title:
An open-source integrated framework for the automation of citation
  collection and screening in systematic reviews

abstract:
  The exponential growth of scientific production makes secondary literature
abridgements increasingly demanding. We introduce a new open-source framework
for systematic reviews that significantly reduces time and workload for
collecting and screening scientific literature. The framework provides three
main tools: 1) an automatic citation search engine and manager that collects
records from multiple online sources with a unified query syntax, 2) a
Bayesian, active machine learning, citation screening tool based on iterative
human-machine interaction to increase predictive accuracy and, 3) a
semi-automatic, data-driven query generator to create new search queries from
existing citation data sets. To evaluate the automatic screener's performance,
we estimated the median posterior sensitivity and efficiency [90% Credible
Intervals] using Bayesian simulation to predict the distribution of undetected
potentially relevant records. Tested on an example topic, the framework
collected 17,755 unique records through the citation manager; 766 records
required human evaluation while the rest were excluded by the automatic
classifier; the theoretical efficiency was 95.6% [95.3%, 95.7%] with a
sensitivity of 100% [93.5%, 100%]. A new search query was generated from the
labelled dataset, and 82,579 additional records were collected; only 567
records required human review after automatic screening, and six additional
positive matches were found. The overall expected sensitivity decreased to
97.3% [73.8%, 100%] while the efficiency increased to 98.6% [98.2%, 98.7%]. The
framework can significantly reduce the workload required to conduct large
literature reviews by simplifying citation collection and screening while
demonstrating exceptional sensitivity. Such a tool can improve the
standardization and repeatability of systematic reviews.


title:
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal
  Channel Selection

abstract:
  Brain-computer interface systems aim to facilitate human-computer
interactions in a great deal by direct translation of brain signals for
computers. Recently, using many electrodes has caused better performance in
these systems. However, increasing the number of recorded electrodes leads to
additional time, hardware, and computational costs besides undesired
complications of the recording process. Channel selection has been utilized to
decrease data dimension and eliminate irrelevant channels while reducing the
noise effects. Furthermore, the technique lowers the time and computational
costs in real-time applications. We present a channel selection method, which
combines a sequential search method with a genetic algorithm called Deep GA
Fitness Formation (DGAFF). The proposed method accelerates the convergence of
the genetic algorithm and increases the system's performance. The system
evaluation is based on a lightweight deep neural network that automates the
whole model training process. The proposed method outperforms other channel
selection methods in classifying motor imagery on the utilized dataset.


title:
DFT-Spread Orthogonal Time Frequency Space System with Superimposed
  Pilots for Terahertz Integrated Sensing and Communication

abstract:
  Terahertz (THz) integrated sensing and communication (ISAC) is a promising
interdisciplinary technology that realizes simultaneously transmitting
Terabit-per-second (Tbps) and millimeter-level accurate environment or human
activity sensing. However, both communication performance and sensing accuracy
are influenced by the Doppler effects, which are especially severe in the THz
band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power
amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread
orthogonal time frequency space (DFT-s-OTFS) system is proposed to improve the
robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase
sensing parameter estimation algorithm is developed to integrate sensing
functionality into the DFT-s-OTFS waveform. Meanwhile, a scheme of superimposed
pilots is designed, which reduces the pilot overhead and improves the spectral
efficiency. Based on the superimposed pilots, a low-complexity iterative
channel estimation and data detection method is proposed to recover the data
symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA
efficiency by 10% on average compared to OTFS. Simulation results demonstrate
that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS
systems is able to realize millimeter-level range estimation accuracy and
decimeter-per-second-level velocity estimation accuracy. Moreover, the
effectiveness of the iterative method for data detection aided by superimposed
pilots in DFT-s-OTFS systems is validated by the simulations and the bit error
rate performance is not degraded by the Doppler effects.


title:
Guided Visual Attention Model Based on Interactions Between Top-down and
  Bottom-up Information for Robot Pose Prediction

abstract:
  Learning to control a robot commonly requires mapping between robot states
and camera images, where conventional deep vision models require large training
dataset. Existing visual attention models, such as Deep Spatial Autoencoders,
have improved the data-efficiency by training the model to selectively extract
only the task relevant image area. However, since the models are unable to
select attention targets on demand, the diversity of trainable tasks are
limited. This paper proposed a novel Key-Query-Value formulated visual
attention model which can be guided to a certain attention target. The model
creates an attention heatmap from Key and Query, and selectively extracts the
attended data represented in Value. Such structure is capable of incorporating
external inputs to create the Query, which will be trained to represent the
target objects. The separation of Query creation improved the model's
flexibility, enabling to simultaneously obtain and switch between multiple
targets in a top-down manner. The proposed model is experimented on a simulator
and a real-world environment, showing better performance compared to existing
end-to-end robot vision models. The results of real-world experiments indicated
the model's high scalability and extendiblity on robot controlling tasks.


title: Stabilization-free virtual element method for plane elasticity

abstract:
  We present the construction and application of a first order
stabilization-free virtual element method to problems in plane elasticity.
Well-posedness and error estimates of the discrete problem are established. The
method is assessed on a series of well-known benchmark problems from linear
elasticity and numerical results are presented that affirm the optimal
convergence rate of the virtual element method in the $L^2$ norm and the energy
seminorm.


title:
Channel Estimation and Projection for RIS-assisted MIMO Using Zadoff-Chu
  Sequences

abstract:
  The reconfigurable intelligent surface (RIS) technology is a promising
enabler for millimeter wave (mmWave) wireless communications, as it can
potentially provide spectral efficiency comparable to the conventional massive
multiple-input multiple-output (MIMO) but with significantly lower hardware
complexity. In this paper, we focus on the estimation and projection of the
uplink RIS-aided massive MIMO channel, which can be time-varying. We propose to
let the user equipments (UE) transmit Zadoff-Chu (ZC) sequences and let the
base station (BS) conduct maximum likelihood (ML) estimation of the uplink
channel. The proposed scheme is computationally efficient: it uses ZC sequences
to decouple the estimation of the frequency and time offsets; it uses the
space-alternating generalized expectation-maximization (SAGE) method to reduce
the high-dimensional problem due to the multipaths to multiple
lower-dimensional ones per path. Owing to the estimation of the Doppler
frequency offsets, the time-varying channel state can be projected, which can
significantly lower the overhead of the pilots for channel estimation. The
numerical simulations verify the effectiveness of the proposed scheme.


title:
Space Group Symmetry of BaFe$_2$Se$_3$: ab initio-Experiment Phonon
  Study

abstract:
  This paper presents a study of the structure dynamics in BaFe$_2$Se$_3$. We
combined first-principle calculations, infrared measurements and a thorough
symmetry analysis. Our study confirms that $Pnma$ cannot be the space group of
BaFe$_2$Se$_3$, even at room temperature. The phonons assignment requires $Pm$
to be the BaFe$_2$Se$_3$ space group, not only in the magnetic phase, but also
in the paramagnetic phase at room temperature. This is due to a strong coupling
between a short range spin-order along the ladders, and the lattice degrees of
freedom associated with the Fe-Fe bond length. This coupling induces a change
in the bond-length pattern from an alternated trapezoidal one (as in $Pnma$) to
an alternated small/large rectangular one. Out of the two patterns, only the
latter is fully compatible with the observed block-type magnetic structure.
Finally, we propose a complete symmetry analysis of the BaFe$_2$Se$_3$ phase
diagram in the 0-600\,K range.


title: Phase-field fracture irreversibility using the slack variable approach

abstract:
  In this manuscript, the phase-field fracture irreversibility constraint is
transformed into an equality-based constraint using the slack variable
approach. The equality-based fracture irreversibility constraint is then
introduced in the phase-field fracture energy functional using the Lagrange
Multiplier Method and the Penalty method. Both methods are variationally
consistent with the conventional variational inequality phase-field fracture
problem, unlike the history-variable approach. Thereafter, numerical
experiments are carried out on benchmark problems in brittle and quasi-brittle
fracture to demonstrate the efficacy of the proposed method.


title:
Revelations on Jupiter's Formation, Evolution and Interior: Challenges
  from Juno Results

abstract:
  The Juno mission has revolutionized and challenged our understanding of
Jupiter. As Juno transitioned to its extended mission, we review the major
findings of Jupiter's internal structure relevant to understanding Jupiter's
formation and evolution. Results from Juno's investigation of Jupiter's
interior structure imply that the planet has compositional gradients and is
accordingly non-adiabatic, with a complex internal structure. These new results
imply that current models of Jupiter's formation and evolution require a
revision. In this paper, we discuss potential formation and evolution paths
that can lead to an internal structure model consistent with Juno data, and the
constraints they provide. We note that standard core accretion formation
models, including the heavy-element enrichment during planetary growth is
consistent with an interior that is inhomogeneous with composition gradients in
its deep interior. However, such formation models typically predict that this
region, which could be interpreted as a primordial dilute core, is confined to
about 10% of Jupiter's total mass. In contrast, structure models that fit Juno
data imply that this region contains 30% of the mass or more. One way to
explain the origin of this extended region is by invoking a relatively long
(about 2 Myrs) formation phase where the growing planet accretes gas and
planetesimals delaying the runaway gas accretion. Alternatively, Jupiter's
fuzzy core could be a result of a giant impact or convection post-formation.
These novel scenarios require somewhat special and specific conditions. Clarity
on the plausibility of such conditions could come from future high-resolution
observations of planet-forming regions around other stars, from the observed
and modeled architectures of extrasolar systems with giant planets, and future
Juno data obtained during its extended mission.


title: Fast Sinkhorn I: An O(N) algorithm for the Wasserstein-1 metric

abstract:
  The Wasserstein metric is broadly used in optimal transport for comparing two
probabilistic distributions, with successful applications in various fields
such as machine learning, signal processing, seismic inversion, etc.
Nevertheless, the high computational complexity is an obstacle for its
practical applications. The Sinkhorn algorithm, one of the main methods in
computing the Wasserstein metric, solves an entropy regularized minimizing
problem, which allows arbitrary approximations to the Wasserstein metric with
O(N^2) computational cost. However, higher accuracy of its numerical
approximation requires more Sinkhorn iterations with repeated matrix-vector
multiplications, which is still unaffordable. In this work, we propose an
efficient implementation of the Sinkhorn algorithm to calculate the
Wasserstein-1 metric with O(N) computational cost, which achieves the optimal
theoretical complexity. By utilizing the special structure of Sinkhorn's
kernel, the repeated matrix-vector multiplications can be implemented with O(N)
times multiplications and additions, using the Qin Jiushao or Horner's method
for efficient polynomial evaluation, leading to an efficient algorithm without
losing accuracy. In addition, the log-domain stabilization technique, used to
stabilize the iterative procedure, can also be applied in this algorithm. Our
numerical experiments show that the newly developed algorithm is one to three
orders of magnitude faster than the original Sinkhorn algorithm.


title:
Convergence of Discontinuous Galerkin Schemes for the Euler Equations
  via Dissipative Weak Solutions

abstract:
  In this paper, we present convergence analysis of high-order finite element
based methods, in particular, we focus on a discontinuous Galerkin scheme using
summation-by-parts operators. To this end, it is crucial that structure
preserving properties, such as positivity preservation and entropy inequality
hold. We demonstrate how to ensure them and prove the convergence of our
multidimensional high-order DG scheme via dissipative weak solutions. In
numerical simulations, we verify our theoretical results.


title:
Determining star formation rates of active galactic nuclei host galaxies
  based on SED fitting with sub-mm data

abstract:
  We present the star formation rate (SFR) measurements based on the spectral
energy distribution (SED) analysis with new sub-mm fluxes combined with
archival multi-wavelength data for a sample of 52 AGN host galaxies at z
$<0.2$. We carried out sub-mm observations using the SCUBA-2 camera at the
James Clerk Maxwell Telescope, and obtained flux or an upper limit at 450 and
850 $\mu$m for each target. By experimenting the effect of the AGN dust
component in the SED fit, we find that dust luminosity can be overestimated if
AGN contribution is ignored. While the SFR based on 4000{\AA} break shows a
significant offset compared to dust luminosity based SFR, the SFR obtained by
the artificial neural network (Ellison et al. 2016) generally shows consistency
albeit with a large scatter. We find that SFR correlates with AGN outflow
strength manifested by the [OIII] $\lambda 5007$ emission line, and that AGNs
with higher Eddington ratios and stronger outflows are in general hosted by
galaxies with higher SFR, which is consistent with the correlation reported by
Woo et al. (2020). This suggests no instantaneous quenching of star formation
due to AGN feedback.


title:
Analysis of Two-Station Polling Queues with Setups using Continuous Time
  Markov Chain

abstract:
  The paper analyzes the performance of tandem network of polling queue with
setups. For a system with two-products and two-stations, we propose a new
approach based on a partially-collapsible state-space characterization to
reduce state-space complexity. In this approach, the size of the state-space is
varied depending on the information needed to determine buffer levels and
waiting times. We evaluate system performance under different system setting
and comment on the numerical accuracy of the approach as well as provide
managerial insights. Numerical results show that approach yields reliable
estimates of the performance measures. We also show how product and station
asymmetry significantly affect the systems performance.


title: The nature of gas giant planets

abstract:
  Revealing the true nature of the gas giant planets in our Solar System is
challenging. The masses of Jupiter and Saturn are about 318 and 95 Earth
masses, respectively. While they mostly consist of hydrogen and helium, the
total mass and distribution of the heavier elements, which reveal information
on their origin, are still unknown. Recent accurate measurements of the
gravitational fields of Jupiter and Saturn together with knowledge of the
behavior of planetary materials at high pressures allow us to better constrain
their interiors. Updated structure models of Jupiter and Saturn suggest that
both planets have complex interiors that include composition inhomogeneities,
non-convective regions, and fuzzy cores. In addition, it is clear that there
are significant differences between Jupiter and Saturn and that each giant
planet is unique. This has direct implications for giant exoplanet
characterization and for our understanding of gaseous planets as a class of
astronomical objects. In this review we summarize the methods used to model
giant planet interiors and recent developments in giant planet structure
models.


title:
PCSCNet: Fast 3D Semantic Segmentation of LiDAR Point Cloud for
  Autonomous Car using Point Convolution and Sparse Convolution Network

abstract:
  The autonomous car must recognize the driving environment quickly for safe
driving. As the Light Detection And Range (LiDAR) sensor is widely used in the
autonomous car, fast semantic segmentation of LiDAR point cloud, which is the
point-wise classification of the point cloud within the sensor framerate, has
attracted attention in recognition of the driving environment. Although the
voxel and fusion-based semantic segmentation models are the state-of-the-art
model in point cloud semantic segmentation recently, their real-time
performance suffer from high computational load due to high voxel resolution.
In this paper, we propose the fast voxel-based semantic segmentation model
using Point Convolution and 3D Sparse Convolution (PCSCNet). The proposed model
is designed to outperform at both high and low voxel resolution using point
convolution-based feature extraction. Moreover, the proposed model accelerates
the feature propagation using 3D sparse convolution after the feature
extraction. The experimental results demonstrate that the proposed model
outperforms the state-of-the-art real-time models in semantic segmentation of
SemanticKITTI and nuScenes, and achieves the real-time performance in LiDAR
point cloud inference.


title:
On perfectly matched layers of nonlocal wave equations in unbounded
  multi-scale media

abstract:
  A nonlocal perfectly matched layer (PML) is formulated for the nonlocal wave
equation in the whole real axis and numerical discretization is designed for
solving the reduced PML problem on a bounded domain. The nonlocal PML poses
challenges not faced in PDEs. For example, there is no derivative in nonlocal
models, which makes it impossible to replace derivates with complex ones. Here
we provide a way of constructing the PML for nonlocal models, which decays the
waves exponentially impinging in the layer and makes reflections at the
truncated boundary very tiny. To numerically solve the nonlocal PML problem, we
design the asymptotically compatible (AC) scheme for spatially nonlocal
operator by combining Talbot's contour, and a Verlet-type scheme for time
evolution. The accuracy and effectiveness of our approach are illustrated by
various numerical examples.


title:
Counterfactual Regret Minimization for Anti-jamming Game of Frequency
  Agile Radar

abstract:
  The competition between radar and jammer is one emerging issue in modern
electronic warfare, which in principle can be viewed as a non-cooperative game
with two players. In this work, the competition between a frequency agile (FA)
radar and a noise-modulated jammer is considered. As modern FA radar adopts
coherent processing with several pulses, the competition is hence in a
multiple-round way where each pulse can be modeled as one round interaction
between the radar and jammer. To capture such multiple-round property as well
as imperfect information inside the game, i.e., radar and jammer are unable to
know the upcoming signal, we propose an extensive-form game formulation for
such competition. Since the number of game information states grows
exponentially with respect to number of pulses, finding Nash Equilibrium (NE)
strategies may be a computationally intractable task. To effectively solve the
game, a learning-based algorithm called deep Counterfactual Regret Minimization
(CFR) is utilized. Numerical simulations demonstrates the effectiveness of deep
CFR algorithm for approximately finding NE and obtaining the best response
strategy.


title:
Probing the diffusive behaviour of beam-halo dynamics in circular
  accelerators

abstract:
  Circular particle accelerators at the energy frontier are based on
superconducting magnets that are extremely sensitive to beam losses as these
might induce quenches, i.e.\ transitions to the normal-conducting state.
Furthermore, the energy stored in the circulating beam is so large that
hardware integrity is put in serious danger, and machine protection becomes
essential for reaching the nominal accelerator performance. In this challenging
context, the beam halo becomes a potential source of performance limitations
and its dynamics needs to be understood in detail to assess whether it could be
an issue for the accelerator. In this paper, we discuss in detail a novel
framework, based on a diffusive approach, to model beam-halo dynamics. The
functional form of the optimal estimate of the perturbative series, as given by
Nekhoroshev's theorem, is used to provide the functional form of the action
diffusion coefficient. The goal is to propose an effective model for the
beam-halo dynamics and to devise an efficient experimental procedure to obtain
an accurate measurement of the diffusion coefficient.


title:
Tensor renormalization group study of (3+1)-dimensional $Z_2$
  gauge-Higgs model at finite density

abstract:
  We investigate the critical endpoint of the (3+1)-dimensional $Z_2$
gauge-Higgs model at finite density with the tensor renormalization group
method. We focus on the phase transition between the Higgs phase and the
confinement phase at finite chemical potential along the critical end line. As
a benchmark, we also study the (2+1)-dimensional $Z_2$ gauge-Higgs model whose
critical endpoint is compared with a recent numerical result. This is the first
application of the tensor renormalization group method to a four-dimensional
lattice gauge theory and a key stepping stone toward the future investigation
of the phase structure of the finite density QCD.


title:
Global convergence and acceleration of fixed point iterations of union
  upper semicontinuous operators: proximal algorithms, alternating and averaged
  nonconvex projections, and linear complementarity problems

abstract:
  We propose a unified framework to analyze fixed point iterations of a
set-valued operator that is the union of a finite number of upper
semicontinuous maps, each with nonempty closed domain and compact values. We
discuss global convergence, local linear convergence under a calmness
condition, and acceleration through extrapolation and component identification.
Our framework is applied to analyze a class of proximal algorithms for
minimizing the sum of a piecewise smooth function and the difference between
the pointwise minimum of finitely many weakly-convex functions and a piecewise
smooth convex function. When realized on two-set feasibility problems, this
algorithm class recovers alternating projections and averaged projections as
special cases, and our framework thus equips these classical methods with
global convergence and possibilities for acceleration. By specializing the
framework to a feasibility problem reformulation of the linear complementarity
problem, we show global convergence to a solution with a local linear rate of
the alternating projection as well as the averaged projection methods.
Numerical results further exemplify that the proposed acceleration algorithms
significantly improve upon their non-accelerated counterparts in efficiency.


title:
Boundary effects on the emergence of quasi-periodic solutions for Euler
  equations

abstract:
  In this paper, we highlight the importance of the boundary effects on the
construction of quasi-periodic vortex patches solutions close to Rankine
vortices and whose existence is not known in the whole space due to the
resonances of the linear frequencies. Availing of the lack of invariance by
radial dilation of Euler equations in the unit disc and using a Nash-Moser
implicit function iterative scheme we show the existence of such structures
when the radius of the Rankine vortex belongs to a suitable massive Cantor-like
set with almost full Lebesgue measure.


title:
Fine-Tuning can Distort Pretrained Features and Underperform
  Out-of-Distribution

abstract:
  When transferring a pretrained model to a downstream task, two popular
methods are full fine-tuning (updating all the model parameters) and linear
probing (updating only the last linear layer -- the "head"). It is well known
that fine-tuning leads to better accuracy in-distribution (ID). However, in
this paper, we find that fine-tuning can achieve worse accuracy than linear
probing out-of-distribution (OOD) when the pretrained features are good and the
distribution shift is large. On 10 distribution shift datasets
(Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR $\to$ STL, CIFAR10.1, FMoW,
ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on
average 2% higher accuracy ID but 7% lower accuracy OOD than linear probing. We
show theoretically that this tradeoff between ID and OOD accuracy arises even
in a simple setting: fine-tuning overparameterized two-layer linear networks.
We prove that the OOD error of fine-tuning is high when we initialize with a
fixed or random head -- this is because while fine-tuning learns the head, the
lower layers of the neural network change simultaneously and distort the
pretrained features. Our analysis suggests that the easy two-step strategy of
linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning
heuristic, combines the benefits of both fine-tuning and linear probing.
Empirically, LP-FT outperforms both fine-tuning and linear probing on the above
datasets (1% better ID, 10% better OOD than full fine-tuning).


title:
Improved constraint on primordial gravitational waves in light of the
  Hubble tension and BICEP/Keck

abstract:
  The Hubble tension that the standard $\Lambda$CDM model is suffering from can
be resolved with pre-recombination early dark energy. We present the first
constraint on the tensor-to-scalar ratio $r$ in corresponding
Hubble-tension-free cosmologies using the most recent BICEP/Keck cosmic
microwave background (CMB) B-mode polarization data. We find, combining
BICEP/Keck with Planck18 CMB and baryon acoustic oscillation data, that the
models with larger Hubble constant $H_0$ will have tighter upper bound on $r$,
and resolution $H_0\sim73$ km/s/Mpc of the Hubble tension tightens the upper
bound to $r<0.028\ (95\%\text{C.L.})$, $25\%$ tighter than the $\Lambda$CDM
constraint $r<0.036$. We clarify the origin of this tightening bound.


title:
General Helicity Formalism for Two-hadron Production in $e^+e^-$
  Collisions and the $\Lambda$ Polarizing Fragmentation Function

abstract:
  We present the complete structure of the azimuthal dependences and
polarization observables for two-hadron production in $e^+e^-$ annihilation
processes within a transverse momentum dependent (TMD) approach adopting the
helicity formalism. The leading-twist TMD fragmentation functions (TMD-FFs) for
spin-1/2 hadrons are fully accounted for. The role of the polarizing FF,
together with its extraction from Belle data for the transverse polarization of
$\Lambda$'s, are discussed as well. Finally, predictions for SIDIS processes at
EIC are presented.


title:
CCPT: Automatic Gameplay Testing and Validation with
  Curiosity-Conditioned Proximal Trajectories

abstract:
  This paper proposes a novel deep reinforcement learning algorithm to perform
automatic analysis and detection of gameplay issues in complex 3D navigation
environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method
combines curiosity and imitation learning to train agents to methodically
explore in the proximity of known trajectories derived from expert
demonstrations. We show how CCPT can explore complex environments, discover
gameplay issues and design oversights in the process, and recognize and
highlight them directly to game designers. We further demonstrate the
effectiveness of the algorithm in a novel 3D navigation environment which
reflects the complexity of modern AAA video games. Our results show a higher
level of coverage and bug discovery than baselines methods, and it hence can
provide a valuable tool for game designers to identify issues in game design
automatically.


title: Unitarity Constraints and Collider Searches for Dark Photons

abstract:
  Dark photons are predicted by various new physics models, and are being
intensively studied in a variety of experiments. In the first part of this
paper, we obtain partial wave unitarity constraints on the dark photon
parameter space from the allowed $VV\rightarrow VV$ scattering processes in the
limit of large center-of-mass energy, where $V=W,Z$. In the second part of the
paper, searches are performed using the expected differential rates with a
realistic detector simulation including a comprehensive set of background
processes on dilepton and dilepton plus a photon events at the High Luminosity
LHC. In these searches, sensitive differential distributions are used in an
optimized way to determine the sensitivity to dark photon parameter space. It
is shown that remarkable sensitivity to the dark photon model is achieved and
kinetic mixing strength can be probed down to $(1-4)\times 10^{-4}$ for dark
photon mass between $15$ GeV to $2$ TeV. We also investigate the sensitivity of
a future muon collider suggested by the Muon Accelerator Program (MAP) to the
dark photon model at different center-of-mass energies. It is shown that a
future muon collider is able to reach a sensitivity to kinetic mixing at the
order of $10^{-4}$.


title:
Improving the performance of twin-field quantum key distribution with
  advantage distillation technology

abstract:
  In this work, we apply two methods to improve the performance of a practical
twin-field quantum key distribution system under collective attack. Firstly, to
improve the secure key rate, we apply the error rate in X basis, Y basis and Z
basis to characterize the quantum channel. Secondly, we apply the advantage
distillation method to further improve the secure key rate and transmission
distance. Compared with the previous analysis result given by Maeda, Sasaki and
Koashi [Nature Communication 10, 3140 (2019)], the secure key rate obtained by
our analysis method will be increased at least 7%. By increasing the
loss-independent misalignment error to 12%, the previous analysis method cannot
overcome the rate-distance bound. However, our analysis method can still
overcome the rate-distance bound when the misalignment error is as large as
41%. More surprisingly, we prove that twin-field quantum key distribution can
generate positive secure key even if the misalignment error is arbitrarily
close to 50%, thus our analysis method can significantly improve the
performance of a practical twin-field quantum key distribution system.


title: Polar Coding for Efficient Transport Layer Multicast

abstract:
  In this paper, we shed light on how an adaptive, efficient error coding in
the transport layer helps ensure the application requirements. We recap the use
of MDS codes and show that binary coding can significantly reduce the
complexity and hence increase the applicability also for embedded devices. We
exploit the persymmetric structure of the generator matrix in polar codes to
establish a duality of dispersion over channels (the polarization effect) and
over packets (the generality required for multicast transmission), thereby
constructing systematic polar codes for incremental redundancy whose
performance, despite a much lower complexity, is near to MDS codes for
medium-range residual loss rates.


title:
The dynamically screened ladder approximation: Simultaneous treatment of
  strong electronic correlations and dynamical screening out of equilibrium

abstract:
  Dynamical screening is a key property of charged many-particle systems. Its
theoretical description is based on the $GW$ approximation that is extensively
applied for ground-state and equilibrium situations but also for systems driven
out of equilibrium. The main limitation of the $GW$ approximation is the
neglect of strong electronic correlation effects that are important in many
materials as well as in dense plasmas. Here we derive the dynamically screened
ladder (DSL) approximation that selfconsistently includes, in addition to the
$GW$ diagrams, also particle--particle and particle--hole $T$-matrix diagrams.
The derivation is based on reduced-density-operator theory and the result is
equivalent to the recently presented G1--G2 scheme [Schl\"unzen \textit{et
al.}, Phys. Rev. Lett. \textbf{124}, 076601 (2020); Joost \textit{et al.},
Phys. Rev. B \textbf{101}, 245101 (2020)]. We perform extensive time-dependent
DSL simulations for finite Hubbard clusters and present tests against exact
results that confirm excellent accuracy as well as total energy conservation of
the approximation. At strong coupling and for long simulation durations,
instabilities are observed. These problems are solved by enforcing contraction
consistency and applying a purification approach.


title:
USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics
  for Machine Translation

abstract:
  The vast majority of evaluation metrics for machine translation are
supervised, i.e., (i) assume the existence of reference translations, (ii) are
trained on human scores, or (iii) leverage parallel data. This hinders their
applicability to cases where such supervision signals are not available. In
this work, we develop fully unsupervised evaluation metrics. To do so, we
leverage similarities and synergies between evaluation metric induction,
parallel corpus mining, and MT systems. In particular, we use an unsupervised
evaluation metric to mine pseudo-parallel data, which we use to remap deficient
underlying vector spaces (in an iterative manner) and to induce an unsupervised
MT system, which then provides pseudo-references as an additional component in
the metric. Finally, we also induce unsupervised multilingual sentence
embeddings from pseudo-parallel data. We show that our fully unsupervised
metrics are effective, i.e., they beat supervised competitors on 4 out of our 5
evaluation datasets.


title:
Assessment of Effective-One-Body Radiation Reactions for Generic Planar
  Orbits

abstract:
  In this paper we assess the performances of different analytical
prescriptions for the effective-one-body (EOB) radiation reaction along generic
planar orbits using exact numerical result in the test-mass limit. We consider
three prescriptions put forward in the recent literature: (i) the
quasi-circular prescription QC, (ii) the QC with 2 post-Newtonian noncircular
corrections (QC2PN), (iii) the QC corrected by the noncircular Newtonian
prefactor (NCN). The analytical fluxes are then compared against the exact
fluxes that are computed by solving the Teukolsky equation with a test-mass
source in geodesic motion. We find that the NCN prescription is the most
accurate for both eccentric and hyperbolic orbits and it is in robust agreement
also for large values of the eccentricity. This result carries over to the
comparable masses, as we discuss for a numerical-relativity (NR) case study. We
also demonstrate that, while the EOB/NR waveform unfaithfulness is a necessary
check for the precision of EOB models, the direct comparison of EOB/NR fluxes
is a more stringent and informative test to select the best prescription.
Finally, we propose an improved NCN2PN radiation reaction that includes
noncircular 2PN corrections, in resummed form, as a further multiplicative
contribution and that is valid for any mass ratio.


title: Two-Stage Auction Mechanism for Long-Term Participation in Crowdsourcing

abstract:
  Crowdsourcing has become an important tool to collect data for various
artificial intelligence applications and auction can be an effective way to
allocate work and determine reward in a crowdsourcing platform. In this paper,
we focus on the crowdsourcing of small tasks such as image labelling and voice
recording where we face a number of challenges. First, workers have different
limits on the amount of work they would be willing to do, and they may also
misreport these limits in their bid for work. Secondly, if the auction is
repeated over time, unsuccessful workers may drop out of the system, reducing
competition and diversity. To tackle these issues, we first extend the results
of the celebrated Myerson's optimal auction mechanism for a single-parameter
bid to the case where the bid consists of the unit cost of work, the maximum
amount of work one is willing to do, and the actual work completed. We show
that a simple payment mechanism is sufficient to ensure a dominant strategy
from the workers, and that this dominant strategy is robust to the true utility
function of the workers. Secondly, we propose a novel, flexible work allocation
mechanism, which allows the requester to balance between cost efficiency and
equality. While cost minimization is obviously important, encouraging equality
in the allocation of work increases the diversity of the workforce as well as
promotes long-term participation on the crowdsourcing platform. Our main
results are proved analytically and validated through simulations.


title:
Exploring the Effects of AI-assisted Emotional Support Processes in
  Online Mental Health Community

abstract:
  Social support in online mental health communities (OMHCs) is an effective
and accessible way of managing mental wellbeing. In this process, sharing
emotional supports is considered crucial to the thriving social supports in
OMHCs, yet often difficult for both seekers and providers. To support
empathetic interactions, we design an AI-infused workflow that allows users to
write emotional supporting messages to other users' posts based on the
elicitation of the seeker's emotion and contextual keywords from writing. Based
on a preliminary user study (N = 10), we identified that the system helped
seekers to clarify emotion and describe text concretely while writing a post.
Providers could also learn how to react empathetically to the post. Based on
these results, we suggest design implications for our proposed system.


title: Multi-task Representation Learning with Stochastic Linear Bandits

abstract:
  We study the problem of transfer-learning in the setting of stochastic linear
bandit tasks. We consider that a low dimensional linear representation is
shared across the tasks, and study the benefit of learning this representation
in the multi-task learning setting. Following recent results to design
stochastic bandit policies, we propose an efficient greedy policy based on
trace norm regularization. It implicitly learns a low dimensional
representation by encouraging the matrix formed by the task regression vectors
to be of low rank. Unlike previous work in the literature, our policy does not
need to know the rank of the underlying matrix. We derive an upper bound on the
multi-task regret of our policy, which is, up to logarithmic factors, of order
$\sqrt{NdT(T+d)r}$, where $T$ is the number of tasks, $r$ the rank, $d$ the
number of variables and $N$ the number of rounds per task. We show the benefit
of our strategy compared to the baseline $Td\sqrt{N}$ obtained by solving each
task independently. We also provide a lower bound to the multi-task regret.
Finally, we corroborate our theoretical findings with preliminary experiments
on synthetic data.


title:
A formula by $LDL^{T}$ decomposition for the minimal type-I seesaw
  mechanism and conditions of $CP$ symmetry in an arbitrary basis

abstract:
  In this paper, defining a formula by $LDL^{T}$ decomposition for the minimal
type-I seesaw mechanism, we obtain conditions of $CP$ symmetry for the neutrino
mass matrix $m$ in an arbitrary basis. The conditions are found to be ${\rm
Re\,} (M_{22} a_{i} - M_{12} b_{i}) \, {\rm Im \,} ( M_{22} a_{j} - M_{12}
b_{j}) = - \det M \, {\rm Re\,} b_{i} \, {\rm Im \,} b_{j}$ or $ = - \det M \,
{\rm Im \,} b_{i} \, {\rm Re\,} b_{j}$ for the Yukawa matrix $Y_{ij} = (a_{j},
b_{j})$ and the right-handed neutrino mass matrix $M_{ij}$. In other words, the
real or imaginary part of $b_{i}$ must be proportional to the real or imaginary
part of the quantity $(M_{22} a_{i} - M_{12} b_{i})$.


title:
Reentrant localization transition in the Su-Schrieffer-Heeger model with
  random-dimer disorder

abstract:
  The question of whether the topological insulators can host a stable
nontrivial phase in the presence of spatially correlated disorder is a
fundamental question of interest. Based on theoretical investigations, we
analyze the effect of random-dimer disorder on the quantum phase transitions of
the Su-Schrieffer-Heeger model. We explicitly demonstrate that due to the
absence of symmetry, there are no in-gap edge states in certain disordered
topological nontrivial gapped phase and the bulk-boundary correspondence breaks
down. However, the fractionalized end charges still appear at the ends of the
chain. The energy distribution possibility of the in-gap edge states and the
possible values of fractionalized end charge are dependent on the concentration
of random-dimer disorder. On the other hand, the random-dimer disorder and
dimer hopping intertwine in an interesting way. The reentrant localization
transition behavior appears, which is evidenced by the fingerprint of the
inverse participation ratio, normalized participation ratio, and tunneling
conductivity.


title:
Analysis of Digital Sovereignty and Identity: From Digitization to
  Digitalization

abstract:
  Advances in emerging technologies have accelerated digital transformation
with the pervasive digitalization of the economy and society, driving
innovations such as smart cities, industry 4.0 and FinTech. Unlike
digitization, digitalization is a transformation to improve processes by
leveraging digital technologies and digitized data. The cyberspace has evolved
from a hardware internetworking infrastructure to the notion of a virtual
environment, transforming how people, business and government interact and
operate. Through this transformation, lots of personal data are captured which
individuals have no ownership or control over, threatening their privacy. It is
therefore necessary for the data owners to have control over the ownership,
custody and utilization of their data and to protect one's digital assets and
identity through proper data governance, cybersecurity control and privacy
protection. This results in the notions of data sovereignty and digital
sovereignty - two conceptually related terms, but different focuses. This paper
first explains these two concepts in terms of their guiding principles, laws
and regulations requirements, and analyse and discuss the technical challenges
of implementing these requirements. Next, to understand the emerging trend
shift in digital sovereignty towards individuals to take complete control of
the security and privacy of their own digital assets, this paper conducts a
systematic study and analysis of Self-Sovereign Identity, and discuss existing
solutions and point out that an efficient key management system, scalability
and interoperability of the solutions and well established standards are some
of its challenges and open problems to wide deployments.


title:
Carleman Estimates and Controllability of Stochastic degenerate
  parabolic Heat Equations

abstract:
  This paper concerns the null controllability for a class of stochastic
degenerate parabolic equations. We first establish a global Carleman estimate
for a linear forward stochastic degenerate equation with multiplicative noise.
Using this estimate we prove the null controllability of the backward equation
and obtain a partial result for the controllability of the forward equation.
Also, using a new Carleman estimate for backward equation with weighted
function which does not vanish at time t = 0 and the duality method HUM we get
the null controllability of a forward stochastic degenerate equation under the
action of two controls.


title:
Structural reorientation and compaction of porous MoS2 coatings during
  wear testing

abstract:
  Industrial upscaling frequently results in a different coating microstructure
than the laboratory prototypes presented in the literature. Here, we
investigate the wear behavior of physical vapor deposited (PVD) MoS2 coatings:
A dense, nanocrystalline MoS2 coating, and a porous, prismatic-textured MoS2
coating. Transmission electron microscopy (TEM) investigations before and after
wear testing evidence a crystallographic reorientation towards a basal texture
in both samples. A basal texture is usually desirable due to its low-friction
properties. This favorable reorientation is associated to a tribological
compaction of the porous specimens. Following running-in, sliding under high
contact pressure ultimately leads to a wear rate as small as for an ideal
chemical vapor deposited (CVD) grown bulk MoS2 single crystal reference. This
suggests that the imperfections of industrial grade MoS2 coatings can be
remediated by a suitable pretreatment.


title: Equilibria of Attacker-Defender Games

abstract:
  Attempts at predatory capture may provoke a defensive response that reduces
the very value of the predated resource. We provide a game-theoretic analysis
of simultaneous-move, two-player Attacker-Defender games that model such
interactions. When initial endowments are equal, Attackers win about a third of
such games in equilibrium. Under power disparities, Attackers become
particularly aggressive when they are approximately one-third poorer than
Defenders. With non-conflictual outside options Attackers become exceptionally
aggressive when their opponent has access to high-benefit, low-cost production,
and refrain from attack most when they are unilaterally provided with a
high-benefit, high-cost production option.


title:
Use of algebraic dual representations in domain decomposition methods
  for Darcy flow in 3D domain

abstract:
  In this work we use algebraic dual representations in conjunction with domain
decomposition methods for Darcy equations. We define the broken Sobolev spaces
and their finite dimensional counterparts. In addition, a global trace space is
defined that connects the solution between the broken spaces. Use of dual
representations results in a sparse metric free representation of the
constraint on divergence of velocity, the pressure gradient term and on the
continuity constraint across the sub domains. To demonstrate this, we solve two
test cases: i) manufactured solution case, and ii) industrial benchmark
reservoir modelling problem SPE10. The results demonstrate that the domain
decomposition scheme, although with more unknowns, requires less memory and
simulation time as compared to the continuous Galerkin formulation.


title:
Uniqueness of solutions to the logarithmic Minkowski problem in
  $\mathbb{R}^3$

abstract:
  In this paper, we prove the uniqueness of solutions to the logarithmic
Minkowski problem in $\mathbb{R}^3$ without symmetry condition, provided the
density of the measure is close to $1$ in $C^{\alpha}$ norm. This result also
implies the uniqueness of self-similar solutions to the anisotropic Gauss
curvature flow in $\mathbb{R}^3$ when the speed function is $C^{\alpha}$ close
to a positive constant.


title:
ICSML: Industrial Control Systems Machine Learning Inference Framework
  natively executing on IEC 61131-3 compliant devices

abstract:
  Industrial Control Systems (ICS) have played a catalytic role in enabling the
4th Industrial Revolution. ICS devices like Programmable Logic Controllers
(PLCs), automate, monitor, and control critical processes in industrial,
energy, and commercial environments. The convergence of traditional Operational
Technology (OT) with Information Technology (IT) has opened a new and unique
threat landscape. This has inspired defense research that focuses heavily on
Machine Learning (ML) based anomaly detection methods that run on external IT
hardware, which means an increase in costs and the further expansion of the
threat landscape. To remove this requirement, we introduce the ICS machine
learning inference framework (ICSML) which enables the execution of ML model
inference natively on the PLC. ICSML is implemented in IEC 61131-3 code and
provides several optimizations to bypass the limitations imposed by the
domain-specific languages. Therefore, it works \emph{on every PLC without the
need for vendor support}. ICSML provides a complete set of components for the
creation of full ML models similarly to established ML frameworks. We run a
series of benchmarks studying memory and performance and compare our solution
to the TFLite inference framework. At the same time, we develop domain-specific
model optimizations to improve the efficiency of ICSML. To demonstrate the
abilities of ICSML, we evaluate a case study of a real defense for
process-aware attacks targeting a desalination plant.


title:
Model-independent classification of events from the first CHIME/FRB Fast
  Radio Burst catalog

abstract:
  The CHIME/FRB collaboration has recently published a catalog containing about
half a thousand fast radio bursts (FRBs) including their spectra and several
reconstructed properties, like signal widths, amplitudes, etc. We have
developed a model-independent approach for a classification of these bursts
using cross-correlation and clustering algorithms applied to one-dimensional
intensity profiles of the bursts (i.e., to amplitudes as a function of time
averaged over the frequency). Using this algorithm we identified two major
classes of FRBs featuring different waveform morphology, and, simultaneously,
different distribution of brightness temperature. Bursts from one of the
identified cluster have lower brightness temperatures and larger widths than
events from the other cluster. Both groups include bursts from the repeaters
and one-off events.


title:
Bilinear magnetoresistance in topological insulators: role of magnetic
  disorder

abstract:
  Bilinear magnetoresistance is a nonlinear transport phenomenon that scales
linearly with the electric and magnetic fields, and appears in nonmagnetic
systems with strong spin-orbit coupling, such as topological insulators (TIs).
Using the semiclassical Boltzmann theory and generalized relaxation time
approximation, we consider in detail the bilinear magnetoresistance in an
effective model describing surface states of three-dimensional topological
insulators. We show that the presence of magnetic impurities remarkably
modifies the BMR signal. In general, scattering on magnetic impurities reduces
magnitude of BMR. Apart from this, an additional modulation of the angular
dependence of BMR appears when the spin-dependent component of the impurity
potential dominates the scalar one.


title:
Asymptotic properties of the normalized discrete associated-kernel
  estimator for probability mass function

abstract:
  Discrete kernel smoothing is now gaining importance in nonparametric
statistics. In this paper, we investigate some asymptotic properties of the
normalized discrete associated-kernel estimator of a probability mass function.
We show, under some regularity and non-restrictive assumptions on the
associated-kernel, that the normalizing random variable converges in mean
square to 1. We then derive the consistency and the asymptotic normality of the
proposed estimator. Various families of discrete kernels already exhibited
satisfy the conditions, including the refined CoM-Poisson which is
underdispersed and of second-order. Finally, the first-order binomial kernel is
discussed and, surprisingly, its normalized estimator has a suitable asymptotic
behaviour through simulations.


title: Laminar Chaos

abstract:
  We show that the output of systems with time-varying delay can exhibit a new
kind of chaotic behavior characterized by laminar phases, which are
periodically interrupted by irregular bursts. Within each laminar phase the
output intensity remains almost constant, but its level varies chaotically from
phase to phase. In scalar systems the periodic dynamics of the lengths and the
chaotic dynamics of the intensity levels can be understood and also tuned via
two one-dimensional maps, which can be deduced from the nonlinearity of the
delay equation and from the delay variation, respectively.


title:
bAdvertisement: Attacking Advanced Driver-Assistance Systems Using Print
  Advertisements

abstract:
  In this paper, we present bAdvertisement, a novel attack method against
advanced driver-assistance systems (ADASs). bAdvertisement is performed as a
supply chain attack via a compromised computer in a printing house, by
embedding a "phantom" object in a print advertisement. When the compromised
print advertisement is observed by an ADAS in a passing car, an undesired
reaction is triggered from the ADAS. We analyze state-of-the-art object
detectors and show that they do not take color or context into account in
object detection. Our validation of these findings on Mobileye 630 PRO shows
that this ADAS also fails to take color or context into account. Then, we show
how an attacker can take advantage of these findings to execute an attack on a
commercial ADAS, by embedding a phantom road sign in a print advertisement,
which causes a car equipped with Mobileye 630 PRO to trigger a false
notification to slow down. Finally, we discuss multiple countermeasures which
can be deployed in order to mitigate the effect of our proposed attack.


title:
Thorough characterisation of the 16 Cygni system. Part II. Seismic
  inversions of the internal structure

abstract:
  The advent of space-based photometry observations provided high-quality
asteroseismic data for a large number of stars. These observations enabled the
adaptation of advanced techniques, until then restricted to helioseismology, to
study the best asteroseismic targets. Amongst these, the 16Cyg binary system
holds a special place, being the brightest solar twins observed by Kepler. For
this system, modellers have access to high-quality asteroseismic, spectroscopic
and interferometric data, making it the perfect testbed for the limitations of
stellar models. We aim to further constrain the internal structure and
fundamental parameters of 16CygA&B using linear seismic inversion techniques of
both global indicators and localised corrections of the internal structure. We
start from the models defined by detailed modelling in our previous paper and
extend our analysis by applying variational inversions to these models. We
carried out inversions of so-called seismic indicators and provided local
corrections of the internal structure of the two stars. Our results indicate
that linear seismic inversions alone are not able to discriminate between
standard and non-standard models for 16CygA&B. We confirm the results of our
previous studies that used linear inversion techniques, but consider that the
differences could be linked to small fundamental parameters variations rather
than to a missing process in the models. We confirm the robustness and
reliability of the results of the modelling performed in our previous paper. We
conclude that non-linear inversions are likely required to further investigate
the properties of 16CygA&B from a seismic point of view, but that these
inversions should be coupled to analyses of the depletion of light elements
such as lithium and beryllium to constrain the macroscopic transport of
chemicals and potential non-standard evolutionary paths.


title:
Contactless pressure measurement of an underwater shock wave in a
  microtube using a high-resolution background-oriented schlieren technique

abstract:
  A high-resolution background-oriented schlieren (BOS) technique, which
utilizes a high-resolution camera and a microdot background pattern, is
proposed and used to measure the pressure field of an underwater shock wave in
a microtube. The propagation of the shock wave subsequently reaches a concave
water-air interface set in the microtube resulting in the ejection of a focused
microjet. This high spatial-resolution BOS technique can measure the pressure
field of a shock front with a width as narrow as the order of only 101 {\mu}m
with a peak pressure as large as almost 3 MPa, which is significantly narrower
and larger, respectively, than a previous study [1]. This significant
breakthrough has enabled the simultaneous measurement of the pressure impulse
of the shock front and the velocity of the microjet tip. As a result, we have
experimentally observed the linear relation between the velocity of the
microjet tip and the pressure impulse of the shock front for the cases without
secondary cavitation in the liquid bulk. Such relation was
theorectically/numerically predicted by Peters [2]. This study demonstrated the
capability of the proposed high-resolution BOS technique as a microscale
contactless pressure measurement tool for underwater shock waves and
potentially other micro- and nano-fluids.


title: A note on semicompleteness of graph products of abelian groups

abstract:
  In this short note we prove that a graph product $G_\Gamma$ of finitely
generated abelian groups is semicomplete -- that is the kernel of the natural
homomorphism ${\rm Aut}(G_\Gamma)\to{\rm Aut}(G_\Gamma^{ab})$ induced by the
abelianization of $G_\Gamma$ is equal to the inner automorphisms -- if and only
if $\Gamma$ does not have a separating star.


title: Massive MIMO with Dual-Polarized Antennas

abstract:
  This paper considers a single-cell massive MIMO (multiple-input
multiple-output) system with dual-polarized antennas at both the base station
and users. We study a channel model that takes into account several practical
aspects that arise when utilizing dual-polarization, such as channel
cross-polar discrimination (XPD) and cross-polar correlations (XPC) at the
transmitter and receiver. We analyze uplink and downlink achievable spectral
efficiencies (SE) with and without successive interference cancellation (SIC)
for the linear minimum mean squared error (MMSE), zero-forcing (ZF), and
maximum ratio (MR) combining/precoding schemes. In addition, we derive the
statistical properties of the MMSE channel estimator for the dual-polarized
channel model. These estimates are used to implement different precoding and
combining schemes when the uplink and downlink SE expressions are calculated
for the case. Closed-form uplink and downlink SE expressions for MR
combining/precoding are derived. Based on these results, we also provide power
control algorithms to maximize the uplink and downlink sum SEs. Moreover, we
compare the SEs achieved in dual-polarized and uni-polarized setups numerically
and evaluate the impact of XPD and XPC.


title: Bettors' reaction to match dynamics -- Evidence from in-game betting

abstract:
  It is still largely unclear to what extent bettors update their prior
assumptions about the strength or form of competing teams considering the
dynamics during the match. This is of interest not only from the psychological
perspective, but also as the pricing of live odds ideally should be driven both
by the (objective) outcome probabilities and also the bettors' behaviour.
Analysing large and high-resolution data from live betting in German football,
we find that stakes in the live-betting market are driven both by perceived
pre-game strength and by in-game strength as implied by measurable events such
as shots and passes. Both effects vary over the course of the match.


title:
The internal rotation of low-mass stars from solar and stellar
  seismology

abstract:
  The possibility of measuring the internal rotation of the Sun and stars
thanks to helio- and asteroseismology offers tremendous constraints on hydro-
and magnetohydrodynamical processes acting in stellar interiors. Understanding
the processes responsible for the transport of angular momentum in stellar
interiors is crucial as they will also influence the transport of chemicals and
thus the evolution of stars. Here we present some of the key results obtained
in both fields and how detailed seismic analyses can provide stringent
constraints on the physics of angular momentum transport in the interior of low
mass stars and potentially rule out some candidates.


title:
Finite groups admitting a coprime automorphism satisfying an additional
  polynomial identity

abstract:
  It is known that a finite group with an automorphism $\varphi$ of coprime
order has a soluble radical of $(|\varphi|,|C_G(\varphi)|)$-bounded Fitting
height and index. We extend this classic result as follows. Let $f(x) = a_0 +
a_1 \cdot x + \cdots + a_d \cdot x^d \in \mathbb{Z}[x]$ be a primitive
polynomial and let $G$ be a finite group with an automorphism $\varphi$ of
coprime order satisfying $ g^{a_0} \cdot \varphi(g)^{a_1} \cdots
\varphi^d(g)^{a_d} = 1 $, for all $g \in G$. Then the soluble radical of $G$
has $(d,|C_G(\varphi)|)$-boundex Fitting height and index. The bounds are made
explicit and are particularly good for small values of the degree $d$.


title:
Optical signature of cascade transitions between moir\'e interlayer
  excitons

abstract:
  Cascade transition between energy levels has important applications, such as
in quantum information protocols and quantum cascade lasers. In two-dimensional
heterostructure, the moir\'e superlattice potential can result in multiple
interlayer exciton (IX) energy levels. We demonstrate the cascade transitions
between such moir\'e IXs by performing time- and energy-resolved
photoluminescence measurements. We show that the lower-energy moir\'e IX can be
excited to higher-energy ones, facilitating IX population inversion.


title:
Radiative neutrino masses from modular $A_4$ symmetry and supersymmetry
  breaking

abstract:
  We investigate a modular $A_4$ invariant two-loop neutrino mass model in a
supersymmetric framework, where we introduce new fields as minimum as possible,
expecting contributions of superpartners to the neutrino masses. We
successfully reproduce the neutrino oscillation data thanks to the superpartner
contributions in case of normal hierarchy, and predict several observables such
as phases and neutrino masses, concentrating on three specific regions at
nearby fixed points of modulus $\tau=i,\omega,i\times \infty$, where
$\omega\equiv e^{2\pi i/3}$. These points are statistically favored in flux
compactifications of the string theory. We show several results in each the
points by performing global $\chi^2$ analysis, and demonstrate benchmark points
with the minimum $\chi^2$.


title: Almost Disjoint Paths and Separating by Forbidden Pairs

abstract:
  By Menger's theorem the maximum number of arc-disjoint paths from a vertex s
to a vertex t in a directed graph equals the minumum number of arcs needed to
disconnect s and t, i.e., the minimum size of an s-t-cut. The max-flow problem
in a network with unit capacities is equivalent to the arc-disjoint paths
problem. Moreover the max-flow and min-cut problems form a strongly dual pair.
We relax the disjointedness requirement on the paths, allowing them to be
almost disjoint, meaning they may share up to one arc. The resulting almost
disjoint paths problem (ADP) asks for k s-t-paths such that any two of them are
almost disjoint. The separating by forbidden pairs problem (SFP) is the
corresponding dual problem and calls for a set of k arc pairs such that every
s-t-path contains both arcs of at least one such pair.
  In this paper, we explore these two problems, showing that they have an
unbounded duality gap in general and analyzing their complexity. We prove that
ADP is NP-complete when k is part of the input and that SFP is
Sigma_2P-complete, even for acyclic graphs. Furthermore, we efficiently solve
ADP when k<=2 is fixed and present a polynomial time algorithm based on dynamic
programming for ADP when k is constant and the considered graphs are acyclic.


title: Control of protein-based pattern formation via guiding cues

abstract:
  Proteins control many vital functions in living cells, such as cell growth
and cell division. Reliable coordination of these functions requires the
spatial and temporal organizaton of proteins inside cells, which encodes
information about the cell's geometry and the cell-cycle stage. Such protein
patterns arise from protein transport and reaction kinetics, and they can be
controlled by various guiding cues within the cell. Here, we review how protein
patterns are guided by cell size and shape, by other protein patterns that act
as templates, and by the mechanical properties of the cell. The basic
mechanisms of guided pattern formation are elucidated with reference to recent
observations in various biological model organisms. We posit that understanding
the controlled formation of protein patterns in cells will be an essential part
of understanding information processing in living systems.


title:
A summary of several meteorological properties of the moist-air entropy
  variables $\theta_s$ and $PV(\theta_s)$

abstract:
  The aim of this note is to describe several meteorological properties shown
in a conference talk (Marquet, January 2022) for the moist-air specific
entropy, the associated potential temperature ($\theta_s$) defined in Marquet
(2011) and the associated potential vorticity $PV(\theta_s)$ defined in Marquet
(2014), with in particular regions of negative values of $PV(\theta_s)$ close
to the cold fronts that seems to indicate regions of symmetric instability.


title: Polarization of the conformal vacuum in the Milne universe

abstract:
  The vacuum expectation values (VEVs) of the field squared and energy-momentum
tensor for a massless scalar field are investigated in the Milne universe with
general number of spatial dimensions. The vacuum state depends on the choice of
the mode functions in the canonical quantization procedure and we assume that
the field is prepared in the conformal vacuum. As the first step an integral
representation for the difference of the Wightman functions corresponding to
the conformal and Minkowski vacua is derived. The mean field squared and
energy-momentum tensor are obtained in the coincidence limit. It is shown that
the Minkowski vacuum state is interpreted as a thermal one with respect to the
conformal vacuum. The thermal factor is of the Bose-Einstein type in odd
dimensional space and of the Fermi-Dirac type in even number of spatial
dimensions.


title: Point Cloud Denoising via Momentum Ascent in Gradient Fields

abstract:
  To achieve point cloud denoising, traditional methods heavily rely on
geometric priors, and most learning-based approaches suffer from outliers and
loss of details. Recently, the gradient-based method was proposed to estimate
the gradient fields from the noisy point clouds using neural networks, and
refine the position of each point according to the estimated gradient. However,
the predicted gradient could fluctuate, leading to perturbed and unstable
solutions, as well as a large inference time. To address these issues, we
develop the momentum gradient ascent method that leverages the information of
previous iterations in determining the trajectories of the points, thus
improving the stability of the solution and reducing the inference time.
Experiments demonstrate that the proposed method outperforms state-of-the-art
methods with a variety of point clouds and noise levels.


title: Complete Excitation of Discrete Quantum Systems by Single Free Electrons

abstract:
  We reveal a wealth of nonlinear and recoil effects in the interaction between
individual low-energy electrons and samples comprising a discrete number of
states. Adopting a quantum theoretical description of combined free-electron
and two-level systems, we find a maximum achievable excitation probability of
$100\%$, which requires specific conditions relating to the coupling strength
and the transition symmetry, as we illustrate through calculations for dipolar
and quadrupolar modes. Strong recoil effects are observed when the kinetic
energy of the probe lies close to the transition threshold, although the
associated probability remains independent of the electron wave function even
when fully accounting for nonlinear interactions with arbitrarily complex
multilevel samples. Our work reveals the potential of free electrons to control
localized excitations and delineates the boundaries of such control.


title: Generally Curved Magnetic Flux Rope Structures

abstract:
  We present a new analytical approach with the aim to describe generally
curved and twisted magnetic flux ropestructures, that are embedded within
interplanetary coronal mass ejections, under the constraint of invariant
axialflux. In this paper we showcase the simplest case of a generally curved
flux rope with a circular cross-sectionwhich can be described in terms of the
curvature and the torsion of the Frenet-Serret equations. The magneticfield
configuration, for the axial and poloidal field components, are described in
terms of a radial expansion usinga Legendre basis. We further derive equations
that allow us to configure our model for any arbitrary magnetictwist and also
evaluate the force distribution. We show the effects and differences of our
proposed modelcompared to a purely cylindrical or toroidal geometry using an
arbitrarily twisted exemplary flux rope structurewith an uniformly twisted
magnetic field configuration. In order to indirectly compare our model with
realin-situ measurements we generate two synthetic in-situ profiles using
virtual spacecraft trajectories, realisticallysimulating apex and flank
encounters of an interplanetary coronal mass ejection. This proposed model
presentsan intermediate steps towards describing more complex flux rope
structures with arbitrary cross-section shapes.


title: Equivariant Lagrangian Floer homology via cotangent bundles of $EG_N$

abstract:
  We provide a construction of equivariant Lagrangian Floer homology $HF_G(L_0,
L_1)$, for a compact Lie group $G$ acting on a symplectic manifold $M$ in a
Hamiltonian fashion, and a pair of $G$-Lagrangian submanifolds $L_0, L_1
\subset M$.
  We do so by using symplectic homotopy quotients involving cotangent bundles
of an approximation of $EG$. Our construction relies on Wehrheim and Woodward's
theory of quilts, and the telescope construction.
  We show that these groups are independent in the auxilliary choices involved
in their construction, and are $H^*(BG)$-bimodules. In the case when $L_0 =
L_1$, we show that their chain complex $CF_G(L_0, L_1)$ is homotopy equivalent
to the equivariant Morse complex of $L_0$.
  Furthermore, if zero is a regular value of the moment map $\mu$ and if $G$
acts freely on $\mu^{-1}(0)$, we construct two "Kirwan morphisms" from
$CF_G(L_0, L_1)$ to $CF(L_0/G, L_1/G)$ (respectively from $CF(L_0/G, L_1/G)$ to
$CF_G(L_0, L_1)$).
  Our construction applies to the exact and monotone settings, as well as in
the setting of the extended moduli space of flat $SU(2)$-connections of a
Riemann surface, considered in Manolescu and Woodward's work. Applied to the
latter setting, our construction provides an equivariant symplectic side for
the Atiyah-Floer conjecture.


title:
Applications of blockchain and artificial intelligence technologies for
  enabling prosumers in smart grids: A review

abstract:
  Governments' net zero emission target aims at increasing the share of
renewable energy sources as well as influencing the behaviours of consumers to
support the cost-effective balancing of energy supply and demand. These will be
achieved by the advanced information and control infrastructures of smart grids
which allow the interoperability among various stakeholders. Under this
circumstance, increasing number of consumers produce, store, and consume
energy, giving them a new role of prosumers. The integration of prosumers and
accommodation of incurred bidirectional flows of energy and information rely on
two key factors: flexible structures of energy markets and intelligent
operations of power systems. The blockchain and artificial intelligence (AI)
are innovative technologies to fulfil these two factors, by which the
blockchain provides decentralised trading platforms for energy markets and the
AI supports the optimal operational control of power systems. This paper
attempts to address how to incorporate the blockchain and AI in the smart grids
for facilitating prosumers to participate in energy markets. To achieve this
objective, first, this paper reviews how policy designs price carbon emissions
caused by the fossil-fuel based generation so as to facilitate the integration
of prosumers with renewable energy sources. Second, the potential structures of
energy markets with the support of the blockchain technologies are discussed.
Last, how to apply the AI for enhancing the state monitoring and decision
making during the operations of power systems is introduced.


title:
Simplified Learning of CAD Features Leveraging a Deep Residual
  Autoencoder

abstract:
  In the domain of computer vision, deep residual neural networks like
EfficientNet have set new standards in terms of robustness and accuracy. One
key problem underlying the training of deep neural networks is the immanent
lack of a sufficient amount of training data. The problem worsens especially if
labels cannot be generated automatically, but have to be annotated manually.
This challenge occurs for instance if expert knowledge related to 3D parts
should be externalized based on example models. One way to reduce the necessary
amount of labeled data may be the use of autoencoders, which can be learned in
an unsupervised fashion without labeled data. In this work, we present a deep
residual 3D autoencoder based on the EfficientNet architecture, intended for
transfer learning tasks related to 3D CAD model assessment. For this purpose,
we adopted EfficientNet to 3D problems like voxel models derived from a STEP
file. Striving to reduce the amount of labeled 3D data required, the networks
encoder can be utilized for transfer training.


title: Enabling On-Device Smartphone GPU based Training: Lessons Learned

abstract:
  Deep Learning (DL) has shown impressive performance in many mobile
applications. Most existing works have focused on reducing the computational
and resource overheads of running Deep Neural Networks (DNN) inference on
resource-constrained mobile devices. However, the other aspect of DNN
operations, i.e. training (forward and backward passes) on smartphone GPUs, has
received little attention thus far. To this end, we conduct an initial analysis
to examine the feasibility of on-device training on smartphones using mobile
GPUs. We first employ the open-source mobile DL framework (MNN) and its OpenCL
backend for running compute kernels on GPUs. Next, we observed that training on
CPUs is much faster than on GPUs and identified two possible bottlenecks
related to this observation: (i) computation and (ii) memory bottlenecks. To
solve the computation bottleneck, we optimize the OpenCL backend's kernels,
showing 2x improvements (40-70 GFLOPs) over CPUs (15-30 GFLOPs) on the
Snapdragon 8 series processors. However, we find that the full DNN training is
still much slower on GPUs than on CPUs, indicating that memory bottleneck plays
a significant role in the lower performance of GPU over CPU. The data movement
takes almost 91% of training time due to the low bandwidth. Lastly, based on
the findings and failures during our investigation, we present limitations and
practical guidelines for future directions.


title:
BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for
  Transformer-based Models

abstract:
  Recent developments in transfer learning have boosted the advancements in
natural language processing tasks. The performance is, however, dependent on
high-quality, manually annotated training data. Especially in the biomedical
domain, it has been shown that one training corpus is not enough to learn
generic models that are able to efficiently predict on new data. Therefore,
state-of-the-art models need the ability of lifelong learning in order to
improve performance as soon as new data are available - without the need of
retraining the whole model from scratch. We present WEAVER, a simple, yet
efficient post-processing method that infuses old knowledge into the new model,
thereby reducing catastrophic forgetting. We show that applying WEAVER in a
sequential manner results in similar word embedding distributions as doing a
combined training on all data at once, while being computationally more
efficient. Because there is no need of data sharing, the presented method is
also easily applicable to federated learning settings and can for example be
beneficial for the mining of electronic health records from different clinics.


title:
Chemical Composition Of Bright Stars In The Northern Hemisphere:
  Star-Planet Connection

abstract:
  In fulfilling the aims of the planetary and asteroseismic research missions,
such as that of the NASA Transiting Exoplanet Survey Satellite (TESS) space
telescope, accurate stellar atmospheric parameters and a detailed chemical
composition are required as input. We have observed high-resolution spectra for
all 848 bright (V<8 mag) stars that are cooler than F5 spectral class in the
area up to 12 deg surrounding the northern TESS continuous viewing zone and
uniformly determined the main atmospheric parameters, ages, orbital parameters,
velocity components, and precise abundances of up to 24 chemical species
(C(C2), N(CN), [O I], Na I, Mg I, Al I, Si I, Si I, Ca I, Ca II, Sc I, Sc II,
Ti I, Ti II, V I, Cr I, Cr II, Mn I, Fe I, Fe II, Co I, Ni I, Cu I, and Zn I)
for 740 slowly rotating stars. The analysis of 25 planet-hosting stars in our
sample drove us to the following conclusions: the dwarf stars hosting high-mass
planets are more metal rich than those with low-mass planets. We find slightly
negative C/O and Mg/Si slopes toward the stars with high-mass planets. All the
low-mass planet hosts in our sample show positive $\Delta$[El/Fe] versus
condensation temperature slopes, in particular, the star with the large number
of various planets. The high-mass planet hosts have a diversity of slopes, but
in more metal rich, older, and cooler stars, the positive elemental abundance
slopes are more common.


title: Robustness and Accuracy Could Be Reconcilable by (Proper) Definition

abstract:
  The trade-off between robustness and accuracy has been widely studied in the
adversarial literature. Although still controversial, the prevailing view is
that this trade-off is inherent, either empirically or theoretically. Thus, we
dig for the origin of this trade-off in adversarial training and find that it
may stem from the improperly defined robust error, which imposes an inductive
bias of local invariance -- an overcorrection towards smoothness. Given this,
we advocate employing local equivariance to describe the ideal behavior of a
robust model, leading to a self-consistent robust error named SCORE. By
definition, SCORE facilitates the reconciliation between robustness and
accuracy, while still handling the worst-case uncertainty via robust
optimization. By simply substituting KL divergence with variants of distance
metrics, SCORE can be efficiently minimized. Empirically, our models achieve
top-rank performance on RobustBench under AutoAttack. Besides, SCORE provides
instructive insights for explaining the overfitting phenomenon and semantic
input gradients observed on robust models.


title: Reducing FEC-Complexity in Cross-Layer Predictable Data Communication

abstract:
  The PRRT protocol enables applications with strict performance requirements
such as Cyber-Physical Systems, as it provides predictably low, end-to-end
delay via cross-layer pacing and timely error correction via Hybrid ARQ (HARQ).
However, the implemented HARQ uses computationally complex Maximum Distance
Separable (MDS) codes to generate redundancy. In this paper we propose code
partitioning for the complexity reduction of MDS codes, thereby enabling their
deployment on constrained embedded devices.


title:
On the limiting amplitude principle for the wave equation with variable
  coefficients

abstract:
  In this paper, we prove new results on the validity of the limiting ampitude
principle (LAP) for the wave equation with nonconstant coefficients, not
necessarily in divergence form. Under suitable assumptions on the coefficients
and on the source term, we establish the LAP for space dimensions 2 and 3. This
result is extended to one space dimension with an appropriate modification. We
also quantify the LAP and thus provide estimates for the convergence of the
time-domain solution to the frequency-domain solution. Our proofs are based on
time-decay results of solutions of some auxiliary problems.


title: 2d Integrable systems, 4d Chern-Simons theory and Affine Higgs bundles

abstract:
  In this short review we compare constructions of 2d integrable models by
means of two gauge field theories. The first one is the 4d Chern-Simons (4d-CS)
theory proposed by Costello and Yamazaki. The second one is the 2d
generalization of the Hitchin integrable systems constructed by means the
Affine Higgs bundles (AHB). We illustrate this approach by considering 1+1
field versions of elliptic integrable systems including the Calogero-Moser
field theory, the Landau-Lifshitz model and the field theory generalization of
the elliptic Gaudin model.


title: Model-Agnostic Augmentation for Accurate Graph Classification

abstract:
  Given a graph dataset, how can we augment it for accurate graph
classification? Graph augmentation is an essential strategy to improve the
performance of graph-based tasks, and has been widely utilized for analyzing
web and social graphs. However, previous works for graph augmentation either a)
involve the target model in the process of augmentation, losing the
generalizability to other tasks, or b) rely on simple heuristics that lead to
unreliable results. In this work, we introduce five desired properties for
effective augmentation. Then, we propose NodeSam (Node Split and Merge) and
SubMix (Subgraph Mix), two model-agnostic approaches for graph augmentation
that satisfy all desired properties with different motivations. NodeSam makes a
balanced change of the graph structure to minimize the risk of semantic change,
while SubMix mixes random subgraphs of multiple graphs to create rich soft
labels combining the evidence for different classes. Our experiments on social
networks and molecular graphs show that NodeSam and SubMix outperform existing
approaches in graph classification.


title:
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for
  Image Recognition and Beyond

abstract:
  Vision transformers have shown great potential in various computer vision
tasks owing to their strong capability to model long-range dependency using the
self-attention mechanism. Nevertheless, they treat an image as a 1D sequence of
visual tokens, lacking an intrinsic inductive bias (IB) in modeling local
visual structures and dealing with scale variance, which is instead learned
implicitly from large-scale training data with longer training schedules. In
this paper, we propose a Vision Transformer Advanced by Exploring intrinsic IB
from convolutions, i.e., ViTAE. Technically, ViTAE has several spatial pyramid
reduction modules to downsample and embed the input image into tokens with rich
multi-scale context using multiple convolutions with different dilation rates.
In this way, it acquires an intrinsic scale invariance IB and can learn robust
feature representation for objects at various scales. Moreover, in each
transformer layer, ViTAE has a convolution block parallel to the multi-head
self-attention module, whose features are fused and fed into the feed-forward
network. Consequently, it has the intrinsic locality IB and is able to learn
local features and global dependencies collaboratively. The proposed two kinds
of cells are stacked in both isotropic and multi-stage manners to formulate two
families of ViTAE models, i.e., the vanilla ViTAE and ViTAEv2. Experiments on
the ImageNet dataset as well as downstream tasks on the MS COCO, ADE20K, and
AP10K datasets validate the superiority of our models over the baseline
transformer models and concurrent works. Besides, we scale up our ViTAE model
to 644M parameters and obtain the state-of-the-art classification performance,
i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the
best 91.2% Top-1 accuracy on ImageNet real validation set, without using extra
private data.


title:
Self-consistent Description of Bose-Bose Droplets: Modified Gapless
  Hartree-Fock-Bogoliubov Method

abstract:
  We define a formalism of a self-consistent description of the ground state of
a weakly interacting Bose system, accounting for higher order terms in
expansion of energy in the diluteness parameter. The approach is designed to be
applied to a Bose-Bose mixture in a regime of weak collapse where quantum
fluctuations lead to stabilization of the system and formation of quantum
liquid droplets. The approach is based on the Generalized Gross -- Pitaevskii
equation accounting for quantum depletion and anomalous density terms. The
equation is self-consistently coupled to modified Bogoliubov equations.
  The modification we introduce resolves the longstanding issue of missing
phonon-branch excitations when higher order terms are included. Our method
ensures a gapless phononic low-energy excitation spectrum, crucial to correctly
account for quantum fluctuations. We pay particular attention to the case of
droplets harmonically confined in some directions. The method allows to
determine the Lee-Huang-Yang-type contribution to the chemical potential of
inhomogeneous droplets when the local density approximation fails.


title:
Evidence for a doubly charm tetraquark pole in $DD^*$ scattering on the
  lattice

abstract:
  The doubly charm tetraquark with flavor $cc\bar u\bar d$ and isospin
$I\!=\!0$ is investigated by calculating the $DD^*$ scattering amplitude with
lattice QCD. The simulation is done on CLS ensembles with dynamical $u/d,s$
quarks and $m_\pi\simeq 280~$MeV for two charm quark masses, one slightly
larger and one slightly lower than the physical value. The scattering
amplitudes for partial waves $l=0,1$ are extracted near-threshold via the
L\"uscher's method by considering systems with total momenta
$PL/(2\pi)=0,1,\sqrt{2},2$ on two spatial volumes. A virtual bound state pole
in the $DD^*$ scattering amplitude with $l=0$ is found $9.9_{-7.1}^{+3.6}~$MeV
below $DD^*$ threshold for the charm quark mass closer to the physical value.
This pole is likely related to the doubly charm tetraquark discovered by LHCb
less than $1~$MeV below $D^0D^{*+}$ threshold.


title:
Optical, luminescence, and scintillation properties of advanced ZnWO$_4$
  crystal scintillators

abstract:
  Zinc tungstate (ZnWO$_4$) crystal scintillators are promising detection
material for the experiments searching for double beta decay, dark matter, and
investigating rare alpha decays. An extended R&D was performed to develop
advanced quality ZnWO$_4$ crystal scintillators. The R&D programme included the
selection of the initial materials, the variation of the compound
stoichiometry, the application of single and double crystallization, and the
annealing of the crystal boules. The optical transmittance of the produced
boules was measured, and the luminescence under X-ray excitation in the
temperature region from 85 K to room temperature was studied (thermally
stimulated luminescence was measured till 350 K). The energy resolution and the
relative scintillation pulse amplitude were measured with gamma-sources
demonstrating high scintillation properties of the samples produced by single
crystallization from deeply purified zinc and tungsten oxides, with
stoichiometric composition, annealed in air atmosphere.


title:
Accurate Single-Ended Measurement of Propagation Delay in Fiber Using
  Correlation Optical Time Domain Reflectometry

abstract:
  A correlation optical time-domain reflectometry (COTDR) method is presented,
which measures the propagation delay with an accuracy of a few picoseconds.
This accuracy is achieved using a test signal data rate of 10 Gbit/s and
employing cross-correlation and pulse fitting techniques. In this paper we
introduce and evaluate the basic signal processing steps, investigate the
measurement accuracy, and discuss applications for monitoring link delay and
chromatic dispersion of long fiber spans as well as temperature sensing
applications.


title:
Self-consistent Description of Bose-Bose Droplets: Harmonically Trapped
  Quasi-2D Droplets

abstract:
  We describe a quantum droplet of a Bose-Bose mixture squeezed by an external
harmonic forces in one spatial direction. Our approach is based on the
self-consistent method formulated in [1]. The true spatial droplet profile in
the direction of confinement is accounted for, however local density
approximation is assumed in the free directions. We define a numerical approach
to find the beyond-mean-field contribution to the chemical potential
(Lee-Huang-Yang chemical potential) -- the quantity that determines the
droplet's profile. In addition to the numerical approach, we find the
Lee-Huang-Yang potential in the analytic form in two limiting cases: a
perturbative result for a strong confinement and a semiclassical expression
when confinement is very weak.


title: Path regularity of Brownian motion and Brownian sheet

abstract:
  By the work of P. L\'evy, the sample paths of the Brownian motion are known
to satisfy a certain H\"older regularity condition almost surely. This was
later improved by Ciesielski, who studied the regularity of these paths in
Besov and Besov-Orlicz spaces. We review these results and propose new function
spaces of Besov type, strictly smaller than those of Ciesielski and L\'evy,
where the sample paths of the Brownian motion lie in almost surely. In the same
spirit, we review and extend the work of Kamont, who investigated the same
question for the multivariate Brownian sheet and function spaces of dominating
mixed smoothness.


title:
A Smoothing and Thresholding Image Segmentation Framework with Weighted
  Anisotropic-Isotropic Total Variation

abstract:
  In this paper, we propose a multi-stage image segmentation framework that
incorporates a weighted difference of anisotropic and isotropic total variation
(AITV). The segmentation framework generally consists of two stages: smoothing
and thresholding, thus referred to as SaT. In the first stage, a smoothed image
is obtained by an AITV-regularized Mumford-Shah (MS) model, which can be solved
efficiently by the alternating direction method of multipliers (ADMM) with a
closed-form solution of a proximal operator of the $\ell_1 -\alpha \ell_2$
regularizer. Convergence of the ADMM algorithm is analyzed. In the second
stage, we threshold the smoothed image by $k$-means clustering to obtain the
final segmentation result. Numerical experiments demonstrate that the proposed
segmentation framework is versatile for both grayscale and color images,
efficient in producing high-quality segmentation results within a few seconds,
and robust to input images that are corrupted with noise, blur, or both. We
compare the AITV method with its original convex and nonconvex TV$^p (0<p<1)$
counterparts, showcasing the qualitative and quantitative advantages of our
proposed method.


title:
From harmonic analysis of translation-invariant valuations to geometric
  inequalities for convex bodies

abstract:
  The Alesker-Bernig-Schuster theorem asserts that each irreducible
representation of the special orthogonal group appears with multiplicity at
most one as a subrepresentation of the space of continuous
translation-invariant valuations with fixed degree of homogeneity. Moreover,
the theorem describes in terms of highest weights which irreducible
representations appear with multiplicity one. In this paper, we present a
refinement of this result, namely the explicit construction of a highest weight
vector in each irreducible subrepresentation. We then describe how important
natural operations on valuations (pullback, pushforward, Fourier transform,
Lefschetz operator, Alesker-Poincar\'e pairing) act on these highest weight
vectors. We use this information to prove the Hodge-Riemann relations for
valuations in the case of Euclidean balls as reference bodies. Since special
cases of the Hodge-Riemann relations have recently been used to prove new
geometric inequalities for convex bodies, our work immediately extends the
scope of these inequalities.


title: Gelfand residuated lattices

abstract:
  In this paper, a combination of algebraic and topological methods is applied
to obtain new and structural results on Gelfand residuated lattices. It is
demonstrated that Gelfand's residuated lattices strongly tied up with the
hull-kernel topology. Especially, it is shown that a residuated lattice is
Gelfand if and only if its prime spectrum, equipped with the hull-kernel
topology, is normal. The class of soft residuated lattices is introduced, and
it is shown that a residuated lattice is soft if and only if it is Gelfand and
semisimple. Gelfand residuated lattices are characterized using the pure part
of filters. The relation between pure filters and radicals in a Gelfand
residuated lattice is described. It is shown that a residuated lattice is
Gelfand if and only if its pure spectrum is homeomorphic to its usual maximal
spectrum. The pure filters of a Gelfand residuated lattice are characterized.
Finally, it is proved that a residuated lattice is Gelfand if and only if the
hull-kernel and the $\mathscr{D}$-topology coincide on the set of maximal
filters.


title:
Demonstration of latency-aware 5G network slicing on optical metro
  networks

abstract:
  The H2020 METRO-HAUL European project has architected a latency-aware,
cost-effective, agile, and programmable optical metro network. This includes
the design of semidisaggregated metro nodes with compute and storage
capabilities, which interface effectively with both 5G access and multi-Tbit/s
elastic optical networks in the core. In this paper, we report the automated
deployment of 5G services, in particular, a public safety video surveillance
use case employing low-latency object detection and tracking using on-camera
and on-the-edge analytics. The demonstration features flexible deployment of
network slice instances, implemented in terms of European Telecommunications
Standards Institute (ETSI) network function virtualization network services. We
summarize the key findings in a detailed analysis of end-to-end quality of
service, service setup time, and soft-failure detection time. The results show
that the round-trip time over an 80 km link is under 800s and the service
deployment time is under 180s.


title:
In-situ force microscopy to investigate fracture in stretchable
  electronics: insights on local surface mechanics and conductivity

abstract:
  Stretchable conductors are of crucial relevance for emerging technologies
such as wearable electronics, low-invasive bioelectronic implants or soft
actuators for robotics. A critical issue for their development regards the
understanding of defect formation and fracture of conducting pathways during
stress-strain cycles. Here we present a novel atomic force microscopy (AFM)
method that provides multichannel images of surface morphology, conductivity,
and elastic modulus during sample deformation. To develop the method, we
investigate in detail the mechanical interactions between the AFM tip and a
stretched, free-standing thin film sample. Our findings reveal the conditions
to avoid artifacts related to sample bending modes or resonant excitations. As
an example, we analyze strain effects in thin gold films deposited on a soft
silicone substrate. Our technique allows to observe the details of microcrack
opening during tensile strain and their impact on local current transport and
surface mechanics. We find that although the film fractures into separate
fragments, at higher strain a current transport is sustained by a tunneling
mechanism. The microscopic observation of local defect formation and their
correlation to local conductivity will provide novel insight to design more
robust and fatigue resistant stretchable conductors.


title:
Computing a spectral sequence of finite Heisenberg groups of prime power
  order

abstract:
  Let $p\geq 5$ be a prime number, let $n\geq 2$ be a natural number and let
$\text{Heis}(p^n)$ denote the Heisenberg group modulo $p^n$. We study the
Lyndon-Hochschild-Serre spectral sequence $E(\text{Heis}(p^n))$ associated to
$\text{Heis}(p^n)$ considered as a split extension, and show that,
$E(\text{Heis}(p^n))$ collapses in the third page. Moreover, for a fixed $p$,
the spectral sequences $E(\text{Heis}(p^n))$ are isomorphic from the second
page on.


title: A Dutch book argument for belief consistency

abstract:
  An agent progressively learns about a state of the world. A bookmaker is
ready to offer one bet after every new discovery. I say that the agent is
Dutch-booked when she is willing to accept every single bet, but her expected
payoff is negative under each state, where the expected payoff is computed with
the objective probabilities of different discoveries conditional on the state.
I introduce a rule of coherence among beliefs after counterfactual discoveries
that is necessary and sufficient to avoid being Dutch-booked. This rule
characterizes an agent who derives all her beliefs with Bayes rule from the
same Lexicographic Conditional Probability System (Blume, Brandenburger and
Dekel, 1991).