title: Actuator Scheduling for Linear Systems: A Convex Relaxation Approach

abstract:
  In this letter, we investigate the problem of actuator scheduling for
networked control systems. Given a stochastic linear system with a number of
actuators, we consider the case that one actuator is activated at each time.
This problem is combinatorial in nature and NP hard to solve. We propose a
convex relaxation to the actuator scheduling problem, and use its solution as a
reference to design an algorithm for solving the original scheduling problem.
Using dynamic programming arguments, we provide a suboptimality bound of our
proposed algorithm. Furthermore, we show that our framework can be extended to
incorporate multiple actuators scheduling at each time and actuation costs. A
simulation example is provided, which shows that our proposed method
outperforms a random selection approach and a greedy selection approach.


title:
On the processing of anatase La-doped TiO2 nanopowders: structural phase
  transition and surface defect engineering

abstract:
  The efforts in the optimization of the physical and chemical properties
related to the photocatalytic efficiency of TiO2 nanoparticles relies mainly on
defect engineering strategies, been the structural and the doping the most
common ones. The doping of the anatase TiO2 structure with lanthanides elements
potentially can turns the TiO2 optical absorption into the visible range
increasing drastically its photocatalytic efficiency. Here we show that the
processing of TiO2 nanoparticles in oxygen atmosphere and the La doping
stabilizes the anatase phase. The La incorporation takes place mainly in the
surface of the nanoparticles leading to small nanoparticles as compared to the
undoped TiO2. In spite of the effective La doping, the decrease of the energy
bandgap is compensated by a quantum confinement effect related to the decrease
in the nanoparticle size. However, the location of the La3+ ions on the surface
of the nanoparticles induces a local concentration of oxygen vacancies that can
increase the quantum efficiency of the system leading to higher photocatalytic
activity.


title:
Solution of integrals with fractional Brownian motion for different
  Hurst indices

abstract:
  In this paper, we will evaluate integrals that define the conditional
expectation, variance and characteristic function of stochastic processes with
respect to fractional Brownian motion (fBm) for all relevant Hurst indices,
i.e. $H \in (0,1)$. The fractional Ornstein-Uhlenbeck (fOU) process, for
example, gives rise to highly nontrivial integration formulas that need careful
analysis when considering the whole range of Hurst indices. We will show that
the classical technique of analytic continuation, from complex analysis,
provides a way of extending the domain of validity of an integral, from
$H\in(1/2,1)$, to the larger domain, $H\in(0,1)$. Numerical experiments for
different Hurst indices confirm the robustness and efficiency of the integral
formulations presented here. Moreover, we provide accurate and highly efficient
financial option pricing results for processes that are related to the fOU
process, with the help of Fourier cosine expansions.


title:
MHD study of planetary magnetospheric response during extreme solar wind
  conditions: Earth and exoplanet magnetospheres applications

abstract:
  Context: The stellar wind and the interplanetary magnetic field modify the
topology of planetary magnetospheres. Consequently, the hazardous effect of the
direct exposition to the stellar wind, for example regarding the integrity of
satellites orbiting the Earth or the habitability of exoplanets, depend upon
the space weather conditions. Aims: The aim of the study is to analyze the
response of an Earth-like magnetosphere for various space weather conditions
and interplanetary coronal mass ejections. The magnetopause stand off distance,
open-close field line boundary and plasma flows towards the planet surface are
calculated. Methods: We use the MHD code PLUTO in spherical coordinates to
perform a parametric study regarding the dynamic pressure and temperature of
the stellar wind as well as the interplanetary magnetic field intensity and
orientation. The range of the parameters analyzed extends from regular to
extreme space weather conditions consistent with coronal mass ejections at the
Earth orbit for the present and early periods of the Sun main sequence. In
addition, implications of sub-Afvenic solar wind configurations for the Earth
and exoplanet magnetospheres are analyzed. Results: The direct precipitation of
the solar wind at the Earth day side in equatorial latitudes is extremely
unlikely even during super coronal mass ejections. On the other hand, for early
evolution phases along the Sun main sequence once the Sun rotation rate was at
least $5$ times faster (< 440 Myr), the Earth surface was directly exposed to
the solar wind during coronal mass ejections. Nowadays, satellites at High,
Geosynchronous and Medium orbits are directly exposed to the solar wind during
coronal mass ejections, because part of the orbit at the Earth day side is
beyond the nose of the bow shock.


title:
Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use
  Cases

abstract:
  Quantum(-inspired) annealers show promise in solving combinatorial
optimisation problems in practice. There has been extensive researches
demonstrating the utility of D-Wave quantum annealer and quantum-inspired
annealer, i.e., Fujitsu digital annealer on various applications, but few works
are comparing these platforms. In this paper, we benchmark quantum(-inspired)
annealers with three combinatorial optimisation problems ranging from generic
scientific problems to complex problems in practical use. In the case where the
problem size goes beyond the capacity of a quantum(-inspired) computer, we
evaluate them in the context of decomposition. Experiments suggest that both
annealers are effective on problems with small size and simple settings, but
lose their utility when facing problems in practical size and settings.
Decomposition methods extend the scalability of annealers, but they are still
far away from practical use. Based on the experiments and comparison, we
discuss on the advantages and limitations of quantum(-inspired) annealers, as
well as the research directions that may improve the utility and scalability of
the these emerging computing technologies.


title:
Critical points in higher dimensions, I: Reverse order of periodic orbit
  creations in the Lozi family

abstract:
  We introduce a renormalization model which explains how the behavior of a
discrete-time continuous dynamical system changes as the dimension of the
system varies. The model applies to some two-dimensional systems, including
H\'enon and Lozi maps. Here, we focus on the orientation preserving Lozi
family, a two-parameter family of continuous piecewise affine maps, and treat
the family as a perturbation of the tent family from one to two dimensions.
  First, we prove that all periodic orbits depend on the parameters
analytically whenever they exist. The creation or annihilation of periodic
orbits happens when a border collision bifurcation occurs. Next, we prove that
the bifurcation parameters of some types of periodic orbits form analytic
curves in the parameter space. This improves a theorem of Ishii (1997).
Finally, we use the model and the analytic curves to prove that, when the Lozi
family is arbitrary close to the tent family, the order of periodic orbit
creation reverses. This shows that a forcing relation (Guckenheimer 1979 and
Collett and Eckmann 1980) on orbit creations breaks down in two dimensions. In
fact, the forcing relation does not have a continuation to two dimensions even
when the family is arbitrary close to one dimension.


title: Distributed Online Learning for Coexistence in Cognitive Radar Networks

abstract:
  This work addresses the coexistence problem for radar networks. Specifically,
we model a network of cooperative, independent, and non-communicating radar
nodes which must share resources within the network as well as with
non-cooperative nearby emitters. We approach this problem using online Machine
Learning (ML) techniques. Online learning approaches are specifically preferred
due to the fact that each radar node has no prior knowledge of the environment
nor of the positions of the other radar nodes, and due to the sequential nature
of the problem. For this task we specifically select the multi-player
multi-armed bandit (MMAB) model, which poses the problem as a sequential game,
where each radar node in a network makes independent selections of center
frequency and waveform with the same goal of improving tracking performance for
the network as a whole. For accurate tracking, each radar node communicates
observations to a fusion center on set intervals. The fusion center has
knowledge of the radar node placement, but cannot communicate to the individual
nodes fast enough for waveform control. Every radar node in the network must
learn the behavior of the environment, which includes rewards, interferer
behavior, and target behavior. Our contributions include a mathematical
description of the MMAB framework adapted to the radar network scenario. We
conclude with a simulation study of several different network configurations.
Experimental results show that iterative, online learning using MMAB
outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation
approaches.


title: Multijoints and Factorisation

abstract:
  We solve the dual multijoint problem and prove the existence of so-called
"factorisations" for arbitrary fields and multijoints of $k_j$-planes. More
generally, we deduce a discrete analogue of a theorem due in essence to
Bourgain and Guth. Our result is a universal statement which describes a
property of the discrete wedge product without any explicit reference to
multijoints and is stated as follows: Suppose that $k_1 + \ldots + k_d = n$.
There is a constant $C=C(n)$ so that for any field $\mathbb{F}$ and for any
finitely supported function $S : \mathbb{F}^n \rightarrow \mathbb{R}_{\geq 0}$,
there are factorising functions $s_{k_j} : \mathbb{F}^n\times \mathrm{Gr}(k_j,
\mathbb{F}^n)\rightarrow \mathbb{R}_{\geq 0}$ such that $$(V_1
\wedge\cdots\wedge V_d)S(p)^d \leq C\prod_{j=1}^d s_{k_j}(p, V_j),$$ for every
$p\in \mathbb{F}^n$ and every tuple of planes $V_j\in \mathrm{Gr}(k_j,
\mathbb{F}^n)$, and $$\sum_{p\in \pi_j} s(p, e(\pi_j)) =||S||_d$$ for every
$k_j$-plane $\pi_j\subset \mathbb{F}^n$, where $e(\pi_j)\in
\mathrm{Gr}(k_j,\mathbb{F}^n)$ denotes the translate of $\pi_j$ that contains
the origin and $\wedge$ denotes the discrete wedge product.


title:
Accelerated Inertial Regime in the Spinodal Decomposition of Magnetic
  Fluids

abstract:
  Furukawa predicted that at late times, the domain growth in binary fluids
scales as $\ell(t)\sim t^{2/3}$, and the growth is driven by fluid inertia. The
{\it inertial growth regime} has been highly elusive in molecular dynamics (MD)
simulations. We perform coarsening studies of the Stockmayer (SM) model
comprising of magnetic dipoles that interact via long-range dipolar
interactions as well as the usual Lennard-Jones (LJ) potential. This
fascinating polar fluid exhibits a gas-liquid phase coexistence, and magnetic
order even in the absence of an external field. From comprehensive MD
simulations, we observe the inertial scaling [$\ell(t)\sim t^{2/3}$] in the SM
fluid for an extended time window. Intriguingly, the fluid inertia is
overwhelming from the outset - our simulations do not show the early diffusive
regime [$\ell(t)\sim t^{1/3}$] and the intermediate viscous regime
[$\ell(t)\sim t$] prevalent in LJ fluids.


title: Gluon Digitization via Character Expansion for Quantum Computers

abstract:
  Efficient digitization is required for quantum simulations of gauge theories.
Schemes based on discrete subgroups use a smaller, fixed number of qubits at
the cost of systematic errors. We systematize this approach by deriving the
single plaquette action through matching the continuous group action to that of
a discrete one via group character expansions modulo the field fluctuation
contributions. We accompany this scheme by simulations of pure gauge over the
largest discrete crystal-like subgroup of $SU(3)$ up to the fifth-order in the
coupling constant.


title: F2DNet: Fast Focal Detection Network for Pedestrian Detection

abstract:
  Two-stage detectors are state-of-the-art in object detection as well as
pedestrian detection. However, the current two-stage detectors are inefficient
as they do bounding box regression in multiple steps i.e. in region proposal
networks and bounding box heads. Also, the anchor-based region proposal
networks are computationally expensive to train. We propose F2DNet, a novel
two-stage detection architecture which eliminates redundancy of current
two-stage detectors by replacing the region proposal network with our focal
detection network and bounding box head with our fast suppression head. We
benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it
against the existing state-of-the-art detectors and conduct cross dataset
evaluation to test the generalizability of our model to unseen data. Our F2DNet
achieves 8.7%, 2.2%, and 6.1% MR-2 on City Persons, Caltech Pedestrian, and
Euro City Person datasets respectively when trained on a single dataset and
reaches 20.4% and 26.2% MR-2 in heavy occlusion setting of Caltech Pedestrian
and City Persons datasets when using progressive fine-tunning. On top of that
F2DNet have significantly lesser inference time compared to the current
state-of-the-art. Code and trained models will be available at
https://github.com/AbdulHannanKhan/F2DNet.


title:
Optimization study of the electrode design of a 5 mm thick
  orthogonal-strip CdZnTe detector system

abstract:
  The geometry of electrodes is one of the most important factors in
determining the performance of orthogonal-strip detectors. The aim of this work
is to study the performance of a 5 mm thick cross-strip CdZnTe detector with
different electrode widths. Our study consists of two main parts, simulations
and experiments. We utilized four different anode sizes ranging from 0.1 mm to
0.6 mm. The anodes were interspersed with steering electrodes with varying
sizes from 0.3 mm to 0.85 mm. The maximum gap size between the anodes and
steering electrode strips was set to 0.3 mm, while the minimum gap size was
0.125 mm. The performance of the detector was investigated in terms of the
steering electrode bias voltage, the energy resolution, and the charge sharing
effect. For simulations, we developed a C++ based simulation program for charge
transport inside the CdZnTe detector and charge collection at the electrodes.
For photon interactions we used GEANT4 toolkit and for electric field and
weighting potential simulations we used COMSOL software. The results
demonstrated that -50 V is the optimal steering electrode bias for our detector
when -500 V was applied to the cathodes and that the energy resolution
performance drops with increasing steering electrode width. Also, the charge
sharing effect becomes more dominant for larger steering electrode sizes. The
experimental result are further compared with the simulations. The results are
in a good agreement and the comparison validates our simulation model.
Although, our simulation framework has need of better estimation for the
intrinsic noise of CdZnTe. These results suggest that an optimization study
between electrode widths and steering electrode bias is required to obtain the
best performance in orthogonal-strip CdZnTe detectors.

categories: physics.ins-det astro-ph.IM hep-ex nucl-ex physics.space-ph


title:
Family of asymptotic solutions to the two-dimensional kinetic equation
  with a nonlocal cubic nonlinearity

abstract:
  We apply the original semiclassical approach to the kinetic ionization
equation with the nonlocal cubic nonlinearity in order to construct the family
of its asymptotic solutions. The approach proposed relies on an auxiliary
dynamical system of moments of the desired solution to the kinetic equation and
the associated linear partial differential equation. The family of asymptotic
solutions to the kinetic equation is constructed using the symmetry operators
acting on functions concentrated in a neighborhood of a point determined by the
dynamical system. Based on these solutions, we introduce the nonlinear
superposition principle for the nonlinear kinetic equation. Our formalism based
on the Maslov germ method is applied to the Cauchy problem for the specific
two-dimensional kinetic equation. The evolution of the ion distribution in the
kinetically enhanced metal vapor active medium is obtained as the nonlinear
superposition using the numerical-analytical calculations.


title:
Local thermodynamic description of isothermal single-phase flow in
  porous media

abstract:
  Darcy's law for porous media transport is given a new local thermodynamic
basis in terms of the grand potential of confined fluids. The local effective
pressure gradient is determined using non-equilibrium molecular dynamics, and
the hydraulic conductivity and permeability are investigated. The transport
coefficients are determined for single-phase flow in face-centered cubic
lattices of solid spheres. The porosity changed from that in the closest
packing of spheres to near unity in a pure fluid, while the fluid mass density
varied from that of a dilute gas to a dense liquid. The permeability varied
between \SI{5.7e-20}{\meter^2} and \SI{5.5e-17}{\meter^2}, showing a
porosity-dependent Klinkenberg effect. Both transport coefficients depended on
the average fluid mass density and porosity but in different ways. These
results set the stage for a non-equilibrium thermodynamic investigation of
coupled transport of multi-phase fluids in complex media.


title: Cross-sections for heavy atmospheres: H$_2$O self-broadening

abstract:
  The discovery of super-Earth and mini-Neptune exoplanets means that
atmospheric signals from low-mass, temperate exoplanets are being increasingly
studied. The signal acquired as the planet transits its host star, known as the
transit depth, is smaller for these planets and, as such, more difficult to
analyze. The launch of the space telescopes James Webb (JWST) & Ariel will give
rise to an explosion in the quality and quantity of spectroscopic data
available for an unprecedented number of exoplanets in our galaxy. Accurately
extracting the information content, thereby permitting atmospheric science, of
such data-sets will require robust models and techniques. We present here the
analysis of simulated transmission spectra for water-rich atmospheres, giving
evidence for non-negligible differences in simulated transit depths when
self-broadening of H$_2$O is correctly accounted for, compared with the
currently typically accepted standard of using H$_2$ and He-broadened
cross-sections. Our case-study analysis is carried out on two super-Earths,
focusing on water-based atmospheres, ranging from H$_2$-rich to H$_2$O-rich.
The transit depth is considerably affected, increasing values by up to 60 ppm,
which is shown to be detectable with JWST and Ariel. The differences are most
pronounced for the lighter (i.e. $mu$ $\sim$ 4) atmospheres. Our work
illustrates that it is imperative that the field of exoplanet spectroscopy
moves toward adapted cross-sections, increasingly optimized for high-mu
atmospheres for studies of super-Earths and mini-Neptunes.


title: Differentiable Causal Discovery Under Latent Interventions

abstract:
  Recent work has shown promising results in causal discovery by leveraging
interventional data with gradient-based methods, even when the intervened
variables are unknown. However, previous work assumes that the correspondence
between samples and interventions is known, which is often unrealistic. We
envision a scenario with an extensive dataset sampled from multiple
intervention distributions and one observation distribution, but where we do
not know which distribution originated each sample and how the intervention
affected the system, \textit{i.e.}, interventions are entirely latent. We
propose a method based on neural networks and variational inference that
addresses this scenario by framing it as learning a shared causal graph among
an infinite mixture (under a Dirichlet process prior) of intervention
structural causal models. Experiments with synthetic and real data show that
our approach and its semi-supervised variant are able to discover causal
relations in this challenging scenario.


title:
Basic Block Coverage for Search-based Unit Testing and Crash
  Reproduction

abstract:
  Search-based techniques have been widely used for white-box test generation.
Many of these approaches rely on the approach level and branch distance
heuristics to guide the search process and generate test cases with high line
and branch coverage. Despite the positive results achieved by these two
heuristics, they only use the information related to the coverage of explicit
branches (e.g., indicated by conditional and loop statements), but ignore
potential implicit branchings within basic blocks of code. If such implicit
branching happens at runtime (e.g., if an exception is thrown in a
branchless-method), the existing fitness functions cannot guide the search
process. To address this issue, we introduce a new secondary objective, called
Basic Block Coverage (BBC), which takes into account the coverage level of
relevant basic blocks in the control flow graph. We evaluated the impact of BBC
on search-based unit test generation (using the DynaMOSA algorithm) and
search-based crash reproduction (using the STDistance and WeightedSum fitness
functions). Our results show that for unit test generation, BBC improves the
branch coverage of the generated tests. Although small (around 1.5%), this
improvement in the branch coverage is systematic and leads to an increase of
the output domain coverage and implicit runtime exception coverage, and of the
diversity of runtime states. In terms of crash reproduction, in the combination
of STDistance and WeightedSum, BBC helps in reproducing 3 new crashes for each
fitness function. BBC significantly decreases the time required to reproduce
43.5% and 45.1% of the crashes using STDistance and WeightedSum, respectively.
For these crashes, BBC reduces the consumed time by 71.7% (for STDistance) and
68.7% (for WeightedSum) on average.


title:
Circularly polarized periodic gravitational wave and the Pais-Uhlenbeck
  oscillator

abstract:
  The "Bargmann framework" allows us to prove the equivalence of the
Pais-Uhlenbeck oscillator with a spin-zero particle in a circularly polarized
periodic gravitational wave. Pushing forward the symmetries (which include
Carroll symmetry) of the latter, we derive conserved charges for the
Pais-Uhlenbeck oscillator.


title: Convergence Rates for Oversmoothing Banach Space Regularization

abstract:
  This paper studies Tikhonov regularization for finitely smoothing operators
in Banach spaces when the penalization enforces too much smoothness in the
sense that the penalty term is not finite at the true solution. In a Hilbert
space setting, Natterer (1984) showed with the help of spectral theory that
optimal rates can be achieved in this situation. ('Oversmoothing does not
harm.') For oversmoothing variational regularization in Banach spaces only very
recently progress has been achieved in several papers on different settings,
all of which construct families of smooth approximations to the true solution.
In this paper we propose to construct such a family of smooth approximations
based on $K$-interpolation theory. We demonstrate that this leads to simple,
self-contained proofs and to rather general results. In particular, we obtain
optimal convergence rates for bounded variation regularization, general Besov
penalty terms and $\ell^p$ wavelet penalization with $p<1$ which cannot be
treated by previous approaches. We also derive minimax optimal rates for white
noise models. Our theoretical results are confirmed in numerical experiments.


title:
Deoptless: Speculation with Dispatched On-Stack Replacement and
  Specialized Continuations

abstract:
  Just-in-time compilation provides significant performance improvements for
programs written in dynamic languages. These benefits come from the ability of
the compiler to speculate about likely cases and generate optimized code for
these. Unavoidably, speculations sometimes fail and the optimizations must be
reverted. In some pathological cases, this can leave the program stuck with
suboptimal code. In this paper we propose deoptless, a technique that replaces
deoptimization points with dispatched specialized continuations. The goal of
deoptless is to take a step towards providing users with a more transparent
performance model in which mysterious slowdowns are less frequent and grave.


title:
Interplay of magnetism and superconductivity in 2D extended Hubbard
  model

abstract:
  The interplay between magnetic and superconducting states on a square lattice
is studied using the extended Hubbard model, which takes into account the
attraction of electrons located at nearest neighbor sites. Ferro-, antiferro-,
and spiral magnetic states with all possible sets of spiral wave vectors, as
well as singlet superconducting states with s-wave and d-wave pairing order
parameters, are considered. Formation of a state with the intermediate
s+id-symmetry and phase separation between different phases are allowed. The
results of the Hartree-Fock and slave boson approximations are compared in
order to study the role of correlation effects. Both macroscopic phase
separation and microscopic coexistence of the superconducting and magnetic
phases are found under certain model parameters.


title:
Robust Event-Based Control: Bridge Time-Domain Triggering and
  Frequency-Domain Uncertainties

abstract:
  This paper considers the robustness of event-triggered control of general
linear systems against additive or multiplicative frequency-domain
uncertainties. It is revealed that in static or dynamic event triggering
mechanisms, the sampling errors are images of affine operators acting on the
sampled outputs. Though not belonging to $\mathcal{RH}_\infty$, these operators
are finite-gain $\mathcal{L}_2$ stable with operator-norm depending on the
triggering conditions and the norm bound of the uncertainties. This
characterization is further extended to the general integral quadratic
constraint (IQC)-based triggering mechanism. As long as the triggering
condition characterizes an $\mathcal{L}_2$-to-$\mathcal{L}_2$ mapping
relationship (in other words, small-gain-type constraints) between the sampled
outputs and the sampling errors, the robust event-triggered controller design
problem can be transformed into the standard $H_\infty$ synthesis problem of a
linear system having the same order as the controlled plant. Algorithms are
provided to construct the robust controllers for the static, dynamic and
IQC-based event triggering cases.


title: Approval with Runoff

abstract:
  We define a family of runoff rules that work as follows: voters cast approval
ballots over candidates; two finalists are selected; and the winner is decided
by majority. With approval-type ballots, there are various ways to select the
finalists. We leverage known approval-based committee rules and study the
obtained runoff rules from an axiomatic point of view. Then we analyze the
outcome of these rules on single-peaked profiles, and on real data.


title:
A comprehensive study on the processing of Co:ZnO ceramics: defect
  chemistry engineering and grain growth kinetics

abstract:
  In this report we present a systematic study on the preparation of Co:ZnO
ceramics via standard solid-state route from different Co precursors (Co3O4,
CoO and metallic Co) and atmospheres (O2 and Ar). Particular emphasis was done
on the defect chemistry engineering and on the sintering growth kinetics.
First-principles calculations based on density functional theory were employed
to determine the formation energy of the main point defects in ZnO and Co:ZnO
systems. Based on the theoretical results a set of chemical reactions was
proposed. A detailed microstructural characterization was performed in order to
determine the degree of Co incorporation into the ZnO lattice. The samples
prepared in Ar atmosphere and from metallic Co presents the highest Co
solubility limit (lower apparent Co incorporation activation energy) due to the
incongruent ZnO decomposition. The determination of the parameters of the
sintering growth kinetics reveals that Co3O4 is the best sintering additive in
order to achieve higher densities in both sintering atmospheres. The results
give evidences that the sintering in O2 is effective in promoting zinc
vacancies in the ZnO structure, while the sintering in Ar promotes zinc
interstitial defects. Our findings give valuable contribution to the
understanding of the preparation of Co-doped ZnO ceramics and the sintering
growth kinetics, what would allow to improve the state of the art on the
processing of the material at both bulk and nanometric scales.


title:
Statistical analysis of the causes of excess variance in the 21 cm
  signal power spectra obtained with the Low-Frequency Array

abstract:
  The detection of the 21 cm signal of neutral hydrogen from the Epoch of
Reionization (EoR) is challenging due to bright foreground sources, radio
frequency interference (RFI), the ionosphere, and instrumental effects. Even
after correcting for these effects in the calibration step and applying
foreground removal techniques, the remaining residuals in the observed 21 cm
power spectra are still above the thermal noise, which is referred to as the
"excess variance." We study potential causes of this excess variance based on
13 nights of data obtained with the Low-Frequency Array (LOFAR). We focused on
the impact of gain errors, the sky model, and ionospheric effects on the excess
variance by correlating the relevant parameters such as the gain variance over
time or frequency, local sidereal time (LST), diffractive scale, and phase
structure-function slope with the level of excess variance. Our analysis shows
that excess variance has an LST dependence, which is related to the power from
the sky. And the simulated Stokes I power spectra from bright sources and the
excess variance show a similar progression over LST with the minimum power
appearing at LST bin 6h to 9h. This LST dependence is also present in sky
images of the residual Stokes I of the observations. In very-wide sky images,
we demonstrate that the extra power comes exactly from the direction of bright
and distant sources Cassiopeia A and Cygnus A with the array beam patterns.
These results suggest that the level of excess variance in the 21 cm signal
power spectra is related to sky effects and, hence, it depends on LST. In
particular, very bright and distant sources such as Cassiopeia A and Cygnus A
can dominate the effect. This is in line with earlier studies and offers a path
forward toward a solution since the correlation between the sky-related effects
and the excess variance is non-negligible.


title: An XMM-Newton EPIC X-ray view of the Symbiotic Star R~Aquarii

abstract:
  We present the analysis of archival XMM-Newton European Photon Imaging Camera
(EPIC) X-ray observations of the symbiotic star R Aquarii. We used the Extended
Source Analysis Software (ESAS) package to disclose diffuse soft X-ray emission
extending up to 2.2 arcmin ($\approx$0.27 pc) from this binary system. The
depth of these XMM-Newton EPIC observations reveal in unprecedented detail the
spatial distribution of this diffuse emission, with a bipolar morphology
spatially correlated with the optical nebula. The extended X-ray emission
shares the same dominant soft X-ray-emitting temperature as the clumps in the
jet-like feature resolved by Chandra in the vicinity of the binary system. The
harder component in the jet might suggest that the gas cools down, however, the
possible presence of non-thermal emission produced by the presence of a
magnetic field collimating the mass ejection can not be discarded. We propose
that the ongoing precessing jet creates bipolar cavities filled with
X-ray-emitting hot gas that feeds the more extended X-ray bubble as they get
disrupted. These EPIC observations demonstrate that the jet feedback mechanism
produced by an accreting disk around an evolved, low-mass star can blow hot
bubbles, similar to those produced by jets arising from the nuclei of active
galaxies.


title:
Non-Local thermal equilibrium spectra of atmospheric molecules for
  exoplanets

abstract:
  Here we present a study of non-LTE effects on the exoplanetary spectra of a
collection of molecules which are key in the investigation of exoplanet
atmospheres: water, methane, carbon monoxide and titanium oxide. These
molecules are chosen as examples of different spectral ranges (IR and UV),
molecular types (diatomics and polyatomics) and spectral types (electronic and
ro-vibrational); the importance of different vibrational bands in forming
distinct non-LTE spectral features are investigated. Most notably, such key
spectral signatures for distinguishing between the LTE and non-LTE cases
include: for CH4 the 3.15 $\mu$m band region; for H2O the 2.0 $\mu$m and 2.7
$\mu$m band regions; for TiO, a strong variation in intensity in the bands
between 0.5 and 0.75 $\mu$m; and a sole CO signature between 5 and 6 $\mu$m.
The analysis is based on the ExoMol cross sections and takes advantage of the
extensive vibrational assignment of these molecular line lists in the ExoMol
database. We examine LTE and non-LTE cross sections under conditions consistent
with those on WASP-12b and WASP-76b using the empirically motivated
bi-temperature Treanor model. In addition, we make a simplistic forward model
simulation of transmission spectra for H2O in the atmosphere of WASP-12b using
the TauREx 3 atmospheric modelling code.


title: Bottom Quark Mass with Calibrated Uncertainty

abstract:
  We determine the bottom quark mass $\hat{m}_b$ from QCD sum rules of moments
of the vector current correlator calculated in perturbative QCD to ${\cal O}
(\hat\alpha_s^3)$. Our approach is based on the mutual consistency across a set
of moments where experimental data are required for the resonance contributions
only. Additional experimental information from the continuum region can then be
used for stability tests and to assess the theoretical uncertainty. We find
$\hat{m}_b(\hat{m}_b) = (4180.2 \pm 7.9)$ MeV for $\hat\alpha_s(M_Z) = 0.1182$.


title:
Study of the ground state and thermodynamic properties of Cu5-NIPA-like
  molecular nanomagnets

abstract:
  The thermodynamic properties of a spatially anisotropic spin-1/2 Heisenberg
model for a Cu$_5$ pentameric molecule is studied through exact
diagonalization. The elementary geometry of the finite lattice is defined on
nanomolecules consisting of an hourglass structure of two corner-sharing
scalene triangles which are related by inversion symmetry. This microscopic
magnetic model is quite suitable to describe the molecular nanomagnetic
compound Cu5-NIPA. The ground-state phase diagram, as well as the corresponding
total magnetization, are obtained as a function of the anisotropic exchange
interactions and the external magnetic field. The thermodynamic behavior of the
model at finite temperatures is also studied and the corresponding
magnetocaloric effects are analyzed for various values of the Hamiltonian
parameters.


title:
Effect of the field self-interaction of General Relativity on the Cosmic
  Microwave Background Anisotropies

abstract:
  Field self-interactions are at the origin of the non-linearities inherent to
General Relativity. We study their effects on the Cosmic Microwave Background
anisotropies. We find that they can reduce or alleviate the need for dark
matter and dark energy in the description of the Cosmic Microwave Background
power spectrum.


title: Uncertainty Estimation for Heatmap-based Landmark Localization

abstract:
  Automatic anatomical landmark localization has made great strides by
leveraging deep learning methods in recent years. The ability to quantify the
uncertainty of these predictions is a vital ingredient needed to see these
methods adopted in clinical use, where it is imperative that erroneous
predictions are caught and corrected. We propose Quantile Binning, a
data-driven method to categorise predictions by uncertainty with estimated
error bounds. This framework can be applied to any continuous uncertainty
measure, allowing straightforward identification of the best subset of
predictions with accompanying estimated error bounds. We facilitate easy
comparison between uncertainty measures by constructing two evaluation metrics
derived from Quantile Binning. We demonstrate this framework by comparing and
contrasting three uncertainty measures (a baseline, the current gold standard,
and a proposed method combining aspects of the two), across two datasets (one
easy, one hard) and two heatmap-based landmark localization model paradigms
(U-Net and patch-based). We conclude by illustrating how filtering out gross
mispredictions caught in our Quantile Bins significantly improves the
proportion of predictions under an acceptable error threshold, and offer
recommendations on which uncertainty measure to use and how to use it.


title:
Search for the anomalous $ZZ\gamma$ and $Z\gamma\gamma$ couplings via
  $\nu \nu \gamma$ production at the CLIC

abstract:
  The non-Abelian gauge structure of the Standard Model implies the presence of
the multi-boson self-interactions. Precise measurements in experimental and
theoretical studies of these interactions allow not only testing the nature of
the Standard Model but also new physics contribution coming from the beyond
Standard Model. These interactions can be examined using a model-independent
way in effective theory approach that composes the motivation part of this
study. In this paper, we examine the anomalous $ZZ\gamma$ and $Z\gamma\gamma$
neutral triple gauge couplings via the process $e^{-} e^{+}\to Z\gamma$ for the
neutrino-antineutrino pair decay of $Z$ boson. It has performed with both
unpolarized and polarized electron beams at the Compact Linear Collider with
$\sqrt{s}= 3$ TeV. The study focused on $CP$-conserving
$C_{\widetilde{B}W}/{\Lambda^4}$ and $CP$-violating $C_{BB}/{\Lambda^4}$,
$C_{BW}/{\Lambda^4}$, $C_{WW}/{\Lambda^4}$ couplings. Obtained sensitivities on
the anomalous neutral triple gauge couplings with $95\%$ Confidence Level are
given with systematic uncertainties of $0\%$, $5\%$ and $10\%$ for unpolarized,
$-80\%$ and $80\%$ polarized electron beams with integrated luminosities of
${\cal L}_{\text{int}}=5$ $\rm ab^{-1}$, ${\cal L}_{\text{int}}=4$ $\rm
ab^{-1}$ and ${\cal L}_{\text{int}}=1$ $\rm ab^{-1}$, respectively. Comparing
the latest experimental limits and related phenomenological studies, our
results on the anomalous neutral gauge couplings are set more stringent
sensitivity between 10-30 times of magnitude.


title:
A remark on the higher torsion invariants for flat vector bundles with
  finite holonomy

abstract:
  We show that the Igusa-Klein topological torsion and the Bismut-Lott analytic
torsion are equivalent for any flat vector bundle whose holonomy is a finite
subgroup of $\mathrm{GL}_n(\mathbb{Q})$. Our proof uses Artin's induction
theorem in representation theory to reduce the problem to the special case of
trivial flat line bundles, which is a recent result of Puchol, Zhu and the
second author. The idea of using Artin's induction theorem appeared in a paper
of Ohrt on the same topic, of which our present work is an improvement.


title:
Benchmarking real-time algorithms for in-phase auditory stimulation of
  low amplitude slow waves with wearable EEG devices during sleep

abstract:
  Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement
(NREM) sleep has shown to improve cognitive function when it is delivered at
the up-phase of SW. SW enhancement is particularly desirable in subjects with
low-amplitude SW such as older adults or patients suffering from
neurodegeneration such as Parkinson disease (PD). However, existing algorithms
to estimate the up-phase suffer from a poor phase accuracy at low EEG
amplitudes and when SW frequencies are not constant. We introduce two novel
algorithms for real-time EEG phase estimation on autonomous wearable devices.
The algorithms were based on a phase-locked loop (PLL) and, for the first time,
a phase vocoder (PV). We compared these phase tracking algorithms with a simple
amplitude threshold approach. The optimized algorithms were benchmarked for
phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and
60 microV, and SW frequencies above 1 Hz on 324 recordings from healthy older
adults and PD patients. Furthermore, the algorithms were implemented on a
wearable device and the computational efficiency and the performance was
evaluated on simulated sleep EEG, as well as prospectively during a recording
with a PD patient. All three algorithms delivered more than 70% of the
stimulation triggers during the SW up-phase. The PV showed the highest capacity
on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing
on real-time hardware revealed that both PV and PLL have marginal impact on
microcontroller load, while the efficiency of the PV was 4% lower than the PLL.
Active auditory stimulation did not influence the phase tracking. This work
demonstrated that phase-accurate auditory stimulation can be delivered during
home-based sleep interventions with a wearable device also in populations with
low-amplitude SW.


title: Computer-Aided Road Inspection: Systems and Algorithms

abstract:
  Road damage is an inconvenience and a safety hazard, severely affecting
vehicle condition, driving comfort, and traffic safety. The traditional manual
visual road inspection process is pricey, dangerous, exhausting, and
cumbersome. Also, manual road inspection results are qualitative and
subjective, as they depend entirely on the inspector's personal experience.
Therefore, there is an ever-increasing need for automated road inspection
systems. This chapter first compares the five most common road damage types.
Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art
machine vision and intelligence-based road damage detection algorithms are
introduced.


title:
Intercorrelated anomalous Hall and spin Hall effect in kagome-lattice
  Co$_3$Sn$_2$S$_2$-based shandite films

abstract:
  Magnetic Weyl semimetals (mWSMs) are characterized by linearly dispersive
bands with chiral Weyl node pairs associated with broken time reversal
symmetry. One of the hallmarks of mWSMs is the emergence of large intrinsic
anomalous Hall effect. On heating the mWSM above its Curie temperature, the
magnetism vanishes while exchange-split Weyl point pairs collapse into
doubly-degenerated gapped Dirac states. Here, we reveal the attractive
potential of these Dirac nodes in paramagnetic state for efficient spin current
generation at room temperature via the spin Hall effect. Ni and In are
introduced to separately substitute Co and Sn in a prototypal mWSM
Co$_3$Sn$_2$S$_2$ shandite film and tune the Fermi level. Composition
dependence of spin Hall conductivity for paramagnetic shandite at room
temperature resembles that of anomalous Hall conductivity for ferromagnetic
shandite at low temperature; exhibiting peak-like dependence centering around
the Ni-substituted Co$_2$Ni$_1$Sn$_2$S$_2$ and undoped Co$_3$Sn$_2$S$_2$
composition, respectively. The peak shift is consistent with the redistribution
of electrons' filling upon crossing the ferromagnetic-paramagnetic transition,
suggesting intercorrelation between the two Hall effects. Our findings
highlight a novel strategy for the quest of spin Hall materials, guided by the
abundant experimental anomalous Hall effect data of ferromagnets in the
literature.


title:
Detecting Relatively Quasiconvex Subgroups and Their Induced Peripheral
  Structure

abstract:
  This paper proves under certain conditions the existence of an algorithm,
which detects relatively quasiconvex subgroups $H$ of relatively hyperbolic
groups $(G,\mathbb{P})$. Additionally, this algorithm outputs an induced
peripheral structure of $(G,\mathbb{P})$ on $H$.


title: ViT-P: Rethinking Data-efficient Vision Transformers from Locality

abstract:
  Recent advances of Transformers have brought new trust to computer vision
tasks. However, on small dataset, Transformers is hard to train and has lower
performance than convolutional neural networks. We make vision transformers as
data-efficient as convolutional neural networks by introducing multi-focal
attention bias. Inspired by the attention distance in a well-trained ViT, we
constrain the self-attention of ViT to have multi-scale localized receptive
field. The size of receptive field is adaptable during training so that optimal
configuration can be learned. We provide empirical evidence that proper
constrain of receptive field can reduce the amount of training data for vision
transformers. On Cifar100, our ViT-P Base model achieves the state-of-the-art
accuracy (83.16%) trained from scratch. We also perform analysis on ImageNet to
show our method does not lose accuracy on large data sets.


title: Quasimodes in the cusp continuum in nonuniform magnetic flux tubes

abstract:
  The study of MHD waves is important both for understanding heating in the
solar atmosphere and for solar atmospheric seismology. The analytical
investigation of wave mode properties in a cylinder is of particular interest
in this domain, as many atmospheric structures can be modeled as such in a
first approximation. We use linearized ideal MHD to study quasimodes (global
modes that are damped through resonant absorption) with a frequency in the cusp
continuum, in a straight cylinder with a circular base and an inhomogeneous
layer at its boundary which separates two homogeneous plasma regions inside and
outside. We are in particular interested in the damping of these modes, and
shall hence try to determine their frequency as a function of background
parameters. After linearizing the ideal MHD equations, we find solutions to the
second-order differential equation for the perturbed total pressure in the
inhomogeneous layer in the form of Frobenius series around the regular singular
points that are the Alfv\'en and cusp resonant positions, as well as power
series around regular points. By connecting these solutions appropriately
through the inhomogeneous layer and with the solutions of the homogeneous
regions inside and outside the cylinder, we derive a dispersion relation for
the frequency of the eigenmodes of the system. From the dispersion relation, it
is also possible to find the frequency of quasimodes even though they are not
eigenmodes. As an example, we find the frequency of the slow surface sausage
quasimode as a function of the inhomogeneous layer's width, for values of the
longitudinal wavenumber relevant for photospheric conditions. The results were
found to match well the results found in another paper which studied the
resistive slow surface sausage eigenmode. We also discuss the perturbation
profiles of the quasimode and the eigenfunctions of continuum modes.


title: Boosting the Performance of Quantum Annealers using Machine Learning

abstract:
  Noisy intermediate-scale quantum (NISQ) devices are spearheading the second
quantum revolution. Of these, quantum annealers are the only ones currently
offering real world, commercial applications on as many as 5000 qubits. The
size of problems that can be solved by quantum annealers is limited mainly by
errors caused by environmental noise and intrinsic imperfections of the
processor. We address the issue of intrinsic imperfections with a novel error
correction approach, based on machine learning methods. Our approach adjusts
the input Hamiltonian to maximize the probability of finding the solution. In
our experiments, the proposed error correction method improved the performance
of annealing by up to three orders of magnitude and enabled the solving of a
previously intractable, maximally complex problem.


title:
Data aggregation can lead to biased inferences in Bayesian linear mixed
  models

abstract:
  Bayesian linear mixed-effects models are increasingly being used in the
cognitive sciences to perform null hypothesis tests, where a null hypothesis
that an effect is zero is compared with an alternative hypothesis that the
effect exists and is different from zero. While software tools for Bayes factor
null hypothesis tests are easily accessible, how to specify the data and the
model correctly is often not clear. In Bayesian approaches, many authors
recommend data aggregation at the by-subject level and running Bayes factors on
aggregated data. Here, we use simulation-based calibration for model inference
to demonstrate that null hypothesis tests can yield biased Bayes factors, when
computed from aggregated data. Specifically, when random slope variances differ
(i.e., violated sphericity assumption), Bayes factors are too conservative for
contrasts where the variance is small and they are too liberal for contrasts
where the variance is large. Moreover, Bayes factors for by-subject aggregated
data are biased (too liberal) when random item variance is present but ignored
in the analysis. We also perform corresponding frequentist analyses (type I and
II error probabilities) to illustrate that the same problems exist and are well
known from frequentist tools. These problems can be circumvented by running
Bayesian linear mixed-effects models on non-aggregated data such as on
individual trials and by explicitly modeling the full random effects structure.
  Reproducible code is available from https://osf.io/mjf47/.


title: Classification of non-solvable groups whose power graph is a cograph

abstract:
  Cameron, Manna and Mehatari investigated the question of which finite groups
admit a power graph that is a cograph, also called power-cograph groups
(Journal of Algebra 591 (2022)). They give a classification for nilpotent
groups and partial results for general groups. However, the authors point out
number theoretic obstacles towards a classification. These arise when the
groups are assumed to be isomorphic to PSL 2 (q) or Sz(q) and are likely to be
hard. In this paper, we prove that these number theoretic problems are in fact
the only obstacles to the classification of non-solvable power-cograph groups.
Specifically, for the non-solvable case, we give a classification of
power-cograph groups in terms of such groups isomorphic to PSL 2 (q) or Sz(q).
For the solvable case, we are able to precisely describe the structure of
solvable power-cograph groups. We obtain a complete classification of solvable
power-cograph groups whose Gruenberg-Kegel graph is connected. Moreover, we
reduce the case where the Gruenberg-Kegel graph is disconnected to the
classification of p-groups admitting fixed-point-free automorphisms of prime
power order, which is in general an open problem.


title:
An Operator-Theoretic Approach to Robust Event-Triggered Control of
  Network Systems with Frequency-Domain Uncertainties

abstract:
  In this paper, we study the robustness of the event-triggered consensus
algorithms against frequency-domain uncertainties. It is revealed that the
sampling errors resulted by event triggering are essentially images of linear
finite-gain $\mathcal{L}_2$-stable operators acting on the consensus errors of
the sampled states and the event-triggered mechanism is equivalent to a
negative feedback loop introduced additionally to the feedback system. In
virtue of this, the robust consensus problem of the event-triggered network
systems subject to additive dynamic uncertainties and network multiplicative
uncertainties are considered, respectively. In both cases, quantitative
relationships among the parameters of the controllers, the Laplacian matrix of
the network topology, and the robustness against aperiodic event triggering and
frequency-domain uncertainties are unveiled. Furthermore, the event-triggered
dynamic average consensus (DAC) problem is also investigated, wherein the
sampling errors are shown to be images of nonlinear finite-gain operators. The
robust performance of the proposed DAC algorithm is analyzed, which indicates
that the robustness and the performance are negatively related to the
eigenratio of the Laplacian matrix. Simulation examples are also provided to
verify the obtained results.


title:
Some recent advances in the understanding of ${\cal N}=4$ supersymmetric
  Yang-Mills thermodynamics

abstract:
  The interest in the thermodynamics of supersymmetric Yang-Mills started after
Maldacena proposed the duality between string theory on AdS backgrounds and the
large-N limit of SYM theories. One of the motivations to study the thermal
properties of ${\cal N}=4$ supersymmetric Yang-Mills in four dimensions
(SYM$_{4,4}$) is that at high temperatures, the weak-coupling limit of this
theory has many similarities with high temperature quantum chromodynamics
(QCD). In this proceedings contribution, we review recent calculations of the
resummed perturbative free energy of ${\cal N}=4$ supersymmetric Yang-Mills in
four spacetime dimensions through second order in the 't Hooft coupling
$\lambda$ at finite temperature and zero chemical potential. We compare our
final result with prior results obtained in the weak and strong-coupling limits
and construct a generalized Pad\'{e} approximant that interpolates between the
weak-coupling result and the large-$N_c$ strong-coupling result.


title:
Analytic meronic black holes, gravitating solitons and higher spins in
  the Einstein $SU(N)$-Yang-Mills theory

abstract:
  We construct meronic black holes and solitons in the Einstein
$SU(N)$-Yang-Mills theory in $D=4$ and $D=5$ dimensions. These analytical
solutions are found by combining the generalized hedgehog ansatz with the Euler
parameterization of the $SU(N)$ group from which the Yang-Mills equations are
automatically satisfied for all values of $N$ while the Einstein equations can
be solved analytically. We explicitly show the role that the color number $N$
plays in the black hole thermodynamics as well as in the gravitational spin
from isospin effect. Two remarkable results of our analysis are that, firstly,
meronic black holes can be distinguished by colored black holes by looking at
the spin from isospin effect (which is absent in the latter but present in the
former). Secondly, using the theory of non-embedded ansatz for $SU(N)$ together
with the spin from isospin effect, one can build fields of arbitrary high spin
out of scalar fields charged under the gauge group. Hence, one can analyze
interacting higher spin fields in asymptotically flat space-times without
"introducing by hand" higher spin fields. Our analysis also discloses an
interesting difference between the spin from isospin effect in $D=4$ and in
$D=5$.


title:
A Star-sized Impact-Produced Dust Clump in the Terrestrial Zone of the
  HD 166191 System

abstract:
  We report on five years of 3-5 micron photometry measurements obtained by
warm Spitzer to track the dust debris emission in the terrestrial zone of HD
166191 in combination with simultaneous optical data. We show that the debris
production in this young (~10 Myr) system increased significantly in early 2018
and reached a record high level (almost double by mid 2019) by the end of the
Spitzer mission (early 2020), suggesting intense collisional activity in its
terrestrial zone likely due to either initial assembling of terrestrial planets
through giant impacts or dynamical shake-up from unseen planet-mass objects or
recent planet migration. This intense activity is further highlighted by
detecting a star-size dust clump, passing in front of the star, in the midst of
its infrared brightening. We constrain the minimum size and mass of the clump
using multiwavelength transit profiles and conclude that the dust clump is most
likely created by a large impact involving objects of several hundred km in
size with an apparent period of 142 days (i.e., 0.62 au assuming a circular
orbit). The system's evolutionary state (right after the dispersal of its
gas-rich disk) makes it extremely valuable to learn about the process of
terrestrial planet formation and planetary architecture through future
observations.


title:
Compositional Mapping of Europa using MCMC Modelling of Near-IR
  VLT/SPHERE and Galileo/NIMS Observations

abstract:
  We present maps of surface composition of Europa's anti-jovian hemisphere
acquired using high spatial resolution IFU multi-spectral data from the SPHERE
instrument on the Very Large Telescope (0.95 to 1.65$\mu$m) and the NIMS
instrument on the Galileo orbiter (0.7 to 5.2$\mu$m). Spectral modelling was
performed using a Markov Chain Monte Carlo method to estimate endmember
abundances and to quantify their associated uncertainties. Modelling results
support the leading-trailing hemisphere difference in hydrated sulphuric acid
abundances caused by exogenic plasma bombardment. Water ice grains are found to
be in the 100$\mu$m to 1mm range, with larger grains present on the trailing
hemisphere, consistent with radiation driven sputtering destroying smaller
grains. Modelling best estimates suggest a mixture of sulphate and chlorinated
salts, although uncertainties derived from the MCMC modelling suggest that it
is difficult to confidently detect individual salt abundances with low spectral
resolution spectra from SPHERE and NIMS. The high spatial resolution offered by
SPHERE allows the small scale spatial distribution (<150km) of potential
species to be mapped, including ground-based detection of lineae and impact
features. This could be used in combination with other higher spectral
resolution observations to confirm the presence of these species.


title: Tameness and Rosenthal type locally convex spaces

abstract:
  Motivated by Rosenthal's famous $l^1$-dichotomy in Banach spaces, Haydon's
theorem, and additionally by recent works on tame dynamical systems, we
introduce the class of tame locally convex spaces. This is a natural locally
convex analogue of Rosenthal Banach spaces (for which any bounded sequence
contains a weak Cauchy subsequence). Our approach is based on a bornology of
tame subsets which in turn is closely related to eventual fragmentability. This
leads, among others, to the following results:
  $\bullet$ extending Haydon's characterization of Rosenthal Banach spaces, by
showing that a lcs $E$ is tame iff every weak-star compact, equicontinuous
convex subset of $E^{*}$ is the strong closed convex hull of its extreme points
iff $\overline{\rm{co\,}}^{w^{*}}(K) = \overline{\rm{co\,}}(K)$ for every
weak-star compact equicontinuous subset $K$ of $E^{*}$;
  $\bullet$ $E$ is tame iff there is no bounded sequence equivalent to the
generalized $l^{1}$-sequence;
  $\bullet$ strengthening some results of W.M. Ruess about Rosenthal's
dichotomy;
  $\bullet$ applying the Davis-Figiel-Johnson-Pelczy\'nski (DFJP) technique one
may show that every tame operator $T \colon E \to F$ between a lcs $E$ and a
Banach space $F$ can be factored through a tame (i.e., Rosenthal) Banach space.


title: Hamiltonian Monte Carlo reconstruction from peculiar velocities

abstract:
  The problem of the reconstruction of the large scale density and velocity
fields from peculiar velocities surveys is addressed here within a Bayesian
framework by means of Hamiltonian Monte Carlo (HMC) sampling. The HAmiltonian
Monte carlo reconstruction of the Local EnvironmenT (Hamlet) algorithm is
designed to reconstruct the linear large scale density and velocity fields in
conjunction with the undoing of lognormal bias in the derived distances and
velocities of peculiar velocities surveys such as the Cosmicflows data. The
Hamlet code has been tested against Cosmicflows mock catalogs consisting of up
to 30 000 data points with mock errors akin to those of the Cosmicflows-3 data,
within the framework of the LCDM standard model of cosmology.
  The Hamlet code outperforms previous applications of Gibbs sampling MCMC
reconstruction from the Cosmicflows-3 data by two to four orders of magnitude
in CPU time. The gain in performance is due to the inherent higher efficiency
of the HMC algorithm and due to parallel computing on GPUs rather than CPUs.
This gain will enable an increase in the reconstruction of the large scale
structure from the upcoming Cosmicfows-4 data and the setting of constrained
initial conditions for cosmological high resolution simulations.


title:
dApps: Distributed Applications for Real-time Inference and Control in
  O-RAN

abstract:
  The Open Radio Access Network (Open RAN)-which is being standardized, among
others, by the O-RAN Alliance-is bringing a radical transformation to the
cellular ecosystem through the notions of disaggregation and RAN Intelligent
Controllers (RICs). The latter enable closed-loop control through custom logic
applications, namely xApps and rApps, supporting control decisions at different
timescales. However, the current O-RAN specifications lack of a practical
approach to execute real-time control loops operating at timescales below 10ms.
In this paper, we propose the notion of dApps, distributed applications that
complement existing xApps/rApps by allowing operators to implement fine-grained
data-driven management and control in real-time at the Central Units
(CUs)/Distributed Units (DUs). dApps receive real-time data from the RAN, as
well as enrichment information from the near-real-time RIC, and execute
inference and control of lower-layer functionalities, thus enabling use cases
with stricter timing requirements than those considered by the RICs, such as
beam management and user scheduling. We propose feasible ways to integrate
dApps in the O-RAN architecture by leveraging and extending interfaces and
components already present therein. Finally, we discuss challenges specific to
dApps, and provide preliminary results that show the benefits of executing
network intelligence through dApps.


title:
Quantum researcher mobility: the wonderful wizard of Oz who paid for
  Dorothy's Visa fees

abstract:
  Historically, science has benefited greatly through the mobility of
researchers, whether it has been due to large-scale conflict, the search for
new opportunities or a lack thereof. Today's world of strict global immigration
policies, exacerbated by the COVID-19 pandemic, places inordinate hurdles on
the mobility of all researchers, let alone quantum ones. Exorbitant Visa fees,
the difficulty of navigating a foreign immigration system, lack of support for
researchers' families, and explicit government policy targeting selected groups
of immigrants are all examples of things that have severely impacted the
ability of quantum researchers to cross both physical and scientific borders.
Here we clearly identify some key problems affecting quantum researcher
mobility and discuss examples of good practice on the governmental,
institutional, and societal level that have helped, or might help, overcome
these hurdles. The adoption of such practices worldwide can ensure that quantum
scientists can reach their fullest potential, irrespective of where they were
born.


title:
UV and X-ray pulse amplitude variability in the transitional millisecond
  pulsar PSR J1023+0038

abstract:
  The transitional millisecond pulsar PSR\,J1023+0038 is the first millisecond
pulsar discovered to emit UV and optical pulses. Here we present the results of
the UV and X-ray phase-resolved timing analysis of observations performed with
the Hubble Space Telescope, \textit{XMM-Newton} and NuSTAR satellites between
2014 and 2021. Ultraviolet pulsations are detected in the high luminosity mode
and disappear during low and flaring modes, similar to what is observed in the
X-ray band. In the high mode, we find variability in both the UV and X-ray
pulse amplitudes. The root mean square pulsed amplitude in the UV band ranges
from $\sim$2.1\% down to $\sim$0.7\%, while it oscillates in the interval
$5.5-12\%$ in the X-ray band. This variability is not correlated with the
orbital phase, like what has been observed in the optical band. Notwithstanding
the rather low statistics, we have marginal evidence that variations in the
pulse amplitude do not occur simultaneously in the UV and X-ray bands. When the
UV pulsed amplitude decreases below the detection threshold, no significant
variation in the X-ray pulsed amplitude is observed. These oscillations in the
pulse amplitude could be caused by small random variations in the mass
accretion rate leading to a variation in the size of the intra-binary shock
region. Finally, we find that the pulsed flux spectral distribution from the
X-ray to the UV band is well fitted using a power-law relation of the form $\nu
F_{\nu}^{pulsed} \sim \nu^{0.4}$. This supports the hypothesis of a common
physical mechanism underlying the X-ray, UV, and optical pulsed emissions in
PSR\,J1023+0038.


title: On the Cylinder Theorem in $M^2\times \mathbb{R}^n $

abstract:
  Consider a surface $M^2$ with Gaussian curvature either $< 0$ or $> 0$. We
prove that in $M^2\times \mathbb{R}^n$ cylinders are characterized as the
hypersurfaces with both the extrinsic and intrinsic curvatures equal to zero.


title: Valued fields with a total residue map

abstract:
  When $k$ is a finite field, Becker-Denef-Lipschitz (1979) observed that the
total residue map $\text{res}:k(\!(t)\!)\to k$, which picks out the constant
term of the Laurent series, is definable in the language of rings with a
parameter for $t$. Driven by this observation, we study the theory
$\text{VF}_{\text{res},\iota}$ of valued fields equipped with a linear form
$\text{res}:K\to k$ which specializes to the residue map on the valuation ring.
We prove that $\text{VF}_{\text{res},\iota}$ does not admit a model companion.
In addition, we show that the power series field $(k(\!(t)\!),\text{res})$,
equipped with such a total residue map, is undecidable whenever $k$ is an
infinite field. As a consequence, we get that $(\mathbb{C}(\!(t)\!),
\text{Res}_0)$ is undecidable, where $\text{Res}_0:\mathbb{C}(\!(t)\!)\to
\mathbb{C}:f\mapsto \text{Res}_0(f)$ maps $f$ to its complex residue at $0$.


title: Nonlinear and Linearized Models in Thermoviscoelasticity

abstract:
  We consider a quasistatic nonlinear model in thermoviscoelasticity at a
finite-strain setting in the Kelvin-Voigt rheology where both the elastic and
viscous stress tensors comply with the principle of frame indifference under
rotations. The force balance is formulated in the reference configuration by
resorting to the concept of nonsimple materials whereas the heat transfer
equation is governed by the Fourier law in the deformed configurations. Weak
solutions are obtained by means of a staggered in-time discretization where the
deformation and the temperature are updated alternatingly. Our result refines a
recent work by Mielke & Roub\'{\i}\v{c}ek [arXiv:1903.11094] since our
approximation does not require any regularization of the viscosity term.
Afterwards, we focus on the case of deformations near the identity and small
temperatures, and we show by a rigorous linearization procedure that weak
solutions of the nonlinear system converge in a suitable sense to solutions of
a system in linearized thermoviscoelasticity. The same property holds for
time-discrete approximations and we provide a corresponding commutativity
result.


title: String modes, propagators and loops on fuzzy spaces

abstract:
  We present a systematic organization of functions and operators on the fuzzy
2-sphere in terms of string modes, which are optimally localized in position
and momentum space. This allows to separate the semi-classical and the deep
quantum regime of non-commutative quantum field theory and exhibits its
nonlocal nature. This organization greatly simplifies the computation of loop
contributions, avoiding oscillatory integrals and providing the effective
action directly in position space. UV/IR mixing is understood as nonlocality
arising from long string modes in the loops. The method is suited for any
quantized symplectic space.


title: Complex analysis of divergent perturbation theory at finite temperature

abstract:
  We investigate the convergence properties of finite-temperature perturbation
theory by considering the mathematical structure of thermodynamic potentials
using complex analysis. We discover that zeros of the partition function lead
to poles in the internal energy and logarithmic singularities in the Helmholtz
free energy which create divergent expansions in the canonical ensemble.
Analysing these zeros reveals that the radius of convergence increases for
higher temperatures. In contrast, when the reference state is degenerate, these
poles in the internal energy create a zero radius of convergence in the
zero-temperature limit. Finally, by showing that the poles in the internal
energy reduce to exceptional points in the zero-temperature limit, we unify the
two main mathematical representations of quantum phase transitions.


title: DiT: Self-supervised Pre-training for Document Image Transformer

abstract:
  Image Transformer has recently achieved significant progress for natural
image understanding, either using supervised (ViT, DeiT, etc.) or
self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we
propose DiT, a self-supervised pre-trained Document Image Transformer model
using large-scale unlabeled text images for Document AI tasks, which is
essential since no supervised counterparts ever exist due to the lack of human
labeled document images. We leverage DiT as the backbone network in a variety
of vision-based Document AI tasks, including document image classification,
document layout analysis, table detection as well as text detection for OCR.
Experiment results have illustrated that the self-supervised pre-trained DiT
model achieves new state-of-the-art results on these downstream tasks, e.g.
document image classification (91.11 $\rightarrow$ 92.69), document layout
analysis (91.0 $\rightarrow$ 94.9), table detection (94.23 $\rightarrow$ 96.55)
and text detection for OCR (93.07 $\rightarrow$ 94.29). The code and
pre-trained models are publicly available at \url{https://aka.ms/msdit}.


title:
Enhanced clamshell swimming with asymmetric beating at low Reynolds
  number

abstract:
  A single flexible filament can be actuated to escape from the scallop theorem
and generate net propulsion at low Reynolds number. In this work, we study the
dynamics of a simple boundary-driven multi-filament swimmer, a two-arm
clamshell actuated at the hinged point, using a nonlocal slender body
approximation with full hydrodynamic interactions. We first consider an elastic
clamshell consisted of flexible filaments with intrinsic curvature, and then
build segmental models consisted of rigid segments connected by different
mechanical joints with different forms of response torques. The simplicity of
the system allows us to fully explore the effect of various parameters on the
swimming performance. Optimal included angles and elastoviscous numbers are
identified. The segmental models capture the characteristic dynamics of the
elastic clamshell. We further demonstrate how the swimming performance can be
significantly enhanced by the asymmetric beating patterns induced by biased
torques.


title:
Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge
  Health Monitoring

abstract:
  Modern real-time Structural Health Monitoring systems can generate a
considerable amount of information that must be processed and evaluated for
detecting early anomalies and generating prompt warnings and alarms about the
civil infrastructure conditions. The current cloud-based solutions cannot scale
if the raw data has to be collected from thousands of buildings. This paper
presents a full-stack deployment of an efficient and scalable anomaly detection
pipeline for SHM systems which does not require sending raw data to the cloud
but relies on edge computation. First, we benchmark three algorithmic
approaches of anomaly detection, i.e., Principal Component Analysis (PCA),
Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE).
Then, we deploy them on an edge-sensor, the STM32L4, with limited computing
capabilities. Our approach decreases network traffic by
$\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a
single installation and minimize network and cloud resource utilization,
enabling the scaling of the monitoring infrastructure. A real-life case study,
a highway bridge in Italy, demonstrates that combining near-sensor computation
of anomaly detection algorithms, smart pre-processing, and low-power wide-area
network protocols (LPWAN) we can greatly reduce data communication and cloud
computing costs, while anomaly detection accuracy is not adversely affected.


title:
Where to Look Next: Learning Viewpoint Recommendations for Informative
  Trajectory Planning

abstract:
  Search missions require motion planning and navigation methods for
information gathering that continuously replan based on new observations of the
robot's surroundings. Current methods for information gathering, such as Monte
Carlo Tree Search, are capable of reasoning over long horizons, but they are
computationally expensive. An alternative for fast online execution is to
train, offline, an information gathering policy, which indirectly reasons about
the information value of new observations. However, these policies lack safety
guarantees and do not account for the robot dynamics. To overcome these
limitations we train an information-aware policy via deep reinforcement
learning, that guides a receding-horizon trajectory optimization planner. In
particular, the policy continuously recommends a reference viewpoint to the
local planner, such that the resulting dynamically feasible and collision-free
trajectories lead to observations that maximize the information gain and reduce
the uncertainty about the environment. In simulation tests in previously unseen
environments, our method consistently outperforms greedy next-best-view
policies and achieves competitive performance compared to Monte Carlo Tree
Search, in terms of information gains and coverage time, with a reduction in
execution time by three orders of magnitude.


title:
Can decay heat measurements tell us something about the Reactor
  Antineutrino Anomaly?

abstract:
  Measurements of the decay energy released as a function of time following the
thermal neutron induced fission on $^{235}$U and $^{239,241}$Pu were performed
in the 1970s at Oak Ridge National Laboratory with the purpose of quantifying
possible Loss Of Coolant Accident scenarios. This decay energy, known in
technical parlance as decay heat, is mainly composed of two terms, that of the
electrons produced together with antineutrinos in the beta-minus decay of the
neutron-rich fission products, and that of the gammas produced in the
subsequent decay of excited nuclear levels. In this work we study if this
extensive set of decay heat measurements can be used to assess the Reactor
Antineutrino Anomaly, that is, the approximately 5\% deficit of electron
antineutrinos produced by nuclear reactors, first deduced by Mention and
collaborators in 2011, and observed by the major reactor antineutrino
experiments near nuclear power plants since. With the assistance of nuclear
databases, we are able to obtain the ratio of electron spectra under
equilibrium conditions for $^{235}$U to $^{239}$Pu, in better agreement with
the lower trend recently reported by Kopeikin and collaborators, as well as
those for $^{235}$U to $^{241}$Pu and $^{241}$Pu to $^{239}$Pu, which do not
agree well with those measured at the Insitut Laue-Langevin in the 1980s. We
conclude that a new experimental campaign is needed to measure the electron
spectra utilizing a high-resolution and signal-to-noise-ratio electron
spectrometer and a highly precise fission normalization procedure.


title: Distributed Methods with Absolute Compression and Error Compensation

abstract:
  Distributed optimization methods are often applied to solving huge-scale
problems like training neural networks with millions and even billions of
parameters. In such applications, communicating full vectors, e.g.,
(stochastic) gradients, iterates, is prohibitively expensive, especially when
the number of workers is large. Communication compression is a powerful
approach to alleviating this issue, and, in particular, methods with biased
compression and error compensation are extremely popular due to their practical
efficiency. Sahu et al. (2021) propose a new analysis of Error Compensated SGD
(EC-SGD) for the class of absolute compression operators showing that in a
certain sense, this class contains optimal compressors for EC-SGD. However, the
analysis was conducted only under the so-called $(M,\sigma^2)$-bounded noise
assumption. In this paper, we generalize the analysis of EC-SGD with absolute
compression to the arbitrary sampling strategy and propose the first analysis
of EC-LSVRG with absolute compression for (strongly) convex problems. Our rates
improve upon the previously known ones in this setting. Our theoretical
findings are corroborated by several numerical experiments.


title:
AutoMO-Mixer: An automated multi-objective Mixer model for balanced,
  safe and robust prediction in medicine

abstract:
  Accurately identifying patient's status through medical images plays an
important role in diagnosis and treatment. Artificial intelligence (AI),
especially the deep learning, has achieved great success in many fields.
However, more reliable AI model is needed in image guided diagnosis and
therapy. To achieve this goal, developing a balanced, safe and robust model
with a unified framework is desirable. In this study, a new unified model
termed as automated multi-objective Mixer (AutoMO-Mixer) model was developed,
which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer)
as base. To build a balanced model, sensitivity and specificity were considered
as the objective functions simultaneously in training stage. Meanwhile, a new
evidential reasoning based on entropy was developed to achieve a safe and
robust model in testing stage. The experiment on an optical coherence
tomography dataset demonstrated that AutoMO-Mixer can obtain safer, more
balanced, and robust results compared with MLP-Mixer and other available
models.


title:
MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in
  Conversations

abstract:
  Emotion Recognition in Conversations (ERC) has considerable prospects for
developing empathetic machines. For multimodal ERC, it is vital to understand
context and fuse modality information in conversations. Recent graph-based
fusion methods generally aggregate multimodal information by exploring unimodal
and cross-modal interactions in a graph. However, they accumulate redundant
information at each layer, limiting the context understanding between
modalities. In this paper, we propose a novel Multimodal Dynamic Fusion Network
(MM-DFN) to recognize emotions by fully understanding multimodal conversational
context. Specifically, we design a new graph-based dynamic fusion module to
fuse multimodal contextual features in a conversation. The module reduces
redundancy and enhances complementarity between modalities by capturing the
dynamics of contextual information in different semantic spaces. Extensive
experiments on two public benchmark datasets demonstrate the effectiveness and
superiority of MM-DFN.


abstract:
  The von Neumann and quantum R\'enyi entropies characterize fundamental
properties of quantum systems and lead to theoretical and practical
applications in many fields. Quantum algorithms for estimating quantum
entropies, using a quantum query model that prepares the purification of the
input state, have been established in the literature. {However, constructing
such a model is almost as hard as state tomography.} In this paper, we propose
quantum algorithms to estimate the von Neumann and quantum $\alpha$-R\'enyi
entropies of an $n$-qubit quantum state $\rho$ using independent copies of the
input state. We also show how to efficiently construct the quantum circuits for
{quantum entropy estimation} using primitive single/two-qubit gates. We prove
that the number of required copies scales polynomially in $1/\epsilon$ and
$1/\Lambda$, where $\epsilon$ denotes the additive precision and $\Lambda$
denotes the lower bound on all non-zero eigenvalues. Notably, our method
outperforms previous methods in the aspect of practicality since it does not
require any quantum query oracles, which are usually necessary for previous
methods. Furthermore, we conduct experiments to show the efficacy of our
algorithms to single-qubit states and study the noise robustness. We also
discuss the applications to some quantum states of practical interest as well
as some meaningful tasks such as quantum Gibbs state preparation and
entanglement estimation.


title: Impedance spectroscopy: Impedance spectroscopy of nanomaterials

abstract:
  Solid state impedance spectroscopy enables the various contributions to the
resistive and capacitive properties of electronically inhomogeneous condensed
matter to be deconvoluted and characterized separately. The different
contributions arise from electronically distinct areas in the sample, which can
in an ideal case be represented each by one standard RC element. In the
following, the basic principles of impedance spectroscopy, different types of
experimental setups and several examples of experimental impedance data sets
from nanostructured materials are reviewed and discussed. The data analysis and
equivalent circuit modelling processes which are relevant for the application
of this technique to nanomaterials are emphasized. The different dimensions and
structure of nanomaterials as compared to macroscopic bulk samples leads to
quite different and sometimes more complex data that require detailed analysis
and advanced equivalent circuit models.


title:
Contribution of the non-resonant mechanism to the double and single
  differential distributions over the invariant variables in the reaction $e^+
  + e^- \to N + \bar N +\pi^0$

abstract:
  The general analysis of the differential cross section and various
polarization observables is performed for the process $e^+ + e^- \to N + \bar N
+\pi^0 $ assuming that the annihilation occurs through the exchange of one
virtual photon. The dependence of the differential distributions over invariant
variables is derived for the reaction $e^+ + e^- \to N + \bar N +\pi^0 $ in the
so-called non-resonant mechanism, applying the conservation of the hadron
electromagnetic currents and the P-invariance of the hadron electromagnetic
interaction. The detection in an exclusive experimental set up where the
nucleon (or antinucleon) and pion are detected in coincidence is considered. A
number of single and double differential distributions have been calculated
analytically and numerical estimates are given for the $p\bar p\pi^0$ and
$n\bar n\pi^0$ channels, in the Born (non-resonant) approximation, in the
energy range from threshold up to $s=16$ GeV$^2$.


title: Learning Goal-Oriented Non-Prehensile Pushing in Cluttered Scenes

abstract:
  Pushing objects through cluttered scenes is a challenging task, especially
when the objects to be pushed have initially unknown dynamics and touching
other entities has to be avoided to reduce the risk of damage. In this paper,
we approach this problem by applying deep reinforcement learning to generate
pushing actions for a robotic manipulator acting on a planar surface where
objects have to be pushed to goal locations while avoiding other items in the
same workspace. With the latent space learned from a depth image of the scene
and other observations of the environment, such as contact information between
the end effector and the object as well as distance to the goal, our framework
is able to learn contact-rich pushing actions that avoid collisions with other
objects. As the experimental results with a six degrees of freedom robotic arm
show, our system is able to successfully push objects from start to end
positions while avoiding nearby objects. Furthermore, we evaluate our learned
policy in comparison to a state-of-the-art pushing controller for mobile robots
and show that our agent performs better in terms of success rate, collisions
with other objects, and continuous object contact in various scenarios.


title:
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D
  Networks for 3D Coherent Layer Segmentation of Retina OCT Images

abstract:
  Automated surface segmentation of retinal layer is important and challenging
in analyzing optical coherence tomography (OCT). Recently, many deep learning
based methods have been developed for this task and yield remarkable
performance. However, due to large spatial gap and potential mismatch between
the B-scans of OCT data, all of them are based on 2D segmentation of individual
B-scans, which may loss the continuity information across the B-scans. In
addition, 3D surface of the retina layers can provide more diagnostic
information, which is crucial in quantitative image analysis. In this study, a
novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) is
proposed to obtain continuous 3D retinal layer surfaces from OCT. The 2D
features of individual B-scans are extracted by an encoder consisting of 2D
convolutions. These 2D features are then used to produce the alignment
displacement field and layer segmentation by two 3D decoders, which are coupled
via a spatial transformer module. The entire framework is trained end-to-end.
To the best of our knowledge, this is the first study that attempts 3D retinal
layer segmentation in volumetric OCT images based on CNNs. Experiments on a
publicly available dataset show that our framework achieves superior results to
state-of-the-art 2D methods in terms of both layer segmentation accuracy and
cross-B-scan 3D continuity, thus offering more clinical values than previous
works.


title:
Machine-learning enabled thermodynamic model for the design of new
  rare-earth compounds

abstract:
  We employ a descriptor based machine-learning approach to assess the effect
of chemical alloying on formation-enthalpy of rare-earth intermetallics.
Application of machine-learning approaches in rare-earth intermetallic design
have been sparse due to limited availability of reliable datasets. In this
work, we developed an `in-house' rare-earth database with more than 600$+$
compounds, each entry was populated with formation enthalpy and related atomic
features using high-throughput density-functional theory (DFT). The SISSO (sure
independence screening and sparsifying operator) based machine-learning method
with meaningful atomic features was used for training and testing the formation
enthalpies of rare earth compounds. The complex lattice function coupled with
the machine-learning model was used to explore the effect of transition metal
alloying on the energy stability of Ce based cubic Laves phases (MgCu$_{2}$
type). The SISSO predictions show good agreement with high-fidelity DFT
calculations and X$-$ray powder diffraction measurements. Our study provides
quantitative guidance for compositional considerations within a
machine-learning model and discovering new metastable materials. The
electronic-structure of Ce$-$Fe$-$Cu based compound was also analyzed
in$-$depth to understand the electronic origin of phase stability. The
interpretable analytical models in combination with density$-$functional theory
and experiments provide a fast and reliable design guide for discovering
technologically useful materials.


title:
Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable
  Topics for the Russian Language

abstract:
  Toxicity on the Internet, such as hate speech, offenses towards particular
users or groups of people, or the use of obscene words, is an acknowledged
problem. However, there also exist other types of inappropriate messages which
are usually not viewed as toxic, e.g. as they do not contain explicit offences.
Such messages can contain covered toxicity or generalizations, incite harmful
actions (crime, suicide, drug use), provoke "heated" discussions. Such messages
are often related to particular sensitive topics, e.g. on politics, sexual
minorities, social injustice which more often than other topics, e.g. cars or
computing, yield toxic emotional reactions. At the same time, clearly not all
messages within such flammable topics are inappropriate.
  Towards this end, in this work, we present two text collections labelled
according to binary notion of inapropriateness and a multinomial notion of
sensitive topic. Assuming that the notion of inappropriateness is common among
people of the same culture, we base our approach on human intuitive
understanding of what is not acceptable and harmful. To objectivise the notion
of inappropriateness, we define it in a data-driven way though crowdsourcing.
Namely we run a large-scale annotation study asking workers if a given chatbot
textual statement could harm reputation of a company created it. Acceptably
high values of inter-annotator agreement suggest that the notion of
inappropriateness exists and can be uniformly understood by different people.
To define the notion of sensitive topics in an objective way we use on
guidelines suggested commonly by specialists of legal and PR department of a
large public company as potentially harmful.


title:
How human-robot collaboration impacts construction productivity: an
  agent-based multi-fidelity modeling approach

abstract:
  Though construction robots have drawn attention in research and practice for
decades, human-robot collaboration (HRC) remains important to conduct complex
construction tasks. Considering its complexity and uniqueness, it is still
unclear how HRC process will impact construction productivity. To this end, an
agent-based (AB) multi-fidelity modeling approach is introduced to simulate and
evaluate how HRC influences construction productivity. A high-fidelity model is
first proposed for a scenario with one robot. Then, a low-fidelity model is
established to extract key parameters that capture the inner relationship among
scenarios. The multi-fidelity models work together to simulate complex
scenarios. Simulation and experiements show that: 1) the proposed approach is
feasible and flexible for simulation of complex HRC processes, and can cover
multiple collaboration and interaction modes; 2) the influence of the
supplement strategy is simple when there is only one robot, where lower Check
Interval (CI) and higher Supplement Limit (SL) will improve productivity. But
the influence becomes much more complicated when there are more robots due to
the internal competition among robots for the limited time of workers; 3) the
productivity per robot improves when there are more robots and workers, even if
the human-robot ratio remains the same; 4) introducing proactive interaction
between robots and workers could improve productivity significantly, up to 22%
in our experiments, which further depends on the supplement strategy and the
human-robot ratio. Overall, this research contributes an integrated approach to
simulate and evaluate HRC's impacts on productivity as well as valuable
insights on how to optimize HRC for better performance.


title: Microwave Assisted Hydrothermal Synthesis of Nanoparticles

abstract:
  The general topic of energy is regarded to be one of the most important areas
for any research and development activities at the start of the twentyfirst
century. Therefore, large research efforts are already based on or will be
targeted on the development of clean fuels to replace fossil fuels, the
improvement of the existing resources and the discovery of new sustainable
energy resources, the establishment of new efficient ways to store sustainable
energy, and the replacement of the existing high energy consuming technologies
with the more energy efficient ones. The latter concept has resulted in
considerable efforts recently to renovate the conventional synthetic chemical
industry by introducing new green chemistry concepts, where the energy
requirements for chemical processing or synthesis would be drastically reduced.
In this context, a vast amount of novel chemical synthesis techniques have been
developed during the last decades. Such innovative techniques include
combustion, sol gel and coprecipitation methods, sonochemistry, hydrothermal
synthesis, and microwave-assisted techniques. Significant qualitative drawbacks
in materials synthesized by such novel ways in terms of crystal quality and
physical properties are usually not encountered. In some cases, even
quantitative improvements, novel crystal arrangements, and interesting particle
shapes can be achieved. The concept of combining several of the recently
developed nonconventional and innovative synthesis techniques is an interesting
concept to even further reduce the energy requirements and particle sizes as
compared to one technique alone. Microwave assisted combustion and sol gel
synthesis are now employed frequently, as well as the combination of
hydrothermal and microwave assisted synthesis, where the latter technique is
the topic of this review.


title:
iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating
  Inverse Short-Time Fourier Transform

abstract:
  In recent text-to-speech synthesis and voice conversion systems, a
mel-spectrogram is commonly applied as an intermediate representation, and the
necessity for a mel-spectrogram vocoder is increasing. A mel-spectrogram
vocoder must solve three inverse problems: recovery of the original-scale
magnitude spectrogram, phase reconstruction, and frequency-to-time conversion.
A typical convolutional mel-spectrogram vocoder solves these problems jointly
and implicitly using a convolutional neural network, including temporal
upsampling layers, when directly calculating a raw waveform. Such an approach
allows skipping redundant processes during waveform synthesis (e.g., the direct
reconstruction of high-dimensional original-scale spectrograms). By contrast,
the approach solves all problems in a black box and cannot effectively employ
the time-frequency structures existing in a mel-spectrogram. We thus propose
iSTFTNet, which replaces some output-side layers of the mel-spectrogram vocoder
with the inverse short-time Fourier transform (iSTFT) after sufficiently
reducing the frequency dimension using upsampling layers, reducing the
computational cost from black-box modeling and avoiding redundant estimations
of high-dimensional spectrograms. During our experiments, we applied our ideas
to three HiFi-GAN variants and made the models faster and more lightweight with
a reasonable speech quality. Audio samples are available at
https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/istftnet/.


title: On the Convergence Analysis of Aggregated Heavy-Ball Method

abstract:
  Momentum first-order optimization methods are the workhorses in various
optimization tasks, e.g., in the training of deep neural networks. Recently,
Lucas et al. (2019) proposed a method called Aggregated Heavy-Ball (AggHB) that
uses multiple momentum vectors corresponding to different momentum parameters
and averages these vectors to compute the update direction at each iteration.
Lucas et al. (2019) show that AggHB is more stable than the classical
Heavy-Ball method even with large momentum parameters and performs well in
practice. However, the method was analyzed only for quadratic objectives and
for online optimization tasks under uniformly bounded gradients assumption,
which is not satisfied for many practically important problems. In this work,
we address this issue and propose the first analysis of AggHB for smooth
objective functions in non-convex, convex, and strongly convex cases without
additional restrictive assumptions. Our complexity results match the best-known
ones for the Heavy-Ball method. We also illustrate the efficiency of AggHB
numerically on several non-convex and convex problems.


title: Mobile authentication of copy detection patterns

abstract:
  In the recent years, the copy detection patterns (CDP) attracted a lot of
attention as a link between the physical and digital worlds, which is of great
interest for the internet of things and brand protection applications. However,
the security of CDP in terms of their reproducibility by unauthorized parties
or clonability remains largely unexplored. In this respect this paper addresses
a problem of anti-counterfeiting of physical objects and aims at investigating
the authentication aspects and the resistances to illegal copying of the modern
CDP from machine learning perspectives. A special attention is paid to a
reliable authentication under the real life verification conditions when the
codes are printed on an industrial printer and enrolled via modern mobile
phones under regular light conditions. The theoretical and empirical
investigation of authentication aspects of CDP is performed with respect to
four types of copy fakes from the point of view of (i) multi-class supervised
classification as a baseline approach and (ii) one-class classification as a
real-life application case. The obtained results show that the modern
machine-learning approaches and the technical capacities of modern mobile
phones allow to reliably authenticate CDP on end-user mobile phones under the
considered classes of fakes.


title:
Curvature and Torsion estimation of 3D functional data: A geometric
  approach to build the mean shape under the Frenet Serret framework

abstract:
  The analysis of curves has been routinely dealt with using tools from
functional data analysis. However its extension to multi-dimensional curves
poses a new challenge due to its inherent geometric features that are difficult
to capture with the classical approaches that rely on linear approximations. We
develop an alternative characterization of a mean that reflects shape variation
of the curves. Based on a geometric representation of the curves through the
Frenet-Serret ordinary differential equations, we introduce a new definition of
mean curvature and mean torsion, as well as mean shape through the notion of
mean vector field. This new formulation of the mean for multi-dimensional
curves allows us to integrate the parameters for the shape features into the
unified functional data modelling framework. We formulate the estimation
problem of the functional parameters in a penalized regression and develop an
efficient algorithm. We demonstrate our approach with both simulated data and
real data examples.