title:
Orbital entangled antiferromagnetoc order and spin-orbit-distortion
  exciton in $\rm Sr_2VO_4$

abstract:
  With electron filling $n=1$ in the $\rm Sr_2VO_4$ compound, the octahedrally
coordinated $t_{\rm 2g}$ orbitals are strongly active due to tetragonal
distortion induced crystal field tuning by external agent such as pressure.
Considering the full range of crystal field induced tetragonal splitting in a
realistic three-orbital model, collective spin-orbital excitations are
investigated using the generalized self consistent and fluctuation approach.
The variety of self consistent states obtained including orbital entangled
ferromagnetic and antiferromagnetic orders reflects the rich spin-orbital
physics resulting from the interplay between the band, spin-orbit coupling,
crystal field, and Coulomb interaction terms. The behavior of the calculated
energy scales of collective excitations with crystal field is consistent with
that of the transition temperatures with pressure as obtained from
susceptibility and resistivity anomalies in high-pressure studies.


title:
Frequency characterization of diode lasers for vapor cell clocks
  applications

abstract:
  The knowledge of the frequency noise spectrum of a diode laser is of interest
in several high resolution experiments. Specifically, in laser-pumped vapor
cell clocks, it is well established that the laser frequency noise plays a role
in affecting the clock performances. It is then important to characterize the
frequency noise of a diode laser, especially since such measurements are rarely
found in the literature and hardly ever provided by vendors. In this paper, we
describe a technique based on a frequency-to-voltage converter that transforms
the laser frequency fluctuations into voltage fluctuations. In this way, it is
possible to characterize the laser frequency noise power spectral density in a
wide range of Fourier frequencies, as required in cell clock applications.


title:
Looking for the exotic $X_0(2866)$ and its $J^P=1^+$ partner in the
  $\bar{B}^0 \to D^{(*)+} K^- K^{(*)0} $ reactions

abstract:
  We propose two reactions, $\bar{B}^0 \to K^0 D^{+} K^-$ and $\bar{B}^0 \to
K^{*0} D^{*+} K^-$, already measured at Belle, to look into the $J^P=0^+$,
$X_0(2866)$ state and a $1^+$ partner of molecular $D^*\bar{K}^*$ nature, by
looking at the $D^+ K^-$ and $D^{*+} K^-$ invariant mass distributions,
respectively. Very clear peaks over the background are predicted and the
branching ratios for the production of these states are evaluated to facilitate
the task of determining the needed statistics for their observation. The fact
that the $K^{*0} K^-$ mass distribution from accumulation of events of
different reactions is already available, indicates that one is not far away
from having the needed statistics to see peaks in the $D^{(*)+} K^-$ mass
distributions, so far not yet analyzed.


title:
Influences of internal forcing on atmospheric circulations of irradiated
  giant planets

abstract:
  Close-in giant planets with strong stellar irradiation show atmospheric
circulation patterns with strong equatorial jets and global-scale stationary
waves. So far, almost all modeling works on atmospheric circulations of such
giant planets have mainly considered external radiation alone, without taking
into account the role of internal heat fluxes or just treating it in very
simplified ways. Here, we study atmospheric circulations of strongly irradiated
giant planets by considering the effect of internal forcing, which is
characterized by small-scale stochastic interior thermal perturbations, using a
three-dimensional atmospheric general circulation model. We show that the
perturbation-excited waves can largely modify atmospheric circulation patterns
in the presence of relatively strong internal forcing. Specifically, our
simulations demonstrate three circulation regimes: superrotation regime,
midlatitude-jet regime, and quasi-periodic oscillation regime, depending on the
relative importance of external and internal forcings. It is also found that
strong internal forcing can cause noticeable modifications of the thermal phase
curves.


title:
Spin-liquid signatures in the quantum critical regime of pressurized
  CePdAl

abstract:
  CePdAl is a prototypical frustrated Kondo lattice with partial long-range
order (LRO) at $T_\mathrm{N}=2.7$ K. Previous bulk experiments under
hydrostatic pressure found signatures for a quantum critical regime that
extends from $p_\mathrm{c} \approx 0.9$ GPa, where LRO disappears, up to $\sim
1.7$ GPa. We employed extensive muon spin relaxation and rotation ($\mu$SR)
experiments under pressure. The continuous and complete suppression of LRO at
$p_\mathrm{c}$ is confirmed. Above $T_\mathrm{N}(p)$ and beyond $p_\mathrm{c}$,
an additional crossover scale $T^\ast(p)$ characterizes the change from pure to
stretched exponential relaxation in zero field. Remarkably $T^\ast(p)$ agrees
with previously determined signatures of entropy accumulation above LRO. This
coincidence microscopically evidences fluctuating frustrated spins at $T\leq
T^\ast$ with spin-liquid behavior. Power-law divergences of the temperature and
longitudinal field dependences of the relaxation rate, with time-field scaling,
at pressures between $p_c$ and 1.7 GPa characterize this regime as quantum
critical.


title: Hadronic effects in M{\o}ller scattering at NNLO

abstract:
  Two-loop electroweak corrections to polarized Moller scattering are studied
in two different schemes at low energies. We find the finite $Q^2$ corrections
to be well under control. The hadronic and perturbative QCD corrections to the
$\gamma Z$ two-point function are incorporated through the weak mixing angle at
low energies, which introduce an error of $0.08\times10^{-3}$ in the weak
charge of the electron $Q^e_W$. Furthermore, by studying the scheme dependence,
we obtain an estimate of the current perturbative electroweak uncertainty,
$\delta Q^e_W\approx0.23\times10^{-3}$, which is five times smaller than the
precision estimated for the MOLLER experiment $(\delta
Q_W^e=1.1\times10^{-3})$. Future work is possible to reduce the theory error
further.


title:
A Cylindrical GEM Inner Tracker for the BESIII Experiment: from
  Construction to Electronic Noise Studies

abstract:
  This thesis reports the various phases of the life cycle of a cylindrical GEM
detector, developed within the context of the CGEM-IT project. This represents
the Italian proposal for the upgrade of the BESIII inner tracker, which has
been suffering from aging phenomena due to the high luminosity. The
construction of the detector at Frascati National Laboratories and the early
phases of its commissioning at IHEP are described in detail. In the last two
chapters, noise studies on data collected using a CGEM detector with a full
readout chain near the BEPC-II interaction point will be presented, starting
from the quality assurance tests of the on-detector electronics. The
measurements are realized exploiting the different operative phases of the
BEPC-II storage ring and aim to provide an estimate of the noise level
variation due to the surrounding environment.


title:
From Model Selection to Model Averaging: A Comparison for Nested Linear
  Models

abstract:
  Model selection (MS) and model averaging (MA) are two popular approaches when
having many candidate models. Theoretically, the estimation risk of an oracle
MA is not larger than that of an oracle MS because the former one is more
flexible, but a foundational issue is: does MA offer a {\it substantial}
improvement over MS? Recently, a seminal work: Peng and Yang (2021), has
answered this question under nested models with linear orthonormal series
expansion. In the current paper, we further reply this question under linear
nested regression models. Especially, a more general nested framework,
heteroscedastic and autocorrelated random errors, and sparse coefficients are
allowed in the current paper, which is more common in practice. In addition, we
further compare MAs with different weight sets. Simulation studies support the
theoretical findings in a variety of settings.


title: From BeyondPlanck to Cosmoglobe: Preliminary WMAP Q-band analysis

abstract:
  We present the first application of the Cosmoglobe analysis framework by
analyzing 9-year WMAP time-ordered observations using similar machinery as
BeyondPlanck utilizes for Planck LFI. We analyze only the Q1-band (41 GHz) data
and report on the low-level analysis process from uncalibrated time-ordered
data to calibrated maps. Most of the existing BeyondPlanck pipeline may be
reused for WMAP analysis with minimal changes to the existing codebase. The
main modification is the implementation of the same preconditioned biconjugate
gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample
requires 44 CPU-hrs, which is less than the cost of a Planck 70 GHz sample of
69 CPU-hrs; this demonstrates that full end-to-end Bayesian processing of the
WMAP data is computationally feasible. Our recovered maps are generally similar
to the maps released by the WMAP team, but more work is needed on sidelobe
modeling before the new pipeline is ready for science grade production. While
comparing the Cosmoglobe and WMAP polarization maps, we identify the same
large-scale pattern in the Q1-band as was previously reported in the V- and
W-band polarization maps by the WMAP team, and we show that this morphology may
be reproduced deterministically in terms of temperature-to-polarization leakage
arising from the coupling between the CMB Solar dipole, transmission imbalance,
and sidelobes. Given that this structure is present in the Q1-, V-, and
W-bands, it could have a nonnegligible impact on cosmological and astrophysical
conclusions drawn from these polarized maps.


title:
Spin-Orbit Interaction of Light Enabled by Negative Coupling in High
  Quality Factor Optical Metasurfaces

abstract:
  We study the negative couplings amid local resonances of photonic
metasurfaces. In our analysis, we discover pseudo-spin-orbit coupled bulk modes
leading to lines of circularly polarized radiation eigenstates in
two-dimensional momentum space which were considered to exist only in
three-dimensions. Our theoretical model is exemplified via a guided resonance
dielectric metasurface that possesses Type-II Non-Hermitian diabolical points,
from where the circular polarization lines emanate. The designed metasurface
carries circular polarized radiation eigenstates in both the 0th and the 1st
diffraction orders, allowing spin-selective light deflections. The high quality
factor nature and field enhancement of the designed metasurface could lead to
applications for spin-selective sensing, beam control and nonlinear optics. Our
findings open a gateway for the design of near-field couplings assisted
metasurfaces.


title: Fully Self-Supervised Learning for Semantic Segmentation

abstract:
  In this work, we present a fully self-supervised framework for semantic
segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation,
which saves efforts for the huge amount of annotation, is crucial for building
customized models from end-to-end for open-world domains. This application is
eagerly needed in realistic scenarios. Even though recent self-supervised
semantic segmentation methods have gained great progress, these works however
heavily depend on the fully-supervised pretrained model and make it impossible
a fully self-supervised pipeline. To solve this problem, we proposed a
bootstrapped training scheme for semantic segmentation, which fully leveraged
the global semantic knowledge for self-supervision with our proposed PGG
strategy and CAE module. In particular, we perform pixel clustering and
assignments for segmentation supervision. Preventing it from clustering a mess,
we proposed 1) a pyramid-global-guided (PGG) training strategy to supervise the
learning with pyramid image/patch-level pseudo labels, which are generated by
grouping the unsupervised features. The stable global and pyramid semantic
pseudo labels can prevent the segmentation from learning too many clutter
regions or degrading to one background region; 2) in addition, we proposed
context-aware embedding (CAE) module to generate global feature embedding in
view of its neighbors close both in space and appearance in a non-trivial way.
We evaluate our method on the large-scale COCO-Stuff dataset and achieved 7.19
mIoU improvements on both things and stuff objects


title:
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating
  Networks

abstract:
  Monocular depth estimation has been a popular area of research for several
years, especially since self-supervised networks have shown increasingly good
results in bridging the gap with supervised and stereo methods. However, these
approaches focus their interest on dense 3D reconstruction and sometimes on
tiny details that are superfluous for autonomous navigation. In this paper, we
propose to address this issue by estimating the navigation map under a quadtree
representation. The objective is to create an adaptive depth map prediction
that only extract details that are essential for the obstacle avoidance. Other
3D space which leaves large room for navigation will be provided with
approximate distance. Experiment on KITTI dataset shows that our method can
significantly reduce the number of output information without major loss of
accuracy.


title:
GIAOTracker: A comprehensive framework for MCMOT with global information
  and optimizing strategies in VisDrone 2021

abstract:
  In recent years, algorithms for multiple object tracking tasks have benefited
from great progresses in deep models and video quality. However, in challenging
scenarios like drone videos, they still suffer from problems, such as small
objects, camera movements and view changes. In this paper, we propose a new
multiple object tracker, which employs Global Information And some Optimizing
strategies, named GIAOTracker. It consists of three stages, i.e., online
tracking, global link and post-processing. Given detections in every frame, the
first stage generates reliable tracklets using information of camera motion,
object motion and object appearance. Then they are associated into trajectories
by exploiting global clues and refined through four post-processing methods.
With the effectiveness of the three stages, GIAOTracker achieves
state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place
in the VisDrone2021 MOT Challenge.


title: Fine-grained TLS Services Classification with Reject Option

abstract:
  The recent success and proliferation of machine learning and deep learning
have provided powerful tools, which are also utilized for encrypted traffic
analysis, classification, and threat detection. These methods, neural networks
in particular, are often complex and require a huge corpus of training data.
Therefore, this paper focuses on collecting a large up-to-date dataset with
almost 200 fine-grained service labels and 140 million network flows extended
with packet-level metadata. The number of flows is three orders of magnitude
higher than in other existing public labeled datasets of encrypted traffic. The
number of service labels, which is important to make the problem hard and
realistic, is four times higher than in the public dataset with the most class
labels. The published dataset is intended as a benchmark for identifying
services in encrypted traffic. Service identification can be further extended
with the task of "rejecting" unknown services, i.e., the traffic not seen
during the training phase. Neural networks offer superior performance for
tackling this more challenging problem. To showcase the dataset's usefulness,
we implemented a neural network with a multi-modal architecture, which is the
state-of-the-art approach, and achieved 97.04% classification accuracy and
detected 91.94% of unknown services with 5% false positive rate.


title:
Can deep neural networks learn process model structure? An assessment
  framework and analysis

abstract:
  Predictive process monitoring concerns itself with the prediction of ongoing
cases in (business) processes. Prediction tasks typically focus on remaining
time, outcome, next event or full case suffix prediction. Various methods using
machine and deep learning havebeen proposed for these tasks in recent years.
Especially recurrent neural networks (RNNs) such as long short-term memory nets
(LSTMs) have gained in popularity. However, no research focuses on whether such
neural network-based models can truly learn the structure of underlying process
models. For instance, can such neural networks effectively learn parallel
behaviour or loops? Therefore, in this work, we propose an evaluation scheme
complemented with new fitness, precision, and generalisation metrics,
specifically tailored towards measuring the capacity of deep learning models to
learn process model structure. We apply this framework to several process
models with simple control-flow behaviour, on the task of next-event
prediction. Our results show that, even for such simplistic models, careful
tuning of overfitting countermeasures is required to allow these models to
learn process model structure.


title:
Correlation between spin-phonon coupling and magneto-electric effects in
  CoFe2O4/PMN-PT nanocomposite: Raman Spectroscopy and XMCD study

abstract:
  We have investigated the coupling of lattice with spin via strain
interactions in the CoFe2O4/PMN-PT composite system. X-ray diffraction and
Raman spectroscopic studies illustrate a remarkable modification in
CoFe2O4lattice across Curie temperature (450 K) of PMN-PT. Subsequently,
CoFe2O4/PMN-PT composite reveals a sudden drop in magnetic moment across Tc of
PMN-PT (450 K). However,theindependent CoFe2O4phasedisplaystypical
ferromagnetic behaviour across this temperature. These findings establish
spin-lattice coupling owing to th interfacial strain transfer between CoFe2O4
and PMN-PT in composite. The strain intractions leads to magneto-electric
coupling, evidenced by measuring magentization and magneto-electric coefficient
for the electric field poled and unploed CoFe2O4/PMN-PT composite samples.
X-ray magnetic circular dichroism (XMCD) analysis establishes that the cation
(Fe3+/Co2+) redistribution occurs on tetrahedral and octahedral site in the
electrically poled CoFe2O4/PMN-PT composite, confirming the coupling between
magnetic and electric ordering in the composite. The magneto-electric coupling
coefficient alpha vs dc magnetic field curves revealed hysteretic behavior and
enhanced {\alpha} values after electric poling, which originates from the
strain induced modifications in the magnetic domains configuration of composite
in the poled samples. These findings suggest that the existence of spin lattice
coupling may leads to the mechanism of strong magneto-electric effects via
strain interactions in CoFe2O4/PMN-PT composite.


title:
Predicting the impact of treatments over time with uncertainty aware
  neural differential equations

abstract:
  Predicting the impact of treatments from observational data only still
represents a majorchallenge despite recent significant advances in time series
modeling. Treatment assignments are usually correlated with the predictors of
the response, resulting in a lack of data support for counterfactual
predictions and therefore in poor quality estimates. Developments in causal
inference have lead to methods addressing this confounding by requiring a
minimum level of overlap. However,overlap is difficult to assess and usually
notsatisfied in practice. In this work, we propose Counterfactual ODE (CF-ODE),
a novel method to predict the impact of treatments continuously over time using
Neural Ordinary Differential Equations equipped with uncertainty estimates.
This allows to specifically assess which treatment outcomes can be reliably
predicted. We demonstrate over several longitudinal data sets that CF-ODE
provides more accurate predictions and more reliable uncertainty estimates than
previously available methods.


title: Exact Matching in Graphs of Bounded Independence Number

abstract:
  In the Exact Matching Problem (EM), we are given a graph equipped with a
fixed coloring of its edges with two colors (red and blue), as well as a
positive integer $k$. The task is then to decide whether the given graph
contains a perfect matching exactly $k$ of whose edges have color red. EM
generalizes several important algorithmic problems such as perfect matching and
restricted minimum weight spanning tree problems.
  When introducing the problem in 1982, Papadimitriou and Yannakakis
conjectured EM to be $\textbf{NP}$-complete. Later however, Mulmuley et
al.~presented a randomized polynomial time algorithm for EM, which puts EM in
$\textbf{RP}$. Given that to decide whether or not $\textbf{RP}=\textbf{P}$
represents a big open challenge in complexity theory, this makes it unlikely
for EM to be $\textbf{NP}$-complete, and in fact indicates the possibility of a
deterministic polynomial time algorithm. EM remains one of the few natural
combinatorial problems in $\textbf{RP}$ which are not known to be contained in
$\textbf{P}$, making it an interesting instance for testing the hypothesis
$\textbf{RP}=\textbf{P}$.
  Despite EM being quite well-known, attempts to devise deterministic
polynomial algorithms have remained illusive during the last 40 years and
progress has been lacking even for very restrictive classes of input graphs. In
this paper we finally push the frontier of positive results forward by proving
that EM can be solved in deterministic polynomial time for input graphs of
bounded independence number, and for bipartite input graphs of bounded
bipartite independence number. This generalizes previous positive results for
complete (bipartite) graphs which were the only known results for EM on dense
graphs.


title:
Analysis of Genotype-Phenotype Association using Fields and Information
  Theory

abstract:
  We show how field- and information theory can be used to quantify the
relationship between genotype and phenotype in cases where phenotype is a
continuous variable. Given a sample population of phenotype measurements, from
various known genotypes, we show how the ordering of phenotype data can lead to
quantification of the effect of genotype. This method does not assume that the
data has a Gaussian distribution, it is particularly effective at extracting
weak and unusual dependencies of genotype on phenotype. However, in cases where
data has a special form, (eg Gaussian), we observe that the effective phenotype
field has a special form. We use asymptotic analysis to solve both the forward
and reverse formulations of the problem. We show how $p$-values can be
calculated so that the significance of correlation between phenotype and
genotype can be quantified. This provides a significant generalisation of the
traditional methods used in genome-wide association studies GWAS. We derive a
field-strength which can be used to deduce how the correlations between
genotype and phenotype, and their impact on the distribution of phenotypes.


title:
Search for planets in hot Jupiter systems with multi-sector TESS
  photometry. II. Constraints on planetary companions in 12 systems

abstract:
  Uninterrupted observations from space-borne telescopes provide the
photometric precision that is required to detect shallow transits of small
planets missed by ground-based surveys. We used data from the Transiting
Exoplanet Survey Satellite (TESS) to search for nearby planetary companions in
12 planetary systems with hot Jupiters: HD 2685, Qatar-10, WASP-4, WASP-48,
WASP-58, WASP-91, WASP-120, WASP-121, WASP-122, WASP-140, XO-6, and XO-7. We
also applied the transit timing method based on homogeneously determined
mid-transit times in order to search for non-transiting companions that could
gravitationally perturb the already known planets. We found no additional
planets in those systems down to the regime of sub-Neptunian globes. This
negative result is in line with statistical studies, supporting the
high-eccentricity migration as a pathway of the investigated giant planets to
the tight orbits observed today.


title:
Spectral form factor in the Hadamard-Gutzwiller model: orbit pairs
  contributing in the third order

abstract:
  In this paper we consider orbit pairs contributing in the third order of the
spectral form factor in the Hadamard-Gutzwiller model. We prove that periodic
orbits including two 2-encounters in certain structures have partner orbits.
The action differences are estimated at $\ln(1+u_1s_1)(1+u_2s_2)$ with explicit
error bounds, where $(u_1,s_1)$ and $(u_2,s_2)$ are the coordinates of the
piercing points. A new symbolic dynamics for orbit pairs via conjugacy classes
is also provided.


title:
Magnetic resonance probing of ferroelectricity and magnetism in
  metal-organic frameworks

abstract:
  We employ electron paramagnetic resonance (EPR) of the spin probe Mn2+ to
study the paraelectric ferroelectric transition in DMAMnF and Mn2 doped DMZnF,
which are considered to be model metal organic frameworks (MOF) with a Pb free
perovskite architecture. In DMAMnF, we study the variation of the Mn2+ EPR line
shape and intensity at the X-band (9.4 GHz) and over 80 to 300 K, and we show
the absence of magnetoelectric coupling at the ferroelectric transition. At the
antiferromagnetic transition in DMMnF, we detect a magnetoelectric coupling
caused by weak ferromagnetism in the AFM phase. In DMZnF, the combination of
EPR of the Mn2+ probe and DFT show that the crystal field is predominantly
determined by the DMA+ cations.


title:
Redundant apodization for direct imaging of exoplanets I: Robustness to
  primary mirror segmentation-induced errors outside the segment diffraction
  limit

abstract:
  Direct imaging and spectroscopy of Earth-like planets and young Jupiters
require contrasts up to 10^6-10^10 at angular separations of a few dozen
milliarcseconds. To achieve this goal, one of the most promising approaches
consists of using large segmented primary mirror telescopes with coronagraphic
instruments. However, coronagraphs are highly sensitive to wavefront errors.
The segmentation itself is responsible for phasing errors and segment
vibrations to be controlled at a subnanometric accuracy. We propose an
innovative method for a coronagraph design that allows a consequent relaxation
of the segment phasing constraints for low segment-count mirrors and generates
an instrument that is more robust to segment-level wavefront errors. It is
based on an optimization of the coronagraph that includes a segment-level
apodization. This is repeated over the pupil to match the segmentation
redundancy and improves the contrast stability beyond the minimum separation
set by the single-segment diffraction limit. We validate this method on a
GMT-like pupil for two coronagraph types: apodized pupil Lyot coronagraphs
(APLC) and apodizing phase plate coronagraphs (APP). For the APLC, redundant
apodization enables releasing the piston phasing constraints by a factor of 5
to 20 compared to classical designs. For the APP, the contrast remains almost
constant up to 1 radian RMS of the phasing errors. We also show that redundant
apodizations increase the robustness of the coronagraph to segment tip-tilt
errors, as well as to missing segments. This method cannot be applied to
higher-segment count mirrors such as the ELT or the TMT, but it is particularly
suitable for low segment-count mirrors (fewer than 20 segments) such as the GMT
aperture. These mirrors aim for high-contrast imaging of debris disks or
exoplanets down to 100 mas.


title: Variable elimination, graph reduction and efficient g-formula

abstract:
  We study efficient estimation of an interventional mean associated with a
point exposure treatment under a causal graphical model represented by a
directed acyclic graph without hidden variables. Under such a model, it may
happen that a subset of the variables are uninformative in that failure to
measure them neither precludes identification of the interventional mean nor
changes the semiparametric variance bound for regular estimators of it. We
develop a set of graphical criteria that are sound and complete for eliminating
all the uninformative variables so that the cost of measuring them can be saved
without sacrificing estimation efficiency, which could be useful when designing
a planned observational or randomized study. Further, we construct a reduced
directed acyclic graph on the set of informative variables only. We show that
the interventional mean is identified from the marginal law by the g-formula
(Robins, 1986) associated with the reduced graph, and the semiparametric
variance bounds for estimating the interventional mean under the original and
the reduced graphical model agree. This g-formula is an irreducible, efficient
identifying formula in the sense that the nonparametric estimator of the
formula, under regularity conditions, is asymptotically efficient under the
original causal graphical model, and no formula with such property exists that
only depends on a strict subset of the variables.


title:
A 6.4yr optical quasi-periodic oscillations in SDSS J075217.84+193542.2:
  a new candidate for central binary black hole system

abstract:
  In this manuscript, a 6.4yr optical quasi-periodic oscillations (QPOs) is
detected in the quasar SDSS J075217.84+193542.2 (=\obj) at a redshift 0.117, of
which 13.6yr-long light curve from CSS and ASAS-SN directly described by a
sinusoidal function with a periodicity 6.4yr. The 6.4yr QPOs can be further
confirmed through the Generalized Lomb-Scargle periodogram with confidence
level higher than 99.99\%, and through the auto-correlation analysis results,
and through the WWZ technique. The optical QPOs strongly indicate a central
binary black hole (BBH) system in \obj. The determined two broad Gaussian
components in the broad H$\alpha$ can lead to the BBH system with expected
space separation about 0.02pc between the expected two central BHs with
determined virial BH masses about $8.8\times10^7{\rm M_\odot}$ and
$1.04\times10^9{\rm M_\odot}$. Meanwhile, we check the disk precessions applied
to explain the optical QPOs. However, under the disk precession assumption, the
determined optical emission regions from central BH have sizes about $40{\rm
R_G}$ two times smaller than sizes of the expected NUV emission regions through
the correlation between disk size and BH mass, indicating the disk precessions
are not preferred. And due to the lower radio loudness around 0.28, jet
precessions can be also totally ruled out. Furthermore, only 0.08\% probability
can determined as the QPOs mis-detected through light curves randomly created
by the CAR process, re-confirming the reported optical QPOs.


title: Supersolvable posets and fiber-type abelian arrangements

abstract:
  We present a combinatorial analysis of fiber bundles of generalized
configuration spaces on connected abelian Lie groups. These bundles are akin to
those of Fadell-Neuwirth for configuration spaces, and their existence is
detected by a combinatorial property of an associated finite partially ordered
set. This is consistent with Terao's fibration theorem connecting bundles of
hyperplane arrangements to Stanley's lattice supersolvability. We obtain a
combinatorially determined class of K(\pi,1) toric and elliptic arrangements.
Under a stronger combinatorial condition, we prove a factorization of the
Poincar\'e polynomial when the Lie group is noncompact. In the case of toric
arrangements, this provides an analogue of Falk-Randell's formula relating the
Poincar\'e polynomial to the lower central series of the fundamental group.


title:
Supervised Learning based Sparse Channel Estimation for RIS aided
  Communications

abstract:
  An reconfigurable intelligent surface (RIS) can be used to establish
line-of-sight (LoS) communication when the direct path is compromised, which is
a common occurrence in a millimeter wave (mmWave) network. In this paper, we
focus on the uplink channel estimation of a such network. We formulate this as
a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs)
at the base station (BS). On-grid and off-grid AoAs are considered separately.
In the on-grid case, we propose an algorithm to estimate the direct and RIS
channels. Neural networks trained based on supervised learning is used to
estimate the residual angles in the off-grid case, and the AoAs in both cases.
Numerical results show the performance gains of the proposed algorithms in both
cases.


title: Effective Actor-centric Human-object Interaction Detection

abstract:
  While Human-Object Interaction(HOI) Detection has achieved tremendous
advances in recent, it still remains challenging due to complex interactions
with multiple humans and objects occurring in images, which would inevitably
lead to ambiguities. Most existing methods either generate all human-object
pair candidates and infer their relationships by cropped local features
successively in a two-stage manner, or directly predict interaction points in a
one-stage procedure. However, the lack of spatial configurations or reasoning
steps of two- or one- stage methods respectively limits their performance in
such complex scenes. To avoid this ambiguity, we propose a novel actor-centric
framework. The main ideas are that when inferring interactions: 1) the
non-local features of the entire image guided by actor position are obtained to
model the relationship between the actor and context, and then 2) we use an
object branch to generate pixel-wise interaction area prediction, where the
interaction area denotes the object central area. Moreover, we also use an
actor branch to get interaction prediction of the actor and propose a novel
composition strategy based on center-point indexing to generate the final HOI
prediction. Thanks to the usage of the non-local features and the
partly-coupled property of the human-objects composition strategy, our proposed
framework can detect HOI more accurately especially for complex images.
Extensive experimental results show that our method achieves the
state-of-the-art on the challenging V-COCO and HICO-DET benchmarks and is more
robust especially in multiple persons and/or objects scenes.


title: Proactive Libraries: Enforcing Correct Behaviors in Android Apps

abstract:
  The Android framework provides a rich set of APIs that can be exploited by
developers to build their apps. However, the rapid evolution of these APIs
jointly with the specific characteristics of the lifecycle of the Android
components challenge developers, who may release apps that use APIs
incorrectly. In this demo, we present Proactive Libraries, a tool that can be
used to decorate regular libraries with the capability of proactively detecting
and healing API misuses at runtime. Proactive Libraries blend libraries with
multiple proactive modules that collect data, check the compliance of API
usages with correctness policies, and heal executions as soon as the possible
violation of a policy is detected. The results of our evaluation with 27
possible API misuses show the effectiveness of Proactive Libraries in
correcting API misuses with negligible runtime overhead.


title:
The MURAVES experiment: study of the Vesuvius Great Cone with Muon
  Radiography

abstract:
  The MURAVES experiment aims at the muographic imaging of the internal
structure of the summit of Mt. Vesuvius, exploiting muons produced by cosmic
rays. Though presently quiescent, the volcano carries a dramatic hazard in its
highly populated surroundings. The challenging measurement of the rock density
distribution in its summit by muography, in conjunction with data from other
geophysical techniques, can help the modeling of possible eruptive dynamics.
The MURAVES apparatus consists of an array of three independent and identical
muon trackers, with a total sensitive area of 3 square meters. In each tracker,
a sequence of 4 XY tracking planes made of plastic scintillators is
complemented by a 60 cm thick lead wall inserted between the two downstream
planes to improve rejection of background from low energy muons. The apparatus
is currently acquiring data. Preliminary results from the analysis of a first
data sample are presented.


title: Approximations of the balanced triple product $p$-adic $L$-function

abstract:
  The main purpose of this note is to provide an algorithm for approximating
the value of the balanced $p$-adic $L$-function, as constructed in [Hsi21], at
the point $(2,1,1)$, which is lying outside of the interpolation region. The
algorithmic procedure is obtained building on the work of [FM14] and
considering finite length geodesics on the Bruhat--Tits tree for
$GL_2(\mathbb{Q}_p)$. We are interested in the case where at least one of the
Hida families is associated with an elliptic curve over the rationals and we
further restrict ourselves to the case where only one finite local sign of the
functional equation is $-1$.


title: Rare Gems: Finding Lottery Tickets at Initialization

abstract:
  It has been widely observed that large neural networks can be pruned to a
small fraction of their original size, with little loss in accuracy, by
typically following a time-consuming "train, prune, re-train" approach. Frankle
& Carbin (2018) conjecture that we can avoid this by training lottery tickets,
i.e., special sparse subnetworks found at initialization, that can be trained
to high accuracy. However, a subsequent line of work presents concrete evidence
that current algorithms for finding trainable networks at initialization, fail
simple baseline comparisons, e.g., against training random sparse subnetworks.
Finding lottery tickets that train to better accuracy compared to simple
baselines remains an open problem. In this work, we partially resolve this open
problem by discovering rare gems: subnetworks at initialization that attain
considerable accuracy, even before training. Refining these rare gems - "by
means of fine-tuning" - beats current baselines and leads to accuracy
competitive or better than magnitude pruning methods.


title:
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate
  operations on Clique Trees

abstract:
  Exact inference in Bayesian networks is intractable and has an exponential
dependence on the size of the largest clique in the corresponding clique tree,
necessitating approximations. Techniques for approximate inference typically
use iterative BP in graphs with bounded cluster sizes. We propose an
alternative approach for approximate inference based on an incremental
build-infer-approximate (IBIA) paradigm. In the build stage of this approach,
bounded-clique size partitions are obtained by building the clique tree (CT)
incrementally. Nodes are added to the CT as long as the sizes are within a
user-specified clique size constraint. Once the clique size constraint is
reached, the infer and approximate part of the algorithm finds an approximate
CT with lower clique sizes to which new nodes can be added. This step involves
exact inference to calibrate the CT and a combination of exact and approximate
marginalization for approximation. The approximate CT serves as a starting
point for the construction of CT for the next partition. The algorithm returns
a forest of calibrated clique trees corresponding to all partitions. We show
that our algorithm for incremental construction of clique trees always
generates a valid CT and our approximation technique automatically maintains
consistency of within-clique beliefs. The queries of interest are prior and
posterior singleton marginals and the partition function. More than 500
benchmarks were used to test the method and the results show a significant
reduction in error when compared to other approximate methods, with competitive
runtimes.


title: The multiphase Muskat problem with general viscosities in two dimensions

abstract:
  In this paper we study the two-dimensional multiphase Muskat problem
describing the motion of three immiscible fluids with general viscosities in a
vertical homogeneous porous medium under the influence of gravity. Employing
Rellich type identities in the regime where the fluids are ordered according to
their viscosities, respectively a Neumann series argument when the fluids are
not ordered by viscosity, we may recast the governing equations as a strongly
coupled nonlinear and nonlocal evolution problem for the functions that
parameterize the sharp interfaces that separate the fluids. This problem is of
parabolic type if the Rayleigh-Taylor condition is satisfied at each interface.
Based on this property, we then show that the multiphase Muskat problem is
well-posed in all $L_2$-subcritical Sobolev spaces and that it features some
parabolic smoothing properties.


title:
On isosupremic vectorial minimisation problems in $L^\infty$ with
  general nonlinear constraints

abstract:
  We study minimisation problems in $L^\infty$ for general quasiconvex first
order functionals, where the class of admissible mappings is constrained by the
sublevel sets of another supremal functional and by the zero set of a nonlinear
operator. Examples of admissible operators include those expressing pointwise,
unilateral, integral isoperimetric, elliptic quasilinear differential, jacobian
and null Lagrangian constraints. Via the method of $L^p$ approximations as
$p\to \infty$, we illustrate the existence of a special $L^\infty$ minimiser
which solves a divergence PDE system involving certain auxiliary measures as
coefficients. This system can be seen as a divergence form counterpart of the
Aronsson PDE system which is associated with the constrained $L^\infty$
variational problem.


title:
Parameterized Intractability for Multi-Winner Election under the
  Chamberlin-Courant Rule and the Monroe Rule

abstract:
  Answering an open question by Betzler et al. [Betzler et al., JAIR'13], we
resolve the parameterized complexity of the multi-winner determination problem
under two famous representation voting rules: the Chamberlin-Courant (in short
CC) rule [Chamberlin and Courant, APSR'83] and the Monroe rule [Monroe,
APSR'95]. We show that under both rules, the problem is W[1]-hard with respect
to the sum $\beta$ of misrepresentations, thereby precluding the existence of
any $f(\beta) \cdot |I|^{O(1)}$ -time algorithm, where $|I|$ denotes the size
of the input instance.


title:
Characterization of the effect of plastic gas pipes with the use of a
  Single Wire Proportional Chamber

abstract:
  Plastic gas pipes are widely used in experimental setups for gaseous
detectors, due to their flexibility and easiness of use. This report describes
the characterization studies realized on the use of plastic gas pipes,
particularly in relation to the accumulation of impurities in gas systems that
they may cause. A laboratory setup has been implemented to evaluate the
possible effects caused by plastic pipes, in terms of their specific impact on
O2 and H2O accumulation and the consequences on detectors performance. As
Single Wire Proportional Chambers are very sensitive to the presence of
pollutants and gas mixture variations, this type of detector was chosen to
perform the study.


title: A fair pricing model via adversarial learning

abstract:
  At the core of insurance business lies classification between risky and
non-risky insureds, actuarial fairness meaning that risky insureds should
contribute more and pay a higher premium than non-risky or less-risky ones.
Actuaries, therefore, use econometric or machine learning techniques to
classify, but the distinction between a fair actuarial classification and
"discrimination" is subtle. For this reason, there is a growing interest about
fairness and discrimination in the actuarial community Lindholm, Richman,
Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can
serve as substitutes or proxies for protected attributes. For example, the
color and model of a car, combined with the driver's occupation, may lead to an
undesirable gender bias in the prediction of car insurance prices.
Surprisingly, we will show that debiasing the predictor alone may be
insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing
model is currently built in a two-stage structure that considers many
potentially biased components such as car or geographic risks. We will show
that this traditional structure has significant limitations in achieving
fairness. For this reason, we have developed a novel pricing model approach.
Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau
(2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In
this paper, we will show that (2) this can be generalized to multiple pricing
factors (geographic, car type), (3) it perfectly adapted for a fairness context
(since it allows to debias the set of pricing components): We extend this main
idea to a general framework in which a single whole pricing model is trained by
generating the geographic and car pricing components needed to predict the pure
premium while mitigating the unwanted bias according to the desired metric.


title:
First implementation of full-workflow automation in radiotherapy: the
  All-in-One solution on rectal cancer

abstract:
  The aim of this work is to describe the technical characteristics of an
AI-powered radiotherapy workflow that enables full-process automation
(All-in-One), evaluate its performance implemented for on-couch initial
treatment of rectal cancer, and provide insight into the behavior of
full-workflow automation in the specialty of radiotherapy. The All-in-One
workflow was developed based on a CT-integrated linear accelerator. It
incorporates routine radiotherapy procedures from simulation, autosegmentation,
autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA)
into one scheme, with critical decision points involved, while the patient is
on the treatment couch during the whole process. For the enrolled ten patients
with rectal cancer, minor modifications of the autosegmented target volumes
were required, and the Dice similarity coefficient and 95% Hausdorff distance
before and after modifications were 0.892{\pm}0.061 and 18.2{\pm}13.0 mm,
respectively. The autosegmented normal tissues and automatic plans were
clinically acceptable without any modifications or reoptimization. The
pretreatment IGRT corrections were within 2 mm in all directions, and the
EPID-based in vivo QA showed a {\gamma} passing rate better than 97{\%}
(3{\%}/3 mm/10{\%} threshold). The duration of the whole process was
23.2{\pm}3.5 minutes, depending mostly on the time required for manual
modification and plan evaluation. The All-in-One workflow enables full
automation of the entire radiotherapy process by seamlessly integrating
multiple routine procedures. The one-stop solution shortens the time scale it
takes to ready the first treatment from days to minutes, significantly
improving the patient experience and the efficiency of the workflow, and shows
potential to facilitate the clinical application of online adaptive replanning.


title: Theoretical methods for excitonic physics in two-dimensional materials

abstract:
  In this tutorial we introduce the reader to several theoretical methods of
determining the exciton wave functions and the corresponding eigenenergies. The
methods covered are either analytical, semi-analytical, or numeric. We make
explicit all the details associated with the different methods, thus allowing
newcomers to do research on their own, without experiencing a steep learning
curve. The tutorial starts with a variational method and ends with a simple
semi-analytical approach to solve the Bethe-Salpeter equation in
two-dimensional (2D) gapped materials. For the first methods addressed in this
tutorial, we focus on a single layer of hexagonal Boron Nitride (hBN) and of
transition metal dichalcogenide (TMD), as these are exemplary materials in the
field of 2D excitons. For explaining the Bethe- Salpeter method we choose the
biased bilayer graphene, which presents a tunnable band gap. The system has the
right amount of complexity (without being excessive). This allows the
presentation of the solution of the Bethe-Salpeter equation in a context that
can be easily generalized to more complex systems or to apply it to simpler
models.


title:
Three-dimensional imaging of convective cells in the photosphere of
  Betelgeuse

abstract:
  Understanding convection in red supergiants and the mechanisms that trigger
the mass loss from these evolved stars are the general goals of most
observations of Betelgeuse and its inner circumstellar environment. Linear
spectropolarimetry of the atomic lines of the spectrum of Betelgeuse reveals
information about the three-dimensional (3D) distribution of brightness in its
atmosphere. We model the distribution of plasma and its velocities and use
inversion algorithms to fit the observed linear polarization. We obtain the
first 3D images of the photosphere of Betelgeuse. Within the limits of the used
approximations, we recover vertical convective flows and measure the velocity
of the rising plasma at different heights in the photosphere. In several cases,
we find this velocity to be constant with height, indicating the presence of
forces other than gravity acting on the plasma and counteracting it. In some
cases, these forces are sufficient to maintain plasma rising at 60\,\kms to
heights where this velocity is comparable to the escape velocity. Forces are
present in the photosphere of Betelgeuse that allow plasma to reach velocities
close to the escape velocity. These mechanisms may suffice to trigger mass loss
and sustain the observed large stellar winds of these evolved stars.


title: Strict universes for Grothendieck topoi

abstract:
  Hofmann and Streicher famously showed how to lift Grothendieck universes into
presheaf topoi, and Streicher has extended their result to the case of sheaf
topoi by sheafification. In parallel, van den Berg and Moerdijk have shown in
the context of algebraic set theory that similar constructions continue to
apply even in weaker metatheories. Unfortunately, sheafification seems not to
preserve an important realignment property enjoyed by the presheaf universes
that plays a critical role in models of univalent type theory as well as
synthetic Tait computability, a recent technique to establish syntactic
properties of type theories and programming languages. In the context of
multiple universes, the realignment property also implies a coherent choice of
codes for connectives at each universe level, thereby interpreting the
cumulativity laws present in popular formulations of Martin-L\"of type theory.
  We observe that a slight adjustment to an argument of Shulman constructs a
cumulative universe hierarchy satisfying the realignment property at every
level in any Grothendieck topos. Hence one has direct-style interpretations of
Martin-L\"of type theory with cumulative universes into all Grothendieck topoi.
A further implication is to extend the reach of recent synthetic methods in the
semantics of cubical type theory and the syntactic metatheory of type theory
and programming languages to all Grothendieck topoi.


title: Art of Unlocking

abstract:
  We describe our recent results concerning the rigidity/unlockability
properties of clusters of rigid bodies sliding over the unit sphere.


title:
TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a
  comparison with alternative sources

abstract:
  Rapid impact assessment in the immediate aftermath of a natural disaster is
essential to provide adequate information to international organisations, local
authorities, and first responders. Social media can support emergency response
with evidence-based content posted by citizens and organisations during ongoing
events. In the paper, we propose TriggerCit: an early flood alerting tool with
a multilanguage approach focused on timeliness and geolocation. The paper
focuses on assessing the reliability of the approach as a triggering system,
comparing it with alternative sources for alerts, and evaluating the quality
and amount of complementary information gathered. Geolocated visual evidence
extracted from Twitter by TriggerCit was analysed in two case studies on floods
in Thailand and Nepal in 2021.


title: Learning to Merge Tokens in Vision Transformers

abstract:
  Transformers are widely applied to solve natural language understanding and
computer vision tasks. While scaling up these architectures leads to improved
performance, it often comes at the expense of much higher computational costs.
In order for large-scale models to remain practical in real-world systems,
there is a need for reducing their computational overhead. In this work, we
present the PatchMerger, a simple module that reduces the number of patches or
tokens the network has to process by merging them between two consecutive
intermediate layers. We show that the PatchMerger achieves a significant
speedup across various model sizes while matching the original performance both
upstream and downstream after fine-tuning.


title: Practical Abstraction for Model Checking of Multi-Agent Systems

abstract:
  Model checking of multi-agent systems (MAS) is known to be hard, both
theoretically and in practice. A smart abstraction of the state space may
significantly reduce the model, and facilitate the verification. In this paper,
we propose and study an intuitive agent-based abstraction scheme, based on the
removal of variables in the representation of a MAS. This allows to do the
reduction without generating the global model of the system. Moreover, the
process is easy to understand and control even for domain experts with little
knowledge of computer science. We formally prove the correctness of the
approach, and evaluate the gains experimentally on models of a postal voting
procedure.


title: Support singulier et homologie des fibres de Springer affines

abstract:
  We develop a theory of singular support for various infinite dimensional
stacks and establish several functoriality properties. Then we apply this
theory to compute the singular support of the Grothendieck-Springer affine
Springer sheaf and rely it to the local constancy conjecture of
Goresky-Kottwitz-McPherson on the homology of affine Springer fibers along the
root valuation stratification.


title: Counterfactual Explanations for Predictive Business Process Monitoring

abstract:
  Predictive business process monitoring increasingly leverages sophisticated
prediction models. Although sophisticated models achieve consistently higher
prediction accuracy than simple models, one major drawback is their lack of
interpretability, which limits their adoption in practice. We thus see growing
interest in explainable predictive business process monitoring, which aims to
increase the interpretability of prediction models. Existing solutions focus on
giving factual explanations.While factual explanations can be helpful, humans
typically do not ask why a particular prediction was made, but rather why it
was made instead of another prediction, i.e., humans are interested in
counterfactual explanations. While research in explainable AI produced several
promising techniques to generate counterfactual explanations, directly applying
them to predictive process monitoring may deliver unrealistic explanations,
because they ignore the underlying process constraints. We propose LORELEY, a
counterfactual explanation technique for predictive process monitoring, which
extends LORE, a recent explainable AI technique. We impose control flow
constraints to the explanation generation process to ensure realistic
counterfactual explanations. Moreover, we extend LORE to enable explaining
multi-class classification models. Experimental results using a real, public
dataset indicate that LORELEY can approximate the prediction models with an
average fidelity of 97.69\% and generate realistic counterfactual explanations.


title: Functional Classification of Bitcoin Addresses

abstract:
  This paper proposes a classification model for predicting the main activity
of bitcoin addresses based on their balances. Since the balances are functions
of time, we apply methods from functional data analysis; more specifically, the
features of the proposed classification model are the functional principal
components of the data. Classifying bitcoin addresses is a relevant problem for
two main reasons: to understand the composition of the bitcoin market, and to
identify accounts used for illicit activities. Although other bitcoin
classifiers have been proposed, they focus primarily on network analysis rather
than curve behavior. Our approach, on the other hand, does not require any
network information for prediction. Furthermore, functional features have the
advantage of being straightforward to build, unlike expert-built features.
Results show improvement when combining functional features with scalar
features, and similar accuracy for the models using those features separately,
which points to the functional model being a good alternative when
domain-specific knowledge is not available.


title:
Point Containment Queries on Ray Tracing Cores for AMR Flow
  Visualization

abstract:
  Modern GPUs come with dedicated hardware to perform ray/triangle
intersections and bounding volume hierarchy (BVH) traversal. While the primary
use case for this hardware is photorealistic 3D computer graphics, with careful
algorithm design scientists can also use this special-purpose hardware to
accelerate general-purpose computations such as point containment queries. This
article explains the principles behind these techniques and their application
to vector field visualization of large simulation data using particle tracing.


title:
Development of a compact high-resolution absolute gravity gradiometer
  based on atom interferometers

abstract:
  We present a compact high-resolution gravity gradiometer based on dual Rb-85
atom interferometers using stimulated Raman transitions. A baseline L=44.5 cm
and an interrogation time T=130 ms are realized in a sensor head with volume of
less than 100 liters. Experimental parameters are optimized to improve the
short-term sensitivity while a rejection algorithm relying on inversion of the
Raman wave vector is implemented to improve the long-term stability. After an
averaging time of 17000 s, a phase resolution of 104 {\mu}rad is achieved,
which corresponds to a gravity gradient resolution of 0.86 E. As far as we
know, this is the sub-E atom gravity gradiometer with the highest level of
compactness to date. After the evaluation and correction of system errors
induced by light shift, residual Zeeman shift, Coriolis effect and
self-attraction effect, the instrument serves as an absolute gravity
gradiometer and with it the local gravity gradient is measured to be 3114 (53)
E.


title: Diagram supermodules for $0$-Hecke-Clifford algebras

abstract:
  We introduce a general method for constructing modules for $0$-Hecke algebras
and supermodules for $0$-Hecke-Clifford algebras from diagrams of boxes in the
plane, and give formulas for the images of these modules in the algebras of
quasisymmetric functions and peak functions under the relevant characteristic
map. As initial applications, we resolve a question of Jing and Li (2015),
introduce a new basis of the peak algebra analogous to the quasisymmetric Schur
functions, uncover a new connection between Schur $Q$-functions and
quasisymmetric Schur functions, give a representation-theoretic interpretation
of families of tableaux used in constructing certain functions in the peak
algebra, and establish a common framework for known $0$-Hecke module
interpretations of bases of quasisymmetric functions.


title:
Validating an SVM-based neonatal seizure detection algorithm for
  generalizability, non-inferiority and clinical efficacy

abstract:
  Neonatal seizure detection algorithms (SDA) are approaching the benchmark of
human expert annotation. Measures of algorithm generalizability and
non-inferiority as well as measures of clinical efficacy are needed to assess
the full scope of neonatal SDA performance. We validated our neonatal SDA on an
independent data set of 28 neonates. Generalizability was tested by comparing
the performance of the original training set (cross-validation) to its
performance on the validation set. Non-inferiority was tested by assessing
inter-observer agreement between combinations of SDA and two human expert
annotations. Clinical efficacy was tested by comparing how the SDA and human
experts quantified seizure burden and identified clinically significant periods
of seizure activity in the EEG. Algorithm performance was consistent between
training and validation sets with no significant worsening in AUC (p>0.05, n
=28). SDA output was inferior to the annotation of the human expert, however,
re-training with an increased diversity of data resulted in non-inferior
performance ($\Delta\kappa$=0.077, 95% CI: -0.002-0.232, n=18). The SDA
assessment of seizure burden had an accuracy ranging from 89-93%, and 87% for
identifying periods of clinical interest. The proposed SDA is approaching human
equivalence and provides a clinically relevant interpretation of the EEG.


title:
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language
  Models Better

abstract:
  Effectively finetuning pretrained language models (PLMs) is critical for
their success in downstream tasks. However, PLMs may have risks in overfitting
the pretraining tasks and data, which usually have gap with the target
downstream tasks. Such gap may be difficult for existing PLM finetuning methods
to overcome and lead to suboptimal performance. In this paper, we propose a
very simple yet effective method named NoisyTune to help better finetune PLMs
on downstream tasks by adding some noise to the parameters of PLMs before
fine-tuning. More specifically, we propose a matrix-wise perturbing method
which adds different uniform noises to different parameter matrices based on
their standard deviations. In this way, the varied characteristics of different
types of parameters in PLMs can be considered. Extensive experiments on both
GLUE English benchmark and XTREME multilingual benchmark show NoisyTune can
consistently empower the finetuning of different PLMs on different downstream
tasks.


title:
Scenario Parameter Generation Method and Scenario Representativeness
  Metric for Scenario-Based Assessment of Automated Vehicles

abstract:
  The development of assessment methods for the performance of Automated
Vehicles (AVs) is essential to enable the deployment of automated driving
technologies, due to the complex operational domain of AVs. One candidate is
scenario-based assessment, in which test cases are derived from real-world road
traffic scenarios obtained from driving data. Because of the high variety of
the possible scenarios, using only observed scenarios for the assessment is not
sufficient. Therefore, methods for generating additional scenarios are
necessary.
  Our contribution is twofold. First, we propose a method to determine the
parameters that describe the scenarios to a sufficient degree without relying
on strong assumptions on the parameters that characterize the scenarios. By
estimating the probability density function (pdf) of these parameters,
realistic parameter values can be generated. Second, we present the Scenario
Representativeness (SR) metric based on the Wasserstein distance, which
quantifies to what extent the scenarios with the generated parameter values are
representative of real-world scenarios while covering the actual variety found
in the real-world scenarios.
  A comparison of our proposed method with methods relying on assumptions of
the scenario parametrization and pdf estimation shows that the proposed method
can automatically determine the optimal scenario parametrization and pdf
estimation. Furthermore, we demonstrate that our SR metric can be used to
choose the (number of) parameters that best describe a scenario. The presented
method is promising, because the parameterization and pdf estimation can
directly be applied to already available importance sampling strategies for
accelerating the evaluation of AVs.


title: Zinbiel algebras are nilpotent

abstract:
  In this paper we show that every finite-dimensional Zinbiel algebra over an
arbitrary field is nilpotent, extending a previous result by other authors that
they are solvable.


title:
Mixed-mode oscillations in coupled FitzHugh-Nagumo oscillators: blowup
  analysis of cusped singularities

abstract:
  In this paper, we use geometric singular perturbation theory and blowup, as
our main technical tool, to study the mixed mode oscillations (MMOs) that occur
in two coupled FitzHugh-Nagumo units with repulsive coupling. In particular, we
demonstrate that the MMOs in this model are not due generic folded
singularities, but rather due to singularities at a cusp -- not a fold -- of
the critical manifold. Using blowup, we determine the number of SAOs
analytically, showing -- as for the folded nodes -- that they are determined by
the Weber equation and the ratio of eigenvalues. We also show that the model
undergoes a saddle-node bifurcation in the desingularized reduced problem,
which -- although resembling a folded saddle-node (type II) at the level of the
reduced problem -- also occurs on a cusp, and not a fold. We demonstrate that
this bifurcation is associated with the emergence of an invariant cylinder, the
onset of SAOs, as well as SAOs of increasing amplitude. We relate our findings
with numerical computations and find excellent agreement.


title:
Evolutionary Multi-Objective Reinforcement Learning Based Trajectory
  Control and Task Offloading in UAV-Assisted Mobile Edge Computing

abstract:
  This paper studies the trajectory control and task offloading (TCTO) problem
in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system,
where a UAV flies along a planned trajectory to collect computation tasks from
smart devices (SDs). We consider a scenario that SDs are not directly connected
by the base station (BS) and the UAV has two roles to play: MEC server or
wireless relay. The UAV makes task offloading decisions online, in which the
collected tasks can be executed locally on the UAV or offloaded to the BS for
remote processing. The TCTO problem involves multi-objective optimization as
its objectives are to minimize the task delay and the UAV's energy consumption,
and maximize the number of tasks collected by the UAV, simultaneously. This
problem is challenging because the three objectives conflict with each other.
The existing reinforcement learning (RL) algorithms, either single-objective
RLs or single-policy multi-objective RLs, cannot well address the problem since
they cannot output multiple policies for various preferences (i.e. weights)
across objectives in a single run. This paper adapts the evolutionary
multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO
problem. This algorithm can output multiple optimal policies in just one run,
each optimizing a certain preference. The simulation results demonstrate that
the proposed algorithm can obtain more excellent nondominated policies by
striking a balance between the three objectives regarding policy quality,
compared with two evolutionary and two multi-policy RL algorithms.


title:
Systematic Prevention of On-Core Timing Channels by Full Temporal
  Partitioning

abstract:
  Microarchitectural timing channels enable unwanted information flow across
security boundaries, violating fundamental security assumptions. They leverage
timing variations of several state-holding microarchitectural components and
have been demonstrated across instruction set architectures and hardware
implementations. Analogously to memory protection, Ge et al. have proposed time
protection for preventing information leakage via timing channels. They also
showed that time protection calls for hardware support. This work leverages the
open and extensible RISC-V instruction set architecture (ISA) to introduce the
temporal fence instruction fence.t, which provides the required mechanisms by
clearing vulnerable microarchitectural state and guaranteeing a
history-independent context-switch latency. We propose and discuss three
different implementations of fence.t and implement them on an experimental
version of the seL4 microkernel and CVA6, an open-source, in-order, application
class, 64-bit RISC-V core. We find that a complete, systematic, ISA-supported
erasure of all non-architectural core components is the most effective
implementation while featuring a low implementation effort, a minimal
performance overhead of approximately 2%, and negligible hardware costs.


title:
Electronic and magnetic properties of epitaxial thin film of
  Nd\tsubscript{0.5}Ba\tsuscriptb{0.5}MnO\tsubscript{3}

abstract:
  Contradictory reports about the electronic and magnetic behavior of bulk
Nd0:5Ba0:5MnO3 with uniform disorder exist in the literature. In this work, we
investigate the thin films of Nd0:5Ba0:5MnO3, grown by pulsed laser deposition.
The success of epitaxial growth in a layer-by-layer mode and high quality of
the films have been confirmed by in-situ reflection high energy electron
diffraction, atomic force microscopy, X-ray reflectivity, and, X-ray
diffraction. These films are found to be insulting and exhibit spin-glass
behavior at low temperatures. The study offers an opportunity to tune the
competition between charge/orbital ordering and ferromagnetism in half-doped
manganites through heterostructure engineering.


title:
Assessing generalisability of deep learning-based polyp detection and
  segmentation methods through a computer vision challenge

abstract:
  Polyps are well-known cancer precursors identified by colonoscopy. However,
variability in their size, location, and surface largely affect identification,
localisation, and characterisation. Moreover, colonoscopic surveillance and
removal of polyps (referred to as polypectomy ) are highly operator-dependent
procedures. There exist a high missed detection rate and incomplete removal of
colonic polyps due to their variable nature, the difficulties to delineate the
abnormality, the high recurrence rates, and the anatomical topography of the
colon. There have been several developments in realising automated methods for
both detection and segmentation of these polyps using machine learning.
However, the major drawback in most of these methods is their ability to
generalise to out-of-sample unseen datasets that come from different centres,
modalities and acquisition systems. To test this hypothesis rigorously we
curated a multi-centre and multi-population dataset acquired from multiple
colonoscopy systems and challenged teams comprising machine learning experts to
develop robust automated detection and segmentation methods as part of our
crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this
paper, we analyse the detection results of the four top (among seven) teams and
the segmentation results of the five top teams (among 16). Our analyses
demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy
> 80% on overall Dice score on different validation sets) over real-time
performance required for clinical applicability. We further dissect the methods
and provide an experiment-based hypothesis that reveals the need for improved
generalisability to tackle diversity present in multi-centre datasets.


title:
Density of rational points on manifolds and Diophantine approximation on
  hypersurfaces

abstract:
  In this article, we establish an analogue of the dimension growth conjecture,
which is regarding the density of rational points on projective varieties, for
compact submanifolds of $\mathbb{R}^n$ with non-vanishing curvature. We also
establish the convergence theory for the set of simultaneously
$\psi$-approximable points lying on a generic hypersurface, thereby settling
the generalized Baker-Schmidt problem in the simultaneous setting for generic
hypersurfaces. These results are obtained as consequences of an optimal upper
bound for the density of rational points near manifolds of the form $\{
(\mathbf{x}, f(\mathbf{x})) \in \mathbb{R}^{d+1}: \mathbf{x} \in
B_{\nu}(\mathbf{x}_0) \}$ with non-zero Hessian matrix of $f$ at $\mathbf{x}_0$
and $\nu > 0$ sufficiently small.


title:
Multi-Modal Legged Locomotion Framework with Automated Residual
  Reinforcement Learning

abstract:
  While quadruped robots usually have good stability and load capacity, bipedal
robots offer a higher level of flexibility / adaptability to different tasks
and environments. A multi-modal legged robot can take the best of both worlds.
In this paper, we propose a multi-modal locomotion framework that is composed
of a hand-crafted transition motion and a learning-based bipedal controller --
learnt by a novel algorithm called Automated Residual Reinforcement Learning.
This framework aims to endow arbitrary quadruped robots with the ability to
walk bipedally. In particular, we 1) design an additional supporting structure
for a quadruped robot and a sequential multi-modal transition strategy; 2)
propose a novel class of Reinforcement Learning algorithms for bipedal control
and evaluate their performances in both simulation and the real world.
Experimental results show that our proposed algorithms have the best
performance in simulation and maintain a good performance in a real-world
robot. Overall, our multi-modal robot could successfully switch between biped
and quadruped, and walk in both modes. Experiment videos and code are available
at https://chenaah.github.io/multimodal/.


title: A Greedy Approach to the Canny-Emiris Formula

abstract:
  The Canny-Emiris formula gives the sparse resultant as a ratio between the
determinant of a Sylvester-type matrix and a minor of it, by a subdivision
algorithm. The most complete proof of the formula was given by D'Andrea et al.
under general conditions on the underlying mixed subdivision. Before the proof,
Canny and Pedersen had proposed a greedy algorithm which provides smaller
matrices, in general. The goal of this paper is to give an explicit class of
mixed subdivisions for the greedy approach such that the formula holds, and the
greedy approach reduces the dimensions of the matrices are reduced compared to
the subdivision algorithm We measure this reduction for the case when the
Newton polytopes are zonotopes generated by n line segments (where n is the
rank of the underlying lattice), and for the case of multihomogeneous systems.
This article comes with a JULIA implementation of the treated cases.


title: Semidefinite games

abstract:
  We introduce and study the class of semidefinite games, which generalizes
bimatrix games and finite $N$-person games, by replacing the simplex of the
mixed strategies for each player by a slice of the positive semidefinite cone.
  For semidefinite two-player zero-sum games, we show that the optimal
strategies can be computed by semidefinite programming. Furthermore, we show
that two-player semidefinite zero-sum games are almost equivalent to
semidefinite programming, generalizing Dantzig's result on the almost
equivalence of bimatrix games and linear programming.
  For general two-player semidefinite games, we prove a spectrahedral
characterization of the Nash equilibria. Moreover, we give constructions of
semidefinite games with many Nash equilibria. In particular, we give a
construction of semidefinite games whose number of connected components of Nash
equilibria exceeds the long standing best known construction for many Nash
equilibria in bimatrix games, which was presented by von Stengel in 1999.


title:
Generalized phase-space description of non-linear Hamiltonian systems
  and the Harper-like dynamics

abstract:
  Phase-space features of the Wigner flow for generic one-dimensional systems
with a Hamiltonian, $H^{W}(q,\,p)$, constrained by the $\partial ^2 H^{W} /
\partial q \partial p = 0$ condition are analytically obtained in terms of
Wigner functions and Wigner currents. Liouvillian and stationary profiles are
identified for thermodynamic (TD) and Gaussian quantum ensembles to account for
exact corrections due to quantum modifications over a classical phase-space
pattern. General results are then specialized to the Harper Hamiltonian system
which, besides working as a feasible test platform for the framework here
introduced, admits a statistical description in terms of TD and Gaussian
ensembles, where the Wigner flow properties are all obtained through analytical
tools. Quantum fluctuations over the classical regime are therefore quantified
through probability and information fluxes whenever the classical Hamiltonian
background is provided. Besides allowing for a broad range of theoretical
applications, our results suggest that such a generalized Wigner approach works
as a probe for quantumness and classicality of Harper-like systems, in a
framework which can be extended to any quantum system described by Hamiltonians
in the form of $H^{W}(q,\,p) = K(p) + V(q)$.


title: Warped vacuum energy by black holes

abstract:
  The belief still persists, at least for an important part of the community,
that in general relativity the cosmological constant $\Lambda$ must remain
immaculate. However, Bianchi identities show that $\Lambda$ could acquire local
properties in the presence of matter, i.e.,
$\Lambda\rightarrow\,\Lambda(x^\mu)$. In this paper we go even further, and we
show that a non-uniform vacuum energy does not even require coexistence with
any additional form of matter-energy, nor of any theory beyond general
relativity, at least for extreme environments, such as the proximity of a black
hole.


title:
Construction of a bi-infinite power free word with a given factor and a
  non-recurrent letter

abstract:
  Let $L_{k,\alpha}^{\mathbb{Z}}$ denote the set of all bi-infinite
$\alpha$-power free words over an alphabet with $k$ letters, where $\alpha$ is
a positive rational number and $k$ is positive integer. We prove that if
$\alpha\geq 5$, $k\geq 3$, $v\in L_{k,\alpha}^{\mathbb{Z}}$, and $w$ is a
finite factor of $v$, then there are $\widetilde v\in
L_{k,\alpha}^{\mathbb{Z}}$ and a letter $x$ such that $w$ is a factor of
$\widetilde v$ and $x$ has only a finitely many occurrences in $\widetilde v$.


title:
Metacognitive Agents for Ethical Decision Support: Conceptual Model and
  Research Roadmap

abstract:
  An ethical value-action gap exists when there is a discrepancy between
intentions and actions. This discrepancy may be caused by social and structural
obstacles as well as cognitive biases. Computational models of cognition and
affect can provide insights into the value-action gap and how it can be
reduced. In particular, metacognition ("thinking about thinking") plays an
important role in many of these models as a mechanism for self-regulation and
reasoning about mental attitudes. This paper outlines a roadmap for translating
cognitive-affective models into assistant agents to help make value-aligned
decisions.


title: Self-Training: A Survey

abstract:
  In recent years, semi-supervised algorithms have received a lot of interest
in both academia and industry. Among the existing techniques, self-training
methods have arguably received more attention in the last few years. These
models are designed to search the decision boundary on low density regions
without making extra assumptions on the data distribution, and use the unsigned
output score of a learned classifier, or its margin, as an indicator of
confidence. The working principle of self-training algorithms is to learn a
classifier iteratively by assigning pseudo-labels to the set of unlabeled
training samples with a margin greater than a certain threshold. The
pseudo-labeled examples are then used to enrich the labeled training data and
train a new classifier in conjunction with the labeled training set. We present
self-training methods for binary and multiclass classification and their
variants which were recently developed using Neural Networks. Finally, we
discuss our ideas for future research in self-training. To the best of our
knowledge, this is the first thorough and complete survey on this subject.


title:
Enhanced nonlinear circular dichroism in resonant dielectric
  metasurfaces

abstract:
  We study both linear and nonlinear circular dichroism in dielectric resonant
metasurfaces. We predict and demonstrate experimentally the enhancement of
circular dichroism for normal propagation of light due to the excitation of
multipolar Mie-type modes in silicon resonators and the formation of a complex
vectorial field structure. We also reveal that high-quality Mie resonances
satisfying optimal coupling condition can lead to giant circular dichroism in
the nonlinear regime observed for the generation of the third-harmonic signal
by normally propagating circularly polarized fundamental frequency fields.


title: Parameterized Complexity of Graph Partitioning into Connected Clusters

abstract:
  Given an undirected graph $G$ and $q$ integers $n_1,n_2,n_3, \cdots, n_q$,
balanced connected $q$-partition problem ($BCP_q$) asks whether there exists a
partition of the vertex set $V$ of $G$ into $q$ parts $V_1,V_2,V_3,\cdots, V_q$
such that for all $i\in[1,q]$, $|V_i|=n_i$ and the graph induced on $V_i$ is
connected. A related problem denoted as the balanced connected $q$-edge
partition problem ($BCEP_q$) is defined as follows. Given an undirected graph
$G$ and $q$ integers $n_1,n_2,n_3, \cdots, n_q$, $BCEP_q$ asks whether there
exists a partition of the edge set of $G$ into $q$ parts $E_1,E_2,E_3,\cdots,
E_q$ such that for all $i\in[1,q]$, $|E_i|=n_i$ and the graph induced on the
edge set $E_i$ is connected. Here we study both the problems for $q=2$ and
prove that $BCP_q$ for $q\geq 2$ is $W[1]$-hard. We also show that $BCP_2$ is
unlikely to have a polynomial kernel on the class of planar graphs. Coming to
the positive results, we show that $BCP_2$ is fixed parameter tractable (FPT)
parameterized by treewidth of the graph, which generalizes to FPT algorithm for
planar graphs. We design another FPT algorithm and a polynomial kernel on the
class of unit disk graphs parameterized by $\min(n_1,n_2)$. Finally, we prove
that unlike $BCP_2$, $BCEP_2$ is FPT parameterized by $\min(n_1,n_2)$.


title:
Visualization of carrier transport in lateral metal-perovskite-metal
  structures and its influence on device operation

abstract:
  The high performance of hybrid perovskite based devices is attributed to its
excellent bulk-transport properties. However, carrier dynamics, especially at
the metal-perovskite interface, and its influence on device operation are not
widely understood. This work presents the dominant transport mechanisms in
methylammonium lead iodide (MAPbI3) perovskite-based asymmetric metal-electrode
lateral devices. The device operation is studied with inter-electrode lengths
varying from 4 {\mu}m to 120 {\mu}m. Device characteristics indicate distinct
ohmic and space-charge limited current (SCLC) regimes that are controlled by
the inter-electrode length and applied bias. The electric-potential mapping
using Kelvin-Probe microscopy across the device indicates minimal ion-screening
effects and the presence of a transport barrier at the metal-MAPbI3 junction.
Further, photocurrent imaging of the channel using near-field
excitation-scanning microscopy reveals dominant recombination and
charge-separation zones. These lateral devices exhibit photodetector
characteristics with a responsivity of about 51 mA/W in self-powered mode and
5.2 A/W at 5 V bias, in short-channel devices (4 {\mu}m). The low device
capacitance enables a fast light-switching response of ~12 ns.


title:
Robust Subthermionic Topological Transistor Action via Antiferromagnetic
  Exchange

abstract:
  The topological quantum field-effect transition in buckled 2D-Xenes can
potentially be engineered to enable sub-thermionic transistor operation coupled
with dissipationless ON-state conduction. Substantive device design strategies
to harness this will necessitate delving into the physics of the quantum field
effect transition between the dissipationless topological phase and the band
insulator phase. Investigating workable device structures, we uncover
fundamental sub-threshold limits posed by the gating mechanism that effectuates
such a transition, thereby emphasizing the need for innovations on materials
and device structures. Detailing the complex band translation physics related
to the quantum spin Hall effect phase transition, it is shown that a gating
strategy to beat the thermionic limit can be engineered at the cost of
sacrificing the dissipationless ON-state conduction. It is then demonstrated
that an out-of-plane antiferromagnetic exchange introduced in the material via
proximity coupling can incite transitions between the quantum spin-valley Hall
and the spin quantum anomalous Hall phase, which can ultimately ensure the
topological robustness of the ON state while surpassing the thermionic limit.
Our work thus underlines the operational criteria for building topological
transistors using quantum materials that can overcome the Boltzmann's tyranny
while preserving the topological robustness.


title: Pushing Blocks by Sweeping Lines

abstract:
  We investigate the reconfiguration of $n$ blocks, or "tokens", in the square
grid using "line pushes". A line push is performed from one of the four
cardinal directions and pushes all tokens that are maximum in that direction to
the opposite direction. Tokens that are in the way of other tokens are
displaced in the same direction, as well.
  Similar models of manipulating objects using uniform external forces match
the mechanics of existing games and puzzles, such as Mega Maze, 2048 and
Labyrinth, and have also been investigated in the context of self-assembly,
programmable matter and robotic motion planning. The problem of obtaining a
given shape from a starting configuration is know to be NP-complete.
  We show that, for every $n$, there are "sparse" initial configurations of $n$
tokens (i.e., where no two tokens are in the same row or column) that can be
compacted into any $a\times b$ box such that $ab=n$. However, only $1\times k$,
$2\times k$ and $3\times 3$ boxes are obtainable from any arbitrary sparse
configuration with a matching number of tokens. We also study the problem of
rearranging labeled tokens into a configuration of the same shape, but with
permuted tokens. For every initial configuration of the tokens, we provide a
complete characterization of what other configurations can be obtained by means
of line pushes.


title: Selective Independence and $h$-Perfect Tree Forcing Notions

abstract:
  Generalizing the proof for Sacks forcing, we show that the $h$-perfect tree
forcing notions introduced by Goldstern, Judah and Shelah preserve selective
independent families even when iterated. As a result we obtain new proofs of
the consistency of $\mathfrak{i} = \mathfrak{u} < \mathrm{non} (\mathcal N) =
\mathrm{cof} (\mathcal N)$ and $\mathfrak{i} < \mathfrak{u} = \mathrm{non}
(\mathcal N) = \mathrm{cof}( \mathcal N)$ as well as some related results.


title:
Conditioned diffusion processes with an absorbing boundary condition for
  finite or infinite horizon

abstract:
  When the unconditioned process is a diffusion living on the half-line $x \in
]-\infty,a[$ in the presence of an absorbing boundary condition at position
$x=a$, we construct various conditioned processes corresponding to finite or
infinite horizon. When the time horizon is finite $T<+\infty$, the conditioning
consists in imposing the probability $P^*(y,T ) $ to be surviving at time $T$
and at the position $y \in ]-\infty,a[$, as well as the probability
$\gamma^*(T_a ) $ to have been absorbed at the previous time $T_a \in [0,T]$.
When the time horizon is infinite $T=+\infty$, the conditioning consists in
imposing the probability $\gamma^*(T_a ) $ to have been absorbed at the time
$T_a \in [0,+\infty[$, whose normalization $[1- S^*(\infty )]$ determines the
conditioned probability $S^*(\infty ) \in [0,1]$ of forever-survival. This case
of infinite horizon $T=+\infty$ can be thus reformulated as the conditioning of
diffusion processes with respect to their first-passage-time properties at
position $a$. This general framework is applied to the explicit case where the
unconditioned process is the Brownian motion with uniform drift $\mu$ in order
to generate stochastic trajectories satisfying various types of conditioning
constraints. Finally, we describe the links with the dynamical large deviations
at Level 2.5 and the stochastic control theory.


title: Mass Spectra and Decay Properties of Singly Heavy Bottom-Strange Baryons

abstract:
  We enumerated ground and excited state masses of singly heavy bottom-strange
baryons using the Hypercentral Constituent Quark Model (hCQM). The screening
potential is used with color-Coulomb potential, as the confining potential. We
determine the possible $J^P$ values to recently observed excited states like,
$\Xi_b(6100)$, $\Xi_b(6227)$, $\Xi_b(6327)$, $\Xi_b(6333)$ and four
$\Omega_b(6316,6330,6340,6350)$ states. The Regge trajectories are plotted in
$(J, M^2)$ plane to assign the $J^P$ values and quantum numbers to recently
observed states. Using the calculated spectroscopic data, properties such as
magnetic moment, transition magnetic moment, radiative decay width, and strong
decay width are also investigated for all singly heavy bottom-strange baryonic
system.


title:
When is Software a Medical Device? Understanding and Determining the
  'Intention' and Requirements for Software as a Medical device in EU law

abstract:
  The role of software in society has changed drastically since the start of
the 21st century. Software can now partially or fully facilitate anything from
diagnosis to treatment of a disease, regardless of whether it is psychological
or pathological, with the consequence of software being comparable to any other
type of medical equipment, and this makes discovering when software must comply
with such rules vital to both manufacturers and regulators. In lieu of the
Medical Device Regulation we expand on the idea of intention, and identify the
criteria software must fulfil to be considered medical devices within EU-law.


title:
Experiments as Code: A Concept for Reproducible, Auditable, Debuggable,
  Reusable, & Scalable Experiments

abstract:
  A common concern in experimental research is the auditability and
reproducibility of experiments. Experiments are usually designed, provisioned,
managed, and analyzed by diverse teams of specialists (e.g., researchers,
technicians and engineers) and may require many resources (e.g. cloud
infrastructure, specialized equipment). Even though researchers strive to
document experiments accurately, this process is often lacking, making it hard
to reproduce them. Moreover, when it is necessary to create a similar
experiment, very often we end up "reinventing the wheel" as it is easier to
start from scratch than trying to reuse existing work, thus losing valuable
embedded best practices and previous experiences. In behavioral studies this
has contributed to the reproducibility crisis. To tackle this challenge, we
propose the "Experiments as Code" paradigm, where the whole experiment is not
only documented but additionally the automation code to provision, deploy,
manage, and analyze it is provided. To this end we define the Experiments as
Code concept, provide a taxonomy for the components of a practical
implementation, and provide a proof of concept with a simple desktop VR
experiment that showcases the benefits of its "as code" representation, i.e.,
reproducibility, auditability, debuggability, reusability, and scalability.


title:
Adsorption of SARS CoV-2 Spike Proteins on Various Functionalized
  Surfaces Correlates with the Strong Infectivity of Delta and Omicron Variants

abstract:
  The SARS CoV-2 virus emerged at the end of 2019 and rapidly developed several
mutated variants, specifically the Delta and Omicron, which demonstrate higher
infectivity and escalating infection cases worldwide. The dominant transmission
pathway of this virus is via human-to-human contact and aerosols, but another
possible route is through contact with surfaces contaminated with SARS-CoV-2,
often exhibiting long-period survival. Here we compare the adsorption
capacities of the S1 and S2 subunits of the spike (S) protein from the original
variant to that of the S1 subunit from the Delta and Omicron variants. The
results clearly show a significant difference in adsorption capacity between
the different variants, as well as between the S1 and S2 subunits. Overall, our
study demonstrates that while the Omicron variant is able to adsorb much more
successfully than the Delta, both variants show enhanced adsorption capacity
than the original strain. We also examined the influence of pH conditions on
the adsorption ability of the S1 subunit and found that adsorption was
strongest at pH 7.4, which is the physiological pH. The main conclusion of this
study is that there is a strong correlation between the adsorption capacity and
the infectivity of the various SARS CoV-2 variants.


title: Noise thresholds for classical simulability of non-linear Boson sampling

abstract:
  Boson sampling, a computational problem conjectured to be hard to simulate on
a classical machine, is a promising candidate for an experimental demonstration
of quantum advantage using bosons. However, inevitable experimental noise and
imperfections, such as loss in the interferometer and random counts at the
detectors, could challenge the sampling task from entering the regime where
quantum advantage is achievable. In this work we introduce higher order
non-linearities as a mean to enhance the computational complexity of the
problem and the protocol's robustness against noise, i.e. increase the noise
threshold that allows to perform an efficient classical simulation of the
problem. Using a phase-space method based on the negativity volume of the
relevant quasi-probability distributions, we establish a necessary
non-classicality condition that any experimental proof of quantum advantage
must satisfy. Our results indicate that the addition of single-mode Kerr
non-linearity at the input state preparation level, while retaining a
linear-optical evolution, makes the Boson sampling protocol more robust against
noise and consequently relaxes the constraints on noise parameters required to
show quantum advantage.


title: Deep learning-based UAV detection in the low altitude clutter background

abstract:
  Unmanned aerial vehicles (UAVs) are widely used due to their low cost and
versatility, but they also pose security and privacy threats. Therefore,
reliable detection for low-altitude UAVs is an important issue. The strong
ground clutter makes the radar echoes from small UAVs submerged in noise,
resulting in low radar detection reliability. A low-altitude UAV detection
method based on deep contrastive learning is proposed to address the above
problems: Concretely, a low-altitude UAV radar echo model under low-altitude
clutter interference is first established. Based on the echo components and the
UAV Doppler domain identifiable mechanism, a time-frequency transformation
method combining ZAM transform and morphological operations is used to suppress
the ambiguity problem under clutter. Then feature extraction and fusion method
introducing contrast learning is utilized to suppress non-target ground clutter
interference. Finally, a detector relying on semantic features is designed for
the reliable identification of low-altitude UAVs. The experiments carried out
on both real and simulated data confirm that the proposed method can
effectively suppress ground clutter and reliably extract recognizable semantic
features of UAVs. The proposed method achieves lower false and missing alarms
compared with recent state-of-art solutions and improves the detection accuracy
by more than 5% for the same signal-to-noise ratio, which effectively improves
the detection reliability.


title:
On monoids of weighted zero-sum sequences and applications to norm
  monoids in Galois number fields and binary quadratic forms

abstract:
  Let $G$ be an additive finite abelian group and $\Gamma \subset
\operatorname{End} (G)$ be a subset of the endomorphism group of $G$. A
sequence $S = g_1 \cdot \ldots \cdot g_{\ell}$ over $G$ is a
($\Gamma$-)weighted zero-sum sequence if there are $\gamma_1, \ldots,
\gamma_{\ell} \in \Gamma$ such that $\gamma_1 (g_1) + \ldots + \gamma_{\ell}
(g_{\ell})=0$. We construct transfer homomorphisms from norm monoids (of Galois
algebraic number fields with Galois group $\Gamma$) and from monoids of
positive integers, represented by binary quadratic forms, to monoids of
weighted zero-sum sequences. Then we study algebraic and arithmetic properties
of monoids of weighted zero-sum sequences.


title: Counting Temporal Paths

abstract:
  The betweenness centrality of a vertex v is an important centrality measure
that quantifies how many optimal paths between pairs of other vertices visit v.
Computing betweenness centrality in a temporal graph, in which the edge set may
change over discrete timesteps, requires us to count temporal paths that are
optimal with respect to some criterion. For several natural notions of
optimality, including foremost or fastest temporal paths, this counting problem
reduces to #Temporal Path, the problem of counting all temporal paths between a
fixed pair of vertices; like the problems of counting foremost and fastest
temporal paths, #Temporal Path is #P-hard in general. Motivated by the many
applications of this intractable problem, we initiate a systematic study of the
prameterised and approximation complexity of #Temporal Path. We show that the
problem presumably does not admit an FPT-algorithm for the feedback vertex
number of the static underlying graph, and that it is hard to approximate in
general. On the positive side, we proved several exact and approximate
FPT-algorithms for special cases.


title:
Reconstructing anisotropic conductivities on two-dimensional Riemannian
  manifolds from power densities

abstract:
  We consider an electrically conductive compact two-dimensional Riemannian
manifold with a smooth boundary. This setting defines a natural conductive
Laplacian on the manifold and hence also voltage potentials, current fields and
corresponding power densities arising from suitable boundary conditions.
Motivated by Acousto-Electric Tomography we show that if the manifold has genus
zero and the metric is known, then the conductivity can be recovered uniquely
and constructively from knowledge of a few power densities. We illustrate the
reconstruction procedure numerically by an example of a conductivity on a
non-simply connected surface in three-space.