title:
Exploring Multi-Modal Representations for Ambiguity Detection &
  Coreference Resolution in the SIMMC 2.0 Challenge

abstract:
  Anaphoric expressions, such as pronouns and referential descriptions, are
situated with respect to the linguistic context of prior turns, as well as, the
immediate visual environment. However, a speaker's referential descriptions do
not always uniquely identify the referent, leading to ambiguities in need of
resolution through subsequent clarificational exchanges. Thus, effective
Ambiguity Detection and Coreference Resolution are key to task success in
Conversational AI. In this paper, we present models for these two tasks as part
of the SIMMC 2.0 Challenge (Kottur et al. 2021). Specifically, we use TOD-BERT
and LXMERT based models, compare them to a number of baselines and provide
ablation experiments. Our results show that (1) language models are able to
exploit correlations in the data to detect ambiguity; and (2) unimodal
coreference resolution models can avoid the need for a vision component,
through the use of smart object representations.


title:
Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled
  Manipulator

abstract:
  In order to enable on-purpose robotic impact tasks, predicting joint-velocity
jumps is essential to enforce controller feasibility and hardware integrity. We
observe a considerable prediction error of a commonly-used approach in robotics
compared against 250 benchmark experiments with the Panda manipulator. We
reduce the average prediction error by 81.98% as follows: First, we focus on
task-space equations without inverting the ill-conditioned joint-space inertia
matrix. Second, before the impact event, we compute the equivalent inertial
properties of the end-effector tip considering that a high-gains (stiff)
kinematic-controlled manipulator behaves like a composite-rigid body.


title: On the orthogonality of multilinear maps and application to smoothness

abstract:
  We characterize Birkhoff-James orthogonality of bounded multilinear maps in
its full generality, probably for the first time. In the special case, the said
result reduces to the Birkhoff-James orthogonality of bounded linear operators
on an infinite-dimensional complex normed linear space, a new addition to the
existing literature. The said characterization, whenever restricted to the
setting of Hilbert space, leads to a multilinear generalization of the
well-known Bhatia-\v{S}emrl Theorem in both finite and infinite-dimensional
cases. As a natural outcome of our investigations, we obtain necessary and
sufficient conditions of smoothness for bounded multilinear maps. Contrary to
most of the previous results that address the smoothness of linear operators in
the context of real normed linear spaces, we study the smoothness of
multilinear maps in the realm of complex normed linear spaces. Our results
extend and improve some recent results related to the smoothness of linear
operators.


title:
Crossing two-component dark matter models and implications for 511 keV
  $\gamma$-ray and XENON1T excesses

abstract:
  We study scalar and fermionic crossing two-component dark matter (C2CDM)
models with $ U(1)_X $ dark gauge symmetry. This $ U(1)_X $ gauge symmetry is
broken via the dark Higgs mechanism of a dark Higgs field $\Phi$ and the dark
photon becomes massive. On the other hand, the same dark Higgs field $\Phi$
also serves as a bridge between each component of two DM sectors such that the
dark flavor-changing neutral current (DFCNC) interaction between them is
induced. Moreover, the mass splitting between these two DM sectors is generated
after $ U(1)_X $ gauge symmetry breaking by nonzero $\langle \Phi \rangle$. We
discuss the stability of DM candidates and allowed parameter space from the
relic density and other constraints in C2CDM models. Some novel signatures with
displaced vertices at Belle II are studied. Finally, the possible explanations
of 511 keV $\gamma$-ray line and XENON1T excess in C2CDM models are discussed.


title: Rotating black holes at large $D$ in Einstein-Gauss-Bonnet theory

abstract:
  Applying the large $D$ approach to the Einstein-Gauss-Bonnet theory, we
construct equally rotating black hole solutions in odd dimensions. This
provides the first example of the analytic solutions which describes not-slowly
rotating black holes. For the next-leading order solutions in the $1/D$
expansion, we discuss the physical aspects such as thermodynamics and the phase
diagram.


title: Time-coded Spiking Fourier Transform in Neuromorphic Hardware

abstract:
  After several decades of continuously optimizing computing systems, the
Moore's law is reaching itsend. However, there is an increasing demand for fast
and efficient processing systems that can handlelarge streams of data while
decreasing system footprints. Neuromorphic computing answers thisneed by
creating decentralized architectures that communicate with binary events over
time. Despiteits rapid growth in the last few years, novel algorithms are
needed that can leverage the potential ofthis emerging computing paradigm and
can stimulate the design of advanced neuromorphic chips.In this work, we
propose a time-based spiking neural network that is mathematically equivalent
tothe Fourier transform. We implemented the network in the neuromorphic chip
Loihi and conductedexperiments on five different real scenarios with an
automotive frequency modulated continuouswave radar. Experimental results
validate the algorithm, and we hope they prompt the design of adhoc
neuromorphic chips that can improve the efficiency of state-of-the-art digital
signal processorsand encourage research on neuromorphic computing for signal
processing.


title:
Ideals, bands and direct sum decompositions in mixed lattice vector
  spaces

abstract:
  A mixed lattice vector space is a partially ordered vector space with two
partial orderings and certain lattice-type properties. In this paper we first
give some fundamental results in mixed lattice groups, and then we investigate
the structure theory of mixed lattice vector spaces, which can be viewed as a
generalization of the theory of Riesz spaces. More specifically, we study the
properties of ideals and bands in mixed lattice spaces, and the related idea of
representing a mixed lattice space as a direct sum of disjoint bands. Under
certain conditions, these decompositions can also be given in terms of order
projections.


title:
Mildly version of Hurewicz Basis covering property and Hurewicz measure
  zero spaces

abstract:
  In this paper, we introduced the mildly version of the Hurewicz basis
covering property, studied by Babinkostova, Ko\v{c}inac, and Scheepers. A space
$X$ is said to have mildly-Hurewicz property if for each sequence $\langle
\mathcal{U}_n : n\in \omega \rangle$ of clopen covers of $X$ there is a
sequence $\langle \mathcal{V}_n : n\in \omega \rangle$ such that for each $n$,
$\mathcal{V}_n$ is a finite subset of $\mathcal{U}_n$ and for each $x\in X$,
$x$ belongs to $\bigcup\mathcal{V}_n$ for all but finitely many $n$. Then we
characterized mildly-Hurewicz property by mildly-Hurewicz Basis property and
mildly-Hurewicz measure zero property for metrizable spaces.


title:
Bayesian autoencoders with uncertainty quantification: Towards
  trustworthy anomaly detection

abstract:
  Despite numerous studies of deep autoencoders (AEs) for unsupervised anomaly
detection, AEs still lack a way to express uncertainty in their predictions,
crucial for ensuring safe and trustworthy machine learning systems in
high-stake applications. Therefore, in this work, the formulation of Bayesian
autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty,
comprising epistemic and aleatoric uncertainties. To evaluate the quality of
uncertainty, we consider the task of classifying anomalies with the additional
option of rejecting predictions of high uncertainty. In addition, we use the
accuracy-rejection curve and propose the weighted average accuracy as a
performance metric. Our experiments demonstrate the effectiveness of the BAE
and total anomaly uncertainty on a set of benchmark datasets and two real
datasets for manufacturing: one for condition monitoring, the other for quality
inspection.


title:
Machine Learning based refinement strategies for polyhedral grids with
  applications to Virtual Element and polyhedral Discontinuous Galerkin methods

abstract:
  We propose two new strategies based on Machine Learning techniques to handle
polyhedral grid refinement, to be possibly employed within an adaptive
framework. The first one employs the k-means clustering algorithm to partition
the points of the polyhedron to be refined. This strategy is a variation of the
well known Centroidal Voronoi Tessellation. The second one employs
Convolutional Neural Networks to classify the "shape" of an element so that
"ad-hoc" refinement criteria can be defined. This strategy can be used to
enhance existing refinement strategies, including the k-means strategy, at a
low online computational cost. We test the proposed algorithms considering two
families of finite element methods that support arbitrarily shaped polyhedral
elements, namely the Virtual Element Method (VEM) and the Polygonal
Discontinuous Galerkin (PolyDG) method. We demonstrate that these strategies do
preserve the structure and the quality of the underlaying grids, reducing the
overall computational cost and mesh complexity.


title:
Emergent quantum correlations and collective behavior in non-interacting
  quantum systems subject to stochastic resetting

abstract:
  We investigate the dynamics of a non-interacting spin system, undergoing
coherent Rabi oscillations, in the presence of stochastic resetting. We show
that resetting generally induces long-range quantum and classical correlations
both in the emergent dissipative dynamics and in the non-equilibrium stationary
state. Moreover, for the case of conditional reset protocols -- where the
system is reinitialized to a state dependent on the outcome of a preceding
measurement -- we show that, in the thermodynamic limit, the spin system can
feature collective behavior which results in a phenomenology reminiscent of
that occurring in non-equilibrium phase transitions. The discussed reset
protocols can be implemented on quantum simulators and quantum devices that
permit fast measurement and readout of macroscopic observables, such as the
magnetisation. Our approach does not require the control of coherent
interactions and may therefore highlight a route towards a simple and robust
creation of quantum correlations and collective non-equilibrium states, with
potential applications in quantum enhanced metrology and sensing.

categories: quant-ph cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el physics.atom-ph


title:
Relation between Quantum Coherence and Quantum Entanglement in Quantum
  Measurements

abstract:
  Quantum measurement is a class of quantum channels that sends quantum states
to classical states. We set up resource theories of quantum coherence and
quantum entanglement for quantum measurements and find relations between them.
For this, we conceive a relative entropy type quantity to account for the
quantum resources of quantum measurements. The quantum coherence of a quantum
measurement can be converted into the entanglement in a bipartite quantum
measurement through coherence-non generating transformations. Conversely, a
quantum entanglement monotone of quantum measurements induces a quantum
coherence monotone of quantum measurement. Our results confirm that the
intuition on the link between quantum coherence and quantum entanglement is
valid even for quantum measurements which do not generate any quantum resource.


title: Dynamics of reconfigurable straw-like elements

abstract:
  In this paper, we discuss the dynamic modeling of fluid-filled straw-like
elements consisting of serially interconnected elastic frusta with both
axisymmetric and antisymmetric degrees of freedom, assuming planar motion.
Under appropriate conditions each sub-structure has four stable equilibrium
states. This gives the system under investigation the ability to remain stable
in a large number of complex states, which is a vital ability for myriad of
applications, including reconfigurable structures and soft robots. The
theoretical model explains the dynamics of a single straw-like element in a
discrete manner, considering inertial, damping, and gravitational effects,
while taking into account the nonlinear elasticity of the elastic frusta, and
assuming hydrostatic behavior of the entrapped fluid. After identifying the
geometric and elastic parameters of the theoretical model based on relatively
simple experiments, the model is validated compared to numerical simulations
and experiments. The numerical simulations validate the theoretical elasticity
of the elastic frusta, whereas the overall dynamic behavior of the system and
the influence of unmodeled fluidic effects are examined experimentally. It is
demonstrated both theoretically and empirically that straw-like elements cannot
be adequately modeled using simple uniaxial deformations. In addition, the
experimental validation indicates that the suggested model can accurately
capture their overall dynamics.


title:
Convergence of sparse grid Gaussian convolution approximation for
  multi-dimensional periodic function

abstract:
  We consider the problem of approximating $[0,1]^{d}$-periodic functions by
convolution with a scaled Gaussian kernel. We start by establishing convergence
rates to functions from periodic Sobolev spaces and we show that the saturation
rate is $O(h^{2}),$ where $h$ is the scale of the Gaussian kernel. Taken from a
discrete point of view, this result can be interpreted as the accuracy that can
be achieved on the uniform grid with spacing $h.$ In the discrete setting, the
curse of dimensionality would place severe restrictions on the computation of
the approximation. For instance, a spacing of $2^{-n}$ would provide an
approximation converging at a rate of $O(2^{-2n})$ but would require
$(2^{n}+1)^{d}$ grid points. To overcome this we introduce a sparse grid
version of Gaussian convolution approximation, where substantially fewer grid
points are required, and
  show that the sparse grid version delivers a saturation rate of
$O(n^{d-1}2^{-2n}).$ This rate is in line with what one would expect in the
sparse grid setting (where the full grid error only deteriorates by a factor of
order $n^{d-1}$) however the analysis that leads to the result is novel in that
it draws on results from the theory of special functions and key observations
regarding the form of certain weighted geometric sums.


title:
The hidden uncertainties in particle balance calculations and their
  implications for assessment of plasma performance

abstract:
  Predictive simulations of mixed plasma discharges, like deuterium-tritium
plasmas, rely on self-consistent models for particle transport. These models
have to be validated with interpretive analysis of existing experiments.
Accounting for uncertainties in the particle balance equation is important for
modelling of mixed-plasma discharges because simulation results are directly
affected by plasma composition. JET deuterium and mixed hydrogen-deuterium
plasma discharges heated by neutral beams only are analysed with the TRANSP
code. Influence of prescribed quantities and terms entering into the particle
balance equation is quantitatively assessed on the computed plasma parameters,
in particular on the neutron rate and the plasma energy. Large uncertainties in
the prescribed ion temperature, plasma rotation and impurity content might
result in a noticeable over- or underestimate of the computed neutron rate. A
significant difference in the time evolution of the measured and computed
neutron rate is observed in the interpretive simulations if same diffusivity
for electrons and thermal ions is assumed. Increased thermal deuterium ion
transport is required to reach agreement with the plasma composition measured
at the plasma edge and the measured neutron rate. Thermal ion density computed
with the particle balance equation is mostly affected by the ion outflux term.


title: From Biological Synapses to Intelligent Robots

abstract:
  This review explores biologically inspired learning as a model for
intelligent robot control and sensing technology on the basis of specific
examples. Hebbian synaptic learning is discussed as a functionally relevant
model for machine learning and intelligence, as explained on the basis of
examples from the highly plastic biological neural networks of invertebrates
and vertebrates. Its potential for adaptive learning and control without
supervision, the generation of functional complexity, and control architectures
based on self organization is brought forward. Learning without prior knowledge
based on excitatory and inhibitory neural mechanisms accounts for the process
through which survival or task relevant representations are either reinforced
or suppressed. The basic mechanisms of unsupervised biological learning drive
synaptic plasticity and adaptation for behavioral success in living brains with
different levels of complexity. The insights collected here point toward the
Hebbian model as a choice solution for intelligent robotics and sensor systems.
Keywords: Hebbian learning, synaptic plasticity, neural networks, self
organization, brain, reinforcement, sensory processing, robot control


title: Lower bounds for the depth of second power of edge ideals

abstract:
  Assume that $G$ is a graph with edge ideal $I(G)$. We provide sharp lower
bounds for the depth of $I(G)^2$ in terms of the star packing number of $G$.


title:
VLSM: Validating Labelled State Transition and Message Production
  Systems

abstract:
  In this paper we introduce the notion of a validating labelled state
transition and message production system (VLSM), a tool for formal analysis and
specification of faulty distributed systems. We give theories of VLSM
composition, state equivocation, message equivocation, and validators. Then we
show that the traces of equivocation-limited compositions of validators are
precisely the traces of honest components in the context of limited Byzantine
faults in an asynchronous network without a guarantee of eventual message
arrival. This result shows that the validation strategies of honest nodes can
restrict the effect that Byzantine nodes have on them enough that we can
express all of their possible faulty traces in terms of equivocation over valid
transitions, circumventing the need for Byzantine fault analysis altogether.
Our definitions and results have been formalised and machine-checked in the Coq
proof assistant.


title: Smooth quotients of generalized Fermat curves

abstract:
  A closed Riemann surface $S$ is called a generalized Fermat curve of type
$(p,n)$, where $n,p \geq 2$ are integers such that $(p-1)(n-1)>2$, if it admits
a group $H \cong {\mathbb Z}_{p}^{n}$ of conformal automorphisms with quotient
orbifold $S/H$ of genus zero with exactly $n+1$ cone points, each one of order
$p$; in this case $H$ is called a generalized Fermat group of type $(p,n)$. In
this case, it is known that $S$ is non-hyperelliptic and that $H$ is its unique
generalized Fermat group of type $(p,n)$. Also, explicit equations for them, as
a fiber product of classical Fermat curves of degree $p$, are known. For $p$ a
prime integer, we describe those subgroups $K$ of $H$ acting freely on $S$,
together with algebraic equations for $S/K$, and determine those $K$ such that
$S/K$ is hyperelliptic.


title: Automorphism Group of Marked Interval Graphs in FPT

abstract:
  An interval graph is the intersection graph of a set of intervals on the real
line. By a classical result of [Colbourn and Booth], the automorphism group of
an interval graph can be computed in linear time using so-called PQ-trees. We
consider the following generalization; some cliques of our interval graph are
marked with colors, and we restrict to only such automorphisms that map every
marked clique to a marked clique of the same color. We call such automorphisms
marking preserving, and we are in fact interested in the action of their group
on the marked sets. While this problem is at least as hard as general graph
isomorphism (i.e., GI-hard), we give an FPT-time algorithm with respect to the
parameter equal to the largest number of marked sets incomparable by inclusion.
This result is useful, e.g., for the isomorphism problem of chordal graphs of
bounded leafage.


title: Energy exchange between relativistic fluids: the polytropic case

abstract:
  We present a simple, analytic and straightforward method to elucidate the
effects produced by polytropic fluids on any other gravitational source, no
matter its nature, for static and spherically symmetric spacetimes. As a direct
application, we study the interaction between polytropes and perfect fluids
coexisting inside a self-gravitating stellar object


title:
Isometry groups of regular and context-free languages for the
  Levenshtein distance

abstract:
  This article is a partial answer to the question of which groups can be
represented as isometry groups of languages of a certain type. Namely, from two
arbitrary finite groups $G$ and $H$, a method is indicated for constructing a
regular language whose isometry group is isomorphic to $G \times H^\mathbb{N}$,
and it is proved that context-free isometry groups embed into $\Pi_{n=1}^\infty
S_{n}$, and there exists a regular language with isometry group
$\Pi_{n=1}^\infty S_{2n}$.


title: Low-Temperature Thermal Conductivity of CeRh$_{2}$As$_{2}$

abstract:
  CeRh$_2$As$_2$ is a rare unconventional superconductor ($T_c=0.26$ K)
characterized by two adjacent superconducting phases for a magnetic field $H
\parallel c$-axis of the tetragonal crystal structure. Antiferromagnetic order,
quadrupole-density-wave order ($T_0 = 0.4$ K) and the proximity of this
material to a quantum-critical point have also been reported: The coexistence
of these phenomena with superconductivity is currently under discussion. Here,
we present thermal conductivity and electrical resistivity measurements on a
single crystal of CeRh$_2$As$_2$ between 60 mK and 200 K and in magnetic fields
($H \parallel c$) up to 8 T. Our measurements at low $T$ verify the
Wiedemann-Franz law within the error bars. The $T$ dependence of the thermal
conductivity $\kappa(T)$ shows a pronounced drop below $T_c$ which is also
field dependent and thus interpreted as the signature of superconductivity.
However, the large residual resistivity and the lack of sharp anomalies in
$\kappa(T)$ at the expected transition temperatures clearly indicate that
samples of much higher purity are required to gain more information about the
superconducting gap structure.


title: On $p$-gonal fields of definition

abstract:
  Let $S$ be a closed Riemann surface of genus $g \geq 2$ and $\varphi$ be a
conformal automorphism of $S$ of prime order $p$ such that $S/\langle \varphi
\rangle$ has genus zero. Let ${\mathbb K} \leq {\mathbb C}$ be a field of
definition of $S$. We provide an argument for the existence of a field
extension ${\mathbb F}$ of ${\mathbb K}$, of degree at most $2(p-1)$, for which
$S$ is definable by a curve of the form $y^{p}=F(x) \in {\mathbb F}[x]$, in
which case $\varphi$ corresponds to $(x,y) \mapsto (x,e^{2 \pi i/p} y)$. If,
moreover, $\varphi$ is also definable over ${\mathbb K}$, then ${\mathbb F}$
can be chosen to be at most a quadratic extension of ${\mathbb K}$. For $p=2$,
that is when $S$ is hyperelliptic and $\varphi$ is its hyperelliptic
involution, this fact is due to Mestre (for even genus) and Huggins and
Lercier-Ritzenthaler-Sijslingit in the case that ${\rm Aut}(S)/\varphi\rangle$
is non-trivial.


title:
Realising countable groups as automorphisms of origamis on the Loch Ness
  monster

abstract:
  It is known that every finite group can be represented as the full group of
automorphisms of a suitable compact origami. In this paper, we provide a short
argument to note that the same holds for any countable group by considering
origamis on the Loch Ness monster.


title: Hex-Mesh Generation and Processing: a Survey

abstract:
  In this article, we provide a detailed survey of techniques for hexahedral
mesh generation. We cover the whole spectrum of alternative approaches to mesh
generation, as well as post processing algorithms for connectivity editing and
mesh optimization. For each technique, we highlight capabilities and
limitations, also pointing out the associated unsolved challenges. Recent
relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes,
are also discussed. The required background, pertaining to geometrical as well
as combinatorial aspects, is introduced along the way.


title:
Upper bound for the $(d-2)$-leaky forcing number of $Q_d$ and
  $\ell$-leaky forcing number of $GP(n,1)$

abstract:
  Leaky-forcing is a recently introduced variant of zero-forcing that has been
studied for families of graphs including paths, cycles, wheels, grids, and
trees. In this paper, we extend previous results on the leaky forcing number of
the d-dimensional hypercube, $Q_d$, to show that the $(d-2)$-leaky forcing
number of $Q_d$ is at most $2^{d-1}$. We also examine a question about the
relationship between the size of a minimum $\ell$-leaky-forcing set and a
minimum zero-forcing set for a graph $G$.


title: Insights into electron transport in a ferroelectric tunnel junction

abstract:
  The success of a ferroelectric tunnel junction (FTJ) depends on the asymmetry
of electron tunneling as given by the tunneling electroresistance (TER) effect.
This characteristic is mainly assessed considering three transport mechanisms:
direct tunneling, thermionic emission, and Fowler-Nordheim tunneling. Here, by
analyzing the effect of temperature on TER, we show that taking into account
only these mechanisms may not be enough in order to fully characterize the
performance of FTJ devices. We approach the electron tunneling in FTJ with the
non-equilibrium Green function (NEGF) method, which is able to overcome the
limitations affecting the three mechanisms mentioned above. We bring evidence
that the performance of FTJs is also affected by temperature, in a non-trivial
way, via resonance (Gamow-Siegert) states, which are present in the electron
transmission probability and are usually situated above the barrier. Although
the NEGF technique does not provide direct access to the wavefunctions, we show
that, for single-band transport, one can find the wavefunction at any given
energy and in particular at resonant energies in the system.


title: $\mathbf{{N}_{eff}}$ from Excited DM state

abstract:
  For a cold dark matter (DM) originating from the co-annihilation processes,
there will be excited state(s) in the dark sector, that may decay or annihilate
away shortly after their freeze-out. In this paper we investigate the impact of
the decay of these excited states on the effective number of neutrino species,
$N_{\rm eff}^{}$, which is an important cosmological parameter and will be
tested by the future CMB-S4 project. We work in the framework of pseudo-Dirac
DM mode via the Higgs portal. The relic density and the direct detection signal
of the DM are calculated, which gives the available parameter space. Impacts of
the excited state (the heavy component of the pseudo-Dirac fermion) on the
decoupling of active neutrinos are investigated by solving Boltzmann equations
of photon and neutrinos with collision term induced by the decay of the excited
state. The numerical result shows that $|N_{\rm eff}^{}|$ can be of the order
$10^{-4}$, which depends on the mass and lifetime of excited DM state.


title: PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine

abstract:
  Machine learning algorithms must be able to efficiently cope with massive
data sets. Therefore, they have to scale well on any modern system and be able
to exploit the computing power of accelerators independent of their vendor. In
the field of supervised learning, Support Vector Machines (SVMs) are widely
used. However, even modern and optimized implementations such as LIBSVM or
ThunderSVM do not scale well for large non-trivial dense data sets on
cutting-edge hardware: Most SVM implementations are based on Sequential Minimal
Optimization, an optimized though inherent sequential algorithm. Hence, they
are not well-suited for highly parallel GPUs. Furthermore, we are not aware of
a performance portable implementation that supports CPUs and GPUs from
different vendors.
  We have developed the PLSSVM library to solve both issues. First, we resort
to the formulation of the SVM as a least squares problem. Training an SVM then
boils down to solving a system of linear equations for which highly parallel
algorithms are known. Second, we provide a hardware independent yet efficient
implementation: PLSSVM uses different interchangeable backends--OpenMP, CUDA,
OpenCL, SYCL--supporting modern hardware from various vendors like NVIDIA, AMD,
or Intel on multiple GPUs. PLSSVM can be used as a drop-in replacement for
LIBSVM. We observe a speedup on CPUs of up to 10 compared to LIBSVM and on GPUs
of up to 14 compared to ThunderSVM. Our implementation scales on many-core CPUs
with a parallel speedup of 74.7 on up to 256 CPU threads and on multiple GPUs
with a parallel speedup of 3.71 on four GPUs.
  The code, utility scripts, and documentation are all available on GitHub:
https://github.com/SC-SGS/PLSSVM.


title: Non-Hermitian Many-Body Localization of Coupled Hatano-Nelson Chains

abstract:
  The explorations of non-Hermiticity have been devoted to study
disorder-induced localization, but little is known about the sensitivity of
non-Hermitian many-body phenomena to the spatial boundary conditions. In this
work, we investigate the eigenspectrum and localization properties of the
interacting coupled Hatano-Nelson open chains in the presence of random
disorder potential. In the delocalized regime, a complex-real spectral
transition occurs, accompanied with the nearest-level-spacing distribution of
eigenenergies transforming from the Ginibre random matrix ensemble to the
Gaussian orthogonal ensemble. In contrast, a disorder-induced complex-real
transition occurs with Ginibre to real Poisson level-spacing distribution
transition. Moreover, we characterize wavefunctions through the (biorthogonal)
inverse participation ratio and fractal dimension. Unlike the delocalized
regime, we reveal that for strong disorder, the non-Hermitian skin effect is
suppressed and the system is insensitive to the boundary conditions. Finally,
the many-body localization is corroborated by the dynamics of entanglement
entropy and spin imbalance.


title: The E10 Wheeler-DeWitt operator at low levels

abstract:
  We consider the Wheeler-DeWitt operator associated with the bosonic part of
the Hamiltonian of D=11 supergravity in a formulation with only the spatial
components of the three-form and six-form fields, and compare it with the E10
Casimir operator at low levels, to show that these two operators precisely
match modulo spatial gradients up to and including gl(10) level two. The
uniqueness of the E10 Casimir operator eliminates all ordering ambiguities in
the quantum Hamiltonian, at least up to the level considered. Beyond level
three the two operators are expected to start to differ from each other, as
they do so for the classical expressions. We then consider truncations of the
E10 Wheeler-DeWitt operator for various finite-dimensional subgroups of E10 in
order to exhibit the automorphic properties of the associated wave functions
and to show that physically sensible wave functions generically vanish at the
cosmological singularity, thus providing new and more sophisticated examples of
DeWitt's proposed mechanism for singularity resolution in quantum gravity. Our
construction provides novel perspectives on several unresolved conceptual
issues with the Wheeler-DeWitt equation, such as the question of observables in
quantum gravity, or the issue of emergent space and time in a purely algebraic
framework. We also highlight remaining open questions of the E10 framework.


title: Baxter $d$-permutations and other pattern avoiding classes

abstract:
  A permutation of size $n$ can be identified to its diagram in which there is
exactly one point per row and column in the grid $[n]^2$. In this paper we
consider multidimensional permutations (or $d$-permutations), which are
identified to their diagrams on the grid $[n]^d$ in which there is exactly one
point per hyperplane $x_i=j$ for $i\in[d]$ and $j\in[n]$. We first investigate
exhaustively all small pattern avoiding classes. We provide some bijection to
enumerate some of these classes and we propose some conjectures for others. We
then give a generalization of well-studied Baxter permutations into this
multidimensional setting. In addition, we provide a vincular pattern avoidance
characterization of Baxter $d$-permutations.


title:
Deep Learning, Natural Language Processing, and Explainable Artificial
  Intelligence in the Biomedical Domain

abstract:
  In this article, we first give an introduction to artificial intelligence and
its applications in biology and medicine in Section 1. Deep learning methods
are then described in Section 2. We narrow down the focus of the study on
textual data in Section 3, where natural language processing and its
applications in the biomedical domain are described. In Section 4, we give an
introduction to explainable artificial intelligence and discuss the importance
of explainability of artificial intelligence systems, especially in the
biomedical domain.


title:
Shapley effect estimation in reliability-oriented sensitivity analysis
  with correlated inputs by importance sampling

abstract:
  Reliability-oriented sensitivity analysis aims at combining both reliability
and sensitivity analyses by quantifying the influence of each input variable of
a numerical model on a quantity of interest related to its failure. In
particular, target sensitivity analysis focuses on the occurrence of the
failure, and more precisely aims to determine which inputs are more likely to
lead to the failure of the system. The Shapley effects are quantitative global
sensitivity indices which are able to deal with correlated input variables.
They have been recently adapted to the target sensitivity analysis framework.
In this article, we investigate two importance-sampling-based estimation
schemes of these indices which are more efficient than the existing ones when
the failure probability is small. Moreover, an extension to the case where only
an i.i.d. input/output N-sample distributed according to the importance
sampling auxiliary distribution is proposed. This extension allows to estimate
the Shapley effects only with a data set distributed according to the
importance sampling auxiliary distribution stemming from a reliability analysis
without additional calls to the numerical model. In addition, we study
theoretically the absence of bias of some estimators as well as the benefit of
importance sampling. We also provide numerical guidelines and finally,
realistic test cases show the practical interest of the proposed methods.


title:
Physics and modeling of multi-domain FeFET with domain wall induced
  Negative Capacitance

abstract:
  In this paper, we present the dynamics and modeling of multi-domains in the
ferroelectric FET (FeFET). Due to the periodic texture of domains, the
electrostatics of the FeFET exhibit an oscillatory conduction band profile. To
capture such oscillations, we solve coupled 2-D Poisson's equation with the net
ferroelectric energy density (gradient energy + free energy + depolarization
energy) equation. Multi-domain dynamics are captured by minimizing the net
ferroelectric energy leading to a thermodynamically stable state. Furthermore,
we show that the motion of domain walls originates local bound charge density
in the ferroelectric region, which induces the negative capacitance (NC)
effect. The strength of domain wall-induced NC is determined by the gradient
energy of the ferroelectric material. FeFET exhibits variability in the drain
current with domain period due to the inherent NC effect. Additionally, the
impact of domain wall transition (soft$\rightleftharpoons$hard) on the device's
electrostatic/transport is also analyzed. The model also accurately captures
both nucleations of a new domain and the motion of the domain wall.
Furthermore, the model is thoroughly validated against experimental results and
phase-field simulations.


title:
Take the A-Metric: Interpretations of Some Known Solutions of Einstein's
  Vacuum Field Equations

abstract:
  In this work, we present a new interpretation of the only static vacuum
solution of Einstein's field equations with planar symmetry, the Taub solution.
This solution is a member of the $AIII$ class of metrics, along with the type D
Kasner solution. Various interpretations of these solutions have been put
forward previously in the literature, however, some of these interpretations
have suspect features and are not generally considered physical. Using a simple
mathematical analysis, we show that a novel interpretation of the Taub solution
is possible and that it naturally emerges from the radial, near-singularity
limit of negative-mass Schwarzschild spacetime. A new, more transparent
derivation is also given showing that the type D Kasner metric can be
interpreted as a region of spacetime deep within a positive-mass Schwarzschild
black hole. The dual nature of this class of $A$-metrics is thereby
demonstrated.


title:
Charge-state dependent vibrational relaxation in a single-molecule
  junction

abstract:
  The interplay between nuclear and electronic degrees of freedom strongly
influences molecular charge transport. Herein, we report on transport through a
porphyrin dimer molecule, weakly coupled to graphene electrodes, that displays
sequential tunneling within the Coulomb-blockade regime. The sequential
transport is initiated by current-induced phonon absorption and proceeds by
rapid sequential transport via a non-equilibrium vibrational distribution. We
demonstrate this is possible only when the vibrational dissipation is slow
relative to sequential tunneling rates, and obtain a lower bound for the
vibrational relaxation time of 8 ns, a value that is dependent on the molecular
charge state.


title:
Bridging the Urban-Rural Connectivity Gap through Intelligent Space,
  Air, and Ground Networks

abstract:
  Connectivity in rural areas is one of the main challenges of communication
networks. To overcome this challenge, a variety of solutions for different
situations are required. Optimizing the current networking paradigms is
therefore mandatory. The high costs of infrastructure and the low revenue of
cell sites in rural areas compared with urban areas are especially unattractive
for telecommunication operators. Therefore, space, air, and ground networks
should all be optimized for achieving connectivity in rural areas. We highlight
the latest works on rural connectivity, discuss the solutions for terrestrial
networks, and study the potential benefits of nonterrestrial networks.
Furthermore, we present an overview of artificial intelligence (AI) techniques
for improving space, air, and ground networks, hence improving connectivity in
rural areas. AI enables intelligent communications and can integrate space,
air, and ground networks for rural connectivity. We discuss the rural
connectivity challenges and highlight the latest projects and research and the
empowerment of networks using AI. Finally, we discuss the potential positive
impacts of providing connectivity to rural communities.


title:
A deep learning approach for direction of arrival estimation using
  automotive-grade ultrasonic sensors

abstract:
  In this paper, a deep learning approach is presented for direction of arrival
estimation using automotive-grade ultrasonic sensors which are used for driving
assistance systems such as automatic parking. A study and implementation of the
state of the art deterministic direction of arrival estimation algorithms is
used as a benchmark for the performance of the proposed approach. Analysis of
the performance of the proposed algorithms against the existing algorithms is
carried out over simulation data as well as data from a measurement campaign
done using automotive-grade ultrasonic sensors. Both sets of results clearly
show the superiority of the proposed approach under realistic conditions such
as noise from the environment as well as eventual errors in measurements. It is
demonstrated as well how the proposed approach can overcome some of the known
limitations of the existing algorithms such as precision dilution of
triangulation and aliasing.


title:
Machine learning flow control with few sensor feedback and measurement
  noise

abstract:
  A comparative assessment of machine learning (ML) methods for active flow
control is performed. The chosen benchmark problem is the drag reduction of a
two-dimensional K\'arm\'an vortex street past a circular cylinder at a low
Reynolds number ($Re=100$). The flow is manipulated with two blowing/suction
actuators on the upper and lower side of a cylinder. The feedback employs
several velocity sensors. Two probe configurations are evaluated: 5 and 11
velocity probes located at different points around the cylinder and in the
wake. The control laws are optimized with Deep Reinforcement Learning (DRL) and
Linear Genetic Programming Control (LGPC). By interacting with the unsteady
wake, both methods successfully stabilize the vortex alley and effectively
reduce drag while using small mass flow rates for the actuation. DRL has shown
higher robustness with respect to variable initial conditions and to noise
contamination of the sensor data; on the other hand, LGPC is able to identify
compact and interpretable control laws, which only use a subset of sensors,
thus allowing reducing the system complexity with reasonably good results. Our
study points at directions of future machine learning control combining
desirable features of different approaches.


title:
Dark Energy Stars in Tolman-Kuchowicz spacetime in the context of
  Einstein Gravity

abstract:
  Dark energy is the component in the present Universe with the greatest
abundance, and it is responsible for the accelerating expansion of the
Universe. As a result, dark energy is likely to interact with any compact
astrophysical object [Muhammad F.A.R. Sakti and Anto Sulaksono, {\it Phys. Rev.
D} {\bf 103}, 084042 (2021)]. In present paper, we propose a model for a dark
energy star made up of dark and ordinary matter in which the density of dark
energy is proportional to the density of isotropic perfect fluid matter. In the
context of general relativity, the model is derived in the curved
Tolman-Kuchowicz spacetime geometry [Tolman, Phys Rev 55:364, (1939);
Kuchowicz, Acta Phys Pol 33:541, (1968)]. Here, we look at how dark energy
affects stellar mass, compactness, and equilibrium etc. The physical parameters
of the model e.g., pressure, density, mass function, surface redshift etc. are
investigated, and the stability of stellar configuration is studied in detail.
The model has interesting properties because it meets all energy criteria and
is free from central singularities. The maximum allowable mass has been
obtained from our model with the help of $M-R$ diagram. We analyse many
physical properties of the model and checked that it meets all regularity
constraints, is stable, and therefore physically realistic.


title: Improving Amharic Handwritten Word Recognition Using Auxiliary Task

abstract:
  Amharic is one of the official languages of the Federal Democratic Republic
of Ethiopia. It is one of the languages that use an Ethiopic script which is
derived from Gee'z, ancient and currently a liturgical language. Amharic is
also one of the most widely used literature-rich languages of Ethiopia. There
are very limited innovative and customized research works in Amharic optical
character recognition (OCR) in general and Amharic handwritten text recognition
in particular. In this study, Amharic handwritten word recognition will be
investigated. State-of-the-art deep learning techniques including convolutional
neural networks together with recurrent neural networks and connectionist
temporal classification (CTC) loss were used to make the recognition in an
end-to-end fashion. More importantly, an innovative way of complementing the
loss function using the auxiliary task from the row-wise similarities of the
Amharic alphabet was tested to show a significant recognition improvement over
a baseline method. Such findings will promote innovative problem-specific
solutions as well as will open insight to a generalized solution that emerges
from problem-specific domains.


title:
Lorentz invariance violation (LIV) in some basic phenomena in quantum
  physics

abstract:
  Lorentz symmetry is one of the cornerstone of both general relativity and the
standard model of particle physics. We study the violation of Lorentz symmetry
in some basic phenomena in atomic physics. Using the Green's function, and the
source 4-current, the differential equation of 4-vector of electromagnetic
potential is solved and the modified coulomb potential is obtained by some
researchers. Using modified Coulomb potential, we find the corrections due to
LIV on the spectrum of Hydrogen and Helium atoms. We also investigate the
consequences of LIV on Stark, Zeeman and Spin orbit effects and obtain some
upper bounds for the LIV coefficients.


title:
Domain Adaptation: the Key Enabler of Neural Network Equalizers in
  Coherent Optical Systems

abstract:
  We introduce the domain adaptation and randomization approach for calibrating
neural network-based equalizers for real transmissions, using synthetic data.
The approach renders up to 99\% training process reduction, which we
demonstrate in three experimental setups.


title: On Modality Bias Recognition and Reduction

abstract:
  Making each modality in multi-modal data contribute is of vital importance to
learning a versatile multi-modal model. Existing methods, however, are often
dominated by one or few of modalities during model training, resulting in
sub-optimal performance. In this paper, we refer to this problem as modality
bias and attempt to study it in the context of multi-modal classification
systematically and comprehensively. After stepping into several empirical
analysis, we recognize that one modality affects the model prediction more just
because this modality has a spurious correlation with instance labels. In order
to primarily facilitate the evaluation on the modality bias problem, we
construct two datasets respectively for the colored digit recognition and video
action recognition tasks in line with the Out-of-Distribution (OoD) protocol.
Collaborating with the benchmarks in the visual question answering task, we
empirically justify the performance degradation of the existing methods on
these OoD datasets, which serves as evidence to justify the modality bias
learning. In addition, to overcome this problem, we propose a plug-and-play
loss function method, whereby the feature space for each label is adaptively
learned according to the training set statistics. Thereafter, we apply this
method on eight baselines in total to test its effectiveness. From the results
on four datasets regarding the above three tasks, our method yields remarkable
performance improvements compared with the baselines, demonstrating its
superiority on reducing the modality bias problem.


title:
Regions without invariant tori of given class for the planar circular
  restricted three-body problem

abstract:
  A method to establish regions of phase space through which pass no invariant
tori transverse to a given direction field is applied to the planar circular
restricted three-body problem. Implications for the location of stable orbits
for planets around a binary star are deduced. It is expected that lessons
learnt from this problem will be useful for applications of the method to other
contexts such as flux surfaces for magnetic fields, guiding centre motion in
magnetic fields, and classical models of chemical reaction dynamics.


title:
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain
  Exploration using Instance-Conditioned GANs

abstract:
  Reconstructing perceived natural images from fMRI signals is one of the most
engaging topics of neural decoding research. Prior studies had success in
reconstructing either the low-level image features or the semantic/high-level
aspects, but rarely both. In this study, we utilized an Instance-Conditioned
GAN (IC-GAN) model to reconstruct images from fMRI patterns with both accurate
semantic attributes and preserved low-level details. The IC-GAN model takes as
input a 119-dim noise vector and a 2048-dim instance feature vector extracted
from a target image via a self-supervised learning model (SwAV ResNet-50);
these instance features act as a conditioning for IC-GAN image generation,
while the noise vector introduces variability between samples. We trained ridge
regression models to predict instance features, noise vectors, and dense
vectors (the output of the first dense layer of the IC-GAN generator) of
stimuli from corresponding fMRI patterns. Then, we used the IC-GAN generator to
reconstruct novel test images based on these fMRI-predicted variables. The
generated images presented state-of-the-art results in terms of capturing the
semantic attributes of the original test images while remaining relatively
faithful to low-level image details. Finally, we use the learned regression
model and the IC-GAN generator to systematically explore and visualize the
semantic features that maximally drive each of several regions-of-interest in
the human brain.


title:
Online handwriting, signature and touch dynamics: tasks and potential
  applications in the field of security and health

abstract:
  Background: An advantageous property of behavioural signals ,e.g.
handwriting, in contrast to morphological ones, such as iris, fingerprint, hand
geometry, etc., is the possibility to ask a user for a very rich amount of
different tasks. Methods: This article summarises recent findings and
applications of different handwriting and drawing tasks in the field of
security and health. More specifically, it is focused on on-line handwriting
and hand-based interaction, i.e. signals that utilise a digitizing device
(specific devoted or general-purpose tablet/smartphone) during the realization
of the tasks. Such devices permit the acquisition of on-surface dynamics as
well as in-air movements in time, thus providing complex and richer information
when compared to the conventional pen and paper method. Conclusions: Although
the scientific literature reports a wide range of tasks and applications, in
this paper, we summarize only those providing competitive results (e.g. in
terms of discrimination power) and having a significant impact in the field.


title: The effect of fatigue on the performance of online writer recognition

abstract:
  Background: The performance of biometric modalities based on things done by
the subject, like signature and text-based recognition, may be affected by the
subject state. Fatigue is one of the conditions that can significantly affect
the outcome of handwriting tasks. Recent research has already shown that
physical fatigue produces measurable differences in some features extracted
from common writing and drawing tasks. It is important to establish to which
extent physical fatigue contributes to the intra-person variability observed in
these biometric modalities and also to know whether the performance of
recognition methods is affected by fatigue. Goal: In this paper we assess the
impact of fatigue on intra-user variability and on the performance of
signature-based and text-based writer recognition approaches encompassing both
identification and verification. Methods: Several signature and text
recognition methods are considered and applied to samples gathered after
different levels of induced fatigue, measured by metabolic and mechanical
assessment and, also by subjective perception. The recognition methods are
Dynamic Time Warping and Multi Section Vector Quantization, for signatures, and
Allographic Text-Dependent Recognition for text in capital letters. For each
fatigue level, the identification and verification performance of these methods
is measured. Results: Signature shows no statistically significant intra-user
impact, but text does. On the other hand, performance of signature-based
recognition approaches is negatively impacted by fatigue whereas the impact is
not noticeable in text-based recognition, provided long enough sequences are
considered.


title: Measuring Shocks to Central Bank Independence using Legal Rulings

abstract:
  We investigate the consequences of legal rulings on the conduct of monetary
policy. Several unconventional monetary policy measures of the European Central
Bank have come under scrutiny before national courts and the European Court of
Justice. These lawsuits have the potential to severely impact the scope and
flexibility of central bank policies, and central bank independence in a wide
sense, with important consequences for the real and financial economy. Since
the number of relevant legal challenges is small, we develop an econometric
approach that searches for minimum variance regimes which we use to isolate and
measure the effects of these events. Our results suggest that legal rulings
addressing central bank policies have a powerful effect on financial markets.
Expansionary shocks ease financial conditions along various dimensions, and
inflation swap reactions suggest inflationary pressures with stronger effects
in the short term.


title: Direct probing of strong magnon-photon coupling in a planar geometry

abstract:
  We demonstrate direct probing of strong magnon-photon coupling using
Brillouin light scattering spectroscopy in a planar geometry. The magnonic
hybrid system comprises a split-ring resonator loaded with epitaxial yttrium
iron garnet thin films of 200 nm and 2.46 $\mu$m thickness. The Brillouin light
scattering measurements are combined with microwave spectroscopy measurements
where both biasing magnetic field and microwave excitation frequency are
varied. The cooperativity for the 200 nm-thick YIG films is 4.5, and larger
cooperativity of 137.4 is found for the 2.46 $\mu$m-thick YIG film. We show
that Brillouin light scattering is advantageous for probing the magnonic
character of magnon-photon polaritons, while microwave absorption is more
sensitive to the photonic character of the hybrid excitation. A miniaturized,
planar device design is imperative for the potential integration of magnonic
hybrid systems in future coherent information technologies, and our results are
a first stepping stone in this regard. Furthermore, successfully detecting the
magnonic hybrid excitation by Brillouin light scattering is an essential step
for the up-conversion of quantum signals from the optical to the microwave
regime in hybrid quantum systems.


title: On Bibasic Humbert hypergeometric function $\Phi_1$

abstract:
  The main aim of this work is to derive the $q$-recurrence relations,
$q$-partial derivative relations and summation formula of bibasic Humbert
hypergeometric function $\Phi_1$ on two independent bases $q$ and $q_{1}$ of
two variables and some developments formulae, believed to be new, by using the
conception of $q$-calculus.


title: Aspects of Nonrelativistic Strings

abstract:
  We review recent developments on nonrelativistic string theory. In flat
spacetime, the theory is defined by a two-dimensional relativistic quantum
field theory with nonrelativistic global symmetries acting on the worldsheet
fields. This theory arises as a self-contained corner of relativistic string
theory. It has a string spectrum with a Galilean dispersion relation, and a
spacetime S-matrix with nonrelativistic symmetry. This string theory also gives
a unitary and ultraviolet complete framework that connects different corners of
string theory, including matrix string theory and noncommutative open strings.
In recent years, there has been a resurgence of interest in the non-Lorentzian
geometries and quantum field theories that arise from nonrelativistic string
theory in background fields. In this review, we start with an introduction to
the foundations of nonrelativistic string theory in flat spacetime. We then
give an overview of recent progress, including the appropriate target-space
geometry that nonrelativistic strings couple to. This is known as (torsional)
string Newton-Cartan geometry, which is neither Lorentzian nor Riemannian. We
also give a review of nonrelativistic open strings and effective field theories
living on D-branes. Finally, we discuss applications of nonrelativistic strings
to decoupling limits in the context of the AdS/CFT correspondence.


title:
Turnpike Properties for Stochastic Linear-Quadratic Optimal Control
  Problems

abstract:
  This paper analyzes the limiting behavior of stochastic linear-quadratic
optimal control problems in finite time horizon $[0,T]$ as
$T\rightarrow\infty$. The so-called turnpike properties are established for
such problems, under stabilizability condition which is weaker than the
controllability, normally imposed in the similar problem for ordinary
differential systems. In dealing with the turnpike problem, a crucial issue is
to determine the corresponding static optimization problem. Intuitively
mimicking deterministic situations, it seems to be natural to include both the
drift and the diffusion as constraints in the static optimization problem.
However, this would lead us to a wrong direction. It is found that the correct
static problem should contain the diffusion as a part of the objective
function, which reveals a deep feature of the stochastic turnpike problem.


title:
The Theory, Practice, and Ethical Challenges of Designing a
  Diversity-Aware Platform for Social Relations

abstract:
  Diversity-aware platform design is a paradigm that responds to the ethical
challenges of existing social media platforms. Available platforms have been
criticized for minimizing users' autonomy, marginalizing minorities, and
exploiting users' data for profit maximization. This paper presents a design
solution that centers the well-being of users. It presents the theory and
practice of designing a diversity-aware platform for social relations. In this
approach, the diversity of users is leveraged in a way that allows like-minded
individuals to pursue similar interests or diverse individuals to complement
each other in a complex activity. The end users of the envisioned platform are
students, who participate in the design process. Diversity-aware platform
design involves numerous steps, of which two are highlighted in this paper: 1)
defining a framework and operationalizing the "diversity" of students, 2)
collecting "diversity" data to build diversity-aware algorithms. The paper
further reflects on the ethical challenges encountered during the design of a
diversity-aware platform.


title:
Electron-positron pairs and radioactive nuclei production by irradiation
  of high-Z target with \gamma-photon flash generated by an ultra-intense laser
  in the $\lambda^3$ regime

abstract:
  This paper studies the interaction of laser-driven $\gamma$-photons and high
energy charged particles with high-Z targets through Monte-Carlo simulations.
The interacting particles are taken from particle-in-cell simulations of the
interaction of a tightly-focused ultraintense laser pulse with a titanium
target. Lead is chosen as the secondary high-Z target owing to its high cross
section of the giant dipole resonance and electron-positron pair production.
The results reveal an ultra-short ultra-relativistic collimated positron
population and their energy spectra, angular distribution, and temporal profile
are found. We investigate the target thickness dependence of the resulting
total numbers and total kinetic energies of various particle species emitted
from the lead target irradiated with laser-generated $\gamma$-photons and
charged particles separately. We plot the charts of residual high-Z nuclides
generated by irradiation of the lead target. Owing to the short pulse duration,
the $\gamma$-photon, electron-positron, and neutron sources can find
applications in material science, nuclear physics, laboratory astrophysics, and
as injectors in laser-based accelerators of charged particles.


title:
Ab initio Circular Dichroism with the Yambo code: beyond the Independent
  Particle approximation

abstract:
  Circular dichroism (CD) spectroscopy is a useful technique for characterizing
chiral molecules. It is more sensitive than total absorption to molecule
conformation, and it is routinely used to identify enantiomers. We present here
absorption and CD spectra within the Time Dependent (TD) B3LYP approximation in
c-GlyPhe, a cyclo-dipeptide containing an aromatic group. Results from codes in
localized basis-set (Orca and MolGW) are carefully compared with the novel
TD-B3LYP implementation we developed in the Yambo code, that uses a plane-wave
basis set.


title:
Novel techniques for improvement the NNetEn entropy calculation for
  short and noisy time series

abstract:
  Entropy is a fundamental concept of information theory. It is widely used in
the analysis of analog and digital signals. Conventional entropy measures have
drawbacks, such as sensitivity to the length and amplitude of time series and
low robustness to external noise. Recently, the NNetEn entropy measure has been
introduced to overcome these problems. The NNetEn entropy uses a modified
version of the LogNNet neural network classification model. The algorithm
contains a reservoir matrix with N = 19625 elements, which the given time
series should fill. Many practical time series have less than 19625 elements.
Against this background, this paper investigates different duplicating and
stretching techniques for filling to overcome this difficulty. The most
successful technique is identified for practical applications. The presence of
external noise and bias are other important issues affecting the efficiency of
entropy measures. In order to perform meaningful analysis, three time series
with different dynamics (chaotic, periodic, and binary), with a variation of
signal-to-noise ratio (SNR) and offsets, are considered. It is shown that the
error in the calculation of the NNetEn entropy does not exceed 10% when the SNR
exceeds 30 dB. This opens the possibility of measuring the NNetEn of
experimental signals in the presence of noise of various nature, white noise,
or 1/f noise, without the need for noise filtering.


title: Modeling moving boundary value problems in electrochemical machining

abstract:
  This work presents a new approach to efficiently model the cathode in the
moving boundary value problem of electrochemical machining. Until recently, the
process simulation with finite elements had the drawback of remeshing required
by the changing surface geometries. This disadvantage was overcome by a novel
model formulation for the anodic dissolution that utilizes effective material
parameters as well as the dissolution level as an internal variable and,
thereby, does not require remeshing. Now, we extend this concept to model
arbitrarily shaped and moving cathodes. Two methodologies are investigated to
describe the time varying position of the cathode. In the first approach, we
change the electric conductivity of elements within the cathode and, in a
second approach, we apply Dirichlet boundary conditions on the nodes of
corresponding elements. For both methods, elements on the cathode's surface are
treated with effective material parameters. This procedure allows for the
efficient simulation of industrially relevant, complex geometries without mesh
adaptation. The model's performance is validated by means of analytical,
numerical and experimental results from the literature. The short computation
times make the approach interesting for industrial applications.


title: State-of-the-art in speaker recognition

abstract:
  Recent advances in speech technologies have produced new tools that can be
used to improve the performance and flexibility of speaker recognition While
there are few degrees of freedom or alternative methods when using fingerprint
or iris identification techniques, speech offers much more flexibility and
different levels for performing recognition: the system can force the user to
speak in a particular manner, different for each attempt to enter. Also with
voice input the system has other degrees of freedom, such as the use of
knowledge/codes that only the user knows, or dialectical/semantical traits that
are difficult to forge. This paper offers and overview of the state of the art
in speaker recognition, with special emphasis on the pros and contras, and the
current research lines. The current research lines include improved
classification systems, and the use of high level information by means of
probabilistic grammars. In conclusion, speaker recognition is far away from
being a technology where all the possibilities have already been explored.


title: On sufficient conditions for planar graphs to be 5-flexible

abstract:
  In this paper, we study the flexibility of two planar graph classes
$\mathcal{H}_1$, $\mathcal{H}_2$, where $\mathcal{H}_1$, $\mathcal{H}_2$ denote
the set of all hopper-free planar graphs and house-free planar graphs,
respectively. Let $G$ be a planar graph with a list assignment $L$. Suppose a
preferred color is given for some of the vertices. We prove that if $G\in
\mathcal{H}_1$ or $G\in \mathcal{H}_2$ such that all lists have size at least
$5$, then there exists an $L$-coloring respecting at least a constant fraction
of the preferences.


title: Benchmarking Generative Latent Variable Models for Speech

abstract:
  Stochastic latent variable models (LVMs) achieve state-of-the-art performance
on natural image generation but are still inferior to deterministic models on
speech. In this paper, we develop a speech benchmark of popular temporal LVMs
and compare them against state-of-the-art deterministic models. We report the
likelihood, which is a much used metric in the image domain, but rarely, or
incomparably, reported for speech models. To assess the quality of the learned
representations, we also compare their usefulness for phoneme recognition.
Finally, we adapt the Clockwork VAE, a state-of-the-art temporal LVM for video
generation, to the speech domain. Despite being autoregressive only in latent
space, we find that the Clockwork VAE can outperform previous LVMs and reduce
the gap to deterministic models by using a hierarchy of latent variables.


title:
Three-body relative equilibria on $S^2$ II: Extended Lagrangian
  configurations

abstract:
  This is a natural continuation of our first paper \cite{pre}, where we
develop a new geometrical technique which allow us to study relative equilibria
on the two sphere. We consider a system of three positive masses on
$\mathbb{S}^2$ moving under the influence of an generic attractive potential
which only depends on the mutual distances among the masses. We reduce the
problem of finding extended Lagrangian relative equilibria to the analysis of
the inertia tensor, then we obtain a more manageable equivalent inertia tensor
which allow us to find new families of Lagrangian configurations.


title:
Activate index: an integrated index to reveal disrupted brain network
  organizations of major depressive disorder patients

abstract:
  Altered functional brain networks have been a typical manifestation that
distinguishes major depressive disorder (MDD) patients from healthy control
(HC) subjects in functional magnetic resonance imaging (fMRI) studies.
Recently, rich club and diverse club metrics have been proposed for network or
network neuroscience analyses. The rich club defines a set of nodes that tend
to be the hubs of specific communities, and the diverse club defines the nodes
that span more communities and have edges diversely distributed across
different communities. Considering the heterogeneity of rich clubs and diverse
clubs, combining them and on the basis to derive a novel indicator may reveal
new evidence of brain functional integration and separation, which might
provide new insights into MDD. This study for the first time discussed the
differences between MDD and HC using both rich club and diverse club metrics
and found the complementarity of them in analyzing brain networks. Besides, a
novel index, termed "active index", has been proposed in this study. The active
index defines a group of nodes that tend to be diversely distributed across
communities while avoiding being a hub of a community. Experimental results
demonstrate the superiority of active index in analyzing MDD brain mechanisms.


title: Brain Principles Programming

abstract:
  In the monograph, STRONG ARTIFICIAL INTELLIGENCE. On the Approaches to
Superintelligence, published by Sberbank, provides a cross-disciplinary review
of general artificial intelligence. As an anthropomorphic direction of
research, it considers Brain Principles Programming, BPP) the formalization of
universal mechanisms (principles) of the brain's work with information, which
are implemented at all levels of the organization of nervous tissue. This
monograph provides a formalization of these principles in terms of the category
theory. However, this formalization is not enough to develop algorithms for
working with information. In this paper, for the description and modeling of
Brain Principles Programming, it is proposed to apply mathematical models and
algorithms developed by us earlier that model cognitive functions, which are
based on well-known physiological, psychological and other natural science
theories. The paper uses mathematical models and algorithms of the following
theories: P.K.Anokhin's Theory of Functional Brain Systems, Eleonor Rosh's
prototypical categorization theory, Bob Rehter's theory of causal models and
natural classification. As a result, the formalization of the BPP is obtained
and computer examples are given that demonstrate the algorithm's operation.


title:
Application and reduction of a nonlinear hyperelastic wall model
  capturing ex vivo relationships between fluid pressure, area and wall
  thickness in normal and hypertensive murine left pulmonary arteries

abstract:
  Pulmonary hypertension is a cardiovascular disorder manifested by elevated
arterial blood pressure together with vessel wall stiffening and thickening due
to alterations in collagen, elastin and smooth muscle cells. Hypoxia-induced
(type 3) pulmonary hypertension can be studied in animals exposed to a low
oxygen environment for prolonged time periods leading to biomechanical
alterations in vessel wall structure. This study formulates and systematically
reduces a nonlinear elastic structural wall model for a large pulmonary artery,
generating a novel pressure-area relation capturing remodeling in type 3
pulmonary hypertension. The model is calibrated using {\em ex vivo}
measurements of vessel diameter and wall thickness changes, under controlled
flow conditions, in left pulmonary arteries isolated from control and
hypertensive mice. A two-layer, hyperelastic, anisotropic model incorporating
residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex
relations predicting vessel area and wall thickness with increasing blood
pressure are derived and calibrated using the data. Sensitivity analysis,
parameter estimation and subset selection are used to systematically reduce the
16-parameter model to one in which a much smaller subset of identifiable
parameters is estimated via solution of an inverse problem. Our final reduced
model includes a single set of three elastic moduli. Estimated ranges of these
parameters demonstrate that nonlinear stiffening is dominated by elastin in the
control animals and by collagen in the hypertensive group. The novel
pressure-area relation developed in this study has potential impact on
one-dimensional fluids network models of vessel wall remodeling in the presence
of cardiovascular disease.


title: Radiative pulsar magnetospheres: oblique rotators

abstract:
  Pulsar magnetospheres are filled with relativistic pairs copiously emitting
photons detected from the radio wavelengths up to high and very high energies,
in the GeV and sometimes in the TeV range. Efficient particle acceleration
converts the stellar rotational kinetic energy into radio, X-ray and gamma-ray
photons. Force-free magnetospheres, being dissipationless, cannot operate this
conversion. Some non ideal plasma effects must set in within the magnetosphere.
In this paper, we compute numerical solutions of pulsar radiative
magnetospheres in the radiation reaction limit, where radiation fully balances
single particle acceleration. Using an appropriate Ohm's law, the dissipation
is only controlled by the pair multiplicity factor~$\kappa$. Moreover we allow
for either a minimal radiative region where dissipation is added only where
required or for a force-free inside radiative outside model. This approach
naturally and self-consistently connects the particle dynamics to its radiation
field in the ultra-relativistic regime. Our solutions tend to the force-free
limit for moderately large multiplicities, $\kappa \gg 1$, decreasing the
spin-down energy conversion into radiation. Nevertheless, for sufficiently low
multiplicity $\kappa \lesssim1$, a significant fraction of the spin-down energy
flows into radiation via particle acceleration. The work done by the
electromagnetic field on the plasma mainly occurs in the current sheet of the
striped wind, right outside the light-cylinder. Nevertheless the impact on the
magnetic topology is negligible whatever the model. Therefore the associated
sky maps and light-curves are only weakly impacted as shown.


title:
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window
  Training Strategy for Temporal Link Prediction

abstract:
  With the development of temporal networks such as E-commerce networks and
social networks, the issue of temporal link prediction has attracted increasing
attention in recent years. The Temporal Link Prediction task of WSDM Cup 2022
expects a single model that can work well on two kinds of temporal graphs
simultaneously, which have quite different characteristics and data properties,
to predict whether a link of a given type will occur between two given nodes
within a given time span. Our team, named as nothing here, regards this task as
a link prediction task in heterogeneous temporal networks and proposes a
generic model, i.e., Heterogeneous Temporal Graph Network (HTGN), to solve such
temporal link prediction task with the unfixed time intervals and the diverse
link types. That is, HTGN can adapt to the heterogeneity of links and the
prediction with unfixed time intervals within an arbitrary given time period.
To train the model, we design a Bi-Time-Window training strategy (BTW) which
has two kinds of mini-batches from two kinds of time windows. As a result, for
the final test, we achieved an AUC of 0.662482 on dataset A, an AUC of 0.906923
on dataset B, and won 2nd place with an Average T-scores of 0.628942.


title:
Characterizing elastic turbulence in the three-dimensional von Karman
  swirling flow using the Oldroyd-B model

abstract:
  We present the full three-dimensional numerical investigation of the von
Karman swirling flow between two parallel plates using the Oldroyd-B model and
characterize the onset and development of elastic turbulence. We quantify the
flow state with the secondary-flow strength, a measure of the average strength
of the velocity fluctuations, and then define an order parameter as the time
average of the secondary-flow strength. The order parameter displays a
subcritical transition from the laminar to a bistable flow that switches
between weakly chaotic flow and elastic turbulence. The transition to the
bistable flow occurs at the critical Weissenberg number $\mathrm{Wi_c}=12$.
Above $\mathrm{Wi_c}$, in the elastic turbulent state, we observe a strong
increase in velocity fluctuations and flow resistance, which we define as the
total work performed on the fluid. Upon starting simulations in the turbulent
state and subsequently lowering $\mathrm{Wi}$ below its critical value, we
observe hysteretic behavior in the order parameter and the flow resistance,
which is a common feature of a subcritical transition. Hysteresis has also been
found in experiments. Additionally, we find power-law scaling in the spatial
and temporal power spectra of the velocity fluctuations, characteristic for
elastic turbulence. The maximum values of the power-law exponents in our
simulations are $\alpha_t= 3.41$ for the temporal exponent and $\alpha_s= 3.17$
for the spatial exponent, which are remarkably close to the values obtained in
experiments.


title:
Early Disease Stage Characterization in Parkinson's Disease from
  Resting-state fMRI Data Using a Long Short-term Memory Network

abstract:
  Parkinson's disease (PD) is a common and complex neurodegenerative disorder
with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it
is challenging to classify early stages 1 and 2 and detect brain function
alterations. Functional magnetic resonance imaging (fMRI) is a promising tool
in revealing functional connectivity (FC) differences and developing biomarkers
in PD. Some machine learning approaches like support vector machine and
logistic regression have been successfully applied in the early diagnosis of PD
using fMRI data, which outperform classifiers based on manually selected
morphological features. However, the early-stage characterization in FC changes
has not been fully investigated. Given the complexity and non-linearity of fMRI
data, we propose the use of a long short-term memory (LSTM) network to
characterize the early stages of PD. The study included 84 subjects (56 in
stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative
(PPMI), the largest available public PD dataset. Under a repeated 10-fold
stratified cross-validation, the LSTM model reached an accuracy of 71.63%,
13.52% higher than the best traditional machine learning method, indicating
significantly better robustness and accuracy compared with other machine
learning classifiers. We used the learned LSTM model weights to select the top
brain regions that contributed to model prediction and performed FC analyses to
characterize functional changes with disease stage and motor impairment to gain
better insight into the brain mechanisms of PD.


title:
Reality: Physics, the Physical Universe Language and Mathematics,
  Physics Formalisms in Human Brain Machine Language

abstract:
  The nature of the existence, revealed through Human cognitive system, has
been evolving since the development of the languages. Part of such revelations
were the geometrical forms and the numbers, whose beauty and order, wondrous
and mysterious, conveyed a sense of unreality beyond the physical reality
normally discerned; to Plato they were indications of the other reality of
which only those few glimpses had occurred. And full access for the earthlings
required breaking out of their shackles of captivity; beautifully portrayed in
the Masters cave allegory. The Present era sense of wonder of the effectiveness
of the mathematics in the formulations of the discoveries of the fundamental
and natural laws of existence, and the advances in computation, and especially
in artificial intelligence (AI), have revived the platonic idea with some
adherents among the very minds who contribute to furthering of our
understanding of our physical universe. The work presented here is an attempt
in proving that a physical world precludes the prospects for the mathematics to
be its other reality. This end is achieved by bringing to light the processes
involved in the discernment of the world by humans. The fundamentals involved
make it clear that the events of the physical universe, are played out in our
physical brains operational language of physics; and the symbolic language of
mathematics is another machine language of our computational brains for the
specific expressions of the underlying physics of some of the phenomena it
processes. This rapidly developing language, presently much in appearances
strange to the physical reality, and taken by many great minds; Plato, Galileo,
Kant, Winger and the likes, as indication of an incontrovertible and
mysteriousness mathematical world, is in fact inadvertently, blazing the trail
for discoveries of more novelties of the physics of the physical universe.


title: Towards quantum contact friction

abstract:
  A thermal model of kinetic friction is assigned to a classical loaded
particle moving on a fluctuating smooth surface. A sinusoidal wave approximates
surface fluctuations with a relaxation time. The Hamiltonian is approximated to
the mean energy of the wave describing a system of Harmonic oscillators. The
quantization of amplitudes yields in terms of annihilation and creation
operators multiplied by a quantum phase. Further, we consider acoustic
dispersion relation and evaluate the friction coefficient from the force
autocorrelation function. The model results in the temperature dependence of
the friction coefficient. It follows an asymptotic value for higher
temperatures and supperslipperiness at low temperatures. Moreover, a
correspondence between quantum and classical views gives rise to a relation
between the maximum amplitudes of the surface fluctuations and temperature.


title: A general improvement in the WENO-Z-type schemes

abstract:
  A new type of finite volume WENO schemes for hyperbolic problems was devised
in [36] by introducing the order-preserving (OP) criterion. In this continuing
work, we extend the OP criterion to the WENO-Z-type schemes. We firstly rewrite
the formulas of the Z-type weights in a uniform form from a mapping perspective
inspired by extensive numerical observations. Accrodingly, we build the concept
of the locally order-preserving (LOP) mapping which is an extension of the
order-preserving (OP) mapping and the resultant improved WENO-Z-type schemes
are denoted as LOP-GMWENO-X. There are four major advantages of the
LOP-GMWENO-X schemes superior to the existing WENO-Z-type schemes. Firstly, the
new schemes can amend the serious drawback of the existing WENO-Z-type schemes
that most of them suffer from either producing severe spurious oscillations or
failing to obtain high resolutions in long calculations of hyperbolic problems
with discontinuities. Secondly, they can maintain considerably high resolutions
on solving problems with high-order critical points at long output times.
Thirdly, they can obtain evidently higher resolution in the region with
high-frequency but smooth waves. Finally, they can significantly decrease the
post-shock oscillations for simulations of some 2D problems with strong shock
waves. Extensive benchmark examples are conducted to illustrate these
advantages.


title: Ask2Mask: Guided Data Selection for Masked Speech Modeling

abstract:
  Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn
representations over speech frames which are randomly masked within an
utterance. While these methods improve performance of Automatic Speech
Recognition (ASR) systems, they have one major limitation. They treat all
unsupervised speech samples with equal weight, which hinders learning as not
all samples have relevant information to learn meaningful representations. In
this work, we address this limitation. We propose ask2mask (ATM), a novel
approach to focus on specific samples during MSM pre-training. ATM employs an
external ASR model or \textit{scorer} to weight unsupervised input samples in
two different ways: 1) A fine-grained data selection is performed by masking
over the highly confident input frames as chosen by the scorer. This allows the
model to learn meaningful representations. 2) ATM is further extended to focus
at utterance-level by weighting the final MSM loss with the utterance-level
confidence score. We conduct fine-tuning experiments on two well-benchmarked
corpora: LibriSpeech (matching the pre-training data) and Commonvoice,
TED-LIUM, AMI and CHiME-6 (not matching the pre-training data). The results
substantiate the efficacy of ATM on significantly improving the recognition
performance under mismatched conditions (up to 11.6\% relative over published
results and upto 4.46\% relative over our internal baseline) while still
yielding modest improvements under matched conditions.


title:
Statistics and Deep Learning-based Hybrid Model for Interpretable
  Anomaly Detection

abstract:
  Hybrid methods have been shown to outperform pure statistical and pure deep
learning methods at both forecasting tasks, and at quantifying the uncertainty
associated with those forecasts (prediction intervals). One example is
Multivariate Exponential Smoothing Long Short-Term Memory (MES-LSTM), a hybrid
between a multivariate statistical forecasting model and a Recurrent Neural
Network variant, Long Short-Term Memory. It has also been shown that a model
that ($i$) produces accurate forecasts and ($ii$) is able to quantify the
associated predictive uncertainty satisfactorily, can be successfully adapted
to a model suitable for anomaly detection tasks. With the increasing ubiquity
of multivariate data and new application domains, there have been numerous
anomaly detection methods proposed in recent years. The proposed methods have
largely focused on deep learning techniques, which are prone to suffer from
challenges such as ($i$) large sets of parameters that may be computationally
intensive to tune, $(ii)$ returning too many false positives rendering the
techniques impractical for use, $(iii)$ requiring labeled datasets for training
which are often not prevalent in real life, and ($iv$) understanding of the
root causes of anomaly occurrences inhibited by the predominantly black-box
nature of deep learning methods. In this article, an extension of MES-LSTM is
presented, an interpretable anomaly detection model that overcomes these
challenges. With a focus on renewable energy generation as an application
domain, the proposed approach is benchmarked against the state-of-the-art. The
findings are that MES-LSTM anomaly detector is at least competitive to the
benchmarks at anomaly detection tasks, and less prone to learning from spurious
effects than the benchmarks, thus making it more reliable at root cause
discovery and explanation.


title: The impact of accumulative pension policy on welfare of individuals

abstract:
  Many countries around the world have had to carry out radical reforms
periodically in their pension systems. Global experience shows that it is
important to optimize the costs of pension and social security systems in order
to ensure a decent old age in addition to reducing the pressure on budgetary
resources. By Georgia is changing demographic situation, special attention is
paid to proper functioning of the pension policy. The pension reform carried
out in Georgia in 2019 caused a difference of opinion among experts. This issue
in today is conditions does not lose relevance. The presented thesis discusses
the impact of the mandatory funded pension system on the well-being of people.
Thesis includes the following issues: peculiarities of the formation of pension
systems in Georgia. It is presented a small historical excursion in terms of
the development of pension systems. In addition, are discussed the
international experience of pension systems and comparative analysis in
relation to Georgia. This paper specifically focuses on the essence of the
mandatory funded pension system and assesses the current situation in terms of
investment potential of the resource accumulated in the pension fund. In
conclusion, are presented the challenges of this system and the ways of
perfection.


title:
Integrating Immersive Technologies for Algorithmic Design in
  Architecture

abstract:
  Architectural design practice has radically evolved over the course of its
history, due to technological improvements that gave rise to advanced automated
tools for many design tasks. Traditional paper drawings and scale models are
now accompanied by 2D and 3D Computer-Aided Architectural Design (CAAD)
software.
  While such tools improved in many ways, including performance and accuracy
improvements, the modalities of user interaction have mostly remained the same,
with 2D interfaces displayed on 2D screens. The maturation of Augmented Reality
(AR) and Virtual Reality (VR) technology has led to some level of integration
of these immersive technologies into architectural practice, but mostly limited
to visualisation purposes, e.g. to show a finished project to a potential
client.
  We posit that there is potential to employ such technologies earlier in the
architectural design process and therefore explore that possibility with a
focus on Algorithmic Design (AD), a CAAD paradigm that relies on (often visual)
algorithms to generate geometries. The main goal of this dissertation is to
demonstrate that AR and VR can be adopted for AD activities.
  To verify that claim, we follow an iterative prototype-based methodology to
develop research prototype software tools and evaluate them. The three
developed prototypes provide evidence that integrating immersive technologies
into the AD toolset provides opportunities for architects to improve their
workflow and to better present their creations to clients. Based on our
contributions and the feedback we gathered from architectural students and
other researchers that evaluated the developed prototypes, we additionally
provide insights as to future perspectives in the field.


title:
A low accretion efficiency of planetesimals formed at planetary gap
  edges

abstract:
  Observations and models of giant planets indicate that such objects are
enriched in heavy elements compared to solar abundances. The prevailing view is
that giant planets accreted multiple Earth masses of heavy elements after the
end of core formation. Such late solid enrichment is commonly explained by the
accretion of planetesimals. Planetesimals are expected to form at the edges of
planetary gaps, and here we address the question of whether these planetesimals
can be accreted in large enough amounts to explain the inferred high heavy
element contents of giant planets. We perform a series of N-body simulations of
the dynamics of planetesimals and planets during the planetary growth phase,
taking into account gas drag as well as the enhanced collision cross-section
caused by the extended envelopes. We consider the growth of Jupiter and Saturn
via gas accretion after reaching the pebble isolation mass and we include their
migration in an evolving disk. We find that the accretion efficiency of
planetesimals formed at planetary gap edges is very low: less than 10% of the
formed planetesimals are accreted even in the most favorable cases, which in
our model corresponds to a few Earth-masses. When planetesimals are assumed to
form beyond the feeding zone of the planets, extending to a few Hill radii from
a planet, accretion becomes negligible. Furthermore, we find that the accretion
efficiency increases when the planetary migration distance is increased and
that the efficiency does not increase when the planetesimal radii are
decreased. Based on these results we conclude that it is difficult to explain
the large heavy element content of giant planets with planetesimal accretion
during the gas accretion phase. Alternative processes most likely are required,
e.g. accretion of vapor deposited by drifting pebbles.


title: Counting flags of primitive lattices

abstract:
  We count flags of primitive lattices, which are objects of the form
${0}=\Lambda^{(0)}<\Lambda^{(1)}< \cdots <\Lambda^{(\ell)}= \mathbb{Z}^n$,
where every $\Lambda^{(i)}$ is a primitive lattice in $\mathbb{Z}^n$. The
counting is with respect to two different natural height functions, allowing us
to give a new proof of the Manin conjecture for flag varieties over rational
numbers. We deduce the equidistribution of rational points in flag varieties,
as well as the equidistribution of the shapes of the successive quotient
lattices, $\Lambda^{(i)}/\Lambda^{(i-1)}$. In doing so, we generalize previous
work of Schmidt, as well as our own, on counting primitive lattices of rank
$d<n$.


title:
An Improvement on the Hotelling $T^2$ Test Using the Ledoit-Wolf
  Nonlinear Shrinkage Estimator

abstract:
  Hotelling's $T^2$ test is a classical approach for discriminating the means
of two multivariate normal samples that share a population covariance matrix.
Hotelling's test is not ideal for high-dimensional samples because the
eigenvalues of the estimated sample covariance matrix are inconsistent
estimators for their population counterparts. We replace the sample covariance
matrix with the nonlinear shrinkage estimator of Ledoit and Wolf 2020. We
observe empirically for sub-Gaussian data that the resulting algorithm
dominates past methods (Bai and Saranadasa 1996, Chen and Qin 2010, and Li et
al. 2020) for a family of population covariance matrices that includes matrices
with high or low condition number and many or few nontrivial -- i.e., spiked --
eigenvalues.


title:
Self-assembly of dodecagonal and octagonal quasicrystals in hard spheres
  on a plane

abstract:
  Quasicrystals are fascinating structures, characterized by strong positional
order but lacking the periodicity of a crystal. In colloidal systems,
quasicrystals are typically predicted for particles with complex or highly
specific interactions, which makes experimental realization difficult. Here, we
propose an ideal colloidal model system for quasicrystal formation: binary
mixtures of hard spheres sedimented onto a flat substrate. Using computer
simulations, we explore both the close-packing and spontaneous self-assembly of
these systems over a wide range of size ratios and compositions. Surprisingly,
we find that this simple, effectively two-dimensional model systems forms not
only a variety of crystal phases, but also two quasicrystal phases: one
dodecagonal and one octagonal. Intriguingly, the octagonal quasicrystal
consists of three different tiles, whose relative concentrations can be
continuously tuned via the composition of the binary mixture. Both phases form
reliably and rapidly over a significant part of parameter space, making hard
spheres on a plane an ideal model system for exploring quasicrystal
self-assembly on the colloidal scale.


title:
Sensorless wavefront correction in two-photon microscopy across
  different turbidity scales

abstract:
  Adaptive optics (AO) is a powerful tool to increase the imaging depth of
multiphoton scanning microscopes. For highly scattering tissues, sensorless
wavefront correction techniques exhibit robust performance and present a
straight-forward implementation of AO. However, for many applications such as
live-tissue imaging, the speed of aberration correction remains a critical
bottleneck. Dynamic Adaptive Scattering compensation Holography (DASH) -- a
fast-converging sensorless AO technique introduced recently for scatter
compensation in nonlinear scanning microscopy -- addresses this issue. DASH has
been targeted at highly turbid media, but to-date it has remained an open
question how it performs for mild turbidity, where limitations imposed by
phase-only wavefront shaping are expected to impede its convergence. In this
work, we study the performance of DASH across different turbidity regimes, in
simulation as well as experiments. We further provide a direct comparison
between DASH and a novel, modified version of the Continuous Sequential
Algorithm (CSA) which we call Amplified CSA (a-CSA).


title:
Weak convergence of the iterations for asymptotically $G$-nonexpansive
  maps on Banach spaces with a graph

abstract:
  We have derived that on certain Banach spaces having a graph structure $G$,
the iterations for asymptotically $G$-nonexpansive map will converge weakly
towards a fixed point. This result unifies and extends several theorems on
fixed points proved by various authors for class of nonexpansive and
asymptotically nonexpansive maps. As an application of this result, we derive
that for maps satisfying the nonexpansive condition locally on special Banach
spaces, the successive approximations converge weakly towards a fixed point.


title:
The Uncertainty Aware Salted Kalman Filter: State Estimation for Hybrid
  Systems with Uncertain Guards

abstract:
  In this paper we present a method for updating robotic state belief through
contact with uncertain surfaces and apply this update to a Kalman filter for
more accurate state estimation. Examining how guard surface uncertainty affects
the time spent in each mode, we derive a guard saltation matrix - which maps
perturbations prior to hybrid events to perturbations after - accounting for
additional variation in the resulting state. Additionally, we propose the use
of parameterized reset functions - capturing how unknown parameters change how
states are mapped from one mode to the next - the Jacobian of which accounts
for the additional uncertainty in the resulting state. The accuracy of these
mappings is shown by simulating sampled distributions through uncertain
transition events and comparing the resulting covariances. Finally, we
integrate these additional terms into the "uncertainty aware Salted Kalman
Filter", uaSKF, and show a peak reduction in average estimation error by 24-60%
on a variety of test conditions and systems.


title: Adaptive Third-Order Methods for Composite Convex Optimization

abstract:
  In this paper we propose third-order methods for composite convex
optimization problems in which the smooth part is a three-times continuously
differentiable function with Lipschitz continuous third-order derivatives. The
methods are adaptive in the sense that they do not require the knowledge of the
Lipschitz constant. Trial points are computed by the inexact minimization of
models that consist in the nonsmooth part of the objective plus a quartic
regularization of third-order Taylor polynomial of the smooth part.
Specifically, approximate solutions of the auxiliary problems are obtained by
using a Bregman gradient method as inner solver. Different from existing
adaptive approaches for high-order methods, in our new schemes the
regularization parameters are adjusted taking into account the progress of the
inner solver. With this technique, we show that the basic method finds an
$\epsilon$-approximate minimizer of the objective function performing at most
$\mathcal{O}\left(|\log(\epsilon)|\epsilon^{-\frac{1}{3}}\right)$ iterations of
the inner solver. An accelerated adaptive third-order method is also presented
with total inner iteration complexity of
$\mathcal{O}\left(|\log(\epsilon)|\epsilon^{-\frac{1}{4}}\right)$.