  Speaker identification, determining which character said each utterance in
literary text, benefits many downstream tasks. Most existing approaches use
expert-defined rules or rule-based features to directly approach this task, but
these approaches come with significant drawbacks, such as lack of contextual
reasoning and poor cross-lingual generalization. In this work, we propose a
speaker identification framework that addresses these issues. We first extract
large-scale distant supervision signals in English via general-purpose tools
and heuristics, and then apply these weakly-labeled instances with a focus on
encouraging contextual reasoning to train a cross-lingual language model. We
show that the resulting model outperforms previous state-of-the-art methods on
two English speaker identification benchmarks by up to 9% in accuracy and 5%
with only distant supervision, as well as two Chinese speaker identification
datasets by up to 4.7%.


  Far-UV circular dichroism (CD) spectroscopy provides a rapid, sensitive,
nondestructive tool to analyze protein conformation by monitoring secondary
structure composition. Originally intended for educational purposes, a
spreadsheet-based program that implements a rudimentary routine for fitting and
simulating far-UV protein CD spectra became quite used in research papers too,
as it allowed very quick deconvolution of spectra into secondary structure
compositions and easy simulation of spectra expected for defined secondary
structure contents. To make such software more readily available, I present
here an online version that runs directly on web browsers allowing even faster
analyses on any modern device and without the need of any spreadsheet or third
programs. The new version further extends the original capabilities to fit and
simulate alpha, beta and random coil contents now including also beta turns; it
allows to quickly select the effective spectral window used for fitting and
enables interactive exploration of the effects of changes in secondary
structure composition on the resulting spectra. The web app allows ubiquitous
implementation in biophysics courses for example right on student smartphones,
and seamless, rapid tests in research settings before moving to more advanced
analysis programs (a few proposed here too). The web app is freely available
without registration at
http://lucianoabriata.altervista.org/jsinscience/cd/cd3.html


  Recently, GaAsSb/GaAsN type II short-period superlattices (SLs) have been
proposed as suitable structures to be implemented in the optimal design of
monolithic multi-junction solar cells. However, due to strong surface Sb
segregation, experimental Sb composition profiles differ greatly from the
nominal square-wave design. In this work, the improvement of the interface
quality of these SLs in terms of compositional abruptness and surface roughness
has been evaluated by implementing different growth interruption times under
Sb4/As4 (soaking) and As4 (desorption) overpressure conditions before and after
the growth of GaAsSb layers, respectively. The combined effects of both
processes enhance Sb distribution, achieving squarer compositional profiles
with reduced surface roughness interfaces. It has been found that the
improvement in compositional abruptness is quantitatively much higher at the
lower interface, during soaking, than at the upper interface during desorption.
Conversely, a larger decrease in surface roughness is achieved at the upper
interface than at the lower interface. Fitting of the Sb segregation profiles
using the 3-layer kinetic fluid model has shown that the increase in Sb
incorporation rate is due to the decrease in segregation energy, presumably to
changes in the surface reconstruction of the floating layer at the surface.


  We consider the problem of finding the bound-state spectrum of an impurity
immersed in a weakly interacting two-dimensional Bose-Einstein condensate
supporting a single vortex. We obtain approximate expressions for the energy
levels and show that, due to the finite size of the condensate, the impurity
can access only a finite number of physical bound states. By virtue of the
topological quantization of the vorticity and of the emergence of the Tkachenko
lattice, this system is promising as a robust and scalable platform for the
realization of qubits. Moreover, it provides a potentially new paradigm for
polaron physics in Bose-Einstein condensates and a glimpse towards the study of
quantum turbulence in low-dimensionality systems.


  We present a novel Probabilistic Flux Variation Gradient (PFVG) approach to
to separate the contributions of active galactic nuclei (AGN) and host galaxies
in the context of photometric reverberation mapping (PRM) of AGN. We explored
the ability of recovering the fractional contribution in a model-independent
way using the entire set of light curves obtained through different filters and
photometric apertures simultaneously. The method is based on the observed bluer
when brighter phenomenon that is attributed to the superimposition of a
two-component structure; the red host galaxy, which is constant in time, and
the varying blue AGN. We describe the PFVG mathematical formalism and
demonstrate its performance using simulated light curves and available PRM
observations. The new probabilistic approach is able to recover host-galaxy
fluxes to within 1% precision as long as the light curves do not show a
significant contribution from time delays. This represents a significant
improvement with respect to previous applications of the traditional FVG method
to PRM data. The proposed PFVG provides an efficient and accurate way to
separate the AGN and host-galaxy luminosities in PRM monitoring data. The
method will be especially helpful in the case of large upcoming photometric
survey telescopes such as the public optical/near-infrared Legacy Survey of
Space and Time (LSST) at the Vera C. Rubin Observatory. Finally, we have made
the algorithms freely available as part of our Julia PFVG package.


  Decades of research on Internet congestion control (CC) has produced a
plethora of algorithms that optimize for different performance objectives.
Applications face the challenge of choosing the most suitable algorithm based
on their needs, and it takes tremendous efforts and expertise to customize CC
algorithms when new demands emerge. In this paper, we explore a basic question:
can we design a single CC algorithm to satisfy different objectives? We propose
MOCC, the first multi-objective congestion control algorithm that attempts to
address this challenge. The core of MOCC is a novel multi-objective
reinforcement learning framework for CC that can automatically learn the
correlations between different application requirements and the corresponding
optimal control policies. Under this framework, MOCC further applies transfer
learning to transfer the knowledge from past experience to new applications,
quickly adapting itself to a new objective even if it is unforeseen. We provide
both user-space and kernel-space implementation of MOCC. Real-world experiments
and extensive simulations show that MOCC well supports multi-objective,
competing or outperforming the best existing CC algorithms on individual
objectives, and quickly adapting to new applications (e.g., 14.2x faster than
prior work) without compromising old ones.


  We propose a novel data-driven framework for assessing the a-priori epidemic
risk of a geographical area and for identifying high-risk areas within a
country. Our risk index is evaluated as a function of three different
components: the hazard of the disease, the exposure of the area and the
vulnerability of its inhabitants. As an application, we discuss the case of
COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions
by using available historical data on air pollution, human mobility, winter
temperature, housing concentration, health care density, population size and
age. We find that the epidemic risk is higher in some of the Northern regions
with respect to Central and Southern Italy. The corresponding risk index shows
correlations with the available official data on the number of infected
individuals, patients in intensive care and deceased patients, and can help
explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto
have suffered much more than the rest of the country. Although the COVID-19
outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at
the same time, when the first cases were officially certified at the beginning
of 2020, the disease has spread faster and with heavier consequences in regions
with higher epidemic risk. Our framework can be extended and tested on other
epidemic data, such as those on seasonal flu, and applied to other countries.
We also present a policy model connected with our methodology, which might help
policy-makers to take informed decisions.


  The integration of Large Language Models (LLMs) in education offers both
opportunities and challenges, particularly in fields like physics that demand
precise conceptual understanding. This study examines the capabilities of six
state-of-the-art LLMs in explaining the law of conservation of momentum, a
fundamental principle in physics. By analyzing responses to a consistent,
simple prompt in Japanese, we assess the models' explanatory approaches, depth
of understanding, and adaptability to different educational levels.Our
comprehensive analysis, encompassing text characteristics, response similarity,
and keyword usage, unveils significant diversity in explanatory styles across
models. ChatGPT4.0 and Coral provided more comprehensive and technically
detailed explanations, while Gemini models tended toward more intuitive
approaches. Key findings include variations in the treatment of critical
concepts such as net force, and differing emphases on mathematical rigor and
real-world applications.The results indicate that different AI models may be
more suitable for various educational contexts, ranging from introductory to
advanced levels. ChatGPT4.0 and Coral demonstrated potential for advanced
discussions, while Gemini models appeared more appropriate for introductory
explanations. Importantly, the study underscores the necessity of educator
guidance in effectively leveraging these AI tools, as models varied in their
ability to convey nuanced aspects of physical principles.This research
establishes a foundation for understanding the educational potential of LLMs in
physics, providing insights for educators on integrating these tools into their
teaching practices. It also highlights the need for further investigation into
AI-assisted learning in STEM fields, paving the way for more sophisticated
applications of AI in physics education.


  In the so-called unified dark fluid models, the dark sector gets simplified
because dark matter and dark energy are replaced by a single fluid that behaves
as the former at early times and as the latter at late times. In this short
paper we analyze this class of models from the thermodynamic viewpoint. While
the second law of thermodynamics is satisfied, the first two derivatives of the
entropies of the apparent horizon and of the energy components suffer such a
sharp oscillation that doubts are raised about the soundness of this class of
models.


  We use dimensional regularization to compute the one loop quantum
gravitational contribution to the vacuum polarization on de Sitter background.
Adding the appropriate BPHZ counterterms gives a fully renormalized result
which can be used to quantum correct Maxwell's equations. We use the Hartree
approximation to argue that the electric field strengths of photons experience
a secular suppression during inflation.


  The big bang singularity could be understood as a breakdown of Einstein's
General Relativity at very high energies. Adopting this viewpoint, other
theories, that implement Einstein Cosmology at high energies, might solve the
problem of the primeval singularity. One of them is Loop Quantum Cosmology
(LQC) with a small cosmological constant that models a universe moving along an
ellipse, which prevents singularities like the big bang or the big rip, in the
phase space $(H,\rho)$, where $H$ is the Hubble parameter and $\rho$ the energy
density of the universe. Using LQC when one considers a model of universe
filled by radiation and matter where, due to the cosmological constant, there
are a de Sitter and an anti de Sitter solution. This means that one obtains a
bouncing non-singular universe which is in the contracting phase at early
times. After leaving this phase, i.e., after bouncing, it passes trough a
radiation and matter dominated phase and finally at late times it expands in an
accelerated way (current cosmic acceleration). This model does not suffer from
the horizon and flatness problems as in big bang cosmology, where a period of
inflation that increases the size of our universe in more than 60 e-folds is
needed in order to solve both problems. The model has two mechanisms to avoid
these problems: The evolution of the universe through a contracting phase and a
period of super-inflation ($\dot{H}> 0$).


  The hindered diffusion model is introduced. It is a continuum model giving
the dynamics of a conserved density. Similar to the spin-facilitated models,
the kinetics are hindered by a fluctuating diffusion coefficient that decreases
as the local density approaches some geometrically constrained value where the
mobility goes to zero. The model leads in a natural way to activated dynamics
at low temperatures and high densities. In a well defined approximation the
theory is shown to be compatible with mode-coupling theory. Indeed in the
simplest form of the model we find an ergodic-nonergodic transition into a
phase that supports activated kinetics and nonergodic behavior.


  In this paper, it is shown that some of the results of torus actions on
Poincar\'{e} duality spaces, Borel's dimension formula and topological
splitting principle to local weights, hold if `torus' is replaced by
`pro-torus'.


  We study the one-dimensional hard-core Bose-Hubbard model with three-body
interactions. In previous work[1], the two-thirds filling solid with both
bond-wave order (BOW) and charge-density wave order (CDW) is found and it
undergoes a second order phase transition to the superfluid phase. However, in
recent work[2], we found the BOW not independent with CDW order. Thus, by
utilizing the density matrix renormalization group, we make use of both
entanglement entropy and fidelity susceptibility to detect the phase
transition. In two-thirds filling, the extreme point of the entanglement
entropy also indicates the superfluid phase transition point. However, the
pairwise entanglement entropy drop quickly after additional critical point,
such disentangled behaviour demonstrates another melting point to the CDW
phase. Thus, a novel intermediate phase between CDW phase and superfluid phase
is found. At last, we analyzed the half filling case with entanglement entropy.


  Low-dissipation model and the endoreversible model of heat engines are two of
the most commonly studied models of machines in finite-time thermodynamics. In
this paper, we compare the performance characteristics of these two models
under optimal power output. We point out a basic equivalence between them, in
the linear response regime.


  We describe a first-principles method for calculating electronic structure,
vibrational modes and frequencies, electron-phonon couplings, and inelastic
electron transport properties of an atomic-scale device bridging two metallic
contacts under nonequilibrium conditions. The method extends the
density-functional codes SIESTA and TranSIESTA that use atomic basis sets. The
inelastic conductance characteristics are calculated using the nonequilibrium
Green's function formalism, and the electron-phonon interaction is addressed
with perturbation theory up to the level of the self-consistent Born
approximation. While these calculations often are computationally demanding, we
show how they can be approximated by a simple and efficient lowest order
expansion. Our method also addresses effects of energy dissipation and local
heating of the junction via detailed calculations of the power flow. We
demonstrate the developed procedures by considering inelastic transport through
atomic gold wires of various lengths, thereby extending the results presented
in [Frederiksen et al., Phys. Rev. Lett. 93, 256601 (2004)]. To illustrate that
the method applies more generally to molecular devices, we also calculate the
inelastic current through different hydrocarbon molecules between gold
electrodes. Both for the wires and the molecules our theory is in quantitative
agreement with experiments, and characterizes the system-specific mode
selectivity and local heating.


  Thirteen spectroscopic orbits of late-type stars are determined from the
high-resolution spectra taken with the CHIRON echelle spectrometer at the 1.5-m
CTIO telescope. Most (HIP 14194B, 40523A, 41171A, 51578A, 57572B, 59426A,
62852B, 66438A, 87813B, and 101472A) are inner subsystems in hierarchical
multiple stars with three or four components. The periods range from 2.2 to
1131 days. Masses of the components, orbital inclinations, and projected
rotation velocities are estimated, the presence or absence of the lithium line
is noted. In addition to those systems, HIP 57021 is a simple 54-day twin
binary, and HIP 111598 is a compact triple-lined system with periods of 5.9 and
271 days. This object is likely old, but, nevertheless, the secondary component
in the inner pair does not rotate synchronously with the orbit. The
period-eccentricity diagram of 528 known inner low-mass spectroscopic
subsystems (including 36 from this paper series) is given. The distribution of
the inner periods is smooth, without any details around the tidal
circularization period of ~10 d.


  Learning invariant representations has been the longstanding approach to
self-supervised learning. However, recently progress has been made in
preserving equivariant properties in representations, yet do so with highly
prescribed architectures. In this work, we propose an invariant-equivariant
self-supervised architecture that employs Capsule Networks (CapsNets) which
have been shown to capture equivariance with respect to novel viewpoints. We
demonstrate that the use of CapsNets in equivariant self-supervised
architectures achieves improved downstream performance on equivariant tasks
with higher efficiency and fewer network parameters. To accommodate the
architectural changes of CapsNets, we introduce a new objective function based
on entropy minimisation. This approach which we name CapsIE (Capsule Invariant
Equivariant Network) achieves state-of-the-art performance across invariant and
equivariant tasks on the 3DIEBench dataset compared to prior equivariant SSL
methods, while outperforming supervised baselines. Our results demonstrate the
ability of CapsNets to learn complex and generalised representations for
large-scale, multi-task datasets compared to previous CapsNet benchmarks. Code
is available at https://github.com/AberdeenML/CapsIE.


  We construct two practical algorithms for twisted conjugacy classes of
polycyclic-by-finite groups. The first algorithm determines whether two
elements of a group are twisted conjugate for two given endomorphisms, under
the condition that the Reidemeister coincidence number of these endomorphisms
is finite. The second algorithm determines representatives of the Reidemeister
coincidence classes of two endomorphisms if their Reidemeister coincidence
number is finite, or returns "fail" if the Reidemeister coincidence number is
infinite.


  We study shock statistics in the scalar conservation law $\partial_t u +
\partial_x f(u)=0$, $x \in \R$, $t>0$, with a convex flux $f$ and spatially
random initial data. We show that the Markov property (in $x$) is preserved for
a large class of random initial data (Markov processes with downward jumps and
derivatives of L\'evy processes with downward jumps). The kinetics of shock
clustering is then described completely by an evolution equation for the
generator of the Markov process $u(x,t)$, $x\in \R$. We present four distinct
derivations for this evolution equation, and show that it takes the form of a
Lax pair. The Lax equation admits a spectral parameter as in Manakov (1976),
and has remarkable exact solutions for Burgers equation ($f(u)=u^2/2$). This
suggests the kinetic equations of shock clustering are completely integrable.


  The origin of the spiral spin-order in perovskite multiferroic manganites
$R$MnO$_{3}$ ($RE=$ Tb or Dy) is here investigated using a two $e_{\rm
g}$-orbitals double-exchange model. Our main result is that the experimentally
observed spiral phase can be stabilized by introducing a relatively weak
next-nearest-neighbor superexchange coupling ($\sim10%$ of the nearest-neighbor
superexchange). Moreover, the Jahn-Teller lattice distortion is also shown to
be essential to obtain a realistic spiral period. Supporting our conclusions,
the generic phase diagram of undoped perovskite manganites is obtained using
Monte Carlo simulations, showing phase transitions from the A-type
antiferromagnet, to the spiral phase, and finally to the E-type
antiferromagnet, with decreasing size of the $R$ ions. These results are
qualitatively explained by the enhanced relative intensity of the
superexchanges.


  We report on the discovery of compact, narrow OH line emission from the
hydroxl molecule at 1720 MHz toward the extended TeV source J1834-087. The
origin of this high energy emission is unknown; it could be powered by one or
more candidate neutron stars (leptonic) or by cosmic rays interacting with
dense gas (hadronic). The OH emission is detected near the center of J1834-087,
coincident with the radio continuum of the supernova remnant W41, and the
radial velocity of the line is the same velocity as a giant molecular cloud
along the line of sight. We argue that the OH is maser emission stimulated by
the interaction of the W41 shock with the molecular cloud. The known
correlation between gamma-ray bright supernova remnants and OH masers favors a
hadronic interpretation for this high energy emission.


  Inspired by the work of [Kempe, Kleinberg, Oren, Slivkins, EC13] we introduce
and analyze a model on opinion formation; the update rule of our dynamics is a
simplified version of that of Kempe et. al. We assume that the population is
partitioned into types whose interaction pattern is specified by a graph.
Interaction leads to population mass moving from types of smaller mass to those
of bigger. We show that starting uniformly at random over all population
vectors on the simplex, our dynamics converges point-wise with probability one
to an independent set. This settles an open problem of Kempe et. al., as
applicable to our dynamics. We believe that our techniques can be used to
settle the open problem for the Kempe et. al. dynamics as well.
  Next, we extend the model of Kempe et. al. by introducing the notion of birth
and death of types, with the interaction graph evolving appropriately. Birth of
types is determined by a Bernoulli process and types die when their population
mass is less than a parameter $\epsilon$. We show that if the births are
infrequent, then there are long periods of "stability" in which there is no
population mass that moves. Finally we show that even if births are frequent
and "stability" is not attained, the total number of types does not explode: it
remains logarithmic in $1/\epsilon$.


  Higher KdV flows on spaces of closed equicentroaffine plane curves are
studied and it is shown that the flows are described as certain
multi-Hamiltonian systems on the spaces. Multi-Hamiltonian systems describing
higher mKdV flows are also given on spaces of closed Euclidean plane curves via
the geometric Miura transformation.


  The discovery of dark energy by the first generation of high-redshift
supernova surveys has generated enormous interest beyond cosmology and has
dramatic implications for fundamental physics. Distance measurements using
supernova explosions are the most direct probes of the expansion history of the
Universe, making them extremely useful tools to study the cosmic fabric and the
properties of gravity at the largest scales. The past decade has seen the
confirmation of the original results. Type Ia supernovae are among the leading
techniques to obtain high-precision measurements of the dark energy equation of
state parameter, and in the near future, its time dependence. The success of
these efforts depends on our ability to understand a large number of effects,
mostly of astrophysical nature, influencing the observed flux at Earth. The
frontier now lies in understanding if the observed phenomenon is due to vacuum
energy, albeit its unnatural density, or some exotic new physics. Future
surveys will address the systematic effects with improved calibration
procedures and provide thousands of supernovae for detailed studies.


  We are interested in this paper with the connection between the dynamics of a
model related to Nuclear Magnetic Resonance (NMR) in Quantum Field Theory (QFT)
with its classical counterpart known as the Maxwell-Bloch equations. The model
in QFT is a model of Quantum Electrodynamics (QED) considering fixed spins
interacting with the quantized electromagnetic field in an external constant
magnetic field. This model is close to the common spin-boson model. The
classical model goes back to F. Bloch [15] in 1946. Our goal is not only to
study the derivation of the Maxwell-Bloch equations but to also establish a
semiclassical asymptotic expansion of arbitrary high orders with control of the
error terms of this standard nonlinear classical motion equations. This
provides therefore quantum corrections of any order in powers of the
semiclassical parameter of the Bloch equations. Besides, the asymptotic
expansion for the photon number is also analyzed and a law describing the
photon number time evolution is written down involving the radiation field
polarization. Since the quantum photon state Hilbert space (radiation field) is
infinite dimensional we are thus concerned in this article with the issue of
semiclassical calculus in an infinite dimensional setting. In this regard, we
are studying standard notions as Wick and anti-Wick quantizations, heat
operator, Beals characterization theorem and compositions of symbols in the
infinite dimensional context which can have their own interest.


  By computing the operator product expansions between the first two ${\cal
N}=4$ higher spin multiplets in the unitary coset model, the (anti)commutators
of higher spin currents are obtained under the large $(N,k)$ 't Hooft-like
limit. The free field realization with complex bosons and fermions is
presented. The (anti)commutators for generic spins $s_1$ and $s_2$ with
manifest $SO(4)$ symmetry at vanishing 't Hooft-like coupling constant are
completely determined. The structure constants can be written in terms of the
ones in the ${\cal N}=2$ ${\cal W}_{\infty}$ algebra found by Bergshoeff, Pope,
Romans, Sezgin and Shen previously, in addition to the spin-dependent
fractional coefficients and two $SO(4)$ invariant tensors. We also describe the
${\cal N}=4$ higher spin generators, by using the above coset construction
results, for general super spin $s$ in terms of oscillators in the matrix
generalization of $AdS_3$ Vasiliev higher spin theory at nonzero 't Hooft-like
coupling constant. We obtain the ${\cal N}=4$ higher spin algebra for low spins
and present how to determine the structure constants, which depend on the
higher spin algebra parameter, in general, for fixed spins $s_1$ and $s_2$.


  We define the new description of the electromagnetic current to hold the
current conservation in the momentum-dependent Dirac fields from the Ward
Takahashi identity. To describe the momentum-dependence we solve the
relativistic Hartree-Fock approximation by using the one-pion exchange. In
addition we discuss on contribution from the one-pion exchange current and the
core polarization. It is shown that this current can reduce the convection
current in the isovector case, whose value has been too big due to the small
effective mass in the usual relativistic Hartree approximation.


  This work focuses on the task of property targeting: that is, generating
molecules conditioned on target chemical properties to expedite candidate
screening for novel drug and materials development. DiGress is a recent
diffusion model for molecular graphs whose distinctive feature is allowing
property targeting through classifier-based (CB) guidance. While CB guidance
may work to generate molecular-like graphs, we hint at the fact that its
assumptions apply poorly to the chemical domain. Based on this insight we
propose a classifier-free DiGress (FreeGress), which works by directly
injecting the conditioning information into the training process. CF guidance
is convenient given its less stringent assumptions and since it does not
require to train an auxiliary property regressor, thus halving the number of
trainable parameters in the model. We empirically show that our model yields up
to 79% improvement in Mean Absolute Error with respect to DiGress on property
targeting tasks on QM9 and ZINC-250k benchmarks. As an additional contribution,
we propose a simple yet powerful approach to improve chemical validity of
generated samples, based on the observation that certain chemical properties
such as molecular weight correlate with the number of atoms in molecules.


  In this paper, we consider a novel task, Spatio-Temporal Video Grounding for
Multi-Form Sentences (STVG). Given an untrimmed video and a
declarative/interrogative sentence depicting an object, STVG aims to localize
the spatio-temporal tube of the queried object. STVG has two challenging
settings: (1) We need to localize spatio-temporal object tubes from untrimmed
videos, where the object may only exist in a very small segment of the video;
(2) We deal with multi-form sentences, including the declarative sentences with
explicit objects and interrogative sentences with unknown objects. Existing
methods cannot tackle the STVG task due to the ineffective tube pre-generation
and the lack of object relationship modeling. Thus, we then propose a novel
Spatio-Temporal Graph Reasoning Network (STGRN) for this task. First, we build
a spatio-temporal region graph to capture the region relationships with
temporal object dynamics, which involves the implicit and explicit spatial
subgraphs in each frame and the temporal dynamic subgraph across frames. We
then incorporate textual clues into the graph and develop the multi-step
cross-modal graph reasoning. Next, we introduce a spatio-temporal localizer
with a dynamic selection method to directly retrieve the spatio-temporal tubes
without tube pre-generation. Moreover, we contribute a large-scale video
grounding dataset VidSTG based on video relation dataset VidOR. The extensive
experiments demonstrate the effectiveness of our method.


  This work studies the Zipf Law for cities in Brazil. Data from censuses of
1970, 1980, 1991 and 2000 were used to select a sample containing only cities
with 30,000 inhabitants or more. The results show that the population
distribution in Brazilian cities does follow a power law similar to the ones
found in other countries. Estimates of the power law exponent were found to be
2.22 +/- 0.34 for the 1970 and 1980 censuses, and 2.26 +/- 0.11 for censuses of
1991 and 2000. More accurate results were obtained with the maximum likelihood
estimator, showing an exponent equal to 2.41 for 1970 and 2.36 for the other
three years.


  We introduce higher dimensions into the problem of Bose-Einstein condensates
in a double-well potential, taking into account orbital angular momentum. We
completely characterize the eigenstates of this system, delineating new regimes
via both analytical high-order perturbation theory and numerical exact
diagonalization. Among these regimes are mixed Josephson- and Fock-like
behavior, crossings in both excited and ground states, and shadows of
macroscopic superposition states.


  Implementing nonlinear interactions between single photons and single atoms
is at the forefront of optical physics. Motivated by the prospects of
deterministic all-optical quantum logic, many efforts are currently underway to
find suitable experimental techniques. Focusing the incident photons onto the
atom with a lens yielded promising results, but is limited by diffraction to
moderate interaction strengths. However, techniques to exceed the diffraction
limit are known from high-resolution imaging. In this work, we adapt a
super-resolution imaging technique, 4Pi microscopy, to efficiently couple light
to a single atom. We observe 36.6(3)% extinction of the incident field, and a
modified photon statistics of the transmitted field -- indicating nonlinear
interaction at the single-photon level.


  Data valuation -- quantifying the contribution of individual data sources to
certain predictive behaviors of a model -- is of great importance to enhancing
the transparency of machine learning and designing incentive systems for data
sharing. Existing work has focused on evaluating data sources with the shared
feature or sample space. How to valuate fragmented data sources of which each
only contains partial features and samples remains an open question. We start
by presenting a method to calculate the counterfactual of removing a fragment
from the aggregated data matrix. Based on the counterfactual calculation, we
further propose 2D-Shapley, a theoretical framework for fragmented data
valuation that uniquely satisfies some appealing axioms in the fragmented data
context. 2D-Shapley empowers a range of new use cases, such as selecting useful
data fragments, providing interpretation for sample-wise data values, and
fine-grained data issue diagnosis.


  We study the temporal evolution of the market efficiency in the stock markets
using the complexity, entropy density, standard deviation, autocorrelation
function, and probability distribution of the log return for Standard and
Poor's 500 (S&P 500), Nikkei stock average index, and Korean composition stock
price index (KOSPI). Based on the microscopic spin model, we also find that
these statistical quantities in stock markets depend on the market efficiency.


  Fourth-order accurate compact schemes for variable coefficient convection
diffusion equations are considered. A sufficient condition for the stability of
the fully discrete problem is derived using a difference equation based
approach. The constant coefficient problems are considered as a special case,
and the unconditional stability of compact schemes for such case is proved
theoretically. The condition number of the amplification matrix is also
analyzed, and an estimate for the same is derived. The examples are provided to
support the assumption taken to assure stability.


  An investigation of CP violation was performed using a total of 24 candidates
for B^0 -> J/psi K^0_S decay, with a purity of about 60%. These events were
selected from 4.4 million hadronic Z^0 decays recorded by the OPAL detector at
LEP. An analysis procedure, involving techniques to reconstruct the proper
decay times and tag the produced b-flavours, B^0 or B^0(bar), has been
developed to allow a first direct study of the time dependent CP asymmetry
that, in the Standard Model, is sin(2beta). The result is sin(2beta) =
3.2_{-2.0}^{+1.8} +/- 0.5, where the first error is statistical and the second
systematic. This result is used to determine probabilities for different values
of sin(2beta) in the physical region from -1 to +1.


  Ordinary polytopes were introduced by Bisztriczky as a (nonsimplicial)
generalization of cyclic polytopes. We show that the colex order of facets of
the ordinary polytope is a shelling order. This shelling shares many nice
properties with the shellings of simplicial polytopes. We also give a shallow
triangulation of the ordinary polytope, and show how the shelling and the
triangulation are used to compute the toric h-vector of the ordinary polytope.
As one consequence, we get that the contribution from each shelling component
to the h-vector is nonnegative. Another consequence is a combinatorial proof
that the entries of the h-vector of any ordinary polytope are simple sums of
binomial coefficients.


  Joint communication and sensing (JCAS) is a promising technology for 6th
Generation (6G) mobile networks, such as intelligent vehicular networks,
intelligent manufacturing, and so on. Equipped with two spatially separated
antenna arrays, the base station (BS) can perform downlink active JCAS in a
mono-static setup. This paper proposes a Concurrent Downlink and Uplink (CDU)
JCAS system where the BS can use the echo of transmitted dedicated signals for
sensing in the uplink timeslot, while performing reliable uplink communication.
A novel successive interference cancellation-based CDU JCAS processing method
is proposed to enable the estimation of uplink communication symbols and
downlink sensing parameters. Extensive simulation results verify the
feasibility of the CDU JCAS system, showing a performance improvement of more
than 10 dB compared to traditional JCAS methods while maintaining reliable
uplink communication.


  We study the rotational structures of aperiodic tilings in Euclidean space of
arbitrary dimension using topological methods. Classical topological approaches
to the study of aperiodic patterns have largely concentrated just on
translational structures, studying an associated space, the continuous hull,
here denoted $\Omega_t$. In this article we consider two further spaces
$\Omega_r$ and $\Omega_G$ (the rotational hulls) which capture the full rigid
motion properties of the underlying patterns. The rotational hull $\Omega_r$ is
shown to be a matchbox manifold which contains $\Omega_t$ as a sub-matchbox
manifold. We develop new S-MLD invariants derived from the homotopical and
cohomological properties of these spaces demonstrating their computational as
well as theoretical utility. We compute these invariants for a variety of
examples, including a class of 3-dimensional aperiodic patterns, as well as for
the space of periodic tessellations of $\mathbb{R}^3$ by unit cubes. We show
that the classical space group of symmetries of a periodic pattern may be
recovered as the fundamental group of our space $\Omega_G$. Similarly, for
those patterns associated to quasicrystals, the crystallographers' aperiodic
space group may be recovered as a quotient of our fundamental invariant.


  Droplet-based high throughput biomolecular screening and combinatorial
synthesis entail a viable indexing strategy to be developed for the
identification of each micro-reactor. Here, we propose a novel indexing scheme
based on the generation of droplet sequences on demand to form unique encoding
droplet chains in fluidic networks. These codes are represented by multiunit
and multilevel droplets packages, with each code unit possessing several
distinct signal levels, potentially allowing large encoding capacity. For proof
of concept, we use magnetic nanoparticles as the encoding material and a giant
magnetoresistance (GMR) sensor-based active sorting system supplemented with an
optical detector to generate and decode the sequence of one exemplar sample
droplet reactor and a 4-unit quaternary magnetic code. The indexing capacity
offered by 4-unit multilevel codes with this indexing strategy is estimated to
exceed 104, which holds great promise for large-scale droplet-based screening
and synthesis.


  We have used the RXTE and INTEGRAL satellites simultaneously to observe the
High Mass X-ray binary IGR J19140+0951. The spectra obtained in the 3-80 keV
range have allowed us to perform a precise spectral analysis of the system
along its binary orbit. The spectral evolution confirms the supergiant nature
of the companion star and the neutron star nature of the compact object. Using
a simple stellar wind model to describe the evolution of the photoelectric
absorption, we were able to restrict the orbital inclination angle in the range
37-75 degrees. This analysis leads to a wind mass-loss rate from the companion
star of ~ 10e-7 Msun/year, consistent with its expected spectral type. We have
detected a soft excess in at least three observations, for the first time for
this source. Such soft excesses have been reported in several HMXBs in the
past. We discuss the possible origin of this excess, and suggest, based on its
spectral properties and occurrences prior to the superior conjunction, that it
may be explained as the reprocessing of the X-ray emission originating from the
neutron star by the surrounding ionised gas.


  This paper considers the problem of error correction for a cooperative data
exchange (CDE) system, where some clients are compromised or failed and send
false messages. Assuming each client possesses a subset of the total messages,
we analyze the error correction capability when every client is allowed to
broadcast only one linearly-coded message. Our error correction capability
bound determines the maximum number of clients that can be compromised or
failed without jeopardizing the final decoding solution at each client. We show
that deterministic, feasible linear codes exist that can achieve the derived
bound. We also evaluate random linear codes, where the coding coefficients are
drawn randomly, and then develop the probability for a client to withstand a
certain number of compromised or failed peers and successfully deduce the
complete message for any network size and any initial message distributions.


  We give an explicit description of the (lowering) Kashiwara operators on
Mirkovi\'c-Vilonen polytopes in types $B$ and $C$, which provides a simple
method for generating Mirkovi\'c-Vilonen polytopes inductively. This
description can be thought of as a modification of the original
Anderson-Mirkovi\'c conjecture, which Kamnitzer proved in the case of type $A$,
and presented a counterexample in the case of type $C_{3}$.


  We use a nanofluidic system to investigate the emergence of thermally driven
collective phenomena along a single polymer chain. In our approach, a single
DNA molecule is confined in a nanofluidic slit etched with arrays of embedded
nanocavities; the cavity lattice is designed so that a single chain occupies
multiple cavities. Fluorescent video-microscopy data shows that waves of excess
fluorescence propagate across the cavity-straddling molecule, corresponding to
propagating fluctuations of contour overdensity in the cavities. The waves are
quantified by examining the correlation in intensity fluctuations between
neighbouring cavities. Correlations grow from an anti-correlated minimum to a
correlated maximum before decaying, corresponding to a transfer of contour
between neighbouring cavities at a fixed transfer time-scale. The observed
dynamics can be modelled using Langevin dynamics simulations and a minimal
lattice model of coupled diffusion. This study shows how confinement-based
sculpting of the polymer equilibrium configuration, by renormalizing the
physical system into a series of discrete cavity states, can lead to new types
of dynamic collective phenomena.


  We discuss theoretical aspects of the product rule for classification
problems in supervised machine learning for the case of combining classifiers.
We show that (1) the product rule arises from the MAP classifier supposing
equivalent priors and conditional independence given a class; (2) under some
conditions, the product rule is equivalent to minimizing the sum of the squared
distances to the respective centers of the classes related with different
features, such distances being weighted by the spread of the classes; (3)
observing some hypothesis, the product rule is equivalent to concatenating the
vectors of features.


  Diverse demonstration datasets have powered significant advances in robot
learning, but the dexterity and scale of such data can be limited by the
hardware cost, the hardware robustness, and the ease of teleoperation. We
introduce ALOHA 2, an enhanced version of ALOHA that has greater performance,
ergonomics, and robustness compared to the original design. To accelerate
research in large-scale bimanual manipulation, we open source all hardware
designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of
ALOHA 2 with system identification. See the project website at
aloha-2.github.io.


  Stabilizer circuits arise in almost every area of quantum computation and
communication, so there is an interest in studying them from
information-theoretic perspective, i.e. as quantum channels. We consider
several natural approaches to what can be called a Clifford channel: the
channel that can be realised by a stabilizer circuit without classical control,
the channel that sends pure stabilizer states to mixed stabilizer states, the
channel with stabilizer Choi state, the channel whose Stinespring dilation can
have a Clifford unitary. We show the equivalence of these definitions. Up to
unitary encoding and decoding maps any Clifford channel is a product of
stabilizer state preparations, qubit discardings, identity channels and full
dephasing channels. This simple structure allows to compute information
capacities of such channels.


  Image registration is a fundamental building block for various applications
in medical image analysis. To better explore the correlation between the fixed
and moving images and improve registration performance, we propose a novel deep
learning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net
employs a co-attention block to learn a new representation of the inputs, which
drives the registration of the fixed and moving images. Experiments on UK
Biobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net
obtains higher registration accuracy and smoother deformation fields than
state-of-the-art unsupervised registration methods, while achieving comparable
or better registration performance than corresponding weakly-supervised
variants. In addition, our approach can provide critical structural information
of the input fixed and moving images simultaneously in a completely
unsupervised manner.


  This manuscript presents an ultra-wideband Microstrip
multiple-input-multiple-output (MIMO) antenna covering the 2.4GHz to 6.5 GHz
wireless local networks (WLAN) and Sub-6G bands. The applied MIMO antenna is
composed of two symmetrical microstrip antenna elements. Each microstrip is
designed based on the stepped impedance resonator (SIR) technique and closely
positioned with about 0.25lambda of the lower band (2.5 GHz). The proposed MIMO
antenna is incorporated with the EBG structure to enhance the radiation
characteristic and isolation between two radiating components. The return loss
(S11 under -10) covers 2.4 GHz to 6.5 GHz, and the proposed configuration
obtains about 20 dB isolation within the 2.4 GHz to 6.5 GHz Sub-6G and WLAN
bands, which shows an improvement compared to the initial design of the MIMO
antenna. The envelope correlation coefficient (ECC) is under 0.02 over the
frequency band. The simulation results offer that the proposed microstrip MIMO
antenna system with the different levels of the EBG structure is suitable for
WLAN and Sub-6G applications.


  Research in psychology and neuroscience has successfully modeled decision
making as a process of noisy evidence accumulation to a decision bound. While
there are several variants and implementations of this idea, the majority of
these models make use of a noisy accumulation between two absorbing boundaries.
A common assumption of these models is that decision parameters, e.g., the rate
of accumulation (drift rate), remain fixed over the course of a decision,
allowing the derivation of analytic formulas for the probabilities of hitting
the upper or lower decision threshold, and the mean decision time. There is
reason to believe, however, that many types of behavior would be better
described by a model in which the parameters were allowed to vary over the
course of the decision process.
  In this paper, we use martingale theory to derive formulas for the mean
decision time, hitting probabilities, and first passage time (FPT) densities of
a Wiener process with time-varying drift between two time-varying absorbing
boundaries. This model was first studied by Ratcliff (1980) in the two-stage
form, and here we consider the same model for an arbitrary number of stages
(i.e. intervals of time during which parameters are constant). Our calculations
enable direct computation of mean decision times and hitting probabilities for
the associated multistage process. We also provide a review of how martingale
theory may be used to analyze similar models employing Wiener processes by
re-deriving some classical results. In concert with a variety of numerical
tools already available, the current derivations should encourage mathematical
analysis of more complex models of decision making with time-varying evidence.


  In engineering projects involving various parts from global suppliers, one
common task is to determine which parts are best suited for the project
requirements. Information about specific parts' characteristics is published in
so called data sheets. However, these data sheets are oftentimes only published
in textual form, e.g., as a PDF. Hence, they have to be transformed into a
machine-interpretable format. This transformation process still requires a lot
of manual intervention and is prone to errors. Automated approaches make use of
ontologies to capture the given domain and thus improve automated information
extraction from the data sheets. However, ontologies rely solely on experiences
and perspectives of their creators at the time of creation and cannot
accumulate knowledge over time on their own. This paper presents ConTrOn --
Continuously Trained Ontology -- a system that automatically augments
ontologies. ConTrOn tackles terminology problems by combining the knowledge
extracted from data sheets with an ontology created by domain experts and
external knowledge bases such as WordNet and Wikidata. To demonstrate how the
enriched ontology can improve the information extraction process, we selected
data sheets from spacecraft development as a use case. The evaluation results
show that the amount of information extracted from data sheets based on
ontologies is significantly increased after the ontology enrichment.


  We give a brief review of a research made in the field of differential
geometry in Estonia in the period from the beginning of the 19th century to the
present time. The biographic data of mathematicians who made a valuable
contribution to the development of differential geometry in Estonia in
mentioned period are presented. The material presented in the introduction
covers the period from the beginning of the 19th century to the middle of the
20th century and it can be considered as a brief historical sketch of the
development of differential geometry in Estonia in this period. The next
sections give an idea of the modern trends of development of differential
geometry in Estonia.


  The influence of dilution with non--magnetic impurities on the order in the
ground state of the $S=1/2$ Heisenberg antiferromagnet is investigated by
Quantum Monte Carlo simulations. Data of the spin correlation functions and
thermodynamic properties for system sizes up to $L\times L=16\times16$ and for
impurity concentrations up to $\delta=37.5\%$ are presented. In the low doping
regime the correlation length shows similiar dependence on temperature as in
the pure case. The staggered magnetization $\langle N_z^2(\delta)\rangle$ in
the ground state which is the order parameter of the present model is estimated
by extrapolating data at low temperatures. Impurity concentration dependence of
$\langle N_z^2(\delta)\rangle$ is presented. $\langle N_z^2(\delta)\rangle$
decreases monotonically with impurity concentration and becomes very small
around $\delta_c \sim 0.35$ which is much smaller than the classical
percolation threshold.


  The underlying waveform has always been a shaping factor for each generation
of the cellular networks, such as orthogonal frequency division multiplexing
(OFDM) for the 4th generation cellular networks (4G). To meet the diversified
and pronounced expectations upon the upcoming 5G cellular networks, here we
present an enabler for flexible waveform configuration, named as filtered-OFDM
(f-OFDM). With the conventional OFDM, a unified numerology is applied across
the bandwidth provided, balancing among the channel characteristics and the
service requirements, and the spectrum efficiency is limited by the compromise
we made. In contrast, with f-OFDM, the assigned bandwidth is split up into
several subbands, and different types of services are accommodated in different
subbands with the most suitable waveform and numerology, leading to an improved
spectrum utilization. After outlining the general framework of f-OFDM, several
important design aspects are also discussed, including filter design and guard
tone arrangement. In addition, an extensive comparison among the existing 5G
waveform candidates is also included to illustrate the advantages of f-OFDM.
Our simulations indicate that, in a specific scenario with four distinct types
of services, f-OFDM provides up to 46% of throughput gains over the
conventional OFDM scheme.


  Reconstruction and fast prediction of flow fields are important for the
improvement of data center operations and energy savings. In this study, an
artificial neural network (ANN) and variational autoencoder (VAE) composite
model is proposed for the reconstruction and prediction of 3D flowfields with
high accuracy and efficiency. The VAE model is trained to extract features of
the problem and to realize 3D physical field reconstruction. The ANN is
employed to achieve the constructability of the extracted features. A dataset
of steady temperature/velocity fields is acquired by computational fluid
dynamics and heat transfer (CFD/HT) and fed to train the deep learning model.
The proposed ANN-VAE model is experimentally proven to achieve promising field
prediction accuracy with a significantly reduced computational cost. Compared
to the CFD/HT method, the ANN-VAE method speeds up the physical field
prediction by approximately 380,000 times, with mean accuracies of 97.3% for
temperature field prediction and 97.9% for velocity field prediction, making it
feasible for real-time physical field acquisition.


  The dual-functional radar-communication (DFRC) system is an attractive
technique, since it can support both wireless communications and radar by a
unified hardware platform with real-time cooperation. Considering the appealing
feature of multiple beams, this paper proposes a precoding scheme that
simultaneously support multiuser transmission and target detection, with an
integrated continuous phase modulation (CPM) and linear frequency modulation
(LFM) signal, based on the designed dual mode framework. Similarly to the
conception of communication rate, this paper defines radar rate to unify the
DFRC system. Then, the maximum sum-rate that includes both the communication
and radar rates is set to be the objective function. Regarding as the optimal
issue is non-convex, the optimal problem is divided into two sub-issues, one is
the user selection issue, and the other is the joint beamforming design and
power allocation issue. A successive maximum iteration (SMI) algorithm is
presented for the former issue, which can balance the performances between the
sum-rate and complexity; and maximum minimization Lagrange multiplier (MMLM)
iteration algorithm is utilized to solve the latter optimal issue. Moreover, we
deduce the spectrum characteristic, bit error rate (BER) and ambiguity function
(AF) for the proposed system. Simulation results show that our proposed system
can provide appreciated sum-rate than the classical schemes, validating the
efficiency of the proposed system.


  We discuss the Kirchhoff-type $p$-Laplacian Dirichlet problem containing the
left and right fractional derivative operators. By using the mountain pass
theorem and the genus properties in critical point theory, we get some new
results on the existence and multiplicity of nontrivial weak solutions for such
Dirichlet problem.


  The notion of 'bifurcating continued fractions' is introduced. Two coupled
sequences of non-negative integers are obtained from an ordered pair of
positive real numbers in a manner that generalizes the notion of continued
fractions. These sequences enable simple representations of roots of cubic
equations. In particular, remarkably simple and elegant 'bifurcating continued
fraction' representations of Tribonacci and Moore numbers, the cubic variations
of the 'golden mean', are obtained. This is further generalized to associate m
non-negative integer sequences with a set of m given real numbers so as to
provide simple 'bifurcating continued fraction' representation of roots of
polynomial equations of degree m+1.


  Without any further adjusting of parameters, a relativistic constituent quark
model, successful in the description of the data for the nucleon elastic form
factors and of the dominant contribution for the nucleon to Delta
electromagnetic transition, is used here to predict the dominant
electromagnetic form factors of the Delta baryon. The model is based on a
simple Delta wave function corresponding to a quark-diquark system in an
S-state. The results for E0 and M1 are consistent both with experimental
results and lattice calculations. The remaining form factors E2 and M3 vanish,
given the symmetric structure taken for the Delta.


  In heavy atoms and molecules, on the distances $a \ll Z^{-1/3}$ from one of
the nuclei (with a charge $Z_m$) we prove that $\rho_\Psi (x)$ is approximated
in $L^p$-norm, by the electronic density for a single atom in the model with no
interactions between electrons. We cover also the relativistic case.


  It is known that the $n$-qubit system has no unextendible product bases
(UPBs) of cardinality $2^n-1$, $2^n-2$ and $2^n-3$. On the other hand the
$n$-qubit UPBs of cardinality $2^n-4$ exist for all $n\ge3$. We prove that they
do not exist for cardinality $2^n-5$.


  Human perception is structured around objects which form the basis for our
higher-level cognition and impressive systematic generalization abilities. Yet
most work on representation learning focuses on feature learning without even
considering multiple objects, or treats segmentation as an (often supervised)
preprocessing step. Instead, we argue for the importance of learning to segment
and represent objects jointly. We demonstrate that, starting from the simple
assumption that a scene is composed of multiple entities, it is possible to
learn to segment images into interpretable objects with disentangled
representations. Our method learns -- without supervision -- to inpaint
occluded parts, and extrapolates to scenes with more objects and to unseen
objects with novel feature combinations. We also show that, due to the use of
iterative variational inference, our system is able to learn multi-modal
posteriors for ambiguous inputs and extends naturally to sequences.


  Pipelining is a well understood and often used implementation technique for
increasing the performance of a hardware system. We develop several SystemC/C++
modeling techniques that allow us to quickly model, simulate, and evaluate
pipelines. We employ a small domain specific language (DSL) based on resource
usage patterns that automates the drudgery of boilerplate code needed to
configure connectivity in simulation models. The DSL is embedded directly in
the host modeling language SystemC/C++. Additionally we develop several
techniques for parameterizing a pipeline's behavior based on policies of
function, communication, and timing (performance modeling).


  For vertex and edge connectivity we construct infinitely many pairs of
regular graphs with the same spectrum, but with different connectivity.


  Here we present a quantum-statistical formulation of third-order polarization
P(3)(t), which is induced in a sample by a sequence of incident external
fields, and serves as a source of an emitted radiation field detected as a
signal in pump-probe (PP) experiments. Our treatment is based on the
perturbation expansion of the non-equilibrium density matrix for calculation of
multi-time correlation functions, and the corresponding response function, at
finite temperature. As a model for our study, the high-temperature
superconductor MgB2 has been selected. Knowledge of the electronic structure of
the studied system, and of the corresponding Eliashberg function that
represents pertinent electron-phonon (EP) interactions, enabled us to
distinguish non-equilibrium processes running over different time-periods in a
sequence of interactions with laser pulses on a microscopic level. We have also
derived temperature-dependent relaxation dynamics as a function of delay time
between the pump and probe pulses. For the studied model system of MgB2, we
have shown that an abrupt increase of the relaxation time at Tc, as detected by
experiments, is the direct consequence of sudden changes in the character of EP
coupling in transition from an adiabatic to a stabilized superconducting
anti-adiabatic state, as it predicts the anti-adiabatic theory of
electron-vibration interactions. The BCS model, which preserves the adiabatic
character of EP coupling also below the critical temperature of MgB2, is
basically unable to reflect the enormous sudden increase of the relaxation
time. Based on diagrammatic perturbation theory, differences in the optical
pump-optical probe and the optical pump-terahertz probe settings of MgB2 PP
relaxation dynamics are discussed.


  In this paper we analyze the earlier constructions of the type IIA dual pairs
through orientifolding. By an appropriate choice of $\Gamma$-matrix basis for
the spinor representations of the $U$-duality group, we give an explicit
relationship between the orientifold models and their dual pairs.


  Quantitative phase microscopy (QPM) has found significant applications in the
field of biomedical imaging which works on the principle of interferometry. The
theory behind achieving interference in QPM with conventional light sources
such as white light and lasers is very well developed. Recently, the use of
dynamic speckle illumination (DSI) in QPM has attracted attention due to its
advantages over conventional light sources such as high spatial phase
sensitivity, single shot, scalable field of view (FOV) and resolution. However,
the understanding behind obtaining interference fringes in QPM with DSI has not
been convincingly covered previously. This imposes a constraint on obtaining
interference fringes in QPM using DSI and limits its widespread penetration in
the field of biomedical imaging. The present article provides the basic
understanding of DSI through both simulation and experiments that is essential
to build interference optical microscopy systems such as QPM, digital
holographic microscopy and optical coherence tomography. Using the developed
theory of DSI we demonstrate its capabilities of using non-identical objective
lenses in both arms of the interference microscopy without degrading the
interference fringe contrast and providing the flexibility to use user-defined
microscope objective lens. It is also demonstrated that the interference
fringes are not washed out over a large range of optical path difference (OPD)
between the object and the reference arm providing competitive edge over low
temporal coherence light sources. The theory and explanation developed here
would enable wider penetration of DSI based QPM for applications in biology and
material sciences.


  We observe a dramatic lack of robustness of the DPG method when solving
problems on large domains and where stability is based on a Poincar\'e-type
inequality. We show how robustness can be re-established by using appropriately
scaled test norms. As model cases we study the Poisson problem and the
Kirchhoff--Love plate bending model, and also include fully discrete variants
where optimal test functions are approximated. Numerical experiments for both
model problems, including an-isotropic domains and mixed boundary conditions,
confirm our findings.


  We consider a time-stepping scheme of Crank-Nicolson type for the heat
equation on a moving domain in Eulerian coordinates. As the spatial domain
varies between subsequent time steps, an extension of the solution from the
previous time step is required. Following Lehrenfeld \& Olskanskii [ESAIM:
M2AN, 53(2):\,585-614, 2019], we apply an implicit extension based on so-called
ghost-penalty terms. For spatial discretisation, a cut finite element method is
used. We derive a complete a priori error analysis in space and time, which
shows in particular second-order convergence in time under a parabolic CFL
condition. Finally, we present numerical results in two and three space
dimensions that confirm the analytical estimates, even for much larger time
steps.


  Fair representations are a powerful tool for establishing criteria like
statistical parity, proxy non-discrimination, and equality of opportunity in
learned models. Existing techniques for learning these representations are
typically model-agnostic, as they preprocess the original data such that the
output satisfies some fairness criterion, and can be used with arbitrary
learning methods. In contrast, we demonstrate the promise of learning a
model-aware fair representation, focusing on kernel-based models. We leverage
the classical Sufficient Dimension Reduction (SDR) framework to construct
representations as subspaces of the reproducing kernel Hilbert space (RKHS),
whose member functions are guaranteed to satisfy fairness. Our method supports
several fairness criteria, continuous and discrete data, and multiple protected
attributes. We further show how to calibrate the accuracy tradeoff by
characterizing it in terms of the principal angles between subspaces of the
RKHS. Finally, we apply our approach to obtain the first Fair Gaussian Process
(FGP) prior for fair Bayesian learning, and show that it is competitive with,
and in some cases outperforms, state-of-the-art methods on real data.


  The scientific goals of the AMIGA project are based on the analysis of a
significant amount of spectroscopic 3D data. In order to perform this work we
present an initiative to develop a new VO compliant package, including present
core applications and tasks offered by the Groningen Image Processing System
(GIPSY), and new ones based on use cases elaborated in collaboration with ad-
vanced users. One of the main goals is to provide local interoperability
between GIPSY (visualization and data analysis) and other VO software. The
connectivity with the Virtual Observatory environment will provide general
access to 3D data VO archives and services, maximizing the potential for
scientific discovery.


  When dealing with privatized data, it is important to be able to protect
against malformed user inputs. This becomes difficult in MPC systems as each
server should not contain enough information to know what values any user has
submitted. In this paper, we implement an MPC technique to verify blinded user
inputs are unit vectors. In addition, we introduce a BGW circuit which can
securely aggregate the blinded inputs while only releasing the result when it
is above a public threshold. These distributed techniques take as input a unit
vector. While this initially seems limiting compared to real number input, it
is quite powerful for cases such as selecting from a list of options,
indicating a location from a set of possibilities, or any system which uses
one-hot encoding.


  We present a new calculation of $W^+ W^-$ production in the semi-exclusive
channel, that is either with intact outgoing protons or rapidity gaps present
in the final state, and with no colour flow between the colliding protons. This
study provides the first complete prediction of the $W^+ W^-$ semi-exclusive
cross section, as well as the breakdown between elastic and proton dissociative
channels. It combines the structure function calculation for a precise
modelling of the region of low momentum transfers with a parton-level
calculation in the region of high momentum transfers. The survival factor
probability of no additional proton-proton interactions is fully accounted for,
including its kinematic and process dependence. We analyse in detail the role
that the pure photon-initiated ($\gamma\gamma \to W^+ W^-$) subprocess plays, a
comparison that is only viable by working in the electroweak axial gauge. In
this way, we find that the dominance of this is not complete in the proton
dissociative cases, although once $Z$-initiated production is included a
significantly better matching to the complete calculation is achieved. A direct
consequence of this is that the relative elastic, single and double
dissociative fractions are in general different in comparison to the case of
lepton pair production. We present a direct comparison to the recent ATLAS data
on semi-exclusive $W^+ W^-$ production, finding excellent agreement within
uncertainties. Our calculation is provided in the publicly available SuperChic
4.1 Monte Carlo (MC) generator, and can be passed to a general purpose MC for
showering and hadronization of the final state.


  Whether the new line at ~3.5 keV, recently detected in different samples of
galaxy clusters, Andromeda galaxy and central part of our Galaxy, is due to
Potassium emission lines, is now unclear. By using the latest astrophysical
atomic emission line database AtomDB v. 3.0.2, we show that the most
prospective method to directly check its Potassium origin will be the study of
K XIX emission line complex at ~3.7 keV with future X-ray imaging spectrometers
such as Soft X-ray spectometer on-board Astro-H mission or microcalorimeter
on-board Micro-X sounding rocket experiment. To further reduce the remaining
(factor ~3-5) uncertainty of the 3.7/3.5 keV ratio one should perform more
precise modeling including removal of significant spatial inhomogeneities,
detailed treatment of background components, and further extension of the
modeled energy range.


  We study two interacting quantum particles forming a bound state in
$d$-dimensional free space, and constrain the particles in $k$ directions to
$(0,\infty)^k \times \mathbb{R}^{d-k}$, with Neumann boundary conditions.
First, we prove that the ground state energy strictly decreases upon going from
$k$ to $k+1$. This shows that the particles stick to the corner where all
boundary planes intersect. Second, we show that for all $k$ the resulting
Hamiltonian, after removing the free part of the kinetic energy, has only
finitely many eigenvalues below the essential spectrum. This paper generalizes
the work of Egger, Kerner and Pankrashkin (J. Spectr. Theory 10(4):1413--1444,
2020) to dimensions $d>1$.


  In stars with temperatures above 20*10^6 K, hydrogen burning is dominated by
the CNO cycle. Its rate is determined by the slowest process, the
14N(p,gamma)15O reaction. Deep underground in Italy's Gran Sasso laboratory, at
the LUNA 400 kV accelerator, the cross section of this reaction has been
measured at energies much lower than ever achieved before. Using a windowless
gas target and a 4pi BGO summing detector, direct cross section data has been
obtained down to 70 keV, reaching a value of 0.24 picobarn. The Gamow peak has
been covered by experimental data for several scenarios of stable and explosive
hydrogen burning. In addition, the strength of the 259 keV resonance has been
remeasured. The thermonuclear reaction rate has been calculated for
temperatures 90 - 300 *10^6 K, for the first time with negligible impact from
extrapolations.


  "Glitches" -- transient noise artifacts in the data collected by
gravitational wave interferometers like LIGO and Virgo -- are an ever-present
obstacle for the search and characterization of gravitational wave signals.
With some having morphology similar to high mass, high mass-ratio, and
extreme-spin binary black hole events, they limit sensitivity to such sources.
They can also act as a contaminant for all sources, requiring targeted
mitigation before astrophysical inferences can be made. We propose a data
driven, parametric model for frequently encountered glitch types using
probabilistic principal component analysis. As a noise analog of parameterized
gravitational wave signal models, it can be easily incorporated into existing
search and detector characterization techniques. We have implemented our
approach with the open source glitschen package. Using LIGO's currently most
problematic glitch types, the 'blip' and 'tomte', we demonstrate that
parametric models of modest dimension can be constructed and used for effective
mitigation in both frequentist and Bayesian analyses.


  In this paper, we establish a large deviation principle for a type of
stochastic partial differential equations (SPDEs) with locally monotone
coefficients driven by L\'evy noise. The weak convergence method plays an
important role.


  The broadband X-ray spectrum of the prototypical starburst galaxy M82 is very
complex. At least three spectral components are required to fit the combined
ROSAT and ASCA spectrum in the 0.1--10 keV range. The observed X-ray flux in
this band is dominated by a hard Gamma = 1.7, heavily absorbed power law
component which originates in the nucleus and near-nuclear disk of the galaxy.
Among the candidates for the origin of this hard X-ray emission, the most
plausible appears to be inverse-Compton scattered emission from the interaction
of M82's copious infrared photon flux with supernova-generated relativistic
electrons. The measured intrinsic luminosity of the power law component agrees
closely with calculations of the expected inverse-Compton luminosity. Moreover,
the radio and X-ray emission in the nucleus of M82 have the same spectral
slope, which should be the case if both types of emission are non- thermal and
are associated with a common population of electrons. The other two spectral
components, thermal plasmas with characteristic temperatures kT = 0.6 and 0.3
keV, are associated with the star formation and starburst-driven wind in M82.
The warmer thermal component is heavily absorbed as well and must also
originate in the central region of the galaxy. The softer thermal component,
however, is not absorbed, and is likely to represent the X-ray emission which
extends along M82's minor axis. The amount of absorption required in the
three-component model suggests that the intrinsic luminosity of M82 in the
0.1--10 keV band is about four times greater than its observed luminosity of 4
x 10(40) ergs/s.


  This technical report summarizes the winning solution for the 3D Occupancy
Prediction Challenge, which is held in conjunction with the CVPR 2023 Workshop
on End-to-End Autonomous Driving and CVPR 23 Workshop on Vision-Centric
Autonomous Driving Workshop. Our proposed solution FB-OCC builds upon FB-BEV, a
cutting-edge camera-based bird's-eye view perception design using
forward-backward projection. On top of FB-BEV, we further study novel designs
and optimization tailored to the 3D occupancy prediction task, including joint
depth-semantic pre-training, joint voxel-BEV representation, model scaling up,
and effective post-processing strategies. These designs and optimization result
in a state-of-the-art mIoU score of 54.19% on the nuScenes dataset, ranking the
1st place in the challenge track. Code and models will be released at:
https://github.com/NVlabs/FB-BEV.


  In this paper we investigate the $(2+1)$-dimensional topological non-Fermi
liquid in strongly correlated electron system, which has a holographic dual
description by Einstein gravity in $(3+1)$-dimensional anti-de Sitter (AdS)
space-time. In a dyonic Reissner-Nordstrom black hole background, we consider a
Dirac fermion coupled to the background $U(1)$ gauge theory and an intrinsic
chiral gauge field $b_M$ induced by chiral anomaly. UV retarded Green's
function of the charged fermion in the UV boundary from AdS$_4$ gravity is
calculated, by imposing in-falling wave condition at the horizon. We also
obtain IR correlation function of the charged fermion at the IR boundary
arising from the near horizon geometry of the topological black hole with index
$k=0,\pm 1$. By using the UV retarded Green's function and IR correlation
function, we analyze the low frequency behavior of the topological non-Fermi
liquid at zero and finite temperatures, especially the relevant non-Fermi
liquid behavior near the quantum critical region. In addition, we find that
when $k=\pm 1$, the effective mass of bulk Dirac fermion at IR fixed point is
topologically quantized, consequently the Fermi momentum presents a discrete
sparse distributed pattern, in contrary to the Ricci flat case with $k=0$. As
demonstrated examples, we calculate the spectral functions of topological
non-Fermi liquid due to anomalous Hall effect (AHE) ($|\vec{b}|\ne 0, b_0=0$)
and chiral magnetic effect (CME) ($|\vec{b}| = 0, b_0\ne 0$), which can be
tested in topological materials such as topological insulator and topological
metal with strong couplings.


  (Abridged) Specific angular momentum is one of the key parameters that
control the evolution of galaxies. We derive the baryonic specific angular
momentum of disc galaxies and study its relation with the dark matter specific
angular momentum. Using a combination of high-quality HI rotation curves and
HI/near-IR surface densities, we homogeneously measure the stellar ($j_{\rm
*}$) and gas ($j_{\rm gas}$) specific angular momenta for a large sample of
local disc galaxies. This allows us to determine the baryonic specific angular
momentum ($j_{\rm bar}$) with high accuracy and across a very wide range of
masses. The $j_{\ast}-M_\ast$ relation is an unbroken power-law from $7
\lesssim$ log($M_\ast$/$M_\odot) \lesssim 11.5$, with slope $0.54 \pm 0.02$.
For the gas component, we find that the $j_{\rm gas}-M_{\rm gas}$ relation is
also an unbroken power-law from $6 \lesssim$ log($M_{\rm gas}$/$M_\odot)
\lesssim 11$, with a steeper slope of $1.02 \pm 0.04$. Regarding the baryonic
relation, our data support a correlation characterized by single power-law with
slope $0.60 \pm 0.02$. Our most massive spirals and smallest dwarfs lie along
the same $j_{\rm bar}-M_{\rm bar}$ sequence. While the relations are tight and
unbroken, we find internal correlations inside them: At fixed $M_\ast$,
galaxies with larger $j_\ast$ have larger disc scale lengths, and at fixed
$M_{\rm bar}$, gas-poor galaxies have lower $j_{\rm bar}$ than expected. We
estimate the retained fraction of baryonic specific angular momentum, finding
it constant across our entire mass range with a value of $\sim 0.6$, indicating
that the $j_{\rm bar}$ of present-day disc galaxies is comparable to the
initial specific angular momentum of their dark matter haloes. These results
set important constraints for hydrodynamical simulations and semi-analytical
models aiming to reproduce galaxies with realistic specific angular momenta.


  Several scenarios have been suggested to explain the phase-space distribution
of the Milky Way (MW) satellite galaxies in a disc of satellites (DoS). To
quantitatively compare these different possibilities, a new method analysing
angular momentum directions in modelled data is presented. It determines how
likely it is to find sets of angular momenta as concentrated and as close to a
polar orientation as is observed for the MW satellite orbital poles. The method
can be easily applied to orbital pole data from different models. The observed
distribution of satellite orbital poles is compared to published angular
momentum directions of subhalos derived from six cosmological state-of-the-art
simulations in the Aquarius project. This tests the possibility that
filamentary accretion might be able to naturally explain the satellite orbits
within the DoS. For the most likely alignment of main halo and MW disc spin,
the probability to reproduce the MW satellite orbital pole properties turns out
to be less than 0.5 per cent in Aquarius models. Even an isotropic distribution
of angular momenta has a higher likelihood to produce the observed
distribution. The two Via Lactea cosmological simulations give results similar
to the Aquarius simulations. Comparing instead with numerical models of
galaxy-interactions gives a probability of up to 90 per cent for some models to
draw the observed distribution of orbital poles from the angular momenta of
tidal debris. This indicates that the formation as tidal dwarf galaxies in a
single encounter is a viable, if not the only, process to explain the
phase-space distribution of the MW satellite galaxies.


  Simulations of protein folding and protein association happen on timescales
that are orders of magnitude larger than what can typically be covered in
all-atom molecular dynamics simulations. Use of low-resolution models
alleviates this problem but may reduce the accuracy of the simulations. We
introduce a replica-exchange-based multiscale sampling technique that combines
the faster sampling in coarse-grained simulations with the potentially higher
accuracy of all-atom simulations. After testing the efficiency of our
Resolution Exchange with Tunneling (ResET) in simulations of the Trp-cage
protein, an often used model to evaluate sampling techniques in protein
simulations, we use our approach to compare the landscape of wild type and A2T
mutant Abeta 1-42 peptides. Our results suggest a mechanism by that the
mutation of a small hydrophobic Alanine (A) into a bulky polar Threonine (T)
may interfere with the self-assembly of Abeta - fibrils.


  Pre-sales customer service is of importance to E-commerce platforms as it
contributes to optimizing customers' buying process. To better serve users, we
propose AliMe KG, a domain knowledge graph in E-commerce that captures user
problems, points of interests (POI), item information and relations thereof. It
helps to understand user needs, answer pre-sales questions and generate
explanation texts. We applied AliMe KG to several online business scenarios
such as shopping guide, question answering over properties and recommendation
reason generation, and gained positive results. In the paper, we systematically
introduce how we construct domain knowledge graph from free text, and
demonstrate its business value with several applications. Our experience shows
that mining structured knowledge from free text in vertical domain is
practicable, and can be of substantial value in industrial settings.


  The issue of group fairness in machine learning models, where certain
sub-populations or groups are favored over others, has been recognized for some
time. While many mitigation strategies have been proposed in centralized
learning, many of these methods are not directly applicable in federated
learning, where data is privately stored on multiple clients. To address this,
many proposals try to mitigate bias at the level of clients before aggregation,
which we call locally fair training. However, the effectiveness of these
approaches is not well understood. In this work, we investigate the theoretical
foundation of locally fair training by studying the relationship between global
model fairness and local model fairness. Additionally, we prove that for a
broad class of fairness metrics, the global model's fairness can be obtained
using only summary statistics from local clients. Based on that, we propose a
globally fair training algorithm that directly minimizes the penalized
empirical loss. Real-data experiments demonstrate the promising performance of
our proposed approach for enhancing fairness while retaining high accuracy
compared to locally fair training methods.


  Detection of common events and scenes from audio is useful for extracting and
understanding human contexts in daily life. Prior studies have shown that
leveraging knowledge from a relevant domain is beneficial for a target acoustic
event detection (AED) process. Inspired by the observation that many
human-centered acoustic events in daily life involve voice elements, this paper
investigates the potential of transferring high-level voice representations
extracted from a public speaker dataset to enrich an AED pipeline. Towards this
end, we develop a dual-branch neural network architecture for the joint
learning of voice and acoustic features during an AED process and conduct
thorough empirical studies to examine the performance on the public AudioSet
[1] with different types of inputs. Our main observations are that: 1) Joint
learning of audio and voice inputs improves the AED performance (mean average
precision) for both a CNN baseline (0.292 vs 0.134 mAP) and a TALNet [2]
baseline (0.361 vs 0.351 mAP); 2) Augmenting the extra voice features is
critical to maximize the model performance with dual inputs.


  We theoretically investigate the nonlinear response of bent graphene, in the
presence of artificially-enhanced spin-orbit coupling, which can occur either
via adatom deposition, or by placing the sheet of bent graphene in contact with
a spin-orbit active substrate. We discuss the interplay between the spin-orbit
coupling and the artificial magnetic field generated by the bending, for both
the cases of Rashba and intrinsic spin-orbit coupling. For the latter, we
introduce a spin-field interaction Hamiltonian addressing directly the electron
spin as a degree of freedom. Our findings reveal that in this case, by
controlling the amount of spin-orbit coupling, it is possible to significantly
tune the spectrum of the nonlinear signal, achieving, in principle, efficient
conversion of light from THz to UV region.


  Israel's proof of the third law of black hole dynamics is based on the
semi-rigidity assumption which requires that the areas of the outermost trapped
surfaces are preserved. We argue that the condition that the area of the event
horizon should not decrease is necessary -but not sufficient- for the
semi-rigidity assumption to hold. It is known that the presence of a naked
singularity in the exterior region invalidates Hawking's proof of the area
theorem. In this sense, the third law assumes the validity of the strong form
of the cosmic censorship conjecture, contrary to general acceptance. Still, one
cannot guarantee that the semi-rigidity assumption holds. Therefore the current
proof is incomplete. Recent studies on the interactions of black holes with
fermionic fields imply a generic formation of naked singularities. The presence
of these singularities invalidates the laws of black hole dynamics and cosmic
censorship conjecture for the processes that satisfy the weak energy condition.


  This study unveils the elusive presence of criminal signatures in cyberspace,
validating for the first time their existence through statistical evidence. By
applying the A priori algorithm to the modus operandi of Advanced Persistent
Threats, extracted from an extensive corpus of over 17,000 articles spanning
2007 to 2020, we highlight the enduring patterns leveraged by sophisticated
cyber criminals. Our findings verify the existence of unique signatures
associated with advanced cybercriminals, bridging a crucial gap in current
understanding of human behavior in cyber-attacks. This pivotal research sets
the foundation for an entirely new academic intersection in cybersecurity and
computational criminology.


  The spin-frustrated conductor Mn$_3$Pt exhibits a characteristic magnetic
structure called partial disorder in which some spin sites can form magnetic
order through the generation of non-ordered sites that locally relieve the
frustration. Here we report the results of a single-crystal inelastic neutron
scattering study of this compound. The measured momentum $\vec{Q}$ correlations
of diffusive magnetic scattering reveal that the paramagnetic phase exhibits
short-range spin fluctuations with the same type of partial disorder. Its
relation to conductivity is also discussed.


  A code, Epic5, has been developed which computes, in the two-dimensional
case, the initially circular orbits of guiding centra in an arbitrary
axisymmetric potential with an arbitrary, weak perturbing potential in solid
body rotation. This perturbing potential is given by its Fourier expansion. The
analytic solution solves the linear epicyclic approximation of the equations of
motion. To simulate the motion of interstellar matter and to damp the Lindblad
resonances, we have in these equations introduced a friction which is
proportional to the deviation from circular velocity. The corotation resonance
is also damped by a special parameter. The program produces, in just a few
seconds, orbital and density maps, as well as line of sight velocity maps for a
chosen orientation of the galaxy.
  We test Epic5 by comparing its results with previous simulations and
observations from the literature, which gives satisfactory agreement. The aim
is that this program should be a useful complement to elaborate numerical
simulations. Particularly so are its abilities to quickly explore the parameter
space, to construct artificial galaxies, and to isolate various single agents
important for developing structure of interstellar matter in disc galaxies.


  We have studied the physical properties of a sample of narrow-line Seyfert 1
(NLS1) galaxies, and present a summary of our previous results, and new
results. In particular, we have previously shown that (1) the locus of NLS1
galaxies on the M_BH-sigma_[OIII] plane does follow the relation of non-active
galaxies after removing objects obviously dominated by outflows as evidenced by
their [OIII] core blueshifts. We have (2) identified a number of so-called
'blue outliers' with large outflow velocities revealed by their emission-line
kinematic shifts. We also (3) present new correlations and trends which link
black hole mass, Eddington ratio and physical parameters of the emission-line
regions.


  In this article we produce Groebner bases for the defining ideal of a
monomial curve that corresponds to an almost arithmetic sequence of positive
integers, correcting previous work of Sengupta,(2003).


  We study the effect of disorder on the propagation of collective excitations
in a disordered Bose superfluid. We incorporate local density depletion induced
by strong disorder at the meanfield level, and formulate the transport of the
excitations in terms of a screened scattering problem. We show that the
competition of disorder, screening, and density depletion induces a strongly
non-monotonic energy dependence of the disorder parameter. In three dimensions,
it results in a rich localization diagram with four different classes of
mobility spectra, characterized by either no or up to three mobility edges.
Implications on experiments with disordered ultracold atoms are discussed.


  In this paper, we have used the reconstructed DBI-essence dark energy density
to modify the mass accretions of black holes and wormholes. In general, the
black hole mass accretion does not depend on the metric or local Einstein
geometry. That is why we have used a generalized mechanism by reconstructing
the DBI-essence dark energy reconstruction with f(R) gravity. We have used some
particular forms of the scale factor to analyze the accretion phenomena. We
have shown the effect of cosmic evolution in the proper time variation of black
hole mass accretion. Finally, we have studied the validity of energy conditions
and analyzed the type I - IV singularities for our reconstructed model.


  As part of the Selected Areas Starcount Survey (SASS), a CCD survey to V>21,
we have obtained VI photometry of two fields at b=+-40 aligned roughly with an
extrapolation of the major axis of the Sagittarius (Sgr) dwarf spheroidal
galaxy. Comparison of the color-magnitude diagram (CMD) for some of these
fields to the CMDs of fields reflected about the Galactic l=0 meridian reveals
an excess of stars at V_o=17.85 and 0.9 < (V-I)_o < 1.1 in the (l,b) = (11,-40)
field. The excess stars have colors consistent with the Sgr red clump, and
deeper CMD imaging in these locations shows evidence of a main-sequence turnoff
at V=21 with the main-sequence extending to the limit of our data (V=24). The
surface brightnesses we derive from either the potential excess of red clump
stars or the apparent excess of MSTO stars are consistent with each other and
with the results of other surveys at this latitude. No similar excess appears
in our northern Galactic hemisphere fields near l=353, b=+41 field.
  We have obtained spectroscopy of all 30 candidate red clump stars in the
range 0.9 < (V-I)_o < 1.1 and 17.75 < V_o < 17.95. The radial velocity
distribution of the stars, while dissimilar from expectations of Galactic
structure models, does not show a contribution by stars near the Galactocentric
radial velocity seen in other studies near the Sgr core. It is difficult to
reconcile a photometric result that is consistent with other explorations of
the Sagittarius stream with a radial velocity distribution that is apparently
inconsistent.
  In a companion paper (Johnston et al. 1999), we discuss how some of the
discrepancies are resolved if ur potential Sgr detection corresponds to a
different Sgr tidal streamer than that detected by most other surveys.


  In this study, we investigated the thermodynamic features of a system based
on oppositely charged polyelectrolytes, sodium alginate, and
poly(diallyldimethylammonium chloride) (PDADMAC) at different pH values.
Additionally, a comparison of the effects of the thermodynamic parameters on
the growth of the layers based on the same polymers is presented. For this
investigation, different techniques were combined to compare results from the
association in solution and co-assembled layers at the silicon surface. Dynamic
light scattering (DLS) and isothermal titration calorimetry (ITC) were used for
studies in solution, and the layer-by-layer technique was employed for the
preparation of the polymer layers. Ellipsometry and atomic force microscopy
(AFM) were used to characterize the layer thickness growth as a function of the
solution pH, and interferometric confocal microscopy was employed to analyze
the topography and roughness of the films. The layers grown for the
PDADMAC/sodium alginate system demonstrated pH sensitivity with either linear
or exponential behavior, depending on the pH values of the polyelectrolyte
solutions. The layer thicknesses measured using ellipsometry and AFM data were
in good agreement. Additionally, a pH influence on the roughness and topography
of the films was observed. Films from basic dipping solutions resulted in
surfaces that were more homogeneous with less roughness; in contrast, films
with more layers and those formed in a low-pH dipping solution were rougher and
less homogeneous.


  Every field of Science is undergoing unprecedented changes in the discovery
process, and Astronomy has been a main player in this transition since the
beginning. The ongoing and future large and complex multi-messenger sky surveys
impose a wide exploiting of robust and efficient automated methods to classify
the observed structures and to detect and characterize peculiar and unexpected
sources. We performed a preliminary experiment on KiDS DR4 data, by applying to
the problem of anomaly detection two different unsupervised machine learning
algorithms, considered as potentially promising methods to detect peculiar
sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random
Forest. The former method, working directly on images, is considered
potentially able to identify peculiar objects like interacting galaxies and
gravitational lenses. The latter instead, working on catalogue data, could
identify objects with unusual values of magnitudes and colours, which in turn
could indicate the presence of singularities.


  We study non-Abelian flavor symmetries on orbifolds, $S^1/Z_2$ and $T^2/Z_3$.
Our extra dimensional models realize $D_N$, $\Sigma(2N^2)$, $\Delta(3N^2)$ and
$\Delta(6N^2)$ including $A_4$ and $S_4$. In addition, one can also realize
their subgroups such as $Q_N$, $T_7$, etc. The $S_3$ flavor symmetry can be
realized on both $S^1/Z_2$ and $T^2/Z_3$ orbifolds.


  In this work we have proposed a scheme for generating $N$ qubit entangled
states which can teleport an unknown state perfectly. By switching on the
exchange interaction ($J$) between the qubits one can get the desired states
periodically. A multi electron quantum dot can be a possible realization for
generating such $N$ qubit states with high fidelity. In the limit of $N \to
\infty$, there exists a unique time $t=\frac{1}{J}\cos^{-1}(-1/8)$ where the
Hamiltonian dynamics gives the $N$ qubit state that can assist perfect
teleportation. We have also discussed the effect of the nuclear spin
environment on the fidelity of teleportation for a general $N$ qubit entangled
channel.


  We propose a Nieh-Yan extension of f (R) models in the presence of Immirzi
field and the resulting theory is studied in the context of first order
formalism. We claim as in our model the ambiguities related to the Palatini
vacuum case are solved, allowing us to extend the range of applicability of the
method proposed in [4] to a wider class of f (R) theories. In particular we
analyze within such a theoretical framework the propagation of gravitational
waves in vacuum and outline as a dynamical Immirzi field could be regarded as a
propagating torsion field with non-trivial and detectable effects on standard
gravitational polarizations.


  2DEG states of Al/$\delta$-GaAs structures were investigated in the
persistent tunneling photoconductivity (PTPC) regime at low temperatures.
"Thickening" of the unoccupied subbands to the ground state of 2DEG was
observed at $T=4.2$~$K$. It was found that there is a uniform shift of the
subband levels at $T=77$~$K$. The behavior of the persistent 2D states in
tunneling spectra after various illumination and at different temperatures
allows us to assume that there are two PTPC mechanisms, namely, the broadening
of the potential well profile of the $\delta$-layer and photoionization of the
DX-centers.


  We demonstrate the use of a fiber-based femtosecond laser locked onto an
ultra-stable optical cavity to generate a low-noise microwave reference signal.
Comparison with both a liquid Helium cryogenic sapphire oscillator (CSO) and a
Ti:Sapphire-based optical frequency comb system exhibit a stability about
$3\times10^{-15}$ between 1 s and 10 s. The microwave signal from the fiber
system is used to perform Ramsey spectroscopy in a state-of-the-art Cesium
fountain clock. The resulting clock system is compared to the CSO and exhibits
a stability of $3.5\times10^{-14}\tau^{-1/2}$. Our continuously operated
fiber-based system therefore demonstrates its potential to replace the CSO for
atomic clocks with high stability in both the optical and microwave domain,
most particularly for operational primary frequency standards.