  An operad (this paper deals with non-symmetric operads)may be conceived as a
partial algebra with a family of insertion operations, Gerstenhaber's circle-i
products, which satisfy two kinds of associativity, one of them involving
commutativity. A Cat-operad is an operad enriched over the category Cat of
small categories, as a 2-category with small hom-categories is a category
enriched over Cat. The notion of weak Cat-operad is to the notion of Cat-operad
what the notion of bicategory is to the notion of 2-category. The equations of
operads like associativity of insertions are replaced by isomorphisms in a
category. The goal of this paper is to formulate conditions concerning these
isomorphisms that ensure coherence, in the sense that all diagrams of canonical
arrows commute. This is the sense in which the notions of monoidal category and
bicategory are coherent. The coherence proof in the paper is much simplified by
indexing the insertion operations in a context-independent way, and not in the
usual manner. This proof, which is in the style of term rewriting, involves an
argument with normal forms that generalizes what is established with the
completeness proof for the standard presentation of symmetric groups. This
generalization may be of an independent interest, and related to matters other
than those studied in this paper. Some of the coherence conditions for weak
Cat-operads lead to the hemiassociahedron, which is a polyhedron related to,
but different from, the three-dimensional associahedron and permutohedron.


  Hot line lists for two isotopologues of water, \octo\ and \heto, are
presented. The calculations employ newly constructed potential energy surfaces
(PES) which take advantage of a novel method for using the large set of
experimental energy levels for \hato\ to give high quality predictions for
\octo\ and \heto. This procedure greatly extends the energy range for which a
PES can be accurately determined, allowing accurate prediction of higher-lying
energy levels than are currently known from direct laboratory measurements.
This PES is combined with a high-accuracy, {\it ab initio} dipole moment
surface of water in the computation of all energy levels, transition
frequencies and associated Einstein A coefficients for states with rotational
excitation up to $J=50$ and energies up to 30~000 \cm. The resulting HotWat78
line lists complement the well-used BT2 \hato\ line list (Barber et.al, 2006,
MNRAS, {\bf 368}, 1087). Full line lists are made available in the electronic
form as supplementary data to this article and at \url{www.exomol.com}.


  In the present paper, we characterize the behavior of supercritical branching
processes in random environment with linear fractional offspring distributions,
conditioned on having small, but positive values at some large generation. As
it has been noticed in previous works, there is a phase transition in the
behavior of the process. Here, we examine the strongly and intermediately
supercritical regimes The main result is a conditional limit theorem for the
rescaled associated random walk in the intermediately case.


  We investigate the pseudospin symmetry case of a spin-1/2 particle governed
by the generalized isotonic oscillator, by presenting quasi exact polynomial
solutions to Dirac equation with pseudospin symmetry vector and scalar
potentials. The resulting equation is found to be quasi-exactly solvable owing
to the existence of a hidden $sl(2)$ algebraic structure. A systematic and
closed form solution to the basic equation is obtained using the Bethe ansatz
method. Analytic expression for the energy is obtain and the wavefunction is
derived in terms of the roots to a set of Bethe ansatz equations.


  We study the dependence on $\varepsilon$ in the critical dimension
$k(n,p,\varepsilon)$ for which one can find random sections of the
$\ell_p^n$-ball which are $(1+\varepsilon)$-spherical. We give lower (and
upper) estimates for $k(n,p,\varepsilon)$ for all eligible values $p$ and
$\varepsilon$ as $n\to \infty$, which agree with the sharp estimates for the
extreme values $p=1$ and $p=\infty$. Toward this end, we provide tight bounds
for the Gaussian concentration of the $\ell_p$-norm.


  In this study, grazing incidence small-angle X-ray scattering (GISAXS) is
used to collect statistical information on dimensional parameters in an area of
20 mm x 15 mm on photonic structures produced by nanoimprint lithography. The
photonic structures are composed of crystalline and locally quasicrystalline
two-dimensional patterns with structure sizes between about 100 nm and 10
$\mu$m to enable broadband visible light absorption for use in solar energy
harvesting. These first GISAXS measurements on locally quasicrystalline samples
demonstrate that GISAXS is capable of showing the locally quasicrystalline
nature of the samples while at the same time revealing the long-range
periodicity introduced due to the lattice design. We describe the scattering
qualitatively in the framework of the distorted wave Born approximation using a
hierarchical model mirroring the sample design, which consists of a rectangular
and locally quasicrystalline supercell which is repeated periodically to fill
the whole surface.
  The nanoimprinted samples are compared to a sample manufactured using
electron beam lithography and the distortions of the periodic and locally
quasiperiodic samples are quantified statistically. Due to the high sensitivity
of GISAXS to deviations from the perfect lattice, the misalignment of the
crystallographic axes was measured with a resolution of 0.015{\deg}, showing
distortions up to +/- 0.15{\deg} in the investigated samples.


  We analyze the two-species reaction-diffusion system including trapping
reaction $A + B \to A$ as well as coagulation/annihilation reactions $A + A \to
(A,0)$ where particles of both species are performing L\'evy flights with
control parameter $0 < \sigma < 2$, known to lead to superdiffusive behaviour.
The density, as well as the correlation function for target particles $B$ in
such systems, are known to scale with nontrivial universal exponents at space
dimension $d \leq d_{c}$. Applying the renormalization group formalism we
calculate these exponents in a case of superdiffusion below the critical
dimension $d_c=\sigma$. The numerical simulations in one-dimensional case are
performed as well. The quantitative estimates for the decay exponent of the
density of survived particles $B$ are in good agreement with our analytical
results. In particular, it is found that the surviving probability of the
target particles in a superdiffusive regime is higher than that in a system
with ordinary diffusion.


  Deep generative models have shown success in generating 3D shapes with
different representations. In this work, we propose Neural Volumetric Mesh
Generator(NVMG) which can generate novel and high-quality volumetric meshes.
Unlike the previous 3D generative model for point cloud, voxel, and implicit
surface, the volumetric mesh representation is a ready-to-use representation in
industry with details on both the surface and interior. Generating this such
highly-structured data thus brings a significant challenge. We first propose a
diffusion-based generative model to tackle this problem by generating voxelized
shapes with close-to-reality outlines and structures. We can simply obtain a
tetrahedral mesh as a template with the voxelized shape. Further, we use a
voxel-conditional neural network to predict the smooth implicit surface
conditioned on the voxels, and progressively project the tetrahedral mesh to
the predicted surface under regularizations. The regularization terms are
carefully designed so that they can (1) get rid of the defects like flipping
and high distortion; (2) force the regularity of the interior and surface
structure during the deformation procedure for a high-quality final mesh. As
shown in the experiments, our pipeline can generate high-quality artifact-free
volumetric and surface meshes from random noise or a reference image without
any post-processing. Compared with the state-of-the-art voxel-to-mesh
deformation method, we show more robustness and better performance when taking
generated voxels as input.


  We investigate roads for evaluating model-independent cross-section
distributions for the sequential hyperon decay $\Sigma^0\rightarrow\Lambda
\gamma;\Lambda\rightarrow p\pi^- $ and its corresponding antihyperon decay. The
hyperons are produced in the reaction $e^+e^-\rightarrow J/{\psi}\rightarrow
\Sigma^0\bar{\Sigma}^0$. Cross-section distributions are calculated using the
folding technique.


  We analyze the stability of the Einstein static universe by considering
homogeneous perturbations in the context of f(G) modified Gauss-Bonnet theories
of gravity. By considering a generic form of f(G), the stability region of the
Einstein static universe is parameterized by the linear equation of state
parameter w=p/rho and the second derivative f"(G) of the Gauss-Bonnet term.


  A composite plasmonic slab based on double-layer metallic gratings and a
dielectric film is experimentally and numerically demonstrated in terahertz
(THz) regions, which can support two resonance modes and then form a broad
bandgap (40%). As compared to the double-layer metal grating, the dielectric
film in composite THz slabs significantly enhances the transmission of the
transverse magnetic (TM) mode. Electric field vector proved that the
low-frequency resonance mode originates from the symmetric plasmonic mode and
the high-frequency resonance mode is induced by the hybrid mode of plasmonic
and dielectric modes. The inherently near field coupling between metal gratings
and dielectric film has been analyzed by changing the structural parameters. We
further demonstrate that by tuning the metallic grating width, the plasmonic
bandgap can be manipulated. These results suggest that this composite plasmonic
slab has great potential for use as a filter, polarizer, and sensor in THz
regions.


  Frame dragging, one of the outstanding phenomena predicted by General
Relativity, is efficiently studied by means of the laser-ranged satellites
LARES, LAGEOS and LAGEOS 2. The accurate analysis of the orbital perturbations
of Earth's solid and ocean tides has been relevant for increasing the accuracy
in the test of frame-dragging using these three satellites. The Earth's tidal
perturbations acting on the LARES satellite are obtained for the 110
significant modes of corresponding Doodson number and are exhibited to enable
the comparison to those of the LAGEOS and LAGEOS-2 satellites. For LARES we
represent 29 perturbation modes for l=2,3,4 for ocean tides.


  Large language models (LLMs) are excellent in-context learners. However, the
sensitivity of data contained in prompts raises privacy concerns. Our work
first shows that these concerns are valid: we instantiate a simple but highly
effective membership inference attack against the data used to prompt LLMs. To
address this vulnerability, one could forego prompting and resort to
fine-tuning LLMs with known algorithms for private gradient descent. However,
this comes at the expense of the practicality and efficiency offered by
prompting. Therefore, we propose to privately learn to prompt. We first show
that soft prompts can be obtained privately through gradient descent on
downstream data. However, this is not the case for discrete prompts. Thus, we
orchestrate a noisy vote among an ensemble of LLMs presented with different
prompts, i.e., a flock of stochastic parrots. The vote privately transfers the
flock's knowledge into a single public prompt. We show that LLMs prompted with
our private algorithms closely match the non-private baselines. For example,
using GPT3 as the base model, we achieve a downstream accuracy of 92.7% on the
sst2 dataset with ($\epsilon=0.147, \delta=10^{-6}$)-differential privacy vs.
95.2% for the non-private baseline. Through our experiments, we also show that
our prompt-based approach is easily deployed with existing commercial APIs.


  Person-Job Fit is the process of matching the right talent for the right job
by identifying talent competencies that are required for the job. While many
qualitative efforts have been made in related fields, it still lacks of
quantitative ways of measuring talent competencies as well as the job's talent
requirements. To this end, in this paper, we propose a novel end-to-end
data-driven model based on Convolutional Neural Network (CNN), namely
Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to
the requirements of a job. To be specific, PJFNN is a bipartite neural network
which can effectively learn the joint representation of Person-Job fitness from
historical job applications. In particular, due to the design of a hierarchical
representation structure, PJFNN can not only estimate whether a candidate fits
a job, but also identify which specific requirement items in the job posting
are satisfied by the candidate by measuring the distances between corresponding
latent representations. Finally, the extensive experiments on a large-scale
real-world dataset clearly validate the performance of PJFNN in terms of
Person-Job Fit prediction. Also, we provide effective data visualization to
show some job and talent benchmark insights obtained by PJFNN.


  We show that a variation of Milnor's $\bar\mu$-invariants, the so-called
Campbell-Hausdorff invariants introduced recently by Stefan Papadima, are of
finite type with respect to {\it marked singular links}. These link invariants
are stronger than quantum invariants in the sense that they detect easily the
non-invertibility of links with more than one components. It is still open
whether some effectively computable knot invariants, e.g. finite type knot
invariants (Vassiliev invariants), could detect the non-invertibility of knots.


  Research in artificial intelligence (AI)-assisted decision-making is
experiencing tremendous growth with a constantly rising number of studies
evaluating the effect of AI with and without techniques from the field of
explainable AI (XAI) on human decision-making performance. However, as tasks
and experimental setups vary due to different objectives, some studies report
improved user decision-making performance through XAI, while others report only
negligible effects. Therefore, in this article, we present an initial synthesis
of existing research on XAI studies using a statistical meta-analysis to derive
implications across existing research. We observe a statistically positive
impact of XAI on users' performance. Additionally, the first results indicate
that human-AI decision-making tends to yield better task performance on text
data. However, we find no effect of explanations on users' performance compared
to sole AI predictions. Our initial synthesis gives rise to future research
investigating the underlying causes and contributes to further developing
algorithms that effectively benefit human decision-makers by providing
meaningful explanations.


  An optimal estimation inverse method is presented which can be used to
retrieve simultaneously vertical profiles of temperature and specific humidity,
in addition to surface pressure, from satellite-to-satellite radio occultation
observations of the Earth's atmosphere. The method is a non-linear, maximum
{\it a posteriori} technique which can accommodate most aspects of the real
radio occultation problem and is found to be stable and to converge rapidly in
most cases. The optimal estimation inverse method has two distinct advantages
over the analytic inverse method in that it accounts for some of the effects of
horizontal gradients and is able to retrieve optimally temperature and humidity
simultaneously from the observations. It is also able to account for
observation noise and other sources of error. Combined, these advantages ensure
a realistic retrieval of atmospheric quantities.
  A complete error analysis emerges naturally from the optimal estimation
theory, allowing a full characterisation of the solution. Using this analysis a
quality control scheme is implemented which allows anomalous retrieval
conditions to be recognised and removed, thus preventing gross retrieval
errors.
  The inverse method presented in this paper has been implemented for bending
angle measurements derived from GPS/MET radio occultation observations of the
Earth. Preliminary results from simulated data suggest that these observations
have the potential to improve NWP model analyses significantly throughout their
vertical range.


  Given a replicated database, a divergent design tunes the indexes in each
replica differently in order to specialize it for a specific subset of the
workload. This specialization brings significant performance gains compared to
the common practice of having the same indexes in all replicas, but requires
the development of new tuning tools for database administrators. In this paper
we introduce RITA (Replication-aware Index Tuning Advisor), a novel
divergent-tuning advisor that offers several essential features not found in
existing tools: it generates robust divergent designs that allow the system to
adapt gracefully to replica failures; it computes designs that spread the load
evenly among specialized replicas, both during normal operation and when
replicas fail; it monitors the workload online in order to detect changes that
require a recomputation of the divergent design; and, it offers suggestions to
elastically reconfigure the system (by adding/removing replicas or
adding/dropping indexes) to respond to workload changes. The key technical
innovation behind RITA is showing that the problem of selecting an optimal
design can be formulated as a Binary Integer Program (BIP). The BIP has a
relatively small number of variables, which makes it feasible to solve it
efficiently using any off-the-shelf linear-optimization software. Experimental
results demonstrate that RITA computes better divergent designs compared to
existing tools, offers more features, and has fast execution times.


  Many application problems that lead to solving linear systems make use of
preconditioned Krylov subspace solvers to compute their solution. Among the
most popular preconditioning approaches are incomplete factorization methods
either as single-level approaches or within a multilevel framework. We will
present a block incomplete factorization that is based on skillfully blocking
the system initially and throughout the factorization. This approach allows for
the use of cache-optimized dense matrix kernels such as level-3 BLAS or LAPACK.
We will demonstrate how this block approach outperforms the scalar method often
by orders of magnitude on modern architectures, paving the way for its
prospective use inside various multilevel incomplete factorization approaches
or other applications where the core part relies on an incomplete
factorization.


  Ovadia and Rodriguez-Hertz (Neutralized local entropy, arXiv:2302.10874)
defined the neutralized Bowen open ball for an autonomous dynamical system on a
compact metric space. Replacing the usual Bowen open ball with neutralized
Bowen open ball, we introduce the notions of neutralized Bowen topological
entropy of subsets, neutralized weighted Bowen topological entropy of subsets,
lower neutralized Brin-Katok's local entropy of Borel probability measures, and
neutralized Katok's entropy of Borel probability measures for a non-autonomous
dynamical system on a compact metric space. Then, we establish variational
principles for neutralized Bowen topological entropy and neutralized weighted
Bowen topological entropy of non-empty compact subsets in terms of lower
neutralized Brin-Katok's local entropy and neutralized Katok's entropy. In
particular, this extends the main result of (YANG, R., CHEN, E., ZHOU, X.:
Variational principle for neutralized Bowen topological entropy,
arXiv:2303.01738v1) for autonomous dynamical systems.


  Two-dimensional van der Waals (vdW) materials exhibit a broad palette of
unique and superlative properties, including high electrical and thermal
conductivities, paired with the ability to exfoliate or grow and transfer
single layers onto a variety of substrates thanks to the relatively weak vdW
interlayer bonding. However, the same vdW bonds also lead to relatively low
thermal boundary conductance (TBC) between the 2D layer and its 3D substrate,
which is the main pathway for heat removal and thermal management in devices,
leading to a potential thermal bottleneck and dissipation-driven performance
degradation. Here we use first-principles phonon dispersion with our 2D-3D
Boltzmann phonon transport model to compute the TBC of 156 unique 2D/3D
interface pairs, many of which are not available in the literature. We then
employ machine learning (ML) to develop streamlined predictive models, of which
Neural Network and Gaussian process display the highest predictive accuracy
(RMSE $<$ 5 MWm$^{-2}$K$^{-1}$ and $R^2>$0.99) on the complete descriptor set.
Then we perform sensitivity analysis to identify the most impactful
descriptors, consisting of the vdW spring coupling constant, 2D thermal
conductivity, ZA phonon bandwidth, the ZA phonon resonance gap, and the
frequency of the first van Hove singularity or Boson peak. On that reduced set,
we find that a decision-tree algorithm can make accurate predictions (RMSE $<$
20 MWm$^{-2}$K$^{-1}$ and $R^2>$0.9) on materials it has not been trained on by
performing a transferability analysis. Our model allows optimal selection of
2D-substrate pairings to maximize heat transfer and will improve thermal
management in future 2D nanoelectronics.


  Atoms confined in a harmonic potential show universal oscillations in 2D. We
point out the connection of these ''breathing'' modes to the presence of a
hidden symmetry. The underlying symmetry SO(2,1), i.e. the two dimensional
Lorentz group, allows pulsating solutions to be constructed for the interacting
quantum system and for the corresponding nonlinear Gross-Pitaevskii equation.
We point out how this symmetry can be used as a probe for recently proposed
experiments of trapped atoms in 2D.


  In this article we study effects that small perturbations in the noise have
to the solution of differential equations driven by H\"older continuous
functions of order $H>\frac12$. As an application, we consider stochastic
differential equations driven by a fractional Brownian motion. We introduce a
Wong--Zakai type stationary approximation to the fractional Brownian motions
and prove that it converges in a suitable space. Moreover, we provide sharp
results on the rate of convergence in the $p$-norm. Our stationary
approximation is suitable for all values of $H\in (0,1)$.


  A planet's Lyman-{\alpha} (Ly{\alpha}) emission is sensitive to its
thermospheric structure. Here, we report joint Hubble Space Telescope (HST) and
Cassini cross-calibration observations of the Saturn Ly{\alpha} emission made
two weeks before the Cassini grand finale. To investigate the long-term Saturn
Ly{\alpha} airglow observed by different ultraviolet instruments, we
cross-correlate their calibration, finding that while the official Cassini/UVIS
sensitivity should be lowered by ~75%, the Voyager 1/UVS sensitivities should
be enhanced by ~20% at the Ly{\alpha} channels. This comparison also allowed us
to discover a permanent feature of the Saturn disk Ly{\alpha} brightness that
appears at all longitudes as a brightness excess (Ly{\alpha} bulge) of ~30%
(~12{\sigma}) extending over the latitude range ~5-35N compared to the regions
at equator and ~60N. This feature is confirmed by three distinct instruments
between 1980 & 2017 in the Saturn north hemisphere. To analyze the Ly{\alpha}
observations, we use a radiation transfer (RT) model of resonant scattering of
solar and interplanetary Ly{\alpha} photons, and a latitude-dependent
photochemistry model of the upper atmosphere constrained by occultation and
remote-sensing observations. For each latitude, we show that the Ly{\alpha}
observations are sensitive to the temperature profile in the upper stratosphere
and lower thermosphere, thus providing useful information in a region of the
atmosphere that is difficult to probe by other means. In the Saturn Ly{\alpha}
bulge region, at latitudes between ~5 to ~35{\deg}, the observed brightening
and line broadening support seasonal effects, variation of the temperature
vertical profile, and potential superthermal atoms that require confirmation.


  Nowadays, autonomous mobile robots support people in many areas where human
presence either redundant or too dangerous. They have successfully proven
themselves in expeditions, gas industry, mines, warehouses, etc. However, even
legged robots may stuck in rough terrain conditions requiring human cognitive
abilities to navigate the system. While gamepads and keyboards are convenient
for wheeled robot control, the quadruped robot in 3D space can move along all
linear coordinates and Euler angles, requiring at least 12 buttons for
independent control of their DoF. Therefore, more convenient interfaces of
control are required.
  In this paper we present HyperPalm: a novel gesture interface for intuitive
human-robot interaction with quadruped robots. Without additional devices, the
operator has full position and orientation control of the quadruped robot in 3D
space through hand gesture recognition with only 5 gestures and 6 DoF hand
motion.
  The experimental results revealed to classify 5 static gestures with high
accuracy (96.5%), accurately predict the position of the 6D position of the
hand in three-dimensional space. The absolute linear deviation Root mean square
deviation (RMSD) of the proposed approach is 11.7 mm, which is almost 50% lower
than for the second tested approach, the absolute angular deviation RMSD of the
proposed approach is 2.6 degrees, which is almost 27% lower than for the second
tested approach. Moreover, the user study was conducted to explore user's
subjective experience from human-robot interaction through the proposed gesture
interface. The participants evaluated their interaction with HyperPalm as
intuitive (2.0), not causing frustration (2.63), and requiring low physical
demand (2.0).


  It is the last paper of a long series. We present the spectral side of the
twisted trace formula and its stable version. Using the method of Arthur, we
finish the proof of the stabilization of the two sides of this twisted trace
formula.


  Language models have become increasingly popular in recent years for tasks
like information retrieval. As use-cases become oriented toward specific
domains, fine-tuning becomes default for standard performance. To fine-tune
these models for specific tasks and datasets, it is necessary to carefully tune
the model's hyperparameters and training techniques. In this paper, we present
an in-depth analysis of the performance of four transformer-based language
models on the task of biomedical information retrieval. The models we consider
are DeepMind's RETRO (7B parameters), GPT-J (6B parameters), GPT-3 (175B
parameters), and BLOOM (176B parameters). We compare their performance on the
basis of relevance, accuracy, and interpretability, using a large corpus of
480000 research papers on protein structure/function prediction as our dataset.
Our findings suggest that smaller models, with <10B parameters and fine-tuned
on domain-specific datasets, tend to outperform larger language models on
highly specific questions in terms of accuracy, relevancy, and interpretability
by a significant margin (+50% on average). However, larger models do provide
generally better results on broader prompts.


  The linear transport properties of a model molecular transistor with
electron-electron and electron-phonon interactions were investigated
analytically and numerically. The model takes into account phonon modulation of
the electronic energy levels and of the tunnelling barrier between the molecule
and the electrodes. When both effects are present they lead to asymmetries in
the dependence of the conductance on gate voltage. The Kondo effect is observed
in the presence of electron-phonon interactions. There are important
qualitative differences between the cases of weak and strong coupling. In the
first case the standard Kondo effect driven by spin fluctuations occurs. In the
second case, it is driven by charge fluctuations. The Fermi-liquid relation
between the spectral density of the molecule and its charge is altered by
electron-phonon interactions. Remarkably, the relation between the
zero-temperature conductance and the charge remains unchanged. Therefore, there
is perfect transmission in all regimes whenever the average number of electrons
in the molecule is an odd integer.


  We discuss simple integration methods for the calculation of rotating black
hole scattering resonances both in the complex frequency plane (quasinormal
modes) and the complex angular momentum plane (Regge poles). Our numerical
schemes are based on variations of "phase-amplitude" methods. In particular, we
discuss the Pruefer transformation, where the original (frequency domain)
Teukolsky wave equation is replaced by a pair of first-order non-linear
equations governing the introduced phase functions. Numerical integration of
these equations, performed along the real coordinate axis, or along rotated
contours in the complex coordinate plane, provides the required S-matrix
element (the ratio of amplitudes of the outgoing and ingoing waves at
infinity). Mueller's algorithm is then employed to conduct searches in the
complex plane for the poles of this quantity (which are, by definition, the
desired resonances). We have tested this method by verifying known results for
Schwarzschild quasinormal modes and Regge poles, and provide new results for
the Kerr black hole problem. We also describe a new method for estimating the
"excitation coefficients" for quasinormal modes. The method is applied to
scalar waves moving in the Kerr geometry, and the obtained results shed light
on the long-lived quasinormal modes that exist for black holes rotating near
the extreme Kerr limit.


  A smooth intersection $Y$ of two quadrics in $\mathbb{P}^{2g+1}$ has Hodge
level 1. We show that such varieties $Y$ have a multiplicative Chow-K\"unneth
decomposition, in the sense of Shen-Vial. As a consequence, a certain
tautological subring of the Chow ring of powers of $Y$ injects into cohomology.


  The Near Horizon Geometry (NHG) equation with a cosmological constant
{\Lambda} is considered on compact 2-dimensional manifolds. It is shown that
every solution satisfies the Type D equation at every point of the manifold. A
similar result known in the literature was valid only for non-degenerate in a
suitable way points of a given solution. At the degenerate points the Type D
equation was not applicable. In the current paper we prove that the degeneracy
is ruled out by the compactness. Using that result we find all the solutions to
the NHG equation on compact 2-dimensional manifolds of non-positive Euler
characteristics. Some integrability conditions known earlier in the {\Lambda} =
0 case are generalized to arbitrary value of {\Lambda}. They may be still
useful for compact 2-manifolds of positive Euler characteristic.


  Meta learning with multiple objectives can be formulated as a Multi-Objective
Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to
solve several possible conflicting targets for the meta learner. However,
existing studies either apply an inefficient evolutionary algorithm or linearly
combine multiple objectives as a single-objective problem with the need to tune
combination weights. In this paper, we propose a unified gradient-based
Multi-Objective Meta Learning (MOML) framework and devise the first
gradient-based optimization algorithm to solve the MOBLP by alternatively
solving the lower-level and upper-level subproblems via the gradient descent
method and the gradient-based multi-objective optimization method,
respectively. Theoretically, we prove the convergence properties of the
proposed gradient-based optimization algorithm. Empirically, we show the
effectiveness of the proposed MOML framework in several meta learning problems,
including few-shot learning, neural architecture search, domain adaptation, and
multi-task learning.


  We study the Gibbs sampling algorithm for continuous determinantal point
processes. We show that, given a warm start, the Gibbs sampler generates a
random sample from a continuous $k$-DPP defined on a $d$-dimensional domain by
only taking $\text{poly}(k)$ number of steps. As an application, we design an
algorithm to generate random samples from $k$-DPPs defined by a spherical
Gaussian kernel on a unit sphere in $d$-dimensions, $\mathbb{S}^{d-1}$ in time
polynomial in $k,d$.


  We investigate dilepton production in transport-based approaches and show
that the baryon couplings of the $\rho$ meson represent the most important
ingredient for understanding the measured dilepton spectra. At low energies (of
a few GeV), the baryon resonances naturally play a larger role and affect
already the vacuum spectra via Dalitz-like contributions, which can be captured
well in an on-shell-transport scheme. At higher energies, the baryons mostly
affect the in-medium self energy of the $\rho$, which is harder to tackle in
transport models and requires advanced techniques.


  We study elliptic fibrations that geometrically engineer an SU(2)$\times$
G$_2$ gauge theory realized by Weierstrass model for the collision
III+$\text{I}_0^{*\text{ns}}$. We construct the four possible crepant
resolutions of such a Weierstrass model and show that they form a chain of four
minimal models connected by flops. We compute the generating function for the
Euler characteristic of these crepant resolutions. In the case of a Calabi-Yau
threefold, we consider the compactification of M-theory and F-theory on an
SU(2)$\times$ G$_2$-model to a five and six-dimensional supergravity with eight
supercharges. By matching each crepant resolution with each Coulomb chamber of
the five-dimensional theory, we determine the number of multiplets and compute
the prepotential in each Coulomb chamber. In particular, we discuss counting
number of hypermultiplets in presence of singularities. We discuss in detail
the cancellation of anomalies of the six-dimensional theory.


  Rough path analysis is developed in the full Besov scale. This extends, and
essentially concludes, an investigation started by [Pr\"omel--Trabs, Rough
differential equations driven by signals in {B}esov spaces. J. Diff. Equ.
2016], further studied in a series of papers by Liu, Pr\"omel and Teichmann. A
new Besov sewing lemma, a real-analysis result of interest in its own right,
plays a key role, and the flexibility in the choice of Besov parameters allows
for the treatment of equations not available in the H\"older or variation
settings. Important classes of stochastic processes fit in the present
framework.


  Applications such as the analysis of microbiome data have led to renewed
interest in statistical methods for compositional data, i.e., multivariate data
in the form of probability vectors that contain relative proportions. In
particular, there is considerable interest in modeling interactions among such
relative proportions. To this end we propose a class of exponential family
models that accommodate general patterns of pairwise interaction while being
supported on the probability simplex. Special cases include the family of
Dirichlet distributions as well as Aitchison's additive logistic normal
distributions. Generally, the distributions we consider have a density that
features a difficult to compute normalizing constant. To circumvent this issue,
we design effective estimation methods based on generalized versions of score
matching. A high-dimensional analysis of our estimation methods shows that the
simplex domain is handled as efficiently as previously studied full-dimensional
domains.


  Serverless computing is a buzzword that is being used commonly in the world
of technology and among developers and businesses. Using the
Function-as-a-Service (FaaS) model of serverless, one can easily deploy their
applications to the cloud and go live in a matter of days, it facilitates the
developers to focus on their core business logic and the backend process such
as managing the infrastructure, scaling of the application, updation of
software and other dependencies is handled by the Cloud Service Provider. One
of the features of serverless computing is ability to scale the containers to
zero, which results in a problem called cold start. The challenging part is to
reduce the cold start latency without the consumption of extra resources. In
this paper, we use SARIMA (Seasonal Auto Regressive Integrated Moving Average),
one of the classical time series forecasting models to predict the time at
which the incoming request comes, and accordingly increase or decrease the
amount of required containers to minimize the resource wastage, thus reducing
the function launching time. Finally, we implement PBA (Prediction Based
Autoscaler) and compare it with the default HPA (Horizontal Pod Autoscaler),
which comes inbuilt with kubernetes. The results showed that PBA performs
fairly better than the default HPA, while reducing the wastage of resources.


  Transient gravitational waves (aka gravitational wave bursts) within the
nanohertz frequency band could be generated by a variety of astrophysical
phenomena such as the encounter of supermassive black holes, the kinks or cusps
in cosmic strings, or other as-yet-unknown physical processes. Radio-pulses
emitted from millisecond pulsars could be perturbed by passing gravitational
waves, hence the correlation of the perturbations in a pulsar timing array can
be used to detect and characterize burst signals with a duration of
$\mathcal{O}(1\text{-}10)$ years. We propose a fully Bayesian framework for the
analysis of the pulsar timing array data, where the burst waveform is
generically modeled by piecewise straight lines, and the waveform parameters in
the likelihood can be integrated out analytically. As a result, with merely
three parameters (in addition to those describing the pulsars' intrinsic and
background noise), one is able to efficiently search for the existence and the
sky location of {a burst signal}. If a signal is present, the posterior of the
waveform can be found without further Bayesian inference. We demonstrate this
model by analyzing simulated data sets containing a stochastic gravitational
wave background {and a burst signal generated by the parabolic encounter of two
supermassive black holes.


  In this paper, we consider a UAV-enabled MEC platform that serves multiple
mobile ground users with random movements and task arrivals. We aim to minimize
the average weighted energy consumption of all users subject to the average UAV
energy consumption and data queue stability constraints. To control the system
operation in sequential time slots, we formulate the problem as a multi-stage
stochastic optimization, and propose an online algorithm that optimizes the
resource allocation and the UAV trajectory in each stage. We adopt Lyapunov
optimization to convert the multi-stage stochastic problem into per-slot
deterministic problems with much less optimizing variables. To tackle the
non-convex per-slot problem, we use the successive convex approximation (SCA)
technique to jointly optimize the resource allocation and the UAV movement.
Simulation results show that the proposed online algorithm can satisfy the
average UAV energy and queue stability constraints, and significantly
outperform the other considered benchmark methods in reducing the energy
consumption of ground users.


  The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately
classify a target dataset with limited labelled data by exploiting the
knowledge of a richly labelled auxiliary dataset, despite the differences
between the domains of the two datasets. Some existing approaches require
labelled samples from multiple domains for model training. However, these
methods fail when the sample labels are scarce. To overcome this challenge,
this paper proposes a solution that makes use of multiple source domains
without the need for additional labeling costs. Specifically, one of the source
domains is completely tagged, while the others are untagged. An Inter-Source
Stylization Network (ISSNet) is then introduced to enhance stylisation across
multiple source domains, enriching data distribution and model's generalization
capabilities. Experiments on 8 target datasets show that ISSNet leverages
unlabelled data from multiple source data and significantly reduces the
negative impact of domain gaps on classification performance compared to
several baseline methods.


  We study the production characteristics of 557 pairs of charmed hadrons
produced in $\pi^{-}-Cu$\ interactions at 230~GeV/c using a momentum estimator
for charmed hadrons with missing decay products. We find, the mean value of the
transverse momentum squared of the charmed pairs is $<$\pts$>=(1.98\pm 0.11\pm
0.09)\;$ GeV$^2$/c$^2$, the mean rapidity difference is $<|$\yd$|>=0.54\pm
0.02\pm 0.24$, and the mean effective mass is $<$\mef$>=(4.45\pm 0.03\pm
0.13)\;$ GeV/c$^2$. Comparing these results with the next-to-leading order QCD
predictions we find an agreement for the \yd\ and \mef, whilst the measured
mean value of \pts\ is significantly larger than the predicted value.


  We consider the following nonlinear Choquard equation with Dirichlet boundary
condition $$-\Delta u
=\left(\int_{\Omega}\frac{|u|^{2_{\mu}^{\ast}}}{|x-y|^{\mu}}dy\right)|u|^{2_{\mu}^{\ast}-2}u+\lambda
f(u)\hspace{4.14mm}\mbox{in}\hspace{1.14mm} \Omega, $$ where $\Omega$ is a
smooth bounded domain of $\mathbb{R}^N$, $\lambda>0$, $N\geq3$, $0<\mu<N$ and
$2_{\mu}^{\ast}$ is the critical exponent in the sense of the
Hardy-Littlewood-Sobolev inequality. Under suitable assumptions on different
types of nonlinearities $f(u)$, we are able to prove some existence and
multiplicity results for the equation by variational methods.


  We investigate the nonlinear optical properties of BiOBr nanoflakes, a novel
two-dimensional (2D) layered material from the Bismuth oxyhalide family. We
measure the nonlinear absorption and Kerr nonlinearity of BiOBr nanoflakes at
both 800 nm and 1550 nm via the Z scan technique. We observe a large nonlinear
absorption coefficient beta = 10^-7 m/W as well as a large Kerr coefficient n2
= 10^-14 m^2/W. We also observe strong dispersion in n2, with it reversing sign
from negative to positive as the wavelength varies from 800 nm to 1550 nm. In
addition, we characterize the thickness-dependence of the nonlinear optical
properties of BiOBr nanoflakes, observing that both the magnitudes of beta and
n2 increase for very thin flakes. Finally, we integrate BiOBr nanoflakes onto
silicon integrated waveguides and characterize the linear optical properties of
the resulting hybrid integrated devices, with the measurements agreeing with
calculated parameters using independent ellipsometry measurements. These
results verify the strong potential of BiOBr as an advanced nonlinear optical
material for high-performance hybrid integrated photonic devices.


  Video generation primarily aims to model authentic and customized motion
across frames, making understanding and controlling the motion a crucial topic.
Most diffusion-based studies on video motion focus on motion customization with
training-based paradigms, which, however, demands substantial training
resources and necessitates retraining for diverse models. Crucially, these
approaches do not explore how video diffusion models encode cross-frame motion
information in their features, lacking interpretability and transparency in
their effectiveness. To answer this question, this paper introduces a novel
perspective to understand, localize, and manipulate motion-aware features in
video diffusion models. Through analysis using Principal Component Analysis
(PCA), our work discloses that robust motion-aware feature already exists in
video diffusion models. We present a new MOtion FeaTure (MOFT) by eliminating
content correlation information and filtering motion channels. MOFT provides a
distinct set of benefits, including the ability to encode comprehensive motion
information with clear interpretability, extraction without the need for
training, and generalizability across diverse architectures. Leveraging MOFT,
we propose a novel training-free video motion control framework. Our method
demonstrates competitive performance in generating natural and faithful motion,
providing architecture-agnostic insights and applicability in a variety of
downstream tasks.


  While real-world decisions involve many competing objectives, algorithmic
decisions are often evaluated with a single objective function. In this paper,
we study algorithmic policies which explicitly trade off between a private
objective (such as profit) and a public objective (such as social welfare). We
analyze a natural class of policies which trace an empirical Pareto frontier
based on learned scores, and focus on how such decisions can be made in noisy
or data-limited regimes. Our theoretical results characterize the optimal
strategies in this class, bound the Pareto errors due to inaccuracies in the
scores, and show an equivalence between optimal strategies and a rich class of
fairness-constrained profit-maximizing policies. We then present empirical
results in two different contexts -- online content recommendation and
sustainable abalone fisheries -- to underscore the applicability of our
approach to a wide range of practical decisions. Taken together, these results
shed light on inherent trade-offs in using machine learning for decisions that
impact social welfare.


  We consider the problem of inserting a stiff chain into a colloidal
suspension of particles that interact with it through excluded volume forces.
The free energy of insertion is associated with the work of creating a cavity
devoid of colloid and sufficiently large to accomodate the chain. The
corresponding work per unit length is the force that resists the entry of the
chain into the colloidal suspension. In the case of a hard sphere fluid, this
work can be calculated straightforwardly within the scaled particle theory; for
solutions of flexible polymers, on the other hand, we employ simple scaling
arguments. The forces computed in these ways are shown, for nanometer chain and
colloid diameters, to be of the order of tens of pN for solution volume
fraction for biophysical processes such as the ejection of DNA from viral
capsids into the cell cytoplasm.


  We explore the potential of a spin-orbit coupled Bose-Einstein condensate for
thermodynamic cycles. For this purpose we propose a quantum heat engine based
on a condensate with spin-orbit and Zeeman coupling as a working medium. The
cooling and heating are simulated by contacts of the condensate with an
external magnetized media and demagnetized media. We examine the condensate
ground state energy and its dependence on the strength of the synthetic
spin-orbit and Zeeman couplings and interatomic interaction. Then we study the
efficiency of the proposed engine. The cycle has a critical value of spin-orbit
coupling related to the engine maximum efficiency.


  Deterministic protocols are well-known tools to obtain extended formulations,
with many applications to polytopes arising in combinatorial optimization.
Although constructive, those tools are not output-efficient, since the time
needed to produce the extended formulation also depends on the number of rows
of the slack matrix (hence, on the exact description in the original space). We
give general sufficient conditions under which those tools can be implemented
as to be output-efficient, showing applications to e.g.~Yannakakis' extended
formulation for the stable set polytope of perfect graphs, for which, to the
best of our knowledge, an efficient construction was previously not known. For
specific classes of polytopes, we give also a direct, efficient construction of
extended formulations arising from protocols. Finally, we deal with extended
formulations coming from unambiguous non-deterministic protocols.


  Few days ago V.D. Efros submitted a preprint to nucl-th containing criticisms
of our recent research activity on bound and scattering states of A=3, 4
nucleons. As a consequence, we are forced to examine the essence of the
controversy and of the comments contained in that preprint. These are the
motivations of the present paper.


  We introduce a model for predicting the diffusion of content information on
social media. When propagation is usually modeled on discrete graph structures,
we introduce here a continuous diffusion model, where nodes in a diffusion
cascade are projected onto a latent space with the property that their
proximity in this space reflects the temporal diffusion process. We focus on
the task of predicting contaminated users for an initial initial information
source and provide preliminary results on differents datasets.


  Standard exceptional points (EPs) are non-Hermitian degeneracies that occur
in open systems. At an EP, the Taylor series expansion becomes singular and
fails to converge -- a feature that was exploited for several applications.
Here, we theoretically introduce and experimentally demonstrate a new class of
parity-time symmetric systems [implemented using radio frequency (rf) circuits]
that combine EPs with another type of mathematical singularity associated with
the poles of complex functions. These nearly divergent exceptional points can
exhibit an unprecedentedly large eigenvalue bifurcation beyond those obtained
by standard EPs. Our results pave the way for building a new generation of
telemetering and sensing devices with superior performance.


  The massive neutrinos of the Cosmic Neutrino Background (C$\nu$B) are
fundamental ingredients of the radiation-dominated early universe and are
important non-relativistic probes of the large-scale structure formation in the
late universe. The dominant source of anisotropies in the neutrino flux
distribution on the sky are highly amplified integrals of metric perturbations
encountered during the non-relativistic phase of the C$\nu$B. This paper
numerically compares the line-of-sight methods for computing C$\nu$B
anisotropies with the Einstein-Boltzmann hierarchy solutions in linear theory
for a range of neutrino masses. Angular power spectra are computed that are
relevant to a future polarized tritium target run of the PTOLEMY experiment.
Correlations between the C$\nu$B sky maps and galactic survey data are derived
using line-of-sight techniques and discussed in the context of multi-messenger
astrophysics.


  We show that a triangular lattice consisting of dipolar molecules pointing
orthogonal to the plane undergoes a first-order defect melting transition.


  We note that in extensions of the Standard Model that allow for a varying
fine structure constant, alpha, all matter species, apart from right-handed
neutrinos, will gain an intrinsic electric dipole moment (EDM). In a large
subset of varying-alpha theories, all such particle species will also gain an
effective electric charge. This charge will in general not be quantised and can
result in macroscopic non-conservation of electric charge.


  We explain the Elekes Szabo paper in more details and improve the constant in
one dimensional case in \mathbb{R} and \mathbb{C}.


  The question of how to continuously manipulate a photonic system between a
radiating state and a bound state is an important challenge in photonics, as
its solution promises broad technological relevance for optical sensors,
modulators, switches and displays. Existing approaches utilise the inherent
wave-nature of electromagnetic fields to their advantage, and are commonly
identified as bound states in the continuum (BICs), as they resemble singular
bound states embedded in a band of radiating states. Although quasi-BICs have
been demonstrated for numerous symmetric periodic photonic crystals, their
existence so far has been limited to narrow spectral ranges, and their
application in switches or modulators needs large changes of the refractive
index. Here, we show that the incidence of two guided symmetric substrate waves
of opposite phase onto a symmetric periodic film waveguide enables the
excitation of self-stabilising BICs which are electrically switchable in a
broad optical range, with small changes of the refractive index. An
experimental verification of the concept shows a switching contrast C=700 at a
wavelength of 532 nm and C=1000 at a wavelength of 632.8 nm.


  An open problem about finite geometric progressions in syndetic sets leads to
a family of diophantine equations related to the commutativity of translation
and multiplication by squares.


  We study theoretically the stability of flow in superfluid 3He-A. The
calculations are done using a one-dimensional model where the order parameter
depends only on the coordinate in the direction of the superfluid velocity v_s.
We concentrate on the case that the external magnetic field H is perpendicular
to v_s, where only few results are available analytically. We calculate the
critical velocity v_c at which the superflow becomes unstable against the
formation of continuous vortices. The detailed dependence of v_c on the
temperature and on the form of the underlying orbital texture l(r) is
investigated. Both uniform and helical textures and two types of domain-wall
structures are studied. The results are partially in agreement with experiments
made in a rotating cylinder.


  The field-induced multiferroic transition in TbMnO3 has been studied by
neutron scattering. Apart strong hysteresis, the magnetic transition associated
with the flop of electronic polarization exhibits a memory effect: after a
field sweep, TbMnO3 does not exhibit the same phase as that obtained by
zero-field cooling. The strong changes in the magnetic excitations across the
transition perfectly agree with a rotation of the cycloidal spiral plane
indicating that the inverse Dzyaloshinski-Moriya coupling causes the giant
magnetoelectric effect at the field-induced transition. The analysis of the
zone-center magnetic excitations identifies the electromagnon of the
multiferroic high-field phase.


  Let $\mathbb{K}$ be a field and $I$ be a square-free monomial ideal in the
polynomial ring $\mathbb{K}[x_1, \ldots, x_n]$. The Green-Lazarsfeld index,
$\mathrm{index}(I)$, counts the number of steps to reach to a syzygy minimally
generated by a nonlinear form in a graded minimal free resolution of $I$. In
this paper, we study this invariant for $I$ and its powers from a combinatorial
point of view. We characterize all square-free monomial ideals $I$ generated in
degree $3$ such that $\mathrm{index}(I)>1$. Utilizing this result, we also
characterize all square-free monomial ideals generated in degree $3$ such that
$\mathrm{index}(I)>1$ and $\mathrm{index}(I^2)=1$. In case $n\leq5$, it is
shown that $\mathrm{index}(I^k)>1$ for all $k$ if $I$ is any square-free
monomial ideal with $\mathrm{index}(I)>1$.


  This Bachelor's thesis studies Adelman's construction of the universal
abelian category of an additive category. It furthermore extends the
construction such that it also applies to transformations between additive
functors.


  Let $\alpha\in[0,1)$, and let $G$ be a connected graph of order $n$ with
$n\geq f(\alpha)$, where $f(\alpha)=14$ for $\alpha\in[0,\frac{1}{2}]$,
$f(\alpha)=17$ for $\alpha\in(\frac{1}{2},\frac{2}{3}]$, $f(\alpha)=20$ for
$\alpha\in(\frac{2}{3},\frac{3}{4}]$ and $f(\alpha)=\frac{5}{1-\alpha}+1$ for
$\alpha\in(\frac{3}{4},1)$. A path factor is a spanning subgraph $F$ of $G$
such that every component of $F$ is a path with at least two vertices. Let
$k\geq2$ be an integer. A $P_{\geq k}$-factor means a path-factor with each
component being a path of order at least $k$. A graph $G$ is called a $P_{\geq
k}$-factor covered graph if $G$ has a $P_{\geq k}$-factor containing $e$ for
any $e\in E(G)$. Let $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G)$, where $D(G)$
denotes the diagonal matrix of vertex degrees of $G$ and $A(G)$ denotes the
adjacency matrix of $G$. The largest eigenvalue of $A_{\alpha}(G)$ is called
the $A_{\alpha}$-spectral radius of $G$, which is denoted by
$\rho_{\alpha}(G)$. In this paper, it is proved that $G$ is a
$P_{\geq2}$-factor covered graph if $\rho_{\alpha}(G)>\eta(n)$, where $\eta(n)$
is the largest root of $x^{3}-((\alpha+1)n+\alpha-4)x^{2}+(\alpha
n^{2}+(\alpha^{2}-2\alpha-1)n-2\alpha+1)x-\alpha^{2}n^{2}+(5\alpha^{2}-3\alpha+2)n-10\alpha^{2}+15\alpha-8=0$.
Furthermore, we provide a graph to show that the bound on $A_{\alpha}$-spectral
radius is optimal.


  In computational biology, numerous recent studies have been dedicated to the
analysis of the chromatin structure within the cell by two-dimensional
segmentation methods. Motivated by this application, we consider the problem of
retrieving the diagonal blocks in a matrix of observations. The theoretical
properties of the least-squares estimators of both the boundaries and the
number of blocks proposed by L\'evy-Leduc et al. [2014] are investigated. More
precisely, the contribution of the paper is to establish the consistency of
these estimators. A surprising consequence of our results is that, contrary to
the onedimensional case, a penalty is not needed for retrieving the true number
of diagonal blocks. Finally, the results are illustrated on synthetic data.


  In the absence of external driving, a system exposed to thermal fluctuations
will relax to equilibrium. However, the constant input of work makes it
possible to counteract this relaxation, and maintain the system in a
nonequilibrium steady state. In this Article, we use the stochastic
thermodynamics of Markov jump processes to compute the minimum rate at which
energy must be supplied and dissipated to maintain an arbitrary nonequilibrium
distribution in a given energy landscape. This lower bound depends on two
factors: the undriven probability current in the equilibrium state, and the
distance from thermal equilibrium of the target distribution. By showing the
consequences of this result in a few simple examples, we suggest general
implications for the required energetic costs of macromolecular repair and
cytosolic protein localization.


  Strongly correlated electronic systems exhibit a wealth of unconventional
behavior stemming from strong electron-electron interactions. The
LaAlO$_3$/SrTiO$_3$ (LAO/STO) heterostructure supports rich and varied
low-temperature transport characteristics including low-density
superconductivity, and electron pairing without superconductivity for which the
microscopic origins is still not understood. LAO/STO also exhibits inexplicable
signatures of electronic nematicity via nonlinear and anomalous Hall effects.
Nanoscale control over the conductivity of the LAO/STO interface enables
mesoscopic experiments that can probe these effects and address their
microscopic origins. Here we report a direct correlation between electron
pairing without superconductivity, anomalous Hall effect and electronic
nematicity in quasi-1D ballistic nanoscale LAO/STO Hall crosses. The
characteristic magnetic field at which the Hall coefficient changes directly
coincides with the depairing of non-superconducting pairs showing a strong
correlation between the two distinct phenomena. Angle-dependent Hall
measurements further reveal an onset of electronic nematicity that again
coincides with the electron pairing transition, unveiling a rotational symmetry
breaking due to the transition from paired to unpaired phases at the interface.
The results presented here highlights the influence of preformed electron pairs
on the transport properties of LAO/STO and provide evidence of the elusive
pairing ''glue'' that gives rise to electron pairing in SrTiO$_3$-based
systems.


  There is a fundamental relationship between belief propagation and maximum a
posteriori decoding. A decoding algorithm, which we call the Maxwell decoder,
is introduced and provides a constructive description of this relationship.
Both, the algorithm itself and the analysis of the new decoder are reminiscent
of the Maxwell construction in thermodynamics. This paper investigates in
detail the case of transmission over the binary erasure channel, while the
extension to general binary memoryless channels is discussed in a companion
paper.


  We prove that, up to scalar multiples, there exists only one local regular
Dirichlet form on a generalized Sierpinski carpet that is invariant with
respect to the local symmetries of the carpet. Consequently for each such
fractal the law of Brownian motion is uniquely determined and the Laplacian is
well defined.


  In an empirical logic, an experimentally verifiable proposition P relating to
a quantum system is assigned the value of either true of false if the system is
in the pure state that belongs or, respectively, does not belong to the Hilbert
subspace that represents P. Determined in such a way truth or falsity of P can
be termed a factual truth-value of P. In the present paper, it is proposed to
consider a counter-factual truth-value of P, i.e., either of the values, true
or false, that would have been taken by P if the system had been in a pure
state belonging to a Hilbert subspace that does not represent P. The assumption
that it is always possible to speak meaningfully of counter-factual
truth-values of experimental propositions can be called the hypothesis of
propositional counter-factual definiteness. As it is shown in the paper, this
hypothesis lies at the basis of the EPR paradox, a striking and influential
thought experiment intended to defy predictions of quantum mechanics, such as
one that measurements of spin along the different axes are incompatible. The
purpose of this paper is to show that this hypothesis can be falsified by
declining to paste together invariant-subspace lattices of contexts associated
with the system (in other words, Boolean algebras or blocks) into one Hilbert
lattice. Without such pasting, the EPR paradoxical inference cannot be reached.


  We present a two-epoch Sloan Digital Sky Survey and Gemini/GMOS+William
Herschel Telescope/ISIS variability study of 50 broad absorption line quasars
of redshift range 1.9 < z < 4.2, containing 38 Si IV and 59 C IV BALs and
spanning rest-frame time intervals of approximately 10 months to 3.7 years. We
find that 35/50 quasars exhibit one or more variable BALs, with 58% of Si IV
and 46% of C IV BALs showing variability across the entire sample. On average,
Si IV BALs show larger fractional change in BAL pseudo equivalent width than C
IV BALs, as referenced to an unabsorbed continuum+emission-line spectrum
constructed using non-negative matrix factorisation. No correlation is found
between BAL variability and quasar luminosity, suggesting that ionizing
continuum changes do not play a significant role in BAL variability (assuming
the gas is in photoionization equilibrium with the ionizing continuum). A
subset of 14 quasars have one variable BAL from each of Si IV and C IV with
significant overlap in velocity space and for which variations are in the same
sense (strengthening or weakening) and which appear to be correlated (98%
confidence). We find examples of both appearing and disappearing BALs in
weaker/shallower lines with disappearance rates of 2.3% for C IV and 5.3% for
Si IV, suggesting average lifetimes of 142 and 43 years respectively. We
identify 5 objects in which the BAL is coincident with the broad emission-line,
but appears to cover only the continuum source. Assuming a clumpy inhomogeneous
absorber model and a typical size for the continuum source, we infer a maximum
cloud radius of 10^13 to 10^14 cm, assuming Eddington limited accretion.


  In this paper, we study the throughput and delay performances of the slotted
Aloha with batch service, which has wide applications in random access
networks. Different from the classical slotted Aloha, each node in the slotted
Aloha with batch service can transmit up to M packets once it succeeds in
channel competition. The throughput is substantially improved because up to M
packets jointly undertake the overhead due to contention. In an innovative
vacation model developed in this paper, we consider each batch of data
transmission as a busy period of each node, and the process between two
successive busy periods as a vacation period. We then formulate the number of
arrivals during a vacation period in a renewal-type equation, which
characterizes the dependency between busy periods and vacation periods. Based
on this formulation, we derive the mean waiting time of a packet and the
bounded delay region for the slotted Aloha with batch service. Our results
indicate the throughput and delay performances are substantially improved with
the increase of batch sizeM, and the bounded delay region is enlarged
accordingly. As M goes to infinity, we find the saturated throughput can
approach 100% of channel capacity, and the system remains stable irrespective
of the population size and transmission probability.


  Let $(u,v)$ be a nonnegative solution to the semilinear parabolic system \[
\mbox{(P)} \qquad \cases{ \partial_t u=D_1\Delta u+v^p, & $x\in{\bf
R}^N,\,\,\,t>0,$\\ \partial_t v=D_2\Delta v+u^q, & $x\in{\bf R}^N,\,\,\,t>0,$\\
(u(\cdot,0),v(\cdot,0))=(\mu,\nu), & $x\in{\bf R}^N,$ } \] where $D_1$,
$D_2>0$, $0<p\le q$ with $pq>1$ and $(\mu,\nu)$ is a pair of nonnegative Radon
measures or nonnegative measurable functions in ${\bf R}^N$. In this paper we
study sufficient conditions on the initial data for the solvability of
problem~(P) and clarify optimal singularities of the initial functions for the
solvability.


  We consider a quantum network of mid-infrared, graphene plasmons coupled to
the hydrogen-like excited states of group-V donors in silicon. First, we show
how to use plasmon-enhanced light-matter interactions to achieve single-shot
spin readout of the donor qubits via optical excitation and electrical
detection of the emitted plasmons. We then show how plasmons in high mobility
graphene nanoribbons can be used to achieve high-fidelity, two-qubit gates and
entanglement of distant Si donor qubits. The proposed device is readily
compatible with existing technology and fabrication methods.


  I show that the recently proposed (2+2) Hamiltonian reduction of Einstein's
equations of 4-dimensional spacetimes is consistent with general covariance.
The consistency proof is {\it extrinsic}, as it follows from the fact that
Hamilton's equations derived from the non-zero gravitational Hamiltonian are
identical to the Ricci-flat condition of 4-dimensional spacetimes in privileged
coordinates.


  In this paper, we obtain a version of Ekeland's variational principle for
interval-value functions by means of the Dancs-Hegedus-Medvegyev theorem [14].
We also derive two versions of Ekeland's variational principle involving the
generalized Hukuhara Gateaux differentiability of interval-valued functions as
well as a version of Ekeland's variational principle for interval-valued
bifunctions. Finally, we apply these new versions of Ekeland's variational
principle to fixed point theorems, to interval-valued optimization problems, to
the interval-valued Mountain Pass Theorem, to noncooperative interval-valued
games, and to interval-valued optimal control problems described by
interval-valued differential equations.


  Unbiased estimators are introduced for averaged Bregman divergences which
generalize Stein's Unbiased (Predictive) Risk Estimator, and the minimization
of these estimators is proposed as a regularization parameter selection method
for regularization of inverse problems. Numerical experiments are presented in
order to show the performance of the proposed technique. Experimental results
indicate a useful occurence of a concentration of measure phenomena and some
implications of this hypothesis are analyzed.


  Combining our comprehensive investigations of polarization evolution,
soft-mode by Raman scattering and microstructure by TEM, and the results
reported in the literatures, we show that prototypical relaxor Pb(Mg1/3Nb2/3)O3
(PMN) is essentially ferroelectric for T<Tc~225 K. Its anomalous dielectric
behavior over a broad temperature range results from the reorientation of
domains in the crystal. A physic picture of the structure evolution in relaxor
is also revealed. It is found that nanometric ferroelectric domains (gennerally
called as polar nano-region (PNR)) interact cooperatively to form micrometric
domain. Such multiscale inhomogeneities of domain structure in addition to the
well-known inhomogeneities of chemical composition and local symmetry are
considered to play a crucial role in producing the enigmatic phenomena in
relaxor system.


  In this talk I review and generalize an idea of Seiberg that an N=1
supersymmetric gauge theory shows confinement without breaking of chiral
symmetry when the gauge symmetry of its magnetic dual is completely broken by
the Higgs effect. It is shown how the confining spectrum of a supersymmetric
gauge theory can easily be derived when a magnetic dual is known and this
method is applied to many models containing fields in second rank tensor
representations and an appropriate tree-level superpotential.


  Using the density functional theory (DFT) with the generalized gradient
approximation (GGA), the structural and electronic properties of wurtzite AlN,
GaN, InN, and their related alloys, Al$_x$Ga$_{1-x}$N and In$_x$Ga$_{1-x}$N,
were calculated. We have performed accurate {\it ab initio} total energy
calculations using the full--potential linearized augmented plane wave
(FP--LAPW) method to investigate the structural and electronic properties. In
both alloys we found that the fundamental parameters do not follow Vegard's
law. The lattice parameters, $a, c,$ and $u$, for the Al$_x$Ga$_{1-x}$N alloy
are found to exhibit downward bowing, while for In$_x$Ga$_{1-x}$N there is an
upward bowing for the $a$ and $c$ parameters and a downward bowing for the
internal parameter, $u$. Furthermore, we found that for both alloys, the band
gap value does not follow Vegard's law. As a by--product of our electronic band
structure calculations, the effective masses of the binary compounds as well as
their related alloys were calculated. We show that the calculated properties
for the binary compounds, as well as for the studied alloys, show good
agreement with most of the previously reported results. Finally, using the
frozen phonon approach, the A$_1(TO)$ mode for the different systems studied in
this work was calculated. Our calculations show good agreement with
experimental values reported for the binary compounds. For the ternary alloys,
our calculations reproduce experimental values for Al$_x$Ga$_{1-x}$N as well as
theoretical predictions for In$_x$Ga$_{1-x}$N.


  In arXiv:hep-th/0310113 we started a program of creating a reference-book on
matrix-model tau-functions, the new generation of special functions, which are
going to play an important role in string theory calculations. The main focus
of that paper was on the one-matrix Hermitian model tau-functions. The present
paper is devoted to a direct counterpart for the Kontsevich and Generalized
Kontsevich Model (GKM) tau-functions. We mostly focus on calculating resolvents
(=loop operator averages) in the Kontsevich model, with a special emphasis on
its simplest (Gaussian) phase, where exists a surprising integral formula, and
the expressions for the resolvents in the genus zero and one are especially
simple (in particular, we generalize the known genus zero result to genus one).
We also discuss various features of generic phases of the Kontsevich model, in
particular, a counterpart of the unambiguous Gaussian solution in the generic
case, the solution called Dijkgraaf-Vafa (DV) solution. Further, we extend the
results to the GKM and, in particular, discuss the p-q duality in terms of
resolvents and corresponding Riemann surfaces in the example of dualities
between (2,3) and (3,2) models.


  We obtain sufficient conditions for the vanishing of higher homotopy groups
of the complements to hypersurfaces in ${\mathbb C}^n$ in terms of the behavior
at infinity and relate the monodromy of non isolated singularities to the
position of singularities in generic plane sections.


  Over the past years, interest in classifying drivers' behavior from data has
surged. Such interest is particularly relevant for car insurance companies who,
due to privacy constraints, often only have access to data from Inertial
Measurement Units (IMU) or similar. In this paper, we present a semi-supervised
learning solution to classify portions of trips according to whether drivers
are driving aggressively or normally based on such IMU data. Since the amount
of labeled IMU data is limited and costly to generate, we utilize Recurrent
Conditional Generative Adversarial Networks (RCGAN) to generate more labeled
data. Our results show that, by utilizing RCGAN-generated labeled data, the
classification of the drivers is improved in 79% of the cases, compared to when
the drivers are classified with no generated data.


  We present an entanglement criterion for multiqubits by using the quantum
correlation tensors which rely on the expectation values of the Pauli operators
for a multiqubit state. Our criterion explains not only the total entanglement
of the system but also the partial entanglement in subsystems. It shows that we
have to consider the subsystem entanglements in order to obtain the full
description for multiqubit entanglements. Furthermore, we offer an extension of
the entanglement to multiqudits.


  The well-known linear Doppler effect arises from the linear motion between
source and observer, while the less well-known rotational Doppler effect arises
from the rotational motion. Here, we present both theories and experiments
illustrating the relationship between the rotational and linear Doppler
effects. A spiral phaseplate is used to generate a light beam carrying orbital
angular momentum and the frequency shift is measured arising from its
rotational and/or linear motion. By considering either the motion-induced
time-evolving phase or the momentum and energy conservation in light-matter
interactions, we derive the rotational Doppler shift, linear Doppler shift, and
overall Doppler shift associated with rotational and linear motions. We
demonstrate the relationship between rotational and linear Doppler shifts,
either of which can be derived from the other effect, thereby illustrating
their shared origin. Moreover, the close relationship between rotational and
linear Doppler effects is also deduced for a more general moving rough surface.


  Connected and autonomous vehicles (CAVs), unlike conventional cars, will
utilise the whole space of intersections and cross in a lane-free order. This
paper formulates such a lane-free crossing of intersections as a
multi-objective optimal control problem (OCP) that minimises the overall
crossing time, as well as the energy consumption of CAVs. The proposed OCP is
convexified by applying the dual problem theory to the constraints that avoid
collision of vehicles with each other and with road boundaries. The resulting
OCP is smooth and solvable by gradient-based algorithms. Simulation results
show that the proposed algorithm reduces the crossing time by an average of 40%
and 41% as compared to, respectively, the state-of-the-art reservation-based
and lane-free methods, whilst consuming the same amount of energy. Furthermore,
it is shown that the resulting crossing time of the proposed algorithm is i)
fixed to a constant value regardless of the number of CAVs, and ii) very close
to its theoretical limit.


  Two variants of multi-robot search for a stationary object in a priori known
environment represented by a graph are studied in the paper. The first one is a
generalization of the Traveling Deliveryman Problem where more than one
deliveryman is allowed to be used in a solution. Similarly, the second variant
is a generalization of the Graph Search Problem. A novel heuristics suitable
for both problems is proposed which is furthermore integrated into a
cluster-first route second approach. A set of computational experiments was
conducted over the benchmark instances derived from the TSPLIB library. The
results obtained show that even a standalone heuristics significantly
outperforms the standard solution based on k- means clustering in quality of
results as well as computational time. The integrated approach furthermore
improves solutions found by a standalone heuristics by up to 15% at the expense
of higher computational complexity.


  We study the effects of introducing a feedback channel in the erasure
source-broadcast problem for the case of three receivers. In our problem
formulation, we wish to transmit a binary source to three users over the
erasure broadcast channel when causal feedback is universally available to the
transmitter and all three receivers. Each receiver requires a certain fraction
of the source sequence, and we are interested in the minimum latency, or
transmission time, required to serve them all.
  We propose a queue-based hybrid digital-analog coding scheme that achieves
optimal performance for the duration of analog transmissions. We characterize
the number of analog transmissions that can be sent as the solution to a linear
program and furthermore give sufficient conditions for which optimal
performance can be achieved by all users. In some cases, we find that users can
be point-to-point optimal regardless of their distortion constraints.
  When the analog transmissions are insufficient in meeting user demands, we
propose two subsequent coding schemes. The first uses a queue preprocessing
strategy for channel coding. The second method is a novel chaining algorithm,
which involves the transmitter targeting point-to-point optimal performance for
two users as if they were the only users in the network. Meanwhile, the third
user simultaneously builds "chains" of symbols that he may at times be able to
decode based on the channel conditions, and the subsequent reception of
additional symbols.


  We study the problem of coupling Einstein's equations to a relativistic and
physically well-motivated version of the Navier-Stokes equations. Under a
natural evolution condition for the vorticity, we prove existence and
uniqueness in a suitable Gevrey class if the fluid is incompressible, where
this condition is given an appropriate relativistic interpretation, and show
that the solutions enjoy the finite propagation speed property.


  Single crystal tungsten ditelluride (WTe2) has recently been discovered to
exhibit non-saturating extreme magnetoresistance in bulk; it has also emerged
as a new layered material from which atomic layer crystals can be extracted.
While atomically thin WTe2 is attractive for its unique properties, little
study has been conducted on single- and few-layer WTe2. Here we report the
isolation of single- and few-layer WTe2, as well as fabrication and
characterization of the first WTe2 suspended nanostructures. We have observed
new Raman signatures of few-layer WTe2 that have been theoretically predicted
but not yet reported to date, in both on-substrate and suspended WTe2 flakes.
We have further probed the nanomechanical properties of suspended WTe2
structures by measuring their flexural resonances, and obtain a Young's modulus
of E_Y~80GPa for the suspended WTe2 flakes. This study paves the way for future
investigations and utilization of the multiple new Raman fingerprints of
single- and few-layer WTe2, and for exploring mechanical control of WTe2 atomic
layers.


  Two-dimensional singular decomposition (2DSVD) has been widely used for image
processing tasks, such as image reconstruction, classification, and clustering.
However, traditional 2DSVD algorithm is based on the mean square error (MSE)
loss, which is sensitive to outliers. To overcome this problem, we propose a
robust 2DSVD framework based on a generalized kernel risk sensitive loss
(GKRSL-2DSVD) which is more robust to noise and and outliers. Since the
proposed objective function is non-convex, a majorization-minimization
algorithm is developed to efficiently solve it with guaranteed convergence. The
proposed framework has inherent properties of processing non-centered data,
rotational invariant, being easily extended to higher order spaces.
Experimental results on public databases demonstrate that the performance of
the proposed method on different applications significantly outperforms that of
all the benchmarks.


  Suspensions of micro and nano particles made of Polystyrene, Poly(methyl
methacrylate), Silicon dioxide etc. have been a standard model system to
understand colloidal physics. . These systems have proved useful insights into
phenomena such as self-assembly. Colloidal model systems are also extensively
used to simulate many condensed matter phenomena such as dynamics in a quenched
disordered system and glass transition. A precise control of particles using
optical or holographic tweezers is essential for such studies. However, studies
of collective phenomena such as jamming and flocking behaviour in a disordered
space are limited due to the low throughput of the optical trapping
techniques.In this article, we present a technique where we trap and pin
polystyrene microspheres ~ 10 {\mu}m over triangular-crest shaped
microstructures in a microfluidic environment. Trapping/Pinning occurs due to
the combined effect of hydrodynamic interaction and non-specific adhesion
forces. This method allows trapping and pinning of microspheres in any
arbitrary pattern with a high degree of spatial accuracy which can be useful in
studying fundamentals of various collective phenomena as well as in
applications such as bead detachment assay based biosensors.


  Little innovation has been made to low-level attitude flight control used by
uncrewed aerial vehicles (UAVs), which still predominantly uses the classical
PID controller. In this work we introduce Neuroflight, the first open source
neuro-flight controller firmware. We present our toolchain for training a
neural network in simulation and compiling it to run on embedded hardware.
Challenges faced jumping from simulation to reality are discussed along with
our solutions. Our evaluation shows the neural network can execute at over
2.67kHz on an Arm Cortex-M7 processor and flight tests demonstrate a quadcopter
running Neuroflight can achieve stable flight and execute aerobatic maneuvers.


  We study the Noether-Lefschetz locus of a very ample line bundle L on an
arbitrary smooth threefold Y. Building on results of Green, Voisin and
Otwinowska, we give explicit bounds, depending only on the Castelnuovo-Mumford
regularity properties of L, on the codimension of the components of the
Noether-Lefschetz locus of |L|.


  We consider four-point functions of operators in the stress tensor multiplet
of the 3d $\mathcal{N}=6$ $U(N)_k\times U(N+M)_{-k}$ or $SO(2)_{2k}\times
USp(2+2M)_{-k}$ ABJ theories in the limit where $M$ and $k$ are taken to
infinity while $N$ and $\lambda\sim M/k$ are held fixed. In this limit, these
theories have weakly broken higher spin symmetry and are holographically dual
to $\mathcal{N}=6$ higher spin gravity on $AdS_4$, where $\lambda$ is dual to
the bulk parity breaking parameter. We use the weakly broken higher spin Ward
identities, superconformal Ward identities, and the Lorentzian inversion
formula to fully determine the tree level stress tensor multiplet four-point
function up to two free parameters. We then use supersymmetric localization to
fix both parameters for the ABJ theories in terms of $\lambda$, so that our
result for the tree level correlator interpolates between the free theory at
$\lambda=0$ and a parity invariant interacting theory at $\lambda=1/2$. We
compare the CFT data extracted from this correlator to a recent numerical
bootstrap conjecture for the exact spectrum of $U(1)_{2M}\times U(1+M)_{-2M}$
ABJ theory (i.e. $\lambda=1/2$ and $N=1$), and find good agreement in the
higher spin regime.


  The Erd\H{o}s primitive set conjecture states that the sum $f(A) = \sum_{a\in
A}\frac{1}{a\log a}$, ranging over any primitive set $A$ of positive integers,
is maximized by the set of prime numbers. Recently Laib, Derbal, and Mechik
proved that the translated Erd\H{o}s conjecture for the sum $f(A,h) =
\sum_{a\in A}\frac{1}{a(\log a+h)}$ is false starting at $h=81$, by comparison
with semiprimes. In this note we prove that such falsehood occurs already at
$h= 1.04\cdots$, and show this translate is best possible for semiprimes. We
also obtain results for translated sums of $k$-almost primes with larger $k$.


  When gas giant protoplanets grow sufficiently massive, circumplanetary disks
would form. While solid bodies captured by the circumplanetary disks likely
contribute to the growth of the planets and regular satellites around them,
some of captured bodies would remain in planet-centered orbits after the
dispersal of the disk. We examine capture and subsequent orbital evolution of
planetesimals in waning circumplanetary gas disks using three-body orbital
integration. We find that some of captured planetesimals can survive in the
circumplanetary disk for a long period of time under such weak gas drag.
Captured planetesimals have semi-major axes smaller than about one third of the
planet's Hill radius. Distributions of their eccentricities and inclinations
after disk dispersal depend on the strength of gas drag and the timescale of
disk dispersal, and initially strong gas drag and quick disk dispersal
facilitates capture and survival of planetesimals. However, in such a case,
final orbital eccentricities and inclinations of captured bodies remain rather
large. Although our results suggest that some of the present irregular
satellites of gas giant planets with small semi-major axes would have been
captured by gas drag, other mechanisms are required to fully explain their
current orbital characteristics.


  In this paper we consider the problem of estimation of scalar field
distribution (e.g. pollutant, moisture, temperature) from noisy measurements
collected by unmanned autonomous vehicles such as UAVs. The field is modelled
as a sum of Fourier components/modes, where the number of modes retained and
estimated determines in a natural way the approximation quality. An algorithm
for estimating the modes using an online optimization approach is presented,
under the assumption that the noisy measurements are quantized. The algorithm
can also estimate time-varying fields through the introduction of a forgetting
factor. Simulation studies demonstrate the effectiveness of the proposed
approach.


  After photo-excitation of DNA, the excited electron (in the LUMO) and the
remaining hole (in the HOMO) localized on the same DNA base form a bound pair,
called the Frenkel exciton, due to their mutual Coulomb interaction. In this
study, we demonstrate that a tight-binding (TB) approach, parametrized by ab
initio data, allows to correlate relaxation properties, average charge
separation, and dipole moments to a large ensemble of double-stranded DNA
sequences (all 16,384 possible sequences with 14 nucleobases). This way, we are
able to identify a relatively small sub-ensemble of sequences responsible for
long-lived excited states, high average charge separation, and high dipole
moment. Further analysis shows that these sequences are particularly T-rich. By
systematically screening the impact of electron-hole interaction (Coulomb
forces), we verify that these correlations are relatively robust against
finite-size variations of the interaction parameter, not directly accessible
experimentally. This methodology combines simulation methods from quantum
physics and physical chemistry with statistical analysis known from genetics
and epigenetics, thus representing a powerful bridge to combine information
from both fields.


  We examine the Si(111) multi-valley quantum Hall system and show that it
exhibits an exceptionally rich interplay of broken symmetries and quantum Hall
ordering already near integer fillings $\nu$ in the range $\nu=0-6$. This
six-valley system has a large $[SU(2)]^3\rtimes D_3$ symmetry in the limit
where the magnetic length is much larger than the lattice constant. We find
that the discrete ${D}_3$ factor breaks over a broad range of fillings at a
finite temperature transition to a discrete nematic phase. As $T \rightarrow 0$
the $[SU(2)]^3$ continuous symmetry also breaks: completely near $\nu =3$, to a
residual $[U(1)]^2\times SU(2)$ near $\nu=2$ and $4$ and to a residual
$U(1)\times [SU(2)]^2$ near $\nu=1$ and $5$. Interestingly, the symmetry
breaking near $\nu=2,4$ and $\nu=3$ involves a combination of selection by
thermal fluctuations known as "order by disorder" and a selection by the
energetics of Skyrme lattices induced by moving away from the commensurate
fillings, a mechanism we term "order by doping". We also exhibit modestly
simpler analogs in the four-valley Si(110) system.


  Gliomas are the most common and aggressive among brain tumors, which cause a
short life expectancy in their highest grade. Therefore, treatment assessment
is a key stage to enhance the quality of the patients' lives. Recently, deep
convolutional neural networks (DCNNs) have achieved a remarkable performance in
brain tumor segmentation, but this task is still difficult owing to high
varying intensity and appearance of gliomas. Most of the existing methods,
especially UNet-based networks, integrate low-level and high-level features in
a naive way, which may result in confusion for the model. Moreover, most
approaches employ 3D architectures to benefit from 3D contextual information of
input images. These architectures contain more parameters and computational
complexity than 2D architectures. On the other hand, using 2D models causes not
to benefit from 3D contextual information of input images. In order to address
the mentioned issues, we design a low-parameter network based on 2D UNet in
which we employ two techniques. The first technique is an attention mechanism,
which is adopted after concatenation of low-level and high-level features. This
technique prevents confusion for the model by weighting each of the channels
adaptively. The second technique is the Multi-View Fusion. By adopting this
technique, we can benefit from 3D contextual information of input images
despite using a 2D model. Experimental results demonstrate that our method
performs favorably against 2017 and 2018 state-of-the-art methods.


  The magnitude of the anomalous torque acting on a rotating magnetized ball in
vacuum is specified. Its value is shown to depend on the magnetic field
structure inside the body.


  There is evidence that the driving style of an autonomous vehicle is
important to increase the acceptance and trust of the passengers. The driving
situation has been found to have a significant influence on human driving
behavior. However, current driving style models only partially incorporate
driving environment information, limiting the alignment between an agent and
the given situation. Therefore, we propose a situation-aware driving style
model based on different visual feature encoders pretrained on fleet data, as
well as driving behavior predictors, which are adapted to the driving style of
a specific driver. Our experiments show that the proposed method outperforms
static driving styles significantly and forms plausible situation clusters.
Furthermore, we found that feature encoders pretrained on our dataset lead to
more precise driving behavior modeling. In contrast, feature encoders
pretrained supervised and unsupervised on different data sources lead to more
specific situation clusters, which can be utilized to constrain and control the
driving style adaptation for specific situations. Moreover, in a real-world
setting, where driving style adaptation is happening iteratively, we found the
MLP-based behavior predictors achieve good performance initially but suffer
from catastrophic forgetting. In contrast, behavior predictors based on
situationdependent statistics can learn iteratively from continuous data
streams by design. Overall, our experiments show that important information for
driving behavior prediction is contained within the visual feature encoder. The
dataset is publicly available at
huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.


  The progress we are currently witnessing in many computer vision
applications, including automatic face analysis, would not be made possible
without tremendous efforts in collecting and annotating large scale visual
databases. To this end, we propose 4DFAB, a new large scale database of dynamic
high-resolution 3D faces (over 1,800,000 3D meshes). 4DFAB contains recordings
of 180 subjects captured in four different sessions spanning over a five-year
period. It contains 4D videos of subjects displaying both spontaneous and posed
facial behaviours. The database can be used for both face and facial expression
recognition, as well as behavioural biometrics. It can also be used to learn
very powerful blendshapes for parametrising facial behaviour. In this paper, we
conduct several experiments and demonstrate the usefulness of the database for
various applications. The database will be made publicly available for research
purposes.


  In a seminal paper in 2013, Witt showed that the (1+1) Evolutionary Algorithm
with standard bit mutation needs time $(1+o(1))n \ln n/p_1$ to find the optimum
of any linear function, as long as the probability $p_1$ to flip exactly one
bit is $\Theta(1)$. In this paper we investigate how this result generalizes if
standard bit mutation is replaced by an arbitrary unbiased mutation operator.
This situation is notably different, since the stochastic domination argument
used for the lower bound by Witt no longer holds. In particular, starting
closer to the optimum is not necessarily an advantage, and OneMax is no longer
the easiest function for arbitrary starting positions.
  Nevertheless, we show that Witt's result carries over if $p_1$ is not too
small, with different constraints for upper and lower bounds, and if the number
of flipped bits has bounded expectation~$\chi$. Notably, this includes some of
the heavy-tail mutation operators used in fast genetic algorithms, but not all
of them. We also give examples showing that algorithms with unbounded $\chi$
have qualitatively different trajectories close to the optimum.


  We demonstrate the generation of counter-rotating cavity solitons in a
silicon nitride microresonator using a fixed, single-frequency laser. We
demonstrate a dual 3-soliton state with a difference in the repetition rates of
the soliton trains that can be tuned by varying the ratio of pump powers in the
two directions. Such a system enables a highly compact, tunable dual comb
source that can be used for applications such as spectroscopy and distance
ranging.


  The identity of quantum matter can be effectively altered by means of gauge
fields. In two spatial dimensions this is illustrated by the Chern-Simons
flux-attachment mechanism, but such a mechanism is not possible in lower
dimensions. Here, we study a one-dimensional interacting Bose gas in the
presence of a gauge field. This model can be explicitly mapped into an
interacting anyonic system by a large gauge transformation, indicating a
statistical transmutation analogous to that of Chern-Simons. The Bogoliubov
spectrum in the weakly-interacting limit reveals the presence of a roton
minimum arising from the statistical interaction. At a mean-field level chiral
solitons are recovered. Should these be understood as quantum bound states, it
is natural to interpret them as corresponding to localised anyonic
quasiparticles. Hydrodynamic arguments highlight the presence of dispersive
chiral shock waves in the propagation of a wavepacket due to a Riemann-Hopf
nonlinearity. Numerical calculations show the presence of both chiral soliton
trains and shock waves.


  We prove that cycles, wheels and block graphs have sequentially
Cohen-Macaulay binomial edge ideals. Moreover, we provide a construction of new
families of sequentially Cohen-Macaulay graphs by cones.


  This manifesto paper will introduce machine listening intelligence, an
integrated research framework for acoustic and musical signals modelling, based
on signal processing, deep learning and computational musicology.


  The lower critical field, Bc1, is one of the fundamental quantities of a
superconductor which directly manifests the Cooper pair bulk density in the
material. Despite this field can be measured by several techniques, the most
conventional way is to calculate this field from experimentally measured DC
penetration field, Bp, which is defined as the starting point of the deviation
of the DC magnetization curve, M(Bappl), from a linear dependence. Surprisingly
enough we found that there is no mathematical routine which describes how this
starting point of deviation can be found. Here we proposed to extract Bp data
from the fit of M(Bappl) dataset to the power law, where the threshold
criterion Mc can be established by a convention. The advantage of this approach
is that the procedure extracts one additional characteristic parameter, which
is the power-law exponent. We demonstrated the applicability of the approach
for polycrystalline ThIr3, WB4.2, BaTi2Bi2O, Th4H15, for Pb thin films and for
Nb single crystal. Due to in majority of reports Bc1(T) analysis is limited by
the extracting of the London penetration depth, here we advanced the analysis
to extract primary thermodynamic superconducting parameters (i.e., the ground
state superconducting energy gap, ${\Delta}(0)$, the relative jump in
electronic specific heat at transition temperature, ${\Delta}C/C$, and the
gap-to-transition temperature ratio, $2{\Delta}(0)/(k_B T_c )$ from Bc1(T)
data. This extraction was shown for Nb, ThIr3, TaRh2B2 and NbRh2B2.


  We re-examine the well-known Wong formula for heavy-ion fusion cross
sections. Although this celebrated formula yields almost exact results for
single-channel calculations for relatively heavy systems such as
$^{16}$O+$^{144}$Sm, it tends to overestimate the cross section for light
systems such as $^{12}$C+$^{12}$C. We generalise the formula to take account of
the energy dependence of the barrier parameters and show that the
energy-dependent version gives results practically indistinguishable from a
full quantal calculation. We then examine the deviations arising from the
discrete nature of the intervening angular momenta, whose effect can lead to an
oscillatory contribution to the excitation function. We recall some compact,
analytic expressions for these oscillations, and highlight the important
physical parameters that give rise to them. Oscillations in symmetric systems
are discussed, as are systems where the target and projectile identities can be
exchanged via a strong transfer channel.