title: Dynamic stabilisation of Rayleigh-Plateau modes on a liquid cylinder

abstract:
  We demonstrate dynamic stabilisation of axisymmetric Fourier modes
susceptible to the classical Rayleigh-Plateau (RP) instability on a liquid
cylinder by subjecting it to a radial oscillatory body force. Viscosity is
found to play a crucial role in this stabilisation. Linear stability
predictions are obtained via Floquet analysis demonstrating that RP unstable
modes can be stabilised using radial forcing. We also solve the linearised,
viscous initial-value problem for free-surface deformation obtaining an
equation governing the amplitude of a three-dimensional Fourier mode. This
equation generalises the Mathieu equation governing Faraday waves on a cylinder
derived earlier in Patankar et al. (2018), is non-local in time and represents
the cylindrical analogue of its Cartesian counterpart (Beyer & Friedrich 1995).
The memory term in this equation is physically interpreted and it is shown that
for highly viscous fluids, its contribution can be sizeable. Predictions from
the numerical solution to this equation demonstrates RP mode stabilisation upto
several hundred forcing cycles and is in excellent agreement with numerical
simulations of the incompressible, Navier-Stokes equations.


title:
Combining Deep Learning and Reasoning for Address Detection in
  Unstructured Text Documents

abstract:
  Extracting information from unstructured text documents is a demanding task,
since these documents can have a broad variety of different layouts and a
non-trivial reading order, like it is the case for multi-column documents or
nested tables. Additionally, many business documents are received in paper
form, meaning that the textual contents need to be digitized before further
analysis. Nonetheless, automatic detection and capturing of crucial document
information like the sender address would boost many companies' processing
efficiency. In this work we propose a hybrid approach that combines deep
learning with reasoning for finding and extracting addresses from unstructured
text documents. We use a visual deep learning model to detect the boundaries of
possible address regions on the scanned document images and validate these
results by analyzing the containing text using domain knowledge represented as
a rule based system.


title:
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data
  Augmentation

abstract:
  Graph contrastive learning (GCL) has emerged as a dominant technique for
graph representation learning which maximizes the mutual information between
paired graph augmentations that share the same semantics. Unfortunately, it is
difficult to preserve semantics well during augmentations in view of the
diverse nature of graph data. Currently, data augmentations in GCL that are
designed to preserve semantics broadly fall into three unsatisfactory ways.
First, the augmentations can be manually picked per dataset by
trial-and-errors. Second, the augmentations can be selected via cumbersome
search. Third, the augmentations can be obtained by introducing expensive
domain-specific knowledge as guidance. All of these limit the efficiency and
more general applicability of existing GCL methods. To circumvent these crucial
issues, we propose a \underline{Sim}ple framework for \underline{GRA}ph
\underline{C}ontrastive l\underline{E}arning, \textbf{SimGRACE} for brevity,
which does not require data augmentations. Specifically, we take original graph
as input and GNN model with its perturbed version as two encoders to obtain two
correlated views for contrast. SimGRACE is inspired by the observation that
graph data can preserve their semantics well during encoder perturbations while
not requiring manual trial-and-errors, cumbersome search or expensive domain
knowledge for augmentations selection. Also, we explain why SimGRACE can
succeed. Furthermore, we devise adversarial training scheme, dubbed
\textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning
and theoretically explain the reasons. Albeit simple, we show that SimGRACE can
yield competitive or better performance compared with state-of-the-art methods
in terms of generalizability, transferability and robustness, while enjoying
unprecedented degree of flexibility and efficiency.


title:
Validation of the Heuristic Model for the Electron Mobility in Dense
  Helium Gas

abstract:
  The investigation of the mobility \(\mu\) of quasi-free electrons in dense,
cold helium gas as a function of its density \(N\) is made difficult by the
fact that a significant fraction of electrons is localized in low mobility
bubbles. The measured mobility is a weighted average of the contributions of
the slow electron bubbles, whose mobility is determined by hydrodynamics, and
of the fast quasi-free electrons. A precise description of the fast electron
mobility is thus required. In the past we have developed a heuristic model that
proved succesful at rationalizing the electron mobility in argon and neon
gases. In order to validate it, we have carried out new accurate %electron
mobility measurements in helium gas in the temperature range $\textbf{26} \,
\text{K} < \text{\em T} < \textbf{300}\,$K at densities, in which the presence
of localized states is negligible. The analysis of these new data confirms the
validity of the model.


title:
Efficient light-trapping in ultrathin GaAs solar cells using
  quasi-random photonic crystals

abstract:
  Ultrathin solar cells reduce material usage and allow the use of
lower-quality materials thanks to their one order of magnitude smaller
thickness than their conventional counterparts. However, efficient photonic
light-trapping is required to harvest the incident light efficiently for an
otherwise insufficient absorber thickness. Quasi-random photonic crystals are
predicted to have high efficient light-trapping while being more robust under
angle and thickness variations than simple photonic crystals. Here we
experimentally demonstrate a light-trapping solution based on quasi-random
photonic crystals fabricated by polymer blend lithography. We control the
average lattice parameter by modifying the spin-coating speed. We demonstrate
an ultrathin GaAs cell of 260 nm with a rear quasi-random pattern with
submicron features, and a Jsc =26.4 mA/cm2 and an efficiency of 22.35% under
the global solar spectrum.


title: Bubble identification from images with machine learning methods

abstract:
  An automated and reliable processing of bubbly flow images is highly needed
to analyse large data sets of comprehensive experimental series. A particular
difficulty arises due to overlapping bubble projections in recorded images,
which highly complicates the identification of individual bubbles. Recent
approaches focus on the use of deep learning algorithms for this task and have
already proven the high potential of such techniques. The main difficulties are
the capability to handle different image conditions, higher gas volume
fractions and a proper reconstruction of the hidden segment of a partly
occluded bubble. In the present work, we try to tackle these points by testing
three different methods based on Convolutional Neural Networks (CNNs) for the
two former and two individual approaches that can be used subsequently to
address the latter. To validate our methodology, we created test data sets with
synthetic images that further demonstrate the capabilities as well as
limitations of our combined approach. The generated data, code and trained
models are made accessible to facilitate the use as well as further
developments in the research field of bubble recognition in experimental
images.


title: On entropy, entropy-like quantities, and applications

abstract:
  This is a review on entropy in various fields of mathematics and science. Its
scope is to convey a unified vision of the classical as well as some newer
entropy notions to a broad audience with an intermediate background in
dynamical systems and ergodic theory. Due to the breadth and depth of the
subject, we have opted for a compact exposition whose contents are a compromise
between conceptual import and instrumental relevance. The intended technical
level and the space limitation born furthermore upon the final selection of the
topics, which cover the three items named in the title. Specifically, the first
part is devoted to the avatars of entropy in the traditional contexts: many
particle physics, information theory, and dynamical systems. This chronological
order helps to present the materials in a didactic manner. The axiomatic
approach will be also considered at this stage to show that, quite remarkably,
the essence of entropy can be encapsulated in a few basic properties. Inspired
by the classical entropies, further akin quantities have been proposed in the
course of time, mostly aimed at specific needs. A common denominator of those
addressed in the second part of this review is their major impact on research.
The final part shows that, along with its profound role in the theory, entropy
has interesting practical applications beyond information theory and
communications technology. For this sake we preferred examples from applied
mathematics, although there are certainly nice applications in, say, physics,
computer science and even social sciences. This review concludes with a
representative list of references.


title:
Universal aspects of droplet spreading dynamics in Newtonian and
  non-Newtonian Fluids

abstract:
  Droplet impacts are common in many applications such as coating, spraying, or
printing; understanding how droplets spread after impact is thus of utmost
importance. Such impacts may occur with different velocities on a variety of
substrates. The fluids may also be non-Newtonian and thus possess different
rheological properties. How the different properties such as surface roughness
and wettability, droplet viscosity and rheology as well as interfacial
properties affect the spreading dynamics of the droplets and the eventual drop
size after impact are unresolved questions. Most recent work focuses on the
maximum spreading diameter after impact and uses scaling laws to predict this.
In this paper we show that a proper rescaling of the spreading dynamics with
the maximum radius attained by the drop, and the impact velocity leads to a
unique single and thus universal curve for the variation of diameter versus
time. The validity of this universal functional shape is validated for
different liquids with different rheological properties as well as substrates
with different wettabilities. This universal function agrees with a recent
model that proposes a closed set of differential equations for the spreading
dynamics of droplets.


title:
Predicting Default Probabilities for Stress Tests: A Comparison of
  Models

abstract:
  Since the Great Financial Crisis (GFC), the use of stress tests as a tool for
assessing the resilience of financial institutions to adverse financial and
economic developments has increased significantly. One key part in such
exercises is the translation of macroeconomic variables into default
probabilities for credit risk by using macrofinancial linkage models. A key
requirement for such models is that they should be able to properly detect
signals from a wide array of macroeconomic variables in combination with a
mostly short data sample. The aim of this paper is to compare a great number of
different regression models to find the best performing credit risk model. We
set up an estimation framework that allows us to systematically estimate and
evaluate a large set of models within the same environment. Our results
indicate that there are indeed better performing models than the current
state-of-the-art model. Moreover, our comparison sheds light on other potential
credit risk models, specifically highlighting the advantages of machine
learning models and forecast combinations.


title: Symmetry and inverse-closedness of some $p$-Beurling algebras

abstract:
  Let $(G,d)$ be a metric space with the counting measure $\mu$ satisfying some
growth conditions. Let $\omega(x,y)=(1+d(x,y))^\delta$ for some
$0<\delta\leq1$. Let $0<p\leq1$. Let $\mathcal A_{p\omega}$ be the collection
of kernels $K$ on $G\times G$ satisfying $\max\{\sup_x\sum_y
|K(x,y)|^p\omega(x,y)^p, \sup_y\sum_x |K(x,y)|^p\omega(x,y)^p\}<\infty$. Each
$K \in \mathcal A_{p\omega}$ defines a bounded linear operator on $\ell^2(G)$.
If in addition, $\omega$ satisfies the weak growth condition, then we show that
$\mathcal A_{p\omega}$ is inverse closed in $B(\ell^2(G))$. We shall also
discuss inverse-closedness of $p$-Banach algebra of infinite matrices over
$\mathbb Z^d$ and the $p$-Banach algebra of weighted $p$-summable sequences
over $\mathbb Z^{2d}$ with the twisted convolution. In order to show these
results, we prove Hulanicki's lemma and Barnes' lemma for $p$-Banach algebras.


title:
Comprehensive calculations of energy levels, radiative transition
  parameters, hyperfine structure constants A$_J$-B$_J$, Land\'e g$_J$ factors
  and isotope shifts for Sc XX

abstract:
  Large scale calculations for the energy levels, transition rates, oscillator
strengths, hyperfine interaction constants, Land\'e g$_J$ factors and isotope
shifts have been performed for $1s^2$ and $1snl \: (n=2-8 $ and $l \leq (n-1)
)$ levels of He-like scandium, Sc XX. The GRASP2018 package based on the
multi-configurational Dirac-Fock method is used to perform these calculations.
The leading quantum electrodynamics corrections, Breit interactions and nuclear
recoil effects are also included in the succeeding relativistic configuration
interaction calculations. The relativistic isotope shift (RIS4) program is used
to determine the mass and field shifts factors. A detailed comparison of the
present results with the corresponding values from the NIST database and other
theoretical work, wherever available, has been done, and an excellent agreement
is achieved. A large section of the results is reported for the first time in
the present work.


title:
Approximation by Fourier sums in classes of Weyl--Nagy differentiable
  functions with high exponent of smoothness

abstract:
  We establish asymptotic estimates for the least upper bounds of
approximations in the uniform metric by Fourier sums of order $n-1$ of classes
of $2\pi$-periodic Weyl--Nagy differentiable functions, $W^r_{\beta,p}, 1\le
p\le \infty, \beta\in\mathbb{R},$ for high exponents of smoothness $r\ (r-1\ge
\sqrt{n})$. We obtain similar estimates in metrics of the spaces $L_p, 1\le
p\le\infty,$ for functional classes $W^r_{\beta,1}$.


title: Periapsis shifts in dark matter distribution with a dense core

abstract:
  We consider the periapsis shifts in dark matter distribution with a dense
core. We model the dark matter distribution as an isotropic gas sphere, the
Emden polytropic sphere of index 5 in general relativity. This model has a
parameter range where all the energy conditions are satisfied in the entire
region. Within the parameter range, the asymptotic analysis for stellar motion
allows us to identify two competing effects on the bounded motion: the
general-relativistic effect and a local-density effect of matter. Furthermore,
using nearly circular bound orbits, we demonstrate that retrograde periapsis
shifts occur near the center, where the local-density effect dominates over the
general-relativistic effect, whereas prograde periapsis shifts occur in the far
region, where the general-relativistic effect dominates over the local-density
effect. This result means that a natural explanation for the retrograde
periapsis shifts is not the existence of exotic objects (e.g., naked
singularities or wormholes) but the local distribution of physically reasonable
matter on the stellar orbit. Furthermore, it also implies that the periapsis
shift plays a crucial role in distinguishing black hole alternatives, such as
dark matter cores, from a pure black hole.


title: Twisted Rota-Baxter families and NS-family algebras

abstract:
  Family algebraic structures indexed by a semigroup first appeared in the
algebraic aspects of renormalizations in quantum field theory. The concept of
the Rota-Baxter family and its relation with (tri)dendriform family algebras
have been recently discovered. In this paper, we first consider a notion of
$\mathcal{O}$-operator family as a generalization of the Rota-Baxter family and
define two variations of associative Yang-Baxter family that produce
$\mathcal{O}$-operator families. Given a Hochschild $2$-cocycle on the
underlying algebra, we also define a notion of twisted $\mathcal{O}$-operator
family (in particular twisted Rota-Baxter family). We also introduce and study
NS-family algebras as the underlying structure of twisted
$\mathcal{O}$-operator families. Finally, we define suitable cohomology of
twisted $\mathcal{O}$-operator families and NS-family algebras (in particular
cohomology of Rota-Baxter families and dendriform family algebras) that govern
their deformations.


title:
A novel wide-angle Compton Scanner setup to study bulk events in
  germanium detectors

abstract:
  A novel Compton Scanner setup has been built, commissioned and operated at
the Max-Planck-Institute for Physics in Munich to study pulse shapes from bulk
events in high-purity germanium detectors. In this fully automated setup, the
detector under test is irradiated from the top with 661.660 keV gammas, some of
which Compton scatter inside the detector. The interaction points in the
detector can be reconstructed when the scattered gammas are detected with a
pixelated camera placed at the side of the detector. This allows for the
creation of position-dependent pulse shape libraries. The wide range of
accepted Compton angles results in shorter measurement times in comparison to
similar setups where only perpendicularly scattered gammas are selected by slit
collimators. In this paper, the construction of the Compton Scanner, its
alignment and the procedure to reconstruct interaction points in the germanium
detector are described in detail. The creation of a first pulse shape library
for an n-type segmented point-contact germanium detector is described. Selected
pulses are compared to simulated pulses to demonstrate the power of such pulse
shape libraries to better understand the charge carrier drift in germanium.


title: Spin revolution

abstract:
  The classical laws of physics are usually invariant under time reversal.
Here, we reveal a novel class of magnetomechanical effects rigorously breaking
time-reversal symmetry. The effect is based on the mechanical rotation of a
hard magnet around its magnetization axis in the presence of friction and an
external magnetic field, which we call spin revolution. The physical reason for
time-reversal symmetry breaking is the spin revolution and not the dissipation.
The time-reversal symmetry breaking leads to a variety of unexpected effects
including upward propulsion on vertical surfaces defying gravity as well as
magnetic gyroscopic motion that is perpendicular to the applied force. In
contrast to the spin, the angular momentum of spin revolution can be parallel
or antiparallel to the equilibrium magnetization. The spin revolution emerges
spontaneously, without external rotations, and offers various applications in
areas such as magnetism, robotics and energy harvesting.


title:
Fortnet, a software package for training Behler-Parrinello neural
  networks

abstract:
  A new, open source, parallel, stand-alone software package (Fortnet) has been
developed, which implements Behler-Parrinello neural networks. It covers the
entire workflow from feature generation to the evaluation of generated
potentials, coupled with higher-level analysis such as the analytic calculation
of atomic forces. The functionality of the software package is demonstrated by
driving the training for the fitted correction functions of the density
functional tight binding (DFTB) method, which are commonly used to compensate
the inaccuracies resulting from the DFTB approximations to the Kohn-Sham
Hamiltonian. The usual two-body form of those correction functions limits the
transferability of the parameterizations between very different structural
environments. The recently introduced DFTB+ANN approach strives to lift these
limitations by combining DFTB with a near-sighted artificial neural network
(ANN). After investigating various approaches, we have found the combination of
DFTB with an ANN acting on-top of some baseline correction functions (delta
learning) the most promising one. It allowed to introduce many-body corrections
on top of two-body parametrizations, while excellent transferability to
chemical environments with deviating energetics could be demonstrated.


title: Moving Other Way: Exploring Word Mover Distance Extensions

abstract:
  The word mover's distance (WMD) is a popular semantic similarity metric for
two texts. This position paper studies several possible extensions of WMD. We
experiment with the frequency of words in the corpus as a weighting factor and
the geometry of the word vector space. We validate possible extensions of WMD
on six document classification datasets. Some proposed extensions show better
results in terms of the k-nearest neighbor classification error than WMD.


title: To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment

abstract:
  There has been mounting evidence that pretrained language models fine-tuned
on large and diverse supervised datasets can transfer well to a variety of
out-of-domain tasks. In this work, we investigate this transfer ability to the
legal domain. For that, we participated in the legal case entailment task of
COLIEE 2021, in which we use such models with no adaptations to the target
domain. Our submissions achieved the highest scores, surpassing the second-best
team by more than six percentage points. Our experiments confirm a
counter-intuitive result in the new paradigm of pretrained language models:
given limited labeled data, models with little or no adaptation to the target
task can be more robust to changes in the data distribution than models
fine-tuned on it. Code is available at https://github.com/neuralmind-ai/coliee.


title: S-duality and the universal isometries of q-map spaces

abstract:
  The tree-level q-map assigns to a projective special real (PSR) manifold of
dimension $n-1\geq 0$, a quaternionic K\"{a}hler (QK) manifold of dimension
$4n+4$. It is known that the resulting QK manifold admits a
$(3n+5)$-dimensional universal group of isometries (i.e. independently of the
choice of PSR manifold). On the other hand, in the context of Calabi-Yau
compactifications of type IIB string theory, the classical hypermultiplet
moduli space metric is an instance of a tree-level q-map space, and it is known
from the physics literature that such a metric has an
$\mathrm{SL}(2,\mathbb{R})$ group of isometries related to the
$\mathrm{SL}(2,\mathbb{Z})$ S-duality symmetry of the full 10d theory. We
present a purely mathematical proof that any tree-level q-map space admits such
an $\mathrm{SL}(2,\mathbb{R})$ action by isometries, enlarging the previous
universal group of isometries to a $(3n+6)$-dimensional group $G$. As part of
this analysis, we describe how the $(3n+5)$-dimensional subgroup interacts with
the $\mathrm{SL}(2,\mathbb{R})$-action, and find a codimension one normal
subgroup of $G$ that is unimodular. By taking a quotient with respect to a
lattice in the unimodular group, we obtain a quaternionic K\"ahler manifold
fibering over a projective special real manifold with fibers of finite volume,
and compute the volume as a function of the base. We furthermore provide a
mathematical treatment of results from the physics literature concerning the
twistor space of the tree-level q-map space and the holomorphic lift of the
$(3n+6)$-dimensional group of universal isometries to the twistor space.


abstract:
  We use the method of field decomposition, a technique widely used in
relativistic magnetohydrodynamics, to study the small velocity approximation
(SVA) of the Lorentz transformation in Maxwell equations for slowly moving
media. The "deformed" Maxwell equations derived under the SVA in the lab frame
can be put into the conventional form of Maxwell equations in the medium's
comoving frame. Our results show that the Lorentz transformation in the SVA up
to $O(v/c)$ ($v$ is the speed of the medium and $c$ is the speed of light in
vacuum) is essential to derive these equations: the time and charge density
must also change when transforming to a different frame even in the SVA, not
just the position and current density as in the Galilean transformation. This
marks the essential difference of the Lorentz transformation from the Galilean
one. We show that the integral forms of Faraday and Ampere equations for slowly
moving surfaces are consistent with Maxwell equations. We also present Faraday
equation the covariant integral form in which the electromotive force can be
defined as a Lorentz scalar independent of the observer's frame. No evidences
exist to support an extension or modification of Maxwell equations.

categories: physics.class-ph cond-mat.mes-hall cond-mat.mtrl-sci hep-ph nucl-th


title: Two types of metastable magnetic hopfions

abstract:
  Localized magnetic topological solitons with Hopf index of 1 in an unbounded
bulk magnet are studied theoretically, starting with the classical
micromagnetic Hamiltonian. It is shown analytically that (like Bloch and N\'eel
walls in classical magnetism) there are two possible stable configurations of
these solitons, which are equivalent topologically. Their stability regions are
plotted on a phase diagram in the magnetic field -- uniaxial anisotropy space
for different normalized magnetostatic interaction strengths. For some
parameters both types of hopfions can be stable.


title: Scrutinizing $\pi\pi$ scattering in light of recent lattice phase shifts

abstract:
  In this paper, the $IJ=00, 11, 20$ partial wave $\pi\pi$ scattering phase
shifts determined by the lattice QCD approach are analyzed by using a novel
dispersive solution of the S-matrix, i.e. the PKU representation, in which the
unitarity and analyticity of scattering amplitudes are automatically satisfied
and the phase shifts are conveniently decomposed into the contributions of the
cuts and various poles, including bound states, virtual states and resonances.
The contribution of the left-hand cut is estimated by the $SU(2)$ chiral
perturbation theory to $\mathcal{O}(p^4)$. The Balanchandran-Nuyts-Roskies
relations are considered as constraints to meet the requirements of the
crossing symmetry. It is found that the $IJ=00$ $\pi\pi$ scattering phase
shifts obtained at $m_\pi=391$ MeV by Hadron Spectrum Collaboration (HSC)
reveal the presence of both a bound state pole and a virtual state pole below
the $\pi\pi$ threshold rather than only one bound state pole for the $\sigma$.
To reproduce the lattice phase shifts at $m_\pi=391$ MeV, a virtual-state pole
in the $IJ=20$ channel is found to be necessary in order to balance the
left-hand cut effects from the chiral amplitudes. Similar discussions are also
carried out for the lattice results with $m_\pi=236$ MeV from HSC. The observed
behaviors of the pole positions with respect to the variation of the pion
masses can provide deep insights into our understanding of the dynamical origin
of $\sigma$ resonance.


title: Building Synthetic Speaker Profiles in Text-to-Speech Systems

abstract:
  The diversity of speaker profiles in multi-speaker TTS systems is a crucial
aspect of its performance, as it measures how many different speaker profiles
TTS systems could possibly synthesize. However, this important aspect is often
overlooked when building multi-speaker TTS systems and there is no established
framework to evaluate this diversity. The reason behind is that most
multi-speaker TTS systems are limited to generate speech signals with the same
speaker profiles as its training data. They often use discrete speaker
embedding vectors which have a one-to-one correspondence with individual
speakers. This correspondence limits TTS systems and hinders their capability
of generating unseen speaker profiles that did not appear during training. In
this paper, we aim to build multi-speaker TTS systems that have a greater
variety of speaker profiles and can generate new synthetic speaker profiles
that are different from training data. To this end, we propose to use
generative models with a triplet loss and a specific shuffle mechanism. In our
experiments, the effectiveness and advantages of the proposed method have been
demonstrated in terms of both the distinctiveness and intelligibility of
synthesized speech signals.


title:
Reasoning for Complex Data through Ensemble-based Self-Supervised
  Learning

abstract:
  Self-supervised learning deals with problems that have little or no available
labeled data. Recent work has shown impressive results when underlying classes
have significant semantic differences. One important dataset in which this
technique thrives is ImageNet, as intra-class distances are substantially lower
than inter-class distances. However, this is not the case for several critical
tasks, and general self-supervised learning methods fail to learn
discriminative features when classes have closer semantics, thus requiring more
robust strategies. We propose a strategy to tackle this problem, and to enable
learning from unlabeled data even when samples from different classes are not
prominently diverse. We approach the problem by leveraging a novel
ensemble-based clustering strategy where clusters derived from different
configurations are combined to generate a better grouping for the data samples
in a fully-unsupervised way. This strategy allows clusters with different
densities and higher variability to emerge, which in turn reduces intra-class
discrepancies, without requiring the burden of finding an optimal configuration
per dataset. We also consider different Convolutional Neural Networks to
compute distances between samples. We refine these distances by performing
context analysis and group them to capture complementary information. We
consider two applications to validate our pipeline: Person Re-Identification
and Text Authorship Verification. These are challenging applications
considering that classes are semantically close to each other and that training
and test sets have disjoint identities. Our method is robust across different
modalities and outperforms state-of-the-art results with a fully-unsupervised
solution without any labeling or human intervention.


title:
Genuine Activation of Quantum Nonlocality: Stronger Than Local
  Indistinguishability

abstract:
  The structure of quantum theory assures the discrimination of any possible
orthogonal set of states. However, the scenario becomes highly nontrivial in
the limited measurement setting and leads to different classes of
impossibility, viz., indistinguishability, unmarkability, irreducibility etc.
These phenomena, often referred to as other nonlocal aspects of quantum theory,
have utmost importance in the domain of secret sharing etc. It, therefore,
becomes a pertinent question to distill/activate such behaviours from a set,
apparently devoid of these nonlocal features and free from local redundancy.
While the activation of local indisitinguishability in the sets of entangled
states has recently been reported, other stronger notion of quantum nonlocality
has yet not been inspected in the parlance of activation. Here, we explored all
such stronger versions of nonlocality and affirmatively answered to activate
each of them. We also find a locally distinguishable set of multipartite
entangled states which can be converted with certainty to a nontrivial set of
locally irreducible genuinely entangled states.


title: Renormalization of bicritical circle maps

abstract:
  A general ansatz in Renormalization Theory, already established in many
important situations, states that exponential convergence of renormalization
orbits implies that topological conjugacies are actually smooth (when
restricted to the attractors of the original systems). In this paper we
establish this principle for a large class of bicritical circle maps, which are
$C^3$ circle homeomorphisms with irrational rotation number and exactly two
(non-flat) critical points. The proof presented here is an adaptation, to the
bicritical setting, of the one given by de Faria and de Melo for the case of a
single critical point. When combined with some recent papers, our main theorem
implies $C^{1+\alpha}$ rigidity for real-analytic bicritical circle maps with
rotation number of bounded type.


title: Over-the-Air Ensemble Inference with Model Privacy

abstract:
  We consider distributed inference at the wireless edge, where multiple
clients with an ensemble of models, each trained independently on a local
dataset, are queried in parallel to make an accurate decision on a new sample.
In addition to maximizing inference accuracy, we also want to maximize the
privacy of local models. We exploit the superposition property of the air to
implement bandwidth-efficient ensemble inference methods. We introduce
different over-the-air ensemble methods and show that these schemes perform
significantly better than their orthogonal counterparts, while using less
resources and providing privacy guarantees. We also provide experimental
results verifying the benefits of the proposed over-the-air inference approach,
whose source code is shared publicly on Github.


title:
Aspects of Jackiw-Teitelboim Gravity in Anti-de Sitter and de Sitter
  spacetime

abstract:
  We discuss JT gravity in AdS and dS space in the second order formalism. For
the pure dS JT theory without matter, we show that the path integral gives rise
in general to the Hartle-Hawking wave function which describes an arbitrary
number of disconnected universes produced by tunnelling "from nothing", or to
transition amplitudes which describe the tunnelling of an initial state
consisting of several contracting universes to a final state of several
expanding universes. These processes can be described by a hologram consisting
of Random Matrix Theory (RMT) or, we suggest, after some modification on the
gravity side, by a hologram with the RMT being replaced by SYK theory. In the
presence of matter, we discuss the double trumpet path integral and argue that
with suitable twisted boundary conditions, a divergence in the moduli space
integral can be avoided and the system can tunnel from a contracting phase to
an expanding one avoiding a potential big bang/big crunch singularity. The
resulting spectrum of quantum perturbations which are produced can exhibit
interesting departures from scale invariance. We also show that the divergence
in moduli space can be avoided for suitable correlators which involve different
boundaries in the AdS/dS cases, and suggest that a hologram consisting of the
SYK theory with additional matter could get rid of these divergences in
general. Finally, we analyse the AdS double trumpet geometry and show that
going to the micro-canonical ensemble instead of the canonical one, for the
spectral form factor, does not get rid of the divergence in moduli space.


title:
Transformers in Self-Supervised Monocular Depth Estimation with Unknown
  Camera Intrinsics

abstract:
  The advent of autonomous driving and advanced driver assistance systems
necessitates continuous developments in computer vision for 3D scene
understanding. Self-supervised monocular depth estimation, a method for
pixel-wise distance estimation of objects from a single camera without the use
of ground truth labels, is an important task in 3D scene understanding.
However, existing methods for this task are limited to convolutional neural
network (CNN) architectures. In contrast with CNNs that use localized linear
operations and lose feature resolution across the layers, vision transformers
process at constant resolution with a global receptive field at every stage.
While recent works have compared transformers against their CNN counterparts
for tasks such as image classification, no study exists that investigates the
impact of using transformers for self-supervised monocular depth estimation.
Here, we first demonstrate how to adapt vision transformers for self-supervised
monocular depth estimation. Thereafter, we compare the transformer and
CNN-based architectures for their performance on KITTI depth prediction
benchmarks, as well as their robustness to natural corruptions and adversarial
attacks, including when the camera intrinsics are unknown. Our study
demonstrates how transformer-based architecture, though lower in run-time
efficiency, achieves comparable performance while being more robust and
generalizable.


title:
Agricultural quality matrix-based multiomics structural analysis of
  carrots in soils fertilized with thermophile-fermented compost

abstract:
  Compost is used worldwide as a soil conditioner for crops, but its functions
remain unclear. Here, the omics profiles of carrots and soil amended with
compost fermented by thermophilic Bacillaceae, as a compost model, are
classified based on information on carrots' growth, color, and taste
characteristics. The production of carrots and their antioxidant activity were
significantly increased by compost exposure, as was their content of carotenes,
essential carrot nutrients. Furthermore, omics profiles revealed that carrots'
growth, red color, and taste could be increased by altering characteristic
leaf, root, and soil metabolites by compost exposure. In particular, structural
equation modeling statistically predicted a strong association with
nitrogen-cycle related-soil bacteria candidates, which could modulate nitrogen
fixation and anaerobic ammonium oxidation (anammox), affected by compost. These
observations statistically provide a novel perspective on potential sustainable
compost as an organic fertilizer that has the role of preserving the
environment.


title:
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust,
  and Energy-efficient Spiking Neural Networks?

abstract:
  Recent Spiking Neural Networks (SNNs) works focus on an image classification
task, therefore various coding techniques have been proposed to convert an
image into temporal binary spikes. Among them, rate coding and direct coding
are regarded as prospective candidates for building a practical SNN system as
they show state-of-the-art performance on large-scale datasets. Despite their
usage, there is little attention to comparing these two coding schemes in a
fair manner. In this paper, we conduct a comprehensive analysis of the two
codings from three perspectives: accuracy, adversarial robustness, and
energy-efficiency. First, we compare the performance of two coding techniques
with various architectures and datasets. Then, we measure the robustness of the
coding techniques on two adversarial attack methods. Finally, we compare the
energy-efficiency of two coding schemes on a digital hardware platform. Our
results show that direct coding can achieve better accuracy especially for a
small number of timesteps. In contrast, rate coding shows better robustness to
adversarial attacks owing to the non-differentiable spike generation process.
Rate coding also yields higher energy-efficiency than direct coding which
requires multi-bit precision for the first layer. Our study explores the
characteristics of two codings, which is an important design consideration for
building SNNs. The code is made available at
https://github.com/Intelligent-Computing-Lab-Yale/Rate-vs-Direct.


title:
Edge-computing Enabled Next Generation Wireless Networks: A Novel
  Approach to Provide Secure Multicast Services

abstract:
  Smart grids have received much attention in recent years in order to
optimally manage the resources, transmission and consumption of electric
power.In these grids, one of the most important communication services is the
multicast service. Providing multicast services in the smart communicative grid
poses several challenges, including the heterogeneity of different
communication media and the strict requirements of reliability, security and
latency. Wireless technologies and PLC connections are the two most important
media used in this grid, among which PLC connections are very unstable, which
makes it difficult to provide reliability. In this research, the problem of
geographically flooding of multicast data has been considered. First, this
problem has been modeled as an optimization problem which is used as a
reference model in evaluating the proposed approaches. Then, two MKMB and GCBT
multicast tree formation algorithms have been developed based on geographical
information according to the characteristics of smart grids. Comparison of
these two approaches shows the advantages and disadvantages of forming a
core-based tree compared to a source-based tree. Evaluation of these approaches
shows a relative improvement in tree cost and the amount of end-to-end delay
compared to basic algorithms. In the second part, providing security and
reliability in data transmission has been considered. Both Hybrid and Multiple
algorithms have been developed based on the idea of multiple transmission tree.
In the Hybrid algorithm, the aim is to provide higher security and reliability,
but in the Multiple algorithms, minimization of message transmission delay is
targeted. In the section of behavior evaluation, these two algorithms have been
studied in different working conditions, which indicates the achievement of the
desired goals.


title: Effect of Creep on Corrosion-Induced Cracking

abstract:
  For corrosion-induced cracking in reinforced concrete, naturally occurring
corrosion rates are so low that rust accumulates often over tens of years near
the surface of the reinforcement bars before sufficient pressure in the
surrounding concrete is generated to induce cracking in the concrete cover. To
speed up the process, corrosion tests with impressed currents have been
developed in which the corrosion rate is controlled to be so high that cracking
of the concrete cover occurs within a few days. Extrapolating the results of
these accelerated tests to those of naturally occurring corrosion requires an
understanding of the influence of long-term creep deformations of concrete on
the corrosion-induced cracking process. In mathematical models in the
literature, creep deformations are often ignored for accelerated but considered
for natural corrosion rates in the form of an effective modulus. Here, three
numerical models of increasing complexity are proposed with the aim to
investigate the effect of creep on corrosion-induced cracking. The simplest
approach is based on an uncracked axis-symmetric thick-walled cylinder combined
with a plastic limit on the radial pressure-induced by the accumulation of
rust. The model with intermediate complexity comprises a thick-walled cylinder
model divided into an inner cracked and an outer uncracked layer. The most
comprehensive model consists of a thick-walled cylinder discretised by a
three-dimensional lattice approach. Basic creep is predicted in all three
approaches by means of the B3 model developed by Ba\v{z}ant and co-workers.
Time dependence of strength of concrete is modelled using fib Model Code
expressions. It is shown that for the comprehensive lattice model, creep has
limited influence on critical corrosion penetration, which indicates that the
dependence of the critical corrosion penetration on corrosion rate must have
other sources.


title:
Comment on "Exact Solution for Three-Dimensional Ising Model" by Degang
  Zhang

abstract:
  We show that Zhang Degang's claimed solution of the three-dimensional Ising
model [arXiv:2110.11233] has fatal irreparable errors. v2: Spell correction and
minor improvements.


title: Cohomology and deformations of compatible Hom-Lie algebras

abstract:
  In this paper, we consider compatible Hom-Lie algebras as a twisted version
of compatible Lie algebras. Compatible Hom-Lie algebras are characterized as
Maurer-Cartan elements in a suitable bidifferential graded Lie algebra. We also
define a cohomology theory for compatible Hom-Lie algebras generalizing the
recent work of Liu, Sheng and Bai. As applications of cohomology, we study
abelian extensions and deformations of compatible Hom-Lie algebras.


title: Halogenated Greenhouse Gases Made Global Warming Primarily

abstract:
  Time-series observations of global lower stratospheric temperature (GLST),
global land surface air temperature (LSAT), global mean surface temperature
(GMST), sea ice extent (SIE) and snow cover extent (SCE), together with
observations reported in Paper I, combined with theoretical calculations of
GLSTs and GMSTs, have provided strong evidence that ozone depletion and global
climate changes are dominantly caused by human-made halogen-containing
ozone-depleting substances (ODSs) and greenhouse gases (GHGs) respectively.
Both GLST and SCE have become constant since the mid-1990s and GMST/LSAT has
reached a peak since the mid-2000s, while regional continued warmings at the
Arctic coasts (particularly Russia and Alaska) in winter and spring and at some
areas of Antarctica are observed and can be well explained by a sea-ice-loss
warming amplification mechanism. The calculated GMSTs by the parameter-free
warming theory of halogenated GHGs show an excellent agreement with the
observed GMSTs after the natural El Ni\~no southern oscillation (ENSO) and
volcanic effects are removed. These results provide a convincing mechanism of
global climate change and will make profound changes in our understanding of
atmospheric processes. This study also emphasizes the critical importance of
continued international efforts in phasing out all anthropogenic halogenated
ODSs and GHGs.


title:
Free boson realization of the Dunkl intertwining operator in one
  dimension

abstract:
  The operator that intertwines between the $\mathbb{Z}_2$ - Dunkl operator and
the derivative is shown to have a realization in terms of the oscillator
operators in one dimension. This observation rests on the fact that the Dunkl
intertwining operator maps the Hermite polynomials on the generalized Hermite
polynomials.


title: OPP-Miner: Order-preserving sequential pattern mining

abstract:
  A time series is a collection of measurements in chronological order.
Discovering patterns from time series is useful in many domains, such as stock
analysis, disease detection, and weather forecast. To discover patterns,
existing methods often convert time series data into another form, such as
nominal/symbolic format, to reduce dimensionality, which inevitably deviates
the data values. Moreover, existing methods mainly neglect the order
relationships between time series values. To tackle these issues, inspired by
order-preserving matching, this paper proposes an Order-Preserving sequential
Pattern (OPP) mining method, which represents patterns based on the order
relationships of the time series data. An inherent advantage of such
representation is that the trend of a time series can be represented by the
relative order of the values underneath the time series data. To obtain
frequent trends in time series, we propose the OPP-Miner algorithm to mine
patterns with the same trend (sub-sequences with the same relative order).
OPP-Miner employs the filtration and verification strategies to calculate the
support and uses pattern fusion strategy to generate candidate patterns. To
compress the result set, we also study finding the maximal OPPs. Experiments
validate that OPP-Miner is not only efficient and scalable but can also
discover similar sub-sequences in time series. In addition, case studies show
that our algorithms have high utility in analyzing the COVID-19 epidemic by
identifying critical trends and improve the clustering performance.


title: Impact of COVID-19 Pandemic on Energy Demand in Estonia

abstract:
  The COVID-19 pandemic triggered a question of how to measure and evaluate
adequacy of the applied restrictions. Available studies propose various methods
mainly grouped to statistical and machine learning techniques. The current
paper joins this line of research by introducing a simple-yet-accurate linear
regression model which eliminates effects of weekly cycle, available daylight,
temperature, and wind from the electricity consumption data. The model is
validated using real data and enables the qualitative analysis of economical
impact.


title:
A Comprehensive Survey on the Internet of Things with the Industrial
  Marketplace

abstract:
  There is no doubt that new technology has become one of the crucial parts of
most people's lives around the world. By and large, in this era, the Internet
and the Internet of Things (IoT) have become the most indispensable parts of
our lives. Recently, IoT technologies have been regarded as the most broadly
used tools among other technologies. The tools and the facilities of IoT
tech-nologies within the marketplace are part of Industry 4.0. The marketplace
is too regarded as a new area that can be used with IoT technologies. One of
the main purposes of this paper is to highlight using IoT technologies in
Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature
revised. This paper focuses on the value of the IoT in the industrial domain in
general; it reviews the IoT and focuses on its benefits and drawbacks, and
presents some of the IoT applications, such as in transportation and
healthcare. In addition, the trends and facts that are related to the IoT
technologies on the marketplace are reviewed. Finally, the role of IoT in
telemedicine and healthcare and the benefits of IoT technologies for COVID-19
are presented as well.


title: Bounded functional calculi for unbounded operators

abstract:
  This article summarises the theory of several bounded functional calculi for
unbounded operators that have recently been discovered. The extend the
Hille--Phillips calculus for (negative) generators $A$ of certain bounded
$C_0$-semigroups, in particular for bounded semigroups on Hilbert spaces and
bounded holomorphic semigroups on Banach spaces. They include functions outside
the Hille-Phillips class, and they generally give sharper bounds for the norms
of the resulting operators $f(A)$. The calculi are mostly based on appropriate
reproducing formulas for the relevant classes of functions, and they rely on
significant and interesting developments of function theory. They are
compatible with standard functional calculi and they admit appropriate
convergence lemmas and spectral mapping theorems. They can also be used to
derive several well-known operator norm-estimates, provide generalisations of
some of them, and extend the general theory of operator semigroups. Our aim is
to help readers to make use of these calculi without having to understand the
details of their construction.


title: Ptychopy: GPU framework for ptychographic data analysis

abstract:
  X-ray ptychography imaging at synchrotron facilities like the Advanced Photon
Source (APS) involves controlling instrument hardwares to collect a set of
diffraction patterns from overlapping coherent illumination spots on extended
samples, managing data storage, reconstructing ptychographic images from
acquired diffraction patterns, and providing the visualization of results and
feedback. In addition to the complicated workflow, ptychography instrument
could produce up to several TB's of data per second that is needed to be
processed in real time. This brings up the need to develop a high performance,
robust and user friendly processing software package for ptychographic data
analysis. In this paper we present a software framework which provides
functionality of visualization, work flow control, and data reconstruction. To
accelerate the computation and large datasets process, the data reconstruction
part is implemented with three algorithms, ePIE, DM and LSQML using CUDA-C on
GPU.


title:
Jensen-type inequalities for convex and $m$-convex functions via
  fractional calculus

abstract:
  Inequalities play an important role in pure and applied mathematics. In
particular, Jensen's inequality, one of the most famous inequalities, plays a
main role in the study of the existence and uniqueness of initial and boundary
value problems for differential equations. In this work we prove some new
Jensen-type inequalities for $m$-convex functions, and we apply them to
generalized Riemann-Liouville-type integral operators. It is remarkable that,
if we consider $m=1$, we obtain new inequalities for convex functions.


title:
Application of K-means Clustering Algorithm in Evaluation and
  Statistical Analysis of Internet Financial Transaction Data

abstract:
  The purpose is to promote the orderly development of China's Internet
financial transactions and minimize default and delinquency in Internet
financial transactions. Based on the typical big data algorithm (K-means
algorithm), this paper discusses the concepts of the K-means algorithm and
Internet financial transactions, as well as the significance of big data
algorithms for Internet financial transaction data evaluation and statistical
analysis. Meanwhile, the existing Internet financial transaction systems are
reviewed, and their deficiencies are summarized, based on which relevant
countermeasures and suggestions are put forward. At the same time, the K-means
clustering algorithm is applied to evaluate financial transaction data, finding
that it can improve the accuracy of data and reduce the error by 40%. But when
the number of clusters is 7, the output result distribution interval of the
K-means clustering algorithm is 4 days, and when the number of clusters is 10,
the output result distribution interval of the K-means clustering algorithm is
6 days, indicating that the convergence effect of this algorithm is relatively
good. Additionally, many small and micro individuals still hold a negative
attitude towards the innovation and adjustment of Internet financial
transactions, indicating that the construction of China's Internet financial
transaction system needs further optimization. The satisfaction of most small
and micro individuals with innovation and adjustment also shows that the
proposed Internet financial transaction adjustment measures are feasible, can
provide references for relevant Internet financial transactions, and
contributes to the development of Internet financial transactions in China.


title:
The development of a portable elbow exoskeleton with a Twisted Strings
  Actuator to assist patients with upper limb inhabitation

abstract:
  Over the years, the number of exoskeleton devices utilized for upper-limb
rehabilitation has increased dramatically, each with its own set of pros and
cons. Most exoskeletons are not portable, limiting their utility to daily use
for house patients. Additionally, the huge size of some grounded exoskeletons
consumes space while maintaining a sophisticated structure and require more
expensive materials. In other words, to maintain affordability, the device's
structure must be simple. Thus, in this work, a portable elbow exoskeleton is
developed using SolidWorks to incorporate a Twisted Strings Actuator (TSA) to
aid in upper-limb rehabilitation and to provide an alternative for those with
compromised limbs to recuperate. Experiments are conducted to identify the
optimal value for building a more flexible elbow exoskeleton prototype by
analyzing stress, strain conditions, torque, forces, and strings. Preliminary
computational findings reveal that for the proposed intended prototype, a
string length of.033 m and a torque value ranging from 1.5 Nm to 3 Nm are
optimal.


title:
Autonomous Vehicles: Open-Source Technologies, Considerations, and
  Development

abstract:
  Autonomous vehicles are the culmination of advances in many areas such as
sensor technologies, artificial intelligence (AI), networking, and more. This
paper will introduce the reader to the technologies that build autonomous
vehicles. It will focus on open-source tools and libraries for autonomous
vehicle development, making it cheaper and easier for developers and
researchers to participate in the field. The topics covered are as follows.
First, we will discuss the sensors used in autonomous vehicles and summarize
their performance in different environments, costs, and unique features. Then
we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for
each modality. Third, we will review popular open-source driving simulators, a
cost-effective way to train machine learning models and test vehicle software
performance. We will then highlight embedded operating systems and the security
and development considerations when choosing one. After that, we will discuss
Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are
areas that fuse networking technologies with autonomous vehicles to extend
their functionality. We will then review the five levels of vehicle automation,
commercial and open-source Advanced Driving Assistance Systems, and their
features. Finally, we will touch on the major manufacturing and software
companies involved in the field, their investments, and their partnerships.
These topics will give the reader an understanding of the industry, its
technologies, active research, and the tools available for developers to build
autonomous vehicles.


title: Neural Network based Inter bi-prediction Blending

abstract:
  This paper presents a learning-based method to improve bi-prediction in video
coding. In conventional video coding solutions, the motion compensation of
blocks from already decoded reference pictures stands out as the principal tool
used to predict the current frame. Especially, the bi-prediction, in which a
block is obtained by averaging two different motion-compensated prediction
blocks, significantly improves the final temporal prediction accuracy. In this
context, we introduce a simple neural network that further improves the
blending operation. A complexity balance, both in terms of network size and
encoder mode selection, is carried out. Extensive tests on top of the recently
standardized VVC codec are performed and show a BD-rate improvement of -1.4% in
random access configuration for a network size of fewer than 10k parameters. We
also propose a simple CPU-based implementation and direct network quantization
to assess the complexity/gains tradeoff in a conventional codec framework.


title: Modular representation and control of floppy networks

abstract:
  Geometric graph models of systems as diverse as proteins, robots, and
mechanical structures from DNA assemblies to architected materials point
towards a unified way to represent and control them in space and time. While
much work has been done in the context of characterizing the behavior of these
networks close to critical points associated with bond and rigidity
percolation, isostaticity, etc., much less is known about floppy,
under-constrained networks that are far more common in nature and technology.
Here we combine geometric rigidity and algebraic sparsity to provide a
framework for identifying the zero-energy floppy modes via a representation
that illuminates the underlying hierarchy and modularity of the network, and
thence the control of its nestedness and locality. Our framework allows us to
demonstrate a range of applications of this approach that include robotic
reaching tasks with motion primitives, and predicting the linear and nonlinear
response of elastic networks based solely on infinitesimal rigidity and
sparsity, which we test using physical experiments. Our approach is thus likely
to be of use broadly in dissecting the geometrical properties of floppy
networks using algebraic sparsity to optimize their function and performance.

categories: cs.RO cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.soft math.OC


title: Generalized Yang-Mills Theory under Rotor Mechanism

abstract:
  This paper follows the previous work on generalized abelian gauge field
theory of higher-order derivatives under rotor model and extends the study to
the most generalized non-abelian case. We find that the rotor mechanism from
the abelian case applies nicely to the non-abelian case under the Lorentz gauge
condition. Under the rotor mechanism, the gauge field transforms as $T_{\mu}^a
\rightarrow \Box^n T_{\mu}^a$. When the order of field derivative is $n=0$,
this restores back to the original Yang-Mills action. Our work gives an
extensive generalization of the Yang-Mills theory with higher-order field
derivatives. We also compute the equation of motion and Noether's current of
the generalized non-abelian gauge field theory. Finally, we study the dynamic
instability issue of the theory by the Ostrogradsky construction and the
analysis of the 00-component of the energy-momentum tensor.


title:
Optimizing Age of Information in Wireless Uplink Networks with Partial
  Observations

abstract:
  We consider a wireless uplink network consisting of multiple end devices and
an access point (AP). Each device monitors a physical process with stochastic
arrival of status updates and sends these updates to the AP over a shared
channel. The AP aims to schedule the transmissions of these devices to optimize
the network-wide information freshness, quantified by the Age of Information
(AoI) metric. Due to the stochastic arrival of the status updates at the
devices, the AP only has partial observations of system times of the latest
status updates at the devices when making scheduling decisions. We formulate
such a decision-making problem as a belief Markov Decision Process
(belief-MDP). The belief-MDP in its original form is difficult to solve as the
dimension of its states can go to infinity and its belief space is uncountable.
By leveraging the properties of the status update arrival (i.e., Bernoulli)
processes, we manage to simplify the feasible states of the belief-MDP to
two-dimensional vectors. Built on that, we devise a low-complexity scheduling
policy. We derive upper bounds for the AoI performance of the low-complexity
policy and analyze the performance guarantee by comparing its performance with
a universal lower bound. Numerical results validate our analyses.


title: Approaches to Artificial General Intelligence: An Analysis

abstract:
  This paper is an analysis of the different methods proposed to achieve AGI,
including Human Brain Emulation, AIXI and Integrated Cognitive Architecture.
First, the definition of AGI as used in this paper has been defined, and its
requirements have been stated. For each proposed method mentioned, the method
in question was summarized and its key processes were detailed, showcasing how
it functioned. Then, each method listed was analyzed, taking various factors
into consideration, such as technological requirements, computational ability,
and adequacy to the requirements. It was concluded that while there are various
methods to achieve AGI that could work, such as Human Brain Emulation and
Integrated Cognitive Architectures, the most promising method to achieve AGI is
Integrated Cognitive Architectures. This is because Human Brain Emulation was
found to require scanning technologies that will most likely not be available
until the 2030s, making it unlikely to be created before then. Moreover,
Integrated Cognitive Architectures has reduced computational requirements and a
suitable functionality for General Intelligence, making it the most likely way
to achieve AGI.


title: Modified Newtonian dynamics and excited hadrons

abstract:
  We discuss implications that result from the acceptance of MOND parameter
$a_M$ as the third fundamental constant of nature in addition to $c$ and $h$.
To this aim we use the concept of Newtonian triangle in the $(log(r),~ log(m))$
plane and show that excited states on hadronic Regge trajectories lead into the
MOND regime beyond this triangle. The experimentally suggested freezing of one
internal spatial degree of quark freedom in excited baryons corroborates then
the conjecture that MOND is related to the idea of space emergence.


title:
Existence and perception as the basis of AGI (Artificial General
  Intelligence)

abstract:
  As is known, AGI (Artificial General Intelligence), unlike AI, should operate
with meanings. And that's what distinguishes it from AI. Any successful AI
implementations (playing chess, unmanned driving, face recognition etc.) do not
operate with the meanings of the processed objects in any way and do not
recognize the meaning. And they don't need to. But for AGI, which emulates
human thinking, this ability is crucial. Numerous attempts to define the
concept of "meaning" have one very significant drawback - all such definitions
are not strict and formalized, so they cannot be programmed. The meaning search
procedure should use a formalized description of its existence and possible
forms of its perception. For the practical implementation of AGI, it is
necessary to develop such "ready-to-code" descriptions in the context of their
use for processing the related cognitive concepts of "meaning" and "knowledge".
An attempt to formalize the definition of such concepts is made in this
article.


title: Comparative Study of Machine Learning Models for Stock Price Prediction

abstract:
  In this work, we apply machine learning techniques to historical stock prices
to forecast future prices. To achieve this, we use recursive approaches that
are appropriate for handling time series data. In particular, we apply a linear
Kalman filter and different varieties of long short-term memory (LSTM)
architectures to historical stock prices over a 10-year range (1/1/2011 -
1/1/2021). We quantify the results of these models by computing the error of
the predicted values versus the historical values of each stock. We find that
of the algorithms we investigated, a simple linear Kalman filter can predict
the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly
well. However, in the case of high-volatility stocks (e.g., Tesla) the more
complex LSTM algorithms significantly outperform the Kalman filter. Our results
show that we can classify different types of stocks and then train an LSTM for
each stock type. This method could be used to automate portfolio generation for
a target return rate.


title: T-Plots: A Novel Approach to Network Design

abstract:
  It is accepted wisdom that changes in the traffic matrix entail capacity
over-provisioning, but there is no simple measure of just how much
over-provisioning can buy. In this Thesis, we aim to provide the network
designer with a simple view of the network robustness to traffic matrix
changes. We first present the Traffic Load Distribution Plots, or T-Plots, a
class of plots illustrating the percentage of traffic matrices that can be
serviced as a function of the capacity over-provisioning. For instance, from a
simple look at their T- Plots, network designers can guarantee that their
network services all admissible traffic matrices, or 99% of permutation traffic
matrices, or all traffic matrices with ingress/egress load at most half the
maximum. We further show that, unfortunately, in the general case plotting
T-Plots is #P-Complete, i.e., that it is impossible to plot a T-plot in a
polynomial time by the noon tools. However, we show that T-Plots can sometimes
be closely modeled as Gaussian, thus only using two values (mean and variance)
to quantify the robustness of a capacity allocation to traffic matrix changes.
We further utilize these Gaussian T-Plots to provide a more robust capacity
allocation. Finally, we demonstrate the benefits of using T-Plots by showing
results of extensive Monte Carlo simulations in a real backbone network. This
Thesis was submitted in 2007. Since then, the results that appeared in it were
applied in various networking environments. In this newer version, we revisit
the results 13 years later and explain their relevance to state-of-the-art
problems in network design.


title:
Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets
  with Sentiment Measurements

abstract:
  It is a challenging task to predict financial markets. The complexity of this
task is mainly due to the interaction between financial markets and market
participants, who are not able to keep rational all the time, and often
affected by emotions such as fear and ecstasy. Based on the state-of-the-art
approach particularly for financial market predictions, a hybrid convolutional
LSTM Based variational sequence-to-sequence model with attention (CLVSA), we
propose a novel deep learning approach, named dual-CLVSA, to predict financial
market movement with both trading data and the corresponding social sentiment
measurements, each through a separate sequence-to-sequence channel. We evaluate
the performance of our approach with backtesting on historical trading data of
SPDR SP 500 Trust ETF over eight years. The experiment results show that
dual-CLVSA can effectively fuse the two types of data, and verify that
sentiment measurements are not only informative for financial market
predictions, but they also contain extra profitable features to boost the
performance of our predicting system.


title: $L^2$-Betti numbers and computability of reals

abstract:
  We study the computability degree of real numbers arising as $L^2$-Betti
numbers or $L^2$-torsion of groups, parametrised over the Turing degree of the
word problem. Moreover, we explain how such considerations can be modelled in a
proof assistant.


title: Pre-Leibniz algebras

abstract:
  The notion of pre-Leibniz algebras was recently introduced in the study of
Rota-Baxter operators on Leibniz algebras. In this paper, we first construct a
graded Lie algebra whose Maurer-Cartan elements are pre-Leibniz algebras. Using
this characterization, we define the cohomology of a pre-Leibniz algebra with
coefficients in a representation. This cohomology is shown to split the
Loday-Pirashvili cohomology of Leibniz algebras. As applications of our
cohomology, we study formal and finite order deformations of a pre-Leibniz
algebra. Finally, we define homotopy pre-Leibniz algebras and classify some
special types of homotopy pre-Leibniz algebras.


title:
CDW signatures in the electronic structure of LaSb2 at 13 K and
  metal-insulator transition

abstract:
  Light rare-earth antimonide LaSb2 is a material susceptible to nesting and
exhibits a Charge Density Wave (CDW) at 355 K as well as superconductivity
below 1.2 K. In the seek for additional CDW transitions, we have studied the
temperature-dependent of LaSb2 at 200 and 13 K and infer CDW manifestations by
combining angle-resolved photoemission spectroscopy (ARPES) measurements and
density functional theory (DFT) calculations. ARPES measurements at 200 K show
a metallic system while it appears to be semiconducting at 13 K, at odds with
existing resistivity measurements. At 13 K, ARPES shows the band folding of the
inner Fermi surface pockets, with considerable spectral weight on the folded
band. We find a nesting vector at q = 0.25$\pm$0.02 {\AA} -1. In addition, we
observe Umklapps of other bands due to the onset of the new periodicity,
together with a semiconducting behavior in the whole reciprocal space.
Calculations demonstrate that the folded band is associated with the La-Sb
layer and that in-plane distortion is the most probable structural modification
in the system, probably affecting the whole unit cell.


title:
Weighted relative Rota-Baxter operators on Leibniz algebras and
  Post-Leibniz algebra structures

abstract:
  Leibniz algebras are non-skewsymmetric analogue of Lie algebras. In this
paper, we consider weighted relative Rota-Baxter operators on Leibniz algebras.
We define cohomology of such operators and as an application, we study their
deformations. Finally, we introduce and study post-Leibniz algebras as the
structure behind weighted relative Rota-Baxter operators.


title: Patch-Based Stochastic Attention for Image Editing

abstract:
  Attention mechanisms have become of crucial importance in deep learning in
recent years. These non-local operations, which are similar to traditional
patch-based methods in image processing, complement local convolutions.
However, computing the full attention matrix is an expensive step with a heavy
memory and computational load. These limitations curb network architectures and
performances, in particular for the case of high resolution images. We propose
an efficient attention layer based on the stochastic algorithm PatchMatch,
which is used for determining approximate nearest neighbors. We refer to our
proposed layer as a "Patch-based Stochastic Attention Layer" (PSAL).
Furthermore, we propose different approaches, based on patch aggregation, to
ensure the differentiability of PSAL, thus allowing end-to-end training of any
network containing our layer. PSAL has a small memory footprint and can
therefore scale to high resolution images. It maintains this footprint without
sacrificing spatial precision and globality of the nearest neighbours, which
means that it can be easily inserted in any level of a deep architecture, even
in shallower levels. We demonstrate the usefulness of PSAL on several image
editing tasks, such as image inpainting and image colorization.


title: Conversational Agents: Theory and Applications

abstract:
  In this chapter, we provide a review of conversational agents (CAs),
discussing chatbots, intended for casual conversation with a user, as well as
task-oriented agents that generally engage in discussions intended to reach one
or several specific goals, often (but not always) within a specific domain. We
also consider the concept of embodied conversational agents, briefly reviewing
aspects such as character animation and speech processing. The many different
approaches for representing dialogue in CAs are discussed in some detail, along
with methods for evaluating such agents, emphasizing the important topics of
accountability and interpretability. A brief historical overview is given,
followed by an extensive overview of various applications, especially in the
fields of health and education. We end the chapter by discussing benefits and
potential risks regarding the societal impact of current and future CA
technology.


title: SLIDE: a surrogate fairness constraint to ensure fairness consistency

abstract:
  As they have a vital effect on social decision makings, AI algorithms should
be not only accurate and but also fair. Among various algorithms for fairness
AI, learning a prediction model by minimizing the empirical risk (e.g.,
cross-entropy) subject to a given fairness constraint has received much
attention. To avoid computational difficulty, however, a given fairness
constraint is replaced by a surrogate fairness constraint as the 0-1 loss is
replaced by a convex surrogate loss for classification problems. In this paper,
we investigate the validity of existing surrogate fairness constraints and
propose a new surrogate fairness constraint called SLIDE, which is
computationally feasible and asymptotically valid in the sense that the learned
model satisfies the fairness constraint asymptotically and achieves a fast
convergence rate. Numerical experiments confirm that the SLIDE works well for
various benchmark datasets.


title: Filters and ideals in pseudocomplemented posets

abstract:
  We study ideals and filters of posets and of pseudocomplemented posets and
show a version of the Separation Theorem, known for ideals and filters in
lattices and semilattices, within this general setting. We extend the concept
of a *-ideal already introduced by Rao for pseudocomplemented distributive
lattices and by Talukder, Chakraborty and Begum for pseudocomplemented
semilattices to pseudocomplemented posets. We derive several important
properties of such ideals. Especially, we explain connections between prime
filters, ultrafilters, filters satisfying the *-condition and dense elements.
Finally, we prove a Separation Theorem for *-ideals.


title: Bayesian Linear Bandits for Large-Scale Recommender Systems

abstract:
  Potentially, taking advantage of available side information boosts the
performance of recommender systems; nevertheless, with the rise of big data,
the side information has often several dimensions. Hence, it is imperative to
develop decision-making algorithms that can cope with such a high-dimensional
context in real-time. That is especially challenging when the decision-maker
has a variety of items to recommend. In this paper, we build upon the linear
contextual multi-armed bandit framework to address this problem. We develop a
decision-making policy for a linear bandit problem with high-dimensional
context vectors and several arms. Our policy employs Thompson sampling and
feeds it with reduced context vectors, where the dimensionality reduction
follows by random projection. Our proposed recommender system follows this
policy to learn online the item preferences of users while keeping its runtime
as low as possible. We prove a regret bound that scales as a factor of the
reduced dimension instead of the original one. For numerical evaluation, we use
our algorithm to build a recommender system and apply it to real-world
datasets. The theoretical and numerical results demonstrate the effectiveness
of our proposed algorithm compared to the state-of-the-art in terms of
computational complexity and regret performance.


title:
$\mathscr{A}=\mathscr{U}$ for cluster algebras from moduli spaces of
  $G$-local systems

abstract:
  For a finite-dimensional simple Lie algebra $\mathfrak{g}$ admitting a
non-trivial minuscule representation and a connected marked surface $\Sigma$
with at least two marked points and no punctures, we prove that the cluster
algebra $\mathscr{A}_{\mathfrak{g},\Sigma}$ associated with the pair
$(\mathfrak{g},\Sigma)$ coincides with the upper cluster algebra
$\mathscr{U}_{\mathfrak{g},\Sigma}$. The proof is based on the fact that the
function ring $\mathcal{O}(\mathcal{A}^\times_{G,\Sigma})$ of the moduli space
of decorated twisted $G$-local systems on $\Sigma$ is generated by matrix
coefficients of Wilson lines introduced in [IO20]. As an application, we prove
that the Muller-type skein algebras $\mathscr{S}_{\mathfrak{g},
\Sigma}[\partial^{-1}]$ [Mul16, IY21, IY] for $\mathfrak{g}=\mathfrak{sl}_2,
\mathfrak{sl}_3,$ or $\mathfrak{sp}_4$ are isomorphic to the cluster algebras
$\mathscr{A}_{\mathfrak{g}, \Sigma}$.


title: CITRIS: Causal Identifiability from Temporal Intervened Sequences

abstract:
  Understanding the latent causal factors of a dynamical system from visual
observations is a crucial step towards agents reasoning in complex
environments. In this paper, we propose CITRIS, a variational autoencoder
framework that learns causal representations from temporal sequences of images
in which underlying causal factors have possibly been intervened upon. In
contrast to the recent literature, CITRIS exploits temporality and observing
intervention targets to identify scalar and multidimensional causal factors,
such as 3D rotation angles. Furthermore, by introducing a normalizing flow,
CITRIS can be easily extended to leverage and disentangle representations
obtained by already pretrained autoencoders. Extending previous results on
scalar causal factors, we prove identifiability in a more general setting, in
which only some components of a causal factor are affected by interventions. In
experiments on 3D rendered image sequences, CITRIS outperforms previous methods
on recovering the underlying causal variables. Moreover, using pretrained
autoencoders, CITRIS can even generalize to unseen instantiations of causal
factors, opening future research areas in sim-to-real generalization for causal
representation learning.


title: A laser-driven mixed fuel nuclear fusion micro-reactor concept

abstract:
  We propose a laser-driven near-solid density nano-structured micro-reactor
concept operating with mixed nuclear fusion fuels. The micro-reactor is capable
of making use of a range of neutronic and aneutronic fuels. Its core parts
consists of an embedded nanoscopic nuclear fuel based laser-driven
nano-accelerator that is capable of producing non-thermal fuel distributions
almost instantly.


title:
Quantum muon diffusion and the preservation of time-reversal symmetry in
  the superconducting state of type-I rhenium

abstract:
  Elemental rhenium exhibiting type-II superconductivity has been previously
reported to break time-reversal symmetry in the superconducting state. We have
investigated an arc-melted sample of rhenium exhibiting type-I
superconductivity. Low temperature zero-field muon-spin relaxation measurements
indicate that time-reversal symmetry is preserved in the superconducting state.
Muon diffusion is observed, which is due to quantum mechanical tunneling
between interstitial sites. The normal state behavior is characterized by the
conduction electrons screening the muons and thermal broadening, and is typical
for a metal. Energy asymmetries between muon trapping sites and the
superconducting energy gap also characterize the superconducting state
behavior.


title: The 6-Ds of Creating AI-Enabled Systems

abstract:
  We are entering our tenth year of the current Artificial Intelligence (AI)
spring, and, as with previous AI hype cycles, the threat of an AI winter looms.
AI winters occurred because of ineffective approaches towards navigating the
technology valley of death. The 6-D framework provides an end-to-end framework
to successfully navigate this challenge. The 6-D framework starts with problem
decomposition to identify potential AI solutions, and ends with considerations
for deployment of AI-enabled systems. Each component of the 6-D framework and a
precision medicine use case is described in this paper.


title:
Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based
  Reasoning

abstract:
  Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of
inferring missing information from knowledge graphs, is a widely used task in
many applications, such as product recommendation and question answering. The
state-of-the-art approaches of knowledge graph embeddings and/or rule mining
and reasoning are data-driven and, thus, solely based on the information the
input knowledge graph contains. This leads to unsatisfactory prediction results
which make such solutions inapplicable to crucial domains such as healthcare.
To further enhance the accuracy of knowledge graph completion we propose to
loosely-couple the data-driven power of knowledge graph embeddings with
domain-specific reasoning stemming from experts or entailment regimes (e.g.,
OWL2). In this way, we not only enhance the prediction accuracy with domain
knowledge that may not be included in the input knowledge graph but also allow
users to plugin their own knowledge graph embedding and reasoning method. Our
initial results show that we enhance the MRR accuracy of vanilla knowledge
graph embeddings by up to 3x and outperform hybrid solutions that combine
knowledge graph embeddings with rule mining and reasoning up to 3.5x MRR.


title:
Corrigendum and Addendum to "Structure monoids of set-theoretic
  solutions of the Yang--Baxter equation"

abstract:
  One of the results in our article, which appeared in Publ. Mat. 65 (2021),
499--528, is that the structure monoid $M(X,r)$ of a left non-degenerate
solution $(X,r)$ of the Yang-Baxter Equation is a left semi-truss, in the sense
of Brzezi\'nski, with an additive structure monoid that is close to being a
normal semigroup. Let $\eta$ denote the least left cancellative congruence on
the additive monoid $M(X,r)$. It is then shown that $\eta$ also is a congruence
on the multiplicative monoid $M(X,r)$ and that the left cancellative epimorphic
image $\bar{M}=M(X,r)/\eta$ inherits a semi-truss structure and thus one
obtains a natural left non-degenerate solution of the Yang-Baxter equation on
$\bar{M}$. Moreover, it restricts to the original solution $r$ for some
interesting classes, in particular if $(X, r)$ is irretractable. The proof
contains a gap. In the first part of the paper we correct this mistake by
introducing a new left cancellative congruence $\mu$ on the additive monoid
$M(X,r)$ and show that it also yields a left cancellative congruence on the
multiplicative monoid $M(X,r)$ and we obtain a semi-truss structure on
$M(X,r)/\mu$ that also yields a natural left non-degenerate solution.
  In the second part of the paper we start from the least left cancellative
congruence $\nu$ on the multiplicative monoid $M(X,r)$ and show that it also is
a congruence on the additive monoid $M(X,r)$ in case $r$ is bijective. If,
furthermore, $r$ is left and right non-degenerate and bijective then $\nu
=\eta$, the least left cancellative congruence on the additive monoid $M(X,r)$,
extending an earlier result of Jespers, Kubat and Van Antwerpen to the infinite
case.


title: Absorption characteristics of large acoustic metasurfaces

abstract:
  Metasurfaces formed of arrays of subwavelength resonators are often tuned to
'critically couple' with incident radiation, so that at resonance dissipative
and radiative damping are balanced and absorption is maximised. Such design
criteria are typically derived assuming an infinite metasurface, whereas the
absorption characteristics of finite metasurfaces, even very large ones, can be
markedly different in certain frequency intervals. This is due to the
excitation of surface waves, intrinsic to resonant metasurfaces, and especially
meta-resonances, namely collective resonances where the surface waves form
standing-wave patterns over the planar metasurface domain. We illustrate this
issue using a detailed model of a Helmholtz-type acoustic metasurface formed of
cavity-neck pairs embedded into a rigid substrate, with geometric and
dissipation effects included from first principles (R. Brand\~ao and O.
Schnitzer, Wave Motion, 97 102583, 2020).


title: Field-of-View IoU for Object Detection in 360{\deg} Images

abstract:
  360{\deg} cameras have gained popularity over the last few years. In this
paper, we propose two fundamental techniques -- Field-of-View IoU (FoV-IoU) and
360Augmentation for object detection in 360{\deg} images. Although most object
detection neural networks designed for the perspective images are applicable to
360{\deg} images in equirectangular projection (ERP) format, their performance
deteriorates owing to the distortion in ERP images. Our method can be readily
integrated with existing perspective object detectors and significantly
improves the performance. The FoV-IoU computes the intersection-over-union of
two Field-of-View bounding boxes in a spherical image which could be used for
training, inference, and evaluation while 360Augmentation is a data
augmentation technique specific to 360{\deg} object detection task which
randomly rotates a spherical image and solves the bias due to the
sphere-to-plane projection. We conduct extensive experiments on the 360indoor
dataset with different types of perspective object detectors and show the
consistent effectiveness of our method.


title: On discretization of continuous-time LPV control solutions

abstract:
  In recent years, the Linear Parameter-Varying (LPV) framework has become
increasingly useful for analysis and control of time-varying systems.
Generally, LPV control synthesis is performed in the continuous-time (CT)
domain due to significantly more intuitive performance shaping methods in CT.
However, the main complication of CT synthesis approaches is the successive
implementation of the resulting CT control solutions on physical hardware. In
the literature, several discretization methods have been developed for LPV
systems. However, most of these approaches necessitate heavy nonlinear
operations introduced by the discretization of these time-varying matrices or
can introduce significant approximation error, thereby severely limiting
implementation capabilities of CT LPV control solutions. Alternatively, the
$w'$ discretization approach has been introduced in the LTI case to allow for
preservation of the CT control. Based on these observations, this paper aims at
extending the $w'$ discretization approach to LPV systems, such that
implementation of CT LPV control solutions on physical hardware is simplified.


title: A proof of the Kotzig-Ringel-Rosa Conjecture

abstract:
  We provide a short and rigorous proof of the Kotzig-Ringel-Rosa conjecture
using a functional reformulation of the conjecture and a new composition lemma.


title: A Tensor Based Regression Approach for Human Motion Prediction

abstract:
  Collaborative robotic systems will be a key enabling technology for current
and future industrial applications. The main aspect of such applications is to
guarantee safety for humans. To detect hazardous situations, current
commercially available robotic systems rely on direct physical contact to the
co-working person. To further advance this technology, there are multiple
efforts to develop predictive capabilities for such systems. Using motion
tracking sensors and pose estimation systems combined with adequate predictive
models, potential episodes of hazardous collisions between humans and robots
can be predicted. Based on the provided predictive information, the robotic
system can avoid physical contact by adjusting speed or position. A potential
approach for such systems is to perform human motion prediction with machine
learning methods like Artificial Neural Networks. In our approach, the motion
patterns of past seconds are used to predict future ones by applying a linear
Tensor-on-Tensor regression model, selected according to a similarity measure
between motion sequences obtained by Dynamic TimeWarping. For test and
validation of our proposed approach, industrial pseudo assembly tasks were
recorded with a motion capture system, providing unique traceable Cartesian
coordinates $(x, y, z)$ for each human joint. The prediction of repetitive
human motions associated with assembly tasks, whose data vary significantly in
length and have highly correlated variables, has been achieved in real time.


title:
Alexandrov theorem for general nonlocal curvatures: the geometric impact
  of the kernel

abstract:
  For a general radially symmetric, non-increasing, non-negative kernel $h\in L
^ 1 _{loc} ( R ^ d)$, we study the rigidity of measurable sets in $R ^ d$ with
constant nonlocal $h$-mean curvature. Under a suitable "improved integrability"
assumption on $h$, we prove that these sets are finite unions of equal balls,
as soon as they satisfy a natural nondegeneracy condition. Both the radius of
the balls and their mutual distance can be controlled from below in terms of
suitable parameters depending explicitly on the measure of the level sets of
$h$. In the simplest, common case, in which $h$ is positive, bounded and
decreasing, our result implies that any bounded open set or any bounded
measurable set with finite perimeter which has constant nonlocal $h$-mean
curvature has to be a ball.


title: On chromatic parameters of some Regular graphs

abstract:
  In this work, we try to enunciate the Total chromatic number of some Cayley
graphs like the Cayley graph on Symmetric group, Alternating group, Dihedral
group with respect to some generating sets and some other regular graphs.


title: C3IEL: Cluster for Cloud Evolution, ClImatE and Lightning

abstract:
  Clouds play a major role in Earth's energy budget and hydrological cycle.
Clouds dynamical structure and mixing with the ambient air have a large impact
on their vertical mass and energy fluxes and on precipitation. Most of the
cloud evolution and mixing occurs at scales smaller than presently observable
from geostationary orbit, which is less than 1 km. A satellite mission is
planned for bridging this gap, named "Cluster for Cloud evolution, ClImatE and
Lightning" (C3IEL). The mission is a collaboration between the Israeli (ISA)
and French (CNES) space agencies, which is presently at the end of its Phase A.
The planned mission will be constituted of a constellation of 2 to 3
nanosatellites in a sun synchronous early afternoon polar orbit, which will
take multi-stereoscopic images of the field of view during an overpass. C3IEL
will carry 3 instruments: (1) CLOUD visible imager at a spatial resolution of
20 m. The multi-stereoscopic reconstruction of the evolution of cloud envelops
at a resolution better than 100 m and velocity of few m/s will provide an
unprecedented information on the clouds dynamics and evolution. (2) WATER VAPOR
imagers at 3 wavebands with different vapor absorption will provide vertically
integrated water vapor around the cloud and possibly a 3-dimensional structure
of the vapor around the clouds due to their mixing and evaporation with the
ambient air. (3) Lightning Optical Imagers and Photometers (LOIP). The
lightning sensors will provide a link between cloud dynamics and
electrification at higher spatial resolution than previously available. C3IEL
will provide presently missing observational evidence for the role of clouds at
sub-km scale in redistributing the energy and water in the atmosphere, and of
the relation between storm vigor and frequency of lightning activity.

categories: astro-ph.IM astro-ph.EP physics.ao-ph physics.geo-ph


title:
TransFollower: Long-Sequence Car-Following Trajectory Prediction through
  Transformer

abstract:
  Car-following refers to a control process in which the following vehicle (FV)
tries to keep a safe distance between itself and the lead vehicle (LV) by
adjusting its acceleration in response to the actions of the vehicle ahead. The
corresponding car-following models, which describe how one vehicle follows
another vehicle in the traffic flow, form the cornerstone for microscopic
traffic simulation and intelligent vehicle development. One major motivation of
car-following models is to replicate human drivers' longitudinal driving
trajectories. To model the long-term dependency of future actions on historical
driving situations, we developed a long-sequence car-following trajectory
prediction model based on the attention-based Transformer model. The model
follows a general format of encoder-decoder architecture. The encoder takes
historical speed and spacing data as inputs and forms a mixed representation of
historical driving context using multi-head self-attention. The decoder takes
the future LV speed profile as input and outputs the predicted future FV speed
profile in a generative way (instead of an auto-regressive way, avoiding
compounding errors). Through cross-attention between encoder and decoder, the
decoder learns to build a connection between historical driving and future LV
speed, based on which a prediction of future FV speed can be obtained. We train
and test our model with 112,597 real-world car-following events extracted from
the Shanghai Naturalistic Driving Study (SH-NDS). Results show that the model
outperforms the traditional intelligent driver model (IDM), a fully connected
neural network model, and a long short-term memory (LSTM) based model in terms
of long-sequence trajectory prediction accuracy. We also visualized the
self-attention and cross-attention heatmaps to explain how the model derives
its predictions.


title: Toeplitz operators on the weighted Bergman spaces of quotient domains

abstract:
  Let $G$ be a finite pseudoreflection group and $\Omega\subseteq \mathbb C^d$
be a bounded domain which is a $G$-space. We establish identities involving
Toeplitz operators on the weighted Bergman spaces of $\Omega$ and $\Omega/G$
using invariant theory and representation theory of $G.$ This, in turn,
provides techniques to study algebraic properties of Toeplitz operators on the
weighted Bergman space on $\Omega/G.$ We specialize on the generalized
zero-product problem and characterization of commuting pairs of Toeplitz
operators. As a consequence, more intricate results on Toeplitz operators on
the weighted Bergman spaces on some specific quotient domains (namely
symmetrized polydisc, monomial polyhedron, Rudin's domain) have been obtained.


title:
Euclidean preferences in the plane under $\ell_1$, $\ell_2$ and
  $\ell_\infty$ norms

abstract:
  We present various results about Euclidean preferences in the plane under
$\ell_1$, $\ell_2$ and $\ell_{\infty}$ norms. We show that Euclidean preference
profiles under norm $\ell_1$ are the same as those under norm $\ell_{\infty}$,
and that the maximal size of such profiles for four candidates is 19. Whatever
the number of candidates, we prove that at most four distinct candidates can be
ranked in last position of an Euclidean preference profile under norm $\ell_1$,
which generalizes the case of one-dimensional Euclidean preferences (for which
it is well known that at most two candidates can be ranked last). We also
establish that the maximal size of an Euclidean preference profile under norm
$\ell_1$ is in $\Theta(m^4)$, i.e., the same order of magnitude as under norm
$\ell_2$. Finally, we provide a new proof that two-dimensional Euclidean
preference profiles under norm $\ell_2$ for four candidates can be
characterized by three voter-maximal two-dimensional Euclidean profiles. This
proof is a simpler alternative to that proposed by Kamiya et al. in Ranking
patterns of unfolding models of codimension one, Advances in Applied
Mathematics 47(2):379-400.


title:
ALDER: Unlocking blockchain performance by multiplexing consensus
  protocols

abstract:
  Most of today's online services (e.g., social networks, search engines,
market places) are centralized, which is recognized as unsatisfactory by a
majority of users for various reasons (e.g., centralized governance,
censorship, loss of control over personal data). Blockchain technologies
promise a new Web revolution (Web 3.0) through the decentralization of online
services. However, one of the key limitations for this revolution to happen at
a planetary scale is the poor performance of today's blockchains. We propose in
this paper ALDER, a solution for unlocking the performance of off-the-shelf
leader-based blockchains by multiplexing their consensus protocol. Our solution
leverages the existence of multiple potential leaders to alleviate the
bottleneck that exists at different levels of consensus protocols. To
illustrate the benefits it brings to Blockchain performance, we apply ALDER to
three representative blockchains, namely Algorand (Proof-of-Stake), RapidChain
(Sharding-based) and Bitcoin (Proof-of-Work). Our evaluation, involving up to
10,000 nodes deployed on 100 physical machines, shows that using ALDER can
provide up to a 300% improvement in both throughput and latency reduction.