  Observations of the Cosmic Microwave Background (CMB) have cemented the
notion that the large-scale Universe is both statistically homogeneous and
isotropic. But is it invariant also under reflections? To probe this we require
parity-sensitive statistics: for scalar observables, the simplest is the
trispectrum. We make the first measurements of the parity-odd scalar CMB,
focusing on the large-scale ($2<\ell<510$) temperature anisotropies measured by
Planck. This is facilitated by new quasi-maximum-likelihood estimators for
binned correlators, which account for mask convolution and leakage between
even- and odd-parity components, and achieve ideal variances within $\approx
20\%$. We perform a blind test for parity violation by comparing a $\chi^2$
statistic from Planck to theoretical expectations, using two suites of
simulations to account for the possible likelihood non-Gaussianity and residual
foregrounds. We find consistency at the $\approx 0.4\sigma$ level, yielding no
evidence for novel early-Universe phenomena. The measured trispectra allow for
a wealth of new physics to be constrained; here, we use them to constrain eight
primordial models, including Ghost Inflation, Cosmological Collider scenarios,
and Chern-Simons gauge fields. We find no signatures of new physics, with a
maximal detection significance of $2.0\sigma$. Our results also indicate that
the recent parity excesses seen in the BOSS galaxy survey are not primordial in
origin, given that the CMB dataset contains roughly $250\times$ more primordial
modes, and is far easier to interpret, given the linear physics, Gaussian
statistics, and accurate mocks. Tighter CMB constraints can be wrought by
including smaller scales and adding polarization data.


  Annotating nuclei in microscopy images for the training of neural networks is
a laborious task that requires expert knowledge and suffers from inter- and
intra-rater variability, especially in fluorescence microscopy. Generative
networks such as CycleGAN can inverse the process and generate synthetic
microscopy images for a given mask, thereby building a synthetic dataset.
However, past works report content inconsistencies between the mask and
generated image, partially due to CycleGAN minimizing its loss by hiding
shortcut information for the image reconstruction in high frequencies rather
than encoding the desired image content and learning the target task. In this
work, we propose to remove the hidden shortcut information, called
steganography, from generated images by employing a low pass filtering based on
the DCT. We show that this increases coherence between generated images and
cycled masks and evaluate synthetic datasets on a downstream nuclei
segmentation task. Here we achieve an improvement of 5.4 percentage points in
the F1-score compared to a vanilla CycleGAN. Integrating advanced
regularization techniques into the CycleGAN architecture may help mitigate
steganography-related issues and produce more accurate synthetic datasets for
nuclei segmentation.


  The pp -> p K+ Y0 reaction has been studied for hyperon masses m(Y0)<1540
MeV/c2 at COSY-Juelich by using a 3.65 GeV/c circulating proton beam incident
on an internal hydrogen target. Final states comprising two protons, one
positively charged kaon and one negatively charged pion have been identified
with the ANKE spectrometer. Such configurations are sensitive to the production
of the ground state Lambda and Sigma0 hyperons as well as the Sigma0(1385) and
Lambda(1405) resonances. Applying invariant- and missing-mass techniques, the
two overlapping excited states could be well separated, though with limited
statistics. The shape and position of the Lambda(1405) distribution,
reconstructed cleanly in the Sigma0 pion0 channel, are similar to those found
from other decay modes and there is no obvious mass shift. This finding
constitutes a challenging test for models that predict Lambda(1405) to be a
two-state resonance.


  We formulate and justify several proposals utilizing the unique electronic
properties of different types of carbon nanotubes for a broad range of
applications to THz optoelectronics, including THz generation by hot electrons
in quasi-metallic nanotubes, frequency multiplication in chiral-nanotube-based
superlattices controlled by a transverse electric field, and THz radiation
detection and emission by armchair nanotubes in a strong magnetic field.


  We present 1420 MHz (lambda=21cm) observations of polarized emission from an
area of 117 degree square in the Galactic plane in Cygnus, covering 82 < l <
95, -3.5 < b < +5.5, a complex region where the line of sight is directed
nearly along the Local spiral arm. The angular resolution is ~ 1', and
structures as large as 45' are fully represented in the images. Polarization
features bear little resemblance to features detected in total power: while the
polarized signal arises in diffuse Galactic synchrotron emission regions, the
appearance of the polarized sky is dominated by Faraday rotation occurring in
small-scale structure in the intervening Warm Ionized Medium. There is no
concentration of polarization structure towards the Galactic plane, indicating
that both the emission and Faraday rotation occur nearby. We develop a
conceptual framework for interpretation of the observations. We can detect only
that polarized emission which has its origin closer than the polarization
horizon, at a distance d_ph; more distant polarized emission is undetectable
because of depth depolarization (differential Faraday rotation) and/or beam
depolarization (due to internal and external Faraday dispersion). d_ph depends
on the instrument used (frequency and beamwidth) as well as the direction being
studied. In our data we find that d_ph ~ 2 kpc, consistent with the
polarization features originating in the Local arm.


  We propose an extension to time series with several simultaneously measured
variables of the nonlinearity test, which combines the redundancy -- linear
redundancy approach with the surrogate data technique. For several variables
various types of the redundancies can be defined, in order to test specific
dependence structures between/among (groups of) variables. The null hypothesis
of a multivariate linear stochastic process is tested using the multivariate
surrogate data. The linear redundancies are used in order to avoid spurious
results due to imperfect surrogates. The method is demonstrated using two types
of numerically generated multivariate series (linear and nonlinear) and
experimental multivariate data from meteorology and physiology.


  In this paper, we demonstrate the importance of embedding temporal
information for an accurate prediction of solar irradiance. We have used two
sets of models for forecasting solar irradiance. The first one uses only time
series data of solar irradiance for predicting future values. The second one
uses the historical solar irradiance values, together with the corresponding
timestamps. We employ data from the weather station located at Nanyang
Technological University (NTU) Singapore. The solar irradiance values are
recorded with a temporal resolution of $1$ minute, for a period of $1$ year. We
use Multilayer Perceptron Regression (MLP) technique for forecasting solar
irradiance. We obtained significant better prediction accuracy when the time
stamp information is embedded in the forecasting framework, as compared to
solely using historical solar irradiance values.


  The problem of speech separation, also known as the cocktail party problem,
refers to the task of isolating a single speech signal from a mixture of speech
signals. Previous work on source separation derived an upper bound for the
source separation task in the domain of human speech. This bound is derived for
deterministic models. Recent advancements in generative models challenge this
bound. We show how the upper bound can be generalized to the case of random
generative models. Applying a diffusion model Vocoder that was pretrained to
model single-speaker voices on the output of a deterministic separation model
leads to state-of-the-art separation results. It is shown that this requires
one to combine the output of the separation model with that of the diffusion
model. In our method, a linear combination is performed, in the frequency
domain, using weights that are inferred by a learned model. We show
state-of-the-art results on 2, 3, 5, 10, and 20 speakers on multiple
benchmarks. In particular, for two speakers, our method is able to surpass what
was previously considered the upper performance bound.


  We consider a dynamical network model in which two competitors have fixed and
different states, and each normal agent adjusts its state according to a
distributed consensus protocol. The state of each normal agent converges to a
steady value which is a convex combination of the competitors' states, and is
independent of the initial states of agents. This implies that the competition
result is fully determined by the network structure and positions of
competitors in the network. We compute an Influence Matrix (IM) in which each
element characterizing the influence of an agent on another agent in the
network. We use the IM to predict the bias of each normal agent and thus
predict which competitor will win. Furthermore, we compare the IM criterion
with seven node centrality measures to predict the winner. We find that the
competitor with higher Katz Centrality in an undirected network or higher
PageRank in a directed network is much more likely to be the winner. These
findings may shed new light on the role of network structure in competition and
to what extent could competitors adjust network structure so as to win the
competition.


  In the spirit of the International Year of Physics 2005 the author advices
the replacement of the teaching methods based on verbal explanations and to
perform a number of tasks following a given recipe by procedures that actively
involve the students in the physics discovery process.


  We consider a cost sharing problem to connect all nodes in a weighted
undirected graph, where the weight of each edge represents the cost to use the
edge for the connectivity and the cost has to be shared among all connected
nodes. There is one node called the source to which all the other nodes want to
connect and it does not share the costs of the connectivity. As a node may need
to go through other nodes to reach the source, the intermediate nodes may
behave strategically to block the connection by cutting the edges adjacent to
them. To prevent such strategical behavior, we design cost sharing mechanisms
to incentivize all nodes not to cut any edge so that we can minimize the total
cost for connecting all the nodes.


  The secondary polytope of a point configuration A is a polytope whose face
poset is isomorphic to the poset of all regular subdivisions of A. While the
vertices of the secondary polytope - corresponding to the triangulations of A -
are very well studied, there is not much known about the facets of the
secondary polytope. The splits of a polytope, subdivisions with exactly two
maximal faces, are the simplest examples of such facets and the first that were
systematically investigated. The present paper can be seen as a continuation of
these studies and as a starting point of an examination of the subdivisions
corresponding to the facets of the secondary polytope in general. As a special
case, the notion of k-split is introduced as a possibility to classify
polytopes in accordance to the complexity of the facets of their secondary
polytopes. An application to matroid subdivisions of hypersimplices and
tropical geometry is given.


  A class of stationary axisymmetric solutions of Einstein's equations for
isolated differentially rotating dust sources is presented. The low-energy
asymptotic regime is extracted, requiring a self-consistent coupling of
quasilocal energy and angular momentum. The Raychaudhuri equation reduces to a
balance equation, with two important limits. These limits can be interpreted
empirically for rotationally supported configurations such as galaxies. The net
energy including quasilocal kinetic contributions vanishes on the inner vortex
surface, and the outer rotosurface. These new geometrical objects potentially
shed light on virialization. Whether or not abundant collisionless dark matter
exists, the new solutions suggest that the phenomenology of galactic rotation
curves be fundamentally reconsidered, for consistency with general relativity.


  The study of combinatorial designs has a rich history spanning nearly two
centuries. In a recent breakthrough, the notorious Existence Conjecture for
Combinatorial Designs dating back to the 1800s was proved in full by Keevash
via the method of randomized algebraic constructions. Subsequently Glock,
K\"{u}hn, Lo, and Osthus provided an alternate purely combinatorial proof of
the Existence Conjecture via the method of iterative absorption. We introduce a
novel method of refined absorption for designs; here as our first application
of the method we provide a new alternate proof of the Existence Conjecture
(assuming the existence of $K_q^r$-absorbers by Glock, K\"{u}hn, Lo, and
Osthus).


  Combining X-ray tomography with simultaneous shear force measurement, we
investigate shear-induced granular avalanches using spherical particles with
different surface roughness. We find that systems consisting of particles with
large surface roughness display quasi-periodic avalanches interrupted by
crackling-like small ones. In contrast, systems consisting of particles with
small roughness display no detectable avalanches. The stress drop of
quasi-periodic avalanche shows a linear relation with the correlation length of
particle non-affine displacement, suggesting that roughness enhances
inter-particle locking and hence particle-level dynamic correlation length.
However, the nonaffine displacement is two orders of magnitude smaller than
particle size, indicating that stress is mainly released on the length scale of
roughness. The correlation length of non-affine displacements abruptly
increases when a quasi-periodic avalanche occurs, suggesting that
quasi-periodic avalanches can be interpreted as a spinodal nucleation event in
a first-order phase transition.


  Approximate Bayesian Computation (ABC for short) is a family of computational
techniques which offer an almost automated solution in situations where
evaluation of the posterior likelihood is computationally prohibitive, or
whenever suitable likelihoods are not available. In the present paper, we
analyze the procedure from the point of view of k-nearest neighbor theory and
explore the statistical properties of its outputs. We discuss in particular
some asymptotic features of the genuine conditional density estimate associated
with ABC, which is an interesting hybrid between a k-nearest neighbor and a
kernel method.


  The LVD time stability allows to establish a time-shift in the OPERA
experiment, thus providing the first proof against Superluminal neutrinos,
using the horizontal muons of the "Teramo Anomaly". This proof is particularly
interesting since does not need the knowledge of the distance between the place
where the neutrinos are produced (CERN) and the place where they are detected
(LNGS). Since the Superluminal neutrinos generated in the physics community a
vivid interest in good and bad behaviour in physics research, the author
thought it was appropriate to recall the origin of the Gran Sasso Lab, of the
3rd neutrino, of the horizontal muons due to the "Teramo Anomaly" and of the
oscillation between leptonic flavours, when the CERN-Gran Sasso neutrino beam
was included in the project for the most powerful underground Laboratory in the
world.


  We present first results from the Near-infrared Integral Field Spectrograph
(NIFS) located at Gemini North. For the active galaxies Cygnus A and Perseus A
we observe rotationally-supported accretion disks and adduce the existence of
massive central black holes and estimate their masses. In Cygnus A we also see
remarkable high-excitation ionization cones dominated by photoionization from
the central engine. In the T-Tauri stars HV Tau C and DG Tau we see
highly-collimated bipolar outflows in the [Fe II] 1.644 micron line, surrounded
by a slower molecular bipolar outflow seen in the H_2 lines, in accordance with
the model advocated by Pyo et al. (2002).


  Recently, long-tailed image classification harvests lots of research
attention, since the data distribution is long-tailed in many real-world
situations. Piles of algorithms are devised to address the data imbalance
problem by biasing the training process towards less frequent classes. However,
they usually evaluate the performance on a balanced testing set or multiple
independent testing sets having distinct distributions with the training data.
Considering the testing data may have arbitrary distributions, existing
evaluation strategies are unable to reflect the actual classification
performance objectively. We set up novel evaluation benchmarks based on a
series of testing sets with evolving distributions. A corpus of metrics are
designed for measuring the accuracy, robustness, and bounds of algorithms for
learning with long-tailed distribution. Based on our benchmarks, we re-evaluate
the performance of existing methods on CIFAR10 and CIFAR100 datasets, which is
valuable for guiding the selection of data rebalancing techniques. We also
revisit existing methods and categorize them into four types including data
balancing, feature balancing, loss balancing, and prediction balancing,
according the focused procedure during the training pipeline.


  The strongly correlated system Ho11B12 with boron sublattice Jahn-Teller
instability and nanoscale electronic phase separation (dynamic charge stripes)
was studied in detail by inelastic neutron scattering (INS), magnetometry and
heat capacity measurements at temperatures in the range 3-300 K. From the
analysis of registered INS spectra, we determined parameters of the cubic
crystal field at holmium sites, B4=- 0.333 meV and B6= -2.003 meV (in Stevens
notations), with an unconventional large ratio B6/B4 pointing on the dominant
role of conduction electrons in the formation of a crystal field potential. The
molecular field in the antiferromagnetic state, Bloc = (1.75+- 0.1) T has been
directly determined from the INS spectra together with short-range order
effects detected in the paramagnetic state. A comparison of measured
magnetization in diluted Lu0.99Ho0.01B12 and concentrated HoB12 single crystals
showed a strong suppression of Ho magnetic moments by antiferromagnetic
exchange interactions in holmium dodecaboride. To account explicitly for the
short-range antiferromagnetic correlations, a self-consistent holmium dimer
model was developed that allowed us to reproduce successfully field and
temperature variations of the magnetization and heat capacity in the cage-glass
phase of HoB12 in external magnetic fields.


  We revisit the phase diagram of strong-interaction matter for the two-flavor
quark-meson model using the Functional Renormalization Group. In contrast to
standard mean-field calculations, an unusual phase structure is encountered at
low temperatures and large quark chemical potentials. In particular, we
identify a regime where the pressure decreases with increasing temperature and
discuss possible reasons for this unphysical behavior.


  WASP-19b has the shortest orbital period of any known exoplanet, orbiting at
only 1.2 times the Roche tidal radius. By observing the Rossiter-McLaughlin
effect we show that WASP-19b's orbit is aligned, with lambda = 4.6 +/- 5.2
degrees. Using, in addition, a spectroscopic vsini and the observed rotation
period we conclude that the obliquity, psi, is less than 20 degrees. Further,
the eccentricity of the orbit is less than 0.02. We argue that hot Jupiters
with orbital periods as short as that of WASP-19b are two orders of magnitude
less common than hot Jupiters at the 3-4-d `pileup'. We discuss the evolution
of WASP-19b's orbit and argue that most likely it was first moved to near twice
the Roche limit by third-body interactions, and has since spiralled inwards to
its current location under tidal decay. This is compatible with a stellar
tidal-dissipation quality factor, Qs, of order 10^7.


  This paper is concerned with testing global null hypotheses about population
mean vectors of high-dimensional data. Current tests require either strong
mixing (independence) conditions on the individual components of the
high-dimensional data or high-order moment conditions. In this paper, we
propose a novel class of bootstrap hypothesis tests based on
$\ell_p$-statistics with $p \in [1, \infty]$ which requires neither of these
assumptions. We study asymptotic size, unbiasedness, consistency, and Bahadur
slope of these tests. Capitalizing on these theoretical insights, we develop a
modified bootstrap test with improved power properties and a self-normalized
bootstrap test for elliptically distributed data. We then propose two novel
bias correction procedures to improve the accuracy of the bootstrap test in
finite samples, which leverage measure concentration and hypercontractivity
properties of $\ell_p$-norms in high dimensions. Numerical experiments support
our theoretical results in finite samples.


  To date, both quantum theory, and Einstein's theory of general relativity
have passed every experimental test in their respective regimes. Nevertheless,
almost since their inception, there has been debate surrounding whether they
should be unified and by now there exists strong theoretical arguments pointing
to the necessity of quantising the gravitational field. In recent years, a
number of experiments have been proposed which, if successful, should give
insight into features at the Planck scale. Here we review some of the
motivations, from the perspective of semi-classical arguments, to expect new
physical effects at the overlap of quantum theory and general relativity. We
conclude with a short introduction to some of the proposals being made to
facilitate empirical verification.


  We report on a search for the decay B0 --> D_S+ rho- in a sample of 90
million Y(4S) decays into B meson pairs collected between 1999 and 2001 with
the BaBar detector at the PEP-II asymmetric-energy e+e- collider. No
significant excess of signal events above the expected background is observed.
We set a 90% C.L. limit on the branching fraction Br(B0 --> D_S+ rho-)<
1.9*10^{-5}. Assuming a flavor SU(3) symmetry relation between the decays B0
--> D_S+ rho- and B0 --> D+ rho-, we set a limit on the ratio of CKM-suppressed
to CKM-favored amplitudes r(D rho)<9.5*10^{-3} at 90% C.L. All results are
preliminary.


  We study $k$-point correlators of characteristic polynomials in non-Hermitian
ensembles of random matrices, focusing on the real, complex and quaternion $N
\times N$ Ginibre ensembles. Our approach is based on the technique of
character expansions, which expresses the correlator as a sum over partitions
involving Schur functions. We show how to re-sum the expansions in terms of
representations which interchange the roles of $N$ and $k$. We also provide a
probabilistic interpretation of the character expansion analogous to the Schur
measure, linking the correlators to the distribution of the top row in certain
Young diagrams. In more specific examples we evaluate these expressions
explicitly in terms of $k \times k$ determinants or Pfaffians. We show that our
approach extends to other ensembles, such as truncations of random unitary
matrices.


  The size of the vortex core in a clean superconductor is strongly temperature
dependent and shrinks with decreasing temperature, decreasing to zero for T ->
0. We study this so-called Kramer-Pesch effect both for a single gap
superconductor and for the case of a two gap superconductor using parameters
appropriate for Magnesium Diboride. Usually, the Kramer-Pesch effect is absent
in the dirty limit. Here, we show that the Kramer-Pesch effect exists in both
bands of a two gap superconductor even if only one of the two bands is in the
clean limit and the other band in the dirty limit, a case appropriate for MgB2.
In this case an induced Kramer-Pesch effect appears in the dirty band. Besides
numerical results we also present an analytical model for the spatial variation
of the pairing potential in the vicinity of the vortex center that allows a
simple calculation of the vortex core radius even in the limit T -> 0.


  In this short survey we compare aspects of two different approaches for
scaling limits of interacting particle systems, the hydrodynamic limit and the
high density limit. We present some examples, comments and open problems on
each approach for different scenarios: the law of large numbers, the central
limit theorem, and the large deviations principle. It is given a special
attention to a recent result of Franco and Groisman' 12 about the high density
limit of a system exhibiting explosion of the total numbers of particles in
finite time.


  Given a labeled training set and a collection of unlabeled data, the goal of
active learning (AL) is to identify the best unlabeled points to label. In this
comprehensive study, we analyze the performance of a variety of AL algorithms
on deep neural networks trained on 69 real-world tabular classification
datasets from the OpenML-CC18 benchmark. We consider different data regimes and
the effect of self-supervised model pre-training. Surprisingly, we find that
the classical margin sampling technique matches or outperforms all others,
including current state-of-art, in a wide range of experimental settings. To
researchers, we hope to encourage rigorous benchmarking against margin, and to
practitioners facing tabular data labeling constraints that
hyper-parameter-free margin may often be all they need.


  We revisit a K-theoretical invariant that was invented by the first author
some years ago for studying multiparameter bifurcation of branches of critical
points of functionals. Our main aim is to apply this invariant to investigate
bifurcation of homoclinic solutions of families of Hamiltonian systems which
are parametrised by tori.


  Deep Active Learning (DAL) has been advocated as a promising method to reduce
labeling costs in supervised learning. However, existing evaluations of DAL
methods are based on different settings, and their results are controversial.
To tackle this issue, this paper comprehensively evaluates 19 existing DAL
methods in a uniform setting, including traditional
fully-\underline{s}upervised \underline{a}ctive \underline{l}earning (SAL)
strategies and emerging \underline{s}emi-\underline{s}upervised
\underline{a}ctive \underline{l}earning (SSAL) techniques. We have several
non-trivial findings. First, most SAL methods cannot achieve higher accuracy
than random selection. Second, semi-supervised training brings significant
performance improvement compared to pure SAL methods. Third, performing data
selection in the SSAL setting can achieve a significant and consistent
performance improvement, especially with abundant unlabeled data. Our findings
produce the following guidance for practitioners: one should (i) apply SSAL
early and (ii) collect more unlabeled data whenever possible, for better model
performance.


  Recently, stellar collapse involving black hole formation from massive stars
is suggested to emit enormous fluxes of neutrinos on par with ordinary
core-collapse supernovae. We investigate their detectability for the currently
operating neutrino detector, SuperKamiokande. Neutrino oscillation is also
taken into account for the evaluation. We find that the event number is larger
than or comparable to that of supernova neutrinos and, hence, black hole
formation is also a candidate for neutrino astronomy. Moreover, we find that
the event number depends dominantly on the equation of state used in the
computations of the black hole formation. This fact implies that the detection
of neutrinos emitted from the black hole progenitors is very valuable to probe
the properties of the equation of state for hot and/or dense matter.


  More than 40% of the world's population is not connected to the internet,
majorly due to the lack of adequate infrastructure. Our work aims to bridge
this digital divide by proposing solutions for network deployment in remote
areas. Specifically, a number of access points (APs) are deployed as an
interface between the users and backhaul nodes (BNs). The main challenges
include designing the number and location of the APs, and connecting them to
the BNs. In order to address these challenges, we first propose a metric called
connectivity ratio to assess the quality of the deployment. Next, we propose an
agile search algorithm to determine the number of APs that maximizes this
metric and perform clustering to find the optimal locations of the APs.
Furthermore, we propose a novel algorithm inspired by infection dynamics to
connect all the deployed APs to the existing BNs economically. To support the
existing terrestrial BNs, we investigate the deployment of non-terrestrial BNs,
which further improves the network performance in terms of average hop count,
traffic distribution, and backhaul length. Finally, we use real datasets from a
remote village to test our solution.


  In this paper we consider the linearized version of a system of partial
differential equations arising from a fluid-structure interaction model. We
prove the existence and the uniqueness of the solution under natural regularity
assumptions.


  Reinforcement learning from self-play has recently reported many successes.
Self-play, where the agents compete with themselves, is often used to generate
training data for iterative policy improvement. In previous work, heuristic
rules are designed to choose an opponent for the current learner. Typical rules
include choosing the latest agent, the best agent, or a random historical
agent. However, these rules may be inefficient in practice and sometimes do not
guarantee convergence even in the simplest matrix games. In this paper, we
propose a new algorithmic framework for competitive self-play reinforcement
learning in two-player zero-sum games. We recognize the fact that the Nash
equilibrium coincides with the saddle point of the stochastic payoff function,
which motivates us to borrow ideas from classical saddle point optimization
literature. Our method trains several agents simultaneously, and intelligently
takes each other as opponent based on simple adversarial rules derived from a
principled perturbation-based saddle optimization method. We prove
theoretically that our algorithm converges to an approximate equilibrium with
high probability in convex-concave games under standard assumptions. Beyond the
theory, we further show the empirical superiority of our method over baseline
methods relying on the aforementioned opponent-selection heuristics in matrix
games, grid-world soccer, Gomoku, and simulated robot sumo, with neural net
policy function approximators.


  In what follows, essentially two things will be accomplished: Firstly, it
will be proven that a version of the Arzel\`a--Ascoli theorem and the
Fr\'echet--Kolmogorov theorem are equivalent to the axiom of countable choice
for subsets of real numbers. Secondly, some progress is made towards
determining the amount of axioms that have to be added to the Zermelo--Fraenkel
system so that the uniform boundedness principle holds.


  We consider probabilistic cloning of a state chosen from a mutually
nonorthogonal set of pure states, with the help of a party holding
supplementary information in the form of pure states. When the number of states
is 2, we show that the best efficiency of producing m copies is always achieved
by a two-step protocol in which the helping party first attempts to produce m-1
copies from the supplementary state, and if it fails, then the original state
is used to produce m copies. On the other hand, when the number of states
exceeds two, the best efficiency is not always achieved by such a protocol. We
give examples in which the best efficiency is not achieved even if we allow any
amount of one-way classical communication from the helping party.


  Shear induced drainage of a foamy yield stress fluid is investigated using
MRI techniques. Whereas the yield stress of the interstitial fluid stabilizes
the system at rest, a fast drainage is observed when a horizontal shear is
imposed. It is shown that the sheared interstitial material behaves as a
viscous fluid in the direction of gravity, the effective viscosity of which is
controlled by shear in transient foam films between bubbles. Results provided
for several bubble sizes are not captured by the R^2 scaling classically
observed for liquid flow in particulate systems, such as foams and thus
constitute a remarkable demonstration of the strong coupling of drainage flow
and shear induced interstitial flow. Furthermore, foam films are found to be
responsible for the unexpected arrest of drainage, thus trapping irreversibly a
significant amount of interstitial liquid.


  Within the Standard Model, we investigate the weak decays of $\Lambda_b \to
\Lambda + \gamma$ and $\Lambda_b \to \Lambda + l^{+} l^{-}$ with the light-cone
sum rules approach. The higher twist distribution amplitudes of $\Lambda$
baryon to the leading conformal spin are included in the sum rules for
transition form factors. Our results indicate that the higher twist
distribution amplitudes almost have no influences on the transition form
factors retaining the heavy quark spin symmetry, while such corrections can
result in significant impacts on the form factors breaking the heavy quark spin
symmetry. Two phenomenological models (COZ and FZOZ) for the wave function of
$\Lambda$ baryon are also employed in the sum rules for a comparison, which can
give rise to the form factors approximately 5 times larger than that in terms
of conformal expansion. Utilizing the form factors calculated in LCSR, we then
perform a careful study on the decay rate, polarization asymmetry and
forward-backward asymmetry, with respect to the decays of $\Lambda_b \to
\Lambda \gamma$, $\Lambda l^{+}l^{-}$.


  We present a protocol that allows us to obtain the concurrence of any two
qubit pure state by performing a minimal and optimal tomography of one of the
subsystems through measuring a single observable of an ancillary four
dimensional qudit. An implementation for a system of trapped ions is also
proposed, which can be achieved with present day experimental techniques.


  We investigate some well-known (and a few not-so-well-known) many-valued
logics that have a small number (3 or 4) of truth values. For some of them we
complain that they do not have any \emph{logical} use (despite their perhaps
having some intuitive semantic interest) and we look at ways to add features so
as to make them useful, while retaining their intuitive appeal. At the end, we
show some surprising results in the system FDE, and its relationships with
features of other logics. We close with some new examples of "synonymous
logics." An Appendix contains a natural deduction system for our augmented FDE,
and proofs of soundness and completeness.


  Recent progress in high-dispersion spectroscopy has revealed the presence of
vaporized heavy metals and ions in the atmosphere of hot Jupiters whose dayside
temperature is larger than 2000 K, categorized as ultra hot Jupiters (UHJs).
Using the archival data of high resolution transmission spectroscopy obtained
with the Subaru telescope, we searched for neutral metals in HD149026b, a hot
Jupiter cooler than UHJs. By removing stellar and telluric absorption and using
a cross-correlation technique, we report tentative detection of neutral
titanium with 4.4 sigma and a marginal signal of neutral iron with 2.8 sigma in
the atmosphere. This is the first detection of neutral titanium in an
exoplanetary atmosphere. In this temperature range, titanium tends to form
titanium oxide (TiO). The fact that we did not detect any signal from TiO
suggests that the C/O ratio in the atmosphere is higher than the solar value.
The detection of metals in the atmosphere of hot Jupiters cooler than UHJs will
be useful for understanding the atmospheric structure and formation history of
hot Jupiters.


  The Josephson effect describes supercurrent flowing through a junction
connecting two superconducting leads by a thin barrier [1]. This current is
driven by a superconducting phase difference $\phi$ between the leads. In the
presence of chiral and time reversal symmetry of the Cooper pair tunneling
process [2] the current is strictly zero when $\phi$ vanishes. Only if these
underlying symmetries are broken the supercurrent for $\phi=0$ may be finite
[3-5]. This corresponds to a ground state of the junction being offset by a
phase $\phi_{0}$, different from 0 or $\pi$. Here, we report such a Josephson
$\phi_{0}$-junction based on a nanowire quantum dot. We use a quantum
interferometer device in order to investigate phase offsets and demonstrate
that $\phi_{0}$ can be controlled by electrostatic gating. Our results have
possible far reaching implications for superconducting flux and phase defined
quantum bits as well as for exploring topological superconductivity in quantum
dot systems.


  A major challenge at the interface of quantum gravity and cosmology is to
explain how the large-scale structure of the Universe emerges from physics at
the Planck scale. In this letter, we take an important step in this direction
by extracting the dynamics of scalar isotropic cosmological perturbations from
full quantum gravity, as described by the causally complete Barrett-Crane group
field theory model. From the perspective of the underlying quantum gravity
theory, cosmological perturbations are represented as nearest-neighbor two-body
entanglement of group field theory quanta. Their effective dynamics is obtained
via mean-field methods and described relationally with respect to a physical
Lorentz frame causally coupled to the quantum geometry. We quantitatively study
these effective dynamical equations and show that at low energies they are
perfectly consistent with those of General Relativity, while for
trans-Planckian scales quantum effects become important. These results
therefore not only provide crucial insights into the potentially purely quantum
gravitational nature of cosmological perturbations, but also offer rich
phenomenological implications for the physics of the early Universe.


  We show that for a closed hyperbolic 3-manifold the size of the first
eigenvalue of the Hodge Laplacian acting on coexact 1-forms is comparable to an
isoperimetric ratio relating geodesic length and stable commutator length with
comparison constants that depend polynomially on the volume and on a lower
bound on injectivity radius, refining estimates of Lipnowski and Stern. We use
this estimate to show that there exist sequences of closed hyperbolic
3-manifolds with injectivity radius bounded below and volume going to infinity
for which the 1-form Laplacian has spectral gap vanishing exponentially fast in
the volume.


  Let $N$ be a smooth manifold that is homeomorphic but not diffeomorphic to a
closed hyperbolic manifold $M$. In this paper, we study the extent to which $N$
admits as much symmetry as $M$. Our main results are examples of $N$ that
exhibit two extremes of behavior. On the one hand, we find $N$ with maximal
symmetry, i.e. Isom($M$) acts on $N$ by isometries with respect to some
negatively curved metric on $N$. For these examples, Isom($M$) can be made
arbitrarily large. On the other hand, we find $N$ with little symmetry, i.e. no
subgroup of Isom($M$) of "small" index acts by diffeomorphisms of $N$. The
construction of these examples incorporates a variety of techniques including
smoothing theory and the Belolipetsky-Lubotzky method for constructing
hyperbolic manifolds with a prescribed isometry group.


  We consider the construction of pseudospin bases for a time-reversal and
inversion symmetric system, illustrated by a model for Cu:Bi$_2$Se$_3$.
Different methods and bases are compared.


  In today's age, social media reigns as the paramount communication platform,
providing individuals with the avenue to express their conjectures,
intellectual propositions, and reflections. Unfortunately, this freedom often
comes with a downside as it facilitates the widespread proliferation of hate
speech and offensive content, leaving a deleterious impact on our world. Thus,
it becomes essential to discern and eradicate such offensive material from the
realm of social media. This article delves into the comprehensive results and
key revelations from the HASOC-2023 offensive language identification result.
The primary emphasis is placed on the meticulous detection of hate speech
within the linguistic domains of Bengali, Assamese, and Bodo, forming the
framework for Task 4: Annihilate Hates. In this work, we used BERT models,
including XML-Roberta, L3-cube, IndicBERT, BenglaBERT, and BanglaHateBERT. The
research outcomes were promising and showed that XML-Roberta-lagre performed
better than monolingual models in most cases. Our team 'TeamBD' achieved rank
3rd for Task 4 - Assamese, & 5th for Bengali.


  We introduce a novel method for online smoothing in state-space models that
utilises a fixed-lag approximation to overcome the well known issue of path
degeneracy. Unlike classical fixed-lag techniques that only approximate certain
marginals, we introduce an online resampling algorithm, called particle
stitching, that converts these marginal samples into a full posterior
approximation. We demonstrate the utility of our method in the context of
map-matching, the task of inferring a vehicle's trajectory given a road network
and noisy GPS observations. We develop a new state-space model for the
difficult task of map-matching on dense, urban road networks.


  We propose new dictionaries for holographic conductors that enable us to
compute carrier densities and mean velocities of charge carriers in the system.
The carrier density, which differs from the charge density, is the total number
density of both the positive and the negative charge carriers. The mean
velocity is the mean value of the velocities of all charge carriers. These
quantities are not conjugate to the sources that are given by boundary values
of bulk fields, and we cannot compute them by using the conventional method in
holography. In the present work, we introduce a phenomenological model of
charge transport, and we establish the dictionary by comparing the results of
the phenomenological model and those from the holography. We show that the mean
velocity agrees with the velocity of an analog black hole on the worldvolume of
the probe D-brane, and it can be read from the spectrum of the fluctuations.


  We develop the holographic dictionary for pure $\mathrm{AdS}_3$ gravity where
the Lagrangian of the dual $2d$ conformal field theory has been deformed by an
arbitrary function of the energy-momentum tensor. In addition to the $T
\overline{T}$ deformation, examples of such functions include a class of
marginal stress tensor deformations which are special because they leave the
generating functional of connected correlators unchanged up to a redefinition
of the source and expectation value. Within this marginal class, we identify
the unique deformation that commutes with the $T \overline{T}$ flow, which is
the root-$T \overline{T}$ operator, and write down the modified boundary
conditions corresponding to this root-$T \overline{T}$ deformation. We also
identify the unique marginal stress tensor flow for the cylinder spectrum of
the dual CFT which commutes with the inviscid Burgers' flow driven by $T
\overline{T}$, and we propose this unique flow as a candidate root-$T
\overline{T}$ deformation of the energy levels. We study BTZ black holes in
$\mathrm{AdS}_3$ subject to root-$T \overline{T}$ deformed boundary conditions,
and find that their masses flow in a way which is identical to that of our
candidate root-$T \overline{T}$ energy flow equation, which offers evidence
that this flow is the correct one. Finally, we also obtain the root-$T
\overline{T}$ deformed boundary conditions for the gauge field in the
Chern-Simons formulation of $\mathrm{AdS}_3$ gravity.


  We study the ground state of the low energy dense QCD with the assumption of
chiral condensates of quarks. Under an external magnetic field, mesons could
form soliton lattices via the chiral anomaly. For such scenarios, we present a
unified description of pions and $\eta$ meson with a $U(2)$ field in the
framework of the chiral perturbation theory. Our result shows the ground state
is a mixture of the magnetized domain walls formed by neutral pion $\pi^0$ and
$\eta$ meson when they coexist. The winding number of the ground state would
alter according to the strength of the magnetic field. When the magnetic field
is strong or the chemical potential is large, the proportion of the mixture is
determined by the decay constants and the contributions to the anomalous action
of $\pi^0$ and $\eta$ meson. The resulting configuration is either a mixed
soliton lattice or a quasicrystal which could be dubbed a ``chiral soliton
quasicrystal''.


  Recently there have been multiple calls for curricular reforms to develop new
pathways to the science, technology, engineering and math (STEM) disciplines.
The Marble Game answers these calls by providing a conceptual framework for
quantitative scientific modeling skills useful across all the STEM disciplines.
The approach actively engages students in a process of directed scientific
discovery. In a "Student Assessment of their Learning Gains" (SALG) survey,
students identified this approach as producing "great gains" in their
understanding of real world problems and scientific research. Using the marble
game, students build a conceptual framework that applies directly to random
molecular-level processes in biology such as diffusion and interfacial
transport. It is also isomorphic with a reversible first-order chemical
reaction providing conceptual preparation for chemical kinetics. The
computational and mathematical framework can also be applied to investigate the
predictions of quantitative physics models ranging from Newtonian mechanics
through RLC circuits. To test this approach, students were asked to derive a
novel theory of osmosis. The test results confirm that they were able to
successfully apply the conceptual framework to a new situation under final exam
conditions. The marble game thus provides a pathway to the STEM disciplines
that includes quantitative biology concepts in the undergraduate curriculum -
from the very first class.


  We report a robust technique for laser frequency stabilisation that enables
the reproducible loading of in excess of 10$^{9}$ Yb atoms from a Zeeman slower
directly into a magneto-optical trap (MOT) operating on the $^{1}S_{0}
\rightarrow \, ^{3}P_{1}$ transition, without the need for a first stage MOT on
the $^{1}S_{0} \rightarrow \, ^{1}P_{1}$ transition. We use a simple atomic
beam apparatus to generate narrow fluorescence signals on both the 399 nm
$^{1}S_{0} \rightarrow \, ^{1}P_{1}$ transition used for the Zeeman slower and
the 556 nm $^{1}S_{0} \rightarrow \, ^{3}P_{1}$ transition. We present in
detail the methods for obtaining spectra with a high signal-to-noise ratio and
demonstrate error signals suitable for robust frequency stabilisation. Finally
we demonstrate the stability and precision of our technique through sensitive
measurements of the gravitational sag of the Yb MOT as a function of the
intensity of the laser cooling beams, which are in good agreement with theory.
These results will be important for efficient loading of the atoms into an
optical dipole trap.


  We investigate the spatial overlap of nonclassical ultrashort light pulses
produced by self-phase modulation effect in electronic Kerr media and its
relevance in the formation of polarization-squeezed states of light. The light
polarization is treated in terms of four quantum Stokes parameters whose
spectra of quantum fluctuations are investigated. We show that the frequency at
which the suppression of quantum fluctuations of Stokes parameters is the
greatest can be controlled by adjusting the linear phase difference between
pulses. By varying the intensity of one pulse one can suppress effectively the
quantum fluctuations of Stokes parameters. We study the overlap of nonclassical
pulses inside of an anisotropic electronic Kerr medium and we show that the
cross-phase modulation effect can be employed to control the
polarization-squeezed state of light. Moreover, we establish that the change of
the intensity or of the nonlinear phase shift per photon for one pulse controls
effectively the squeezing of Stokes parameters. The spatial overlap of a
coherent pulse field with an interference pulse produced by mixing two
quadrature-squeezed pulses on a beam splitter is analyzed. It is found that
squeezing is produced in three of the four Stokes parameters, with the
squeezing in the first two being simultaneous.


  We prove the following type of discrete entropy monotonicity for sums of
isotropic, log-concave, independent and identically distributed random vectors
$X_1,\dots,X_{n+1}$ on $\mathbb{Z}^d$: $$ H(X_1+\cdots+X_{n+1}) \geq
H(X_1+\cdots+X_{n}) + \frac{d}{2}\log{\Bigl(\frac{n+1}{n}\Bigr)} +o(1), $$
where $o(1)$ vanishes as $H(X_1) \to \infty$. Moreover, for the $o(1)$-term, we
obtain a rate of convergence $ O\Bigl({H(X_1)}{e^{-\frac{1}{d}H(X_1)}}\Bigr)$,
where the implied constants depend on $d$ and $n$. This generalizes to
$\mathbb{Z}^d$ the one-dimensional result of the second named author (2023). As
in dimension one, our strategy is to establish that the discrete entropy
$H(X_1+\cdots+X_{n})$ is close to the differential (continuous) entropy
$h(X_1+U_1+\cdots+X_{n}+U_{n})$, where $U_1,\dots, U_n$ are independent and
identically distributed uniform random vectors on $[0,1]^d$ and to apply the
theorem of Artstein, Ball, Barthe and Naor (2004) on the monotonicity of
differential entropy. In fact, we show this result under more general
assumptions than log-concavity, which are preserved up to constants under
convolution. In order to show that log-concave distributions satisfy our
assumptions in dimension $d\ge2$, more involved tools from convex geometry are
needed because a suitable position is required. We show that, for a log-concave
function on $\mathbb{R}^d$ in isotropic position, its integral, barycenter and
covariance matrix are close to their discrete counterparts. Moreover, in the
log-concave case, we weaken the isotropicity assumption to what we call almost
isotropicity. One of our technical tools is a discrete analogue to the upper
bound on the isotropic constant of a log-concave function, which extends to
dimensions $d\ge1$ a result of Bobkov, Marsiglietti and Melbourne (2022).


  Fragmentation functions for hadrons composed of heavy quarks are calculated
directly from the definitions given by Collins and Soper and are compared with
those calculated in another way. A new fragmentaion function for a P-wave meson
is also obtained and the singularity arising at the leading order is discussed.


  This paper is a very non-rigorous, loose, and extremely basic introduction to
sheaves. This is meant to be a a guide to gaining intuition about sheaves, what
they look like, and how they work, so that after reading this paper, someone
can jump into the extremely abstract definitions and examples seen in textbooks
with at least some idea of what is going on. Most of this material is inspired
and built from the work of Dr. Michael Robinson, and that of Dr. Robert Ghrist
and Dr. Jakob Hansen, as well as Dr. Justin Curry's PhD thesis, who are some of
the only applied sheaf theorists out there and they do an amazing job of
explaining sheaves in a concrete way through their research. The rest of this
paper is populated by mathematical definitions found in textbooks that I have
stretched from two lines into multiple pages, as well as some analogies for
thinking of sheaves I have thought of myself. This paper only assumes knowledge
of basic linear algebra, basic group theory, and the very fundamentals of
topology. If there is anything in the setup that you do not understand it is
probably a quick Wikipedia search away. I hope this paper provides insight,
intuition, and helpful examples of why sheaves are such powerful tools in both
math and science.


  Deciding if a graph is a Hamilton graph, also named the Hamilton cycle
problem, is important for discrete mathematics and computer science. Due to no
characterization to identify Hamilton graphs effectively, there are no
tractable algorithms to solve the Hamilton cycle problem. Grinberg Theorem is a
necessary condition only for planar Hamilton graphs. In this paper, based on
new studies on the Grinberg Theorem, in which we provided new properties of
Hamilton graphs with respect to the cycle bases and improved the Grinberg
Theorem to derive an efficient condition for Hamilton graphs, we present a new
precise algorithm for deciding Hamilton graphs, named the '1+1' algorithm.
Theoretically, the '1+1' algorithm terminates in $O(|E(G)|^3)$ worst time
complexity, where $|\textit{E}(\textit{G})|$ is the size of the given graph
$\textit{G}$.


  Machine learning (ML) has become a commodity in our every-day lives. We
routinely ask ML empowered smartphones to suggest lovely food places or to
guide us through a strange place. ML methods have also become standard tools in
many fields of science and engineering. A plethora of ML applications transform
human lives at unprecedented pace and scale. This book portrays ML as the
combination of three basic components: data, model and loss. ML methods combine
these three components within computationally efficient implementations of the
basic scientific principle "trial and error". This principle consists of the
continuous adaptation of a hypothesis about a phenomenon that generates data.
ML methods use a hypothesis to compute predictions for future events. We
believe that thinking about ML as combinations of three components given by
data, model, and loss helps to navigate the steadily growing offer for
ready-to-use ML methods. Our three-component picture of ML allows a unified
treatment of a wide range of concepts and techniques which seem quite unrelated
at first sight. The regularization effect of early stopping in iterative
methods is due to the shrinking of the effective hypothesis space.
Privacy-preserving ML is obtained by particular choices for the features of
data points. Explainable ML methods are characterized by particular choices for
the hypothesis space. To make good use of ML tools it is instrumental to
understand its underlying principles at different levels of detail. On a lower
level, this tutorial helps ML engineers to choose suitable methods for the
application at hand. The book also offers a higher-level view on the
implementation of ML methods which is typically required to manage a team of ML
engineers and data scientists.


  While contextualized word representations have improved state-of-the-art
benchmarks in many NLP tasks, their potential usefulness for social-oriented
tasks remains largely unexplored. We show how contextualized word embeddings
can be used to capture affect dimensions in portrayals of people. We evaluate
our methodology quantitatively, on held-out affect lexicons, and qualitatively,
through case examples. We find that contextualized word representations do
encode meaningful affect information, but they are heavily biased towards their
training data, which limits their usefulness to in-domain analyses. We
ultimately use our method to examine differences in portrayals of men and
women.


  The Evidence Lower Bound (ELBO) is a quantity that plays a key role in
variational inference. It can also be used as a criterion in model selection.
However, though extremely popular in practice in the variational Bayes
community, there has never been a general theoretic justification for selecting
based on the ELBO. In this paper, we show that the ELBO maximization strategy
has strong theoretical guarantees, and is robust to model misspecification
while most works rely on the assumption that one model is correctly specified.
We illustrate our theoretical results by an application to the selection of the
number of principal components in probabilistic PCA.


  The optical geometric phase encoded by the in-plane spatial orientation of
microstructures has promoted the rapid development of numerous new-type optical
meta-devices. However, pushing the concept of the geometric phase toward the
acoustic community still faces challenges. In this work, we take advantage of
two acoustic nonlocal metagratings that could support the direct conversion
between plane wave and designated vortex mode, of which the orbital angular
momentum conversion process plays a vital role in obtaining the acoustic
geometric phase. We obtain the acoustic geometric phases of different orders by
merely varying the orientation angle of one of the acoustic nonlocal
metagratings. Intriguingly, according to our developed theory, we reveal that
the reflective acoustic geometric phase, which is twice of the transmissive
one, can be readily realized by transferring the transmitted configuration to a
reflected one. Both the theoretical model and experimental measurements
successfully verify the announced transmissive and reflective acoustic
geometric phases. Moreover, the characteristics of reconfigurability and
continuous phase modulation that covers the 2{\pi} range shown by the acoustic
geometric phases provide us with new possibilities in advanced acoustic
wavefront control.


  Multi-task learning with transformer encoders (MTL) has emerged as a powerful
technique to improve performance on closely-related tasks for both accuracy and
efficiency while a question still remains whether or not it would perform as
well on tasks that are distinct in nature. We first present MTL results on five
NLP tasks, POS, NER, DEP, CON, and SRL, and depict its deficiency over
single-task learning. We then conduct an extensive pruning analysis to show
that a certain set of attention heads get claimed by most tasks during MTL, who
interfere with one another to fine-tune those heads for their own objectives.
Based on this finding, we propose the Stem Cell Hypothesis to reveal the
existence of attention heads naturally talented for many tasks that cannot be
jointly trained to create adequate embeddings for all of those tasks. Finally,
we design novel parameter-free probes to justify our hypothesis and demonstrate
how attention heads are transformed across the five tasks during MTL through
label analysis.


  BFO3 ceramics were investigated by means of infrared reflectivity and time
domain THz transmission spectroscopy at temperatures 20 - 950 K, and the
magnetodielectric effect was studied at 10 - 300 K, with the magnetic field up
to 9 T. Below 175 K, the sum of polar phonon contributions into the
permittivity corresponds to the value of measured permittivity below 1 MHz. At
higher temperatures, a giant low-frequency permittivity was observed, obviously
due to the enhanced conductivity and possible Maxwell-Wagner contribution.
Above 200 K the observed magnetodielectric effect is caused essentially through
the combination of magnetoresistance and the Maxwell-Wagner effect, as recently
predicted by Catalan (Appl. Phys. Lett. 88, 102902 (2006)). Since the
magnetodielectric effect does not occur due to a coupling of polarization and
magnetization as expected in magnetoferroelectrics, we call it improper
magnetodielectric effect. Below 175 K the magnetodielectric effect is by
several orders of magnitude lower due to the decreased conductivity. Several
phonons exhibit gradual softening with increasing temperature, which explains
the previously observed high-frequency permittivity increase on heating. The
observed non-complete phonon softening seems to be the consequence of the
first-order nature of the ferroelectric transition.


  We study the analytical solutions of the Schr\"odinger equation with a
repulsive exponential potential $\lambda e^{- r}$, and that with an exponential
wall $\lambda e^r$, both with $\lambda > 0$. We show that the complex
eigenenergies obtained for the latter tend either to those of the former, or to
real rational numbers as $\lambda \rightarrow \infty$. In the light of these
results, we explain the wrong resonance energies obtained in a previous
application of the Riccati-Pad\'e method to the Schr\"odinger equation with a
repulsive exponential potential, and further study the convergence properties
of this approach.


  Narrow spectral features in the 5--6 keV range were recently discovered in
the X-ray spectra of a few active galactic nuclei. We discuss the possibility
that these features are due to localized spots which occur on the surface of an
accretion disc following its illumination by flares. We present detailed line
profiles as a function of orbital phase of the spot and its radial distance
from a central black hole. Comparison of these computed profiles with observed
features can help to estimate parameters of the system. In principle this
method can provide a powerful tool to measure the mass of super-massive black
holes in active galactic nuclei. By comparing our simulations with the Chandra
and XMM-Newton results, we show, however, that spectra from present generation
X-ray satellites are not of good enough quality to fully exploit the method and
determine the black hole mass with sufficient accuracy. This task has to be
deferred to future missions with high throughput and high energy resolution,
such as Constellation-X and Xeus.


  In this paper, we prove that the solution constructed in \cite{BR16}
satisfies the stochastic vorticity equations with the stochastic integration
being understood in the sense of the integration of controlled rough path
introduced in \cite{G04}. As a result, we obtain the existence and uniqueness
of the global solutions to the stochastic vorticity equations in 3D case for
the small initial data independent of time, which can be viewed as a stochastic
version of the Kato-Fujita result (see \cite{KF62}).


  It is well known that phantom energy, which is characterized by the equation
of state $p=\omega\rho$, $\omega <-1$, can support Morris-Thorne wormholes
since it leads to a violation of the null energy condition. The purpose of this
note is to show that the converse is also true in the following sense: for a
typical shape function, the equation of state of exotic matter in the vicinity
of the throat is given by $p_r=\omega\rho$, $\omega <-1$, where $p_r$ is the
radial pressure. Some implications thereof are also considered.


  State-of-the-art studies have demonstrated the superiority of joint modelling
over pipeline implementation for medical named entity recognition and
normalization due to the mutual benefits between the two processes. To exploit
these benefits in a more sophisticated way, we propose a novel deep neural
multi-task learning framework with explicit feedback strategies to jointly
model recognition and normalization. On one hand, our method benefits from the
general representations of both tasks provided by multi-task learning. On the
other hand, our method successfully converts hierarchical tasks into a parallel
multi-task setting while maintaining the mutual supports between tasks. Both of
these aspects improve the model performance. Experimental results demonstrate
that our method performs significantly better than state-of-the-art approaches
on two publicly available medical literature datasets.


  We consider the max-cut and max-$k$-cut problems under graph-based
constraints. Our approach can handle any constraint specified using monadic
second-order (MSO) logic on graphs of constant treewidth. We give a
$\frac{1}{2}$-approximation algorithm for this class of problems.


  We demonstrate experimentally that non-collinear intrinsic spin-orbit
magnetic fields can be realized in a curved carbon nanotube two-segment device.
Each segment, analyzed in the quantum dot regime, shows near four-fold
degenerate shell structure allowing for identification of the spin-orbit
coupling and the angle between the two segments. Furthermore, we determine the
four unique spin directions of the quantum states for specific shells and
magnetic fields. This class of quantum dot systems is particularly interesting
when combined with induced superconducting correlations as it may facilitate
unconventional superconductivity and detection of Cooper pair entanglement. Our
device comprises the necessary elements.


  Very-high-energy (VHE) emission is usually interpreted in the
synchrotron-self Compton (SSC) scenario, and expected from the low-redshift and
high-luminosity gamma-ray bursts (GRBs), as GRB 180720B and GRB 190114C.
Recently, VHE emission was detected by the H.E.S.S. telescopes from one of the
closest burst GRB 190829A which was associated with the supernova (SN) 2019oyw.
In this paper, we present a temporal and spectral analysis from optical bands
to Fermi-LAT energy range over multiple observational periods beginning just
after the BAT trigger time and extending for almost three months. We show that
the X-ray and optical observations are consistent with synchrotron
forward-shock emission evolving between the characteristic and cooling spectral
breaks during the early and late afterglow in a uniform-density medium.
Modeling the light curves together with its spectral energy distribution, it is
shown that the outflow expands with an initial bulk Lorentz factor of
$\Gamma\sim 30$, which is high for a low-luminosity GRBs and low for a
high-luminosity GRBs. The values of the initial bulk Lorentz factor and the
isotropic equivalent energy suggest that GRB 190829A is classified as an
intermediate-luminosity burst and consequently, it becomes the first burst of
this class in being detected in the VHE gamma-ray band by an imaging
atmospheric Cherenkov telescope, and, in turn, the first event without being
simultaneously observed by the Fermi-LAT instrument. Analyzing the
intermediate-luminosity bursts with $z\lesssim 0.2$ such as GRB 130702A, we
show that bursts with intermediate luminosity are potential candidates to be
detected in very-high energies.


  One of the core challenges in Visual Dialogue problems is asking the question
that will provide the most useful information towards achieving the required
objective. Encouraging an agent to ask the right questions is difficult because
we don't know a-priori what information the agent will need to achieve its
task, and we don't have an explicit model of what it knows already. We propose
a solution to this problem based on a Bayesian model of the uncertainty in the
implicit model maintained by the visual dialogue agent, and in the function
used to select an appropriate output. By selecting the question that minimises
the predicted regret with respect to this implicit model the agent actively
reduces ambiguity. The Bayesian model of uncertainty also enables a principled
method for identifying when enough information has been acquired, and an action
should be selected. We evaluate our approach on two goal-oriented dialogue
datasets, one for visual-based collaboration task and the other for a
negotiation-based task. Our uncertainty-aware information-seeking model
outperforms its counterparts in these two challenging problems.


  We develop geometry-of-numbers methods to count orbits in prehomogeneous
vector spaces having bounded invariants over any global field. As our primary
example, we apply these techniques to determine, for any base global field $F$
of characteristic not 2, the density of discriminants of field extensions of
degree at most 5 over $F$.


  Unsupervised learning (UL)-based solvers for combinatorial optimization (CO)
train a neural network whose output provides a soft solution by directly
optimizing the CO objective using a continuous relaxation strategy. These
solvers offer several advantages over traditional methods and other
learning-based methods, particularly for large-scale CO problems. However,
UL-based solvers face two practical issues: (I) an optimization issue where
UL-based solvers are easily trapped at local optima, and (II) a rounding issue
where UL-based solvers require artificial post-learning rounding from the
continuous space back to the original discrete space, undermining the
robustness of the results. This study proposes a Continuous Relaxation
Annealing (CRA) strategy, an effective rounding-free learning method for
UL-based solvers. CRA introduces a penalty term that dynamically shifts from
prioritizing continuous solutions, effectively smoothing the non-convexity of
the objective function, to enforcing discreteness, eliminating the artificial
rounding. Experimental results demonstrate that CRA significantly enhances the
performance of UL-based solvers, outperforming existing UL-based solvers and
greedy algorithms in complex CO problems. It also effectively eliminates the
artificial rounding and accelerates the learning.


  A reputable social media or review account can be a good cover for spamming
activities. It has become prevalent that spammers buy/sell such accounts openly
on the Web. We call these sold/bought accounts the changed-hands (CH) accounts.
They are hard to detect by existing spam detection algorithms as their spamming
activities are under the disguise of clean histories. In this paper, we first
propose the problem of detecting CH accounts, and then design an effective
detection algorithm which exploits changes in content and writing styles of
individual accounts, and a proposed novel feature selection method that works
at a fine-grained level within each individual account. The proposed method not
only determines if an account has changed hands, but also pinpoints the change
point. Experimental results with online review accounts demonstrate the high
effectiveness of our approach.


  Hydrophobic solutes significantly alters water hydrogen bond network. The
local alteration of solvation struc-tures get reflected in the vibrational
spectroscopic signal. Although it is possible to detect this
microscopicfeatures by modern infrared spectroscopy, however, bulk phase
spectra often comes with formidable challengeof establishing the connection
among the experimental spectra to molecular structures. Theoretical
spec-troscopy can serve as more powerful tool even where spectroscopic data
cannot provide microscopic picture.In the present work, we build a theoretical
spectroscopic map based on mixed quantum-classical molecularsimulation approach
using methane in water system. The single oscillator level O-H stretch
frequency is wellcorrelated with a collective variable solvation energy. We
construct the spectroscopic maps for fundamentaltransition frequencies and also
the transition dipoles. A bimodal frequency distribution with a blue
shiftedpopulation of transition frequency illustrates presence of gas like
water molecules in the hydration shell ofmethane. This observation is further
complemented by a shell-wise decomposition of the O-H stretch fre-quencies. We
observe a significant increase in ordering of the first solvation water except
the water molecules,which are directly facing the methane molecule. This is
manifested in redshift of the observed transition fre-quencies. Temperature
dependent simulations depict that the water molecules facing to the methane
moleculebehave similar to the high temperature water and the rest of the first
shell water molecules behave more likecold water.


  The structural, magnetic and transport properties of the layered
RBaCo$_2$O$_{5.5}$ cobaltites are sensitive to the oxygen stoichiometry. In
this present study, we report the presence of a low-temperature magnetic glassy
state in electron-doped polycrystalline YBaCo$_2$O$_{5.5}$ cobaltite. The ac
magnetization studies show the absence of conventional spin glass features,
while the various dc magnetization studies demonstrate the presence of
non-equilibrium magnetic glassy state at low temperature. The magnetic glassy
state of this sample results from the kinetic arrest of the first order ferro
(or ferri) to antiferromagnetic transition. The role of electron doping in the
occurrence of magnetic glassy phenomena is discussed in terms of magnetic phase
separation involving the Co3+/Co2+ clusters of the ferrimagnetic phase in the
Co3+/Co3+ antiferromagnetic matrix.


  We perform a systematical study of possible molecular states composed of the
$S$ wave heavy light mesons, where the $S-D$ mixing and {$\eta-\eta^{\prime}$
mixing} are explicitly included. Our calculation indicates that the observed
$X(3872)$ could be a loosely shallow molecular state composed of $D\bar{D}^\ast
+h.c$, while neither $Z_c(3900)/Z_c(4020)$ nor $Z_b(10610)/Z_b(10650)$ is
supported to be a molecule. Some observed possible molecular states are
predicted, which could be searched for by further experimental measurements.


  Major disasters such as wildfire, tornado, hurricane, tropical storm,
flooding cause disruptions in infrastructure systems such as power outage,
disruption to water supply system, wastewater management, telecommunication
failures, and transportation facilities. Disruptions in electricity
infrastructures has a negative impact on every sector of a region, such as
education, medical services, financial, recreation. In this study, we introduce
a novel approach to investigate the factors which can be associated with longer
restoration time of power service after a hurricane. We consider three types of
factors (hazard characteristics, built-environment characteristics, and
socio-demographic factors) that might be associated with longer restoration
times of power outages during a hurricane. Considering restoration time as the
dependent variable and utilizing a comprehensive set of county-level data, we
have estimated a Generalized Accelerated Failure Time (GAFT) that accounts for
spatial dependence among observations for time to event data. Considering
spatial correlation in time to event data has improved the model fit by 12%.
Using GAFT model and Hurricane Irma as a case study, we examined: (1)
differences in electric power outages and restoration rates among different
types of power companies: investor-owned power companies, rural and municipal
cooperatives; (2) the relationship between the duration of power outage and
power system variables, and socioeconomic attributes. We have found that
factors such as maximum sustained wind speed, percentage of customers facing
power outage, percentage of customers served by investor-owned power company,
median household income, and number of power plants are strongly associated
with restoration time. This paper identifies the key factors in predicting the
restoration time of hurricane-induced power outages.


  We argue that the massive Yang-Mills model of Kunimasa and Goto, Slavnov, and
Cornwall, in which massive gauge vector bosons are introduced in a
gauge-invariant way without resorting to the Higgs mechanism, may be useful for
studying diffractive scattering of strongly interacting particles. We perform
in this model explicit calculations of S-matrix elements between quark states,
at tree level, one loop, and two loops, and discuss issues of renormalisability
and unitarity. In particular, it is shown that the S-matrix element for quark
scattering is renormalisable at one-loop order and is only logarithmically
non-renormalisable at two loops. The discrepancies in the ultraviolet regime
between the one-loop predictions of this model and those of massless QCD are
discussed in detail. Also, some of the similarities and differences between the
massive Yang-Mills model and theories with a Higgs mechanism are analysed at
the level of the S-matrix. Finally, we briefly discuss the high-energy
behaviour of the leading-order amplitude for quark-quark elastic scattering in
the diffractive region. The above analysis sets up the stage for carrying out a
systematic computation of the higher order corrections to the two-gluon
exchange model of the Pomeron using massive gluons inside quantum loops.


  We present an updated calibration of the Swift/UVOT broadband ultraviolet
(uvw1, uvm2, and uvw2) filters. The new calibration accounts for the ~1% per
year decline in the UVOT sensitivity observed in all filters, and makes use of
additional calibration sources with a wider range of colours and with HST
spectrophotometry. In this paper we present the new effective area curves and
instrumental photometric zeropoints and compare with the previous calibration.


  $d$-dimensional (for $d>1$) efficient range-summability ($d$D-ERS) of random
variables (RVs) is a fundamental algorithmic problem that has applications to
two important families of database problems, namely, fast approximate wavelet
tracking (FAWT) on data streams and approximately answering range-sum queries
over a data cube. Whether there are efficient solutions to the $d$D-ERS
problem, or to the latter database problem, have been two long-standing open
problems. Both are solved in this work. Specifically, we propose a novel
solution framework to $d$D-ERS on RVs that have Gaussian or Poisson
distribution. Our $d$D-ERS solutions are the first ones that have
polylogarithmic time complexities. Furthermore, we develop a novel $k$-wise
independence theory that allows our $d$D-ERS solutions to have both high
computational efficiencies and strong provable independence guarantees.
Finally, we show that under a sufficient and likely necessary condition,
certain existing solutions for 1D-ERS can be generalized to higher dimensions.


  By using a new method of forced-field extrapolation, we study the emerging
flux region AR 11850 observed by the Interface Region Imaging Spectrograph
(IRIS) and Solar Dynamical Observatory (SDO). Our results suggest that the
bright points (BPs) in this emerging region have responses in lines formed from
the upper photosphere to the transition region, with a relatively similar
morphology. They have an oscillation of several minutes according to the
Atmospheric Imaging Assembly (AIA) data at 1600 and 1700 A . The ratio between
the BP intensities measured in 1600 A and 1700 A filtergrams reveals that these
BPs are heated differently. Our analysis of the Helioseismic and Magnetic
Imager (HMI) vector magnetic field and the corresponding topology in AR11850
indicates that the BPs are located at the polarity inversion line (PIL) and
most of them related with magnetic reconnection or cancelation. The heating of
the BPs might be different due to different magnetic topology. We find that the
heating due to the magnetic cancelation would be stronger than the case of bald
patch reconnection. The plasma density rather than the magnetic field strength
could play a dominant role in this process. Based on physical conditions in the
lower atmosphere, our forced-field extrapolation shows consistent results
between the bright arcades visible in slit-jaw image (SJI) 1400 A and the
extrapolated field lines that pass through the bald patches. It provides a
reliable observational evidence for testing the mechanism of magnetic
reconnection for the BPs and arcades in emerging flux region, as proposed in
simulation works.


  The Einstein field equations in linear post-Newtonian approximation can be
written in analogy with electromagnetism, in the so-called
gravito-electromagnetic formalism. We use this analogy to study the
gravitational field of a massive ring: in particular, we consider a continuous
mass distribution on Keplerian orbit around a central body, and we work out the
gravitational field generated by this mass distribution in the intermediate
zone between the central body and the ring, focusing on the gravito-magnetic
component that originates from the rotation of the ring. In doing so, we
generalize and complement some previous results that focused on the purely
Newtonian effects of the ring (thus neglecting its rotation) or that were
applied to the case of rotating spherical shells. Eventually, we study in some
simple cases the effect of the the rotation of the ring, and suggest that, in
principle, this approach could be used to infer information about the angular
momentum of the ring.


  We estimate the parameters of the Kennicutt-Schmidt (KS) relationship,
linking the star formation rate (Sigma_SFR) to the molecular gas surface
density (Sigma_mol), in the STING sample of nearby disk galaxies using a
hierarchical Bayesian method. This method rigorously treats measurement
uncertainties, and provides accurate parameter estimates for both individual
galaxies and the entire population. Assuming standard conversion factors to
estimate Sigma_SFR and Sigma_mol from the observations, we find that the KS
parameters vary between galaxies, indicating that no universal relationship
holds for all galaxies. The KS slope of the whole population is 0.76, with the
2sigma range extending from 0.58 to 0.94. These results imply that the
molecular gas depletion time is not constant, but varies from galaxy to galaxy,
and increases with the molecular gas surface density. Therefore, other galactic
properties besides just Sigma_mol affect Sigma_SFR, such as the gas fraction or
stellar mass. The non-universality of the KS relationship indicates that a
comprehensive theory of star formation must take into account additional
physical processes that may vary from galaxy to galaxy.


  The discovery of a chiral anomaly in Weyl semimetals, the non-conservation of
chiral charge and energy across two opposite chirality Weyl nodes, has sparked
immense interest in understanding its impact on various physical phenomena.
Here, we demonstrate the existence of electrical, thermal, and gravitational
quantum chiral anomalies in 3D spin-orbit coupled systems. Notably, these
anomalies involve chiral charge transfer across two Fermi surfaces linked to a
single Weyl-like point, rather than across opposite chirality Weyl nodes as in
Weyl semimetals. Our findings reveal that the Berry curvature flux piercing the
Fermi surface plays a critical role in distinguishing the `chirality' of the
carriers and the corresponding chiral charge and energy transfer. Importantly,
we demonstrate that these quantum chiral anomalies lead to interesting thermal
spin transport such as the spin Nernst effect. Our results suggest that 3D
spin-orbit coupled metals offer a promising platform for investigating the
interplay between quantum chiral anomalies and charge and spin transport in
non-relativistic systems.


  This paper studies the universal first-order Massey product of a
prefactorization algebra, which encodes higher algebraic operations on the
cohomology. Explicit computations of these structures are carried out in the
locally constant case, with applications to factorization envelopes on
$\mathbb{R}^m$ and a compactification of linear Chern-Simons theory on
$\mathbb{R}^2\times \mathbb{S}^1$.


  Pneumothorax, a life threatening disease, needs to be diagnosed immediately
and efficiently. The prognosis in this case is not only time consuming but also
prone to human errors. So an automatic way of accurate diagnosis using chest
X-rays is the utmost requirement. To-date, most of the available medical images
datasets have class-imbalance issue. The main theme of this study is to solve
this problem along with proposing an automated way of detecting pneumothorax.
We first compare the existing approaches to tackle the class-imbalance issue
and find that data-level-ensemble (i.e. ensemble of subsets of dataset)
outperforms other approaches. Thus, we propose a novel framework named as VDV
model, which is a complex model-level-ensemble of data-level-ensembles and uses
three convolutional neural networks (CNN) including VGG16, VGG-19 and
DenseNet-121 as fixed feature extractors. In each data-level-ensemble features
extracted from one of the pre-defined CNN are fed to support vector machine
(SVM) classifier, and output from each data-level-ensemble is calculated using
voting method. Once outputs from the three data-level-ensembles with three
different CNN architectures are obtained, then, again, voting method is used to
calculate the final prediction. Our proposed framework is tested on SIIM ACR
Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For
the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating
Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall
with 95.0% AUC is achieved with random split of data while 85.45% recall with
77.06% AUC is obtained with patient-wise split of data. For RS-NIH, the
obtained results are higher as compared to previous results from literature
However, for first dataset, direct comparison cannot be made, since this
dataset has not been used earlier for Pneumothorax classification.


  We consider the problem of inferring the topology of a network using the
measurements available at the end nodes, without cooperation from the internal
nodes. To this end, we provide a simple method to obtain path interference
which identifies whether two paths in the network intersect with each other.
Using this information, we formulate the topology inference problem as an
integer program and develop polynomial time algorithms to solve it optimally
for networks with tree and ring topologies. Finally, we use the insight
developed from these algorithms to develop a heuristic for identifying general
topologies. Simulation results show that our heuristic outperforms a recently
proposed algorithm that uses distance measurements for topology discovery.


  Scalable photonic quantum technologies require highly efficient sources of
single photons on demand. Although much progress has been done in the field
within the last decade, the requirements impose stringent conditions on the
efficiency of such devices. One of the most promising approaches is to
multiplex a single or several heralded photon sources into temporal modes. In
this work we analyze a specific proposal to synchronize photons from a
continuous source with an external reference clock using imperfect optical
switches, which necessarily degrade the ideal behavior of the devised
arrangement. The performance of the source as a sub-poissonian light emitter is
studied taking into account losses in the multiplexing arrangement, detector
efficiency and dark counts. We estimate a fivefold increase in the single
photon probability achieved for 0.5 dB loss switches.


  We report the detection of two very eccentric planets orbiting HD4113 and
HD156846 with the CORALIE Echelle spectrograph mounted on the 1.2-m Euler Swiss
telescope at La Silla. The first planet, HD4113b, has minimum mass of
$m\sin{i}=1.6\pm0.2 M_{\rm Jup}$, a period of $P=526.59\pm0.21$ days and an
eccentricity of $e=0.903\pm0.02$. It orbits a metal rich G5V star at $a=1.28$
AU which displays an additional radial velocity drift of 28 m s$^{-1}$/yr
observed during 8 years. The combination of the radial-velocity data and the
non-detection of any main sequence stellar companion in our high contrast
images taken at the VLT with NACO/SDI, characterizes the companion as a
probable brown dwarf or as a faint white dwarf. The second planet, \object{HD
156846 b}, has minimum mass of $m\sin{i}=10.45\pm0.05$ M$_{\rm Jup}$, a period
of $P=359.51\pm0.09 $ days, an eccentricity of $e=0.847\pm0.002$ and is located
at $a=1.0$ AU from its parent star. HD156846 is a metal rich G0 dwarf and is
also the primary of a wide binary system ($a>250$ AU, $P>4000$ years). Its
stellar companion, \object{IDS 17147-1914 B}, is a M4 dwarf. The very high
eccentricities of both planets can be explained by Kozai oscillations induced
by the presence of a third object.


  In this paper, we find an identity which connects the overpartition function
and the function of Rogers--Ramanujan--Gordon type overpartitions by
considering the weights and gaps. This identity can be seen as an analogue of
the weighted identity of Alladi.


  By using both ray and wave optics, we show that a simple device which
consists on a film of hyperbolic metamaterial on the surface of a catenoid can
be used to trap light. From the study of the trajectories, we observe a
tendency for the light rays to wrap, and eventually be trapped, around the neck
of the device. The wave equation appears to have an effective attractive
potential, and their solutions confirm the bound states suggested by the
trajectories. The relevant equations are solved numerically using neural
networks.


  Thermal fluid processes are inherently multi-physics and multi-scale,
involving mass-momentum-energy transport phenomena. Thermal fluid simulation
(TFS) is based on solving conservative equations, for which - except for
"first-principle" direct numerical simulation - closure relations (CRs) are
required to provide microscopic interactions. In practice, TFS is realized
through reduced-order modeling, and its CRs can be informed by observations and
data from relevant and adequately evaluated experiments and high-fidelity
simulations. This paper focuses on data-driven TFS models, specifically on the
development using machine learning (ML). Five ML frameworks, dubbed Type I to
V, are introduced. The frameworks vary in their performance for different
applications depending on the level of knowledge of governing physics, the
source, type, amount and quality of available data for training. Notably,
outlined for the first time in this paper, Type III models present stringent
requirements on modeling, substantial computing resources for training, and
high potential in extracting value from "big data" in thermal fluid research.
The paper demonstrates the ML frameworks by three examples. First, we utilize
the heat diffusion equation with nonlinear conductivity formulated by
convolutional neural networks to illustrate the applications of Type I, II, and
III ML. The results indicate a preference for Type II ML under deficient data
support. Type III ML can effectively utilize field data, potentially generating
more robust predictions than Type I and II ML. Second, we illustrate how to
employ Type I ML and Type II ML frameworks for data-driven turbulence modeling
using reference works. Third, we demonstrate Type I ML by building a deep
learning (DL)-based slip closure for two-phase flow modeling. The results showe
that the DL-based closure exhibits a bounded error in prediction domain.


  The structure function of the proton has been investigated and has been found
to possess the power law behaviour in conformity with the empirical fits to the
experimental findings. We have estimated F$_{2}$(x, Q$^{2}$)/F$_{2}$(x,
Q$_{0}$$^{2}$) with the anomalous dimension D$_{A}$ predicted from the
statistical model as an input and the result is found to be in good agreement
with the recent data available in the deep inelastic region.


  We report on continued, $\sim$15 year-long, broad Balmer emission lines in
three metal-poor dwarf emission-line galaxies selected from Sloan Digital Sky
Survey spectroscopy. The persistent luminosity of the broad Balmer emission
indicates the galaxies are active galactic nuclei (AGNs) with virial black hole
masses of $\sim 10^{6.7}{-10^{7.0}\ M_{\odot}}$. The lack of observed hard
X-ray emission and the possibility that the Balmer emission could be due to a
long-lived stellar transient motivated additional follow-up spectroscopy. We
also identify a previously-unreported blueshifted narrow absorption line in the
broad H$\alpha$ feature in one of the AGNs, indicating an AGN-driven outflow
with hydrogen column densities of order $10^{17}$ cm$^{-2}$. We also extract
light curves from the Catalina Real-Time Transient Survey and the Zwicky
Transient Facility. We detect probable AGN-like variability in three galaxies,
further supporting the AGN scenario. This also suggests the AGNs are not
strongly obscured. This sample of galaxies are among the most metal-poor which
host an AGN ($Z=0.05 - 0.16\ Z_\odot$). We speculate they may be analogues to
seed black holes which formed in unevolved galaxies at high redshift. Given the
rarity of metal-poor AGNs and small sample size available, we investigate
prospects for their identification in future spectroscopic and photometric
surveys.


  OpenStack is an open-source private cloud used to run VMs and its related
cloud services. OpenStack deployment, management, and upgradation require lots
of efforts and manual troubleshooting. Also, workloads and services offered by
OpenStack cannot self-heal itself on failures. We present KupenStack, a
Cloud-Native OpenStack as Code model built on top of Kubernetes stack as Custom
Resources. KupenStack is a controller that interacts between Kubernetes and
OpenStack and automates complex operations like scaling, LCM, zero-downtime,
self-healing, version upgrades, configuration management, and offers OpenStack
as a service through code. KupenStack builds cloud-native values like immutable
infrastructure, declarative APIs for OpenStack without changing any OpenStack
code. If a VM workload goes down for some reason, then KupenStack handles it
and automatically spins up a new instance. KupenStack uses OpenStack on
Kubernetes deployment for lifecycle management of OpenStack.


  This report describes a series of experiments that track novice programmer's
engagement during two attention based tasks. The tasks required participants to
watch a tutorial video on introductory programming and to attend to a simple
maze game whilst wearing an electroencephalogram (EEG)device called the Emotiv
EPOC. The EPOC's proprietary software includes a system which tracks emotional
state (specifically: engagement, excitement, meditation, frustration, valence
and long-term excitement). Using this data, a software application written in
the Processing language was developed to track user's engagement levels and
implement a neurofeedback based intervention when engagement fell below an
acceptable level. The aim of the intervention was to prompt learners who
disengaged with the task to re-engage. The intervention used during the video
tutorial was to pause the video if a participant disengaged significantly.
However other interventions such as slowing the video down, playing a noise or
darkening/brightening the screen could also be used. For the maze game, the
caterpillar moving through the maze slowed in line with disengagement and moved
more quickly once the learner re-engaged. The approach worked very well and
successfully re-engaged participants, although a number of improvements could
be made. A number of interesting findings on the comparative engagement levels
of different groups e.g. by gender and by age etc. were identified and provide
useful pointers for future research studies.


  In this paper, we discuss a handover management scheme for Next Generation
Self-Organized Networks. We propose to extract experience from full protocol
stack data, to make smart handover decisions in a multi-cell scenario, where
users move and are challenged by deep zones of an outage. Traditional handover
schemes have the drawback of taking into account only the signal strength from
the serving, and the target cell, before the handover. However, we believe that
the expected Quality of Experience (QoE) resulting from the decision of target
cell to handover to, should be the driving principle of the handover decision.
In particular, we propose two models based on multi-layer many-to-one LSTM
architecture, and a multi-layer LSTM AutoEncoder (AE) in conjunction with a
MultiLayer Perceptron (MLP) neural network. We show that using experience
extracted from data, we can improve the number of users finalizing the download
by 18%, and we can reduce the time to download, with respect to a standard
event-based handover benchmark scheme. Moreover, for the sake of
generalization, we test the LSTM Autoencoder in a different scenario, where it
maintains its performance improvements with a slight degradation, compared to
the original scenario.


  We investigate the magnetic response in the quantum spin Hall phase of the
layered Kane-Mele model with Hubbard interaction, and argue a condition to
obtain the Meissner effect. The effect of Rashba spin orbit coupling is also
discussed.


  Motivated by recent experiments, we present the time-optimal variational
control of bright matter-wave soliton trapped in a quasi-one-dimensional
harmonic trap by manipulating the atomic attraction through Feshbach
resonances. More specially, we first apply a time-dependent variational method
to derive the motion equation for capturing the soliton's shape, and secondly
combine inverse engineering with optimal control theory to design the atomic
interaction for implementing time-optimal decompression. Since the time-optimal
solution is of bang-bang type, the smooth regularization is further adopted to
smooth the on-off controller out, thus avoiding the heating and atom loss,
induced from magnetic field ramp across a Feshbach resonance in practice.


  We develop a statistical framework for conducting inference on collections of
time-varying covariance operators (covariance flows) over a general, possibly
infinite dimensional, Hilbert space. We model the intrinsically non-linear
structure of covariances by means of the Bures-Wasserstein metric geometry. We
make use of the Riemmanian-like structure induced by this metric to define a
notion of mean and covariance of a random flow, and develop an associated
Karhunen-Lo\`eve expansion. We then treat the problem of estimation and
construction of functional principal components from a finite collection of
covariance flows, observed fully or irregularly.
  Our theoretical results are motivated by modern problems in functional data
analysis, where one observes operator-valued random processes -- for instance
when analysing dynamic functional connectivity and fMRI data, or when analysing
multiple functional time series in the frequency domain. Nevertheless, our
framework is also novel in the finite-dimensions (matrix case), and we
demonstrate what simplifications can be afforded then. We illustrate our
methodology by means of simulations and data analyses.


  In this paper, we study Ornstein-Uhlenbeck operators with quadratic
potentials. We use Hamiltonian formalism to characterise the singularities
produced by the potentials by finding explicit geodesics of the operators, and
obtain the heat kernels via a probabilistic ansatz. All the formulae are
closed.