  The LARES satellite, a laser-ranged space experiment to contribute to
geophysics observation, and to measure the general relativistic Lense-Thirring
effect, has been observed to undergo an anomalous along-track orbital
acceleration of -0.4 pm/s$^2$ (pm := picometer). This "drag" is not surprising;
along track drag has previously been observed with the related LAGEOS
satellites (-3.4 pm/s$^2$). It is hypothesized that the drag is principally due
to anisotropic thermal radiation from the satellite's exterior. We report the
results of numerical computations of the along-track orbital decay of the LARES
satellite during the first 126 days after launch. The results depend to a
significant degree on the visual and IR absorbance $\alpha$ and emissivity
$\epsilon$ of the fused silica Cube Corner Reflectors. We present results for
two values of $\alpha_{IR}$ = $\epsilon_{IR}$: 0.82, a standard number for
"clean" fused silica; and 0.60, a possible value for silica with slight surface
contamination subjected to the space environment. The heating and the resultant
along-track acceleration depend on the plane of the orbit, the sun position,
and in particular on the occurrence of eclipses, all of which are functions of
time. Thus we compute the drag for specific days. We compare our model to
observational data, available for a 120-day period starting with the 7th day
after launch, which shows the average acceleration of -0.4 pm/s$^2$. With our
model the average along-track drag over this 120-day period for CCR
$\alpha_{IR}$ = $\epsilon_{IR}$ = 0.82 was computed to be -0.59 pm/s$^2$. For
CCR $\alpha_{IR}$ = $\epsilon_{IR}$ = 0.60 we compute -0.36 pm/s$^2$. Thus, our
model demonstrates that most of the anomalous along-track acceleration can be
explained by thermal thrust, but there could be smaller contributions from
unmodelled effects.


  The field of quantum computing has experienced a rapid expansion in recent
years, with ongoing exploration of new technologies, a decrease in error rates,
and a growth in the number of qubits available in quantum processors. However,
near-term quantum algorithms are still unable to be induced without compounding
consequential levels of noise, leading to non-trivial erroneous results.
Quantum Error Correction and Mitigation are rapidly advancing areas of research
in the quantum computing landscape, with a goal of reducing errors. IBM has
recently emphasized that Quantum Error Mitigation is the key to unlocking the
full potential of quantum computing. A recent work, namely HAMMER, demonstrated
the existence of a latent structure regarding post-circuit induction errors
when mapping to the Hamming spectrum. However, they assumed that errors occur
solely in local clusters, whereas we observe that at higher average Hamming
distances this structure falls away. Our study demonstrates that the correlated
structure is not just limited to local patterns, but it also encompasses
certain non-local clustering patterns that can be accurately characterized
through a Poisson distribution model. This model takes into account the input
circuit, the current state of the device, including calibration statistics, and
the qubit topology. Using this quantum error characterizing model, we developed
an iterative algorithm over the generated Bayesian network state-graph for
post-induction error mitigation. Our Q-Beep approach delivers state-of-the-art
results, thanks to its problem-aware modeling of the error distribution's
underlying structure and the implementation of an Bayesian network state-graph.
This has resulted in an increase of up to 234.6% in circuit execution accuracy
on BV circuits and an average improvement of 71.0% in the quality of QAOA
solutions when tested on 16 IBMQ quantum processors.


  EuCd$_2$P$_2$ is notable for its unconventional transport: upon cooling the
metallic resistivity changes slope and begins to increase, ultimately 100-fold,
before returning to its metallic value. Surprisingly, this giant peak occurs at
18K, well above the N\'{e}el temperature ($T_N$) of 11.5K. Using a suite of
sensitive probes of magnetism, including resonant x-ray scattering and
magneto-optical polarimetry, we have discovered that ferromagnetic order onsets
above $T_N$ in the temperature range of the resistivity peak. The observation
of inverted hysteresis in this regime shows that ferromagnetism is promoted by
coupling of localized spins and itinerant carriers. The resulting carrier
localization is confirmed by optical conductivity measurements.


  Many ultra diffuse galaxies (UDGs) have now been identified in clusters of
galaxies. However, the number of nearby UDGs suitable for detailed follow-up
remain rare. Our aim is to begin to identify UDGs in the environments of nearby
bright early-type galaxies from the VEGAS survey. Here we use a deep g band
image of the NGC 5846 group, taken as part of the VEGAS survey, to search for
UDGs. We found one object with properties of a UDG if it associated with the
NGC 5846 group, which seems likely. The galaxy, we name NGC 5846$\_$UDG1, has
an absolute magnitude of M$_g$ = -14.2, corresponding to a stellar mass of
$\sim$10$^8$ M$_{\odot}$. It also reveals a system of compact sources which are
likely globular clusters. Based on the number of globular clusters detected we
estimate a halo mass that is greater than 8$\times$10$^{10}$ M$_{\odot}$ for
UDG1.


  We consider the notion of canonical attacks, which are the cryptographic
analog of the canonical forms of a one-mode Gaussian channel. Using this
notion, we explore the connections between the degradability properties of the
channel and its security for quantum key distribution. Finally, we also show
some relations between canonical attacks and optimal Gaussian cloners.


  Recently, many collaborative representation-based (CR) algorithms have been
proposed for hyperspectral anomaly detection. CR-based detectors approximate
the image by a linear combination of background dictionaries and the
coefficient matrix, and derive the detection map by utilizing recovery
residuals. However, these CR-based detectors are often established on the
premise of precise background features and strong image representation, which
are very difficult to obtain. In addition, pursuing the coefficient matrix
reinforced by the general $l_2$-min is very time consuming. To address these
issues, a nonnegative-constrained joint collaborative representation model is
proposed in this paper for the hyperspectral anomaly detection task. To extract
reliable samples, a union dictionary consisting of background and anomaly
sub-dictionaries is designed, where the background sub-dictionary is obtained
at the superpixel level and the anomaly sub-dictionary is extracted by the
pre-detection process. And the coefficient matrix is jointly optimized by the
Frobenius norm regularization with a nonnegative constraint and a sum-to-one
constraint. After the optimization process, the abnormal information is finally
derived by calculating the residuals that exclude the assumed background
information. To conduct comparable experiments, the proposed
nonnegative-constrained joint collaborative representation (NJCR) model and its
kernel version (KNJCR) are tested in four HSI data sets and achieve superior
results compared with other state-of-the-art detectors.


  The deuteron yield in Pb+Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV is
consistent with thermal production at a freeze-out temperature of $T = 155$
MeV. The existence of deuterons with binding energy of 2.2 MeV at this
temperature was described as "snowballs in hell". We provide a microscopic
explanation of this phenomenon, utilizing relativistic hydrodynamics and
switching to a hadronic afterburner at the above mentioned temperature of $T =
155$ MeV. The measured deuteron $p_T$-spectra and coalescence parameter
$B_2(p_T)$ are reproduced without free parameters, only by implementing
experimentally known cross-sections of deuteron reactions with hadrons, most
importantly $\pi d \leftrightarrow \pi n p$.


  In the paper we look for an elegant proof of the characterization of
relatively compact families in $L^p-$spaces. At first glance, the suggested
approach may seem convoluted and lengthy, but we spare no effort to argue that
our proof is in fact more natural than the ones existent in the literature. The
key idea is that the three properties which characterize relative compactness
in $L^p-$spaces ($L^p-$boundedness, $L^p-$equicontinuity and
$L^p-$equivanishing) are "preserved" (or rather "inherited") when the family
$\Ffamily\subset L^p$ is convolved with a continuous and compactly supported
function. This new family turns out to be relatively compact in $C_0-$space and
it remains to be demonstrated that relative compactness in $C_0-$space implies
relative compactness of the original family $\Ffamily.$


  We propose a picture for the magnetic properties of
superconductor/ferromagnet (S/F) heterostructures based on Nb and permalloy
(Py: Fe_{20}Ni_{80}). By measuring the magnetic moment as a function of the
temperature in S/F/S trilayers for different thicknesses of the middle F layer,
we give evidence of the presence of a magnetic stray field of the F layer. For
values of F-layer thickness below a threshold, we establish a correlation
between the magnetic measurements of the S/F/S trilayers and the anomalous
magnetic dependence of the critical current in S/insulator/thin superconducting
film/F/S (SIsFS) Josephson junctions (JJs). These complementary investigations
provide a self-consistent method to fully characterize S/F heterostructures and
possibly demonstrate effects arising from the mutual interactions between
ferromagnetism and superconductivity. A shift in the Fraunhofer critical
current oscillations has been observed in the opposite direction to the one
commonly observed in JJs with F barriers, as it has been recently predicted by
inverse and electromagnetic proximity theories. This inverse memory effect is
relevant for the design of these heterostructures as memory cells and
spintronic devices.


  Let $X_t^\sharp$ be a multivariate process of the form $X_t =Y_t - Z_t$,
$X_0=x$, killed at some terminal time $T$, where $Y_t$ is a Markov process
having only jumps of the length smaller than $\delta$, and $Z_t$ is a compound
Poisson process with jumps of the length bigger than $\delta$ for some fixed
$\delta>0$. Under the assumptions that the summands in $Z_t$ are
sub-exponential, we investigate the asymptotic behaviour of the potential
function $u(x)= E^x \int_0^\infty \ell(X_s^\sharp)ds$. The case of heavy-tailed
entries in $Z_t$ corresponds to the case of "big claims" in insurance models
and is of practical interest. The main approach is based on fact that $u(x)$
satisfies a certain renewal equation.


  In this paper we refine a conjecture relating the time-dependent size of an
Einstein-Rosen bridge to the computational complexity of the of the dual
quantum state. Our refinement states that the complexity is proportional to the
spatial volume of the ERB. More precisely, up to an ambiguous numerical
coefficient, we propose that the complexity is the regularized volume of the
largest codimension one surface crossing the bridge, divided by $G_N l_{AdS}$.
We test this conjecture against a wide variety of spherically symmetric shock
wave geometries in different dimensions. We find detailed agreement.


  We solve the quantum Vlasov equation for fermions and bosons, incorporating
spontaneous pair creation in the presence of back-reactions and collisions.
Pair creation is initiated by an external impulse field and the source term is
non-Markovian. A simultaneous solution of Maxwell's equation in the presence of
feedback yields an internal current and electric field that exhibit plasma
oscillations with a period tau_pl. Allowing for collisions, these oscillations
are damped on a time-scale, tau_r, determined by the collision frequency.
Plasma oscillations cannot affect the early stages of the formation of a
quark-gluon plasma unless tau_r >> tau_pl and tau_pl approx. 1/Lambda_QCD
approx 1 fm/c.


  In robotics, there is need of an interactive and expedite learning method as
experience is expensive. Robot Learning from Demonstration (RLfD) enables a
robot to learn a policy from demonstrations performed by teacher. RLfD enables
a human user to add new capabilities to a robot in an intuitive manner, without
explicitly reprogramming it. In this work, we present a novel interactive
framework, where a collaborative robot learns skills for trajectory based tasks
from demonstrations performed by a human teacher. The robot extracts features
from each demonstration called as key-points and learns a model of the
demonstrated skill using Hidden Markov Model (HMM). Our experimental results
show that the learned model can be used to produce a generalized trajectory
based skill.


  The effect of intermolecular interactions on intensity redistribution between
individual electronic transitions in different polymorphs of fluorene and
dibenzofuran was studied by transmittance electronic spectroscopy in the energy
range from onset of absorption to the ionization energy (from ~4.0 eV to ~9.0
eV - Electronic transitions with transition dipoles at oblique angles to the
crystallographic axes (A1 symmetry) in these crystals resemble molecular
spectra. Transitions of B2 symmetry, which in these crystals have parallel
transition dipoles, resemble molecular spectra for surface (substrate
influenced) states. For bulk excitonic states the spectra of these transitions
have a continuous, uniform intensity distribution in the whole energy range
investigated, except for the case when the B2 transition is the onset of
absorption.


  Fully connected multilayer perceptrons are used for obtaining numerical
solutions of partial differential equations in various dimensions. Independent
variables are fed into the input layer, and the output is considered as
solution's value. To train such a network one can use square of equation's
residual as a cost function and minimize it with respect to weights by gradient
descent. Following previously developed method, derivatives of the equation's
residual along random directions in space of independent variables are also
added to cost function. Similar procedure is known to produce nearly machine
precision results using less than 8 grid points per dimension for 2D case. The
same effect is observed here for higher dimensions: solutions are obtained on
low density grids, but maintain their precision in the entire region. Boundary
value problems for linear and nonlinear Poisson equations are solved inside 2,
3, 4, and 5 dimensional balls. Grids for linear cases have 40, 159, 512 and
1536 points and for nonlinear 64, 350, 1536 and 6528 points respectively. In
all cases maximum error is less than $8.8\cdot10^{-6}$, and median error is
less than $2.4\cdot10^{-6}$. Very weak grid requirements enable neural networks
to obtain solution of 5D linear problem within 22 minutes, whereas projected
solving time for finite differences on the same hardware is 50 minutes. Method
is applied to second order equation, but requires little to none modifications
to solve systems or higher order PDEs.


  I discuss the existence of discrete properties in the landscape of free
fermionic heterotic-string vacua that were discovered via their classification
by SO(10) GUT models and its subgroups such as the Pati-Salam, Flipped SU(5)
and SU(4) x SU(2) x U(1) models. The classification is carried out by fixing a
set of basis vectors and varying the GGSO projection coefficients entering the
one-loop partition function. The analysis of the models is facilitated by
deriving algebraic expressions for the GSO projections that enable a
computerised analysis of the entire string spectrum and the scanning of large
spaces of vacua. The analysis reveals discrete symmetries like the
spinor-vector duality observed at the SO(10) level and the existence of
exophobic Pati-Salam vacua. Contrary to the Pati-Salam case the classification
shows that there are no exophobic Flipped SU(5) vacua with an odd number of
generations. It is observed that the SU(4) x SU(2) x U(1) models are
substantially more constrained.


  In this article we prove that, restricted to the row-constrained case,
effective sets of colourings in the $2$-fold horocyclic tessellations of the
hyperbolic plane $\mathbb{H}^2$ are sofic.


  Quantifying the resilience in the food system is important for food security
issues. In this work, we present a geospatial knowledge graph (GeoKG)-based
method for measuring the resilience of a multi-commodity flow network.
Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics
of a multi-commodity flow network comprehensively, and design resilience
metrics that measure the node-level and network-level dependence of
single-sourcing, distant, or non-adjacent suppliers/customers in food supply
chains. We conduct a case study of the US state-level agricultural
multi-commodity flow network with hierarchical commodity types. The results
indicate that, by leveraging GeoKG, our method supports measuring both
node-level and network-level resilience across space and over time and also
helps discover concentration patterns of agricultural resources in the spatial
network at different geographic scales.


  We study a trapped system of fermions with an attractive zero-range two-body
interaction using the Shell-Model Monte Carlo method. The method provides {\em
ab initio} results in the low $N$ limit where mean-field theory is not
applicable. The energy and pairing properties are presented as functions of
interaction strength, particle number, and temperature. In the interesting
region where typical matrix elements of the two-body interaction are comparable
to the level spacing of the trap we find large odd-even effects and signatures
of shell structure. As a function of temperature, we observe the disappearance
of these effects as in a phase transition.


  The stability of ecosystems as well as the relation between topology and
dynamics on multilayer networks are important questions that are usually
discussed in separate communities. Here, we combine these two topics by
investigating the influence of the topology of the migration network on the
stability of a four-species foodweb module on six patches. The parameters are
chosen such that the dynamics on an isolated patch have a periodic attractor
with all four species present as well as an attractor where the prey that is
preferred by the top predator dies out. The stability measure used here is
robustness, which is the average proportion of surviving species in the system,
and which shows a complex dependence on the migration rate. We use principal
component analysis to quantify the migration network structure in terms of the
most relevant network measures, and we evaluate correlations between these
measures and characteristics of the robustness curves. Our most important
findings are that higher connectivity of the migration network leads to a
larger maximum robustness, that a broad distribution of connectivities favors
extinction of the preferred prey at intermediate migration rates, and that
migration topologies with a larger betweenness centrality are more prone to
extinction of the preferred prey at the onset of synchronization. Our study
thus demonstrates a strong correlation between dynamical robustness and spatial
topology and can serve as an example for similar studies in other types of
multilayer networks.


  We introduce the $2$-simplicial Transformer, an extension of the Transformer
which includes a form of higher-dimensional attention generalising the
dot-product attention, and uses this attention to update entity representations
with tensor products of value vectors. We show that this architecture is a
useful inductive bias for logical reasoning in the context of deep
reinforcement learning.


  We present a new technique called "quantile analysis" to classify spectral
properties of X-ray sources with limited statistics. The quantile analysis is
superior to the conventional approaches such as X-ray hardness ratio or X-ray
color analysis to study relatively faint sources or to investigate a certain
phase or state of a source in detail, where poor statistics does not allow
spectral fitting using a model. Instead of working with predetermined energy
bands, we determine the energy values that divide the detected photons into
predetermined fractions of the total counts such as median (50%), tercile (33%
& 67%), and quartile (25% & 75%). We use these quantiles as an indicator of the
X-ray hardness or color of the source. We show that the median is an improved
substitute for the conventional X-ray hardness ratio. The median and other
quantiles form a phase space, similar to the conventional X-ray color-color
diagrams. The quantile-based phase space is more evenly sensitive over various
spectral shapes than the conventional color-color diagrams, and it is naturally
arranged to properly represent the statistical similarity of various spectral
shapes. We demonstrate the new technique in the 0.3-8 keV energy range using
Chandra ACIS-S detector response function and a typical aperture photometry
involving background subtraction. The technique can be applied in any energy
band, provided the energy distribution of photons can be obtained.


  The recent astronomical observation of GeV gamma-ray excess from the Galactic
Center was suggested due to a $b \bar{b}$ mode in tens GeV WIMP (weakly
interacting massive particle) pair annihilations, and this mode was also
explored by the new dwarf galaxy observation. Considering the case the WIMP
pair mass below top quark mass, a pseudoscalar $\phi$ is studied in this
article, which mediates the interactions between the standard model fermions
and fermionic WIMPs, and neutral flavor-changing interactions in standard model
fermion sectors. The $b \bar{b}$ mode is favored in WIMP pair annihilations,
while the WIMP-nucleus scattering is highly suppressed in direct detection.
Alternative schemes of $t \rightarrow c$ decay and single top production are
employed to search WIMPs. Assuming the mass of WIMP around 5-60 GeV and with
the reasonable inputs by the constraints, the branching ratio $\mathcal
{B}_{t\rightarrow c \bar{\chi} \chi}$ of a top quark decaying into a charm
quark and a WIMP pair is derived of order $10^{-8} - 10^{-5}$, thus careful
studies in the future on top-physics may help to gain a better understanding of
WIMPs.


  Rydberg atom-based radio frequency electromagnetic field sensors are drawing
wide-spread interest because of their unique properties, such as small size,
dielectric construction, and self-calibration. These photonic sensors use
lasers to prepare atoms and read out the atomic response to a radio frequency
electromagnetic field based on electromagnetically induced transparency, or
related phenomena. Much of the theoretical work has focused on the
Autler-Townes splitting induced by the radio frequency wave. The amplitude
regime, where the change in transmission observed on resonance is measured to
determine electric field strength, has received less attention. In this paper,
we deliver analytic expressions that are useful for calculating the absorption
coefficient and sensitivity in the amplitude regime. We describe the
approximations that we applied to obtain the analytic expressions and
demonstrate their validity over a large range of the interesting parameter
space. The effect of the thermal motion of the atoms is explicitly addressed.
Residual Doppler shifts are shown to limit sensitivity. An analytic expression
for the amplitude regime of Rydberg atom-based sensing has not, to our
knowledge, been obtained previously. The expressions, approximations and
descriptions presented in the paper are important for maximizing the
sensitivity of Rydberg atom-based sensors and for providing insight into the
physics of multi-level interference phenomena.


  Low-rank tensors are an established framework for high-dimensional
least-squares problems. We propose to extend this framework by including the
concept of block-sparsity. In the context of polynomial regression each
sparsity pattern corresponds to some subspace of homogeneous multivariate
polynomials. This allows us to adapt the ansatz space to align better with
known sample complexity results. The resulting method is tested in numerical
experiments and demonstrates improved computational resource utilization and
sample efficiency.


  The current mediated domain-wall dynamics in a thin ferromagnetic wire is
investigated. We derive the effective equations of motion of the domain wall.
They are used to study the possibility to optimize the power supplied by
electric current for the motion of domain walls in a nanowire. We show that a
certain resonant time-dependent current moving a domain wall can significantly
reduce the Joule heating in the wire, and thus it can lead to a novel proposal
for the most energy efficient memory devices. We discuss how Gilbert damping,
non-adiabatic spin transfer torque, and the presence of Dzyaloshinskii-Moriya
interaction can effect this power optimization.


  One of the key objectives of the COMPASS experiment at CERN is the
determination of the gluon contribution to the nucleon spin. The gluon
polarization is measured via photon-gluon fusion in deep-inelastic scattering
of 160 GeV/c polarized muons on a polarized 6LiD solid-state target.
Photon-gluon fusion is tagged by the observation of charmed mesons or the
production of hadron pairs with large transverse momenta p_t. The status of the
analysis of the D0 and D0* events and of the high-p_t hadron pairs is shown.
The gluon polarization \Delta G/G has been determined from the asymmetry of
high-p_t hadron pairs with Q^2>1 GeV/c^2 in an analysis of the 2002/03 data as
\Delta G/G=0.06 \pm 0.31 (stat.) \pm 0.06 (syst.).


  We investigated the electronic structures of the two-dimensional layered
perovskite Sr$_{2}$\textit{M}O$_{4}$ (\textit{M}=4\textit{d} Ru, 4\textit{d}
Rh, and 5\textit{d} Ir) using optical spectroscopy and polarization-dependent O
1\textit{s} x-ray absorption spectroscopy. While the ground states of the
series of compounds are rather different, their optical conductivity spectra
$\sigma(\omega)$ exhibit similar interband transitions, indicative of the
common electronic structures of the 4\textit{d} and 5\textit{d} layered oxides.
The energy splittings between the two $e_{g}$ orbitals, $i.e.$,
$d_{3z^{2}-r^{2}}$ and $d_{x^{2}-y^{2}}$, are about 2 eV, which is much larger
than those in the pseudocubic and 3\textit{d} layered perovskite oxides. The
electronic properties of the Sr$_{2}$\textit{M}O$_{4}$ compounds are discussed
in terms of the crystal structure and the extended character of the 4\textit{d}
and 5\textit{d} orbitals.


  We prove the optimal strong convergence rate of a fully discrete scheme,
based on a splitting approach, for a stochastic nonlinear Schr\"odinger (NLS)
equation. The main novelty of our method lies on the uniform a priori estimate
and exponential integrability of a sequence of splitting processes which are
used to approximate the solution of the stochastic NLS equation. We show that
the splitting processes converge to the solution with strong order $1/2$. Then
we use the Crank--Nicolson scheme to temporally discretize the splitting
process and get the temporal splitting scheme which also possesses strong order
$1/2$. To obtain a full discretization, we apply this splitting Crank--Nicolson
scheme to the spatially discrete equation which is achieved through the
spectral Galerkin approximation. Furthermore, we establish the convergence of
this fully discrete scheme with optimal strong convergence rate
$\mathcal{O}(N^{-2}+\tau^\frac12)$, where $N$ denotes the dimension of the
approximate space and $\tau$ denotes the time step size. To the best of our
knowledge, this is the first result about strong convergence rates of
temporally numerical approximations and fully discrete schemes for stochastic
NLS equations, or even for stochastic partial differential equations (SPDEs)
with non-monotone coefficients. Numerical experiments verify our theoretical
result.


  The total energy distributions (TEDs) of the emission currents in field
emission and surface photofield emission and the overlayer-induced
modifications in the surface electronic structures from the technologically
important W surfaces with the commensurate W(100)/Na c(2x2), W(110)/Na (2x2)
and W(111)/Na (1x1) overlayers are calculated. The TEDs obtained by our recent
numerical method that extends the full-potential linear augmented plane wave
method for the electronic structures to the study of field and photofield
emission are used to interpret the shifts of the peaks in the experimental TEDs
in field emission and photofield emission from the W(100) and W(110) surfaces
at sub-monolayer and monolayer Na coverage. Hybridization of the 3s Na states
with the pairs of dz2-like surface states of the strong Swanson hump in clean
W(100) and surface resonances in clean W(111) below the Fermi energy shifts
these W states by about -1.2 eV and -1.0 eV, thus stabilizing these states, to
yield new strong peaks in the TEDs in field emission and photofield emission
from W(100)/Na c(2x2) and W(111)/Na (1x1) respectively. The effect of Na
intralayer interactions are discussed and are shown to shift the strong s- and
p-like peaks in the surface density of states of W(110) below and above the
Fermi energy respectively to lower energy with increased Na coverage, in
agreement with experiments.


  We present a method to calibrate a high-resolution wavefront-correcting
device with a single, static camera, located in the focal plane; no moving of
any component is needed. The method is based on a localized diversity and
differential optical transfer functions (dOTF) to compute both the phase and
amplitude in the pupil plane located upstream of the last imaging optics. An
experiment with a spatial light modulator shows that the calibration is
sufficient to robustly operate a focal-plane wavefront sensing algorithm
controlling a wavefront corrector with ~40 000 degrees of freedom. We estimate
that the locations of identical wavefront corrector elements are determined
with a spatial resolution of 0.3% compared to the pupil diameter.


  In this paper, we consider induced subgraphs of the Hamming graph $H(n,3)$.
We show that if $U \subseteq \mathbb{Z}_3^n$ and $U$ induces a subgraph of
$H(n,3)$ with maximum degree at most $1$ then
  1. If $U$ is disjoint from a maximum size independent set of $H(n,3)$ then
$|U| \leq 3^{n-1}+1$. Moreover, all such $U$ with size $3^{n-1}+1$ are
isomorphic to each other.
  2. For $n \geq 6$, there exists such a $U$ with size $|U| = 3^{n-1}+18$ and
this is optimal for $n = 6$.
  3. If $U \cap \{x, x+e_1, x+2e_1\} \ne \phi$ for all $x \in \mathbb{Z}_3^n$
then $|U| \leq 3^{n-1} + 729$.


  We analyze the Optimal Channel Network model for river networks using both
analytical and numerical approaches. This is a lattice model in which a
functional describing the dissipated energy is introduced and minimized in
order to find the optimal configurations. The fractal character of river
networks is reflected in the power law behaviour of various quantities
characterising the morphology of the basin. In the context of a finite size
scaling Ansatz, the exponents describing the power law behaviour are calculated
exactly and show mean field behaviour, except for two limiting values of a
parameter characterizing the dissipated energy, for which the system belongs to
different universality classes. Two modified versions of the model,
incorporating quenched disorder are considered: the first simulates
heterogeneities in the local properties of the soil, the second considers the
effects of a non-uniform rainfall. In the region of mean field behaviour, the
model is shown to be robust to both kinds of perturbations. In the two limiting
cases the random rainfall is still irrelevant, whereas the heterogeneity in the
soil properties leads to new universality classes. Results of a numerical
analysis of the model are reported that confirm and complement the theoretical
analysis of the global minimum. The statistics of the local minima are found to
more strongly resemble observational data on real rivers.


  The continuity method is used to deform the cone angle of a weak conical
K\"ahler-Einstein metric with cone singularities along a smooth anti-canonical
divisor on a smooth Fano manifold. This leads to an alternative proof of
Donaldson's Openness Theorem on deforming cone angle \cite{Don} by combining it
with the regularity result of Guenancia-P$\breve{\text{a}}$un \cite{GP} and
Chen-Wang \cite{CW}. This continuity method uses relatively less regularity of
the metric (only weak conical K\"ahler-Einstein) and bypasses the difficult
Banach space set up; it is also generalized to deform the cone angles of a
\emph{weak conical K\"ahler-Einstein metric} along a simple normal crossing
divisor (pluri-anticanonical) on a smooth Fano manifold (assuming no tangential
holomorphic vector fields).


  Our previous experience building systems for middlebox chain composition and
scaling in software-defined networks has revealed that existing mechanisms of
flow annotation commonly do not survive middlebox-traversals, or suffer from
extreme identifier domain limitations resulting in excessive flow table size.
In this paper, we analyze the structural artifacts resulting in these
challenges, and offer a framework for describing the behavior of middleboxes
based on actions taken on traversing packets. We then present a novel mechanism
for flow annotation that features an identifier domain significantly larger
than existing techniques, that is transparent to hosts traversed, and that
conserves flow-table resources by requiring only a small number of match rules
and actions in most switches. We evaluate said technique, showing that it
requires less per-switch state than conventional techniques. We then describe
extensions allowing implementation of this architecture within a broader class
of systems. Finally, we close with architectural suggestions for enabling
straightforward integration of middleboxes within software-defined networks.


  We implement the Gravitational Decoupling through the Minimal Geometric
Deformation method and explore its effect on exterior solutions by imposing a
regularity condition in the Tolman--Oppenheimer--Volkoff equation of the
decoupling sector. We obtain that the decoupling function can be expressed
formally in terms of an integral involving the $g_{tt}$ component of the metric
of the seed solution. As a particular example, we implement the method by using
the Schwarzschild exterior as a seed and we obtain that the asymptotic behavior
of the extended geometry corresponds to a manifold with constant curvature.


  With the advanced gravitational wave detectors coming on line in the next 5
years, we expect to make the first detections of gravitational waves from
astrophysical sources, and study the properties of the waves themselves as
tests of General Relativity. In addition, these gravitational waves will be
powerful tools for the study of their astrophysical sources and source
populations. They carry information that is quite complementary to what can be
learned from electromagnetic or neutrino observations, probing the central
gravitational engines that power the electromagnetic emissions. Preparations
are being made to enable near-simultaneous observations of both gravitational
wave and electromagnetic observations of transient sources, using low-latency
search pipelines and rapid sky localization. We will review the many
opportunities for multi-messenger astronomy and astrophysics with gravitational
waves enabled by the advanced detectors, and the preparations that are being
made to quickly and fully exploit them.


  We generalize the observable diameter and the separation distance for metric
measure spaces to those for pyramids, and prove some limit formulas for these
invariants for a convergent sequence of pyramids. We obtain various
applications of our limit formulas as follows. We have a criterion of the phase
transition property for a sequence of metric measure spaces or pyramids, and
find some examples of symmetric spaces of noncompact type with the phase
transition property. We also give a simple proof of a theorem by Funano-Shioya
on the limit of an $N$-L\'evy family.


  Let $r\geq 1$ be an integer, $\mathbf a=(a_1,\ldots,a_r)$ a vector of
positive integers and let $D\geq 1$ be a common multiple of $a_1,\ldots,a_r$.
In a continuation of a previous paper we prove that, if $D=1$ or $D$ is a prime
number, the restricted partition function $p_{\mathbf a}(n): = $ the number of
integer solutions $(x_1,\dots,x_r)$ to $\sum_{j=1}^r a_jx_j=n$ with $x_1\geq 0,
\ldots, x_r\geq 0$ can be computed by solving a system of linear equations with
coefficients which are values of Bernoulli polynomials and Bernoulli Barnes
numbers.


  In this paper, the G\"{o}del-type solutions within the k-essence theory are
investigated. The consistency of field equations, causality violation and
existence of closed timelike curves are studied. The conditions for existence
of G\"{o}del and G\"{o}del-type solutions are determined for any function that
defines the k-essence. By taking different sources of matter, it is shown that
this theory of gravity allows causal and non-causal G\"{o}del-type solutions.


  We study the continuity of the maximum-entropy inference map for two
observables in finite dimensions. We prove that the continuity is equivalent to
the strong continuity of the set-valued inverse numerical range map. This gives
a continuity condition in terms of analytic eigenvalue functions which implies
that discontinuities are very rare. It shows also that the continuity of the
MaxEnt inference method is independent of the prior state.


  Recent interest on permutation rank modulation shows the Kendall tau metric
as an important distance metric. This note documents our first efforts to
obtain upper bounds on optimal code sizes (for said metric) ala Delsarte's
approach. For the Hamming metric, Delsarte's seminal work on powerful linear
programming (LP) bounds have been extended to permutation codes, via
association scheme theory. For the Kendall tau metric, the same extension needs
the more general theory of coherent configurations, whereby the optimal code
size problem can be formulated as an extremely huge semidefinite programming
(SDP) problem. Inspired by recent algebraic techniques for solving SDP's, we
consider the dual problem, and propose an LP to search over a subset of dual
feasible solutions. We obtain modest improvement over a recent Singleton bound
due to Barg and Mazumdar. We regard this work as a starting point, towards
fully exploiting the power of Delsarte's method, which are known to give some
of the best bounds in the context of binary codes.


  The ground state of closed Heisenberg spin chains with an odd number of sites
has a chiral degeneracy, in addition to a two-fold Kramers degeneracy. A
non-zero chirality implies that the spins are not coplanar, and is a measure of
handedness. The chirality operator, which can be treated as a spin-1/2
operator, is explicitly constructed in terms of the spin operators, and is
given as commutator of Permutation operators.


  Let $p$ be a prime number, $K$ a finite unramified extension of
$\mathbb{Q}_p$ and $\mathbb{F}$ a finite extension of $\mathbb{F}_p$. For
$\overline{\rho}$ any reducible two-dimensional representation of
$\operatorname{Gal}(\overline{K}/K)$ over $\mathbb{F}$, we compute explicitly
the associated \'etale $(\varphi,\mathcal{O}_K^{\times})$-module
$D_A^{\otimes}(\overline{\rho})$ defined by Breuil-Herzig-Hu-Morra-Schraen.
Then we let $\pi$ be an admissible smooth representation of
$\operatorname{GL}_2(K)$ over $\mathbb{F}$ occurring in some Hecke eigenspaces
of the mod $p$ cohomology and $\overline{\rho}$ be its underlying
two-dimensional representation of $\operatorname{Gal}(\overline{K}/K)$ over
$\mathbb{F}$. Assuming that $\overline{\rho}$ is maximally non-split, we prove
under some genericity assumption that the associated \'etale
$(\varphi,\mathcal{O}_K^{\times})$-module $D_A(\pi)$ defined by
Breuil-Herzig-Hu-Morra-Schraen is isomorphic to
$D_A^{\otimes}(\overline{\rho})$. This extends the results of
Breuil-Herzig-Hu-Morra-Schraen, where $\overline{\rho}$ was assumed to be
semisimple.


  The mean-field pictures based on the standard time-dependent variational
approach have widely been used in the study of nonlinear many-boson systems
such as the Bose-Hubbard model. The mean-field schemes relevant to
Gutzwiller-like trial states $|F>$, number-preserving states $|\xi >$ and
Glauber-like trial states $|Z>$ are compared to evidence the specific
properties of such schemes. After deriving the Hamiltonian picture relevant to
$|Z>$ from that based on $|F>$, the latter is shown to exhibit a Poisson
algebra equipped with a Weyl-Heisenberg subalgebra which preludes to the
$|Z>$-based picture. Then states $|Z>$ are shown to be a superposition of $\cal
N$-boson states $|\xi>$ and the similarities/differences of the $|Z>$-based and
$|\xi>$-based pictures are discussed. Finally, after proving that the simple,
symmetric state $|\xi>$ indeed corresponds to a SU(M) coherent state, a dual
version of states $|Z>$ and $|\xi>$ in terms of momentum-mode operators is
discussed together with some applications.


  We investigate the stability of N\'eel skyrmions against tilted magnetic
fields, in polar magnets with uniaxial anisotropy ranging from easy-plane to
easy-axis type. We construct the corresponding phase diagrams and investigate
the internal structure of skewed skyrmions with displaced cores. We find that
moderate easy-plane anisotropy increases the stability range of N\'eel
skyrmions for fields along the symmetry axis, while moderate easy-axis
anisotropy enhances their robustness against tilted magnetic fields. We stress
that the direction, along which the skyrmion cores are shifted, depends on the
symmetry of the underlying crystal lattice. The cores of N\'eel skyrmions,
realized in polar magnets with C$_{nv}$ symmetry, are displaced either along or
opposite to the off-axis (in-plane) component of the magnetic field depending
on the rotation sense of the magnetization, dictated by the sign of the
Dzyaloshinskii constant. The core shift of antiskyrmions, present in chiral
magnets with D$_{2d}$ symmetry, depends on the in-plane orientation of the
magnetic field and can be parallel, anti-parallel, or perpendicular to it. We
argue that the role of anisotropy in magnets with axially symmetric crystal
structure is different from that in cubic helimagnets. Our results can be
applied to address recent experiments on polar magnets with C$_{3v}$ symmetry,
GaV$_4$S$_8$ and GaV$_4$Se$_8$.


  We investigate the effect of polymer additives on a two-dimensional
Kolmogorov flow at very low Reynolds numbers by direct numerical simulations of
the Oldroyd-B viscoelastic model. We find that above the elastic instability
threshold the flow develops the elastic turbulence regime recently observed in
experiments. We observe that both the turbulent drag and the Lyapunov exponent
increase with Weissenberg, indicating the presence of a disordered,
turbulent-like mixing flow. The energy spectrum develops a power-law scaling
range with an exponent close to the experimental and theoretical expectations.


  Graphs are used to model interactions in a variety of contexts, and there is
a growing need to quickly assess the structure of a graph. Some of the most
useful graph metrics, especially those measuring social cohesion, are based on
triangles. Despite the importance of these triadic measures, associated
algorithms can be extremely expensive. We propose a new method based on wedge
sampling. This versatile technique allows for the fast and accurate
approximation of all current variants of clustering coefficients and enables
rapid uniform sampling of the triangles of a graph. Our methods come with
provable and practical time-approximation tradeoffs for all computations. We
provide extensive results that show our methods are orders of magnitude faster
than the state-of-the-art, while providing nearly the accuracy of full
enumeration. Our results will enable more wide-scale adoption of triadic
measures for analysis of extremely large graphs, as demonstrated on several
real-world examples.


  The lepton flavor violating $Z^{\prime}\to\tau\mu$ decay is studied in the
context of several extended models that predict the existence of the new gauge
boson named $Z^\prime$. A calculation of the strength of the lepton flavor
violating $Z^\prime\mu\tau$ coupling is presented by using the most general
renormalizable Lagrangian that includes lepton flavor violation. We used the
experimental value of the muon magnetic dipole moment to bound this coupling,
from which the $\mathrm{Re}(\Omega_{L\mu\tau}\Omega^\ast_{R\mu\tau})$ parameter
is constrained and it is found that
$\mathrm{Re}(\Omega_{L\mu\tau}\Omega^\ast_{R\mu\tau})\sim 10^{-2}$ for a
$Z^\prime$ boson mass of 2 TeV. Alongside, we employed the experimental
restrictions over the $\tau\to\mu\gamma$ and $\tau\to\mu\mu^+\mu^-$ processes
in the context of several models that predict the existence of the $Z^\prime$
gauge boson to bound the mentioned coupling. The most restrictive bounds come
from the calculation of the three-body decay. For this case, it was found that
the most restrictive result is provided by a vector-like coupling, denoted as
$|\Omega_{\mu\tau}|^2$, for the $Z_\chi$ case, finding around $10^{-2}$ for a
$Z^\prime$ boson mass of 2 TeV. We used this information to estimate the
branching ratio for the $Z^\prime\to\tau\mu$ decay. According to the analyzed
models the least optimistic result is provided by the Sequential $Z$ model,
which is of the order of $10^{-2}$ for a $Z^\prime$ boson mass around 2 TeV.


  We have studied detailed nucleosynthesis in the shocked surface layers of an
Oxygen-Neon-Magnesium core collapse supernova with an eye to determining if the
conditions are suitable for r process nucleosynthesis. We find no such
conditions in an unmodified model, but do find overproduction of N=50 nuclei
(previously seen in early neutron-rich neutrino winds) in amounts that, if
ejected, would pose serious problems for galactic chemical evolution.


  Complex Event Processing (CEP) is an emerging field with important
applications in many areas. CEP systems collect events arriving from input data
streams and use them to infer more complex events according to predefined
patterns. The Non-deterministic Finite Automaton (NFA) is one of the most
popular mechanisms on which such systems are based. During the event detection
process, NFAs incrementally extend previously observed partial matches until a
full match for the query is found. As a result, each arriving event needs to be
processed to determine whether a new partial match is to be initiated or an
existing one extended. This method may be highly inefficient when many of the
events do not result in output matches. We present a lazy evaluation mechanism
that defers processing of frequent event types and stores them internally upon
arrival. Events are then matched in ascending order of frequency, thus
minimizing potentially redundant computations. We introduce a Lazy Chain NFA,
which utilizes the above principle, and does not depend on the underlying
pattern structure. An algorithm for constructing a Lazy Chain NFA for common
pattern types is presented, including conjunction, negation and iteration.
Finally, we experimentally evaluate our mechanism on real-world stock trading
data. The results demonstrate a performance gain of two orders of magnitude
over traditional NFA-based approaches, with significantly reduced memory
resource requirements.


  We develop a sound, complete and practically implementable tableaux-based
decision method for constructive satisfiability testing and model synthesis in
the fragment ATL+ of the full Alternating time temporal logic ATL*. The method
extends in an essential way a previously developed tableaux-based decision
method for ATL and works in 2EXPTIME, which is the optimal worst case
complexity of the satisfiability problem for ATL+ . We also discuss how
suitable parametrizations and syntactic restrictions on the class of input ATL+
formulae can reduce the complexity of the satisfiability problem.


  A five dimensional Sasaki-Einstein (SE) manifold provides a AdS/CFT pair for
four dimensional $\mathcal{N}=1$ SCFT, and those pairs are very useful in
studying field theory and AdS/CFT correspondence. The space of known SE
manifolds is increased significantly in the last decade, and we initiated the
study of various field theory properties through the geometric property of
these new SE manifolds. There is an associated three dimensional log-terminal
singularity $X$ for each SE manifold $L_X$, and for quasi-regular case, there
is an associated two dimensional log Del Pezzo surface $(S,\Delta)$. The
algebraic geometrical methods are quite useful in extracting interesting
physical properties from singularity and log Del Pezzo surface. The necessary
and sufficient condition for the existence of SE metric on $L_X$ is related to
K stability of $X$. Motivated by dual field theory, we propose a conjecture on
how to reduce the check of K stability to possibly finite cases, which
hopefully would give us a guideline to find a much larger space of SE metrics.


  In this paper we give an asymptotic expansion of the Bergman kernel for
certain weakly pseudoconvex tube domains of finite type in C^2. Our asymptotic
formula asserts that the singularity of the Bergman kernel at weakly
pseudoconvex points is essentially expressed by using two variables; moreover
certain real blowing-up is necessary to understand its singularity. The form of
the asymptotic expansion with respect to each variable is similar to that in
the strictly pseudoconvex case due to C. Fefferman. We also give an analogous
result in the case of the Szego kernel.


  The Voice over Internet Protocol (VoIP) is becoming a more available and
popular way of communicating for Internet users. This also applies to
Peer-to-Peer (P2P) systems and merging these two have already proven to be
successful (e.g. Skype). Even the existing standards of VoIP provide an
assurance of security and Quality of Service (QoS), however, these features are
usually optional and supported by limited number of implementations. As a
result, the lack of mandatory and widely applicable QoS and security guaranties
makes the contemporary VoIP systems vulnerable to attacks and network
disturbances. In this paper we are facing these issues and propose the SecMon
system, which simultaneously provides a lightweight security mechanism and
improves quality parameters of the call. SecMon is intended specially for VoIP
service over P2P networks and its main advantage is that it provides
authentication, data integrity services, adaptive QoS and (D)DoS attack
detection. Moreover, the SecMon approach represents a low-bandwidth consumption
solution that is transparent to the users and possesses a self-organizing
capability. The above-mentioned features are accomplished mainly by utilizing
two information hiding techniques: digital audio watermarking and network
steganography. These techniques are used to create covert channels that serve
as transport channels for lightweight QoS measurement's results. Furthermore,
these metrics are aggregated in a reputation system that enables best route
path selection in the P2P network. The reputation system helps also to mitigate
(D)DoS attacks, maximize performance and increase transmission efficiency in
the network.


  Higher-order topological insulators, as a novel family of topological phases,
are a hot frontier in condensed matter physics due to their adherence to
unconventional bulk-boundary correspondence. A three-dimensional second-order
topological insulator can support one-dimensional modes along its hinges
(dubbed as hinge states). Here, we present a simple and direct method to
construct chiral hinge modes based on a Chern-insulator stack. We analyze the
existence of the hinge modes through the nontrivial quadrupole indices, and
then design a photonic crystal to realize the specific flowing pattern of the
hinge mode in our model. The experimental results align well with full-wave
simulations, clearly demonstrating the existence of chiral hinge states. We
also verify the robustness of these hinge states against defects in our
photonic system.


  We consider the interaction among agents engaging in a driving task and we
model it as general-sum game. This class of games exhibits a plurality of
different equilibria posing the issue of equilibrium selection. While selecting
the most efficient equilibrium (in term of social cost) is often impractical
from a computational standpoint, in this work we study the (in)efficiency of
any equilibrium players might agree to play. More specifically, we bound the
equilibrium inefficiency by modeling driving games as particular type of
congestion games over spatio-temporal resources. We obtain novel guarantees
that refine existing bounds on the Price of Anarchy (PoA) as a function of
problem-dependent game parameters. For instance, the relative trade-off between
proximity costs and personal objectives such as comfort and progress. Although
the obtained guarantees concern open-loop trajectories, we observe efficient
equilibria even when agents employ closed-loop policies trained via
decentralized multi-agent reinforcement learning.


  Motivated by a recent experimental report[1] claiming the likely observation
of the Majorana mode in a semiconductor-superconductor hybrid
structure[2,3,4,5], we study theoretically the dependence of the zero bias
conductance peak associated with the zero-energy Majorana mode in the
topological superconducting phase as a function of temperature, tunnel barrier
potential, and a magnetic field tilted from the direction of the wire for
realistic wires of finite lengths. We find that higher temperatures and tunnel
barriers as well as a large magnetic field in the direction transverse to the
wire length could very strongly suppress the zero-bias conductance peak as
observed in Ref.[1]. We also show that a strong magnetic field along the wire
could eventually lead to the splitting of the zero bias peak into a doublet
with the doublet energy splitting oscillating as a function of increasing
magnetic field. Our results based on the standard theory of topological
superconductivity in a semiconductor hybrid structure in the presence of
proximity-induced superconductivity, spin-orbit coupling, and Zeeman splitting
show that the recently reported experimental data are generally consistent with
the existing theory that led to the predictions for the existence of the
Majorana modes in the semiconductor hybrid structures in spite of some apparent
anomalies in the experimental observations at first sight. We also make several
concrete new predictions for future observations regarding Majorana splitting
in finite wires used in the experiments.


  This work represents a first contribution on the problem of boundary
stabilization for the phase field system of Cahn-Hilliard type, which models
the phase separation in a binary mixture. The feedback controller we design
here is with actuation only on the temperature flow of the system, on one part
of the boundary only. Moreover, it is of proportional type, given in an
explicit form, expressed only in terms of the eigenfunctions of the Laplace
operator, being easy to manipulate from the computational point of view.
Furthermore, it ensures that the closed loop nonlinear system exponentially
reaches the prescribed stationary solution provided that the initial datum is
close enough to it.


  A molecule can be optically accelerated to rotate unidirectionally at a
frequency of a few terahertzes which is many orders higher than the classical
mechanical rotor. Such a photon-induced ultrafast molecular unidirectional
rotation has been well explored as a controllable spin of the molecular nuclear
wave packet. Although it has been observed for more than 10 years, a complete
imaging of the unidirectional rotating nuclear wave packet is still missing,
which is essentially the cornerstone of all the exploring applications. Here,
for the first time, we experimentally visualize the time-dependent evolution of
the double-pulse excited molecular unidirectional rotation by Coulomb explosion
imaging the rotational nuclear wave packet. Our results reveal comprehensive
details undiscovered in pioneering measurements, which exhibits as a joint of
the quantum revival of the impulsively aligned rotational wave packet and its
unidirectional rotation following the angular momentum conservation. The
numerical simulations well reproduce the experimental observations and
intuitively revivify the thoroughgoing evolution of the rotational wave packet.


  The one-bit quantization is implemented by one single comparator that
operates at low power and a high rate. Hence one-bit compressive sensing
(1bit-CS) becomes attractive in signal processing. When measurements are
corrupted by noise during signal acquisition and transmission, 1bit-CS is
usually modeled as minimizing a loss function with a sparsity constraint. The
one-sided $\ell_1$ loss and the linear loss are two popular loss functions for
1bit-CS. To improve the decoding performance on noisy data, we consider the
pinball loss, which provides a bridge between the one-sided $\ell_1$ loss and
the linear loss. Using the pinball loss, two convex models, an elastic-net
pinball model and its modification with the $\ell_1$-norm constraint, are
proposed. To efficiently solve them, the corresponding dual coordinate ascent
algorithms are designed and their convergence is proved. The numerical
experiments confirm the effectiveness of the proposed algorithms and the
performance of the pinball loss minimization for 1bit-CS.


  Intriguing signals with excesses over expected backgrounds have been observed
in many astrophysical and terrestrial settings, which could potentially have a
dark matter origin. Astrophysical excesses include the Galactic Center GeV
gamma-ray excess detected by the Fermi Gamma-Ray Space Telescope, the AMS
antiproton and positron excesses, and the 511 and 3.5 keV X-ray lines. Direct
detection excesses include the DAMA/LIBRA annual modulation signal, the XENON1T
excess, and low-threshold excesses in solid state detectors. We discuss avenues
to resolve these excesses, with actions the field can take over the next
several years.


  This paper presents a novel nonlinear disturbance rejection control for
hydraulic robots. This method requires two third-order filters as well as
inverse dynamics in order to estimate the disturbances. All the parameters for
the third-order filters are pre-defined. The proposed method is nonlinear,
which does not require the linearization of the rigid body dynamics. The
estimated disturbances are used by the nonlinear controller in order to achieve
disturbance attenuation. The performance of the proposed approach is compared
with existing approaches. Finally, the tracking performance and robustness of
the proposed approach is validated extensively on real hardware by performing
different tasks under either internal or both internal and external
disturbances. The experimental results demonstrate the robustness and superior
tracking performance of the proposed approach.


  Model independent tests for symmetry violations in tau decays are important
for determining whether the tau lepton is elementary or, instead, macroscopic.
Such tests are also significant steps towards resolving the outstanding "e - mu
- tau" puzzle. This talk reviews such tests in the context of a general
treatment of two-body tau decays which only assumes Lorentz invariance and the
tree-like structure of the tau-antitau production-decay sequence. Direct
measurement of polarized-partial-widths and of associated
``longitudinal-transverse W-exchange" interference intensities will provide
significant probes for distinguishing elementary/macroscopic theories of the
Higgs and of the tau, and tau-neutrino. The analogous goals and techniques
apply to top quark decays.


  We show that given a trivalent graph in $S^3$, either the graph complement
contains an essential almost meridional planar surface or thin position for the
graph is also bridge position. This can be viewed as an extension of a theorem
of Thompson to graphs. It follows that any graph complement always contains a
useful planar surface.


  In this paper we investigate some properties of first order theories which
prevent them from having universal models under certain cardinal arithmetic
assumptions. Our results give a new syntactical condition, oak property, which
is a sufficient condition for a theory not to have universal models in
cardinality lambda when certain cardinal arithmetic assumptions implying the
failure of GCH (and close to the failure of SCH) hold.


  We consider a model of 2D gravity with the coefficient of the
Einstein-Hilbert action having an imaginary part $\pi/2$. This is equivalent to
introduce a $\Theta$-vacuum structure in the genus expansion whose effect is to
convert the expansion into a series of alternating signs, presumably Borel
summable. We show that the specific heat of the model has a physical behaviour.
It can be represented nonperturbatively as a series in terms of integrals over
moduli spaces of punctured spheres and the sum of the series can be rewritten
as a unique integral over a suitable moduli space of infinitely punctured
spheres. This is an explicit realization \`a la Friedan-Shenker of 2D quantum
gravity. We conjecture that the expansion in terms of punctures and the genus
expansion can be derived using the Duistermaat-Heckman theorem. We briefly
analyze expansions in terms of punctured spheres also for multicritical models.


  In this paper we consider the operator mapping problem for in-network stream
processing applications. In-network stream processing consists in applying a
tree of operators in steady-state to multiple data objects that are continually
updated at various locations on a network. Examples of in-network stream
processing include the processing of data in a sensor network, or of continuous
queries on distributed relational databases. We study the operator mapping
problem in a ``constructive'' scenario, i.e., a scenario in which one builds a
platform dedicated to the application buy purchasing processing servers with
various costs and capabilities. The objective is to minimize the cost of the
platform while ensuring that the application achieves a minimum steady-state
throughput. The first contribution of this paper is the formalization of a set
of relevant operator-placement problems as linear programs, and a proof that
even simple versions of the problem are NP-complete. Our second contribution is
the design of several polynomial time heuristics, which are evaluated via
extensive simulations and compared to theoretical bounds for optimal solutions.


  In this paper, we give a detailed user's guide to the AMIDAS (A
Model-Independent Data Analysis System) package and website, which is developed
for online simulations and data analyses for direct Dark Matter detection
experiments and phenomenology. Recently, the whole AMIDAS package and website
system has been upgraded to the second phase: AMIDAS-II, for including the new
developed Bayesian analysis technique.
  AMIDAS has the ability to do full Monte Carlo simulations as well as to
analyze real/pseudo data sets either generated by another event generating
programs or recorded in direct DM detection experiments. Moreover, the
AMIDAS-II package can include several "user-defined" functions into the main
code: the (fitting) one-dimensional WIMP velocity distribution function, the
nuclear form factors for spin-independent and spin-dependent cross sections,
artificial/experimental background spectrum for both of simulation and data
analysis procedures, as well as different distribution functions needed in
Bayesian analyses.


  Lymph node metastasis (LNM) is a significant prognostic factor in patients
with head and neck cancer, and the ability to predict it accurately is
essential for treatment optimization. PET and CT imaging are routinely used for
LNM identification. However, uncertainties of LNM always exist especially for
small size or reactive nodes. Radiomics and deep learning are the two preferred
imaging-based strategies for node malignancy prediction. Radiomics models are
built based on handcrafted features, and deep learning can learn the features
automatically. We proposed a hybrid predictive model that combines
many-objective radiomics (MO-radiomics) and 3-dimensional convolutional neural
network (3D-CNN) through evidential reasoning (ER) approach. To build a more
reliable model, we proposed a new many-objective radiomics model. Meanwhile, we
designed a 3D-CNN that fully utilizes spatial contextual information. Finally,
the outputs were fused through the ER approach. To study the predictability of
the two modalities, three models were built for PET, CT, and PET&CT. The
results showed that the model performed best when the two modalities were
combined. Moreover, we showed that the quantitative results obtained from the
hybrid model were better than those obtained from MO-radiomics and 3D-CNN.


  Cloud-edge collaborative computing paradigm is a promising solution to
high-resolution video analytics systems. The key lies in reducing redundant
data and managing fluctuating inference workloads effectively. Previous work
has focused on extracting regions of interest (RoIs) from videos and
transmitting them to the cloud for processing. However, a naive Infrastructure
as a Service (IaaS) resource configuration falls short in handling highly
fluctuating workloads, leading to violations of Service Level Objectives (SLOs)
and inefficient resource utilization. Besides, these methods neglect the
potential benefits of RoIs batching to leverage parallel processing. In this
work, we introduce Tangram, an efficient serverless cloud-edge video analytics
system fully optimized for both communication and computation. Tangram
adaptively aligns the RoIs into patches and transmits them to the scheduler in
the cloud. The system employs a unique ``stitching'' method to batch the
patches with various sizes from the edge cameras. Additionally, we develop an
online SLO-aware batching algorithm that judiciously determines the optimal
invoking time of the serverless function. Experiments on our prototype reveal
that Tangram can reduce bandwidth consumption and computation cost up to
74.30\% and 66.35\%, respectively, while maintaining SLO violations within 5\%
and the accuracy loss negligible.


  We report on an artificial synapse, an organic synapse-transistor (synapstor)
working at 1 volt and with a typical response time in the range 100-200 ms.
This device (also called NOMFET, Nanoparticle Organic Memory Field Effect
Transistor) combines a memory and a transistor effect in a single device. We
demonstrate that short-term plasticity (STP), a typical synaptic behavior, is
observed when stimulating the device with input spikes of 1 volt. Both
significant facilitating and depressing behaviors of this artificial synapse
are observed with a relative amplitude of about 50% and a dynamic response <
200 ms. From a series of in-situ experiments, i.e. measuring the
current-voltage characteristic curves in-situ and in real time, during the
growth of the pentacene over a network of gold nanoparticles, we elucidate
these results by analyzing the relationship between the organic film morphology
and the transport properties. This synapstor works at a low energy of about 2
nJ/spike. We discuss the implications of these results for the development of
neuro-inspired computing architectures and interfacing with biological neurons.


  The build up of polyelectrolyte multilayers (PEMs) was observed by a
silicon-on-insulator (SOI) based thin film resistor. Differently charged
polyelectrolytes adsorbing to the sensor surface result in defined potential
shifts, which decrease with the number of layers deposited. We model the
response of the device assuming electrostatic screening of polyelectrolyte
charges by mobile ions within the PEMs. The Debye screening length inside the
PEMs was found to be increased compared to the value corresponding to the bulk
solution. Furthermore the partitioning of mobile ions between the bulk phase
and the polyelectrolyte film was employed to calculate the dielectric constant
of the PEMs and the concentration of mobile charges.


  We study Andreev reflection (AR) tunneling through a quantum dot (QD)
connected to a ferromagnet and a superconductor, in which the intradot
spin-flip interaction is included. By using the nonequibrium-Green-function
method, the formula of the linear AR conductance is derived at zero
temperature. It is found that competition between the intradot spin-flip
scattering and the tunneling coupling to the leads dominantes resonant
behaviours of the AR conductance versus the gate voltage.A weak spin-flip
scattering leads to a single peak resonance.However, with the spin-flip
scattering strength increasing, the AR conductance will develop into a double
peak resonannce implying a novel structure in the tunneling spectrum of the AR
conductance. Besides, the effect of the spin-dependent tunneling couplings, the
matching of Fermi velocity, and the spin polarization of the ferromagnet on the
AR conductance is eximined in detail.


  Utilizing spectroscopic observations taken for the VIMOS Ultra-Deep Survey
(VUDS), new observations from Keck/DEIMOS, and publicly available observations
of large samples of star-forming galaxies, we report here on the relationship
between the star formation rate (SFR) and the local environment
($\delta_{gal}$) of galaxies in the early universe ($2<z<5$). Unlike what is
observed at lower redshifts ($z<2$), we observe a definite, nearly monotonic
increase in the average SFR with increasing galaxy overdensity over more than
an order of magnitude in $\delta_{gal}$. The robustness of this trend is
quantified by accounting for both uncertainties in our measurements and galaxy
populations that are either underrepresented or not present in our sample and
find that the trend remains significant under all circumstances. This trend
appears to be primarily driven by the fractional increase of galaxies in high
density environments that are more massive in their stellar content and are
forming stars at a higher rate than their less massive counterparts. We find
that, even after stellar mass effects are accounted for, there remains a weak
but significant SFR-$\delta_{gal}$ trend in our sample implying that additional
environmentally-related processes are helping to drive this trend. We also find
clear evidence that the average SFR of galaxies in the densest environments
increases with increasing redshift. These results lend themselves to a picture
in which massive gas-rich galaxies coalesce into proto-cluster environments at
$z\sim3$, interact with other galaxies or with a forming large-scale medium,
subsequently using or losing most of their gas in the process, and begin to
seed the nascent red sequence that is present in clusters at slightly lower
redshifts.


  In this paper, we investigate the design of a novel multicarrier (MC)
modulation on delay-Doppler (DD) plane, to couple the modulated signal with a
doubly-selective channel having DD resolutions. A key challenge for the design
of DD plane MC modulation is to find a realizable pulse orthogonal with respect
to the DD plane's fine resolutions. To this end, we first indicate that a
feasible DD plane MC modulation is essentially a type of staggered multitone
modulation. Then, we propose an orthogonal delay-Doppler division multiplexing
(ODDM) modulation, and design the corresponding transmit pulse. Most
importantly, we prove that the proposed transmit pulse is orthogonal with
respect to the DD plane's resolutions and therefore a realizable DD plane
orthogonal pulse does exist. Finally, we demonstrate the superior performance
of the proposed ODDM modulation in terms of out-of-band radiation and bit error
rate.


  Spin-phonon coupling underlies a number of diverse range of phenomena of
recent interest, particularly in transition metal insulators with strong
spin-orbit effects, where it may give rise to hybrid magnetoelastic
excitations, and the controversial phonon thermal Hall effect. In this work, we
describe a general approach to the first principles estimation of generic
spin-orbit-phonon couplings suitable for investigating these diverse effects.
The method is demonstrated by application to study the temperature-dependent
optical phonon lineshapes in MnPSe$_3$, for which we find quantitative
agreement with recent experiments.


  Constructing quantum circuits for efficient state preparation belongs to the
central topics in the field of quantum information and computation. As the
number of qubits grows fast, methods to derive large-scale quantum circuits are
strongly desired. In this work, we propose the automatically differentiable
quantum circuit (ADQC) approach to efficiently prepare arbitrary quantum
many-qubit states. A key ingredient is to introduce the latent gates whose
decompositions give the unitary gates that form the quantum circuit. The
circuit is optimized by updating the latent gates using back propagation to
minimize the distance between the evolved and target states. Taking the ground
states of quantum lattice models and random matrix product states as examples,
with the number of qubits where processing the full coefficients is unlikely,
ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$.
Superior accuracy is reached compared with the existing state-preparation
approach based on the matrix product disentangler. The parameter complexity of
MPS can be significantly reduced by ADQC with the compression ratio $r \sim
O(10^{-3})$. Our work sheds light on the "intelligent construction" of quantum
circuits for many-qubit systems by combining with the machine learning methods.


  Natural language understanding (NLU) has two core tasks: intent
classification and slot filling. The success of pre-training language models
resulted in a significant breakthrough in the two tasks. One of the promising
solutions called BERT can jointly optimize the two tasks. We note that
BERT-based models convert each complex token into multiple sub-tokens by
wordpiece algorithm, which generates a mismatch between the lengths of the
tokens and the labels. This leads to BERT-based models do not do well in label
prediction which limits model performance improvement. Many existing models can
be compatible with this issue but some hidden semantic information is discarded
in the fine-tuning process. We address the problem by introducing a novel joint
method on top of BERT which explicitly models the multiple sub-tokens features
after wordpiece tokenization, thereby contributing to the two tasks. Our method
can well extract the contextual features from complex tokens by the proposed
sub-words attention adapter (SAA), which preserves overall utterance
information. Additionally, we propose an intent attention adapter (IAA) to
obtain the full sentence features to aid users to predict intent. Experimental
results confirm that our proposed model is significantly improved on two public
benchmark datasets. In particular, the slot filling F1 score is improved from
96.1 to 98.2 (2.1% absolute) on the Airline Travel Information Systems (ATIS)
dataset.


  This paper continues the study of superstability in abstract elementary
classes (AECs) satisfying the amalgamation property. In particular, we consider
the definition of $\mu$-superstability which is based on the local character
characterization of superstability from first order logic. Not only is
$\mu$-superstability a potential dividing line in the classification theory for
AECs, but it is also a tool in proving instances of Shelah's Categoricity
Conjecture.
  In this paper, we introduce a formulation, involving towers, of symmetry over
limit models for $\mu$-superstable abstract elementary classes. We use this
formulation to gain insight into the problem of the uniqueness of limit models
for categorical AECs.


  Let $C_n$ be the group of conjugating automorphisms. Valerij G. Bardakov
defined a representation $\rho$ of $C_n$, which is an extension of
Lawrence-Krammer representation of the braid group $B_n$. Bardakov proved that
the representation $\rho$ is unfaithful for $n \geq 5$. The cases $n=3,4$
remain open. M. N. Nasser and M. N. Abdulrahim made attempts towards the
faithfulness of $\rho$ in the case $n=3$. In this work, we prove that $\rho$ is
unfaithful in the both cases $n=3$ and $n=4$.


  We analyze generalized CP symmetries of two-Higgs doublet models, extending
them from the scalar to the fermion sector of the theory. We show that, with a
single exception, those symmetries imply massless fermions. The single model
which accommodates a fermionic mass spectrum compatible with experimental data
possesses a remarkable feature. It displays a new type of spontaneous CP
violation, which occurs not in the scalar sector responsible for the symmetry
breaking mechanism but, rather, in the fermion sector.


  Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.


  Self-organized criticality (SOC) is a popular concept that has been the
subject of more than 3000 articles in the last 25 years. Here we show that SOC
may enable theory to connect standard Web-based (BLAST) short-range amino acid
(aa) similarities to long-range aa roughening form factors that accurately
describe evolutionary trends in water-membrane protein interactions. Our method
utilizes a hydropathic aa metric based on 5526 protein segments and thereby
encapsulates differential geometrical features of the Protein Data Bank. It
easily organizes small aa sequence differences between humans and proximate
species. For rhodopsin, the most studied transmembrane signaling protein
associated with night vision, it shows that short- and long-range aa sequence
properties correlate with 96% success for humans, monkeys, cats, mice and
rabbits. Proper application of SOC promises unprecedented simplifications of
exponentially complex protein sequence-structure-function problems, both
conceptual and practical.


  Pretrained language models have been used in various natural language
processing applications. In the mental health domain, domain-specific language
models are pretrained and released, which facilitates the early detection of
mental health conditions. Social posts, e.g., on Reddit, are usually long
documents. However, there are no domain-specific pretrained models for
long-sequence modeling in the mental health domain. This paper conducts
domain-specific continued pretraining to capture the long context for mental
health. Specifically, we train and release MentalXLNet and MentalLongformer
based on XLNet and Longformer. We evaluate the mental health classification
performance and the long-range ability of these two domain-specific pretrained
models. Our models are released in HuggingFace.


  We consider a bipartite mean-field model in which both the interaction
constant and the external field take different values only depending on the
groups particles belong to. We compute the exact value of the thermodynamic
limit of the model exploiting a tail estimation on the number of configurations
that share the same value of the magnetization and we analyze the critical
points of the pressure functional associated to the symmetric version of the
model as the external field is away or small.


  We study the interplay of Klein tunneling (= interband tunneling) between
n-doped and p-doped regions in graphene and Andreev reflection (= electron-hole
conversion) at a superconducting electrode. The tunneling conductance of an
n-p-n junction initially increases upon lowering the temperature, while the
coherence time of the electron-hole pairs is still less than their lifetime,
but then drops back again when the coherence time exceeds the lifetime. This
reentrance effect, known from diffusive conductors and ballistic quantum dots,
provides a method to detect phase coherent Klein tunneling of electron-hole
pairs.


  We quantitatively investigate multiple algorithms for microlens array grid
estimation for microlens array-based light field cameras. Explicitly taking
into account natural and mechanical vignetting effects, we propose a new method
for microlens array grid estimation that outperforms the ones previously
discussed in the literature. To quantify the performance of the algorithms, we
propose an evaluation pipeline utilizing application-specific ray-traced white
images with known microlens positions. Using a large dataset of synthesized
white images, we thoroughly compare the performance of the different estimation
algorithms. As an example, we apply our results to the decoding and calibration
of light fields taken with a Lytro Illum camera. We observe that decoding as
well as calibration benefit from a more accurate, vignetting-aware grid
estimation, especially in peripheral subapertures of the light field.


  Magnetization, x-ray diffraction and specific-heat measurements reveal that
SmCoAsO undergoes three magnetic phase transitions. A ferromagnetic transition
attributed to the Co ions, emerges at TC=57 K with a small saturation moment of
0.15muB/Co. Reorientation of the Co moment to an antiferromagnetic state is
obtained at TN2=45 K. The relative high paramagnetic effective moment Peff=1.57
MuB/Co indicates an itinerant ferromagnetic state of the Co sublattice. The
third magnetic transition at TN1=5 K is observed clearly in the specific-heat
study only. Both magnetic and 57Fe Mossbauer studies show that substitution of
small quantities of Fe for Co was unsuccessful.


  This paper presents a new compact canonical-based algorithm to solve the
problem of single-output completely specified NPN Boolean matching. We propose
a new signature vector Boolean difference and cofactor (DC) signature vector.
Our algorithm utilizes the Boolean difference, cofactor signature and symmetry
properties to search for canonical transformations. The use of symmetry and
Boolean difference notably reduces the search space and speeds up the Boolean
matching process compared to the algorithm proposed in [1]. We tested our
algorithm on a large number of circuits. The experimental results showed that
the average runtime of our algorithm 37% higher and its average search space
67% smaller compared to [1] when tested on general circuits.


  A stable polymorph of CrO$_2$ is predicted using PBE+U method. The porous
material is isostructural with $\alpha$-MnO$_2$ making it the second transition
metal oxide in sparse hollandite group of materials. However, unlike the
anti-ferromagnetic semiconducting character of the $\alpha$-MnO$_2$, it is
found to be a ferromagnetic half-metal. At Fermi level, the hole pocket has
ample contribution from O-2$p$ orbital, though, the electron pocket is mostly
contributed by Cr-3$d_{xy}$ and Cr-3d$_{x^2-y^2}$. A combination of negative
charge transfer through orbital mixing and extended anti-bonding state near
Fermi level is responsible for the half-metallic ferromagnetic character of the
structure. A comparative study of rutile and hollandite CrO$_2$ and hollandite
MnO$_2$ structures delineate the interplay between structural, electronic and
magnetic properties. The material shows a robust magnetic character under
hydrothermal pressure, as well as, the band topology is conserved under
uniaxial strain. Moderate magneto-crystalline anisotropy is observed and it
shows a correspondence with the anisotropy of elastic constants.


  We prove quenched versions of a central limit theorem, a large deviations
principle as well as a local central limit theorem for expanding on average
cocycles. This is achieved by building an appropriate modification of the
spectral method for non-autonomous dynamics developed by Dragi\v{c}evi\'{c} et
al., to deal with the case of random dynamics that exhibits nonuniform decay of
correlations, which are ubiquitous in the context of the multiplicative ergodic
theory. Our results provide an affirmative answer to a question posed by Buzzi.


  We study the problem of training a two-layer neural network (NN) of arbitrary
width using stochastic gradient descent (SGD) where the input
$\boldsymbol{x}\in \mathbb{R}^d$ is Gaussian and the target $y \in \mathbb{R}$
follows a multiple-index model, i.e.,
$y=g(\langle\boldsymbol{u_1},\boldsymbol{x}\rangle,...,\langle\boldsymbol{u_k},\boldsymbol{x}\rangle)$
with a noisy link function $g$. We prove that the first-layer weights of the NN
converge to the $k$-dimensional principal subspace spanned by the vectors
$\boldsymbol{u_1},...,\boldsymbol{u_k}$ of the true model, when online SGD with
weight decay is used for training. This phenomenon has several important
consequences when $k \ll d$. First, by employing uniform convergence on this
smaller subspace, we establish a generalization error bound of
$O(\sqrt{{kd}/{T}})$ after $T$ iterations of SGD, which is independent of the
width of the NN. We further demonstrate that, SGD-trained ReLU NNs can learn a
single-index target of the form
$y=f(\langle\boldsymbol{u},\boldsymbol{x}\rangle) + \epsilon$ by recovering the
principal direction, with a sample complexity linear in $d$ (up to log
factors), where $f$ is a monotonic function with at most polynomial growth, and
$\epsilon$ is the noise. This is in contrast to the known $d^{\Omega(p)}$
sample requirement to learn any degree $p$ polynomial in the kernel regime, and
it shows that NNs trained with SGD can outperform the neural tangent kernel at
initialization. Finally, we also provide compressibility guarantees for NNs
using the approximate low-rank structure produced by SGD.


  Let $L\geq0$ and $0<\varepsilon\ll1$. Consider the following time-dependent
family of $1D$ Schr\"{o}dinger equations with scaled and translated harmonic
oscillator potentials $ i\varepsilon\partial_t
u_{\varepsilon}=-\tfrac12\partial_x^2u_{\varepsilon}+V(t,x)u_{\varepsilon}$,
$u_{\varepsilon}(-L-1,x)=\pi^{-1/4}\exp(-x^2/2) $, where $ V(t,x)=
(t+L)^2x^2/2$, $t<-L$, $ V(t,x)= 0$, $-L\leq t \leq L$, and $
V(t,x)=(t-L)^2x^2/2$, $t>L$.
  The initial value problem is explicitly solvable in terms of Bessel
functions. Using the explicit solutions we show that the adiabatic theorem
breaks down as $\varepsilon\to 0$.
  For the case $L=0$ complete results are obtained. The survival probability of
the ground state $\pi^{-1/4}\exp(-x^2/2)$ at microscopic time $t=1/\varepsilon$
is $1/\sqrt{2}+O(\varepsilon)$. For $L>0$ the framework for further
computations and preliminary results are given.


  We prove that there exists $C>0$ such that any $(n+Ck)$-vertex tournament
contains a copy of every $n$-vertex oriented tree with $k$ leaves, improving
the previously best known bound of $n+O(k^2)$ vertices to give a result tight
up to the value of $C$. Furthermore, we show that, for each $k$, there exists
$n_0$, such that, whenever $n\geqslant n_0$, any $(n+k-2)$-vertex tournament
contains a copy of every $n$-vertex oriented tree with at most $k$ leaves,
confirming a conjecture of Dross and Havet.


  The HII region IC 1848 harbors a lot of intricate elephant trunk-like
structures that look morphologically different from usual bright-rimmed clouds
(BRCs). Of particular interest is a concentration of thin and long elephant
trunk-like structures in the southeastern part of IC 1848E. Some of them have
an apparently associated star (or two stars) at their very tip. We conducted
$VI_{c}$ photometry of several of these stars. Their positions on the
$V/(V-I_{c})$ color-magnitude diagram as well as the physical parameters
obtained by SED fittings indicate that they are low-mass pre-main-sequence
stars having ages of mostly one Myr or less. This strongly suggests that they
formed from elongated, elephant trunk-like clouds. We presume that such
elephant trunk-like structures are genetically different from BRCs, on the
basis of the differences in morphology, size distributions, and the ages of the
associated young stars. We suspect that those clouds have been caused by
hydrodynamical instability of the ionization/shock front of the expanding HII
region. Similar structures often show up in recent numerical simulations of the
evolution of HII regions. We further hypothesize that this mechanism makes a
third mode of triggered star formation associated with HII regions, in addition
to the two known mechanisms, i.e., collect-and-collapse of the shell
accumulated around an expanding HII region and radiation-driven implosion of
BRCs originated from pre-existing cloud clumps.


  We study the interaction of a Bose-Einstein condensate, which is confined in
an optical lattice, with a largely detuned light field propagating through the
condensate. If the condensate is in its ground state it acts as a periodic
dielectric and gives rise to photonic band gaps at optical frequencies. The
band structure of the combined system of condensed lattice-atoms and photons is
studied by using the concept of polaritons. If elementary excitations of the
condensate are present, they will produce defect states inside the photonic
band gaps. The frequency of localized defect states is calculated using the
Koster-Slater model.


  We describe a new and computationally efficient Bayesian methodology for
inferring species trees and demographics from unlinked binary markers.
Likelihood calculations are carried out using diffusion models of allele
frequency dynamics combined with a new algorithm for numerically computing
likelihoods of quantitative traits. The diffusion approach allows for analysis
of datasets containing hundreds or thousands of individuals. The method, which
we call \snapper, has been implemented as part of the Beast2 package. We
introduce the models, the efficient algorithms, and report performance of
\snapper on simulated data sets and on SNP data from rattlesnakes and
freshwater turtles.


  The kinetic temperature structure of the massive filament DR21 has been
mapped using the IRAM 30 m telescope. This mapping employed the para-H$_2$CO
triplet ($J_{\rm K_aK_c}$ = 3$_{03}$--2$_{02}$, 3$_{22}$--2$_{21}$, and
3$_{21}$--2$_{20}$) on a scale of $\sim$0.1 pc. By modeling the averaged line
ratios of para-H$_{2}$CO with RADEX under non-LTE assumptions, the kinetic
temperature of the dense gas was derived at a density of $n$(H$_{2}$) =
10$^{5}$ cm$^{-3}$. The para-H$_2$CO lines reveal significantly higher
temperatures than NH$_3$ (1,1)/(2,2) and FIR wavelengths. The dense clumps
appear to correlate with the notable kinetic temperature. Among the four dense
cores (N44, N46, N48, and N54), temperature gradients are observed on a scale
of $\sim$0.1-0.3 pc. This suggests that the warm dense gas is influenced by
internal star formation activity. With the exception of N54, the temperature
profiles of these cores were fitted with power-law indices ranging from $-$0.3
to $-$0.5. This indicates that the warm dense gas is heated by radiation
emitted from internally embedded protostar(s) and/or clusters. While there is
no direct evidence supporting the idea that the dense gas is heated by shocks
resulting from a past explosive event in the DR21 region, our measurements
toward the DR21W1 region provide compelling evidence that the dense gas is
indeed heated by shocks originating from the western DR21 flow. Higher
temperatures appear to be associated with turbulence. The physical parameters
of the dense gas in the DR21 filament exhibit a remarkable similarity to the
results obtained in OMC-1 and N113. This may imply that the physical mechanisms
governing the dynamics and thermodynamics of dense gas traced by H$_{2}$CO in
diverse star formation regions may be dominated by common underlying principles
despite variations in specific environmental conditions. (abbreviated)


  The properties of continuous variable teleportation of single photon states
are investigated. The output state is different from the input state due to the
non-maximal entanglement in the EPR beams. The photon statistics of the
teleportation output are determined and the correlation between the field
information beta obtained in the teleportation process and the change in photon
number is discussed. The results of the output photon statistics are applied to
the transmission of a qbit encoded in the polarization of a single photon.


  New contributions in the field of iterative optimisation heuristics are often
made in an iterative manner. Novel algorithmic ideas are not proposed in
isolation, but usually as an extension of a preexisting algorithm. Although
these contributions are often compared to the base algorithm, it is challenging
to make fair comparisons between larger sets of algorithm variants. This
happens because even small changes in the experimental setup, parameter
settings, or implementation details can cause results to become incomparable.
Modular algorithms offer a way to overcome these challenges. By implementing
the algorithmic modifications into a common framework, many algorithm variants
can be compared, while ensuring that implementation details match in all
versions.
  In this work, we propose a version of a modular framework for the popular
Differential Evolution (DE) algorithm. We show that this modular approach not
only aids in comparison, but also allows for a much more detailed exploration
of the space of possible DE variants. This is illustrated by showing that
tuning the settings of modular DE vastly outperforms a set of commonly used DE
versions which have been recreated in our framework. We then investigate these
tuned algorithms in detail, highlighting the relation between modules and
performance on particular problems.


  Metallicity and gas content are intimately related in the baryonic exchange
cycle of galaxies, and galaxy evolution scenarios can be constrained by
quantifying this relation. To this end, we have compiled a sample of ~400
galaxies in the Local Universe, dubbed "MAGMA" (Metallicity And Gas for Mass
Assembly), which covers an unprecedented range in parameter space, spanning
more than 5 orders of magnitude in stellar mass (Mstar), star-formation rate
(SFR), and gas mass (Mgas), and a factor of ~60 in metallicity [Z,
12+log(O/H)]. Stellar masses and SFRs have been recalculated for all the
galaxies using IRAC, WISE and GALEX photometry, and 12+log(O/H) has been
transformed, where necessary, to a common metallicity calibration. To assess
the true dimensionality of the data, we have applied multi-dimensional
principal component analyses (PCAs) to our sample. In confirmation of previous
work, we find that even with the vast parameter space covered by MAGMA, the
relations between Mstar, SFR, Z and Mgas (MHI+MH2) require only two dimensions
to describe the hypersurface. To accommodate the curvature in the Mstar-Z
relation, we have applied a piecewise 3D PCA that successfully predicts
observed 12+log(O/H) to an accuracy of ~0.1dex. MAGMA is a representative
sample of isolated star-forming galaxies in the Local Universe, and can be used
as a benchmark for cosmological simulations and to calibrate evolutionary
trends with redshift.


  Humans are social beings; we pursue social goals in our daily interactions,
which is a crucial aspect of social intelligence. Yet, AI systems' abilities in
this realm remain elusive. We present SOTOPIA, an open-ended environment to
simulate complex social interactions between artificial agents and evaluate
their social intelligence. In our environment, agents role-play and interact
under a wide variety of scenarios; they coordinate, collaborate, exchange, and
compete with each other to achieve complex social goals. We simulate the
role-play interaction between LLM-based agents and humans within this task
space and evaluate their performance with a holistic evaluation framework
called SOTOPIA-Eval. With SOTOPIA, we find significant differences between
these models in terms of their social intelligence, and we identify a subset of
SOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models.
We find that on this subset, GPT-4 achieves a significantly lower goal
completion rate than humans and struggles to exhibit social commonsense
reasoning and strategic communication skills. These findings demonstrate
SOTOPIA's promise as a general platform for research on evaluating and
improving social intelligence in artificial agents.