  The outer parts of the Milky Way's disc are significantly out of equilibrium.
Using only distances and proper motions of stars from Gaia's Early Data Release
3, in the range |b|<10{\deg}, 130{\deg}<l<230{\deg}, we show that for stars in
the disc between around 10 and 14 kpc from the Galactic centre, vertical
velocity is strongly dependent on the angular momentum, azimuth, and position
above or below the Galactic plane. We further show how this behaviour
translates into a bimodality in the velocity distribution of stars in the outer
Milky Way disc. We use an N-body model of an impulse-like interaction of the
Milky Way disc with a perturber similar to the Sagittarius dwarf to demonstrate
that this mechanism can generate a similar disturbance. It has already been
shown that this interaction can produce a phase spiral similar to that seen in
the Solar neighbourhood. We argue that the details of this substructure in the
outer galaxy will be highly sensitive to the timing of the perturbation or the
gravitational potential of the Galaxy, and therefore may be key to
disentangling the history and structure of the Milky Way.


  Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g.,
DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional
T2I benchmarks, at the cost of significant computational resources. The unCLIP
stack comprises T2I prior and diffusion image decoder. The T2I prior model
alone adds a billion parameters compared to the Latent Diffusion Models, which
increases the computational and high-quality data requirements. We introduce
ECLIPSE, a novel contrastive learning method that is both parameter and
data-efficient. ECLIPSE leverages pre-trained vision-language models (e.g.,
CLIP) to distill the knowledge into the prior model. We demonstrate that the
ECLIPSE trained prior, with only 3.3% of the parameters and trained on a mere
2.8% of the data, surpasses the baseline T2I priors with an average of 71.6%
preference score under resource-limited setting. It also attains performance on
par with SOTA big models, achieving an average of 63.36% preference score in
terms of the ability to follow the text compositions. Extensive experiments on
two unCLIP diffusion image decoders, Karlo and Kandinsky, affirm that ECLIPSE
priors consistently deliver high performance while significantly reducing
resource dependency.


  The theory obtained as a singular limit of General Relativity, if the
reciprocal velocity of light is assumed to tend to zero, is known to be not
exactly the Newton-Cartan theory, but a slight extension of this theory. It
involves not only a Coriolis force field, which is natural in this theory
(although not original Newtonian), but also a scalar field which governs the
relation between Newtons time and relativistic proper time. Both fields are or
can be reduced to harmonic functions, and must therefore be constants, if
suitable global conditions are imposed. We assume this reduction of
Newton-Cartan to Newton`s original theory as starting point and ask for a
consistent post-Newtonian extension and for possible differences to usual
post-Minkowskian approximation methods, as developed, for example, by
Chandrasekhar. It is shown, that both post-Newtonian frameworks are formally
equivalent, as far as the field equations and the equations of motion for a
hydrodynamical fluid are concerned.


  We consider the rotational dynamics in an ensemble of globally coupled
identical pendulums. This model is essentially a generalization of the standard
Kuramoto model, which takes into account the inertia and the intrinsic
nonlinearity of the community elements. There exists the wide variety of
in-phase and out-of-phase regimes. Many of these states appear due to broken
symmetry. In the case of small dissipation our theoretical analysis allows one
to find the boundaries of the instability domain of in-phase rotational mode
for ensembles with arbitrary number of pendulums, describe all arising
out-of-phase rotation modes and study in detail their stability. For the system
of three elements parameter sets corresponding to the unstable in-phase
rotations we find a number of out-of-phase regimes and investigate their
stability and bifurcations both analytically and numerically. As a result, we
obtain a sufficiently detailed picture of the symmetry breaking and existence
of various regular and chaotic states.


  For Boolean satisfiability problems, the structure of the solution space is
characterized by the solution graph, where the vertices are the solutions, and
two solutions are connected iff they differ in exactly one variable. For this
implicitly defined graph, we here study the st-connectivity and connectivity
problems.
  Building on the work of Gopalan et al. ("The Connectivity of Boolean
Satisfiability: Computational and Structural Dichotomies", 2006/2009), we first
investigate satisfiability problems given by CSPs, more exactly CNF(S)-formulas
with constants (as considered in Schaefer's famous 1978 dichotomy theorem); we
prove a computational dichotomy for the st-connectivity problem, asserting that
it is either solvable in polynomial time or PSPACE-complete, and an aligned
structural dichotomy, asserting that the maximal diameter of connected
components is either linear in the number of variables, or can be exponential;
further, we show a trichotomy for the connectivity problem, asserting that it
is either in P, coNP-complete, or PSPACE-complete.
  Next we investigate two important variants: CNF(S)-formulas without
constants, and partially quantified formulas; in both cases, we prove analogous
dichotomies for st-connectivity and the diameter; for for the connectivity
problem, we show a trichotomy in the case of quantified formulas, while in the
case of formulas without constants, we identify fragments of a possible
trichotomy.
  Finally, we consider the connectivity issues for B-formulas, which are
arbitrarily nested formulas built from some fixed set B of connectives, and for
B-circuits, which are Boolean circuits where the gates are from some finite set
B; we prove a common dichotomy for both connectivity problems and the diameter;
for partially quantified B-formulas, we show an analogous dichotomy.


  In this study, we have investigated the adequacy of the PGAS parallel
language X10 to implement a Constraint-Based Local Search solver. We decided to
code in this language to benefit from the ease of use and architectural
independence from parallel resources which it offers. We present the
implementation strategy, in search of different sources of parallelism in the
context of an implementation of the Adaptive Search algorithm. We extensively
discuss the algorithm and its implementation. The performance evaluation on a
representative set of benchmarks shows close to linear speed-ups, in all the
problems treated.


  Events serve as fundamental units of occurrence within various contexts. The
processing of event semantics in textual information forms the basis of
numerous natural language processing (NLP) applications. Recent studies have
begun leveraging large language models (LLMs) to address event semantic
processing. However, the extent that LLMs can effectively tackle these
challenges remains uncertain. Furthermore, the lack of a comprehensive
evaluation framework for event semantic processing poses a significant
challenge in evaluating these capabilities. In this paper, we propose an
overarching framework for event semantic processing, encompassing
understanding, reasoning, and prediction, along with their fine-grained
aspects. To comprehensively evaluate the event semantic processing abilities of
models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets
that cover all aspects of event semantic processing. Extensive experiments are
conducted on EVEVAL, leading to several noteworthy findings based on the
obtained results.


  Terahertz molecular motions are often probed by high-frequency molecular
oscillators in different types of non-linear vibrational spectroscopy. Recently
developed two-dimensional terahertz-infrared-visible spectroscopy allows direct
measuring of this coupling and, thus, obtaining site-specific terahertz
vibrational spectrum. However, these data are affected by the intensity and
phase of the employed laser pulses. In this work, we develop a method of
extracting sample response - representing solely physical properties of a
material - from experimental spectra. Using dimethyl sulfoxide (DMSO) as a
model molecule to verify this method, we measure the coupling between C-H
stretch vibration of its methyl groups and terahertz intramolecular twist and
wagging modes.


  A new class of error-correcting quantum codes is introduced capable of
stabilizing qubits against spontaneous decay arising from couplings to
statistically independent reservoirs. These quantum codes are based on the idea
of using an embedded quantum code and exploiting the classical information
available about which qubit has been affected by the environment. They are
immediately relevant for quantum computation and information processing using
arrays of trapped ions or nuclear spins. Interesting relations between these
quantum codes and basic notions of design theory are established.


  This paper introduces Borinot, an open-source flying robotic platform
designed to perform hybrid agile locomotion and manipulation. This platform
features a compact and powerful hexarotor that can be outfitted with
torque-actuated extremities of diverse architecture, allowing for whole-body
dynamic control. As a result, Borinot can perform agile tasks such as
aggressive or acrobatic maneuvers with the participation of the whole-body
dynamics. The extremities attached to Borinot can be utilized in various ways;
during contact, they can be used as legs to create contact-based locomotion, or
as arms to manipulate objects. In free flight, they can be used as tails to
contribute to dynamics, mimicking the movements of many animals. This allows
for any hybridization of these dynamic modes, like the jump-flight of chicken
and locusts, making Borinot an ideal open-source platform for research on
hybrid aerial-contact agile motion. To demonstrate the key capabilities of
Borinot, we have fitted a planar 2DoF arm and implemented whole-body
torque-level model-predictive-control. The result is a capable and adaptable
platform that, we believe, opens up new avenues of research in the field of
agile robotics.


  The Planar Steiner Tree problem is one of the most fundamental NP-complete
problems as it models many network design problems. Recall that an instance of
this problem consists of a graph with edge weights, and a subset of vertices
(often called terminals); the goal is to find a subtree of the graph of minimum
total weight that connects all terminals. A seminal paper by Erickson et al.
[Math. Oper. Res., 1987] considers instances where the underlying graph is
planar and all terminals can be covered by the boundary of $k$ faces. Erickson
et al. show that the problem can be solved by an algorithm using $n^{O(k)}$
time and $n^{O(k)}$ space, where $n$ denotes the number of vertices of the
input graph. In the past 30 years there has been no significant improvement of
this algorithm, despite several efforts.
  In this work, we give an algorithm for Planar Steiner Tree with running time
$2^{O(k)} n^{O(\sqrt{k})}$ using only polynomial space. Furthermore, we show
that the running time of our algorithm is almost tight: we prove that there is
no $f(k)n^{o(\sqrt{k})}$ algorithm for Planar Steiner Tree for any computable
function $f$, unless the Exponential Time Hypothesis fails.


  We review briefly the fundamental equations of a semi-microscopic
core-particle coupling method that makes no reference to an intrinsic system of
coordinates. We then demonstrate how an intrinsic system can be introduced in
the strong coupling limit so as to yield a completely equivalent formulation.
It is emphasized that the conventional core-particle coupling calculation
introduces a further approximation that avoids what has hitherto been the most
time-consuming feature of the full theory, and that this approximation can be
introduced either in the intrinsic system, the usual case, or in the laboratory
system, our preference. A new algorithm is described for the full theory that
largely removes the difference in complexity between the two types of
calculation. Comparison of the full and approximate theories for some
representative cases provides a basis for the assessment of the accuracy of the
traditional approach. We find that for well-deformed nuclei, e.g. 157Gd and
157Tb, the core-coupling method and the full theory give similar results.


  In this study, we derive the infinite product representation of the
$\operatorname{sinc}(\mathrm{z})$ function by expressing it in a trigonometric
form, evoking similarities to Morrie's Law and Euler's Product formula, along
with their generalizations. The results presented in this investigation are
entirely novel and are believed to introduce new insights into the topic.


  This paper is devoted to prove that if an Alexandrov space of curvature not
less than $\kappa$ with a codimension one extremal subset which admits an
isometric involution with respect to the induced length metric, then the metric
space obtained by gluing the extremal subset along the isometry is an
Alexandrov space of curvature not less than $\kappa$. This is a generalization
of Perelman's doubling and Petrunin's gluing theorems.


  The measurement-device-independent quantum key distribution (MDI-QKD)
possesses the highest security among all practical quantum key distribution
protocols. However, existing multi-intensity decoy-state methods may cause
loopholes when modulating light intensities with practical devices. In this
paper, we propose a passive decoy-state MDI-QKD protocol based on a novel
structure of heralded single-photon sources. It does not need to modulate the
light source into different intensities and thus can avoid leaking modulation
information of decoy states to eavesdroppers, enhancing the security of
practical MDI-QKD. Furthermore, by combining the passive decoy states and
biased basis choices, our protocol can exhibit distinct advantages compared
with state-of-the art MDI-QKD schemes even when the finite key-size effect is
taken into account. Therefore, our present passive decoy-state MDI-QKD seems a
promising candidate for practical implementation of quantum key distributions
in the near future.


  The spectrum of quenched Yang-Mills theory in the large-N limit displays
strings and higher dimensional extended objects. The effective dynamics of
string-like excitations is encoded into area preserving Schild action. In this
letter, we bridge the gap between SU(N) gauge models and fully
reparametrization invariant Nambu-Goto string models by introducing an extra
matrix degree of freedom in the Yang-Mills action. In the large-N limit this
matrix variable becomes the world-sheet auxiliary field allowing a smooth
transition between the Schild and Nambu-Goto strings. The new improved matrix
model we propose here can be extended to p-branes provided we enlarge the
dimensionality of the target spacetime.


  "Light Shining Through the Wall" experiments can probe the existence of
"axion like particles" through their weak coupling to photons. We have adapted
such an experiment to the microwave regime and constructed the table top
apparatus. This work presents an overview of the experimental setup and then
focuses on our latest measurement run and its results. By operating the
apparatus within a superconducting MRI magnet, competitive exclusion limits for
axion like particles to the first generation optical light shining through the
wall experiments have been achieved.


  The average minimum number of bits needed for lossless readout of a pixel
detector is calculated, in the regime of interest for particle physics where
only a small fraction of pixels have a non-zero value per frame. This permits a
systematic comparison of the readout efficiency of different encoding imple-
mentations. The calculation is compared to the number of bits used by the FE-I4
pixel readout chip of the ATLAS experiment.


  Electron-phonon interaction in superconducting transition metals (Ta, Mo, W)
is investigated by the method of point-contact spectroscopy. The spectra
obtained are compared with model densities of the phonon states $F(\omega)$
computed from experiments on neutron scattering, and with the tunnel
electron-phonon interaction function $g(\omega)$ for Ta. Close agreement
between the appropriate spectra is obtained for Ta and Mo, while a significant
discrepancy is observed for W, which is the appearance of a blurred maximum
instead of the two expected separated peaks on the point-contact spectra. The
absolute intensities of a number of point-contact spectra $G(eV)$ and
electron-phonon coupling constants $\lambda$ are calculated. For all three
metals the values obtained turned out to be considerably below those expected.
The possible reasons for this disagreement are discussed.


  Object pose estimation is a fundamental computer vision task exploited in
several robotics and augmented reality applications. Many established
approaches rely on predicting 2D-3D keypoint correspondences using RANSAC
(Random sample consensus) and estimating the object pose using the PnP
(Perspective-n-Point) algorithm. Being RANSAC non-differentiable,
correspondences cannot be directly learned in an end-to-end fashion. In this
paper, we address the stereo image-based object pose estimation problem by i)
introducing a differentiable RANSAC layer into a well-known monocular pose
estimation network; ii) exploiting an uncertainty-driven multi-view PnP solver
which can fuse information from multiple views. We evaluate our approach on a
challenging public stereo object pose estimation dataset and a custom-built
dataset we call Transparent Tableware Dataset (TTD), yielding state-of-the-art
results against other recent approaches. Furthermore, in our ablation study, we
show that the differentiable RANSAC layer plays a significant role in the
accuracy of the proposed method. We release with this paper the code of our
method and the TTD dataset.


  Supervised learning of Convolutional Neural Networks (CNNs), also known as
supervised Deep Learning, is a computationally demanding process. To find the
most suitable parameters of a network for a given application, numerous
training sessions are required. Therefore, reducing the training time per
session is essential to fully utilize CNNs in practice. While numerous research
groups have addressed the training of CNNs using GPUs, so far not much
attention has been paid to the Intel Xeon Phi coprocessor. In this paper we
investigate empirically and theoretically the potential of the Intel Xeon Phi
for supervised learning of CNNs. We design and implement a parallelization
scheme named CHAOS that exploits both the thread- and SIMD-parallelism of the
coprocessor. Our approach is evaluated on the Intel Xeon Phi 7120P using the
MNIST dataset of handwritten digits for various thread counts and CNN
architectures. Results show a 103.5x speed up when training our large network
for 15 epochs using 244 threads, compared to one thread on the coprocessor.
Moreover, we develop a performance model and use it to assess our
implementation and answer what-if questions.


  The premise of this note is the following observation: the formalism of
Bohrification is a natural place for the interpretation of general
non-signalling theories. We demonstrate it through an analysis of so-called
box-worlds, a popular framework for the discussion of systems exhibiting
super-quantum correlations. In particular, we show that non-signalling
box-world states are precisely the internal probability valuations on an
internal frame in a Kripke topos naturally associated with a given box world.


  In this paper, we study the discrete classical phase space of loop gravity,
which is expressed in terms of the holonomy-flux variables, and show how it is
related to the continuous phase space of general relativity. In particular, we
prove an isomorphism between the loop gravity discrete phase space and the
symplectic reduction of the continuous phase space with respect to a flatness
constraint. This gives for the first time a precise relationship between the
continuum and holonomy-flux variables. Our construction shows that the fluxes
depend on the three-geometry, but also explicitly on the connection, explaining
their non commutativity. It also clearly shows that the flux variables do not
label a unique geometry, but rather a class of gauge-equivalent geometries.
This allows us to resolve the tension between the loop gravity geometrical
interpretation in terms of singular geometry, and the spin foam interpretation
in terms of piecewise flat geometry, since we establish that both geometries
belong to the same equivalence class. This finally gives us a clear
understanding of the relationship between the piecewise flat spin foam
geometries and Regge geometries, which are only piecewise-linear flat: While
Regge geometry corresponds to metrics whose curvature is concentrated around
straight edges, the loop gravity geometry correspond to metrics whose curvature
is concentrated around not necessarily straight edges.


  This paper aims at reporting magnetic properties of bulk polycrystalline
Bi2Sr2Ca0.8Dy0.2Cu2O8-y samples textured under a magnetic field. The
microstructure of these materials is highly anisotropic and exhibits particular
features needed to be taken into account in order to interpret their magnetic
and electrical properties. First the AC magnetic susceptibility c = c ' - j c"
has been measured for several magnetic fields (H // ab and H // c) and compared
to the electrical resistivity data. The structure of the c" peak is shown to be
related to the chemical content distribution of the superconducting grains.
Next, the magnetic flux profiles have been extracted from the magnetic
measurements using the Campbell - Rollins procedure. The anisotropy of the flux
profiles and their peculiar curvature behaviour for H // c point out the role
of both grain platelet structure and the presence of secondary phases. From
these results, we conclude that the magnetic properties of such magnetically
textured materials do not allow for a reliable extraction of the critical
current density Jc but essentially probe geometric effects. Such effects have
to be taken into account for improving the manufacture of attractive high-Tc
materials.


  Distillation process is a complex process of conduction, mass transfer and
heat conduction, which is mainly manifested as follows: The mechanism is
complex and changeable with uncertainty; the process is multivariate and strong
coupling; the system is nonlinear, hysteresis and time-varying. Neural networks
can perform effective learning based on corresponding samples, do not rely on
fixed mechanisms, have the ability to approximate arbitrary nonlinear mappings,
and can be used to establish system input and output models. The temperature
system of the rectification tower has a complicated structure and high accuracy
requirements. The neural network is used to control the temperature of the
system, which satisfies the requirements of the production process. This
article briefly describes the basic concepts and research progress of neural
network and distillation tower temperature control, and systematically
summarizes the application of neural network in distillation tower control,
aiming to provide reference for the development of related industries.


  Comparison of solar-neutrino signals in SNO and Super-Kamiokande (SK)
detectors results in discovery of $\nu_e \to \nu_{\mu,\tau}$ oscillations at
level $3.1 - 3.3 \sigma$ \cite{SNO}. This comparison involves the assumption of
neutrino spectrum and a choice for the thresholds of detection in both
experiments. In this Letter we obtain an exact formula for the comparison of
the signals which is valid for arbitrary spectra and thresholds. We find that
the no-oscillation hypothesis is excluded at 3.3$\sigma$. If the
energy-dependent component of the survival probability for electron neutrinos
is small as compared with the average value, i.e. in the case of small
distortion of the observed spectrum, the oscillation hypothesis can also be
tested to similar accuracy. The oscillation to sterile neutrino only, is
excluded at $3.3 \sigma$ level, and oscillation to active neutrinos is
confirmed at the same level, though with some reservations.


  Rescaled Mellin-type transforms of the exponential functional of the
Bourgade-Kuan-Rodgers statistic of Riemann zeroes are conjecturally related to
the distribution of the total mass of the limit lognormal stochastic measure of
Mandelbrot-Bacry-Muzy. The conjecture implies that a non-trivial,
log-infinitely divisible probability distribution is associated with Riemann
zeroes. For application, integral moments, covariance structure, multiscaling
spectrum, and asymptotics associated with the exponential functional are
computed in closed form using the known meromorphic extension of the Selberg
integral.


  We study the speed of a biased random walk on a percolation cluster on $\Z^d$
in function of the percolation parameter $p$. We obtain a first order expansion
of the speed at $p=1$ which proves that percolating slows down the random walk
at least in the case where the drift is along a component of the lattice.


  This paper studies the properties of kiloparsec-scale clumps in star-forming
galaxies at z~2 through multi-wavelength broad band photometry. A sample of 40
clumps is identified through auto-detection and visual inspection from 10
galaxies with 1.5<z<2.5 in the Hubble Ultra Deep Field, where deep and
high-resolution HST/WFC3 and ACS images enable us to resolve structures of z~2
galaxies down to kpc scale in the rest-frame UV and optical bands as well as to
detect clumps toward the faint end. The physical properties of clumps are
measured through fitting spatially resolved seven-band (BVizYJH) spectral
energy distribution to models. On average, the clumps are blue and have similar
median rest-frame UV--optical color as the diffuse components of their host
galaxies, but the clumps have large scatter in their colors. Although the star
formation rate (SFR)--stellar mass relation of galaxies is dominated by the
diffuse components, clumps emerge as regions with enhanced specific SFRs,
contributing individually ~10% and together ~50% of the SFR of the host
galaxies. However, the contributions of clumps to the rest-frame UV/optical
luminosity and stellar mass are smaller, typically a few percent individually
and ~20% together. On average, clumps are younger by 0.2 dex and denser by a
factor of 8 than diffuse components. Clump properties have obvious radial
variations in the sense that central clumps are redder, older, more extincted,
denser, and less active on forming stars than outskirts clumps. Our results are
broadly consistent with a widely held view that clumps are formed through
gravitational instability in gas-rich turbulent disks and would eventually
migrate toward galactic centers and coalesce into bulges. Roughly 40% of the
galaxies in our sample contain a massive clump that could be identified as a
proto-bulge, which seems qualitatively consistent with such a bulge-formation
scenario.


  Simulations of ignition-scale fast ignition targets have been performed with
the new integrated Zuma-Hydra PIC-hydrodynamic capability. We consider an
idealized spherical DT fuel assembly with a carbon cone, and an
artificially-collimated fast electron source. We study the role of E and B
fields and the fast electron energy spectrum. For mono-energetic 1.5 MeV fast
electrons, without E and B fields, the energy needed for ignition is E_f^{ig} =
30 kJ. This is about 3.5x the minimal deposited ignition energy of 8.7 kJ for
our fuel density of 450 g/cm^3. Including E and B fields with the resistive
Ohm's law E = \eta J_b gives E_f^{ig} = 20 kJ, while using the full Ohm's law
gives E_f^{ig} > 40 kJ. This is due to magnetic self-guiding in the former
case, and \nabla n \times \nabla T magnetic fields in the latter. Using a
realistic, quasi two-temperature energy spectrum derived from PIC laser-plasma
simulations increases E_f^{ig} to (102, 81, 162) kJ for (no E/B, E = \eta J_b,
full Ohm's law). This stems from the electrons being too energetic to fully
stop in the optimal hot spot depth.


  We prove that the mapping class group of a closed connected orientable
surface of genus $g$ is generated by two elements of order $g$ for $g\geq 6$.
Moreover, for $g\geq 7$ we found a generating set of two elements, of order $g$
and $g'$ which is the least divisor of $g$ such that $g'>2$. Furthermore, we
proved that it is generated by two elements of order $g/\gcd (g,k)$ for $g\geq
3k^2+4k+1$ and any positive integer $k$.


  We consider the coulomb gas model on the upper half plane with different
boundary conditions, namely Drichlet, Neuman and mixed. We related this model
to SLE($\kappa,\rho$) theories. We derive a set of conditions connecting the
total charge of the coulomb gas, the boundary charges, the parameters $\kappa$
and $\rho$. Also we study a free fermion theory in presence of a boundary and
show with the same methods that it would lead to logarithmic boundary changing
operators.


  Heteroepitaxial growth of transition-metal oxide films on the open (111)
surface of SrTiO3 results in significant restructuring due to the polar
mismatch. Monitoring the structural and composition on an atomic scale of
LaNiO3/SrTiO3 (111) interface as a function of processing conditions has
enabled the avoidance of the expected polar catastrophe. Using atomically
resolved transmission electron microscopy and spectroscopy as well as Low
energy electron diffraction, the structure of the thin film, from interface to
the surface, has been studied. In this paper, we show that the proper
processing can lead to a structure that is ordered, coherent with the substrate
without intermediate structural phase. Angle-resolved X-ray photoemission
spectroscopy shows that the oxygen content of thin films increases with the
film thickness, indicating that the polar mismatch is avoided by the presence
of oxygen vacancies.


  Chiral monopoles are hedgehoglike structures in local chiral condensates in
QCD. These monopoles are (i) made of quark and gluon fields; (ii) explicitly
gauge-invariant; and (iii) they carry quantized and conserved chromomagnetic
charge. We argue that the chiral condensate vanishes in a core of the chiral
monopole while the density of these monopoles increases with temperature wiping
out the quark condensate in quark-gluon plasma. We suggest that the dynamics of
the chiral monopoles is responsible for the chiral symmetry restoration in high
temperature phase of QCD. We also argue that the chiral monopoles are unlikely
to be associated with confining degrees of freedom. In our approach the chiral
symmetry restoration and the color deconfinement in QCD are not necessarily
related to each other and the corresponding transitions may occur at different
temperatures.


  Integrability conditions for Lie systems are related to reduction or
transformation processes. We here analyse a geometric method to construct
integrability conditions for Riccati equations following these approaches. This
approach provides us with a unified geometrical viewpoint that allows us to
analyse some previous works on the topic and explain new properties. Moreover,
this new approach can be straightforwardly generalised to describe
integrability conditions for any Lie system. Finally, we show the usefulness of
our treatment in order to study the problem of the linearisability of Riccati
equations.


  We propose a long-distance continuous-variable quantum key distribution
(CVQKD) with four-state protocol using non-Gaussian state-discrimination
detection. A photon subtraction operation, which is deployed at the
transmitter, is used for splitting the signal required for generating the
non-Gaussian operation to lengthen the maximum transmission distance of CVQKD.
Whereby an improved state-discrimination detector, which can be deemed as an
optimized quantum measurement that allows the discrimination of nonorthogonal
coherent states beating the standard quantum limit, is applied at the receiver
to codetermine the measurement result with conventional coherent detector. By
tactfully exploiting multiplexing technique, the resulting signals can be
simultaneously transmitted through an untrusted quantum channel, and
subsequently sent to the state-discrimination detector and coherent detector
respectively. Security analysis shows that the proposed scheme can lengthen the
maximum transmission distance up to hundreds of kilometers. Furthermore, by
taking finite-size effect and composable security into account we obtain the
tightest bound of the secure distance, which is more practical than that
obtained in the asymptotic limit.


  One-dimensional topological superconductors host Majorana zero modes (MZMs),
the non-local property of which could be exploited for quantum computing
applications. Spin- polarized scanning tunneling microscopy measurements show
that MZMs realized in self- assembled Fe chains on the surface of Pb have a
spin polarization that exceeds that due to the magnetism of these chains. This
feature, captured by our model calculations, is a direct consequence of the
nonlocality of the Hilbert space of MZMs emerging from a topological band
structure. Our study establishes spin polarization measurements as a diagnostic
tool to uniquely distinguish topological MZMs from trivial in-gap states of a
superconductor.


  We use a Raman four-wave mixing process to read-out light from atomic
coherence which is continuously written. The light is continuously generated
after an effective delay, allowing the atomic coherence to evolve during the
process. Contrary to slow-light delay, which depends on the medium optical
depth, here the generation delay is determined solely by the intensive
properties of the system, approaching the atomic coherence lifetime at the weak
driving limit. The generated light is background free. We experimentally probe
these properties utilizing spatial diffusion as an 'internal clock' for the
atomic evolution time.


  Topological nodal-line semimetals offer an interesting research platform to
explore novel phenomena associated with its torus-shaped Fermi surface. Here,
we study magnetotransport in the massive nodal-line semimetal with spin-orbit
coupling and finite Berry curvature distribution which exists in many
candidates. The magnetic field leads to a deformation of the Fermi torus
through its coupling to the orbital magnetic moment, which turns out to be the
main scenario of the magnetoresistivity (MR) induced by the Berry curvature
effect. We show that a small deformation of the Fermi surface yields a positive
MR $\propto B^2$, different from the negative MR by pure Berry curvature effect
in other topological systems. As the magnetic field increases to a critical
value, a topological Lifshitz transition of the Fermi surface can be induced,
and the MR inverts its sign at the same time. The temperature dependence of the
MR is investigated, which shows a totally different behavior before and after
the Lifshitz transition. Our work uncovers a novel scenario of the MR induced
solely by the deformation of the Fermi surface and establishes a relation
between the Fermi surface topology and the sign of the MR.


  Traces in symmetric monoidal categories are well-known and have many
applications; for instance, their functoriality directly implies the Lefschetz
fixed point theorem. However, for some applications, such as generalizations of
the Lefschetz theorem, one needs "noncommutative" traces, such as the
Hattori-Stallings trace for modules over noncommutative rings. In this paper we
study a generalization of the symmetric monoidal trace which applies to
noncommutative situations; its context is a bicategory equipped with an extra
structure called a "shadow." In particular, we prove its functoriality and
2-functoriality, which are essential to its applications in fixed-point theory.
Throughout we make use of an appropriate "cylindrical" type of string diagram,
which we justify formally in an appendix.


  The Metropolis-Adjusted Langevin Algorithm (MALA), originally introduced to
sample exactly the invariant measure of certain stochastic differential
equations (SDE) on infinitely long time intervals, can also be used to
approximate pathwise the solution of these SDEs on finite time intervals.
However, when applied to an SDE with a nonglobally Lipschitz drift coefficient,
the algorithm may not have a spectral gap even when the SDE does. This paper
reconciles MALA's lack of a spectral gap with its ergodicity to the invariant
measure of the SDE and finite time accuracy. In particular, the paper shows
that its convergence to equilibrium happens at exponential rate up to terms
exponentially small in time-stepsize. This quantification relies on MALA's
ability to exactly preserve the SDE's invariant measure and accurately
represent the SDE's transition probability on finite time intervals.


  In this work we present a systematic theoretical analysis regarding
dark-bright solitons and their interactions, motivated by recent advances in
atomic two-component repulsively interacting Bose-Einstein condensates. In
particular, we study analytically via a two-soliton ansatz adopted within a
variational formulation the interaction between two dark-bright solitons in a
homogeneous environment beyond the integrable regime, by considering general
inter/intra-atomic interaction coefficients. We retrieve the possibility of a
fixed point in the case where the bright solitons are out of phase. As the
inter-component interaction is increased, we also identify an exponential
instability of the two-soliton state, associated with a subcritical pitchfork
bifurcation. The latter gives rise to an asymmetric partition of the bright
soliton mass and dynamically leads to spontaneous splitting of the bound pair.
In the case of the in-phase bright solitons, we explain via parsing the
analytical approximations and monitoring the direct dynamics why no such pair
is identified, despite its prediction by the variational analysis.


  To study the rare-earth doping effect on the phonon heat transport of
La-based cuprates and shed light on the mechanism of phonon scattering, both
$ab$-plane and c-axis thermal conductivities ($\kappa_{ab}$ and $\kappa_c$) are
measured for La$_{2-y}$Eu$_y$CuO$_4$ ($y$ = 0, 0.02 and 0.2) and
La$_{1.88-y}$Eu$_y$Sr$_{0.12}$CuO$_4$ ($y$ = 0 and 0.2) single crystals. It is
found that the phonon peak (at 20 -- 25 K) in $\kappa_{ab}(T)$ of
La$_{2-y}$Eu$_y$CuO$_4$ shows an anomalous Eu-doping dependence: it completely
disappears for $y$ = 0.02, which is discussed to be due to the local lattice
distortions around Eu dopants, and reappears for $y$ = 0.2 with much smaller
peak magnitude compared to $y$ = 0 sample. In contrast to the strong
suppression of the phonon peak in Eu-doped La$_2$CuO$_4$, Eu-doping to
La$_{1.88}$Sr$_{0.12}$CuO$_4$ enhances the low-$T$ phonon heat transport that
results in the reappearance of the phonon peak in this charge-carrier-doped
system. The data clearly show that the establishment of static stripe phase
rather than the structural change is responsible for the reappearance of phonon
peak.


  Welfare effects of price changes are often estimated with cross-sections;
these do not identify demand with heterogeneous consumers. We develop a
theoretical method addressing this, utilizing uncompensated demand moments to
construct local approximations for compensated demand moments, robust to
unobserved preference heterogeneity. Our methodological contribution offers
robust approximations for average and distributional welfare estimates,
extending to price indices, taxable income elasticities, and general
equilibrium welfare. Our methods apply to any cross-section; we demonstrate
them via UK household budget survey data. We uncover an insight: simple
non-parametric representative agent models might be less biased than complex
parametric models accounting for heterogeneity.


  Last moments of a mini black hole escaping from a brane are studied. It is
argued that at the point of reconnection, where the piece of the brane attached
to the black hole separates from the rest, the worldsheet of the brane becomes
isotropic (light-like). The degenerate mode of evolution, with the worldsheet
isotropic everywhere, is investigated. In particular, it is shown that the
brane approaches the reconnection point from below if it reconnects within a
certain limit distance, and from above if it reconnects beyond that distance.
The rate of relaxation to the degenerate mode is established. If the dimension
of the brane is $p$, the nondegeneracy, measured by the determinant of the
relevant part of the induced metric tensor, falls down as (latitudinal
angle)$^{2(p - 1)}$.


  Despite recent success, state-of-the-art learning-based models remain highly
vulnerable to input changes such as adversarial examples. In order to obtain
certifiable robustness against such perturbations, recent work considers
Lipschitz-based regularizers or constraints while at the same time increasing
prediction margin. Unfortunately, this comes at the cost of significantly
decreased accuracy. In this paper, we propose a Calibrated Lipschitz-Margin
Loss (CLL) that addresses this issue and improves certified robustness by
tackling two problems: Firstly, commonly used margin losses do not adjust the
penalties to the shrinking output distribution; caused by minimizing the
Lipschitz constant $K$. Secondly, and most importantly, we observe that
minimization of $K$ can lead to overly smooth decision functions. This limits
the model's complexity and thus reduces accuracy. Our CLL addresses these
issues by explicitly calibrating the loss w.r.t. margin and Lipschitz constant,
thereby establishing full control over slack and improving robustness
certificates even with larger Lipschitz constants. On CIFAR-10, CIFAR-100 and
Tiny-ImageNet, our models consistently outperform losses that leave the
constant unattended. On CIFAR-100 and Tiny-ImageNet, CLL improves upon
state-of-the-art deterministic $L_2$ robust accuracies. In contrast to current
trends, we unlock potential of much smaller models without $K=1$ constraints.


  It is shown that a highly equidistant optical frequency comb can be generated
by the parametric excitation of an optical bottle microresonator with nanoscale
effective radius variation by its natural mechanical vibrations.


  In this paper, we aim to forecast a future trajectory distribution of a
moving agent in the real world, given the social scene images and historical
trajectories. Yet, it is a challenging task because the ground-truth
distribution is unknown and unobservable, while only one of its samples can be
applied for supervising model learning, which is prone to bias. Most recent
works focus on predicting diverse trajectories in order to cover all modes of
the real distribution, but they may despise the precision and thus give too
much credit to unrealistic predictions. To address the issue, we learn the
distribution with symmetric cross-entropy using occupancy grid maps as an
explicit and scene-compliant approximation to the ground-truth distribution,
which can effectively penalize unlikely predictions. In specific, we present an
inverse reinforcement learning based multi-modal trajectory distribution
forecasting framework that learns to plan by an approximate value iteration
network in an end-to-end manner. Besides, based on the predicted distribution,
we generate a small set of representative trajectories through a differentiable
Transformer-based network, whose attention mechanism helps to model the
relations of trajectories. In experiments, our method achieves state-of-the-art
performance on the Stanford Drone Dataset and Intersection Drone Dataset.


  Recent work in open-domain conversational agents has demonstrated that
significant improvements in model engagingness and humanness metrics can be
achieved via massive scaling in both pre-training data and model size
(Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build
agents with human-like abilities, we must expand beyond handling just text. A
particularly important topic is the ability to see images and communicate about
what is perceived. With the goal of engaging humans in multi-modal dialogue, we
investigate combining components from state-of-the-art open-domain dialogue
agents with those from state-of-the-art vision models. We study incorporating
different image fusion schemes and domain-adaptive pre-training and fine-tuning
strategies, and show that our best resulting model outperforms strong existing
models in multi-modal dialogue while simultaneously performing as well as its
predecessor (text-only) BlenderBot (Roller et al., 2020) in text-based
conversation. We additionally investigate and incorporate safety components in
our final model, and show that such efforts do not diminish model performance
with respect to engagingness metrics.


  We enlarge our results from the study of the hopping mechanism of the
oscillation excitation transport in 1D model of one biology-like macromolecular
chain to the case of a system composed from two 1D parallel macromolecular
chains with consideration of the properties of intramolecular oscillation
excitations. We suppose, that due to the exciton interaction with thermal
oscillation (generated by mechanical phonon subsystem) of structural elements
(consisting of the peptide group) of the chains, the exciton becomes by self
trapped and forms the polaron state. We suggest a model which generalizes the
modified Holstein polaron model to the case of two macromolecular chains and
find that because of the interchain coupling, the exciton energy band is
splitted into two subbands. The hopping process of exciton migration along the
macromolecular chains is studied in dependence of system parameters and
temperature. We pay an special attention to the temperature range (near T=300
K) in which living cells operate. It is found that for the certain values of
the system parameters there exists the abrupt change of the exciton migration
nature from practically free (light) exciton motion to an immobile (heavy,
dressed by phonon cloud) quasiparticle We discuss an application of the
obtained results to the exciton transport both within deoxyribonucleic acid
molecule and in the 2D polymer films organized from such macromolecular chains.


  An orbifold is a topological space modeled on quotient spaces of a finite
group actions. We can define the universal cover of an orbifold and the
fundamental group as the deck transformation group. Let $G$ be a Lie group
acting on a space $X$. We show that the space of isotopy-equivalence classes of
$(G,X)$-structures on a compact orbifold $\Sigma$ is locally homeomorphic to
the space of representations of the orbifold fundamental group of $\Sigma$ to
$G$ following the work of Thurston, Morgan, and Lok. This implies that the
deformation space of $(G, X)$-structures on $\Sigma$ is locally homeomorphic to
the space of representations of the orbifold fundamental group to $G$ when
restricted to the region of proper conjugation action by $G$.


  A deterministic direct quantum communication protocol by using swapping
quantum entanglement and local unitary operations is proposed in this paper. A
set of ordered EPR pairs in one of the four Bell states is used. For each pair,
each of the two legitimate users owns a photon of the entangled pair via
quantum channel. The pairs are divided into two types of group, i.e., the
checking groups and the encoding-decoding groups. In the checking groups,
taking advantage of the swapping quantum entanglement and Alice's (the message
sender's) public announcement, the eavesdropping can be detected provided that
the number of the checking groups is big enough. After insuring the security of
the quantum channel, Alice encodes her bits via the local unitary operations on
the encoding-decoding groups. Then she performs her Bell measurements on her
photons and publicly announces her measurement results. After her announcement,
the message receiver Bob performs his Bell measurements on his photons and
directly extracts the encoding bits by using the property of the quantum
entanglement swapping. The security of the present scheme is also discussed:
under the attack scenarios to our best knowledge, the scheme is secure.


  We calculate the second virial coefficient of anyons whose wave function does
not vanish at coincidence points. This kind of anyons appear naturally when one
generalizes the hard-core boundary condition according to self-adjoint
extension method in quantum mechanics, and also when anyons are treated field
theoretically by applying renormalization procedure to nonrelativistic
Chern-Simons field theory. For the anyons which do not satisfy hard-core
boundary condition, it is argued that the other scale-invariant limit is more
relevant in high-temperature limit where virial expansion is useful.
Furthermore, the cusp existing at the bosonic point for hard-core anyons
disappears in all the other cases; instead it is shown that a new cusp is
generated at the fermionic point. A physical explanation is given.


  In this paper, we study distributional chaos for weighted translations on
locally compact groups. We give a sufficient condition for such operators to be
distributionally chaotic and construct an example of distributionally chaotic
weighted translations by way of the sufficient condition. In particular, we
prove the existence of distributional chaos and Li-Yorke chaos for weighted
translations operators with aperiodic elements.
  Furthermore, we also investigate the set of distributionally irregular
vectors ($DIV$) of weighted translations through the cone and equivalence
classes. When the field is that of complex numbers, we uncover several
properties on certain subsets of $DIV$, including their connectedness and
correspondences with some measurable subsets in locally compact groups.


  We report on measurements of 11-18 cm wavelength radio emission from
interactions of 15.2 MeV pulsed electron bunches at the Argonne Wakefield
Accelerator. The electrons were observed both in a configuration where they
produced primarily transition radiation from an aluminum foil, and in a
configuration designed for the electrons to produce Cherenkov radiation in a
silica sand target. Our aim was to emulate the large electron excess expected
to develop during an electromagnetic cascade initiated by an ultra high-energy
particle. Such charge asymmetries are predicted to produce strong coherent
radio pulses, which are the basis for several experiments to detect high-energy
neutrinos from the showers they induce in Antarctic ice and in the lunar
regolith. We detected coherent emission which we attribute both to transition
and possibly Cherenkov radiation at different levels depending on the
experimental conditions. We discuss implications for experiments relying on
radio emission for detection of electromagnetic cascades produced by ultra
high-energy neutrinos.


  In this paper we consider the interaction of electrons in bilayer graphene
with a constant homogeneous magnetic field which is orthogonal to the bilayer
surface. Departing from the energy eigenstates of the effective Hamiltonian,
the corresponding coherent states will be constructed. For doing this, first we
will determine appropriate creation and annihilation operators in order to
subsequently derive the coherent states as eigenstates of the annihilation
operator with complex eigenvalue. Then, we will calculate some physical
quantities, as the Heisenberg uncertainty relation, the probabilities and
current density as well as the mean energy value. Finally, we will explore the
time evolution for these states and we will compare it with the corresponding
evolution for monolayer graphene coherent states.


  The appearance of numbers enumerating alternating sign matrices in stationary
states of certain stochastic processes is reviewed. New conjectures concerning
nest distribution functions are presented as well as a bijection between
certain classes of alternating sign matrices and lozenge tilings of hexagons
with cut off corners.


  We prove that |A^n| > c_n |A|^{[\frac{n+1}{2}]} for any finite subset A of a
free group if A contains at least two noncommuting elements, where c_n>0 are
constants not depending on A. Simple examples show that the order of these
estimates are the best possible for each n>0.


  The demands of automated shipping address recognition and verification have
increased to handle a large number of packages and to save costs associated
with misdelivery. A previous study proposed a deep learning system where the
shipping address is recognized and verified based on a camera image capturing
the shipping address and barcode area. Because the system performance depends
on the input image quality, inspection of input image quality is necessary for
image preprocessing. In this paper, we propose an input image quality
verification method combining global and local features. Object detection and
scale-invariant feature transform in different feature spaces are developed to
extract global and local features from several independent convolutional neural
networks. The conditions of shipping label images are classified by fully
connected fusion layers with concatenated global and local features. The
experimental results regarding real captured and generated images show that the
proposed method achieves better performance than other methods. These results
are expected to improve the shipping address recognition and verification
system by applying different image preprocessing steps based on the classified
conditions.


  We examine fractional charge and spin errors in self-consistent Green's
function theory within a second-order approximation (GF2). For GF2 it is known
that the summation of diagrams resulting from the self-consistent solution of
the Dyson equation removes the divergences pathological to second-order
Moller-Plesset theory (MP2) for strong correlations. In the language often used
in density functional theory contexts, this means GF2 has a greatly reduced
fractional spin error relative to MP2. The natural question then is what
effect, if any, does the Dyson summation have on the fractional charge error in
GF2? To this end we generalize our previous implementation of GF2 to open-shell
systems and analyze its fractional spin and charge errors. We find that like
MP2, GF2 possesses only a very small fractional charge error, and consequently
little many electron self-interaction error. This shows that GF2 improves on
the critical failings of MP2, but without altering the positive features that
make it desirable. Furthermore, we find that GF2 has both less fractional
charge and fractional spin errors than typical hybrid density functionals as
well as random phase approximation with exchange.


  We show that the RST model describing the exactly soluble black hole model
can have a dynamical wormhole solution along with an appropriate boundary
condition. The necessary exotic matter which is usually negative energy density
is remarkably produced by the quantization of the infalling matter fields. Then
the asymptotic geometry in the past is two-dimensional anti-de Sitter(AdS$_2$),
which implies the exotic matter is negative. As time goes on, the wormhole
eventually evolves into the black hole and its Hawking radiation appears. The
throat of the static RST wormhole is lower-bounded but in the presence of
infalling matter it collapses to a black hole.


  Proton hexality is a discrete symmetry that avoids the problem of too fast
proton decay in the supersymmetric extension of the standard model.
Unfortunately it is inconsistent with conventional grand unification. We show
that proton hexality can be incorporated in the scheme of "Local Grand
Unification" discussed in the framework of model building in (heterotic) string
theory.


  The observation that real complex networks have internal structure has
important implication for dynamic processes occurring on such topologies. Here
we investigate the impact of community structure on a model of information
transfer able to deal with both search and congestion simultaneously. We show
that networks with fuzzy community structure are more efficient in terms of
packet delivery that those with pronounced community structure. We also propose
an alternative packet routing algorithm which takes advantage of the knowledge
of communities to improve information transfer and show that in the context of
the model an intermediate level of community structure is optimal. Finally, we
show that in a hierarchical network setting, providing knowledge of communities
at the level of highest modularity will improve network capacity by the largest
amount.


  In this paper we extend our recent results [Physica A340 (2004)110] on
q-nonextensive statistics with non-Tsallis entropies. In particular, we combine
an axiomatics of Renyi with the q-deformed version of Khinchin axioms to obtain
the entropy which accounts both for systems with embedded self-similarity and
q-nonextensivity. We find that this entropy can be uniquely solved in terms of
a one-parameter family of information measures. The corresponding entropy
maximizer is expressible via a special function known under the name of the
Lambert W-function. We analyze the corresponding "high" and "low-temperature"
asymptotics and make some remarks on the possible applications.


  We present our analysis of the long cadence Kepler data for the well-studied
Blazhko star RR Lyr, gathered during the first two quarters of the satellite's
observations and covering a total of 127d. Besides being of great importance
for our understanding of RR Lyrae stars in general, these RR Lyr data can be
regarded as a case study for observations of bright stars with Kepler. Kepler
can perform high-precision photometry on targets like RR Lyr, as the saturated
flux is conserved to a very high degree. The Kepler data on RR Lyr are
revolutionary in several respects. Even with long-cadence sampling (one
measurement per 29.4 min), the unprecedented precision (< mmag) of the Kepler
photometry allows the study of the star's extreme light curve variations in
detail. The multiplet structures at the main frequency and its harmonics,
typical for Blazhko stars, are clearly detected up to the quintuplets. For the
first time, photometric data of RR Lyr reveal the presence of half-integer
frequencies, linked to a period doubling effect. This phenomenon may be
connected to the still unexplained Blazhko modulation. Moreover, with three
observed Blazhko cycles at our disposal, we observe that there is no exact
repetition in the light curve changes from one modulation cycle to the next for
RR Lyr. This may be due to additional periodicities in the star, or to
transient or quasi-periodic changes.


  There are a number of constraints which limit the current and voltages which
can be applied on a multiple drive electrical imaging system. One obvious
constraint is to limit the maximum Ohmic power dissipated in the body. Current
patterns optimising distinguishability with respect to this constraint are
singular functions of the difference of transconductance matrices with respect
to the power norm. (the optimal currents of Isaacson). If one constrains the
total current ($L^1$ norm) the optimal patterns are pair drives. On the other
hand if one constrains the maximum current on each drive electrode (an
$L^\infty$ norm), the optimal patterns have each drive channel set to the
maximum source or sink current value. In this paper we consider appropriate
safety constraints and discuss how to find the optimal current patterns with
those constraints.


  We have analyzed the total and nuclear SED for NGC 4261 and find that the
dominant process for the mid- and far-infrared emission in this object is
non-thermal emission from the active nucleus. Modeling the emission from the
optically detected 300 pc dust disk yields no significant disk contribution at
any wavelength. To explain the observations, either the disk has an inflated
inner region which partly absorbs the core, or the intrinsic core spectrum is
curved. The inner 10 pc of the disk can potentially be conceived as an
obscuring torus, albeit with optical depth around unity.


  We consider the time slicing approximations of Feynman path integrals,
constructed via piecewice classical paths. A detailed study of the convergence
in the norm operator topology, in the space $\mathcal{B}(L^2(\mathbb{R}^d))$ of
bounded operators on $L^2$, and even in finer operator topologies, was carried
on by D. Fujiwara in the case of smooth potentials with an at most quadratic
growth. In the present paper we show that the result about the convergence in
$\mathcal{B}(L^2(\mathbb{R}^d))$ remains valid if the potential is only assumed
to have second space derivatives in the Sobolev space $H^{d+1}(\mathbb{R}^d)$
(locally and uniformly), uniformly in time. The proof is non-perturbative in
nature, but relies on a precise short time analysis of the Hamiltonian flow at
this Sobolev regularity and on the continuity in $L^2$ of certain oscillatory
integral operators with non-smooth phase and amplitude.


  Food scientists at the U.S. Army's Natick Solider Center have developed a
model for the lifecyle of the bacteria Staphylococcus aureus in intermediate
moisture bread. In this article, we study this model using dynamical systems
and Lie symmetry methods. We calculate center manifolds and Lie symmetries for
different cases of parameter values and compare our results to those of the
food scientists.


  This study suggests a new decomposition of the effect of Foreign Direct
Investment (FDI) on long-term growth in developing countries. It reveals that
FDI not only have a positive direct effect on growth, but also increase the
latter by reducing the recessionary effect resulting from a banking crisis.
Even more, they reduce its occurrence. JEL: F65, F36, G01, G15


  A large number of papers have introduced novel machine learning and feature
extraction methods for automatic classification of AD. However, they are
difficult to reproduce because key components of the validation are often not
readily available. These components include selected participants and input
data, image preprocessing and cross-validation procedures. The performance of
the different approaches is also difficult to compare objectively. In
particular, it is often difficult to assess which part of the method provides a
real improvement, if any. We propose a framework for reproducible and objective
classification experiments in AD using three publicly available datasets (ADNI,
AIBL and OASIS). The framework comprises: i) automatic conversion of the three
datasets into BIDS format, ii) a modular set of preprocessing pipelines,
feature extraction and classification methods, together with an evaluation
framework, that provide a baseline for benchmarking the different components.
We demonstrate the use of the framework for a large-scale evaluation on 1960
participants using T1 MRI and FDG PET data. In this evaluation, we assess the
influence of different modalities, preprocessing, feature types, classifiers,
training set sizes and datasets. Performances were in line with the
state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No
difference in performance was found for the use of different atlases, image
smoothing, partial volume correction of FDG PET images, or feature type. Linear
SVM and L2-logistic regression resulted in similar performance and both
outperformed random forests. The classification performance increased along
with the number of subjects used for training. Classifiers trained on ADNI
generalized well to AIBL and OASIS. All the code of the framework and the
experiments is publicly available at:
https://gitlab.icm-institute.org/aramislab/AD-ML.


  As cosmology has entered a phase of precision experiments, the content of the
universe has been established to contain interesting and not yet fully
understood components, namely dark energy and dark matter. While the cause and
exact nature of the dark energy remains mysterious, there is greater hope to
connect the dark matter to current models of particle physics. Supersymmetric
models provide several excellent candidates for dark matter, with the lightest
neutralino the prime example. This and other dark matter candidates are
discussed, and prospects for their detection summarized. Some methods of
detection are explained, and indications of signals in present data are
critically examined.


  The pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI)
is characterized by the presence of beta-amyloid extracellular plaques and
neurofibrillary tangles containing hyper-phosphorylated tau. Individuals
carrying the apolipoprotein E-e4 (APOE-e4) allele are at increased risk of
cognitive decline and developing AD pathology. The development of positron
emission tomography (PET) radioligands sensitive to tau neurofibrillary
tangles, such as 18F-AV1451, has allowed for visualization and assessment of AD
pathology in vivo. The radioligand used in 18F-AV1451 binds with iron in
addition to tau neurofibrillary tangles. We employ multimodal neuroimaging
analyses, combining iron-sensitive measures from MRI with 18F-AV1451 PET, to
examine off-target binding effects in cohorts of 20 APOE-e4 negative, 20
APOE-e4 positive MCI, and 29 control participants. Increased tau pathology,
after controlling for tissue susceptibility, was found in the temporal lobe and
hippocampus of APOE-e4+ MCI participants as compared to APOE-e4 negative MCI
and control participants. Tau pathology in the hippocampus was significantly
related to memory, but only in APOE-e4+ participants. Correlations between
hippocampal 18F-AV1451 PET uptake and cognitive correlations did not
significantly differ when correcting for the influence of iron on 18F-AV1451
PET signal. However, controlling for susceptibility was found to influence
correlations between tau-PET uptake and diffusion metrics and the change in
this interaction may be due to the influence of iron on diffusivity. Taken
together, these results suggest that iron does not need to be accounted for in
group comparisons of tau-PET uptake or correlations between cognitive measures
and tau-PET SUVR. However, iron should be taken into account in correlations
between diffusion measures and tau-PET uptake.


  We review the area of strange quark contributions to nucleon structure. In
particular, we focus on current models of strange quark vector currents in the
nucleon and the associated parity-violating elastic electron scattering
experiments from which vector- and axial-vector currents are extracted


  Correlated with the trend of increasing degrees of freedom in robotic systems
is a similar trend of rising interest in Spatio-Temporal systems described by
Partial Differential Equations (PDEs) among the robotics and control
communities. These systems often exhibit dramatic under-actuation, high
dimensionality, bifurcations, and multimodal instabilities. Their control
represents many of the current-day challenges facing the robotics and
automation communities. Not only are these systems challenging to control, but
the design of their actuation is an NP-hard problem on its own. Recent methods
either discretize the space before optimization, or apply tools from linear
systems theory under restrictive linearity assumptions in order to arrive at a
control solution. This manuscript provides a novel sampling-based stochastic
optimization framework based entirely in Hilbert spaces suitable for the
general class of \textit{semi-linear} SPDEs which describes many systems in
robotics and applied physics. This framework is utilized for simultaneous
policy optimization and actuator co-design optimization. The resulting
algorithm is based on variational optimization, and performs joint episodic
optimization of the feedback control law and the actuation design over
episodes. We study first and second order systems, and in doing so, extend
several results to the case of second order SPDEs. Finally, we demonstrate the
efficacy of the proposed approach with several simulated experiments on a
variety of SPDEs in robotics and applied physics including an infinite
degree-of-freedom soft robotic manipulator.


  We compare Mercury's precession test in standard General Relativity (GR),
Brans-Dicke theories (BD), and Palatini f(R)-theories. We avoid post Newtonian
(PN) approximation and compute exact precession in these theories. We show that
the well-known mathematical equivalence between Palatini f(R)-theories and a
specific subset of BD theories does not extend to a really physical equivalence
among theories since equivalent models still allow different incompatible
precession for Mercury depending on the solution one chooses. As a result one
cannot use BD equivalence to rule out Palatini f(R)-theories. On the contrary,
we directly discuss that Palatini f(R)-theories can (and specific models do)
easily pass Solar System tests as Mercury's precession.


  We study discrete Schroedinger operators with compactly supported potentials
on the square lattice. Constructing spectral representations and representing
S-matrices by the generalized eigenfunctions, we show that the potential is
uniquely reconstructed from the S-matrix of all energies. We also study the
spectral shift function for the trace class potentials, and estimate the
discrete spectrum in terms of the moments of the spectral shift function and
the potential.


  Preliminary results from an ongoing project to compute a grid of post-AGB
models is presented. Our preliminary results show that stellar evolution
computations that include an updated treatment of the microphysics predict
post-AGB timescales that are several times shorter that predicted by older
models. Also the mass-luminosity relation of post-AGB models deviates from that
of older grids. In addition, our results suggest only a slight metallicity
dependence of the post-AGB timescales. We expect these results to have
significant consequences for models of the formation of planetary nebulae and
their luminosity function.


  We address the description of solutes flow with trapping processes in porous
media. Starting from a small-scale model for tracer particles trajectories, we
derive the corresponding governing equations for the concentration of the
mobile and immobile phases. We show that this formulation is fairly general and
can easily take into account non-constant coefficients and in particular
space-dependent sorption rates. The transport equations are solved numerically
and a comparison with Monte Carlo particle-tracking simulations of spatial
contaminant profiles and breakthrough curves is proposed, so to illustrate the
obtained results.


  Multi-modal entity alignment (MMEA) aims to discover identical entities
across different knowledge graphs (KGs) whose entities are associated with
relevant images. However, current MMEA algorithms rely on KG-level modality
fusion strategies for multi-modal entity representation, which ignores the
variations of modality preferences of different entities, thus compromising
robustness against noise in modalities such as blurry images and relations.
This paper introduces MEAformer, a multi-modal entity alignment transformer
approach for meta modality hybrid, which dynamically predicts the mutual
correlation coefficients among modalities for more fine-grained entity-level
modality fusion and alignment. Experimental results demonstrate that our model
not only achieves SOTA performance in multiple training scenarios, including
supervised, unsupervised, iterative, and low-resource settings, but also has a
limited number of parameters, efficient runtime, and interpretability. Our code
is available at https://github.com/zjukg/MEAformer.


  We present detailed experiments on transient growth of turbulent spots
induced by external forcing in plane Couette-Poiseuille flow, which are studied
in the framework of linear of transient growth. The experimental investigation
is supplemented with full theoretical analysis. We compare quantitatively the
experimental and theoretical results, including maximal gain and the time at
which it occurs. We also present the limits of validity for the application of
the linear theory at high amplitude perturbation and Reynolds number, showing
experiments with self-sustained states.


  We propose a single particle boson mechanism of High T_c Superconductivity
(HTCS) and pseudogap regime. Bosons appear in it due to the coupling of spins
of the two-dimensional (2D) fermions with statistical magnetic field induced by
anyon vector potential. The ground state of 2D gas is pure bosonic if gas is
not dense. At the dense limit of gas the interaction of effective (coupled with
the statistical magnetic field) spins of bosons leads to the increasing of
their fluctuations, which destroy the coupling. An experimental phase diagram
of the hole doped superconducting cuprates discussed in the paper of Tallon and
Loram might qualitatively and quantitatively be clarified in the framework of
this mechanism. The vicinity of the structural phase transition to
superconducting state might strengthen the possible quadratic striction in the
sample and the phase transition of bosons into Bose-Einstein condensate (BEC),
which is responsible for the superconductivity (SC), is not second order, but
first, close to second one. According this treatment the pseudogap regime is
the region of meta stable bosons, which are out of the BEC. At the pseudogap
boundary, E_g, the bosons finally undergo the phase transition into fermions.
Non-Fermi liquid like property of quasi-particles discussed in the literature
might be related to bosons with spins in the pseudogap regime.


  We describe a simple family of analytical coordinate systems for the
Schwarzschild spacetime. The coordinates penetrate the horizon smoothly and are
spatially isotropic. Spatial slices of constant coordinate time $t$ feature a
trumpet geometry with an asymptotically cylindrical end inside the horizon at a
prescribed areal radius $R_0$ (with $0<R_{0}\leq M$) that serves as the free
parameter for the family. The slices also have an asymptotically flat end at
spatial infinity. In the limit $R_{0}=0$ the spatial slices lose their trumpet
geometry and become flat -- in this limit, our coordinates reduce to
Painlev\'e-Gullstrand coordinates.


  We generalize the definition and enumeration of spanning trees from the
setting of graphs to that of arbitrary-dimensional simplicial complexes
$\Delta$, extending an idea due to G. Kalai. We prove a simplicial version of
the Matrix-Tree Theorem that counts simplicial spanning trees, weighted by the
squares of the orders of their top-dimensional integral homology groups, in
terms of the Laplacian matrix of $\Delta$. As in the graphic case, one can
obtain a more finely weighted generating function for simplicial spanning trees
by assigning an indeterminate to each vertex of $\Delta$ and replacing the
entries of the Laplacian with Laurent monomials. When $\Delta$ is a shifted
complex, we give a combinatorial interpretation of the eigenvalues of its
weighted Laplacian and prove that they determine its set of faces uniquely,
generalizing known results about threshold graphs and unweighted Laplacian
eigenvalues of shifted complexes.


  Level curvature is a measure of sensitivity of energy levels of a
disordered/chaotic system to perturbations. In the bulk of the spectrum Random
Matrix Theory predicts the probability distributions of level curvatures to be
given by Zakrzewski-Delande expressions [F. von Oppen {\it Phys. Rev. Lett.}
{\bf 73} 798 (1994) & {\it Phys.Rev. E} {\bf 51} 2647 (1995); Y.V. Fyodorov and
H.-J. Sommers {\it Z.Phys.B} {\bf 99} 123 (1995)]. Motivated by growing
interest in statistics of extreme (maximal or minimal) eigenvalues of
disordered systems of various nature, it is natural to ask about the associated
level curvatures. I show how calculating the distribution for the curvatures of
extreme eigenvalues in GUE ensemble can be reduced to studying asymptotic
behaviour of orthogonal polynomials appearing in the recent work C. Nadal and
S. N. Majumdar {\it J. Stat. Mech.} {\bf 2011} P04001 (2011). The corresponding
asymptotic analysis being yet outstanding, I instead will discuss solution of a
related, but somewhat simpler problem of calculating the level curvature
distribution averaged over all the levels in a spectral window close to the
edge of the semicircle. The method is based on asymptotic analysis of kernels
associated with Hermite polynomials and their Cauchy transforms, and is
straightforwardly extendable to any rotationally-invariant ensemble of random
matrices.


  This paper proposes a framework for time series generation built to
investigate anomaly detection in cloud microservices. In the field of cloud
computing, ensuring the reliability of microservices is of paramount concern
and yet a remarkably challenging task. Despite the large amount of research in
this area, validation of anomaly detection algorithms in realistic environments
is difficult to achieve. To address this challenge, we propose a framework to
mimic the complex time series patterns representative of both normal and
anomalous cloud microservices behaviors. We detail the pipeline implementation
that allows deployment and management of microservices as well as the
theoretical approach required to generate anomalies. Two datasets generated
using the proposed framework have been made publicly available through GitHub.


  We develop an integral version of Deligne cohomology for smooth proper real
varieties. For this purpose the role played by singular cohomology in the
complex case has to be replaced by ordinary bigraded G-equivariant cohomology,
where G=Gal(C/R). This is the G-equivariant counterpart of singular cohomology.
We establish the basic properties of the theory and give a geometric
interpretation for the groups in dimension 2 in weights 1 and 2.


  We study of the role of galaxy-galaxy interactions and disk instabilities in
producing starburst activity in galaxies out to z=4. For this, we use a sample
of 387 galaxies with robust total star formation rate measurements from
Herschel, gas masses from ALMA, stellar masses and redshifts from multi-band
photometry, and JWST/NIRCam rest-frame optical imaging. Using mass-controlled
samples, we find an increased fraction of interacting galaxies in the starburst
regime at all redshifts out to z=4. This increase correlates with star
formation efficiency (SFE), but not with gas fraction. However, the correlation
is weak (and only significant out to z=2), which could be explained by the
short duration of SFE increase during interaction. In addition, we find that
isolated disk galaxies make up a significant fraction of the starburst
population. The fraction of such galaxies with star-forming clumps ("clumpy
disks") is significantly increased compared to the main-sequence disk
population. Furthermore, this fraction directly correlates with SFE. This is
direct observational evidence for a long-term increase of SFE maintained due to
disk instabilities, contributing to the majority of starburst galaxies in our
sample and hence to substantial mass growth in these systems. This result could
also be of importance for explaining the growth of the most massive galaxies at
z>6.


  Ovoids in $\PG(3, \gf(q))$ have been an interesting topic in coding theory,
combinatorics, and finite geometry for a long time. So far only two families of
ovoids are known. The first is the elliptic quadratics and the second is the
Tits ovoids. It is known that an ovoid in $\PG(3, \gf(q))$ corresponds to a
$[q^2+1, 4, q^2-q]$ code over $\gf(q)$, which is called an ovoid code. The
objectives of this paper is to study the subfield codes of the two families of
ovoid codes. The dimensions, minimum weights, and the weight distributions of
the subfield codes of the elliptic quadric codes and Tits ovoid codes are
settled. The parameters of the duals of these subfield codes are also studied.
Some of the codes presented in this paper are optimal, and some are
distance-optimal. The parameters of the subfield codes are new.


  The bootstrap is a popular method of constructing confidence intervals due to
its ease of use and broad applicability. Theoretical properties of bootstrap
procedures have been established in a variety of settings. However, there is
limited theoretical research on the use of the bootstrap in the context of
estimation of a differentiable functional in a nonparametric or semiparametric
model when nuisance functions are estimated using machine learning. In this
article, we provide general conditions for consistency of the bootstrap in such
scenarios. Our results cover a range of estimator constructions, nuisance
estimation methods, bootstrap sampling distributions, and bootstrap confidence
interval types. We provide refined results for the empirical bootstrap and
smoothed bootstraps, and for one-step estimators, plug-in estimators, empirical
mean plug-in estimators, and estimating equations-based estimators. We
illustrate the use of our general results by demonstrating the asymptotic
validity of bootstrap confidence intervals for the average density value and
G-computed conditional mean parameters, and compare their performance in finite
samples using numerical studies. Throughout, we emphasize whether and how the
bootstrap can produce asymptotically valid confidence intervals when standard
methods fail to do so.


  We consider the application of the dual parametrization for the case of gluon
GPDs in the nucleon. This provides opportunities for the more flexible modeling
unpolarized gluon GPDs in a nucleon which in particular contain the invaluable
information on the fraction of nucleon spin carried by gluons. We perform the
generalization of Abel transform tomography approach for the case of gluons. We
also discuss the skewness effect in the framework of the dual parametrization.
We strongly suggest to employ the fitting strategies based on the dual
parametrization to extract the information on GPDs from the experimental data.


  Baryon Acoustic Oscillations (BAOs) in the early Universe are predicted to
leave an as yet undetected signature on the relative clustering of total mass
versus luminous matter. A detection of this effect would provide an important
confirmation of the standard cosmological paradigm and constrain alternatives
to dark matter as well as non-standard fluctuations such as Compensated
Isocurvature Perturbations (CIPs). We conduct the first observational search
for this effect, by comparing the number-weighted and luminosity-weighted
correlation functions, using the SDSS-III BOSS Data Release 10 CMASS sample.
When including CIPs in our model, we formally obtain evidence at $3.2\sigma$ of
the relative clustering signature and a limit that matches the existing upper
limits on the amplitude of CIPs. However, various tests suggest that these
results are not yet robust, perhaps due to systematic biases in the data. The
method developed in this Letter, used with more accurate future data such as
that from DESI, is likely to confirm or disprove our preliminary evidence.


  Face alignment has witnessed substantial progress in the last decade. One of
the recent focuses has been aligning a dense 3D face shape to face images with
large head poses. The dominant technology used is based on the cascade of
regressors, e.g., CNN, which has shown promising results. Nonetheless, the
cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end
training, hand-crafted features and slow training speed. To address these
issues, we propose a new layer, named visualization layer, that can be
integrated into the CNN architecture and enables joint optimization with
different loss functions. Extensive evaluation of the proposed method on
multiple datasets demonstrates state-of-the-art accuracy, while reducing the
training time by more than half compared to the typical cascade of CNNs. In
addition, we compare multiple CNN architectures with the visualization layer to
further demonstrate the advantage of its utilization.


  We survey several Talagrand type inequalities and their application to
influences with the tool of hypercontractivity for both discrete and
continuous, and product and non-product models. The approach covers similarly
by a simple interpolation the framework of geometric influences recently
developed by N. Keller, E. Mossel and A. Sen. Geometric Brascamp-Lieb
decompositions are also considered in this context.


  We model two mergers of orbiting binary neutron stars, the first forming a
black hole and the second a differentially rotating neutron star. We extract
gravitational waveforms in the wave zone. Comparisons to a post-Newtonian
analysis allow us to compute the orbital kinematics, including trajectories and
orbital eccentricities. We verify our code by evolving single stars and
extracting radial perturbative modes, which compare very well to results from
perturbation theory. The Einstein equations are solved in a first order
reduction of the generalized harmonic formulation, and the fluid equations are
solved using a modified convex essentially non-oscillatory method. All
calculations are done in three spatial dimensions without symmetry assumptions.
We use the \had computational infrastructure for distributed adaptive mesh
refinement.


  The Bulge is the least understood major stellar population of the Milky Way.
Most of what we know about the formation and evolution of the Bulge comes from
bright giant stars. The underlying assumption that giants represent all the
stars, and accurately trace the chemical evolution of a stellar population, is
under debate. In particular, recent observations of a few microlensed dwarf
stars give a very different picture of the evolution of the Bulge from that
given by the giant stars. [ABRIDGED] We perform a detailed elemental abundance
analysis of dwarf stars in the Galactic bulge, based on high-resolution spectra
that were obtained while the stars were optically magnified during
gravitational microlensing events. [ABRIDGED] We present detailed elemental
abundances and stellar ages for six new dwarf stars in the Galactic bulge.
Combining these with previous events, here re-analysed with the same methods,
we study a homogeneous sample of 15 stars, which constitute the largest sample
to date of microlensed dwarf stars in the Galactic bulge. We find that the
stars span the full range of metallicities from [Fe/H]=-0.72 to +0.54, and an
average metallicity of <[Fe/H]>=-0.08+/-0.47, close to the average metallicity
based on giant stars in the Bulge. Furthermore, the stars follow well-defined
abundance trends, that for [Fe/H]<0 are very similar to those of the local
Galactic thick disc. This suggests that the Bulge and the thick disc have had,
at least partially, comparable chemical histories. At sub-solar metallicities
we find the Bulge dwarf stars to have consistently old ages, while at
super-solar metallicities we find a wide range of ages. Using the new age and
abundance results from the microlensed dwarf stars we investigate possible
formation scenarios for the Bulge.


  This paper proposes a multivariable extremum seeking scheme using Fast
Fourier Transform (FFT) for a network of subsystems working towards optimizing
the sum of their local objectives, where the overall objective is the only
available measurement. Here, the different inputs are perturbed with different
dither frequencies, and the power spectrum of the overall output signal
obtained using FFT is used to estimate the steady-state cost gradient w.r.t.
each input. The inputs for the subsystems are then updated using integral
control in order to drive the respective gradients to zero. This paper provides
analytical rules for designing the FFT-based gradient estimation algorithm and
analyzes the stability properties of the resulting extremum seeking scheme for
the static map setting. The effectiveness of the proposed FFT-based
multivariable extremum seeking scheme is demonstrated using two examples,
namely, wind farm power optimization problem, and a heat exchanger network for
industrial waste-to-heat recovery.


  Efficient methods for encoding and compression are likely to pave way towards
the problem of efficient trainability on higher dimensional Hilbert spaces
overcoming issues of barren plateaus. Here we propose an alternative approach
to variational autoencoders to reduce the dimensionality of states represented
in higher dimensional Hilbert spaces. To this end we build a variational based
autoencoder circuit that takes as input a dataset and optimizes the parameters
of Parameterized Quantum Circuit (PQC) ansatz to produce an output state that
can be represented as tensor product of 2 subsystems by minimizing Tr(\rho^2).
The output of this circuit is passed through a series of controlled swap gates
and measurements to output a state with half the number of qubits while
retaining the features of the starting state, in the same spirit as any
dimension reduction technique used in classical algorithms. The output obtained
is used for supervised learning to guarantee the working of the encoding
procedure thus developed. We make use of Bars and Stripes dataset (BAS) for an
8x8 grid to create efficient encoding states and report a classification
accuracy of 95% on the same. Thus the demonstrated example shows a proof for
the working of the method in reducing states represented in large Hilbert
spaces while maintaining the features required for any further machine learning
algorithm that follow.


  In practical applications, it is common that wildfire simulators do not
correctly predict the evolution of the fire scar. Usually, this is caused due
to multiple factors including inaccuracy in the input data such as land cover
classification, moisture, improperly represented local winds, cumulative errors
in the fire growth simulation model, high level of discontinuity/heterogeneity
within the landscape, among many others. Therefore in practice, it is necessary
to adjust the propagation of the fire to obtain better results, either to
support suppression activities or to improve the performance of the simulator
considering new default parameters for future events, best representing the
current fire spread growth phenomenon. In this article, we address this problem
through a new methodology using Derivative-Free Optimization (DFO) algorithms
for adjusting the Rate of Spread (ROS) factors in a fire simulation growth
model called Cell2Fire. To achieve this, we solve an error minimization
optimization problem that captures the difference between the simulated and
observed fire, which involves the evaluation of the simulator output in each
iteration as part of a DFO framework, allowing us to find the best possible
factors for each fuel present on the landscape. Numerical results for different
objective functions are shown and discussed, including a performance comparison
of alternative DFO algorithms.


  We address the problem of dynamic variable selection in time series
regression with unknown residual variances, where the set of active predictors
is allowed to evolve over time. To capture time-varying variable selection
uncertainty, we introduce new dynamic shrinkage priors for the time series of
regression coefficients. These priors are characterized by two main
ingredients: smooth parameter evolutions and intermittent zeroes for modeling
predictive breaks. More formally, our proposed Dynamic Spike-and-Slab (DSS)
priors are constructed as mixtures of two processes: a spike process for the
irrelevant coefficients and a slab autoregressive process for the active
coefficients. The mixing weights are themselves time-varying and depend on
lagged values of the series. Our DSS priors are probabilistically coherent in
the sense that their stationary distribution is fully known and characterized
by spike-and-slab marginals. For posterior sampling over dynamic regression
coefficients, model selection indicators as well as unknown dynamic residual
variances, we propose a Dynamic SSVS algorithm based on forward-filtering and
backward-sampling. To scale our method to large data sets, we develop a Dynamic
EMVS algorithm for MAP smoothing. We demonstrate, through simulation and a
topical macroeconomic dataset, that DSS priors are very effective at separating
active and noisy coefficients. Our fast implementation significantly extends
the reach of spike-and-slab methods to large time series data.


  The Maximum Leaf Spanning Arborescence problem (MLSA) is defined as follows:
Given a directed graph $G$ and a vertex $r\in V(G)$ from which every other
vertex is reachable, find a spanning arborescence rooted at $r$ maximizing the
number of leaves (vertices with out-degree zero). The MLSA has applications in
broadcasting, where a message needs to be transferred from a source vertex to
all other vertices along the arcs of an arborescence in a given network. In
doing so, it is desirable to have as many vertices as possible that only need
to receive, but not pass on messages since they are inherently cheaper to
build.
  We study polynomial-time approximation algorithms for the MLSA. For general
digraphs, the state-of-the-art is a
$\min\{\sqrt{\mathrm{OPT}},92\}$-approximation. In the (still APX-hard) special
case where the input graph is acyclic, the best known approximation guarantee
of $\frac{7}{5}$ is due to Fernandes and Lintzmayer: They prove that any
$\alpha$-approximation for the \emph{hereditary $3$-set packing problem}, a
special case of weighted $3$-set packing, yields a
$\max\{\frac{4}{3},\alpha\}$-approximation for the MLSA in acyclic digraphs
(dags), and provide a $\frac{7}{5}$-approximation for the hereditary $3$-set
packing problem. In this paper, we obtain a $\frac{4}{3}$-approximation for the
hereditary $3$-set packing problem, and, thus, also for the MLSA in dags. In
doing so, we manage to leverage the full potential of the reduction provided by
Fernandes and Lintzmayer. The algorithm that we study is a simple local search
procedure considering swaps of size up to $10$. Its analysis relies on a
two-stage charging argument.


  We discover a new Poincar\'e type phenomenon by establishing an optimal
rigidity theorem for local CR mappings between circle bundles that are defined
in a canonical way over (possibly reducible) bounded symmetric domains. We
prove such a local CR map, if nonconstant, must extend to a rational
biholomorphism between the corresponding disk bundles. The result includes as a
special case the classical Poincar\'e--Tanaka--Alexander theorem. Among other
applications, we show, for two irreducible bounded symmetric domains with rank
at least two, a local CR diffeomorphism between (open connected pieces of)
their anti-canonical circle bundles extends to a norm-preserving holomorphic
isomorphism between their anti-canonical bundles. The statement fails in the
rank one case. As another application, we construct, for any $n \geq 2,$ a
countably infinite family of compact locally homogeneous strongly pseudoconvex
CR hypersurfaces (in complex manifolds) of real dimension $2n+1$ with
transverse symmetry such that they are all obstruction flat and Bergman
logarithmically flat. Moreover, their local CR structures are mutually
inequivalent. Such a family cannot exist in dimension three by known results: A
Bergman logarithmically flat CR hypersurface must be spherical, and so is a
compact obstruction flat CR hypersurface with transverse symmetry.


  We characterize ex-post implementable allocation rules for single object
auctions under quasi-linear preferences with convex interdependent value
functions. We show that requiring ex-post implementability is equivalent to
requiring that the allocation rule must satisfy a condition that we call
eventual monotonicity (EM), which is a weakening of monotonicity, a familiar
condition used to characterize dominant strategy implementation.


  We introduce a new species of gap solitons (GSs) supported by an azimuthally
modulated guiding ring in defocusing cubic media. The periodicity in the
azimuthal direction strongly modifies properties and existence domains of GSs.
In addition to the fundamental solitons, we report even and twisted bound
states. The former type is found to be stable, while the twisted states are
always unstable in defocusing media.


  Within two different frameworks of isospin-dependent transport model, i.e.,
Boltzmann-Uehling-Uhlenbeck (IBUU04) and Ultrarelativistic Quantum Molecular
Dynamics (UrQMD) transport models, sensitive probes of nuclear symmetry energy
are simulated and compared. It is shown that neutron to proton ratio of free
nucleons, pi-/pi+ ratio as well as isospin-sensitive transverse and elliptic
flows given by the two transport models with their "best settings", all have
obvious differences. Discrepancy of numerical value of isospin-sensitive n/p
ratio of free nucleon from the two models mainly originates from different
symmetry potentials used and discrepancies of numerical value of charged
pi-/pi+ ratio and isospin-sensitive flows mainly originate from different
isospin-dependent nucleon-nucleon cross sections. These demonstrations call for
more detailed studies on the model inputs (i.e., the density- and
momentum-dependent symmetry potential and the isospin-dependent nucleon-nucleon
cross section in medium) of isospin-dependent transport model used. The studies
of model dependence of isospin sensitive observables can help nuclear
physicists to pin down the density dependence of nuclear symmetry energy
through comparison between experiments and theoretical simulations
scientifically.


  The classical simulation of physical processes using standard models of
computation is fraught with problems. On the other hand, attempts at modelling
real-world computation with the aim of isolating its hypercomputational content
have struggled to convince. We argue that a better basic understanding can be
achieved through computability theoretic deconstruction of those physical
phenomena most resistant to classical simulation. From this we may be able to
better assess whether the hypercomputational enterprise is proleptic computer
science, or of mainly philosophical interest.