  Four-dimensional asymptotically-free large $N$ gauge theories compactified on
$S^3_R \times \mathbb{R}$ have a weakly-coupled confining regime when $R$ is
small compared to the strong scale. We compute the vacuum energy of a variety
of confining large $N$ non-supersymmetric gauge theories in this calculable
regime, where the vacuum energy can be thought of as the $S^3$ Casimir energy.
The $N=\infty$ renormalized vacuum energy turns out to vanish in all of the
large $N$ gauge theories we have examined, confirming a striking prediction of
temperature-reflection symmetry.


  Person re-identification (person Re-Id) aims to retrieve the pedestrian
images of a same person that captured by disjoint and non-overlapping cameras.
Lots of researchers recently focuse on this hot issue and propose deep learning
based methods to enhance the recognition rate in a supervised or unsupervised
manner. However, two limitations that cannot be ignored: firstly, compared with
other image retrieval benchmarks, the size of existing person Re-Id datasets
are far from meeting the requirement, which cannot provide sufficient
pedestrian samples for the training of deep model; secondly, the samples in
existing datasets do not have sufficient human motions or postures coverage to
provide more priori knowledges for learning. In this paper, we introduce a
novel unsupervised pose augmentation cross-view person Re-Id scheme called
PAC-GAN to overcome these limitations. We firstly present the formal definition
of cross-view pose augmentation and then propose the framework of PAC-GAN that
is a novel conditional generative adversarial network (CGAN) based approach to
improve the performance of unsupervised corss-view person Re-Id. Specifically,
The pose generation model in PAC-GAN called CPG-Net is to generate enough
quantity of pose-rich samples from original image and skeleton samples. The
pose augmentation dataset is produced by combining the synthesized pose-rich
samples with the original samples, which is fed into the corss-view person
Re-Id model named Cross-GAN. Besides, we use weight-sharing strategy in the
CPG-Net to improve the quality of new generated samples. To the best of our
knowledge, we are the first try to enhance the unsupervised cross-view person
Re-Id by pose augmentation, and the results of extensive experiments show that
the proposed scheme can combat the state-of-the-arts.


  We investigate multi-monopole solutions of a modified version of the BPS
Yang-Mills-Higgs model in which a term quartic in the covariant derivatives of
the Higgs field (a Skyrme term) is included in the Lagrangian. Using numerical
methods we find that this modification leads to multi-monopole bound states. We
compute axially symmetric monopoles up to charge five and also monopoles with
Platonic symmetry for charges three, four and five. The numerical evidence
suggests that, in contrast to Skyrmions, the minimal energy Skyrmed monopoles
are axially symmetric.


  The hot plasma permeating clusters of galaxies often shows a central peak in
the X-ray surface brightness that is coincident with a drop in entropy. This is
taken as evidence for a cooling flow where the radiative cooling in the central
regions of a cluster causes a slow subsonic inflow. Searches in all wavebands
have revealed significantly less cool gas than predicted indicating that the
mass deposition rate of cooling flows is much lower than expected. However,
most cooling flow clusters host an Active Galactic Nucleus (AGN) at their
centres. AGN can inflate large bubbles of hot plasma that subsequently rise
through the cluster atmosphere, thus stirring this gas. Here we report on the
results from highly resolved hydrodynamic simulations which for the first time
show that buoyant bubbles increase the cooling time in the inner cluster
regions and thereby significantly reduce the deposition of cold gas. This work
demonstrates that the action of AGN in the centres of cooling flow clusters can
explain the observed absence of large quantities of cooled gas.


  Most currently used cryptographic tools for protecting data are based on
certain computational assumptions, which makes them vulnerable with respect to
technological and algorithmic developments, such as quantum computing. One
existing option to counter this potential threat is quantum key distribution,
whose security is based on the laws of quantum physics. Quantum key
distribution is secure against unforeseen technological developments. A second
approach is post-quantum cryptography, which is a set of cryptographic
primitives that are believed to be secure even against attacks with both
classical and quantum computing technologies. From this perspective, this study
reviews recent progress in the deployment of the quantum-secured infrastructure
based on quantum key distribution, post-quantum cryptography, and their
combinations. Various directions in the further development of the full-stack
quantum-secured infrastructure are also indicated. Distributed applications,
such as blockchains and distributed ledgers, are also discussed.


  Applying traditional collaborative filtering to digital publishing is
challenging because user data is very sparse due to the high volume of
documents relative to the number of users. Content based approaches, on the
other hand, is attractive because textual content is often very informative. In
this paper we describe large-scale content based collaborative filtering for
digital publishing. To solve the digital publishing recommender problem we
compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets
(DBN) that both find low-dimensional latent representations for documents.
Efficient retrieval can be carried out in the latent representation. We work
both on public benchmarks and digital media content provided by Issuu, an
online publishing platform. This article also comes with a newly developed deep
belief nets toolbox for topic modeling tailored towards performance evaluation
of the DBN model and comparisons to the LDA model.


  The degeneracy and spatial support of pseudo-Landau levels (pLLs) in strained
honeycomb lattices systematically depends on the geometry -- for instance, in
hexagonal and rectangular flakes the 0th pLL displays a twofold increased
degeneracy, while the characteristic sublattice polarization of the 0th pLL is
only fully realized in a zigzag-terminated triangle. These features are
dictated by algebraic constraints in the atomistic theory, and signify a
departure from the standard picture in which all qualitative differences
between pLLs and Landau levels induced by a magnetic field trace back to the
valley-antisymmetry of the pseudomagnetic field.


  Automated segmentation of individual calf muscle compartments from 3D
magnetic resonance (MR) images is essential for developing quantitative
biomarkers for muscular disease progression and its prediction. Achieving
clinically acceptable results is a challenging task due to large variations in
muscle shape and MR appearance. Although deep convolutional neural networks
(DCNNs) achieved improved accuracy in various image segmentation tasks, certain
problems such as utilizing long-range information and incorporating high-level
constraints remain unsolved. We present a novel fully convolutional network
(FCN), called FilterNet, that utilizes contextual information in a large
neighborhood and embeds edge-aware constraints for individual calf muscle
compartment segmentations. An encoder-decoder architecture with flexible
backbone blocks is used to systematically enlarge convolution receptive field
and preserve information at all resolutions. Edge positions derived from the
FCN output muscle probability maps are explicitly regularized using
kernel-based edge detection in an end-to-end optimization framework. Our
FilterNet was evaluated on 40 T1-weighted MR images of 10 healthy and 30
diseased subjects by 4-fold cross-validation. Mean DICE coefficients of
88.00%--91.29% and mean absolute surface positioning errors of 1.04--1.66 mm
were achieved for the five 3D muscle compartments.


  Generalized Pl\"ucker numbers are defined to count certain types of tangent
lines of generic degree $d$ complex projective hypersurfaces. They can be
computed by identifying them as coefficients of GL(2)-equivariant cohomology
classes of certain invariant subspaces, the so-called coincident root strata,
of the vector space of homogeneous degree $d$ complex polynomials in two
variables. In an earlier paper L\'aszl\'o M. Feh\'er and the author gave a new,
recursive method for calculating these classes. Using this method, we showed
that -- similarly to the classical Pl\"ucker formulas counting the bitangents
and flex lines of a degree $d$ plane curve -- generalized Pl\"ucker numbers are
polynomials in the degree $d$.
  In this paper, by further analyzing our recursive formula, we determine the
leading terms of all the generalized Pl\"ucker formulas.


  Kolyvagin used Heegner points to associate a system of cohomology classes to
an elliptic curve over $\Q$ and conjectured that the system contains a
non-trivial class. His conjecture has profound implications on the structure of
Selmer groups. We provide new computational and theoretical evidence for
Kolyvagin's conjecture. More precisely, we explicitly compute Heegner points
over ring class fields and use these points to verify the conjecture for
specific elliptic curves of rank two. We explain how Kolyvagin's conjecture
implies that if the analytic rank of an elliptic curve is at least two then the
$\Z_p$-corank of the corresponding Selmer group is at least two as well. We
also use explicitly computed Heegner points to produce non-trivial classes in
the Shafarevich-Tate group.


  The spatiality of derivations of quasi *-algebras is investigated by means of
representation theory. Moreover, in view of physical applications, the
spatiality of the limit of a family of spatial derivations is considered.


  We discuss two extensions of results conjectured by Nick Kuhn about the
non-realization of unstable algebras as the mod $p$ singular cohomology of a
space, for $p$ a prime. The first extends and refines earlier work of the
second and fourth authors, using Lannes' mapping space theorem. The second (for
the prime $2$) is based on an analysis of the $-1$ and $-2$ columns of the
Eilenberg-Moore spectral sequence, and of the associated extension. In both
cases, the statements and proofs use the relationship between the categories of
unstable modules and functors between $\Fp$-vector spaces. The second result in
particular exhibits the power of the functorial approach.


  We show that a general $n$-dimensional polarized abelian variety $(A,L)$ of a
given polarization type and satisfying $ h^0(A, L) \geq \dfrac{8^n}{2} \cdot
\dfrac{n^n}{n !}$ is projectively normal. In the process, we also obtain a
sharp lower bound for the volume of a purely one-dimensional complex analytic
subvariety in a geodesic tubular neighborhood of a subtorus of a compact
complex torus.


  For decades, system administrators have been striving to design and tune
cluster scheduling policies to improve the performance of high performance
computing (HPC) systems. However, the increasingly complex HPC systems combined
with highly diverse workloads make such manual process challenging,
time-consuming, and error-prone. We present a reinforcement learning based HPC
scheduling framework named DRAS-CQSim to automatically learn optimal scheduling
policy. DRAS-CQSim encapsulates simulation environments, agents, hyperparameter
tuning options, and different reinforcement learning algorithms, which allows
the system administrators to quickly obtain customized scheduling policies.


  Subsystem quantum error-correcting codes typically involve measuring a
sequence of non-commuting parity check operators. They can sometimes exhibit
greater fault-tolerance than conventional subspace codes, which use commuting
checks. However, unlike subspace codes, it is unclear if subsystem codes -- in
particular their advantages -- can be understood in terms of ground state
properties of a physical Hamiltonian. In this paper, we address this question
for the three-dimensional subsystem toric code (3D STC), as recently
constructed by Kubica and Vasmer [Nat. Comm. 13, 6272(2022)], which exhibits
single-shot error correction (SSEC). Motivated by a conjectured relation
between SSEC and thermal stability, we study the zero and finite temperature
phases of an associated non-commuting Hamiltonian. By mapping the Hamiltonian
model to a pair of 3D Z_2 gauge theories coupled by a kinetic constraint, we
find various phases at zero temperature, all separated by first-order
transitions: there are 3D toric code-like phases with deconfined point-like
excitations in the bulk, and there are phases with a confined bulk supporting a
2D toric code on the surface when appropriate boundary conditions are chosen.
The latter is similar to the surface topological order present in 3D STC.
However, the similarities between the SSEC in 3D STC and the confined phases
are only partial: they share the same sets of degrees of freedom, but they are
governed by different dynamical rules. Instead, we argue that the process of
SSEC can more suitably be associated with a path (rather than a point) in the
zero-temperature phase diagram, a perspective which inspires alternative
measurement sequences enabling SSEC. Moreover, since none of the
above-mentioned phases survives at nonzero temperature, SSEC of the code does
not imply thermal stability of the associated Hamiltonian phase.


  Clouds have a significant impact on the Earth's climate system. They play a
vital role in modulating Earth's radiation budget and driving regional changes
in temperature and precipitation. This makes clouds ideal for climate
intervention techniques like Marine Cloud Brightening (MCB) which refers to
modification in cloud reflectivity, thereby cooling the surrounding region.
However, to avoid unintended effects of MCB, we need a better understanding of
the complex cloud to climate response function. Designing and testing such
interventions scenarios with conventional Earth System Models is
computationally expensive. Therefore, we propose a hybrid AI-assisted visual
analysis framework to drive such scientific studies and facilitate interactive
what-if investigation of different MCB intervention scenarios to assess their
intended and unintended impacts on climate patterns. We work with a team of
climate scientists to develop a suite of hybrid AI models emulating
cloud-climate response function and design a tightly coupled frontend
interactive visual analysis system to perform different MCB intervention
experiments.


  Quantum state tomography aiming at reconstructing the density matrix of a
quantum state plays an important role in various emerging quantum technologies.
Inspired by the intuition that machine learning has favorable robustness and
generalization, we propose a deep neural networks based quantum state
tomography (DNN-QST) approach, which are applied to three
measurement-constrained cases, including few measurement copies and incomplete
measurements as well as noisy measurements. Numerical results demonstrate that
DNN-QST exhibits a great potential to achieve high fidelity for quantum state
tomography with limited measurement resources and can achieve improved
estimation when tomographic measurements suffer from noise. In addition, the
results for 2-qubit states from quantum optical devices demonstrate the
generalization of DNN-QST and its robustness against possible error in the
experimental devices.


  The conjecture made recently by the group at Sherbrooke, that their observed
anomalous thermal Hall effect in the pseudo-gap phase in the cuprates is due to
phonons, is supported on the basis of an earlier result that the observed
loop-current order in this phase must induce lattice distortions which are
linear in the order parameter and an applied magnetic field. The lowered
symmetry of the crystal depends on the direction of the field. A consequence is
that the elastic constants change proportional to the field and are shown to
induce axial thermal transport with the same symmetries as the Lorentz force
enforces for the normal electronic Hall effect. Direct measurements of elastic
constants in a magnetic field are suggested to verify the quantitative aspects
of the results.


  Stellar flares, winds and coronal mass ejections form the space weather. They
are signatures of the magnetic activity of cool stars and, since activity
varies with age, mass and rotation, the space weather that extra-solar planets
experience can be very different from the one encountered by the solar system
planets. How do stellar activity and magnetism influence the space weather of
exoplanets orbiting main-sequence stars? How do the environments surrounding
exoplanets differ from those around the planets in our own solar system? How
can the detailed knowledge acquired by the solar system community be applied in
exoplanetary systems? How does space weather affect habitability? These were
questions that were addressed in the splinter session "Cool stars and Space
Weather", that took place on 9 Jun 2014, during the Cool Stars 18 meeting. In
this paper, we present a summary of the contributions made to this session.


  I briefly review recent work on the comparison between two and three graviton
scattering in supergravity and matrix theory


  We introduce the quasi-topological electromagnetism which is defined to be
the squared norm of the topological 4-form $F\wedge F$. A salient property is
that its energy-momentum tensor is of the isotropic perfect fluid with the
pressure being precisely the opposite to its energy density. It can thus
provide a model for dark energy. We study its application in both black hole
physics and cosmology. The quasi-topological term has no effect on the purely
electric or magnetic Reissner-Nordstr\"om black holes, the dyonic solution is
however completely modified. We find that the dyonic black holes can have four
real horizons. For suitable parameters, the black hole can admit as many as
three photon spheres, with one being stable. Another intriguing property is
that although the quasi-topological term breaks the electromagnetic duality,
the symmetry emerges in the on-shell action in the Wheeler-DeWitt patch. In
cosmology, we demonstrate that the quasi-topological term alone is equivalent
to a cosmological constant, but the model provides a mechanism for the dark
energy to couple with other types of matter. We present a concrete example of
the quasi-topological electromagnetism coupled to a scalar field that admits
the standard FLRW cosmological solutions.


  We prove the Wolff potential estimates for nonlocal equations with
nonstandard growth. As an application, we obtain the Wiener criterion in this
framework, which provides a necessary and sufficient condition for boundary
points to be regular. Our approach relies on the fine analysis of superharmonic
functions in view of nonlocal nonlinear potential theory.


  Determination of qubit initialisation and measurement fidelity is important
for the overall performance of a quantum computer. However, the method by which
it is calculated in semiconductor qubits varies between experiments. In this
paper we present a full theoretical analysis of electronic single-shot readout
and describe critical parameters to achieve high fidelity readout. In
particular, we derive a model for energy selective state readout based on a
charge detector response and examine how to optimise the fidelity by choosing
correct experimental parameters. Although we focus on single electron spin
readout, the theory presented can be applied to other electronic readout
techniques in semiconductors that use a reservoir.


  In this paper, we study the effects of both the amount of open cell walls and
their aperture sizes on solid foams permeability. FEM flow simulations are
performed at both pore and macroscopic scales. For foams with fully
interconnected pores, we obtain a robust power-law relationship between
permeability and membrane aperture size. This result owns to the local pressure
drop mechanism through the membrane aperture as described by Sampson for fluid
flow through a circular orifice in a thin plate. Based on this local law,
pore-network simulation of simple flow is used and is shown to reproduce
successfully FEM results. This low computational cost method allowed to study
in detail the effects of the open wall amount on percolation, percolating
porosity and permeability. A model of effective permeability is proposed and
shows ability to reproduce the results of network simulations. Finally, an
experimental validation of the theoretical model on well controlled solid foam
is presented.


  The magnetic properties of Fe$_3$O$_4$ nanoparticle assemblies have been
investigated in detail through a combination of vibrating sample magnetometry
and muon spin relaxation ($\mu$SR) techniques. Two samples with average
particle sizes of 5 nm and 20 nm, respectively, were studied. For both samples,
the magnetometry and $\mu$SR results exhibit clear signatures of the
superparmagnetic state at high temperature and the magnetically blocked state
at low temperature. The $\mu$SR data demonstrate that the transition from the
superparamagnetic to the blocked state occurs gradually throughout the sample
volume over a broad temperature range due to the finite particle size
distribution of each sample. The transition occurs between approximately 3 K
and 45 K for the 5 nm sample and 150 K and 300 K for the 20 nm sample. The
magnetometry and $\mu$SR data are further analyzed to yield estimates of
microscopic magnetic parameters including the nanoparticle spin-flip activation
energy $E_A$, magnetic anisotropy $K$, and intrinsic nanoparticle spin reversal
attempt time $\tau_0$. These results highlight the complementary information
about magnetic nanoparticles that can be obtained by bulk magnetic probes such
as magnetometry and local magnetic probes such as $\mu$SR.


  This paper is devoted to an in deep investigation of the first fundamental
solution to the linear multi-dimensional space-time-fractional diffusion-wave
equation. This equation is obtained from the diffusion equation by replacing
the first order time-deri\-va\-ti\-ve by the Caputo fractional derivative of
order $\beta,\ 0 <\beta \leq 2$ and the Laplace operator by the fractional
Laplacian $(-\Delta)^{\frac\alpha 2}$ with $0<\alpha \leq 2$. First, a
representation of the fundamental solution in form of a Mellin-Barnes integral
is deduced by employing the technique of the Mellin integral transform. This
representation is then used for establishing of several subordination formulas
that connect the fundamental solutions for different values of the fractional
derivatives $\alpha$ and $\beta$. We also discuss some new cases of completely
monotone functions and probability density functions that are expressed in
terms of the Mittag-Leffler function, the Wright function, and the generalized
Wright function.


  The fusion algebra of the vertex operator algebra $V_L^+$ for a rank 1 even
lattice $L$ is explicitly shown to be isomorphic to the fusion algebra of the
Kac-Moody algebra of type $D^{(1)}$ at level 2 in almost all cases.


  We introduce Doctor XAvIer, a BERT-based diagnostic system that extracts
relevant clinical data from transcribed patient-doctor dialogues and explains
predictions using feature attribution methods. We present a novel performance
plot and evaluation metric for feature attribution methods: Feature Attribution
Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC). FAD curve
analysis shows that integrated gradients outperforms Shapley values in
explaining diagnosis classification. Doctor XAvIer outperforms the baseline
with 0.97 F1-score in named entity recognition and symptom pertinence
classification and 0.91 F1-score in diagnosis classification.


  I describe a new Bayesian based algorithm to infer the full three dimensional
velocity field from observed distances and spectroscopic galaxy catalogues. In
addition to the velocity field itself, the algorithm reconstructs true
distances, some cosmological parameters and specific non-linearities in the
velocity field. The algorithm takes care of selection effects, miscalibration
issues and can be easily extended to handle direct fitting of, e.g., the
inverse Tully-Fisher relation. I first describe the algorithm in details
alongside its performances. This algorithm is implemented in the VIRBIuS
(VelocIty Reconstruction using Bayesian Inference Software) software package. I
then test it on different mock distance catalogues with a varying complexity of
observational issues. The model proved to give robust measurement of velocities
for mock catalogues of 3,000 galaxies. I expect the core of the algorithm to
scale to tens of thousands galaxies. It holds the promises of giving a better
handle on future large and deep distance surveys for which individual errors on
distance would impede velocity field inference.


  We study the $2e^-2e^+$ equal-mass charge-neutral four-body system in the
adiabatic hyperspherical framework. The lowest few adiabatic potentials are
calculated for zero orbital angular momentum, positive parity, and charge
conjugation symmetries. Propagating the R-matrix, the low-energy $s$-wave
scattering lengths of the singlet-singlet and triplet-triplet spin
configurations are calculated. Lastly, we calculate the S-matrix for energies
above the ionic threshold to estimate the transition rates between the single
ionic fragmentation channel and the lowest few dimer-dimer fragmentation
channels.


  We consider a mechanical resonator made of diamond, which contains a
nitrogen-vacancy center (NV center) locating at the end of the oscillator. A
second order magnetic gradient is applied and inducing coupling between
mechanical modes and the NV center. By applying proper external magnetic field,
the energy difference between NV center electron spin levels can be tuned to
match the energy difference between two mechanical modes $a$ and $b$. A laser
is used for continuously initializing the NV center electron spin. The mode $a$
with lower frequency is driven by a thermal bath. We find that the temperature
of the mode $b$ is significantly cooled when the heating bath temperature is
increased. We discuss the conditions that quantum regime cooling requires, and
confirm the results by numerical simulation. Finally we give the intuitive
physical explanation on this unusual effect.


  The advance of smartphones and cellular networks boosts the need of mobile
advertising and targeted marketing. However, it also triggers the unseen
security threats. We found that the phone scams with fake calling numbers of
very short lifetime are increasingly popular and have been used to trick the
users. The harm is worldwide. On the other hand, deceptive advertising
(deceptive ads), the fake ads that tricks users to install unnecessary apps via
either alluring or daunting texts and pictures, is an emerging threat that
seriously harms the reputation of the advertiser. To counter against these two
new threats, the conventional blacklist (or whitelist) approach and the machine
learning approach with predefined features have been proven useless.
Nevertheless, due to the success of deep learning in developing the highly
intelligent program, our system can efficiently and effectively detect phone
scams and deceptive ads by taking advantage of our unified framework on deep
neural network (DNN) and convolutional neural network (CNN). The proposed
system has been deployed for operational use and the experimental results
proved the effectiveness of our proposed system. Furthermore, we keep our
research results and release experiment material on
http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any
update.


  High-energy diffractive physics has several interests for theoreticians and
experimentalists. We first remind the reader of the main characteristics of
diffraction in particles physics and present the calculation of a two-gluon
exchange in which non-perturbative effects are introduced via new singularities
from confinement.


  Momentum distributions (MD) of deep hole proton states in $^{238}$U are
studied paying particular attention to the influence of deformation. Two
essentially different mean-field models, Woods-Saxon (WS) and
Skyrme-Hartree-Fock with the SkM$^*$ force, are used. Noticeable deviations
between the WS and SkM$^*$ results are found. They are mainly due to the
difference in effective nucleon mass. In particular, SkM$^*$ gives much weaker
deformation effects at low momenta than WS. It is shown that, in spite of the
deformation mixing, MD at low momenta can serve for identification of
$K^{\pi}=1/2^+$ hole states originating from $s_{1/2}$ spherical sub-shells.
Moreover, following the WS calculations, the deformation results in additional
$K^{\pi}=1/2^+$ states with strong $l=0$ contributions. A possibility to probe
such states in knock-out experiments is discussed.


  Using the zero-curvature formulation, it is shown that W-algebra
transformations are symmetries of corresponding generalised Drinfel'd-Sokolov
hierarchies. This result is illustrated with the examples of the KdV and
Boussinesque hierarchies, and the hierarchy associated to the
Polyakov-Bershadsky W-algebra.


  Let $0<\gamma<n$ and $I_\gamma$ be the fractional integral operator of order
$\gamma$, $I_{\gamma}f(x)=\int_{\mathbb R^n}|x-y|^{\gamma-n}f(y)\,dy$, and let
$[b,I_\gamma]$ be the linear commutator generated by a symbol function $b$ and
$I_\gamma$, $[b,I_{\gamma}]f(x)=b(x)\cdot I_{\gamma}f(x)-I_\gamma(bf)(x)$. This
paper is concerned with two-weight, weak type norm estimates for such operators
on the weighted Morrey and amalgam spaces. Based on weak-type norm inequalities
on weighted Lebesgue spaces and certain $A_p$-type conditions on pairs of
weights, we can establish the weak-type norm inequalities for fractional
integral operator $I_{\gamma}$ as well as the corresponding commutator in the
framework of weighted Morrey and amalgam spaces. Furthermore, some estimates
for the extreme case are also obtained on these weighted spaces.


  Generating synthetic datasets for training face recognition models is
challenging because dataset generation entails more than creating high fidelity
images. It involves generating multiple images of same subjects under different
factors (\textit{e.g.}, variations in pose, illumination, expression, aging and
occlusion) which follows the real image conditional distribution. Previous
works have studied the generation of synthetic datasets using GAN or 3D models.
In this work, we approach the problem from the aspect of combining subject
appearance (ID) and external factor (style) conditions. These two conditions
provide a direct way to control the inter-class and intra-class variations. To
this end, we propose a Dual Condition Face Generator (DCFace) based on a
diffusion model. Our novel Patch-wise style extractor and Time-step dependent
ID loss enables DCFace to consistently produce face images of the same subject
under different styles with precise control. Face recognition models trained on
synthetic images from the proposed DCFace provide higher verification
accuracies compared to previous works by $6.11\%$ on average in $4$ out of $5$
test datasets, LFW, CFP-FP, CPLFW, AgeDB and CALFW. Code is available at
https://github.com/mk-minchul/dcface


  We show that quantum noise in very sensitive interferometric measurements
such as gravitational-waves detectors can be drastically modified by quantum
feedback. We present a new scheme based on active control to lock the motion of
a mirror to a reference mirror at the quantum level. This simple technique
allows to reduce quantum effects of radiation pressure and to greatly enhance
the sensitivity of the detection.


  Bluetooth Basic Rate/Enhanced Data Rate (BR/EDR) is a wireless technology
used in billions of devices. Recently, several Bluetooth fuzzing studies have
been conducted to detect vulnerabilities in Bluetooth devices, but they fall
short of effectively generating malformed packets. In this paper, we propose
L2FUZZ, a stateful fuzzer to detect vulnerabilities in Bluetooth BR/EDR Logical
Link Control and Adaptation Protocol (L2CAP) layer. By selecting valid commands
for each state and mutating only the core fields of packets, L2FUZZ can
generate valid malformed packets that are less likely to be rejected by the
target device. Our experimental results confirmed that: (1) L2FUZZ generates up
to 46 times more malformed packets with a much less packet rejection ratio
compared to the existing techniques, and (2) L2FUZZ detected five zero-day
vulnerabilities from eight real-world Bluetooth devices.


  Publish-subscribe systems are a popular approach for edge-based IoT use
cases: Heterogeneous, constrained edge devices can be integrated easily, with
message routing logic offloaded to edge message brokers. Message processing,
however, is still done on constrained edge devices. Complex content-based
filtering, the transformation between data representations, or message
extraction place a considerable load on these systems, and resulting
superfluous message transfers strain the network.
  In this paper, we propose Lotus, adding in-transit data processing to an edge
publish-subscribe middleware in order to offload basic message processing from
edge devices to brokers. Specifically, we leverage the Function-as-a-Service
paradigm, which offers support for efficient multi-tenancy, scale-to-zero, and
real-time processing. With a proof-of-concept prototype of Lotus, we validate
its feasibility and demonstrate how it can be used to offload sensor data
transformation to the publish-subscribe messaging middleware.


  The quadratic alpha' corrections to the two-dimensional black hole and to its
T-dual are calculated. These backgrounds are used to write the covariant form
of the quadratic alpha' corrections to the T-duality for general time-dependent
backgrounds of dilaton and diagonal metric in the bosonic string theory.


  A number of recent works have concentrated on a few statistical properties of
complex networks, such as the clustering, the right-skewed degree distribution
and the community, which are common to many real world networks. In this paper,
we address the hierarchy property sharing among a large amount of networks.
Based upon the eigenvector centrality (EC) measure, a method is proposed to
reconstruct the hierarchical structure of a complex network. It is tested on
the Santa Fe Institute collaboration network, whose structure is well known. We
also apply it to a Mathematicians' collaboration network and the protein
interaction network of Yeast. The method can detect significantly hierarchical
structures in these networks.


  Practical measures of electronic decoherence, called distilled purities, that
are applicable to many-body systems are introduced. While usual measures of
electronic decoherence such as the purity employ the full $N$-particle density
matrix which is generally unavailable, the distilled purities are based on the
$r$-body reduced density matrices ($r$-RDMs) which are more accessible
quantities. The $r$-body distilled purities are derivative quantities of the
previously introduced $r$-body reduced purities [I. Franco and H. Appel, J.
Chem. Phys. 139, 094109 (2013)] that measure the non-idempotency of the
$r$-RDMs. Specifically, the distilled purities exploit the structure of the
reduced purities to extract coherences between Slater determinants with integer
occupations defined by a given single-particle basis that compose an electronic
state. In this way, the distilled purities offer a practical platform to
quantify coherences in a given basis that can be used to analyze the quantum
dynamics of many-electron systems. Exact expressions for the one-body and
two-body distilled purities are presented and the utility of the approach is
exemplified via an analysis of the dynamics of oligo-acetylene as described by
the Su-Schrieffer-Heeger Hamiltonian. Last, the advantages and limitations of
the purity, reduced purity and distilled purity as measures of electronic
coherence are discussed.


  We develop an interpretable and learnable Wigner-Ville distribution that
produces a super-resolved quadratic signal representation for time-series
analysis. Our approach has two main hallmarks. First, it interpolates between
known time-frequency representations (TFRs) in that it can reach
super-resolution with increased time and frequency resolution beyond what the
Heisenberg uncertainty principle prescribes and thus beyond commonly employed
TFRs, Second, it is interpretable thanks to an explicit low-dimensional and
physical parameterization of the Wigner-Ville distribution. We demonstrate that
our approach is able to learn highly adapted TFRs and is ready and able to
tackle various large-scale classification tasks, where we reach
state-of-the-art performance compared to baseline and learned TFRs.


  We study (pre-)sheaves in bicategories on geometric categories: smooth
manifolds, manifolds with a Lie group action and Lie groupoids. We present
three main results: we describe equivariant descent, we generalize the plus
construction to our setting and show that the plus construction yields a
2-stackification for 2-prestacks. Finally we show that, for a 2-stack, the
pullback functor along a Morita-equivalence of Lie groupoids is an equivalence
of bicategories. Our results have direct applications to gerbes and 2-vector
bundles. For instance, they allow to construct equivariant gerbes from local
data and can be used to simplify the description of the local data. We
illustrate the usefulness of our results in a systematic discussion of
holonomies for unoriented surfaces.


  In an attempt to uncover any underlying physics in the standard model (SM),
we suggest a $\mu$--$\tau$ power law in the lepton sector, such that relatively
large 13 mixing angle with bi-large ones can be derived. On the basis of this,
we propose a neat and economical model for both the fermion mass hierarchy
problem of the SM and a solution to the strong CP problem, in a way that no
domain wall problem occurs, based on $A_{4}\times U(1)_{X}$ symmetry in a
supersymmetric framework. Here we refer to the global $U(1)_X$ symmetry that
can explain the above problems as "flavored Peccei-Quinn symmetry". In the
model, a direct coupling of the SM gauge singlet flavon fields responsible for
spontaneous symmetry breaking to ordinary quarks and leptons, both of which are
charged under $U(1)_X$, comes to pass through Yukawa interactions, and all
vacuum expectation values breaking the symmetries are connected each other. So,
the scale of Peccei-Quinn symmetry breaking is shown to be roughly located
around $10^{12}$ GeV section through its connection to the fermion masses.
  The model predictions are shown to lie on the testable regions in the very
near future through on-going experiments for neutrino oscillation, neutrinoless
double beta decay and axion. We examine the model predictions, arisen from the
$\mu$--$\tau$ power law, on leptonic $CP$ violation, neutrinoless double beta
decay and atmospheric mixing angle, and show that the fermion mass and mixing
hierarchies are in good agreement with the present data. Interestingly, we show
the model predictions on the axion mass $m_a\simeq2.53\times10^{-5}$ eV and the
axion coupling to photon $g_{a\gamma\gamma}\simeq1.33\times10^{-15}~{\rm
GeV}^{-1}$. And subsequently the square of the ratio between them is shown to
be 1 or 2 orders of magnitude lower than that of the conventional axion model.


  METIS is one of the three first-light instruments planned for the ELT, mainly
dedicated to high contrast imaging in the mid-infrared. On the SPHERE
high-contrast instrument currently installed at the VLT, we observe that one of
the main contrast limitations is the wind driven halo, due to the limited AO
running speed with respect to the atmospheric turbulence temporal evolution.
From this observation, we extrapolate this signature to the ELT/METIS
instrument, which is equipped with a single conjugated adaptive optics system
and with several coronagraphic devices. By making use of an analytic AO
simulator, we compare the amount of wind driven halo observed with SPHERE and
with METIS, under the same turbulence conditions.


  While free and weakly interacting particles are well described by a a
second-quantized nonlinear Schr\"odinger field, or relativistic versions of it,
the fields of strongly interacting particles are governed by effective actions,
whose quadratic terms are extremized by fractional wave equations. Their
particle orbits perform universal L\'evy walks rather than Gaussian random
walks with perturbations.


  We study numerically how the structures of distinct networks influence the
epidemic dynamics in contact process. We first find that the variability
difference between homogeneous and heterogeneous networks is very narrow,
although the heterogeneous structures can induce the lighter prevalence.
Contrary to non-community networks, strong community structures can cause the
secondary outbreak of prevalence and two peaks of variability appeared.
Especially in the local community, the extraordinarily large variability in
early stage of the outbreak makes the prediction of epidemic spreading hard.
Importantly, the bridgeness plays a significant role in the predictability,
meaning the further distance of the initial seed to the bridgeness, the less
accurate the predictability is. Also, we investigate the effect of different
disease reaction mechanisms on variability, and find that the different
reaction mechanisms will result in the distinct variabilities at the end of
epidemic spreading.


  Generating virtual populations of anatomy that capture sufficient variability
while remaining plausible is essential for conducting in-silico trials of
medical devices. However, not all anatomical shapes of interest are always
available for each individual in a population. Hence,
missing/partially-overlapping anatomical information is often available across
individuals in a population. We introduce a generative shape model for complex
anatomical structures, learnable from datasets of unpaired datasets. The
proposed generative model can synthesise complete whole complex shape
assemblies coined virtual chimaeras, as opposed to natural human chimaeras. We
applied this framework to build virtual chimaeras from databases of whole-heart
shape assemblies that each contribute samples for heart substructures.
Specifically, we propose a generative shape compositional framework which
comprises two components - a part-aware generative shape model which captures
the variability in shape observed for each structure of interest in the
training population; and a spatial composition network which assembles/composes
the structures synthesised by the former into multi-part shape assemblies (viz.
virtual chimaeras). We also propose a novel self supervised learning scheme
that enables the spatial composition network to be trained with partially
overlapping data and weak labels. We trained and validated our approach using
shapes of cardiac structures derived from cardiac magnetic resonance images
available in the UK Biobank. Our approach significantly outperforms a PCA-based
shape model (trained with complete data) in terms of generalisability and
specificity. This demonstrates the superiority of the proposed approach as the
synthesised cardiac virtual populations are more plausible and capture a
greater degree of variability in shape than those generated by the PCA-based
shape model.


  In this paper, the asymptotic formulas for Eulerian numbers, refined Eulerian
numbers and the coefficients of descent polynomials are obtained directly from
the spline interpretations of these numbers. Having related these numbers
directly to B-splines [15], we can take advantage of many powerful spline
techniques to derive various results of these numbers. The asymptotic formulas
for the Eulerian numbers Ad;k agree with the previously known results which
were given by L. Carlitz et al.(1972)[2] and S.Tanny (1973) [18], but the
convergence order is much better. We also give the asymptotic representations
of refined Eulerian numbers which is in terms of the Hermite polynomials.


  This paper studies the impact of probabilistic shaping on effective
signal-to-noise ratios (SNRs) and achievable information rates (AIRs) in a
back-to-back configuration and in unrepeated nonlinear fiber transmissions. For
back-to-back, various shaped quadrature amplitude modulation (QAM)
distributions are found to have the same implementation penalty as uniform
input. By demonstrating in transmission experiments that shaped QAM input leads
to lower effective SNR than uniform input at a fixed average launch power, we
experimentally confirm that shaping enhances the fiber nonlinearities. However,
shaping is ultimately found to increase the AIR, which is the most relevant
figure of merit as it is directly related to spectral efficiency. In a detailed
study of these shaping gains for the nonlinear fiber channel, four strategies
for optimizing QAM input distributions are evaluated and experimentally
compared in wavelength division multiplexing (WDM) systems. The first shaping
scheme generates a Maxwell-Boltzmann (MB) distribution based on a linear
additive white Gaussian noise channel. The second strategy uses the
Blahut-Arimoto algorithm to optimize an unconstrained QAM distribution for a
split-step Fourier method based channel model. In the third and fourth
approach, MB-shaped QAM and unconstrained QAM are optimized via the enhanced
Gaussian noise (EGN) model. Although the absolute shaping gains are found to be
relatively small, the relative improvements by EGN-optimized unconstrained
distributions over linear AWGN optimized MB distributions are up to 59%. This
general behavior is observed in 9-channel and fully loaded WDM experiments.


  WOD-2012 aims at facilitating new trends and ideas from a broad range of
topics concerned within the widely-spread Open Data movement, from the
viewpoint of computer science research.
  While being most commonly known from the recent Linked Open Data movement,
the concept of publishing data explicitly as Open Data has meanwhile developed
many variants and facets that go beyond publishing large and highly structured
RDF/S repositories. Open Data comprises text and semi-structured data, but also
open multi-modal contents, including music, images, and videos. With the
increasing amount of data that is published by governments (see, e.g.,
data.gov, data.gov.uk or data.gouv.fr), by international organizations
(data.worldbank.org or data.undp.org) and by scientific communities (tdar.org,
cds.u-strasbg.fr, GenBank, IRIS or KNB) explicitly under an Open Data policy,
new challenges arise not only due to the scale at which this data becomes
available.
  A number of community-based conferences accommodate tracks or workshops which
are dedicated to Open Data. However, WOD aims to be a premier venue to gather
researchers and practitioners who are contributing to and interested in the
emerging field of managing Open Data from a computer science perspective.
Hence, it is a unique opportunity to find in a single place up-to-date
scientific works on Web-scale Open Data issues that have so far only partially
been addressed by different research communities such as Databases, Data Mining
and Knowledge Management, Distributed Systems, Data Privacy, and Data
Visualization.


  The training of neural networks using different deep learning frameworks may
lead to drastically differing accuracy levels despite the use of the same
neural network architecture and identical training hyperparameters such as
learning rate and choice of optimization algorithms. Currently, our ability to
build standardized deep learning models is limited by the availability of a
suite of neural network and corresponding training hyperparameter benchmarks
that expose differences between existing deep learning frameworks. In this
paper, we present a living dataset of models and hyperparameters, called
CrossedWires, that exposes semantic differences between two popular deep
learning frameworks: PyTorch and Tensorflow. The CrossedWires dataset currently
consists of models trained on CIFAR10 images using three different computer
vision architectures: VGG16, ResNet50 and DenseNet121 across a large
hyperparameter space. Using hyperparameter optimization, each of the three
models was trained on 400 sets of hyperparameters suggested by the HyperSpace
search algorithm. The CrossedWires dataset includes PyTorch and Tensforflow
models with test accuracies as different as 0.681 on syntactically equivalent
models and identical hyperparameter choices. The 340 GB dataset and benchmarks
presented here include the performance statistics, training curves, and model
weights for all 1200 hyperparameter choices, resulting in 2400 total models.
The CrossedWires dataset provides an opportunity to study semantic differences
between syntactically equivalent models across popular deep learning
frameworks. Further, the insights obtained from this study can enable the
development of algorithms and tools that improve reliability and
reproducibility of deep learning frameworks. The dataset is freely available at
https://github.com/maxzvyagin/crossedwires through a Python API and direct
download link.


  Recently, convolutional neural networks (CNNs) have achieved excellent
performances in many computer vision tasks. Specifically, for hyperspectral
images (HSIs) classification, CNNs often require very complex structure due to
the high dimension of HSIs. The complex structure of CNNs results in
prohibitive training efforts. Moreover, the common situation in HSIs
classification task is the lack of labeled samples, which results in accuracy
deterioration of CNNs. In this work, we develop an easy-to-implement capsule
network to alleviate the aforementioned problems, i.e., 1D-convolution capsule
network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial
and spectral information on spatial and spectral domains, which is more
lightweight than 3D-convolution due to fewer parameters. Secondly,
1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce
parameter amount and computational complexity of conventional capsule network.
Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input
samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is
verified by three representative HSI datasets. Experimental results demonstrate
that 1D-ConvCapsNet is superior to state-of-the-art methods in both the
accuracy and training effort.


  A homogenization approach is one of effective strategies to solve multiscale
elliptic problems approximately. The finite element heterogeneous multiscale
method (FEHMM) which is based on the finite element makes possible to simulate
such process numerically. In this paper we introduce a FEHMM scheme for
multiscale elliptic problems based on nonconforming spaces. In particular we
use the noconforming element with the periodic boundary condition introduced in
the companion paper. Theoretical analysis derives a priori error estimates in
the standard Sobolev norms. Several numerical results which confirm our
analysis are provided.


  The content of a polynomial over a ring $R$ is a well understood notion. Ohm
and Rush generalized this concept of a content map to an arbitrary ring
extension of $R$, although it can behave quite badly. We examine five
properties an algebra may have with respect to this function -- content
algebra, weak content algebra, semicontent algebra (our own definition),
Gaussian algebra, and Ohm-Rush algebra. We show that the Gaussian, weak
content, and semicontent algebra properties are all transitive. However,
transitivity is unknown for the content algebra property. We then compare the
Ohm-Rush notion with the more usual notion of content in the power series
context. We show that many of the given properties coincide for the power
series extension map over a valuation ring of finite dimension, and that they
are equivalent to the value group being order-isomorphic to the integers or the
reals. Along the way, we give a new characterization of Pr\"ufer domains.


  Based on a specific model with U(3) family gauge symmetry at $10^3$ TeV
scale, we show its experimental signatures to search for. Since the gauge
symmetry is introduced with a special purpose, its gauge coupling constant and
gauge boson mass spectrum are not free. The current structure in this model
leads to family number violations via exchange of extra gauge bosons. We
investigate present constraints from flavor changing processes and discuss
visible signatures at LHC and lepton colliders.


  A growing area of research investigates augmenting language models with tools
(e.g., search engines, calculators) to overcome their shortcomings (e.g.,
missing or incorrect knowledge, incorrect logical inferences). Various few-shot
tool-usage strategies have been proposed. However, there is no systematic and
fair comparison across different strategies, or between these strategies and
strong baselines that do not leverage tools. We conduct an extensive empirical
analysis, finding that (1) across various datasets, example difficulty levels,
and models, strong no-tool baselines are competitive to tool-assisted
strategies, implying that effectively using tools with in-context
demonstrations is a difficult unsolved problem; (2) for knowledge-retrieval
tasks, strategies that *refine* incorrect outputs with tools outperform
strategies that retrieve relevant information *ahead of* or *during
generation*; (3) tool-assisted strategies are expensive in the number of tokens
they require to work -- incurring additional costs by orders of magnitude --
which does not translate into significant improvement in performance. Overall,
our findings suggest that few-shot tool integration is still an open challenge,
emphasizing the need for comprehensive evaluations of future strategies to
accurately assess their *benefits* and *costs*.


  In this paper, we present a generic framework to extend existing uniformly
optimal convex programming algorithms to solve more general nonlinear, possibly
nonconvex, optimization problems. The basic idea is to incorporate a local
search step (gradient descent or Quasi-Newton iteration) into these uniformly
optimal convex programming methods, and then enforce a monotone decreasing
property of the function values computed along the trajectory. Algorithms of
these types will then achieve the best known complexity for nonconvex problems,
and the optimal complexity for convex ones without requiring any problem
parameters. As a consequence, we can have a unified treatment for a general
class of nonlinear programming problems regardless of their convexity and
smoothness level. In particular, we show that the accelerated gradient and
level methods, both originally designed for solving convex optimization
problems only, can be used for solving both convex and nonconvex problems
uniformly. In a similar vein, we show that some well-studied techniques for
nonlinear programming, e.g., Quasi-Newton iteration, can be embedded into
optimal convex optimization algorithms to possibly further enhance their
numerical performance. Our theoretical and algorithmic developments are
complemented by some promising numerical results obtained for solving a few
important nonconvex and nonlinear data analysis problems in the literature.


  An accurate and efficient characterization of the polyelectrolyte properties
for cytoskeleton filaments are key to the molecular understanding of electrical
signal propagation, bundle and network formation, as well as other relevant
physicochemical processes associated with biological functions in eukaryotic
cells and their potential nanotechnological applications. In this article, we
introduce an innovative multi-scale approach able to account for the atomistic
details of a proteins molecular structure, its biological environment, and
their impact on electrical impulses propagating along wild type Actin
filaments. The approach provides a novel, simple, accurate, approximate
analytic expression for the characterization of electrical impulses in the
shape of soliton waveforms. It has been successfully used to determine the
effects of electrolyte conditions and voltage stimulus on the electrical
impulse shape, attenuation and kern propagation velocity in these systems. Our
results predict the propagation of electrical signal impulses in the form of
solitons for the range of voltage stimulus and electrolyte solutions typically
present in intracellular and in-vitro conditions. This multi-scale theory may
also be applicable to other highly charged rod-like polyelectrolytes with
relevance in biomedicine and biophysics. It is also able to account for
molecular structure conformation (mutation) and biological environment
(protonations/deprotonations) changes often present in pathological conditions.


  Let K be a finite field. Let X* be a subset of the affine space Kn, which is
parameterized by odd cycles. In this paper we give an explicit Gr\"obner basis
for the vanishing ideal, I(X*), of X*. We give an explicit formula for the
regularity of I(X*) and finally if X* is parameterized by an odd cycle of
length k, we show that the Hilbert function of the vanishing ideal of X* can be
written as linear combination of Hilbert functions of degenerate torus.


  Mergers of two compact objects, like two neutron stars or a neutron star and
a black hole, are the probable progenitor of short gamma-ray bursts. These
events are also promising sources of gravitational waves, that are currently
motivating related searches by an international network of gravitational wave
detectors. Here we describe a search for gravitational waves from the in-spiral
phase of two coalescing compact objects, in coincidence with short GRBs
occurred during during LIGO's fifth science run and Virgo's first science run.
The search includes 22 GRBs for which data from more than one of the detectors
in the LIGO/Virgo network were available. No statistically significant
gravitational-wave candidate has been found, and a parametric test shows no
excess of weak gravitational-wave signals in our sample of GRBs. The 90\%~C.L.
median exclusion distance for GRBs in our sample is of 6.7 Mpc, under the
hypothesis of a neutron star - black hole progenitor model.


  In this report, we describe our submission to the VoxCeleb Speaker
Recognition Challenge (VoxSRC) 2020. Two approaches are adopted. One is to
apply query expansion on speaker verification, which shows significant progress
compared to baseline in the study. Another is to use Kaldi extract x-vector and
to combine its Probabilistic Linear Discriminant Analysis (PLDA) score with
ResNet score.


  This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a
reinforcement learning algorithm specifically designed for weakly coupled MDP
problems with continuous action spaces. LPCA addresses the challenge of
resource constraints dependent on continuous actions by introducing a Lagrange
relaxation of the weakly coupled MDP problem within a neural network framework
for Q-value computation. This approach effectively decouples the MDP, enabling
efficient policy learning in resource-constrained environments. We present two
variations of LPCA: LPCA-DE, which utilizes differential evolution for global
optimization, and LPCA-Greedy, a method that incrementally and greadily selects
actions based on Q-value gradients. Comparative analysis against other
state-of-the-art techniques across various settings highlight LPCA's robustness
and efficiency in managing resource allocation while maximizing rewards.


  As a comet, asteroid or planet approaches its parent star, the orbit changes
shape due to the curvature of spacetime. For comets in particular, the
deviation at the pericentre may noticeably change their ephemerides and affect
the dynamics of outgassing, tidal disruption or other processes which act on
orbital timescales and are assumed to follow Newtonian gravity. By obtaining
and analysing the unaveraged equations of motion in orbital elements due to the
dominant post-Newtonian contribution (1PN), I derive a simple analytic
expression for the maximum deviation in terms of only the stellar mass and
eccentricity of the orbit. This relation can be used to assess the potential
importance of including short-period relativistic terms in models containing
comets, asteroids or planets, and help determine the level of precision needed
in numerical integrations. The magnitude of the deviation in systems with
Solar-like stars is typically comparable to the size of comet nuclei, and the
direction of the deviation is determined by the eccentricity. I show that for
eccentricities above a critical value of approximately 0.359, the direction is
away from the star.


  Real-world image super-resolution (SR) tasks often do not have paired
datasets, which limits the application of supervised techniques. As a result,
the tasks are usually approached by unpaired techniques based on Generative
Adversarial Networks (GANs), which yield complex training losses with several
regularization terms, e.g., content or identity losses. We theoretically
investigate optimization problems which arise in such models and find two
surprizing observations. First, the learned SR map is always an optimal
transport (OT) map. Second, we theoretically prove and empirically show that
the learned map is biased, i.e., it does not actually transform the
distribution of low-resolution images to high-resolution ones. Inspired by
these findings, we propose an algorithm for unpaired SR which learns an
unbiased OT map for the perceptual transport cost. Unlike the existing
GAN-based alternatives, our algorithm has a simple optimization objective
reducing the need for complex hyperparameter selection and an application of
additional regularizations. At the same time, it provides a nearly
state-of-the-art performance on the large-scale unpaired AIM19 dataset.


  The defining equation $(\ast):\ \dot \omega\_t=-F'(\omega\_t),$ of a gradient
flow is kinetic in essence. This article explores some dynamical (rather than
kinetic) features of gradient flows (i) by embedding equation $(\ast)$ into the
family of slowed down gradient flow equations: $\dot \omega ^{
\varepsilon}\_t=- \varepsilon F'( \omega ^{ \varepsilon}\_t),$ where
$\varepsilon>0$, and (ii) by considering the \emph{accelerations} $\ddot \omega
^{ \varepsilon}\_t$. We shall focus on Wasserstein gradient flows. Our approach
is mainly heuristic. It relies on Otto calculus.A special formulation of the
Schr{\"o}dinger problem consists in minimizing some action on the Wasserstein
space of probability measures on a Riemannian manifold subject to fixed initial
and final data. We extend this action minimization problem by replacing the
usual entropy, underlying Schr{\"o}dinger problem, with a general function of
the Wasserstein space. The corresponding minimal cost approaches the squared
Wasserstein distance when some fluctuation parameter tends to zero. We show
heuristically that the solutions satisfy a Newton equation, extending a recent
result of Conforti. The connection with Wasserstein gradient flows is
established and various inequalities, including evolutional variational
inequalities and contraction inequality under curvature-dimension condition,
are derived with a heuristic point of view. As a rigorous result we prove a new
and general contraction inequality for the Schr{\"o}dinger problem under a
Ricci lower bound on a smooth and compact Riemannian manifold.


  We present a general methodology for performing statistical inference on the
components of a real-valued matrix parameter for which rows and columns are
subject to order restrictions. The proposed estimation procedure is based on an
iterative algorithm developed by Dykstra and Robertson (1982) for simple order
restriction on rows and columns of a matrix. For any order restrictions on rows
and columns of a matrix, sufficient conditions are derived for the algorithm to
converge in a single application of row and column operations. The new
algorithm is applicable to a broad collection of order restrictions. In
practice, it is easy to design a study such that the sufficient conditions
derived in this paper are satisfied. For instance, the sufficient conditions
are satisfied in a balanced design. Using the estimation procedure developed in
this article, a bootstrap test for order restrictions on rows and columns of a
matrix is proposed. Computer simulations for ordinal data were performed to
compare the proposed test with some existing test procedures in terms of size
and power. The new methodology is illustrated by applying it to a set of
ordinal data obtained from a toxicological study.


  We present maps of Neptune in and near the CO (2-1) rotation line at 230.538
GHz. These data, taken with the Combined Array for Research in Millimeter-wave
Astronomy (CARMA) represent the first published spatially-resolved maps in the
millimeter. At large (~5 GHz) offsets from the CO line center, the majority of
the emission originates from depths of 1.1-4.7 bar. We observe a latitudinal
gradient in the brightness temperature at these frequencies, increasing by 2-3
K from 40 degrees N to the south pole. This corresponds to a decrease in the
gas opacity of about 30% near the south pole at altitudes below 1 bar, or a
decrease of order a factor of 50 in the gas opacity at pressures greater than 4
bar. We look at three potential causes of the observed gradient: variations in
the tropospheric methane abundance, variations in the H2S abundance, and
deviations from equilibrium in the ortho/para ratio of hydrogen. At smaller
offsets (0-213 MHz) from the center of the CO line, lower atmospheric pressures
are probed, with contributions from mbar levels down to several bars. We find
evidence of latitudinal variations at the 2-3% level in the CO line, which are
consistent with the variations in zonal-mean temperature near the tropopause
found by Conrath et al. (1998) and Orton et al. (2007).


  According to the Shell-Model Monte Carlo method, basing on the Random Phase
Approximation and the linear response theory, we carried out an estimation on
electron capture of strongly screening nuclides $^{56}$Fe, $^{56}$Co, $^{56}$Ni
, $^{56}$Mn ,$^{56}$Cr and $^{56}$V in strong electron screening (SES)in
presupernova. The
  EC rates are decreased greatly and even exceed $21.5\%$ in SES. We also
compare our results with those of Aufderheide (AFUD), which calculated by the
method of Aufderheide in SES. Our results are agreed reasonably well with AUFD
at higher density-temperature surroundings (e.g. $\rho_7>60, T_9=15.40$) and
the maximum error is $\sim $0.5$\%$. However, the maximum error is $\sim
$13.0$\%$ at lower density surroundings (e.g. $^{56}$Cr at $\rho_7=10,
T_9=15.40, Y_e=0.41$ ). On the other hand, we also compared our results in SES
with those of FFN's and Nabi's, which is in the case without SES. The
comparisons show that our results are lower more than one order magnitude than
FFN's, but about $7.23\%$ than Nabi's.


  We prove that for any open Riemann surface $N,$ natural number $n\geq 3,$
non-constant harmonic map $h:N\to \mathbb{R}^{n-2}$ and holomorphic 2-form $H$
on $N,$ there exists a weakly complete harmonic map $X=(X_j)_{j=1,\ldots,n}:N
\to \mathbb{R}^n$ with Hopf differential $H$ and $(X_j)_{j=3,\ldots,n}=h.$ In
particular, there exists a complete conformal minimal immersion
$Y=(Y_j)_{j=1,\ldots,n}:N \to \mathbb{R}^n$ such that $(Y_j)_{j=3,\ldots,n}=h.$
  As a consequence of these results, complete full non-decomposable minimal
surfaces with arbitrary conformal structure and whose generalized Gauss map is
non-degenerate and fails to intersect $n$ hyperplanes of $\mathbb{CP}^{n-1}$ in
general position are constructed. Moreover, complete non-proper embedded
minimal surfaces in $\mathbb{R}^n,$ $\forall n>3,$ are exhibited.


  This paper presents Very Long Baseline Array (VLBA) observations of 13
double-peaked [O III] emission-line type-2 Active Galactic Nuclei (AGNs) at
redshifts 0.06 < z < 0.41 (with a median redshift of z~0.15) identified in the
Sloan Digital Sky Survey. Such double-peaked emission-line objects may result
from jets or outflows from the central engine or from a dual AGN. The VLBA
provides an angular resolution of <~10 pc at the distance of many of these
galaxies, sufficient to resolve the radio emission from extremely close dual
AGNs and to contribute to understanding the origin of double-peaked [O III]
emission lines. Of the 13 galaxies observed at 3.6 cm (8.4 GHz), we detect six
at a 1\sigma\ sensitivity level of ~0.15 mJy/beam, two of which show clear jet
structures on scales ranging from a few milliarcseconds to tens of
milliarcseconds (corresponding to a few pc to tens of pc at a median redshift
of 0.15). We suggest that radio-loud double-peaked emission-line type-2 AGNs
may be indicative of jet produced structures, but a larger sample of
double-peaked [O III] AGNs with high angular resolution radio observations will
be required to confirm this suggestion.


  We show how to extend the construction of Tulczyjew triples to Lie algebroids
via graded manifolds. We also provide a generalisation of triangular Lie
bialgebroids as higher Poisson and Schouten structures on Lie algebroids.


  The information flow in a quantum system is a fundamental feature of its
dynamics. An important class of dynamics are quantum cellular automata (QCA),
systems with discrete updates invariant in time and space, for which an index
theory has been proposed for the quantification of the net flow of quantum
information across a boundary. While the index is rigid in the sense of begin
invariant under finite-depth local circuits, it is not defined when the system
is coupled to an environment, i.e. for non-unitary time evolution of open
quantum systems. We propose a new measure of information flow for non-unitary
QCA denoted the information current which is not rigid, but can be computed
locally based on the matrix-product operator representation of the map.


  Based on the linearized Eilenberger equations, the upper critical field
$(H_{c2})$ of mixed d- and s-wave superconductors has been microscopically
studied with an emphasis on the competing effects of mass anisotropy and spin
Zeeman coupling. We find the mass anisotropy always enhance $H_{c2}$ while the
Zeeman interaction suppresses $H_{c2}$. As required by the thermodynamics, we
find $H_{c2}$ is saturated at zero temperature. We compare the theoretical
calculations with recent experimental data of
YBa$_{2}$Cu$_{3}$O$_{7-+AFw-delta}$.


  A theoretical analysis of the $(\pi^-,\gamma\gamma)$ reaction in nuclei is
made in order to find the viability of this reaction to test modifications of
the $\sigma$ meson mass in nuclear matter. The $\pi\pi$ correlation in the
scalar-isoscalar channel in nuclear matter could, in principle, manifest itself
in this reaction since it plays an important role in the
$\pi\pi\to\gamma\gamma$ mechanism. But we conclude that this effect is hardly
visible in this reaction due to the strong background of the
pion-Bremsstrahlung terms. Only with some special cuts and for some
polarization states are the effect visible at the cost of a strong reduction in
the cross section.


  The persistent issue of wrongful convictions in the United States emphasizes
the need for scrutiny and improvement of the criminal justice system. While
statistical methods for the evaluation of forensic evidence, including glass,
fingerprints, and DNA, have significantly contributed to solving intricate
crimes, there is a notable lack of national-level standards to ensure the
appropriate application of statistics in forensic investigations. We discuss
the obstacles in the application of statistics in court, and emphasize the
importance of making statistical interpretation accessible to
non-statisticians, especially those who make decisions about potentially
innocent individuals. We investigate the use and misuse of statistical methods
in crime investigations, in particular the likelihood ratio approach. We
further describe the use of graphical models, where hypotheses and evidence can
be represented as nodes connected by arrows signifying association or
causality. We emphasize the advantages of special graph structures, such as
object-oriented Bayesian networks and chain event graphs, which allow for the
concurrent examination of evidence of various nature.


  In this article we study for $p\in (1,\infty)$ the $L^p$-realization of the
vector-valued Schr\"odinger operator $\mathcal{L}u := \mathrm{div} (Q\nabla u)
+ V u$. Using a noncommutative version of the Dore-Venni theorem due to
Monniaux and Pr\"uss, we prove that the $L^p$-realization of $\mathcal{L}$,
defined on the intersection of the natural domains of the differential and
multiplication operators which form $\mathcal{L}$, generates a strongly
continuous contraction semigroup on $L^p(\mathbb{R}^d; \mathbb{R}^m)$. We also
study additional properties of the semigroup such as extension to $L^1$,
positivity, ultracontractivity and prove that the generator has compact
resolvent.


  Machine learning (ML) enables the development of interatomic potentials that
promise the accuracy of first principles methods while retaining the low cost
and parallel efficiency of empirical potentials. While ML potentials
traditionally use atom-centered descriptors as inputs, different models such as
linear regression and neural networks can map these descriptors to atomic
energies and forces. This begs the question: what is the improvement in
accuracy due to model complexity irrespective of choice of descriptors? We
curate three datasets to investigate this question in terms of ab initio energy
and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3)
the superionic conductor LGPS. We further investigate how these errors affect
simulated properties with these models and verify if the improvement in fitting
errors corresponds to measurable improvement in property prediction. Since
linear and nonlinear regression models have different advantages and
disadvantages, the results presented herein help researchers choose models for
their particular application. By assessing different models, we observe
correlations between fitting quantity (e.g. atomic force) error and simulated
property error with respect to ab initio values. Such observations can be
repeated by other researchers to determine the level of accuracy, and hence
model complexity, needed for their particular systems of interest.


  We study a product rule and a difference operator equipped with Leibniz rule
in a general framework of lattice field theory. It is shown that the difference
operator can be determined by the product rule and some initial data through
the Leibniz rule. This observation leads to a no-go theorem that it is
impossible to construct any difference operator and product rule on a lattice
with the properties of (i) translation invariance, (ii) locality and (iii)
Leibniz rule. We present a formalism to overcome the difficulty by an infinite
flavor extension or a matrix expression of a lattice field theory.


  Using three different Monte Carlo generators of high energy proton-proton
collisions (HIJING, NEXUS, and PSM) we study the energy dependence of
multiplicity distributions of charged particles including the LHC energy range.
Results are used for calculation of the information entropy, Renyi's dimensions
and other multifractal characteristics of particle production.


  Massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems
operating at millimeter-wave (mmWave) frequencies enable simultaneous wideband
data transmission to a large number of users. In order to reduce the complexity
of MU precoding in all-digital basestation architectures, we propose a
two-stage precoding architecture that first performs precoding using a sparse
matrix in the beamspace domain, followed by an inverse fast Fourier transform
that converts the result to the antenna domain. The sparse precoding matrix
requires a small number of multipliers and enables regular hardware
architectures, which allows the design of hardware-efficient all-digital
precoders. Simulation results demonstrate that our methods approach the
error-rate of conventional Wiener filter precoding with more than 2x reduced
complexity.


  Topic evolution modeling has been researched for a long time and has gained
considerable interest. A state-of-the-art method has been recently using word
modeling algorithms in combination with community detection mechanisms to
achieve better results in a more effective way. We analyse results of this
approach and discuss the two major challenges that this approach still faces.
Although the topics that have resulted from the recent algorithm are good in
general, they are very noisy due to many topics that are very unimportant
because of their size, words, or ambiguity. Additionally, the number of words
defining each topic is too large, making it difficult to analyse them in their
unsorted state. In this paper, we propose approaches to tackle these challenges
by adding topic filtering and network analysis metrics to define the importance
of a topic. We test different combinations of these metrics to see which
combination yields the best results. Furthermore, we add word filtering and
ranking to each topic to identify the words with the highest novelty
automatically. We evaluate our enhancement methods in two ways: human
qualitative evaluation and automatic quantitative evaluation. Moreover, we
created two case studies to test the quality of the clusters and words. In the
quantitative evaluation, we use the pairwise mutual information score to test
the coherency of topics. The quantitative evaluation also includes an analysis
of execution times for each part of the program. The results of the
experimental evaluations show that the two evaluation methods agree on the
positive feasibility of the algorithm. We then show possible extensions in the
form of usability and future improvements to the algorithm.


  We investigate the topological plasmon polaritons (TPPs) in one-dimensional
dimerized doped silicon nanoparticle chains, as an analogy of the topological
edge states in the Su-Schrieffer-Heeger (SSH) model. The photonic band
structures are analytically calculated by taking all near-field and far-field
dipole-dipole interactions into account. For longitudinal modes, it is
demonstrated that the band topology can be well characterized by the complex
Zak phase irrespective of the lattice constant and doping concentration. By
numerically solving the eigenmodes of a finite system, it is found that a
dimerized chain with a nonzero complex Zak phase supports nontrivial
topological eigenmodes localized over both edges. Moreover, by changing the
doping concentration of Si, it is possible to tune the resonance frequency of
the TPPs from far-infrared to near-infrared, and the localization length of the
edge modes are also modulated accordingly. Since these TPPs are highly
protected modes that can achieve a strong confinement of electromagnetic waves
and are also immune to impurities and disorder, they can provide a potentially
tunable tool for robust and enhanced light-matter interactions light-matter
interaction in the infrared spectrum.


  This work details a scalable framework to orchestrate a swarm of rotary-wing
UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity
and traffic offloading for ground users. First, a Multiscale Adaptive
Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is
developed for a single UAV. Aiming to minimize the time-averaged latency to
serve user requests, subject to an average UAV power constraint, it is shown
that the optimization problem can be cast as a semi-Markov decision process,
and exhibits a multiscale structure: outer actions on radial wait velocities
and terminal service positions minimize the long-term delay-power trade-off,
optimized via value iteration; given these outer actions, inner actions on
angular wait velocities and service trajectories minimize a short-term
delay-energy cost. A novel hierarchical competitive swarm optimization scheme
is developed in the inner optimization, to devise high-resolution trajectories
via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms
(MAESTRO-X) via scalable policy replication: enabled by a decentralized
command-and-control network, the optimal single-agent policy is augmented with
spread maximization, consensus-driven conflict resolution, adaptive frequency
reuse, and piggybacking. Numerical evaluations show that, for user requests of
10 Mbits, generated according to a Poisson arrival process with rate 0.2
req/min/UAV, single-agent MAESTRO offers 3.8x faster service than a
high-altitude platform and 29% faster than a static UAV deployment; moreover,
for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7x faster than
a successive convex approximation scheme; and remarkably, a single UAV
optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.


  In this article, we prove the theorems concerning the trace relation of
SO(3), SU(2), and SO(n) which are representation of SO(3) and SU(2). An
interesting fact we found is the trace relation of SU(2) gives the spherical
law of cosine which in turns is a dihedral angle relation, a constraint that
must be satisfied by closed Euclidean simplices. Moreover, we applied our
results on general group elements to holonomies on the simplicial complex of
Regge Calculus, which is the main motivation of this article. Here, we found
that: (1) in 4-dimensional Euclidean Regge Gravity, all the holonomy circling a
single hinge are simple rotations, and (2) the dihedral angle relation
represents the 'contracted' Bianchi identity for a simplicial complex.


  Let $X$ be a variety with an action by an algebraic group $G$. In this paper
we discuss various properties of $G$-equivariant $D$-modules on $X$, such as
the decompositions of their global sections as representations of $G$ (when $G$
is reductive), and descriptions of the categories that they form. When $G$ acts
on $X$ with finitely many orbits, the category of equivariant $D$-modules is
isomorphic to the category of finite-dimensional representations of a finite
quiver with relations. We describe explicitly these categories for irreducible
$G$-modules $X$ that are spherical varieties, and show that in such cases the
quivers are almost always representation-finite (i.e. with finitely many
indecomposable representations).


  Decentralized services and applications provide a multitude of advantages for
their users, such as improved privacy, control, and independence from third
parties. Anyhow, decentralization comes at the cost of certain disadvantages,
such as increased application complexity or communication overhead. This
aggravates the development and deployment of decentralized services and
applications. In this paper we present Blade, a software platform that aims to
ease the effort of development, deployment, and administration of decentralized
services by implementing reusable solutions for recurring challenges developers
are facing when designing decentralized service architectures. This includes
functionality for e.g. identity management, access control, request handling,
verification of authenticity and integrity, discovery, or routing. Blade
implements all this functionality in a Blade server instance, which can be
deployed on a lightweight device, such as a NAS, Raspberry Pi, or router at
home. This allows users without expert knowledge to run a Blade instance with
already existing hardware with little overhead. Blade supports polyglot Blade
modules that implement extended functionality, such as interfaces, frontends,
and business logic of decentralized applications, e.g. a decentralized instant
messaging service or an online social network. Based on the Oracle GraalVM,
Blade modules can be implemented in a variety of programming languages and
utilize the functionality provided by the Blade server instance. Blade modules
are published in a Ethereum-based decentralized marketplace from where they can
be installed directly via the Blade instances...


  The complex of curves $\mathcal{C}(S_g)$ of a closed orientable surface of
genus $g \geq 2$ is the simplicial complex having its vertices,
$\mathcal{C}^0(S_g)$, are isotopy classes of essential curves in $S_g$. Two
vertices co-bound an edge of the $1$-skeleton, $\mathcal{C}^1(S_g)$, if there
are disjoint representatives in $S_g$. A metric is obtained on
$\mathcal{C}^0(S_g)$ by assigning unit length to each edge of
$\mathcal{C}^1(S_g)$. Thus, the distance between two vertices, $d(v,w)$,
corresponds to the length of a geodesic---a shortest edge-path between $v$ and
$w$ in $\mathcal{C}^1 (S_g)$. Recently, Birman, Margalit and the second author
introduced the concept of {\em initially efficient geodesics} in
$\mathcal{C}^1(S_g)$ and used them to give a new algorithm for computing the
distance between vertices. In this note we introduce the software package MICC
({\em Metric in the Curve Complex}), a partial implementation of the initially
efficient geodesic algorithm. We discuss the mathematics underlying MICC and
give applications. In particular, we give examples of distance four vertex
pairs, for $g=2$ and 3. Previously, there was only one known example, in genus
$2$, due to John Hempel.


  Large-scale magnetic fields are observed today to be coherent on galactic
scales. While there exists an explanation for their amplification and their
specific configuration in spiral galaxies -- the dynamo mechanism -- a
satisfying explanation for the original seed fields required is still lacking.
Cosmic strings are compelling candidates because of their scaling properties,
which would guarantee the coherence on cosmological scales of any resultant
magnetic fields at the time of galaxy formation. We present a mechanism for the
production of primordial seed magnetic fields from heterotic cosmic strings
arising from M theory. More specifically, we make use of heterotic cosmic
strings stemming from M5--branes wrapped around four of the compact internal
dimensions. These objects are stable on cosmological time scales and carry
charged zero modes. Therefore a scaling solution of such defects will generate
seed magnetic fields which are coherent on galactic scales today.


  A numerical thought experiment with two momentum correlated particles is
presented, in which particle A passes through a series of zig-zagging slits and
particle B moves unobstructedly. It is shown that, if particle A's meandering
path is monitored by successive non-detections, particle B will loosely adhere
to a similar trajectory without violating momentum conservation. The discussion
relates this apparent telekinetic influence, which is a standard quantum
mechanical result, to supposedly real telekinesis and to Stapp's hypothesis on
intention in quantum physics.


  We give new deterministic bounds for fully-dynamic graph connectivity. Our
data structure supports updates (edge insertions/deletions) in
$O(\log^2n/\log\log n)$ amortized time and connectivity queries in $O(\log
n/\log\log n)$ worst-case time, where $n$ is the number of vertices of the
graph. This improves the deterministic data structures of Holm, de Lichtenberg,
and Thorup (STOC 1998, J.ACM 2001) and Thorup (STOC 2000) which both have
$O(\log^2n)$ amortized update time and $O(\log n/\log\log n)$ worst-case query
time. Our model of computation is the same as that of Thorup, i.e., a pointer
machine with standard $AC^0$ instructions.


  Spherically symmetric entities filled with matter induced by the 5D Bulk may
be built in the empty 4D space-time. The substance of the entity, the latter
regarded as a fundamental particle, is characterized by the prematter equ. of
state P=-rho. the particle is coverede by a Schwarzschild-like envelope and
from the outside it is characterized by mass and radius. One regard these
entities as neutral fundamental particles being constituents of quarks and
leptons. The presented classical models are developed in the framwork of the
Weyl-Dirac version of Wesson's IMT.


  Handwritten Text Recognition (HTR) is an open problem at the intersection of
Computer Vision and Natural Language Processing. The main challenges, when
dealing with historical manuscripts, are due to the preservation of the paper
support, the variability of the handwriting -- even of the same author over a
wide time-span -- and the scarcity of data from ancient, poorly represented
languages. With the aim of fostering the research on this topic, in this paper
we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR
dataset of Italian ancient manuscripts edited by a single author over 60 years.
The dataset comes in two configurations: a basic splitting and a date-based
splitting which takes into account the age of the author. The first setting is
intended to study HTR on ancient documents in Italian, while the second focuses
on the ability of HTR systems to recognize text written by the same writer in
time periods for which training data are not available. For both
configurations, we analyze quantitative and qualitative characteristics, also
with respect to other line-level HTR benchmarks, and present the recognition
performance of state-of-the-art HTR architectures. The dataset is available for
download at \url{https://aimagelab.ing.unimore.it/go/lam}.


  In a previous article [J. Comp. Phys. $\mathbf{357}$ (2018) 282-304], the
mixed mimetic spectral element method was used to solve the rotating shallow
water equations in an idealized geometry. Here the method is extended to a
smoothly varying, non-affine, cubed sphere geometry. The differential operators
are encoded topologically via incidence matrices due to the use of spectral
element edge functions to construct tensor product solution spaces in
$H(\mathrm{rot})$, $H(\mathrm{div})$ and $L_2$. These incidence matrices
commute with respect to the metric terms in order to ensure that the mimetic
properties are preserved independent of the geometry. This ensures conservation
of mass, vorticity and energy for the rotating shallow water equations using
inexact quadrature on the cubed sphere. The spectral convergence of errors are
similarly preserved on the cubed sphere, with the generalized Piola
transformation used to construct the metric terms for the physical field
quantities.


  We study the azimuthal asymmetries for the distributions of leading pions
inside a jet produced inclusively in high-energy proton-proton collisions
within the framework of the transverse momentum dependent generalized parton
model. We present results for the RHIC center-of-mass energies $\sqrt{s}$ = 200
and 500 GeV, mainly for forward jet rapidities, in particular for the two
mechanisms which dominate such asymmetries: the Sivers and the Collins effects.
We also briefly discuss the case of inclusive jet production and, adopting the
so-called colour gauge invariant parton model, we propose a phenomenological
analysis of the process dependence of the quark Sivers function.


  In various application areas, networked data is collected by measuring
interactions involving some specific set of core nodes. This results in a
network dataset containing the core nodes along with a potentially much larger
set of fringe nodes that all have at least one interaction with a core node. In
many settings, this type of data arises for structures that are richer than
graphs, because they involve the interactions of larger sets; for example, the
core nodes might be a set of individuals under surveillance, where we observe
the attendees of meetings involving at least one of the core individuals. We
model such scenarios using hypergraphs, and we study the problem of core
recovery: if we observe the hypergraph but not the labels of core and fringe
nodes, can we recover the "planted" set of core nodes in the hypergraph?
  We provide a theoretical framework for analyzing the recovery of such a set
of core nodes and use our theory to develop a practical and scalable algorithm
for core recovery. The crux of our analysis and algorithm is that the core
nodes are a hitting set of the hypergraph, meaning that every hyperedge has at
least one node in the set of core nodes. We demonstrate the efficacy of our
algorithm on a number of real-world datasets, outperforming competitive
baselines derived from network centrality and core-periphery measures.


  As evidenced by various recent and significant papers within the frequentist
literature, along with numerous applications in macroeconomics, genomics, and
neuroscience, there continues to be substantial interest to understand the
theoretical estimation properties of high-dimensional vector autoregression
(VAR) models. To date, however, while Bayesian VAR (BVAR) models have been
developed and studied empirically (primarily in the econometrics literature)
there exist very few theoretical investigations of the repeated sampling
properties for BVAR models in the literature. In this direction, we construct
methodology via the $\varepsilon$-$admissible$ subsets (EAS) approach for
posterior-like inference based on a generalized fiducial distribution of
relative model probabilities over all sets of active/inactive components
(graphs) of the VAR transition matrix. We provide a mathematical proof of
$pairwise$ and $strong$ graphical selection consistency for the EAS approach
for stable VAR(1) models which is robust to model misspecification, and
demonstrate numerically that it is an effective strategy in high-dimensional
settings.


  The modernization of existing and new nuclear power plants with digital
instrumentation and control systems (DI&C) is a recent and highly trending
topic. However, there lacks strong consensus on best-estimate reliability
methodologies by both the United States (U.S.) Nuclear Regulatory Commission
(NRC) and the industry. In this work, we develop an approach called
Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to
quantify probabilities of various software failure modes in a DI&C system. The
method utilizes accepted industry methodologies for quality assurance that are
verified by experimental evidence. In essence, the approach combines a semantic
failure classification model with a reliability growth model to predict the
probability of failure modes of a software system. A case study was conducted
on a representative I&C platform (ChibiOS) running a smart sensor acquisition
software developed by Virginia Commonwealth University (VCU). The testing and
evidence collection guidance in ORCAS was applied, and defects were uncovered
in the software. Qualitative evidence, such as modified condition decision
coverage, was used to gauge the completeness and trustworthiness of the
assessment while quantitative evidence was used to determine the software
failure probabilities. The reliability of the software was then estimated and
compared to existing operational data of the sensor device. It is demonstrated
that by using ORCAS, a semantic reasoning framework can be developed to justify
if the software is reliable (or unreliable) while still leveraging the strength
of the existing methods.


  We consider integrability properties of the superstring on $AdS_{5}\times
S^{5}$ background and construct a new one parameter family of currents which
satisfies the vanishing curvature condition. We present the Hamiltonian
analysis for the sigma model action and determine the Poisson algebra of the
transition matrices. We reveal the generalization of the $\mathbb{Z}_{4}$
automorphism analogous to the sigma models defined on a symmetric space coset.
A possible regularization scheme for the ambiguities present, which respects
the generalized automorphism, is also discussed.


  Ensembles of independently trained neural networks are a state-of-the-art
approach to estimate predictive uncertainty in Deep Learning, and can be
interpreted as an approximation of the posterior distribution via a mixture of
delta functions. The training of ensembles relies on non-convexity of the loss
landscape and random initialization of their individual members, making the
resulting posterior approximation uncontrolled. This paper proposes a novel and
principled method to tackle this limitation, minimizing an $f$-divergence
between the true posterior and a kernel density estimator (KDE) in a function
space. We analyze this objective from a combinatorial point of view, and show
that it is submodular with respect to mixture components for any $f$.
Subsequently, we consider the problem of greedy ensemble construction. From the
marginal gain on the negative $f$-divergence, which quantifies an improvement
in posterior approximation yielded by adding a new component into the KDE, we
derive a novel diversity term for ensemble methods. The performance of our
approach is demonstrated on computer vision out-of-distribution detection
benchmarks in a range of architectures trained on multiple datasets. The source
code of our method is made publicly available at
https://github.com/Oulu-IMEDS/greedy_ensembles_training.


  Phase change memory (PCM) devices are known to reduce in power consumption as
the bit volume and contact area of their electrodes are scaled down. Here, we
demonstrate two types of low-power PCM devices with lateral graphene ribbon
electrodes: one in which the graphene is patterned into narrow nanoribbons and
the other where the phase change material is patterned into nanoribbons. The
sharp graphene "edge" contacts enable switching with threshold voltages as low
as ~3 V, low programming currents (<1 {\mu}A SET, <10 {\mu}A RESET) and ON/OFF
ratios >100. Large-scale fabrication with graphene grown by chemical vapor
deposition also enables the study of heterogeneous integration and that of
variability for such nanomaterials and devices.