  Adversarial examples are considered a serious issue for safety critical
applications of AI, such as finance, autonomous vehicle control and medicinal
applications. Though significant work has resulted in increased robustness of
systems to these attacks, systems are still vulnerable to well-crafted attacks.
To address this problem, several adversarial attack detection methods have been
proposed. However, a system can still be vulnerable to adversarial samples that
are designed to specifically evade these detection methods. One recent
detection scheme that has shown good performance is based on uncertainty
estimates derived from Monte-Carlo dropout ensembles. Prior Networks, a new
method of estimating predictive uncertainty, has been shown to outperform
Monte-Carlo dropout on a range of tasks. One of the advantages of this approach
is that the behaviour of a Prior Network can be explicitly tuned to, for
example, predict high uncertainty in regions where there are no training data
samples. In this work, Prior Networks are applied to adversarial attack
detection using measures of uncertainty in a similar fashion to Monte-Carlo
Dropout. Detection based on measures of uncertainty derived from DNNs and
Monte-Carlo dropout ensembles are used as a baseline. Prior Networks are shown
to significantly out-perform these baseline approaches over a range of
adversarial attacks in both detection of whitebox and blackbox configurations.
Even when the adversarial attacks are constructed with full knowledge of the
detection mechanism, it is shown to be highly challenging to successfully
generate an adversarial sample.


  We propose a new approach to generate and detect spin currents in graphene,
based on a large spin-Hall response arising near the neutrality point in the
presence of external magnetic field. Spin currents result from the imbalance of
the Hall resistivity for the spin-up and spin-down carriers induced by Zeeman
interaction, and do not involve spin-orbit interaction. Large values of the
spin-Hall response achievable in moderate magnetic fields produced by on-chip
sources, and up to room temperature, make the effect viable for spintronics
applications.


  The localized fermions on the intersection curve $\Sigma$ of D7-branes, are
connected to a N=2 supersymmetric quantum mechanics algebra. Due to this
algebra the fields obey a global U(1) symmetry. This symmetry restricts the
proton decay operators and the neutrino mass terms. Particularly, we find that
several proton decay operators are forbidden and the Majorana mass term is the
only one allowed in the theory. A special SUSY QM algebra is studied at the end
of the paper. In addition we study the impact of a non-trivial holomorphic
metric perturbation on the localized solutions along each matter curve.
Moreover, we study the connection of the localized solutions to an N=2
supersymmetric quantum mechanics algebra when background fluxes are turned on.


  Current cosmological controversies can be solved if a sufficient level of
precision is achieved by observations. Future surveys with the next generation
of telescopes will offer significantly improved depth and angular resolution
with respect to existing observations, opening the so-called "era of precision
cosmology". But, that era can be considered already started at the radio
wavelengths with Very Long Baseline Interferometry (VLBI). In this paper, we
give an overview on how VLBI is contributing to some open questions in
contemporary cosmology by reaching simultaneously the largest distances and the
smallest scales.


  Conventional pedestrian simulators are inevitable tools in the design process
of a building, as they enable project engineers to prevent overcrowding
situations and plan escape routes for evacuation. However, simulation runtime
and the multiple cumbersome steps in generating simulation results are
potential bottlenecks during the building design process. Data-driven
approaches have demonstrated their capability to outperform conventional
methods in speed while delivering similar or even better results across many
disciplines. In this work, we present a deep learning-based approach based on a
Vision Transformer to predict density heatmaps over time and total evacuation
time from a given floorplan. Specifically, due to limited availability of
public datasets, we implement a parametric data generation pipeline including a
conventional simulator. This enables us to build a large synthetic dataset that
we use to train our architecture. Furthermore, we seamlessly integrate our
model into a BIM-authoring tool to generate simulation results instantly and
automatically.


  Deep VLT/MUSE optical integral field spectroscopy has recently revealed an
abundant population of ultra-faint galaxies ($M_{UV} \approx -15$; 0.01
$L_{\star}$) at $z=$2.9$-$6.7 due to their strong Lyman-$\alpha$ emission with
no detectable continuum. The implied Lyman-$\alpha$ equivalent widths can be in
excess of 100-200 Angstrom, challenging existing models of normal star
formation and indicating extremely young ages, small stellar masses, and a very
low amount of metal enrichment. We use JWST/NIRSpec's microshutter array to
follow-up 45 of these galaxies (11h in G235M/F170LP and 7h in G395M/F290LP), as
well as 45 lower-equivalent width Lyman-$\alpha$ emitters. Our spectroscopy
covers the range 1.7$-$5.1 micron in order to target strong optical emission
lines: H$\alpha$, [N II], [O III], and H$\beta$. Individual measurements as
well as stacks reveal line ratios consistent with a metal poor nature (2$-$40%
$Z_{\odot}$, depending on the calibration). The galaxies with the highest
equivalent widths of Lyman-$\alpha$, in excess of 90 Angstrom, have lower [N
II]/H$\alpha$ (1.9-$\sigma$) and [O III]/H$\beta$ (2.2-$\sigma$) ratios than
those with lower equivalent widths, implying lower gas-phase metallicities at a
combined significance of 2.4-$\sigma$. This implies a selection based on
Lyman-$\alpha$ equivalent width is an efficient technique for identifying
younger, less chemically enriched systems.


  The semileptonic decay of a b-quark, b--> c l nu, is considered in the
relativistic limit where the decay products are approximately collinear.
Analytic results for the double differential lepton energy distributions are
given for finite charm-quark mass. Their use for the fast simulation of
isolated lepton backgrounds from heavy quark decays is discussed.


  It is well known that, in theory, the general secure multi-party computation
problem is solvable using circuit evaluation protocols. However, the
communication complexity of the resulting protocols depend on the size of the
circuit that expresses the functionality to be computed and hence can be
impractical. Hence special solutions are needed for specific problems for
efficiency reasons. The point inclusion problem in computational geometry is a
special multiparty computation and has got many applications. Previous
protocols for the secure point inclusion problem are not adequate. In this
paper we modify some known solutions to the point inclusion problem in
computational geometry to the frame work of secure two-party computation.


  We image simultaneously the geometric, electronic and magnetic structure of a
buckled iron bilayer film that exhibits chiral magnetic order. We achieve this
by combining spin-polarized scanning tunneling microscopy and magnetic exchange
force microscopy (SPEX), to independently characterize the geometric as well as
the electronic and magnetic structure of non-flat surfaces. This new SPEX
imaging technique reveals the geometric height corrugation of the
reconstruction lines resulting from strong strain relaxation in the bilayer,
enabling the decomposition of the real-space from the eletronic structure at
the atomic level, and the correlation with the resultant spin spiral ground
state. By additionally utilizing adatom manipulation, we reveal the chiral
magnetic ground state of portions of the unit cell that were not previously
imaged with SP-STM alone. Using density functional theory (DFT), we investigate
the structural and electronic properties of the reconstructed bilayer and
identify the favorable stoichiometry regime in agreement with our experimental
result.


  Multi-frame approaches for single-microphone speech enhancement, e.g., the
multi-frame minimum-power-distortionless-response (MFMPDR) filter, are able to
exploit speech correlations across neighboring time frames. In contrast to
single-frame approaches such as the Wiener gain, it has been shown that
multi-frame approaches achieve a substantial noise reduction with hardly any
speech distortion, provided that an accurate estimate of the correlation
matrices and especially the speech interframe correlation (IFC) vector is
available. Typical estimation procedures of the IFC vector require an estimate
of the speech presence probability (SPP) in each time-frequency (TF) bin. In
this paper, we propose to use a bi-directional long short-term memory deep
neural network (DNN) to estimate the SPP for each TF bin. Aiming at achieving a
robust performance, the DNN is trained for various noise types and within a
large signal-to-noise-ratio range. Experimental results show that the MFMPDR in
combination with the proposed data-driven SPP estimator yields an increased
speech quality compared to a state-of-the-art model-based SPP estimator.
Furthermore, it is confirmed that exploiting interframe correlations in the
MFMPDR is beneficial when compared to the Wiener gain especially in adverse
scenarios.


  Using an equation of motion (EOM) approach, we calculate excitonic properties
of monolayer transition metal dichalcogenides (TMDs) perturbed by an external
magnetic field. We compare our findings to the widely used Wannier model for
excitons in two-dimensional materials and to recent experimental results. We
find good agreement between the calculated excitonic transition energies and
the experimental results. In addition, we find that the exciton energies
calculated using the EOM approach are slightly lower than the ones calculated
using the Wannier model. Finally, we also show that the effect of the
dielectric environment on the magnetoexciton transition energy is minimal due
to counteracting changes in the exciton energy and the exchange self-energy
correction.


  This is an extended version of the previous paper (hep-th/0701272). Quantum
fields near horizons can be described in terms of an infinite set of
two-dimensional conformal fields. We first generalize the method of Christensen
and Fulling to charged black holes to derive fluxes of energy and charge. These
fluxes can be obtained by employing a conformal field theory technique. We then
apply this technique to obtain the fluxes of higher-spin currents and show that
the thermal distribution of Hawking radiation from a charged black hole can be
completely reproduced by investigating transformation properties of the
higher-spin currents under conformal and gauge transformations.


  This paper deals with the problem of constructing superregular matrices that
lead to MDP convolutional codes. These matrices are a type of lower block
triangular Toeplitz matrices with the property that all the square submatrices
that can possibly be nonsingular due to the lower block triangular structure
are nonsingular. We present a new class of matrices that are superregular over
a suficiently large finite field F. Such construction works for any given
choice of characteristic of the field F and code parameters (n; k; d) such that
(n-k)|d. Finally, we discuss the size of F needed so that the proposed matrices
are superregular.


  We examine the poset $P$ of 132-avoiding $n$-permutations ordered by
descents. We show that this poset is the "coarsening" of the well-studied poset
$Q$ of noncrossing partitions . In other words, if $x<y$ in $Q$, then
$f(y)<f(x)$ in $P$, where $f$ is the canonical bijection from the set of
noncrossing partitions onto that of 132-avoiding permutations. This enables us
to prove many properties of $P$.


  Despite notable successes of Reinforcement Learning (RL), the prevalent use
of an online learning paradigm prevents its widespread adoption, especially in
hazardous or costly scenarios. Offline RL has emerged as an alternative
solution, learning from pre-collected static datasets. However, this offline
learning introduces a new challenge known as distributional shift, degrading
the performance when the policy is evaluated on scenarios that are
Out-Of-Distribution (OOD) from the training dataset. Most existing offline RL
resolves this issue by regularizing policy learning within the information
supported by the given dataset. However, such regularization overlooks the
potential for high-reward regions that may exist beyond the dataset. This
motivates exploring novel offline learning techniques that can make
improvements beyond the data support without compromising policy performance,
potentially by learning causation (cause-and-effect) instead of correlation
from the dataset. In this paper, we propose the MOOD-CRL (Model-based Offline
OOD-Adapting Causal RL) algorithm, which aims to address the challenge of
extrapolation for offline policy training through causal inference instead of
policy-regularizing methods. Specifically, Causal Normalizing Flow (CNF) is
developed to learn the transition and reward functions for data generation and
augmentation in offline policy evaluation and training. Based on the
data-invariant, physics-based qualitative causal graph and the observational
data, we develop a novel learning scheme for CNF to learn the quantitative
structural causal model. As a result, CNF gains predictive and counterfactual
reasoning capabilities for sequential decision-making tasks, revealing a high
potential for OOD adaptation. Our CNF-based offline RL approach is validated
through empirical evaluations, outperforming model-free and model-based methods
by a significant margin.


  The investigation of quantum-classical correspondence may lead to gain a
deeper understanding of the classical limit of quantum theory. We develop a
quantum formalism on the basis of a linear-invariant theorem, which gives an
exact quantum-classical correspondence for damped oscillatory systems that are
perturbed by an arbitrary force. Within our formalism, the quantum trajectory
and expectation values of quantum observables are precisely coincide with their
classical counterparts in the case where we remove the global quantum constant
h from their quantum results. In particular, we illustrate the correspondence
of the quantum energy with the classical one in detail.


  We give a polynomial-time dynamic programming algorithm for solving the
linear complementarity problem with tridiagonal or, more generally, Hessenberg
P-matrices. We briefly review three known tractable matrix classes and show
that none of them contains all tridiagonal P-matrices.


  Two semi-empirical ideas are guiding the present research: 1) one neutrino
and N color quark triple mixing angles have second physical meaning of vector
direction angles in euclidean 3-space, and 2) the difference between neutrino
and quark mixing in the SM weak interactions is mostly conditioned by known
fundamental difference of these particles by numbers of color degree of
freedom. As main physical inference, the elementary particle mixing angles obey
a novel phenomenological quark-neutrino (N+1)-color and 3 mixing angle
symmetric positive definite quadratic Pythagorean equation in terms of double
mixing angles. This equation predicts immediately that two large solar and
atmospheric angles must be accompanied by one small not zero theta-13 neutrino
angle in convincing agreement with recent new theta-13 experimental data.
Benchmark flavor mixing pattern - united bimaximal neutrino and zero quark ones
- follows from solutions of the primary equation without free parameters.
Agreement with known experimental data follows only when the number of quark
colors is equal to the number of particle flavors, N = 3. Together with closely
related to the fine structure constant one empirical small parameter, that
benchmark pattern determines realistic quark and neutrino mixing angles in good
agreement with data and especially accurate for quark Cabibbo and neutrino
solar angles. Coming accurate neutrino mixing angle data are important for
testing the Pythagorean equation.


  We present results from an extensive set of one- and two-dimensional
radiation-hydrodynamic simulations of the supernova core collapse, bounce, and
postbounce phases, and focus on the protoneutron star (PNS) spin periods and
rotational profiles as a function of initial iron core angular velocity, degree
of differential rotation, and progenitor mass. For the models considered, we
find a roughly linear mapping between initial iron core rotation rate and PNS
spin. The results indicate that the magnitude of the precollapse iron core
angular velocities is the single most important factor in determining the PNS
spin. Differences in progenitor mass and degree of differential rotation lead
only to small variations in the PNS rotational period and profile. Based on our
calculated PNS spins, at ~ 200-300 milliseconds after bounce, and assuming
angular momentum conservation, we estimate final neutron star rotation periods.
We find periods of one millisecond and shorter for initial central iron core
periods of below ~ 10 s. This is appreciably shorter than what previous studies
have predicted and is in disagreement with current observational data from
pulsar astronomy. After considering possible spindown mechanisms that could
lead to longer periods we conclude that there is no mechanism that can robustly
spin down a neutron star from ~ 1 ms periods to the "injection" periods of tens
to hundreds of milliseconds observed for young pulsars. Our results indicate
that, given current knowledge of the limitations of neutron star spindown
mechanisms, precollapse iron cores must rotate with periods around 50-100
seconds to form neutron stars with periods generically near those inferred for
the radio pulsar population.


  We present several families of nonlinear reaction diffusion equations with
variable coefficients including Fisher-KPP and Burgers type equations. Special
exact solutions such as traveling wave, rational, triangular wave and N-wave
type solutions are shown. By means of similarity transformations the variable
coefficients are conditioned to satisfy Riccati or Ermakov systems of
equations. When the Riccati system is used, conditions are established so that
finite-time singularities might occur. The solutions presented contain
multi-parameters providing a control on the dynamics of the solutions. In the
suplementary material, we provide a computer algebra verification of the
solutions and exemplify nontrivial dynamics of the solutions.


  We show that an inequality related to Newton's inequality provides one more
relation between skewness and kurtosis. This also gives simple and alternative
proofs of the bounds for skewness and kurtosis.


  Microtransit offers opportunities to enhance urban mobility by combining the
reliability of public transit and the flexibility of ride-sharing. This paper
optimizes the design and operations of a deviated fixed-route microtransit
system that relies on reference lines but is allowed to deviate in response to
passenger demand. We formulate a Microtransit Network Design (MiND) model via
two-stage stochastic optimization. The model features a tight second-stage
formulation thanks to a subpath-based representation of microtransit operations
in a load-expanded network, which optimizes on-demand deviations between
checkpoint stops. We develop a double-decomposition algorithm combining Benders
decomposition and subpath-based column generation armed with a tailored
label-setting algorithm. Using real-world data from Manhattan, results suggest
that our method scales to large practical instances, with up to 10-100
candidate lines and hundreds of stations. Comparisons with transit and
ride-sharing benchmarks suggest that microtransit can provide win-win outcomes
toward efficient mobility (high demand coverage, low operating costs, high
level of service), equitable mobility (broad geographic reach) and sustainable
mobility (limited environmental footprint). We provide an open-source
implementation in an online repository to enable replication.


  The simplicial rook graph ${\rm \mathcal{SR}}(m,n)$ is the graph whose
vertices are vectors in $ \mathbb{N}^m$ such that for each vector the summation
of its coordinates is $n$ and two vertices are adjacent if their corresponding
vectors differ in exactly two coordinates. Martin and Wagner (Graphs Combin.
(2015) 31:1589--1611) asked about the independence number of ${\rm
\mathcal{SR}}(m,n)$ that is the maximum number of non attacking rooks which can
be placed on a $(m-1)$-dimensional simplicial chessboard of side length $n+1$.
In this work, we solve this problem and show that $\alpha({\rm
\mathcal{SR}}(m,n))=\big(1-o(1)\big)\frac{\binom{n+m-1}{n}}{m}$. We also prove
that for the domination number of rook graphs we have $\gamma({\rm
\mathcal{SR}}(m, n))= \Theta (n^{m-2})$. Moreover we show that these graphs are
Hamiltonian.
  The cyclic simplicial rook graph ${\rm \mathcal{CSR}}(m,n)$ is the graph
whose vertices are vectors in $\mathbb{Z}^{m}_{n}$ such that for each vector
the summation of its coordinates modulo $n$ is $0$ and two vertices are
adjacent if their corresponding vectors differ in exactly two coordinates. In
this work we determine several properties of these graphs such as independence
number, chromatic number and automorphism group. Among other results, we also
prove that computing the distance between two vertices of a given ${\rm
\mathcal{CSR}}(m,n)$ is $ \mathbf{NP}$-hard in terms of $n$ and $m$.


  Assume that two algebraic varieties of finite type over the complex numbers
are related by a morphism whose fibers are precisely the orbits for the action
of a unipotent group. We show that the two varieties have the same topological
Euler characteristic. If they are smooth and the morphism is smooth, we show
that the two varieties have the same cohomology groups.


  Starting from the vacuum Einstein Field Equations and a static axisymmetric
ansatz, we find two new solutions describing an axisymmetric static vacuum
spacetime with cylindrical symmetry: One of this exhibits an additional
symmetry in $z$-direction and the other has $z$-coordinate dependent
coefficients. In analogy to the Schwarzschild solution, these metrics describe
a static vacuum spacetime and apply in similar settings except for the changed
symmetry conditions. Analyzing the low-velocity limit corresponding to the
Newtonian approximation of the Schwarzschild metric, we find an effective
logarithmic potential. This yields flat rotation curves for test particles
undergoing rotational motion within the spacetime described by the line
elements, in contrast to Newtonian rotation curves. This analysis highlights
how important the symmetry assumptions are for deriving general relativistic
solutions.
  One example of physical objects that are generally described in the static
vacuum low-velocity limit (reducing to Newtonian gravity in the spherically
symmetric case) and exhibit axial symmetry are disk galaxies. We show that
symmetries and appropriate line elements that respect them are crucial to
consider in such settings. In particular, the solutions presented here result
in flat rotation curves without any need for dark matter. While these exact
solutions are limited to static vacuum spacetimes, their application to
physical galaxies relies on appropriate approximations. Nonetheless, they offer
valuable insights into explanations for flat rotation curves in galaxies and
their implications for dark matter.


  The partial width of the decay channels $f_0(500) \to \pi \pi$, $f_0(980) \to
\pi \pi$ and $f_0(980) \to K \bar K$ are calculated using QCD light-cone sum
rules method and a technique of the soft meson approximation. The scalar
particles are treated as mixtures of the heavy $|H\rangle =([su][\bar s \bar
u]+[sd][\bar s \bar d])/\sqrt 2$ and light $|L\rangle =[ud][\bar u \bar d]$
scalar diquark-antidiquark components. Obtained results for the full width of
the $f_0(500)$ meson $\Gamma_{\mathrm{th}.}=434.7\pm 72.3~\mathrm{MeV}$ and for
the $f_0(980)$ meson $\Gamma_{\mathrm{th.}}=42.12\pm 6.70~\mathrm{MeV }$ are
compared with the world averages for these parameters, and a reasonable
agreement between them is found.


  The approximation classes of modules that arise as components of cotorsion
pairs are tied up by Salce's duality. Here we consider general approximation
classes of modules and investigate possibilities of dualization in dependence
on closure properties of these classes. While some proofs are easily dualized,
other dualizations require large cardinal principles, and some fail in ZFC,
with counterexamples provided by classes of $\aleph_1$-projective modules over
non-perfect rings. For example, we show that Vop\v{e}nka's Principle implies
that each covering class of modules closed under homomorphic images is of the
form Gen($M$) for a module $M$, and that the latter property restricted to
classes generated by $\aleph_1$-free abelian groups implies Weak Vop\v{e}nka's
Principle.


  Due to the high flexibility and remarkable performance, low-rank
approximation methods has been widely studied for color image denoising.
However, those methods mostly ignore either the cross-channel difference or the
spatial variation of noise, which limits their capacity in real world color
image denoising. To overcome those drawbacks, this paper is proposed to denoise
color images with a double-weighted truncated nuclear norm minus truncated
Frobenius norm minimization (DtNFM) method. Through exploiting the nonlocal
self-similarity of the noisy image, the similar structures are gathered and a
series of similar patch matrices are constructed. For each group, the DtNFM
model is conducted for estimating its denoised version. The denoised image
would be obtained by concatenating all the denoised patch matrices. The
proposed DtNFM model has two merits. First, it models and utilizes both the
cross-channel difference and the spatial variation of noise. This provides
sufficient flexibility for handling the complex distribution of noise in real
world images. Second, the proposed DtNFM model provides a close approximation
to the underlying clean matrix since it can treat different rank components
flexibly. To solve the problem resulted from DtNFM model, an accurate and
effective algorithm is proposed by exploiting the framework of the alternating
direction method of multipliers (ADMM). The generated subproblems are discussed
in detail. And their global optima can be easily obtained in closed-form.
Rigorous mathematical derivation proves that the solution sequences generated
by the algorithm converge to a single critical point. Extensive experiments on
synthetic and real noise datasets demonstrate that the proposed method
outperforms many state-of-the-art color image denoising methods.


  Deep convolutional neural networks (CNNs) are the current state-of-the-art
for digital analysis of histopathological images. The large size of whole-slide
microscopy images (WSIs) requires advanced memory handling to read, display and
process these images. There are several open-source platforms for working with
WSIs, but few support deployment of CNN models. These applications use
third-party solutions for inference, making them less user-friendly and
unsuitable for high-performance image analysis. To make deployment of CNNs
user-friendly and feasible on low-end machines, we have developed a new
platform, FastPathology, using the FAST framework and C++. It minimizes memory
usage for reading and processing WSIs, deployment of CNN models, and real-time
interactive visualization of results. Runtime experiments were conducted on
four different use cases, using different architectures, inference engines,
hardware configurations and operating systems. Memory usage for reading,
visualizing, zooming and panning a WSI were measured, using FastPathology and
three existing platforms. FastPathology performed similarly in terms of memory
to the other C++ based application, while using considerably less than the two
Java-based platforms. The choice of neural network model, inference engine,
hardware and processors influenced runtime considerably. Thus, FastPathology
includes all steps needed for efficient visualization and processing of WSIs in
a single application, including inference of CNNs with real-time display of the
results. Source code, binary releases and test data can be found online on
GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.


  Recent advancements in open vocabulary models, like CLIP, have notably
advanced zero-shot classification and segmentation by utilizing natural
language for class-specific embeddings. However, most research has focused on
improving model accuracy through prompt engineering, prompt learning, or
fine-tuning with limited labeled data, thereby overlooking the importance of
refining the class descriptors. This paper introduces a new approach to
text-supervised semantic segmentation using supervision by a large language
model (LLM) that does not require extra training. Our method starts from an
LLM, like GPT-3, to generate a detailed set of subclasses for more accurate
class representation. We then employ an advanced text-supervised semantic
segmentation model to apply the generated subclasses as target labels,
resulting in diverse segmentation results tailored to each subclass's unique
characteristics. Additionally, we propose an assembly that merges the
segmentation maps from the various subclass descriptors to ensure a more
comprehensive representation of the different aspects in the test images.
Through comprehensive experiments on three standard benchmarks, our method
outperforms traditional text-supervised semantic segmentation methods by a
marked margin.


  We study the nature of the ground state of the quantum dimer model proposed
by Rokhsar and Kivelson by diagonalizing the Hamiltonian of the model on square
lattices of size $L\times L$, where $L\leq 8$, with periodic boundary
conditions. Finite-size scaling studies of the columnar order parameter and the
low lying excitation spectrum show no evidence of a dimer liquid state in any
finite region of the zero temperature phase diagram. In addition, we find
evidence of a transition from the columnar dimer state to an intermediate state
at a negative value of $V/J$. This state is identified to be the plaquette
resonating valence bond (RVB) state. The energy gap of this state vanishes as a
power law of $L$. It exhibits columnar dimer order, but has disorder {\it
within} the dimer columns. This state persists up to $V/J<1$, and the system
changes to a dimer liquid state only at $V/J=1$.


  The smaller the angular scales on which the anisotropies of the cosmic
microwave background (CMB) are probed the more important their distortion due
to gravitational lensing becomes. Here we investigate the maxima and minima of
the CMB lensing deflection field using general extreme value statistics. Since
general extreme value statistics applies to uncorrelated data in first place we
consider appropriately low-pass filtered deflection maps. Besides the
suppression of correlations filtering is required for another reason: The
lensing field itself is not directly observable but needs to be (statistically)
reconstructed from the lensed CMB by means of a quadratic estimator. This
reconstruction, though, is noise dominated and therefore requires smoothing,
too. In idealized Gaussian realizations as well as in realistically
reconstructed data we find that both maxima and minima of the deflection angle
components follow consistently a general extreme value distribution of
Weibull-type. However, its shape, location and scale parameters vary
significantly between different realizations. The statistics' potential power
to constrain cosmological models appears therefore rather limited.


  A pentagon in the plane with fixed side-lengths has a two-dimensional shape
space. Considering the pentagon as a mechanical system with point masses at the
corners we answer the question of how much the pentagon can rotate with zero
angular momentum. We show that the shape space of the equilateral pentagon has
genus 4 and find a fundamental region by discrete symmetry reduction with
respect to symmetry group D_5. The amount of rotation \Delta \theta for a loop
in shape space at zero angular momentum is interpreted as a geometric phase and
is obtained as an integral of a function B over the region of shape space
enclosed by the loop. With a simple variational argument we determine locally
optimal loops as the zero contours of the function B. The resulting shape
change is represented as a Fourier series, and the global maximum of \Delta
\theta \approx 45\degree is found for a loop around the regular pentagram. We
also show that restricting allowed shapes to convex pentagons the optimal loop
is the boundary of the convex region and gives \Delta \theta \approx 19\degree.


  We propose a system that learns from artistic pairings of music and
corresponding album cover art. The goal is to 'translate' paintings into music
and, in further stages of development, the converse. We aim to deploy this
system as an artistic tool for real time 'translations' between musicians and
painters. The system's outputs serve as elements to be employed in a joint live
performance of music and painting, or as generative material to be used by the
artists as inspiration for their improvisation.


  We develop a full theoretical approach to clustering in complex networks. A
key concept is introduced, the edge multiplicity, that measures the number of
triangles passing through an edge. This quantity extends the clustering
coefficient in that it involves the properties of two --and not just one--
vertices. The formalism is completed with the definition of a three-vertex
correlation function, which is the fundamental quantity describing the
properties of clustered networks. The formalism suggests new metrics that are
able to thoroughly characterize transitive relations. A rigorous analysis of
several real networks, which makes use of the new formalism and the new
metrics, is also provided. It is also found that clustered networks can be
classified into two main groups: the {\it weak} and the {\it strong
transitivity} classes. In the first class, edge multiplicity is small, with
triangles being disjoint. In the second class, edge multiplicity is high and so
triangles share many edges. As we shall see in the following paper, the class a
network belongs to has strong implications in its percolation properties.


  We present an analysis of line-of-sight extinction measurements obtained
using data from the Magellanic Clouds Photometric Survey, which provides
4-filter photometry for millions of stars in the Large Magellanic Cloud. We
find that visual extinctions are typically larger by several tenths of a
magnitude for stars with effective temperatures > 12000 K, than for stars with
effective temperatures between 5500 K and 6500 K. Several repercussions of this
population-dependent extinction are discussed. In particular, LMC distance
measurements that utilize old stellar populations (such as red clump stars),
but use extinctions derived from OB stars, may be biased low.
Population-dependent extinction affects the interpretation of color-magnitude
diagrams and results in an effective absorption law that is steeper than that
intrinsic to the dust for unresolved stellar systems. We further explore the
relation between the stellar populations and dust by comparing our extinction
map to the 100mu image of the region and identifying potential heating sources
of the dust. We conclude that 100mu flux should be used with caution as a star
formation tracer, particularly for studies of star formation within galaxies.
Finally, we reproduce the observed extinction variation between the hot and
cold stellar populations with a simple model of the distribution of the stars
and dust where the scaleheight of the cooler stars is >> than that of the dust
(which is twice that of the OB stars). (Abridged Abstract)


  The societal impact of pre-trained language models has prompted researchers
to probe them for strong associations between protected attributes and
value-loaded terms, from slur to prestigious job titles. Such work is said to
probe models for bias or fairness-or such probes 'into representational biases'
are said to be 'motivated by fairness'-suggesting an intimate connection
between bias and fairness. We provide conceptual clarity by distinguishing
between association biases (Caliskan et al., 2022) and empirical fairness (Shen
et al., 2022) and show the two can be independent. Our main contribution,
however, is showing why this should not come as a surprise. To this end, we
first provide a thought experiment, showing how association bias and empirical
fairness can be completely orthogonal. Next, we provide empirical evidence that
there is no correlation between bias metrics and fairness metrics across the
most widely used language models. Finally, we survey the sociological and
psychological literature and show how this literature provides ample support
for expecting these metrics to be uncorrelated.


  The inverses of indecomposable Cartan matrices are computed for
finite-dimensional Lie algebras and Lie superalgebras over fields of any
characteristic, and for hyperbolic (almost affine) complex Lie (super)algebras.
We discovered three yet inexplicable new phenomena, of which (a) and (b)
concern hyperbolic (almost affine) complex Lie (super)algebras, except for the
5 Lie superalgebras whose Cartan matrices have 0 on the main diagonal: (a)
several of the inverses of Cartan matrices have all their elements negative
(not just non-positive, as they should be according to an a priori
characterization due to Zhang Hechun); (b) the 0s only occur on the main
diagonals of the inverses; (c) the determinants of inequivalent Cartan matrices
of the simple Lie (super)algebra may differ (in any characteristic).
  We interpret most of the results of Wei Yangjiang and Zou Yi Ming, Inverses
of Cartan matrices of Lie algebras and Lie superalgebras, Linear Alg. Appl.,
521 (2017) 283--298 as inverses of the Gram matrices of non-degenerate
invariant symmetric bilinear forms on the (super)algebras considered, not of
Cartan matrices, and give more adequate references. In particular, the inverses
of Cartan matrices of simple Lie algebras were already published, starting with
Dynkin's paper in 1952, see also Table 2 in Springer's book by Onishchik and
Vinberg (1990).


  We review recent progress in observations of ground-based oscillations.
Excellent observations now exist for a few stars (alpha Cen A and B, mu Ara),
while there is some controversy over others (Procyon, eta Boo). We have reached
the stage where single-site observations are of limited value and where careful
planning is needed to ensure the future of asteroseismology.


  This document reports on a series of experimental and theoretical studies
conducted to assess the astro-particle physics potential of three future
large-scale particle detectors proposed in Europe as next generation
underground observatories. The proposed apparatus employ three different and,
to some extent, complementary detection techniques: GLACIER (liquid Argon TPC),
LENA (liquid scintillator) and MEMPHYS (\WC), based on the use of large mass of
liquids as active detection media. The results of these studies are presented
along with a critical discussion of the performance attainable by the three
proposed approaches coupled to existing or planned underground laboratories, in
relation to open and outstanding physics issues such as the search for matter
instability, the detection of astrophysical- and geo-neutrinos and to the
possible use of these detectors in future high-intensity neutrino beams.


  We study the implications of the LPM effect for the Cherenkov radiation of
EeV electromagnetic showers in the coherent radiowave regime for ice. We show
that for showers above 100~PeV the electric field scales with shower energy but
has a markedly narrower angular distribution than for lower energy showers. We
give an electric field frequency and angular spectrum parameterization valid
for showers having energy up to the EeV regime and discuss the implications for
neutrino detectors based on arrays of radio antennas. Implications of the LPM
effect for under water neutrino detectors in project are also briefly
addressed.


  Under distribution shift (DS) where the training data distribution differs
from the test one, a powerful technique is importance weighting (IW) which
handles DS in two separate steps: weight estimation (WE) estimates the
test-over-training density ratio and weighted classification (WC) trains the
classifier from weighted training data. However, IW cannot work well on complex
data, since WE is incompatible with deep learning. In this paper, we rethink IW
and theoretically show it suffers from a circular dependency: we need not only
WE for WC, but also WC for WE where a trained deep classifier is used as the
feature extractor (FE). To cut off the dependency, we try to pretrain FE from
unweighted training data, which leads to biased FE. To overcome the bias, we
propose an end-to-end solution dynamic IW that iterates between WE and WC and
combines them in a seamless manner, and hence our WE can also enjoy deep
networks and stochastic optimizers indirectly. Experiments with two
representative types of DS on three popular datasets show that our dynamic IW
compares favorably with state-of-the-art methods.


  This is the first of two works concerning the Sobolev calculus on metric
measure spaces and its applications. In this work, we focus on several notions
of metric Sobolev space and on their equivalence. More precisely, we give a
systematic presentation of first-order $p$-Sobolev spaces, with
$p\in[1,\infty)$, defined over a complete and separable metric space equipped
with a boundedly-finite Borel measure. We focus on three different approaches:
via approximation with Lipschitz functions; by studying the behaviour along
curves, in terms either of the curve modulus or of test plans; via
integration-by-parts, using Lipschitz derivations with divergence. Eventually,
we show that all these approaches are fully equivalent. We emphasise that no
doubling or Poincar\'{e} assumption is made, and that we allow also for the
exponent $p=1$.
  A substantial part of this work consists of a self-contained and
partially-revisited exposition of known results, which are scattered across the
existing literature, but it contains also several new results, mostly
concerning the equivalence of metric Sobolev spaces for $p=1$.


  Specifying a prior distribution is an essential part of solving Bayesian
inverse problems. The prior encodes a belief on the nature of the solution and
this regularizes the problem. In this article we completely characterize a
Gaussian prior that encodes the belief that the solution is a structured
tensor. We first define the notion of (A,b)-constrained tensors and show that
they describe a large variety of different structures such as Hankel,
circulant, triangular, symmetric, and so on. Then we completely characterize
the Gaussian probability distribution of such tensors by specifying its mean
vector and covariance matrix. Furthermore, explicit expressions are proved for
the covariance matrix of tensors whose entries are invariant under a
permutation. These results unlock a whole new class of priors for Bayesian
inverse problems. We illustrate how new kernel functions can be designed and
efficiently computed and apply our results on two particular Bayesian inverse
problems: completing a Hankel matrix from a few noisy measurements and learning
an image classifier of handwritten digits. The effectiveness of the proposed
priors is demonstrated for both problems. All applications have been
implemented as reactive Pluto notebooks in Julia.


  We elaborate on quantum moduli spaces in 3d $\mathcal{N}=2$ $SU(N)_k$
Chern-Simons gauge theories with $F$ fundamental and $\bar{F}$ anti-fundamental
matter fields. The quantum flat direction on the Coulomb branch differs so much
from the classical one and from the one of the vector-like theories. In many
cases, the Coulomb branch is parametrized by the dressed monopoles. As is found
from the computation of the superconformal index, these dressed operators at
first sight appear to be dressed by massive elementary fields which don't seem
to contribute to the low-energy physics. We argue that these dressed fields can
be interpreted as a non-abelian monopole dressed (or not dressed) by massless
matter fields. Based on this analysis, we will report on the s-confinement
phases with non-trivial monopole operators, which is consistent with the
duality proposals \cite{Aharony:2014uya, Aharony:2013dha}. Along these studies,
we find that the duality reported in \cite{Aharony:2014uya} must be modified
when $k=\pm \frac{1}{2}(F-\bar{F})$ in order to have a correct duality map of
the baryonic operators.


  In this work, we revisit unitary irreducible representations of the
Bondi-Metzner-Sachs (BMS) group discovered by McCarthy. Representations are
labelled by an infinite number of super-momenta in addition to four-momentum.
Tensor products of these irreducible representations lead to particle-like
states dressed by soft gravitational modes. Conservation of 4-momentum and
supermomentum in the scattering of such states leads to a memory effect encoded
in the outgoing soft modes. We note there exist irreducible representations
corresponding to soft states with strictly vanishing four-momentum, which may
nevertheless be produced by scattering of particle-like states. This fact has
interesting implications for the S-matrix in gravitational theories.


  These are lecture notes on the algebraic approach to regular languages. The
classical algebraic approach is for finite words; it uses semigroups instead of
automata. However, the algebraic approach can be extended to structures beyond
words, e.g.~infinite words, or trees or graphs. The purpose of this book is to
describe the algebraic approach in a way that covers these extensions.


  We have performed a global analysis of muon decay measurements to establish
model-independent limits on the space-time structure of the muon decay matrix
element. We find limits on the scalar, vector and tensor coupling of right- and
left-handed muons to right- and left-handed electrons. The limits on those
terms that involve the decay of right-handed muons to left-handed electrons are
more restrictive than in previous global analyses, while the limits on the
other non-standard model interactions are comparable. The value of the Michel
parameter eta found in the global analysis is -0.0036 \pm 0.0069, slightly more
precise than the value found in a more restrictive analysis of a recent
measurement. This has implications for the Fermi coupling constant G_F.


  Nonlocal patch-based methods, in particular the Bayes' approach of Lebrun,
Buades and Morel (2013), are considered as state-of-the-art methods for
denoising (color) images corrupted by white Gaussian noise of moderate
variance. This paper is the first attempt to generalize this technique to
manifold-valued images. Such images, for example images with phase or
directional entries or with values in the manifold of symmetric positive
definite matrices, are frequently encountered in real-world applications.
Generalizing the normal law to manifolds is not canonical and different
attempts have been considered. Here we focus on a straightforward intrinsic
model and discuss the relation to other approaches for specific manifolds. We
reinterpret the Bayesian approach of Lebrun et al. (2013) in terms of minimum
mean squared error estimation, which motivates our definition of a
corresponding estimator on the manifold. With this estimator at hand we present
a nonlocal patch-based method for the restoration of manifold-valued images.
Various proof of concept examples demonstrate the potential of the proposed
algorithm.


  We show that Andreev reflection in a junction between ferromagnetic (F) and
superconducting (S) graphene regions is fundamentally different from the common
FS junctions. For a weakly doped F graphene with an exchange field $h$ larger
than its Fermi energy $E_{\rm F}$, Andreev reflection of massless Dirac
fermions is associated with a Klein tunneling through an exchange field p-n
barrier between two spin-split conduction and valence subbands. We find that
this Andreev-Klein process results in an enhancement of the subgap conductance
of a graphene FS junction by $h$ up to the point at which the conductance at
low voltages $eV\ll \Delta$ is greater than its value for the corresponding
non-ferromagnetic junction. We also demonstrate that the Andreev reflection can
be of retro or specular types in both convergent and divergent ways with the
reflection direction aligned, respectively, closer to and farther from the
normal to the junction as compared to the incidence direction.


  Creating complex spatial objects from a flat sheet of material using origami
folding techniques has attracted attention in science and engineering. In the
present work, we employ geometric properties of partially folded zigzag strips
to better describe the kinematics of the known zigzag/herringbone-base folded
sheet metamaterials such as the Miura-ori. Inspired by the kinematics of a
one-degree of freedom zigzag strip, we introduce a class of cellular folded
sheet mechanical metamaterials comprising different scales of zigzag strips in
which the class of the patterns combines origami folding techniques with
kirigami. Employing both analytical and numerical models, we study the key
mechanical properties of the folded materials. Particularly, we show that,
depending on the geometry, these materials exhibit both negative and positive
in-plane Poisson's ratio. By expanding the design space of the Miura-ori, our
class of patterns is potentially appropriate for a wide range of applications,
from mechanical metamaterials to deployable structures at both small and large
scales.


  The kagome lattice sits at the crossroad of present research efforts in
quantum spin liquids, chiral phases, emergent skyrmion excitations and
anomalous Hall effects to name but a few. In light of this diversity, our goal
in this paper is to build a unifying picture of the underlying magnetic
degrees-of-freedom on kagome. Motivated by a growing mosaic of materials, we
especially consider a broad range of nearest-neighbour interactions consisting
of Dzyaloshinskii-Moriya as well as anisotropic ferro$-$ and antiferromagnetic
coupling. We present a three-fold mapping on the kagome lattice which
transforms the celebrated Heisenberg antiferromagnet and XXZ model onto two
lines of time-reversal invariant Hamiltonians. The mapping is exact for
classical and quantum spins alike, i.e. it preserves the energy spectrum of the
original Heisenberg and XXZ models. As a consequence, at the classical level,
all phases have an extensive ground-state degeneracy. These ground states
support a variety of phenomena such as ferromagnetically induced pinch points
in the structure factor and the possibility for spontaneous scalar chirality.
For quantum spin$-1/2$, the XXZ model has been recently shown to be a quantum
spin liquid. Applying our three-fold mapping to the XXZ model gives rise to a
connected network of quantum spin liquids, centered around a paragon of quantum
disorder, namely the Ising antiferromagnet. We show that this quantum disorder
spreads over an extended region of the phase diagram at linear order in spin
wave theory, which overlaps with the parameter region of Herbertsmithite
ZnCu$_3$(OH)$_6$Cl$_2$. We conclude this work by discussing the connection of
our results to the chiral spin liquids found on kagome with further
nearest-neighbour interactions, and to the recently synthesized ternary
intermetallic materials.


  We present a model for the radiative output of star clusters in the process
of star formation suitable for use in hydrodynamical simulations of radiative
feedback. Gas in a clump, defined as a region whose density exceeds 10^4 cm^-3,
is converted to stars via the random sampling of the Chabrier IMF. A star
formation efficiency controls the rate of star formation. We have completed a
suite of simulations which follow the evolution of accretion-fed clumps with
initial masses ranging from 0 to 10^5 M_sol and accretion rates ranging from
10^-5 to 10^-1 M_sol yr^-1. The stellar content is tracked over time which
allows the aggregate luminosity, ionizing photon rate, number of stars, and
star formation rate (SFR) to be determined. For a fiducial clump of 10^4 M_sol,
the luminosity is ~4x10^6 L_sol with a SFR of roughly 3x10^-3 M_sol yr^-1. We
identify two regimes in our model. The accretion-dominated regime obtains the
majority of its gas through accretion and is characterized by an increasing SFR
while the reservoir-dominated regime has the majority of its mass present in
the initial clump with a decreasing SFR. We show that our model can reproduce
the expected number of O stars, which dominate the radiative output of the
cluster. We find a nearly linear relationship between SFR and mass as seen in
observations. We conclude that our model is an accurate and straightforward way
to represent the output of clusters in hydrodynamical simulations with
radiative feedback.


  Particle decays are always accompanied by the emission of graviton quanta of
gravity through bremsstrahlung processes. However, the corresponding branching
ratio is suppressed by the square of the ratio of particle's mass to the Planck
scale. The resulting present abundance of gravitational waves (GWs), composed
of gravitons, is analogously suppressed. We show that superheavy particles, as
heavy as the Planck scale, can be naturally produced during the
post-inflationary reheating stage in the early Universe and their decays yield
dramatic amounts of GWs over broad frequency range. GW observations could hence
directly probe Planck-scale physics, notoriously challenging to explore.


  In a recent communication, Parimi et al. (Nature 426, 404 (2003)) reported
the experimental results on imaging by a flat lens made of photonic crystals.
They attributed the observed focusing to the negative refraction expected for
the Left-Handed-Materials (LHMs). Here we demonstrate that the experimental
observation is irrelevant to the negative refraction of LHMs. Rather, the
phenomenon is a natural result of the anisotropic scattering by an array of
scatterers.


  We here introduce a novel scheme for generating smoothly-varying infill
graded microstructural (IGM) configurations from a given menu of generating
cells. The scheme was originally proposed for essentially improving the variety
of describable configurations in a modified asymptotic homogenisation-based
topology optimisation framework [1] for fast IGM design. But the proposed
scheme, after modification, also demonstrates its unique values in two aspects
of applications. First, it provides a fairly simple way of generating an IGM
configuration continuously patching any given cell configurations. Second, it
tenders a straightforward mean for decorating microstructures on a given
manifold. We will further show that the form of topology description function
given here effectively offers a platform for unifying most existing approaches
for IGM generation. Fuelled by asymptotic analysis of the mechanical behaviour
of the resulting IGM configurations, a topology optimisation scheme for
compliance minimisation is introduced. We will finally show that, the use of
the present scheme helps reduce the compliance value of an optimised structure
by nearly a half, if compared with that from the original framework [1].


  The axion is one of the more interesting candidates to make the dark matter
of the universe, and the axion potential plays a fundamental role in the
determination of the dynamics of the axion field. Moreover, the way in which
the $U(1)_A$ anomaly manifests itself in the chiral symmetry restored phase of
$QCD$ at high temperature could be tested when probing the $QCD$ phase
transition in relativistic heavy ion collisions. With these motivations, we
investigate the physical consequences of the survival of the effects of the
$U(1)_A$ anomaly in the chiral symmetric phase of $QCD$, and show that the free
energy density is a singular function of the quark mass $m$, in the chiral
limit, and that the $\sigma$ and $\bar\pi$ susceptibilities diverge in this
limit at any $T\ge T_c$. We also show that the difference between the $\bar\pi$
and $\bar\delta$ susceptibilities diverges in the chiral limit at any $T\ge
T_c$, a result that can be contrasted with the existing lattice calculations;
and discuss on the generalization of these results to the $N_f\ge 3$ model.


  This work shows how non-binary low-density parity-check codes over GF($2^p$)
can be combined with probabilistic amplitude shaping (PAS) (B\"ocherer, et al.,
2015), which combines forward-error correction with non-uniform signaling for
power-efficient communication. Ultra-sparse low-density parity-check codes over
GF(64) and GF(256) gain 0.6 dB in power efficiency over state-of-the-art binary
LDPC codes at a spectral efficiency of 1.5 bits per channel use and a
blocklength of 576 bits. The simulation results are compared to finite length
coding bounds and complemented by density evolution analysis.


  We consider the practicalities of defining, simulating, and characterizing
"Liquids" from a pedagogical standpoint based on atomistic computer
simulations. For simplicity and clarity we study two-dimensional systems
throughout. In addition to the infinite-ranged Lennard-Jones 12/6 potential we
consider two shorter-ranged families of pair potentials. At zero pressure one
of them includes just nearest neighbors. The other longer-ranged family
includes twelve additional neighbors. We find that these further neighbors can
help stabilize the liquid phase.
  What about liquids? To implement Wikipedia's definition of liquids as
conforming to their container we begin by formulating and imposing
smooth-container boundary conditions. To encourage conformation further we add
a vertical gravitational field. Gravity helps stabilize the relatively vague
liquid-gas interface. Gravity reduces the messiness associated with the
curiously-named "spinodal" (tensile) portion of the phase diagram. Our
simulations are mainly isothermal. We control the kinetic temperature with
Nos\'e-Hoover thermostating, extracting or injecting heat so as to impose a
mean kinetic temperature over time. Our simulations stabilizing density
gradients and the temperature provide critical-point estimates fully consistent
with previous efforts from free energy and Gibbs' ensemble simulations. This
agreement validates our approach.


  Methods of eXplainable Artificial Intelligence (XAI) are used in
geoscientific applications to gain insights into the decision-making strategy
of Neural Networks (NNs) highlighting which features in the input contribute
the most to a NN prediction. Here, we discuss our lesson learned that the task
of attributing a prediction to the input does not have a single solution.
Instead, the attribution results and their interpretation depend greatly on the
considered baseline (sometimes referred to as reference point) that the XAI
method utilizes; a fact that has been overlooked so far in the literature. This
baseline can be chosen by the user or it is set by construction in the method s
algorithm, often without the user being aware of that choice. We highlight that
different baselines can lead to different insights for different science
questions and, thus, should be chosen accordingly. To illustrate the impact of
the baseline, we use a large ensemble of historical and future climate
simulations forced with the SSP3-7.0 scenario and train a fully connected NN to
predict the ensemble- and global-mean temperature (i.e., the forced global
warming signal) given an annual temperature map from an individual ensemble
member. We then use various XAI methods and different baselines to attribute
the network predictions to the input. We show that attributions differ
substantially when considering different baselines, as they correspond to
answering different science questions. We conclude by discussing some important
implications and considerations about the use of baselines in XAI research.


  There is a trend towards using wireless technologies in networked control
systems. However, the adverse properties of the radio channels make it
difficult to design and implement control systems in wireless environments. To
attack the uncertainty in available communication resources in wireless control
systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS)
scheme is developed, which takes advantage of the co-design of control and
wireless communications. By exploiting cross-layer design, CLAFS adjusts the
sampling periods of control systems at the application layer based on
information about deadline miss ratio and transmission rate from the physical
layer. Within the framework of feedback scheduling, the control performance is
maximized through controlling the deadline miss ratio. Key design parameters of
the feedback scheduler are adapted to dynamic changes in the channel condition.
An event-driven invocation mechanism for the feedback scheduler is also
developed. Simulation results show that the proposed approach is efficient in
dealing with channel capacity variations and noise interference, thus providing
an enabling technology for control over WLAN.


  The simulation analysis of the Extensive Air Showers (EAS) was executed by
exploring the longitudinal development employing the AIRES system (version
19.04.00) for several hadronic interaction models (SIBYLL, QGSJET, and EPOS)
for high energies. The simulation was performed for different high energies
(10^17, 10^18, and 10^19) eV and two dissimilar primary particles, proton as
well iron nuclei, with several zenith angles values (0^o, 10^o, and 30^o). The
shower size of longitudinal development was parameterized using the sigmoidal
function (Boltzmann model) and gave a new four parameters as functions of the
primary energy between the energy extent (10^17-10^19) eV. The comparison among
the acquired results data (the parameterized number of shower particles) along
with the experimental results (Pierre Auger experiment) had offered a
fascinating matching for the primary proton at the fixed primary energy 10^19
eV for vertical EAS showers.


  The creation of electron-positron pair by a photon and the bremsstrahlung of
an electron in a medium are considered in high-energy region, where influence
of the multiple scattering on the processes (the Landau-Pomeranchuk-Migdal
(LPM) effect) becomes essential. The pair photoproduction probability is
calculated with an accuracy up to the "next to leading logarithm". The integral
characteristics: the radiation length and the total probability of pair
photoproduction are analyzed under influence of the LPM effect, and the
asymptotic expansions of these characteristics are derived.


  The field of Continual Learning (CL) seeks to develop algorithms that
accumulate knowledge and skills over time through interaction with
non-stationary environments. In practice, a plethora of evaluation procedures
(settings) and algorithmic solutions (methods) exist, each with their own
potentially disjoint set of assumptions. This variety makes measuring progress
in CL difficult. We propose a taxonomy of settings, where each setting is
described as a set of assumptions. A tree-shaped hierarchy emerges from this
view, where more general settings become the parents of those with more
restrictive assumptions. This makes it possible to use inheritance to share and
reuse research, as developing a method for a given setting also makes it
directly applicable onto any of its children. We instantiate this idea as a
publicly available software framework called Sequoia, which features a wide
variety of settings from both the Continual Supervised Learning (CSL) and
Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing
suite of methods which are easy to extend and customize, in addition to more
specialized methods from external libraries. We hope that this new paradigm and
its first implementation can help unify and accelerate research in CL. You can
help us grow the tree by visiting www.github.com/lebrice/Sequoia.


  Let $G$ be a group with involution * and $\sigma\colon G\to\{\pm1\}$ a group
homomorphism. The map $\sharp$ that sends $\alpha=\sum\alpha_gg$ in a group
ring $RG$ to $\alpha^{\sharp}=\sum\sigma(g)\alpha_gg^*$ is an involution of
$RG$ called an \emph{oriented group involution}. An element $\alpha\in RG$ is
\emph{symmetric} if $\alpha^{\sharp}=\alpha$ and \emph{skew-symmetric} if
$\alpha^{\sharp}=-\alpha$. The sets of symmetric and skew-symmetric elements
have received a lot of attention in the special cases that * is the inverse map
on $G$ and/or $\sigma$ is identically 1, but not in general. In this paper, we
determine the conditions under which the sets of elements that are symmetric
and skew-symmetric, respectively, relative to a general oriented involution
form subrings of $RG$. The work on symmetric elements is a modification and
correction of previous work.


  Common fully glazed facades and transparent objects present architectural
barriers and impede the mobility of people with low vision or blindness, for
instance, a path detected behind a glass door is inaccessible unless it is
correctly perceived and reacted. However, segmenting these safety-critical
objects is rarely covered by conventional assistive technologies. To tackle
this issue, we construct a wearable system with a novel dual-head Transformer
for Transparency (Trans4Trans) model, which is capable of segmenting general
and transparent objects and performing real-time wayfinding to assist people
walking alone more safely. Especially, both decoders created by our proposed
Transformer Parsing Module (TPM) enable effective joint learning from different
datasets. Besides, the efficient Trans4Trans model composed of symmetric
transformer-based encoder and decoder, requires little computational expenses
and is readily deployed on portable GPUs. Our Trans4Trans model outperforms
state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2
datasets and obtains mIoU of 45.13% and 75.14%, respectively. Through various
pre-tests and a user study conducted in indoor and outdoor scenarios, the
usability and reliability of our assistive system have been extensively
verified.


  Superconducting quantum interference device arrays have been extensively
studied for their high magnetic field sensitivity. The performance of these
devices strongly depends on the characteristic parameters of their Josephson
junctions, i.e. their critical currents and shunt resistances. Using a
resistively shunted junction model and including thermal noise, we perform a
numerical investigation of the effects of asymmetric Josephson junctions by
independently studying variations in the critical currents and junction
resistances. We compare the voltage response of a dc-SQUID with a 1D parallel
SQUID array and study the maximum transfer function dependence on the number of
junctions in parallel, the screening parameter and thermal noise strength. Our
results show that the maximum transfer function and linearity increase with the
number of junctions in parallel for arrays with different junction resistances,
in contrast to SQUID arrays with identical junctions or with spreads in the
critical currents.


  We address the problem of the factors contributing to a peak color trend of
old metal-rich globular cluster (MRGC) populations with mass of their hosts,
early-type galaxies and spheroidal subsystems of spiral ones (spheroids). The
color-mass trend is often converted to a metallicity-mass trend under the
assumption that age effects are small or negligible. While direct estimates of
the ages of MRGC populations neither can rule out nor reliably support the
populations' age trend, key data on timing of the formation of spheroids and
other indirect evidence imply it in the sense: the more massive spheroid the
older on average its MRGC population. We show that the contribution of an
allowable age trend of the MRGC populations to their peak color trend can
achieve up to ~50 % or so. In this event the comparable value of the color
trend, ~30 %, is due to alpha-element ratio systematic variations of the order
of Delta[alpha/Fe] ~ 0.1 to 0.2 dex because of a correlation between the
[alpha/Fe] ratios and age. Hence a systematic variation of exactly [Fe/H]
ratios may turn out to be less significant among the contributors, and its
range many times lower, i.e. of the order of Delta[Fe/H] ~ 0.1 or even none,
than the corresponding range deduced by assuming no age trend.


  Human movements are physical processes combining the classical mechanics of
the human body moving in space and the biomechanics of the muscles generating
the forces acting on the body under sophisticated sensory-motor control. The
characterization of the performance of human movements is a problem with
important applications in clinical and sports research. One way to characterize
movement performance is through measures of energy efficiency that relate the
mechanical energy of the body and metabolic energy expended by the muscles.
Such a characterization provides information about the performance of a
movement insofar as subjects select movements with the aim of maximizing the
energy efficiency. We examine the case of the energy efficiency of asynchronous
arm-cranking doing external mechanical work, that is, using the arms to turn an
asynchronous arm-crank that performs external mechanical work. We construct a
metabolic energy model and use it estimate how cranking may be performed with
maximum energy efficiency, and recover the intuitive result that for larger
external forces the crank-handles should be placed as far from the center of
the crank as is comfortable for the subject to turn. We further examine
mechanical advantage in asynchronous arm-cranking by constructing an idealized
system that is driven by a crank and which involves an adjustable mechanical
advantage, and analyze the case in which the avg. frequency is fixed and derive
the mechanical advantages that maximize energy efficiency.


  Automated decision support systems that are able to infer second opinions
from experts can potentially facilitate a more efficient allocation of
resources; they can help decide when and from whom to seek a second opinion. In
this paper, we look at the design of this type of support systems from the
perspective of counterfactual inference. We focus on a multiclass
classification setting and first show that, if experts make predictions on
their own, the underlying causal mechanism generating their predictions needs
to satisfy a desirable set invariant property. Further, we show that, for any
causal mechanism satisfying this property, there exists an equivalent mechanism
where the predictions by each expert are generated by independent
sub-mechanisms governed by a common noise. This motivates the design of a set
invariant Gumbel-Max structural causal model where the structure of the noise
governing the sub-mechanisms underpinning the model depends on an intuitive
notion of similarity between experts which can be estimated from data.
Experiments on both synthetic and real data show that our model can be used to
infer second opinions more accurately than its non-causal counterpart.


  A problem of analytical continuation of scattering data to the
negative-energy region to obtain information about bound states is discussed
within an exactly solvable potential model. This work is continuation of the
previous one by the same authors [L. D. Blokhintsev et al., Phys. Rev. C 95,
044618 (2017)]. The goal of this paper is to determine the most effective way
of analytic continuation for different systems. The $d+\alpha$ and $\alpha
+^{12}$C systems are considered and, for comparison, an effective-range
function approach and a recently suggested $\Delta$-method [O. L. Ram\'{\i}rez
Su\'arez and J.-M. Sparenberg, Phys. Rev. C 96, 034601 (2017)] are applied. We
conclude that the $\Delta$-method is more effective for heavier systems with
large values of the Coulomb parameter, whereas for light systems with small
values of the Coulomb parameter the effective-range function method might be
preferable.


  The kinetics of main physical processes controlling the growth of nanowires
(NW) via evolution pass " vapor-liquid-solid" is considered. The roles of the
thermodynamics and kinetics of cluster nucleation in the initial stage of NW
formation are studied. Approximate expressions for NW length are obtained in
one-dimensional approximation. The influence of transfer processes in the gas
phase on the growth NW is evaluated. The effect of release of the latent heat
of phase transition and heat conduction along NW is considered. For carbon NW,
grown on Ni catalytic particles, numerical results are obtained. PACS: 64.70Dv,
68.70+w.


  Cylindric skew Schur functions, which are a generalisation of skew Schur
functions, arise naturally in the study of P-partitions. Also, recent work of
A. Postnikov shows they have a strong connection with a problem of considerable
current interest: that of finding a combinatorial proof of the non-negativity
of the 3-point Gromov-Witten invariants. After explaining these motivations, we
study cylindric skew Schur functions from the point of view of
Schur-positivity. Using a result of I. Gessel and C. Krattenthaler, we
generalise a formula of A. Bertram, I. Ciocan-Fontanine and W. Fulton, thus
giving an expansion of an arbitrary cylindric skew Schur function in terms of
skew Schur functions. While we show that no non-trivial cylindric skew Schur
functions are Schur-positive, we conjecture that this can be reconciled using
the new concept of cylindric Schur-positivity.


  It was previously reported, that temperature may significantly influence
neural dynamics on different levels of brain modelling. Due to this fact, while
creating the model in computational neuroscience we would like to make it
scalable for wide-range of various brain temperatures. However currently,
because of a lack of experimental data and an absence of analytical model
describing temperature influence on synapses, it is not possible to include
temperature effects on multi-neuron modelling level. In this paper, we propose
first step to deal with this problem: new analytical model of AMPA-type
synaptic conductance, which is able to include temperature effects in
low-frequency stimulations. It was constructed on basis of Markov model
description of AMPA receptor kinetics and few simplifications motivated both
experimentally and from Monte Carlo simulation of synaptic transmission. The
model may be used for efficient and accurate implementation of temperature
effects on AMPA receptor conductance in large scale neural network simulations.
This in fact, opens wide-range of new possibilities for researching an
influence of temperature on brain functioning.


  We present a method to solve two-stage stochastic problems with fixed
recourse when the uncertainty space can have either discrete or continuous
distributions. Given a partition of the uncertainty space, the method is
addressed to solve a discrete problem with one scenario for each element of the
partition (sub-regions of the uncertainty space). Fixing first stage variables,
we formulate a second stage subproblem for each element, and exploiting
information from the dual of these problems, we provide conditions that the
partition must satisfy to obtain the optimal solution. These conditions provide
guidance on how to refine the partition, converging iteratively to the optimal
solution. Results from computational experiments show how the method
automatically refines the partition of the uncertainty space in the regions of
interest for the problem. Our algorithm is a generalization of the adaptive
partition-based method presented by Song & Luedtke (2015) for discrete
distributions, extending its applicability to more general cases.


  We study a power-law $F(R)$ gravity with an early dark energy term, that can
describe both the early-time and the late-time acceleration of the Universe. We
confront this scenario with recent observational data including the Pantheon
Type Ia supernovae, measurements of the Hubble parameter $H(z)$ (Cosmic
Chronometers), data from Baryon Acoustic Oscillations and standard rulers data
from the Cosmic Microwave Background (CMB) radiation. The model demonstrates
some achievements in confronting with these observations and can be compared
with the $\Lambda$-Cold-Dark-Matter model. In particular, in both models we
obtain very close estimates for the Hubble constant $H_0$, but it is not true
for $\Omega_m^0$. The early dark energy term supports viability of the
considered $F(R)$ gravity model.


  In the present scenario rate of utilization of electrical energy is
continuously increasing, to preserve the non-renewable energy resources,
efficient consumption of electrical energy is one of the most important needs
of this era. Smart Energy Meter (SEM) plays a vital role in efficient use of
electrical energy in consumer premises. Accuracy in the measurement of current
is one of the key factors to be considered while measuring energy consumption.
This paper introduces Arduino based low cost single-phase SEM and proposes a
linear regression based technique for accurate measurement of current. The
proposed current measurement approach gives accurate measurement for linear as
well as highly non-linear loads. The SEM built in the laboratory has data
transfer capability and also embedded with Demand Response (DR) feature. Block
Rate Tariff structure used by Tamil Nadu Generation and Distribution Company is
considered to incorporate DR that warns the consumers whenever their energy
consumption reaches the boundary of a block.


  Fast and accurate simulations of the non-linear evolution of the cosmic
density field are a major component of many cosmological analyses, but the
computational time and storage required to run them can be exceedingly large.
For this reason, we use generative adversarial networks (GANs) to learn a
compressed representation of the 3D matter density field that is fast and easy
to sample, and for the first time show that GANs are capable of generating
samples at the level of accuracy of other conventional methods. Using
sub-volumes from a suite of GADGET-2 N-body simulations, we demonstrate that a
deep-convolutional GAN can generate samples that capture both large- and
small-scale features of the matter density field, as validated through a
variety of n-point statistics. The use of a data scaling that preserves
high-density features and a heavy-tailed latent space prior allow us to obtain
state of the art results for fast 3D cosmic web generation. In particular, the
mean power spectra from generated samples agree to within 5% up to k=3 and
within 10% for k<5 when compared with N-body simulations, and similar accuracy
is obtained for a variety of bispectra. By modeling the latent space with a
heavy-tailed prior rather than a standard Gaussian, we better capture sample
variance in the high-density voxel PDF and reduce errors in power spectrum and
bispectrum covariance on all scales. Furthermore, we show that a conditional
GAN can smoothly interpolate between samples conditioned on redshift. Deep
generative models, such as the ones described in this work, provide great
promise as fast, low-memory, high-fidelity forward models of large-scale
structure.


  One of the most interesting problems discerned when applying the
Black--Scholes model to financial derivatives, is reconciling the deviation
between expected and observed values. In our recent work, we derived a new
model based on the Black--Scholes model and formulated a new mathematical
approach to an inverse problem in financial markets. In this paper, we apply
microlocal analysis to prove a uniqueness of the solution to our inverse
problem. While microlocal analysis is used for various models in physics and
engineering, this is the first attempt to apply it to a model in financial
markets. First, we explain our model, which is a type of arbitrage model. Next
we illustrate our new mathematical approach, and then for space-dependent real
drift, we obtain stable linearization and an integral equation. Finally, by
applying microlocal analysis to the integral equation, we prove our uniqueness
of the solution to our new mathematical model in financial markets.


  We present a study of events with Z bosons and jets produced at the Fermilab
Tevatron Collider in ppbar collisions at a center of mass energy of 1.96 TeV.
The data sample consists of nearly 14,000 Z/G* -> e+e- candidates corresponding
to the integrated luminosity of 0.4 fb-1 collected using the D0 detector.
Ratios of the Z/G* + >= n jet cross sections to the total inclusive Z/G* cross
section have been measured for n = 1 to 4 jet events. Our measurements are
found to be in good agreement with a next-to-leading order QCD calculation and
with a tree-level QCD prediction with parton shower simulation and
hadronization.


  In this digital age organizations depend upon the technologies to provide
customer-centric solutions by understanding well about their customers'
behaviour and continuously improving business process of the organization.
Business intelligence (BI) applications will play a vital role at this stage by
discovering the knowledge hidden in internal as well as external sources. On
the other hand, Knowledge Management (KM) will enhance the organisations
performance by providing collaborative tools to learn, create and share the
knowledge among the employees. The main intention of the BI is to enhance the
employees' knowledge with information that allows them to make decisions to
achieve its organisational strategies. However only twenty percent of data
exist in structured form, majority of banks knowledge is in unstructured or
minds of its employees. Organizations are needed to integrate KM with Knowledge
which is discovered from data and information. The purpose of this paper is to
discuss the need of business insiders in the process of knowledge discovery and
distribution, to make BI more relevant to business of the bank. We have also
discussed about the BI/KM applications in banking industry and provided a
framework to integrate BI and KM in banking industry.


  We study stochastic mean-field games among finite number of teams with large
finite as well as infinite number of decision makers. For this class of games
within static and dynamic settings, we establish the existence of a Nash
equilibrium, and show that a Nash equilibrium exhibits exchangeability in the
finite decision maker regime and symmetry in the infinite one. To arrive at
these existence and structural theorems, we endow the set of randomized
policies with a suitable topology under various decentralized information
structures, which leads to the desired convexity and compactness of the set of
randomized policies. Then, we establish the existence of a randomized Nash
equilibrium that is exchangeable (not necessarily symmetric) among decision
makers within each team for a general class of exchangeable stochastic games.
As the number of decision makers within each team goes to infinity (that is for
the mean-field game among teams), using a de Finetti representation theorem, we
show existence of a randomized Nash equilibrium that is symmetric (i.e.,
identical) among decision makers within each team and also independently
randomized. Finally, we establish that a Nash equilibrium for a class of
mean-field games among teams (which is symmetric) constitutes an approximate
Nash equilibrium for the corresponding pre-limit (exchangeable) game among
teams with large but finite number of decision makers. We thus show that common
randomness is not necessary for large team-against-team games, unlike the case
with small sized teams.


  We study the Seshadri constants on geometrically ruled surfaces. The unstable
case is completely solved. Moreover, we give some bounds for the stable case.
We apply these results to compute the Seshadri constant of the rational and
elliptic ruled surfaces. Both cases are completely determined. The elliptic
case provides an interesting picture of how particular is the behavior of the
Seshadri constants.


  As the largest gravitationally bound structures known, clusters provide clear
constraints on the formation of structure and on the composition of the
universe. Despite their extreme importance for cosmology the number of clusters
at high redshift (z > 0.75) is rather small. There are only a few X-ray
emitting examples reported and a handful of optically-selected ones. These
clusters can provide stringent constrains on theories of large scale structure
formation, if they are massive enough. I will review the status of these
distant X-ray selected clusters. These objects are of special importance
because their X-ray emission implies that they are massive, comparable to low
redshift examples, and their existence is problematic for some theories of
structure formation.


  A general method of the Foldy-Wouthuysen transformation is developed. This
method is applicable to relativistic particles with any spin in arbitrarily
strong external fields. It can be used when the de Broglie wavelength is much
smaller than the characteristic distance. Contrary to previously developed
relativistic methods, the present method satisfies the condition of the exact
Foldy-Wouthuysen transformation and is well substantiated. The derived
relativistic Foldy-Wouthuysen Hamiltonian is expanded in powers of the Planck
constant. In this expansion, terms proportional to the zero and first powers
are determined exactly in accordance with the above condition and terms
proportional to higher powers are not specified. The obtained result agrees
with the corresponding formula for the Foldy-Wouthuysen Hamiltonian previously
deduced by an iterative relativistic method and proves the validity of results
obtained with this formula.


  This paper describes an empirical study of shortfall optimization with Barra
Extreme Risk. We compare minimum shortfall to minimum variance portfolios in
the US, UK, and Japanese equity markets using Barra Style Factors (Value,
Growth, Momentum, etc.). We show that minimizing shortfall generally improves
performance over minimizing variance, especially during down-markets, over the
period 1985-2010. The outperformance of shortfall is due to intuitive tilts
towards protective factors like Value, and away from aggressive factors like
Growth and Momentum. The outperformance is largest for the shortfall that
measures overall asymmetry rather than the extreme losses.


  We have analyzed {\it Rossi} X-ray timing explorer (RXTE) pointed
observations of the outbursts of black hole and neutron star soft X-ray
transients in which an initial low/hard state or `island' state, followed by a
transition to a softer state, was observed. In three sources, the black hole
transient XTE J1550-564, the neutron star transient Aquila X-1 and a
quasi-persistent neutron star low mass X-ray binary (LMXB) 4U 1705-44, two such
outbursts were found. We find that the flux of the soft X-ray peak, which lags
the hard X-ray peak by a few days to several weeks, scales with the flux of the
hard X-ray peak. We conclude that we are able to predict the soft X-ray
outburst peak flux based on the `preceding' hard X-ray peak flux, implying an
early set up of the outbursts. We also find that the X-ray luminosity
corresponding to the peak of the hard X-ray flux, which corresponds to the
X-ray luminosity of the start of the hard-to-soft state transition, varies by a
factor of about 2. If the accretion geometry early in the outburst rise is
composed of two flows (e.g. a hot sub-Keplerian halo flow and a Keplerian disk
flow, or an outflow and a Keplerian disk flow), the correlation indicates that
the two flows are initially related, probably due to processes in the outer
part of the accretion disk. We discuss constraints on a single flow model and a
disk-jet model from these observations.


  The Vicsek simulation model of flocking together with its theoretical
treatment by Toner and Tu in 1995 were two foundational cornerstones of active
matter physics. However, despite the field's tremendous progress, the actual
universality class (UC) governing the scaling behavior of Viscek's "flocking"
phase remains elusive. Here, we use nonperturbative, functional renormalization
group methods to analyze, numerically and analytically, a simplified version of
the Toner-Tu model, and uncover a novel UC with scaling exponents that agree
remarkably well with the values obtained in a recent simulation study by
Mahault et al. [Phys. Rev. Lett. 123, 218001 (2019)], in both two and three
spatial dimensions. We therefore believe that there is strong evidence that the
UC uncovered here describes Vicsek's flocking phase.


  It is claimed that a variety of facts concerning ellipsis, event reference,
and interclausal coherence can be explained by two features of the linguistic
form in question: (1) whether the form leaves behind an empty constituent in
the syntax, and (2) whether the form is anaphoric in the semantics. It is
proposed that these features interact with one of two types of discourse
inference, namely {\it Common Topic} inference and {\it Coherent Situation}
inference. The differing ways in which these types of inference utilize
syntactic and semantic representations predicts phenomena for which it is
otherwise difficult to account.


  We have investigated the stability of a set of non-rotating anisotropic
spherical models with a phase-space distribution function of the
Osipkov-Merritt type. The velocity distribution in these models is isotropic
near the center and becomes radially anisotropic at large radii. They are
special members of the family studied by Dehnen and Tremaine et al. where the
mass density has a power-law cusp $\rho\propto r^{-\gamma}$ at small radii and
decays as $\rho\propto r^{-4}$ at large radii. The radial-orbit instability of
models with $\gamma$ = 0, 1/2, 1, 3/2, and 2, was studied using an N-body code
written by one of us and based on the `self-consistent field' method developed
by Hernquist and Ostriker. These simulations have allowed us to delineate a
boundary in the $(\gamma,r_{a})$-plane that separates the stable from the
unstable models. This boundary is given by $2T_{r}/T_{t} = 2.31 \pm 0.27$, for
the ratio of the total radial to tangential kinetic energy. We also found that
the stability criterion $df/dQ\le 0$, recently raised by Hjorth, gives lower
values compared with our numerical results.


  We reconsider the quantization of symbols defined on the product between a
nilpotent Lie algebra and its dual. To keep track of the non-commutative group
background, the Lie algebra is endowed with the Baker-Campbell-Hausdorff
product, making it via the exponential diffeomorphism a copy of its unique
connected simply connected nilpotent Lie group. Using harmonic analysis tools,
we emphasize the role of a Weyl system, of the associated Fourier-Wigner
transformation and, at the level of symbols, of an important family of
exponential functions. Such notions also serve to introduce a family of
phase-space shifts. These are used to define and briefly study a new class of
coorbit spaces of symbols and its relationship with coorbit spaces of vectors,
defined via the Fourier-Wigner transform.


  Resonances in optical systems are useful for many applications, such as
frequency comb generation, optical filtering, and biosensing. However, many of
these applications are difficult to implement in optical metasurfaces because
traditional approaches for designing multi-resonant nanostructures require
significant computational and fabrication efforts. To address this challenge,
we introduce the concept of Fourier lattice resonances (FLRs) in which multiple
desired resonances can be chosen a priori and used to dictate the metasurface
design. Because each resonance is supported by a distinct surface lattice mode,
each can have a high quality factor. Here, we experimentally demonstrate
several metasurfaces with arbitrarily placed resonances (e.g., at 1310 and 1550
nm) and Q-factors as high as 800 in a plasmonic platform. This flexible
procedure requires only the computation of a single Fourier transform for its
design, and is based on standard lithographic fabrication methods, allowing one
to design and fabricate a metasurface to fit any specific, optical-cavity-based
application. This work represents an important milestone towards the complete
control over the transmission spectrum of a metasurface.


  Light-electron interaction in empty space is the seminal ingredient for
free-electron lasers and also for controlling electron beams to dynamically
investigate materials and molecules. Pushing the coherent control of free
electrons by light to unexplored timescales, below the attosecond, would enable
unprecedented applications in light-assisted electron quantum circuits and
diagnostics at extremely small timescales, such as those governing
intramolecular electronic motion and nuclear phenomena. We experimentally
demonstrate attosecond coherent manipulation of the electron wave function in a
transmission electron microscope, and show that it can be pushed down to the
zeptosecond regime with existing technology. We make a relativistic pulsed
electron beam interact in free space with an appropriately synthesized
semi-infinite light field generated by two femtosecond laser pulses reflected
at the surface of a mirror and delayed by fractions of the optical cycle. The
amplitude and phase of the resulting coherent oscillations of the electron
states in energymomentum space are mapped via momentum-resolved ultrafast
electron energy-loss spectroscopy. The experimental results are in full
agreement with our theoretical framework for light-electron interaction, which
predicts access to the zeptosecond timescale by combining semi-infinite X-ray
fields with free electrons.


  As a critical component for online advertising and marking, click-through
rate (CTR) prediction has draw lots of attentions from both industry and
academia field. Recently, the deep learning has become the mainstream
methodological choice for CTR. Despite of sustainable efforts have been made,
existing approaches still pose several challenges. On the one hand, high-order
interaction between the features is under-explored. On the other hand,
high-order interactions may neglect the semantic information from the low-order
fields. In this paper, we proposed a novel prediction method, named FINT, that
employs the Field-aware INTeraction layer which captures high-order feature
interactions while retaining the low-order field information. To empirically
investigate the effectiveness and robustness of the FINT, we perform extensive
experiments on the three realistic databases: KDD2012, Criteo and Avazu. The
obtained results demonstrate that the FINT can significantly improve the
performance compared to the existing methods, without increasing the amount of
computation required. Moreover, the proposed method brought about 2.72\%
increase to the advertising revenue of a big online video app through A/B
testing. To better promote the research in CTR field, we released our code as
well as reference implementation at: https://github.com/zhishan01/FINT.


  Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible
deployment and access services as air base stations. However, the channel
errors of low-altitude communication links formed by mobile deployment of UAVs
cannot be ignored. And the energy efficiency of the UAVs communication with
imperfect channel state information (CSI) hasnt been well studied yet.
Therefore, we focus on system performance optimization in non-orthogonal
multiple access (NOMA) UAV network considering imperfect CSI between the UAV
and users. A suboptimal resource allocation scheme including user scheduling
and power allocation is designed for maximizing energy efficiency. Because of
the nonconvexity of optimization function with an probability constraint for
imperfect CSI, the original problem is converted into a non-probability problem
and then decoupled into two convex subproblems. First, a user scheduling method
is applied in the two-side matching of users and subchannels by the difference
of convex programming. Then based on user scheduling, the energy efficiency in
UAV cells is optimized through a suboptimal power allocation algorithm by
successive convex approximation method. The simulation results prove that the
proposed algorithm is effective compared with existing resource allocation
schemes.


  We present an experimental comparison between different iterative ghost
imaging algorithms. Our experimental setup utilizes a spatial light modulator
for generating known random light fields to illuminate a partially-transmissive
object. We adapt the weighting factor used in the traditional ghost imaging
algorithm to account for changes in the efficiency of the generated light
field. We show that our normalized weighting algorithm can match the
performance of differential ghost imaging.


  An analytical solution for the flow field of a shear flow over a rectangular
cavity containing a second immiscible fluid is derived. While flow of a
single-phase fluid over a cavity is a standard case investigated in fluid
dynamics, flow over a cavity which is filled with a second immiscible fluid,
has received little attention. The flow filed inside the cavity is considered
to define a boundary condition for the outer flow which takes the form of a
Navier slip condition with locally varying slip length. The slip-length
function is determined from the related problem of lid-driven cavity flow.
Based on the Stokes equations and complex analysis it is then possible to
derive a closed analytical expression for the flow field over the cavity for
both the transversal and the longitudinal case. The result is a comparatively
simple function, which displays the dependence of the flow field on the cavity
geometry and the medium filling the cavity. The analytically computed flow
field agrees well with results obtained from a numerical solution of the
Navier-Stokes equations. The studies presented in this article are of
considerable practical relevance, for example for the flow over
superhydrophobic surfaces.