient check-pointing (GCP)
strategy to save GPU memory and improve the prediction. Extensively validated
on a large 3D US dataset, our method tackles varying fetal poses and achieves
promising results. 3D pose estimation of fetus has potentials in serving as a
map to provide navigation for many advanced studies.


  The climate on early Mars is one of the major unsolved problems. It is
unclear whether early Mars was warm and wet or cold and icy. Morphological
features on Mars such as sinuous ridges could provide critical constraints to
address this issue. Here we investigate several sinuous ridges to the east of
Tempe Terra, Mars and find they may have recorded persistent fluvial activity
on early Mars. Our analysis indicates that these ridges may represent
exhumation of the channel belts and overbank deposits formed from meander
rivers over significant geologic time. Layered smectite-bearing minerals,
distributed along the ridge flanks, could be detrital or authigenic floodplain
clays. Our interpretation of the stratigraphic relationships indicates that the
layered smectite-bearing materials lie between channel belt deposits,
supporting the floodplain interpretation. Our results suggest that a persistent
warming event has persisted for a geologically significant interval (>1500 yr)
during the Noachian period of Mars.


  Physical laws are believed to be invariant under the combined transformations
of charge, parity and time reversal (CPT symmetry). This implies that an
antimatter particle has exactly the same mass and absolute value of charge as
its particle counterpart. Metastable antiprotonic helium ($\bar{p}{\rm He}^+$)
is a three-body atom consisting of a normal helium nucleus, an electron in its
ground state and an antiproton ($\bar{p}$) occupying a Rydberg state with high
principal and angular momentum quantum numbers, respectively $n$ and $\ell$,
such that $n\sim\ell\sim 38$. These atoms are amenable to precision laser
spectroscopy, the results of which can in principle be used to determine the
antiproton-to-electron mass ratio and to constrain the equality between the
antiproton and proton charges and masses. Here we report two-photon
spectroscopy of antiprotonic helium, in which $\bar{p}{\rm ^3He^+}$ and
$\bar{p}{\rm ^4He^+}$ isotopes are irradiated by two counter-propagating laser
beams. This excites nonlinear, two-photon transitions of the antiproton of the
type $(n,\ell)\rightarrow (n-2,\ell-2)$ at deep-ultraviolet wavelengths
($\lambda$=139.8, 193.0 and 197.0nm), which partly cancel the Doppler
broadening of the laser resonance caused by the thermal motion of the atoms.
The resulting narrow spectral lines allowed us to measure three transition
frequencies with fractional precisions of 2.3-5 parts in $10^9$. By comparing
the results with three-body quantum electrodynamics calculations, we derived an
antiproton-to-electron mass ratio of 1,836.1526736(23), where the parenthetical
error represents one standard deviation. This agrees with the
proton-to-electron value known to a similar precision.


  The top-down and bottom-up methods are two mainstreams of referring
segmentation, while both methods have their own intrinsic weaknesses. Top-down
methods are chiefly disturbed by Polar Negative (PN) errors owing to the lack
of fine-grained cross-modal alignment. Bottom-up methods are mainly perturbed
by Inferior Positive (IP) errors due to the lack of prior object information.
Nevertheless, we discover that two types of methods are highly complementary
for restraining respective weaknesses but the direct average combination leads
to harmful interference. In this context, we build Win-win Cooperation (WiCo)
to exploit complementary nature of two types of methods on both interaction and
integration aspects for achieving a win-win improvement. For the interaction
aspect, Complementary Feature Interaction (CFI) provides fine-grained
information to top-down branch and introduces prior object information to
bottom-up branch for complementary feature enhancement. For the integration
aspect, Gaussian Scoring Integration (GSI) models the gaussian performance
distributions of two branches and weightedly integrates results by sampling
confident scores from the distributions. With our WiCo, several prominent
top-down and bottom-up combinations achieve remarkable improvements on three
common datasets with reasonable extra costs, which justifies effectiveness and
generality of our method.


  An unsupervised learning procedure based on maximizing the mutual information
between the outputs of two networks receiving different but statistically
dependent inputs is analyzed (Becker and Hinton, Nature, 355, 92, 161). For a
generic data model, I show that in the large sample limit the structure in the
data is recognized by mutual information maximization. For a more restricted
model, where the networks are similar to perceptrons, I calculate the learning
curves for zero-temperature Gibbs learning. These show that convergence can be
rather slow, and a way of regularizing the procedure is considered.


  We use observations from the APOGEE survey to explore the relationship
between stellar parameters and multiplicity. We combine high-resolution repeat
spectroscopy for 41,363 dwarf and subgiant stars with abundance measurements
from the APOGEE pipeline and distances and stellar parameters derived using
\textit{Gaia} DR2 parallaxes from \cite{Sanders2018} to identify and
characterise stellar multiples with periods below 30 years, corresponding to
\drvm$\gtrsim$ 3 \kms, where \drvm\ is the maximum APOGEE-detected shift in the
radial velocities. Chemical composition is responsible for most of the
variation in the close binary fraction in our sample, with stellar parameters
like mass and age playing a secondary role. In addition to the previously
identified strong anti-correlation between the close binary fraction and \feh\,
we find that high abundances of $\alpha$ elements also suppress multiplicity at
most values of \feh\ sampled by APOGEE. The anti-correlation between $\alpha$
abundances and multiplicity is substantially steeper than that observed for Fe,
suggesting C, O, and Si in the form of dust and ices dominate the opacity of
primordial protostellar disks and their propensity for fragmentation via
gravitational stability. Near \feh{} = 0 dex, the bias-corrected close binary
fraction ($a<10$ au) decreases from $\approx$ 100 per cent at \alh{} = $-$0.2
dex to $\approx$ 15 per cent near \alh{} = 0.08 dex, with a suggestive turn-up
to $\approx$20 per cent near \alh{} = 0.2. We conclude that the relationship
between stellar multiplicity and chemical composition for sun-like dwarf stars
in the field of the Milky Way is complex, and that this complexity should be
accounted for in future studies of interacting binaries.


  We consider a bistatic configuration with a stationary transmitter
transmitting unknown waveforms of opportunity and a moving receiver, and
present a Deep Learning (DL) framework for passive synthetic aperture radar
(SAR) imaging. Existing passive radar methods require two or more antennas
which are either spatially separated or colocated with sufficient directivity
to estimate the underlying waveform prior to imaging. Our approach to passive
radar only requires a single receiver, hence reduces cost and increases
versatility. We approach DL from an optimization perspective and formulate
image reconstruction as a machine learning task. By unfolding the iterations of
a proximal gradient descent algorithm, we construct a deep recurrent neural
network (RNN) that is parameterized by transmitted waveforms. We cascade the
RNN structure with a decoder stage to form a recurrent-auto encoder
architecture. We then utilize backpropagation to learn transmitted waveforms by
training the network in an unsupervised manner using SAR measurements. The
highly non-convex problem of backpropagation is guided to a feasible solution
over the parameter space by initializing the network with the known components
of the SAR forward model. Moreover, prior information regarding the waveform
structure is incorporated during initialization and backpropagation. We
demonstrate the effectiveness of the DL-based approach through extensive
numerical simulations that show focused, high contrast imagery using a single
receiver antenna at realistic SNR levels.


  The classical Robinson--Schensted--Knuth correspondence is a bijection from
nonnegative integer matrices to pairs of semi-standard Young tableaux. Based on
the work of, among others, Burge, Hillman, Grassl, Knuth and Gansner, it is
known that a version of this correspondence gives, for any nonzero integer
partition $\lambda$, a bijection from arbitrary fillings of $\lambda$ to
reverse plane partitions of shape $\lambda$, via Greene--Kleitman invariants.
By bringing out the combinatorial aspects of our recent results on quiver
representations, we construct a family of bijections from fillings of $\lambda$
to reverse plane partitions of shape $\lambda$ parametrized by a choice of
Coxeter element in a suitable symmetric group. We recover the above version of
the Robinson--Schensted--Knuth correspondence for a particular choice of
Coxeter element depending on $\lambda$.


  We present results based on a Baade-Wesselink analysis of Cepheids in the
Small Magellanic Cloud. The Baade-Wesselink analysis provides individual
luminosities for these metal-poor Cepheids which combined with recent
Baade-Wesselink results from Gieren et al. (1998) on solar metallicity Galactic
Cepheids constrain the metallicity effect on the zero-point of the Cepheid P-L
relation. A preliminary analysis leads to an effect of dMv / d[Fe/H]= -0.45
+-0.15, metal-rich Cepheids being brighter, in good agreement with several
recent independent determinations. An effect of this magnitude reduces
significantly the current disagreement between the long and the short distance
estimates to the LMC, and favors a shorter value.


  The seesaw mechanism for the small neutrino mass has been a popular paradigm,
yet it has been believed that there is no way to test it experimentally. We
present a conceivable outcome from future experiments that would convince us of
the seesaw mechanism. It would involve a variety of data from LHC, ILC,
cosmology, underground, and low-energy flavor violation experiments to
establish the case.


  The work fluctuations of an oscillator in contact with a thermostat and
driven out of equilibrium by an external force are studied experimentally and
theoretically within the context of Fluctuation Theorems (FTs). The oscillator
dynamics is modeled by a second order Langevin equation. Both the transient and
stationary state fluctuation theorems hold and the finite time corrections are
very different from those of a first order Langevin equation. The periodic
forcing of the oscillator is also studied; it presents new and unexpected short
time convergences. Analytical expressions are given in all cases.


  We report a search for the doubly charmed baryon $\Xi_{cc}^{+}$ through the
decay $\Xi_{cc}^{+} \to \Lambda_{c}^{+} K^{-} \pi^{+}$, using a data sample
corresponding to an integrated luminosity of $0.65~\mathrm{pb^{-1}}$ of $pp$
collisions at $\mathrm{\sqrt{s}} = 7~\mathrm{TeV}$. In the mass range
3300-3800$~\mathrm{MeV}/c^{2}$ no significant signal is observed. Upper limits
at $95\%$ confidence level are set on $R$, the ratio of the production cross
section of the $\Xi_{cc}^{+}$ times the relevant branching fraction over the
$\Lambda_{c}^{+}$ cross section, as a function of the $\Xi_{cc}^{+}$ mass and
lifetime. The largest upper limits on $R$ over the investigated mass range are
$R<1.5\times10^{-2}$ for a lifetime of $100~\mathrm{fs}$ and
$R<3.9\times10^{-4}$ for a lifetime of $400~\mathrm{fs}$.


  The goal of this paper is to provide an insight into the equilibrium of the
Internet market, when the current balance of the market is disrupted, and one
of the ISPs switches to a non-neutral regime. We consider a content provider
with a subscription revenue model and a continuum of end-users. The CP is also
non-neutral, in the sense that she can charge users of different ISPs different
subscription fees, and use this "leverage" to control the equilibrium outcome.
Results reveal that the CP is able to control the non-neutral ISP to some
extend. However, switching to a non-neutral regime by an ISP tips the balance
of the market in favor of this ISP.


  In the present paper optimal interpolation formulas are constructed in
$W_2^{(m,m-1)}(0,1)$ space. Explicit formulas for coefficients of optimal
interpolation formulas are obtained. Some numerical results are presented.


  We show theoretically that interaction effects in a double-well optical
lattice can induce condensates in an excited band. For a symmetric double-well
lattice, bosons condense into the bottom of the excited band at the edge of the
Brillouin Zone if the chemical potential is above a critical value. For an
asymmetric lattice, a condensate with zero momentum is automatically induced in
the excited band by the condensate in the lowest band. This is due to a
combined effect of interaction and lattice potential, which reduces the band
gap and breaks the inversion symmetry. Our work can be generalized to a
superlattice composed of multiple-well potentials at each lattice site, where
condensates can be induced in even higher bands.