title: All-magnonic Stern-Gerlach effect in antiferromagnets

abstract:
  The Stern-Gerlach (SG) effect is well known as the spin-dependent splitting
of a beam of atoms carrying magnetic moments by a magnetic-field gradient,
leading to the concept of electron spin. Antiferromagnets can accommodate two
magnon modes with opposite spin polarizations, which is equivalent to the spin
property of electrons. Here, we propose the existence of an all-magnonic SG
effect in antiferromagnetic magnonic system, where a linearly polarized
spin-wave beam is deflected by a straight Dzyaloshinskii-Moriya interaction
(DMI) interface into two opposite polarized spin-wave beams propagating in two
discrete directions. Moreover, we observe bi-focusing of antiferromagnetic spin
waves induced by a curved DMI interface, which can also spatially separate
thermal magnons with opposite polarizations. Our findings provide a unique
perspective to understand the rich phenomena associated with antiferromagnetic
magnon spin and would be helpful for polarization-dependent application of
antiferromagnetic spintronic devices.


title:
User Experience Design for Automatic Credibility Assessment of News
  Content About COVID-19

abstract:
  The increasingly rapid spread of information about COVID-19 on the web calls
for automatic measures of quality assurance. In that context, we check the
credibility of news content using selected linguistic features. We present two
empirical studies to evaluate the usability of graphical interfaces that offer
such credibility assessment. In a moderated qualitative interview with six
participants, we identify rating scale, sub-criteria and algorithm authorship
as important predictors of the usability. A subsequent quantitative online
survey with 50 participants reveals a conflict between transparency and
conciseness in the interface design, as well as a perceived hierarchy of
metadata: the authorship of a news text is more important than the authorship
of the credibility algorithm used to assess the content quality. Finally, we
make suggestions for future research, such as proactively documenting
credibility-related metadata for Natural Language Processing and Language
Technology services and establishing an explicit hierarchical taxonomy of
usability predictors for automatic credibility assessment.


title:
Reconstructing solar magnetic fields from historical observations VIII.
  AIA 1600 {\AA} contrast as a proxy of solar magnetic fields

abstract:
  The bright regions in the solar chromosphere and temperature minimum have a
good spatial correspondence with regions of intense photospheric magnetic
field. Their observation started more than a hundred years ago with the
invention of the spectroheliograph. While the historical spectroheliograms are
essential for studying the long-term variability of the Sun, the modern
satellite-borne observations can help us reveal the nature of chromospheric
brightenings in previously unattainable detail. Our aim is to improve the
understanding of the relation between magnetic fields and radiative structures
by studying modern seeing-free observations of far-ultraviolet (FUV) radiation
around 1600 \r{A} and photospheric magnetic fields. We used Helioseismic and
Magnetic Imager (HMI) observations of photospheric magnetic fields and
Atmospheric Imaging Assembly (AIA) observations of FUV contrast around 1600
\r{A}. We developed a robust method to find contrast thresholds defining bright
and dark AIA 1600 \r{A} pixels, and we combine them to bright and dark
clusters. We investigate the relation of magnetic fields and AIA 1600 \r{A}
radiation in bright and dark clusters. We find that the percentage of bright
pixels entirely explains the observed variability of 1600 \r{A} emission. We
developed a multilinear regression model based on the percentages of bright and
dark pixels, which can reliably predict the magnitude of the disk-averaged
unsigned magnetic field. We find that bright and dark clusters closely
correspond respectively to the populations of moderate (B > 55 G) and strong (B
> 1365 G) magnetic field HMI clusters. The largest bright clusters have a
constant mean unsigned magnetic field, as found previously for Ca II K plages.
However, the magnetic field strength of bright clusters is 254.7$\pm$0.1 G,
which is roughly 100 G larger than found earlier for Ca II K plages.


title: Symmetry-protected exceptional and nodal points in non-Hermitian systems

abstract:
  One of the unique features of non-Hermitian (NH) systems is the appearance of
non-Hermitian degeneracies known as exceptional points~(EPs). The occurrence of
EPs in NH systems requires satisfying constraints whose number can be reduced
in the presence of some symmetries. This results in stabilizing the appearance
of EPs. Even though two different types of EPs, namely defective and
non-defective EPs, may emerge in NH systems, exploring the possibilities of
stabilizing EPs has been only addressed for defective EPs, at which the
Hamiltonian becomes non-diagonalizable. In this letter, we show that certain
discrete symmetries, namely parity-time, parity-particle-hole, and
pseudo-Hermitian symmetry, may guarantee the occurrence of both defective and
non-defective EPs. We extend this list of symmetries by including the
non-Hermitian time-reversal symmetry in the two-band systems.
  We further show that the non-defective EPs manifest themselves by i) the
diagonalizability of non-Hermitian Hamiltonian at these points and ii) the
non-diagonalizability of the Hamiltonian along certain intersections of
non-defective EPs. Two-band and four-band models exemplify our findings.
Through an example, we further reveal that ordinary (Hermitian) nodal points
may coexist with defective EPs in non-Hermitian models when the above
symmetries are relaxed.


title: Group equations with abelian predicates

abstract:
  In this paper we begin the systematic study of group equations with abelian
predicates in the main classes of groups where solving equations is possible.
We extend the line of work on word equations with length constraints, and more
generally, on extensions of the existential theory of semigroups, to the world
of groups.
  We use interpretability by equations to establish model-theoretic and
algebraic conditions which are sufficient to get undecidability. We apply our
results to (non-abelian) right-angled Artin groups, and show that the problem
of solving equations with abelian predicates is undecidable for these. We
obtain the same result for hyperbolic groups whose abelianisation has
torsion-free rank at least two. By contrast, we prove that in groups with
finite abelianisation, the problem can be reduced to solving equations with
recognisable constraints, and so this is decidable in right-angled Coxeter
groups, or more generally, graph products of finite groups, as well as
hyperbolic groups with finite abelianisation.


title:
Limiting distribution of extremal eigenvalues of d-dimensional random
  Schr\"odinger operator

abstract:
  We consider Schr\"odinger operator with random decaying potential on $\ell^2
({\bf Z}^d)$ and showed that, (i) IDS coincides with that of free Laplacian in
general cases, and (ii) the set of extremal eigenvalues, after rescaling,
converges to a inhomogeneous Poisson process, under certain condition on the
single-site distribution.


title: Gravitational Lensing by Black Holes with Multiple Photon Spheres

abstract:
  We study gravitational lensing of light by hairy black holes, which, in a
certain parameter regime, can possess two photon spheres of different size
outside the event horizon. In particular, we focus on higher-order images of a
point-like light source and a luminous celestial sphere produced by strong
gravitational lensing near photon spheres. Two photon spheres usually triple
the number of high-order images of a point-like light source. When a hairy
black hole is illuminated by a celestial sphere, two photon spheres would give
rise to two critical curves in the black hole image, and the smaller critical
curve coincides with the shadow edge. In addition to a set of higher-order
images of the celestial sphere outside the shadow edge, two more sets of
higher-order images are observed inside and outside the larger critical curve,
respectively.


title: Finite sequences representing expected order statistics

abstract:
  Characterizations of finite sequences $\beta_{1}<\cdots<\beta_{n}$
representing expected values of order statistics from a random sample of size
$n$ are given. As a by-product, a characterization of binomial mixtures, when
the mixing random variable is supported in the open interval $(0,1)$, is
presented; this enables the exact description of the convex hull of the open
binomial curve, as well as the open moment curve.


title: Active learning BSM parameter spaces

abstract:
  Active learning (AL) has interesting features for parameter scans of new
models. We show on a variety of models that AL scans bring large efficiency
gains to the traditionally tedious work of finding boundaries for BSM models.
In the MSSM, this approach produces more accurate bounds. In light of our prior
publication, we further refine the exploration of the parameter space of the
SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV.
Finally we show that this technique is especially useful in more complex models
like the MDGSSM.


title: Quantum reservoir computing of thermal convection flow

abstract:
  We simulate the nonlinear chaotic dynamics of Lorenz-type models for a
classical two-dimensional thermal convection flow with 3 and 8 degrees of
freedom by a hybrid quantum--classical reservoir computing model. The
high-dimensional quantum reservoir dynamics is established by universal quantum
gates that rotate and entangle the individual qubits of the tensor product
quantum state. A comparison of the quantum reservoir computing model with its
classical counterpart shows that the same prediction and reconstruction
capabilities of classical reservoirs with thousands of perceptrons can be
obtained by a few strongly entangled qubits. We demonstrate that the mean
squared error between model output and ground truth in the test phase of the
quantum reservoir computing algorithm increases when the reservoir is
decomposed into separable subsets of qubits. Furthermore, the quantum reservoir
computing model is implemented on a real noisy IBM quantum computer for up to 7
qubits. Our work thus opens the door to model the dynamics of classical complex
systems in a high-dimensional phase space effectively with an algorithm that
requires a small number of qubits.


title: Deep Geometry Post-Processing for Decompressed Point Clouds

abstract:
  Point cloud compression plays a crucial role in reducing the huge cost of
data storage and transmission. However, distortions can be introduced into the
decompressed point clouds due to quantization. In this paper, we propose a
novel learning-based post-processing method to enhance the decompressed point
clouds. Specifically, a voxelized point cloud is first divided into small
cubes. Then, a 3D convolutional network is proposed to predict the occupancy
probability for each location of a cube. We leverage both local and global
contexts by generating multi-scale probabilities. These probabilities are
progressively summed to predict the results in a coarse-to-fine manner.
Finally, we obtain the geometry-refined point clouds based on the predicted
probabilities. Different from previous methods, we deal with decompressed point
clouds with huge variety of distortions using a single model. Experimental
results show that the proposed method can significantly improve the quality of
the decompressed point clouds, achieving 9.30dB BDPSNR gain on three
representative datasets on average.


title:
"My nose is running.""Are you also coughing?": Building A Medical
  Diagnosis Agent with Interpretable Inquiry Logics

abstract:
  With the rise of telemedicine, the task of developing Dialogue Systems for
Medical Diagnosis (DSMD) has received much attention in recent years. Different
from early researches that needed to rely on extra human resources and
expertise to help construct the system, recent researches focused on how to
build DSMD in a purely data-driven manner. However, the previous data-driven
DSMD methods largely overlooked the system interpretability, which is critical
for a medical application, and they also suffered from the data sparsity issue
at the same time. In this paper, we explore how to bring interpretability to
data-driven DSMD. Specifically, we propose a more interpretable decision
process to implement the dialogue manager of DSMD by reasonably mimicking real
doctors' inquiry logics, and we devise a model with highly transparent
components to conduct the inference. Moreover, we collect a new DSMD dataset,
which has a much larger scale, more diverse patterns and is of higher quality
than the existing ones. The experiments show that our method obtains 7.7%,
10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three
datasets, demonstrating the effectiveness of its rational decision process and
model design. Our codes and the GMD-12 dataset are available at
https://github.com/lwgkzl/BR-Agent.


title: Computational Modeling of Initial Stage Si(100) Surface Oxidation

abstract:
  Silicon together with its native oxide SiO$_2$ was recognized as an
outstanding material system for the semiconductor industry in the 1950s. In
state-of-the-art device technology, SiO$_2$ is widely used as an insulator in
combination with high-$k$ dielectrics such as HfO$_2$, demanding fabrication of
ultra-thin interfacial layers. The classical standard model derived by Deal and
Grove accurately describes the oxidation of Si in a progressed stage, however,
strongly underestimates growth rates for thin oxide layers. Recent studies
report a variety of oxidation mechanisms during the growth of oxide films in
the range of \SI{10}{\angstrom} with various details still under debate. This
paper presents a first-principles based approach to theoretically assess the
thermal oxidation process of the technologically relevant Si(100) surfaceduring
this initial stage. Our investigations range from the chemisorption of single
O$_2$ molecules onto the $p(2\times2)$ reconstructed Si surface to oxidized Si
surface layers with a thickness of up to \SI{20}{\angstrom}. The initially
observed enhanced growth rate is assigned to barrierless O$_2$ chemisorption
events upon which the oxygen molecule dissociate. We present strong evidence
for an immediate amorphization of the oxide layer from the onset of oxidation.
Surface reactions dominate until the surface is saturated with oxygen and
separated from the Si substrate by a \SI{5}{\angstrom} transition region. The
saturated surface becomes inert to dissociative reactions and enables the
diffusion of molecular oxygen to the \interface interface as assumed within the
Deal-Grove model. Further oxidation of the Si substrate is then provided by
O$_2$ dissociations at the interface due to the same charge transfer process
responsible for the chemisorption at the surface.


title:
A Directional Vibrotactile Feedback Interface for Ergonomic Postural
  Adjustment

abstract:
  The objective of this paper is to develop and evaluate a directional
vibrotactile feedback interface as a guidance tool for postural adjustments
during work. In contrast to the existing active and wearable systems such as
exoskeletons, we aim to create a lightweight and intuitive interface, capable
of guiding its wearers towards more ergonomic and healthy working conditions.
To achieve this, a vibrotactile device called ErgoTac is employed to develop
three different feedback modalities that are able to provide a directional
guidance at the body segments towards a desired pose. In addition, an
evaluation is made to find the most suitable, comfortable, and intuitive
feedback modality for the user. Therefore, these modalities are first compared
experimentally on fifteen subjects wearing eight ErgoTac devices to achieve
targeted arm and torso configurations. The most effective directional feedback
modality is then evaluated on five subjects in a set of experiments in which an
ergonomic optimisation module provides the optimised body posture while
performing heavy lifting or forceful exertion tasks. The results yield strong
evidence on the usefulness and the intuitiveness of one of the developed
modalities in providing guidance towards ergonomic working conditions, by
minimising the effect of an external load on body joints. We believe that the
integration of such low-cost devices in workplaces can help address the
well-known and complex problem of work-related musculoskeletal disorders.


title:
FIES and Tull confirm and characterise three giant planets delivered by
  TESS

abstract:
  We report the confirmation and characterisation of TOI-1820 b, TOI-2025 b,
and TOI-2158 b--three Jupiter-sized planets on short period orbits around
G-type stars detected by TESS. Through our ground-based efforts using the FIES
and Tull spectrographs we have confirmed these planets, and characterised their
orbits and find periods of around 4.9 d, 8.9 d, and 8.6 d for TOI-1820 b,
TOI-2025 b, and TOI-2158 b, respectively. The sizes of the planets range from
0.96 to 1.16 Jupiter radii, and their masses are in the range from 0.8 to 4.5
Jupiter masses. For two of the systems, namely TOI-2025 and TOI-2158, we see a
long-term trend in the radial velocities, indicating the presence of an outer
companion in each of the two systems. For TOI-2025 we furthermore find the star
to be well-aligned with the orbit with a projected obliquity of 9$^{+36}_{-34}$
deg. As these planets are all found in relatively bright systems
(V$\sim$10.9-11.6 mag) they are well-suited for further studies, which can help
shed light into the formation and migration of hot and warm Jupiters.


title:
PIE: a Parameter and Inference Efficient Solution for Large Scale
  Knowledge Graph Embedding Reasoning

abstract:
  Knowledge graph (KG) embedding methods which map entities and relations to
unique embeddings in the KG have shown promising results on many reasoning
tasks. However, the same embedding dimension for both dense entities and sparse
entities will cause either over parameterization (sparse entities) or under
fitting (dense entities). Normally, a large dimension is set to get better
performance. Meanwhile, the inference time grows log-linearly with the number
of entities for all entities are traversed and compared. Both the parameter and
inference become challenges when working with huge amounts of entities. Thus,
we propose PIE, a \textbf{p}arameter and \textbf{i}nference \textbf{e}fficient
solution. Inspired from tensor decomposition methods, we find that decompose
entity embedding matrix into low rank matrices can reduce more than half of the
parameters while maintaining comparable performance. To accelerate model
inference, we propose a self-supervised auxiliary task, which can be seen as
fine-grained entity typing. By randomly masking and recovering entities'
connected relations, the task learns the co-occurrence of entity and relations.
Utilizing the fine grained typing, we can filter unrelated entities during
inference and get targets with possibly sub-linear time requirement.
Experiments on link prediction benchmarks demonstrate the proposed key
capabilities. Moreover, we prove effectiveness of the proposed solution on the
Open Graph Benchmark large scale challenge dataset WikiKG90Mv2 and achieve the
state of the art performance.


title:
Band gap opening from displacive instabilities in layered
  covalent-organic frameworks $^\dag$

abstract:
  Covalent organic frameworks (COFs) offer a high degree of chemical and
structural flexibility. There is a large family of COFs built from 2D sheets
that are stacked to form extended crystals. While it has been common to
represent the stacking as eclipsed with one repeating layer ("AA"), there is
growing evidence that a more diverse range of stacking sequences is accessible.
Herein, we report a computational study of layer stacking in two prototypical
COFs, Tp-Azo and DAAQ-TFP, which have shown high performance as Li-ion battery
electrodes. We find a striking preference for slipped structures with
horizontal offsets between layers ranging from 1.8 \r{A} to 3.8 \r{A} in a
potential energy minimum that forms a low energy ring. The associated symmetry
breaking results in a pronounced change in the underlying electronic structure.
A band gap opening of 0.8 - 1.4 eV is found due to modifications of the
underlying valence and conduction band dispersion as explained from changes in
the $\pi$ orbital overlap. The implications for the screening and selection of
COF for energy applications are discussed.


title: $S$- and $P$-wave fully-strange tetraquark states from QCD sum rules

abstract:
  We apply the QCD sum rule method to systematically study the $S$- and
$P$-wave fully-strange tetraquark states within the diquark-antidiquark
picture. We systematically construct their interpolating currents by explicitly
adding the covariant derivative operator. Our results suggest that the
$f_0(2100)$, $X(2063)$, and $f_2(2010)$ may be explained as the $S$-wave $s s
\bar s \bar s$ tetraquark states with the quantum numbers $J^{PC} = 0^{++}$,
$1^{+-}$, and $2^{++}$, respectively. Our results also suggest that both the
$X(2370)$ and $X(2500)$ may be explained as the $P$-wave $s s \bar s \bar s$
tetraquark states of $J^{PC} = 0^{-+}$, and both the $\phi(2170)$ and $X(2400)$
may be explained as the $P$-wave $s s \bar s \bar s$ tetraquark states of
$J^{PC} = 1^{--}$. The masses of the $s s \bar s \bar s$ tetraquark states with
the exotic quantum number $J^{PC} = 1^{-+}$ are extracted from two
non-correlated currents to be $2.45^{+0.20}_{-0.25}$ GeV and
$2.49^{+0.21}_{-0.25}$ GeV.


title: Water Ortho-to-Para ratio in the coma of comet 67P/Churyumov-Gerasimenko

abstract:
  Abundance ratios of the nuclear-spin isomers of H$_2$O and NH$_3$ have been
measured in about two dozen comets, with a mean value corresponding to a
nuclear-spin temperature of $\sim$ 30 K. The real meaning of these
unequilibrated nuclear-spin abundance ratios is still debated. However, an
equilibrated water ortho-to-para ratio (OPR) of 3 is also commonly observed.
The H channel of VIRTIS (VIRTIS-H) on board Rosetta provided high-resolution
2.5--2.9 $\mu$m spectra of H$_2$O vapour in the coma of comet
67P/Churyumov-Gerasimenko (67P), which are suitable for the determination of
the OPR of water in this comet. A large dataset of VIRTIS-H spectra obtained in
limb-sounding viewing geometry was analysed, covering heliocentric distances
from 1.24 to 2.73 au and altitudes from a few hundred metres to $>$ 100 km. The
OPR, together with the H$_2$O rotational temperature and column density, were
derived for each spectra. The weak lines of the $\nu_1$, $\nu_1+\nu_3-\nu_1$
and $\nu_2+\nu_3-\nu_2$ bands in the 2.774--2.910 $\mu$m range were used to
calculate by how much the strong $\nu_3$ band centred at 2.67 $\mu$m is
attenuated due to optical depth effects, expressed by the attenuation factor
$f_{\rm atten}$. Most OPR determinations are strongly affected by opacity
effects, as demonstrated by the observed anti-correlation between the OPR and
the column density, and the correlation between the OPR and attenuation factor
$f_{\rm atten}$. Based on both radiative transfer calculations and OPR values
obtained in low-opacity conditions, we derive an OPR of 2.94 $\pm$ 0.06 for
comet 67P. The water OPR measured in the coma of 67P is consistent with
laboratory experiments showing that water vapour that has thermally desorbed
from water ice has a statistical value of 3, regardless of the past formation
process of water ice.


title:
The rank of the inverse semigroup of all partial automorphisms on a
  finite crown

abstract:
  For $n \in \mathbb{N}$, let $[n] = \{1, 2, \ldots, n\}$ be an $n$ - element
set. As usual, we denote by $I_n$ the symmetric inverse semigroup on $[n]$,
i.e. the partial one-to-one transformation semigroup on $[n]$ under composition
of mappings. The crown (cycle) $\cal{C}_n$ is an $n$-ordered set with the
partial order $\prec$ on $[n]$, where the only comparabilities are $$1 \prec 2
\succ 3 \prec 4 \succ \cdots \prec n \succ 1 ~~\mbox{ or }~~ 1 \succ 2 \prec 3
\succ 4 \prec \cdots \succ n \prec 1.$$ We say that a transformation $\alpha
\in I_n$ is order-preserving if $x \prec y$ implies that $x\alpha \prec
y\alpha$, for all $x, y $ from the domain of $\alpha$. In this paper, we study
the inverse semigroup $IC_n$ of all partial automorphisms on a finite crown
$\cal{C}_n$. We consider the elements, determine a generating set of minimal
size and calculate the rank of $IC_n$.


title:
SCS-Co: Self-Consistent Style Contrastive Learning for Image
  Harmonization

abstract:
  Image harmonization aims to achieve visual consistency in composite images by
adapting a foreground to make it compatible with a background. However,
existing methods always only use the real image as the positive sample to guide
the training, and at most introduce the corresponding composite image as a
single negative sample for an auxiliary constraint, which leads to limited
distortion knowledge, and further causes a too large solution space, making the
generated harmonized image distorted. Besides, none of them jointly constrain
from the foreground self-style and foreground-background style consistency,
which exacerbates this problem. Moreover, recent region-aware adaptive instance
normalization achieves great success but only considers the global background
feature distribution, making the aligned foreground feature distribution
biased. To address these issues, we propose a self-consistent style contrastive
learning scheme (SCS-Co). By dynamically generating multiple negative samples,
our SCS-Co can learn more distortion knowledge and well regularize the
generated harmonized image in the style representation space from two aspects
of the foreground self-style and foreground-background style consistency,
leading to a more photorealistic visual result. In addition, we propose a
background-attentional adaptive instance normalization (BAIN) to achieve an
attention-weighted background feature distribution according to the
foreground-background feature similarity. Experiments demonstrate the
superiority of our method over other state-of-the-art methods in both
quantitative comparison and visual analysis.


title: Tailored Uncertainty Estimation for Deep Learning Systems

abstract:
  Uncertainty estimation bears the potential to make deep learning (DL) systems
more reliable. Standard techniques for uncertainty estimation, however, come
along with specific combinations of strengths and weaknesses, e.g., with
respect to estimation quality, generalization abilities and computational
complexity. To actually harness the potential of uncertainty quantification,
estimators are required whose properties closely match the requirements of a
given use case. In this work, we propose a framework that, firstly, structures
and shapes these requirements, secondly, guides the selection of a suitable
uncertainty estimation method and, thirdly, provides strategies to validate
this choice and to uncover structural weaknesses. By contributing tailored
uncertainty estimation in this sense, our framework helps to foster trustworthy
DL systems. Moreover, it anticipates prospective machine learning regulations
that require, e.g., in the EU, evidences for the technical appropriateness of
machine learning systems. Our framework provides such evidences for system
components modeling uncertainty.


title: Analyticity of positive semigroups is inherited under domination

abstract:
  For positive $C_0$-semigroups $S$ and $T$ on a Banach lattice such that $S(t)
\le T(t)$ for all times $t$, we prove that analyticity of $T$ implies
analyticity of $S$. This answers an open problem posed by Arendt in 2004.
  Our proof is based on a spectral theoretic argument: we apply
Perron-Frobenius theory to multiplication operators that are induced by $S$ and
$T$ on a vector-valued function space.


title:
The advantage of self-protecting interventions in mitigating epidemic
  circulation at the community level

abstract:
  Protecting interventions of many types (both pharmaceutical and
non-pharmaceutical) can be deployed against the spreading of a communicable
disease, as the worldwide COVID-19 pandemic has dramatically shown. Here we
investigate in detail the effects at the population level of interventions that
provide an asymmetric protection between the people involved in a single
interaction. Masks of different filtration types, either protecting mainly the
wearer or the contacts of the wearer, are a prominent example of these
interventions. By means of analytical calculations and extensive simulations of
simple epidemic models on networks, we show that interventions protecting more
efficiently the adopter (e.g the mask wearer) are more effective than
interventions protecting primarily the contacts of the adopter in reducing the
prevalence of the disease and the number of concurrently infected individuals
("flattening the curve"). This observation is backed up by the study of a more
realistic epidemic model on an empirical network representing the patterns of
contacts in the city of Portland. Our results point out that promoting
wearer-protecting face masks and other self-protecting interventions, though
deemed selfish and inefficient, can actually be a better strategy to
efficiently curtail pandemic spreading.


title:
Robust initial orbit determination for short-arc Doppler radar
  observations

abstract:
  A new Doppler radar initial orbit determination algorithm with embedded
uncertainty quantification capabilities is presented. The method is based on a
combination of Gauss' and Lambert's solvers. The whole process is carried out
in the Differential Algebra framework, which provides the Taylor expansion of
the state estimate with respect to the measurements' uncertainties. This
feature makes the approach particularly suited for handling data association
problems. A comparison with the Doppler integration method is performed using
both simulated and real data. The proposed approach is shown to be more
accurate and robust, and particularly suited for short-arc observations.


title:
T-odd proton-helicity asymmetry in semi-inclusive DIS in perturbative
  QCD

abstract:
  We compute the single-spin asymmetry $A_{UL}$ in semi-inclusive
deep-inelastic scattering of unpolarized leptons and longitudinally polarized
protons at large transverse momentum of the produced hadron. Our calculation is
performed in collinear factorization at the lowest order of QCD perturbation
theory. For photon exchange the asymmetry is T-odd and receives contributions
from the interference of the tree level and one-loop absorptive amplitudes. We
consider the behavior of the spin asymmetry at low transverse momentum where
contact to the formalism based on transverse-momentum dependent distribution
functions can be made. We also present some phenomenological results relevant
for the COMPASS and HERMES experiments and the future Electron-Ion Collider.


title:
Simulations for Planning Next-Generation Exoplanet Radial Velocity
  Surveys

abstract:
  Future direct imaging missions such as HabEx and LUVOIR aim to catalog and
characterize Earth-mass analogs around nearby stars. The exoplanet yield of
these missions will be dependent on the frequency of Earth-like planets, and
potentially the a priori knowledge of which stars specifically host suitable
planetary systems. Ground or space based radial velocity surveys can
potentially perform the pre-selection of targets and assist in the optimization
of observation times, as opposed to an uninformed direct imaging survey. In
this paper, we present our framework for simulating future radial velocity
surveys of nearby stars in support of direct imaging missions. We generate
lists of exposure times, observation time-series, and radial velocity
time-series given a direct imaging target list. We generate simulated surveys
for a proposed set of telescopes and precise radial velocity spectrographs
spanning a set of plausible global-network architectures that may be considered
for next generation extremely precise radial velocity surveys. We also develop
figures of merit for observation frequency and planet detection sensitivity,
and compare these across architectures. From these, we draw conclusions, given
our stated assumptions and caveats, to optimize the yield of future radial
velocity surveys in support of direct imaging missions. We find that all of our
considered surveys obtain sufficient numbers of precise observations to meet
the minimum theoretical white noise detection sensitivity for Earth-mass
habitable zone planets, with margin to explore systematic effects due to
stellar activity and correlated noise.


title:
On the nearly freeness of conic-line arrangements with nodes, tacnodes,
  and ordinary triple points

abstract:
  In the present note we provide a partial classification of nearly free
conic-line arrangements in the complex plane having nodes, tacnodes, and
ordinary triple points. In this setting, our theoretical bound tells us that
the degree of such an arrangement is bounded from above by $12$. We construct
examples of nearly free conic-line arrangements having degree $3,4,5,6,7$, and
we prove that in degree $11$ and $12$ there is no such arrangement.


title:
SN 2019va: A Type IIP Supernova with Large Influence of Nickel-56 Decay
  on the Plateau-phase Light Curve

abstract:
  We present multi-band photometric and spectroscopic observations of the type
II supernova, (SN) 2019va, which shows an unusually flat plateau-phase
evolution in its V-band light curve. Its pseudo-bolometric light curve even
shows a weak brightening towards the end of the plateau phase. These uncommon
features are related to the influence of 56Ni decay on the light curve during
the plateau phase, when the SN emission is usually dominated by cooling of the
envelope. The inferred 56Ni mass of SN 2019va is 0.088+/-0.018 solar mass,
which is significantly larger than most SNe II. To estimate the influence of
56Ni decay on the plateau-phase light curve, we calculate the ratio (dubbed as
eta_Ni) between the integrated time-weighted energy from 56Ni decay and that
from envelope cooling within the plateau phase, obtaining a value of 0.8 for SN
2019va, which is the second largest value among SNe II that have been measured.
After removing the influence of 56Ni decay on the plateau-phase light curve, we
found that the progenitor/explosion parameters derived for SN 2019va are more
reasonable. In addition, SN 2019va is found to have weaker metal lines in its
spectra compared to other SNe IIP at similar epochs, implying a low-metallicity
progenitor, which is consistent with the metal-poor environment inferred from
the host-galaxy spectrum. We further discuss the possible reasons that might
lead to SN 2019va-like events.


title:
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning
  Approach

abstract:
  With the advancement of deep learning techniques, major cloud providers and
niche machine learning service providers start to offer their cloud-based
machine learning tools, also known as machine learning as a service (MLaaS), to
the public. According to our measurement, for the same task, these MLaaSes from
different providers have varying performance due to the proprietary datasets,
models, etc. Federating different MLaaSes together allows us to improve the
analytic performance further. However, naively aggregating results from
different MLaaSes not only incurs significant momentary cost but also may lead
to sub-optimal performance gain due to the introduction of possible
false-positive results. In this paper, we propose Armol, a framework to
federate the right selection of MLaaS providers to achieve the best possible
analytic performance. We first design a word grouping algorithm to unify the
output labels across different providers. We then present a deep combinatorial
reinforcement learning based-approach to maximize the accuracy while minimizing
the cost. The predictions from the selected providers are then aggregated
together using carefully chosen ensemble strategies. The real-world
trace-driven evaluation further demonstrates that Armol is able to achieve the
same accuracy results with $67\%$ less inference cost.


title: Size Generalization for Resource Allocation with Graph Neural Networks

abstract:
  Size generalization is important for learning wireless policies, which are
often with dynamic sizes, say caused by time-varying number of users. Recent
works of learning to optimize resource allocation empirically demonstrate that
graph neural networks (GNNs) can generalize to different problem scales.
However, GNNs are not guaranteed to generalize across input sizes. In this
paper, we strive to analyze the size generalization mechanism of GNNs when
learning permutation equivariant (PE) policies. We find that the aggregation
function and activation functions of a GNN play a key role on its size
generalization ability. We take the GNN with mean aggregator, called mean-GNN,
as an example to demonstrate a size generalization condition, and interpret why
several GNNs in the literature of wireless communications can generalize well
to problem scales. To illustrate how to design GNNs with size generalizability
according to our finding, we consider power and bandwidth allocation, and
suggest to select or pre-train activation function in the output layer of
mean-GNN for learning the PE policies. Simulation results show that the
proposed GNN can generalize well to the number of users, which validate our
analysis for the size generalization condition of GNNs when learning the PE
policies.


title:
Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle
  Perception

abstract:
  Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable
spatial perception of their surroundings and help make driving decisions.
Recent works demonstrated attacks that aim to hide objects from AV perception,
which can result in severe consequences. 3D shadows, are regions void of
measurements in 3D point clouds which arise from occlusions of objects in a
scene. 3D shadows were proposed as a physical invariant valuable for detecting
spoofed or fake objects. In this work, we leverage 3D shadows to locate
obstacles that are hidden from object detectors. We achieve this by searching
for void regions and locating the obstacles that cause these shadows. Our
proposed methodology can be used to detect an object that has been hidden by an
adversary as these objects, while hidden from 3D object detectors, still induce
shadow artifacts in 3D point clouds, which we use for obstacle detection. We
show that using 3D shadows for obstacle detection can achieve high accuracy in
matching shadows to their object and provide precise prediction of an
obstacle's distance from the ego-vehicle.


title: Participatory Sensing for Localization of a GNSS Jammer

abstract:
  GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous
such incidents have been reported worldwide in the last decade. It is important
to detect attacks fast and localize attackers, which can be hard if not
impossible without dedicated sensing infrastructure. The notion of
participatory sensing, or crowdsensing, is that a large ensemble of voluntary
contributors provides the measurements, rather than relying on dedicated
sensing infrastructure. This work considers embedded GNSS receivers to provide
measurements for participatory jamming detection and localization.
Specifically, this work proposes a novel jamming localization algorithm, based
on participatory sensing, that exploits AGC and C/N_0 estimates from commercial
GNSS receivers. The proposed algorithm does not require knowledge of the
jamming power nor of the channels, but automatically estimates all parameters.
The algorithm is shown to outperform similar state-of-the-art localization
algorithms in relevant scenarios.


title:
Conditional average treatment effect estimation with treatment offset
  models

abstract:
  Treatment effect estimates are often available from randomized controlled
trials as a single average treatment effect for a certain patient population.
Estimates of the conditional average treatment effect (CATE) are more useful
for individualized treatment decision making, but randomized trials are often
too small to estimate the CATE. There are several examples in medical
literature where the assumption of a known constant relative treatment effect
(e.g. an odds-ratio) is used to estimate CATE models from large observational
datasets. One approach to estimating these CATE models is by using the relative
treatment effect as an offset, while estimating the covariate-specific baseline
risk. Whether this is a valid approach in the presence of unobserved
confounding is unknown. We demonstrate for a simple example that offset models
do not recover the true CATE in the presence of unobserved confounding. We then
explore the magnitude of this bias in numerical experiments. For virtually all
plausible confounding magnitudes, estimating the CATE using offset models is
more accurate than assuming a single absolute treatment effect whenever there
is sufficient variation in the baseline risk. Next, we observe that the
odds-ratios reported in randomized controlled trials are not the odds-ratios
that are needed in offset models because trials often report the marginal
odds-ratio. We introduce a constraint to better use marginal odds-ratios from
randomized controlled trials and find that the newly introduced constrained
offset models have lower bias than standard offset models. Finally, we
highlight directions for future research for exploiting the assumption of a
constant relative treatment effect with offset models.


title: Making sense of violence risk predictions using clinical notes

abstract:
  Violence risk assessment in psychiatric institutions enables interventions to
avoid violence incidents. Clinical notes written by practitioners and available
in electronic health records (EHR) are valuable resources that are seldom used
to their full potential. Previous studies have attempted to assess violence
risk in psychiatric patients using such notes, with acceptable performance.
However, they do not explain why classification works and how it can be
improved. We explore two methods to better understand the quality of a
classifier in the context of clinical note analysis: random forests using topic
models, and choice of evaluation metric. These methods allow us to understand
both our data and our methodology more profoundly, setting up the groundwork to
work on improved models that build upon this understanding. This is
particularly important when it comes to the generalizability of evaluated
classifiers to new data, a trustworthiness problem that is of great interest
due to the increased availability of new data in electronic format.


title: Two-dimensional Fibonacci Words: Tandem Repeats and Factor Complexity

abstract:
  If $x$ is a non-empty string then the repetition $xx$ is called a tandem
repeat. Similarly, a tandem in a two dimensional array $X$ is a configuration
consisting of a same primitive block $W$ that touch each other with one side or
corner. In \cite{Apostolico:2000}, Apostolico and Brimkov have proved various
bounds for the number of tandems in a two dimensional word of size $m \times
n$. Of the two types of tandems considered therein, they also proved that, for
one type, the number of occurrences in an $m \times n$ Fibonacci array attained
the general upper bound, $\mathcal{O}(m^{2}n \hspace{0.1cm} \mbox{log}
\hspace{0.1cm} n)$. In this paper, we derive an expression for the exact number
of tandems in a given finite Fibonacci array $f_{m,n}$. As a required result,
we derive the factor complexities of $f_{m,n}$, $m,n \ge 0$ and that of the
infinite Fibonacci word $f_{\infty, \infty}$. Generations of $f_{\infty,
\infty}$ and $f_{m,n}$, for any given $m,n \ge 1$ using a two-dimensional
homomorphism is also achieved.


title: Tunable nano-plasmonic photodetectors

abstract:
  Visible and infrared photons can be detected with a broadband response via
the internal photoeffect. By using plasmonic nanostructures, i.e. nanoantennas,
wavelength selectivity can be introduced to such detectors through
geometry-dependent resonances. Also, additional functionality, like electronic
responsivity switching and polarization detection have been realized. However,
previous devices consisted of large arrays of nanostructures to achieve
detectable photocurrents. Here we show that this concept can be scaled down to
a single antenna level, resulting in detector dimensions well below the
resonance wavelength of the device. Our design consists of a single
electrically-connected plasmonic nanoantenna covered with a wide-bandgap
semiconductor allowing broadband photodetection in the VIS/NIR via injection of
hot carriers. We demonstrate electrical switching of the color sensitivity as
well as polarization detection. Our results hold promise for the realization of
ultra small, highly integratable photodetectors with advanced functionality.


title:
ExaASC: A General Target-Based Stance Detection Corpus in Arabic
  Language

abstract:
  Target-based Stance Detection is the task of finding a stance toward a
target. Twitter is one of the primary sources of political discussions in
social media and one of the best resources to analyze Stance toward entities.
This work proposes a new method toward Target-based Stance detection by using
the stance of replies toward a most important and arguing target in source
tweet. This target is detected with respect to the source tweet itself and not
limited to a set of pre-defined targets which is the usual approach of the
current state-of-the-art methods. Our proposed new attitude resulted in a new
corpus called ExaASC for the Arabic Language, one of the low resource languages
in this field. In the end, we used BERT to evaluate our corpus and reached a
70.69 Macro F-score. This shows that our data and model can work in a general
Target-base Stance Detection system. The corpus is publicly available1.


title:
Einstein's equation is incompatible with a Galilean limit in
  non-Euclidean spatial classes of topologies

abstract:
  We show that the Galilean limit of Lorentzian structures applied to a
solution of the Einstein equation only exists if the topological class (i.e.
Thurston's geometric structure) of the spatial sections of the spacetime
4-manifold is Euclidean. We argue that this result might be a signature of an
incompleteness of the Einstein equation, which should feature an additional
"topological term" linked to the spatial topological class. This term would
imply that the limit exists in any topological class. However, the term is only
known at the leading order of the Galilean limit, and remains unconstrained at
full order. We propose strategies to determine this topological term. If indeed
such a term is a missing feature of general relativity, the Einstein equation
in its current form would be physically incorrect if the Universe has a
non-Euclidean spatial topology.


title: NP-hardness of computing PL geometric category in dimension 2

abstract:
  The PL geometric category of a polyhedron $P$, denoted $\hbox{plgcat}(P)$,
provides a natural upper bound for the Lusternik--Schnirelmann category and it
is defined as the minimum number of PL collapsible subpolyhedra of $P$ that
cover $P$. In dimension 2 the PL geometric category is at most~3. It is easy to
characterize/recognize $2$-polyhedra $P$ with $\hbox{plgcat}(P) = 1$. Borghini
provided a partial characterization of $2$-polyhedra with $\hbox{plgcat}(P) =
2$. We complement his result by showing that it is NP-hard to decide whether
$\hbox{plgcat}(P)\leq 2$. Therefore, we should not expect much more than a
partial characterization, at least in algorithmic sense. Our reduction is based
on the observation that 2-dimensional polyhedra $P$ admitting a shellable
subdivision satisfy $\hbox{plgcat}(P) \leq 2$ and a (nontrivial) modification
of the reduction of Goaoc, Pat\'{a}k, Pat\'{a}kov\'{a}, Tancer and Wagner
showing that shellability of $2$-complexes is NP-hard.


title: Axial-vector transition form factors of the singly heavy baryons

abstract:
  We investigate the axial-vector transition form factors of the lowest-lying
singly heavy baryons within the framework of the chiral quark-soliton model. We
consider the linear $m_{\mathrm{s}}$ corrections, dealing with the strange
current quark mass $m_{\mathrm{s}}$ as a small perturbation. Since we have
various relations between different transitions because of isospin symmetry and
flavor SU(3) symmetry breaking, only two axial-vector transition form factors
are independent. We present the numerical results for these form factors. The
effects of the flavor SU(3) symmetry breaking turn out tiny, so we neglect
them. We also compute the decay rates for several strong decays of the singly
heavy baryons and compare the results with the experimental data and those from
other models. While the results for the $\Sigma_c\to \Lambda_c^++\pi$ and
$\Sigma_c^*\to \Lambda_c^++\pi$ decays are slightly overestimated in comparison
with the corresponding experimental data, those for the $\Xi_c^*\to \Xi_c+\pi$
are in remarkable agreement with the data.


title:
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time
  Image Enhancement

abstract:
  The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely
sampling it into a discretized 3D lattice. Previous works have made efforts to
learn image-adaptive output color values of LUTs for flexible enhancement but
neglect the importance of sampling strategy. They adopt a sub-optimal uniform
sampling point allocation, limiting the expressiveness of the learned LUTs
since the (tri-)linear interpolation between uniform sampling points in the LUT
transform might fail to model local non-linearities of the color transform.
Focusing on this problem, we present AdaInt (Adaptive Intervals Learning), a
novel mechanism to achieve a more flexible sampling point allocation by
adaptively learning the non-uniform sampling intervals in the 3D color space.
In this way, a 3D LUT can increase its capability by conducting dense sampling
in color ranges requiring highly non-linear transforms and sparse sampling for
near-linear transforms. The proposed AdaInt could be implemented as a compact
and efficient plug-and-play module for a 3D LUT-based method. To enable the
end-to-end learning of AdaInt, we design a novel differentiable operator called
AiLUT-Transform (Adaptive Interval LUT Transform) to locate input colors in the
non-uniform 3D LUT and provide gradients to the sampling intervals. Experiments
demonstrate that methods equipped with AdaInt can achieve state-of-the-art
performance on two public benchmark datasets with a negligible overhead
increase. Our source code is available at https://github.com/ImCharlesY/AdaInt.


title: Optimal quantum optical control of spin in diamond

abstract:
  The nitrogen-vacancy (NV) center spin represents an appealing candidate for
quantum information processing. Besides the widely used microwave control, its
coherent manipulation may also be achieved using laser as mediated by the
excited energy levels. Nevertheless, the multiple levels of the excited state
of NV center spin make the coherent transition process become complex and may
affect the fidelity of coherent manipulation. Here, we adopt the strategy of
optimal quantum control to accelerate coherent state transfer in the ground
state manifold of NV center spin using laser. The results demonstrate improved
performance in both the speed and the fidelity of coherent state transfer which
will be useful for optical control of NV center spin in diamond.


title:
Double-lens Scintillometry: The variable scintillation of pulsar
  B1508+55

abstract:
  We report on observations of PSR B1508+55's scintillation at the Effelsberg
100-m telescope spanning from early 2020 to early 2022. In the autumn of 2020,
close to the time the pulsar was predicted by Wucknitz (2018) to cross echoes
in its pulse profile, a sudden transition in the scintillation arcs from
peculiar stripe-like features to parabolic arclets was observed. To infer a
geometric model of the scattering we measure the effects of the annual velocity
curve of Earth, of the relative movement of the line of sight, and of the
projection of points on a second scattering screen and develop novel methods to
do so. The latter phenomenon was discovered by this study and strongly
indicates a two-screen scattering geometry. We derive an analytical two-screen
model and demonstrate in a Markov Chain Monte Carlo analysis as well as
simulations that it can be successfully applied to explain the observations by
interpreting the transition as a change of relative amplitudes of images as
well as a shift in the orientation of anisotropy. The collection of methods we
demonstrate here is transferable to other pulsars with the potential to
strongly improve constraints on scattering models.


title: Dispersion relations and knot theory

abstract:
  I show that the crossing symmetric dispersion relation (CSDR) for 2-2
scattering leads to a fascinating connection with knot theory. In particular,
the dispersive kernel can be identified naturally in terms of the generating
function for the Alexander polynomials corresponding to the torus knot
$(2,2n+1)$ arising in knot theory. In the low energy expansion, the difference
between the $(n+1)$-th and $n$-th derivatives of the scattering amplitude with
respect to the crossing symmetric variable can be bounded in terms of the torus
$(2,2n+1)$-knot invariants and the resulting bounding curve in the space of
allowed S-matrices can be determined analytically in terms of the
$(2,2n+1)$-torus Alexander polynomial. The agreement with the pion S-matrix
bootstrap is impressive. The global bounds are also derived using Geometric
Function Theory (GFT) techniques and shown to be identical.


title: A flexible propelled arm: Mechanical considerations for the use in UAVs

abstract:
  This paper presents a soft propelled arm, 3D-printed in TPU 70A, designed to
be used in flexible UAVs, an advantageous concept since it reduces risk in the
event of a collision thanks to a high energy absorption. The proposed design
also allows the possibility to adapt to the environment and land on pipelines.
The flexibility of the arm can be controlled during the additive manufacturing
process by adjusting the infill rate or internal density. In this work, a
mechanical design based on simulations with an experimentally adjusted 5-
parameter Mooney-Rivlin non-linear model is proposed. Thrust efficiency is also
maximized through CFD simulations.


title: Prompt Engineering for Text-Based Generative Art

abstract:
  Text-based generative art has seen an explosion of interest in 2021. Online
communities around text-based generative art as a novel digital medium have
quickly emerged. This short paper identifies five types of prompt modifiers
used by practitioners in the community of text-based generative art based on a
3-month ethnographic study on Twitter. The novel taxonomy of prompt modifiers
provides researchers a conceptual starting point for investigating the
practices of text-based generative art, but also may help practitioners of
text-based generative art improve their images. The paper concludes with a
discussion of research opportunities in the space of text-based generative art
and the broader implications of prompt engineering from the perspective of
human-AI interaction in future applications beyond the use case of text-based
generative art.


title:
Dynamic Diagnosis of the Progress and Shortcomings of Student Learning
  using Machine Learning based on Cognitive, Social, and Emotional Features

abstract:
  Student diversity, like academic background, learning styles, career and life
goals, ethnicity, age, social and emotional characteristics, course load and
work schedule, offers unique opportunities in education, like learning new
skills, peer mentoring and example setting. But student diversity can be
challenging too as it adds variability in the way in which students learn and
progress over time. A single teaching approach is likely to be ineffective and
result in students not meeting their potential. Automated support could address
limitations of traditional teaching by continuously assessing student learning
and implementing needed interventions. This paper discusses a novel methodology
based on data analytics and Machine Learning to measure and causally diagnose
the progress and shortcomings of student learning, and then utilizes the
insight gained on individuals to optimize learning. Diagnosis pertains to
dynamic diagnostic formative assessment, which aims to uncover the causes of
learning shortcomings. The methodology groups learning difficulties into four
categories: recall from memory, concept adjustment, concept modification, and
problem decomposition into sub-goals (sub-problems) and concept combination.
Data models are predicting the occurrence of each of the four challenge types,
as well as a student's learning trajectory. The models can be used to
automatically create real-time, student-specific interventions (e.g., learning
cues) to address less understood concepts. We envision that the system will
enable new adaptive pedagogical approaches to unleash student learning
potential through customization of the course material to the background,
abilities, situation, and progress of each student; and leveraging
diversity-related learning experiences.


title:
Particle Swarm Optimization Based Demand Response Using Artificial
  Neural Network Based Load Prediction

abstract:
  In the present study, a Particle Swarm Optimization (PSO) based Demand
Response (DR) model, using Artificial Neural Network (ANN) to predict load is
proposed. The electrical load and climatological data of a residential area in
Austin city in Texas are used as the inputs of the ANN. Then, the outcomes with
the day-ahead prices data are used to solve the load shifting and cost
reduction problem. According to the results, the proposed model has the ability
to decrease payment costs and peak load.


title: Physical Deep Learning with Biologically Plausible Training Method

abstract:
  The ever-growing demand for further advances in artificial intelligence
motivated research on unconventional computation based on analog physical
devices. While such computation devices mimic brain-inspired analog information
processing, learning procedures still relies on methods optimized for digital
processing such as backpropagation. Here, we present physical deep learning by
extending a biologically plausible training algorithm called direct feedback
alignment. As the proposed method is based on random projection with arbitrary
nonlinear activation, we can train a physical neural network without knowledge
about the physical system. In addition, we can emulate and accelerate the
computation for this training on a simple and scalable physical system. We
demonstrate the proof-of-concept using a hierarchically connected
optoelectronic recurrent neural network called deep reservoir computer. By
constructing an FPGA-assisted optoelectronic benchtop, we confirmed the
potential for accelerated computation with competitive performance on
benchmarks. Our results provide practical solutions for the training and
acceleration of neuromorphic computation.


title:
A Validation Procedure for the Estimation of Reachable Regions in
  Football

abstract:
  Modelling the trajectorial motion of humans along the ground is a
foundational task in the quantitative analysis of sports like association
football. Most existing models of football player motion have not been
validated yet with respect to actual data. One of the reasons for this lack is
that performing such a validation is not straightforward, because models of
player motion are usually phrased in a way that emphasises possibly reachable
positions rather than expected positions. Since positional data of football
players typically contains outliers, this data may misrepresent the range of
actually reachable positions.
  This paper proposes a validation routine for trajectorial motion models that
measures and optimises the ability of a motion model to accurately predict all
possibly reachable positions by favoring the smallest predicted area of
reachable positions that encompasses all observed reached positions up to a
manually defined threshold. We demonstrate validation and optimisation on four
different motion models, assuming (a) motion with constant speed, (b) motion
with constant acceleration, (c) motion with constant acceleration with a speed
limit, and (d) motion along two segments with constant speed. Our results show
that assuming motion with constant speed or constant acceleration without a
limit on the achievable speed is particularly inappropriate for an accurate
distinction between reachable and unreachable locations. Motion along two
segments of constant speed provides by far the highest accuracy among the
tested models and serves as an efficient and accurate approximation of
real-world player motion.


title:
SusTrainable: Promoting Sustainability as a Fundamental Driver in
  Software Development Training and Education. Teacher Training, November 1-5,
  Nijmegen, The Netherlands. Revised lecture notes

abstract:
  These are the proceedings of the first teacher training of the Erasmus+
project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this
project is Promoting Sustainability as a Fundamental Driver in Software
Development Training and Education and the interested reader may know more
about it at:
  https://sustrainable.github.io
  The flagship contribution is the organization of two summer schools on
sustainable software production. The first summer school is moved due to the
Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This
summer school will consist of lectures and practical work for master and PhD
students in the area of computing science and closely related fields. There
will be contributions from Plovdiv University, University of Ko\v{s}ice,
University of Coimbra, University of Minho, E\"otv\"os Lor\'and University,
University of Rijeka, Radboud University Nijmegen, University of Pula,
University of Amsterdam and Babe\c{s}-Bolyai University.
  To prepare and streamline this summer school, the consortium organized a
teacher training in Nijmegen, the Netherlands. This was an event of five full
days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and
Pieter Koopman on the campus of the Radboud University Nijmegen, The
Netherlands. Sustainability is one of the themes within the strategy of the
Radboud University called `A Significant Impact', see
https://www.ru.nl/sustainability/organisation/radboud-sustainable/.
Sustainability plays an important role in education, research, and management.
  The contributions were reviewed and give a good overview of what can be
expected from the summer school. Based on these papers and the very positive
and constructive atmosphere, we expect a high quality and pleasant summer
school. We are looking forward to contributing to a sustainable future.


title:
Phase-Field Modeling of Coupled Brittle-Ductile Fracture in Aluminum
  Alloys

abstract:
  Fracture in aluminum alloys with precipitates involves at least two
mechanisms, namely, ductile fracture of the aluminum-rich matrix and brittle
fracture of the precipitates. In this work, a coupled crystal plasticity-phase
field model for mixed ductile-brittle failure modes is formulated and used to
investigate the effect of precipitate morphology and size distribution on
damage evolution in aluminum alloys. A thermodynamically consistent framework
for elastic and plastic work dissipation in the fracture process zone is used
to formulate the coupled constitutive behavior for brittle and ductile damage,
respectively. Representative Volume Elements (RVE) with varying particle
morphology and orientation were generated and their uni-axial loading was
simulated to assess the damage resistance of the different model
microstructures. For critical energy release rate $G_c$ values of the aluminum
matrix ranging from 4 to 8~Jm$^{-2}$ for single crystals and from 10 to
16~Jm$^{-2}$ for polycrystals, the model predicts a change in failure modes
from particle debonding to cracking followed by ductile matrix failure. The
change of particle failure mechanism as a result of increased $G_c$ is observed
for both, single and polycrystalline model microstructures. For the case of
particle debonding, microstructures with circular or ellipsoidal particles
(with the major axis perpendicular to the loading direction) show a higher
ductility and fracture work compared to the other studied cases. In the case of
particle cracking, microstructures with ellipsoidal particles aligned parallel
to the loading axis show a higher ductility and fracture work among the
investigated cases. The simulations qualitatively reproduce the experimentally
observed particle failure mechanisms (cracking and debonding) for two different
matrix alloy classes (commercially pure and 2xxx alloys) reinforced with
ceramic particles.


title:
Beyond the biology: Evaluating the role of political, economic, and
  social factors associated with the incidence and mortality of SARS-CoV-2
  during the first seven months of the COVID-19 pandemic

abstract:
  Purpose: In this study we propose to identify significant economic,
political, and social factors that helped contribute to the incidence and
mortality of SARS-CoV-2 around the world during the first seven months of the
COVID-19 pandemic. Methods: Retrospective analyses were conducted using data
acquired by the World Health Organization regarding the number of new cases and
deaths from 76 countries from January through July 2020. Hierarchical cluster
and principal component analyses were used to categorize different countries
based on their ability to control the outbreak based on number of cases and
deaths throughout these initial months of the pandemic. Furthermore, using
non-parametrical analyses, different variables were correlated with multiple
economic, political, and social indexes. Results: Multiple significant
correlations were found, including the control of corruption, government
effectiveness, and the human life indicator, with indexes containing the higher
correlations demonstrating the capability of individual countries to help drive
the course of this pandemic. Geographical hotspots were identified that
required further attention to aid in outbreak control. Conclusion:
Collectively, the findings in this study indicate that multiple factors beyond
the biological should be considered in the implementation of measures to
contain this pandemic


title:
Leveraging triplet loss and nonlinear dimensionality reduction for
  on-the-fly channel charting

abstract:
  Channel charting is an unsupervised learning method that aims at mapping
wireless channels to a so-called chart, preserving as much as possible spatial
neighborhoods. In this paper, a model-based deep learning approach to this
problem is proposed. It builds on a physically motivated distance measure to
structure and initialize a neural network that is subsequently trained using a
triplet loss function. The proposed structure exhibits a low number of
parameters and clever initialization leads to fast training. These two features
make the proposed approach amenable to on-the-fly channel charting. The method
is empirically assessed on realistic synthetic channels, yielding encouraging
results.


title: Failed Disruption Propagation in Integer Genetic Programming

abstract:
  We inject a random value into the evaluation of highly evolved deep integer
GP trees 9743720 times and find 99.7percent Suggesting crossover and mutation's
impact are dissipated and seldom propagate outside the program. Indeed only
errors near the root node have impact and disruption falls exponentially with
depth at between exp(-depth/3) and exp(-depth/5) for recursive Fibonacci GP
trees, allowing five to seven levels of nesting between the runtime
perturbation and an optimal test oracle for it to detect most errors.
Information theory explains this locally flat fitness landscape is due to FDP.
Overflow is not important and instead, integer GP, like deep symbolic
regression floating point GP and software in general, is not fragile, is
robust, is not chaotic and suffers little from Lorenz' butterfly. Keywords:
genetic algorithms, genetic programming, SBSE, information loss, information
funnels, entropy, evolvability, mutational robustness, optimal test oracle
placement, neutral networks, software robustness, correctness attraction,
diversity, software testing, theory of bloat, introns


title:
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of
  Gripper-Object Interaction

abstract:
  We approach the problem of high-DOF reaching-and-grasping via learning joint
planning of grasp and motion with deep reinforcement learning. To resolve the
sample efficiency issue in learning the high-dimensional and complex control of
dexterous grasping, we propose an effective representation of grasping state
characterizing the spatial interaction between the gripper and the target
object. To represent gripper-object interaction, we adopt Interaction Bisector
Surface (IBS) which is the Voronoi diagram between two close by 3D geometric
objects and has been successfully applied in characterizing spatial relations
between 3D objects. We found that IBS is surprisingly effective as a state
representation since it well informs the fine-grained control of each finger
with spatial relation against the target object. This novel grasp
representation, together with several technical contributions including a fast
IBS approximation, a novel vector-based reward and an effective training
strategy, facilitate learning a strong control model of high-DOF grasping with
good sample efficiency, dynamic adaptability, and cross-category generality.
Experiments show that it generates high-quality dexterous grasp for complex
shapes with smooth grasping motions.


title: Statistical applications of contrastive learning

abstract:
  The likelihood function plays a crucial role in statistical inference and
experimental design. However, it is computationally intractable for several
important classes of statistical models, including energy-based models and
simulator-based models. Contrastive learning is an intuitive and
computationally feasible alternative to likelihood-based learning. We here
first provide an introduction to contrastive learning and then show how we can
use it to derive methods for diverse statistical problems, namely parameter
estimation for energy-based models, Bayesian inference for simulator-based
models, as well as experimental design.


title: Robust Solutions for Multi-Defender Stackelberg Security Games

abstract:
  Multi-defender Stackelberg Security Games (MSSG) have recently gained
increasing attention in the literature. However, the solutions offered to date
are highly sensitive, wherein even small perturbations in the attacker's
utility or slight uncertainties thereof can dramatically change the defenders'
resulting payoffs and alter the equilibrium. In this paper, we introduce a
robust model for MSSGs, which admits solutions that are resistant to small
perturbations or uncertainties in the game's parameters. First, we formally
define the notion of robustness, as well as the robust MSSG model. Then, for
the non-cooperative setting, we prove the existence of a robust approximate
equilibrium in any such game, and provide an efficient construction thereof.
For the cooperative setting, we show that any such game admits a robust
approximate alpha-core, provide an efficient construction thereof, and prove
that stronger types of the core may be empty. Interestingly, the robust
solutions can substantially increase the defenders' utilities over those of the
non-robust ones.


title:
Machine Learning-Based GPS Multipath Detection Method Using Dual
  Antennas

abstract:
  In urban areas, global navigation satellite system (GNSS) signals are often
reflected or blocked by buildings, thus resulting in large positioning errors.
In this study, we proposed a machine learning approach for global positioning
system (GPS) multipath detection that uses dual antennas. A machine learning
model that could classify GPS signal reception conditions was trained with
several GPS measurements selected as suggested features. We applied five
features for machine learning, including a feature obtained from the dual
antennas, and evaluated the classification performance of the model, after
applying four machine learning algorithms: gradient boosting decision tree
(GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was
found that a classification accuracy of 82%-96% was achieved when the test data
set was collected at the same locations as those of the training data set.
However, when the test data set was collected at locations different from those
of the training data, a classification accuracy of 44%-77% was obtained.


title:
The origin of Earth's mantle nitrogen: primordial or early
  biogeochemical cycling?

abstract:
  Earth's mantle nitrogen (N) content is comparable to that found in its N-rich
atmosphere. Mantle N has been proposed to be primordial or sourced by later
subduction, yet its origin has not been elucidated. Here we model N
partitioning during the magma ocean stage following planet formation and the
subsequent cycling between the surface and mantle over Earth history using
argon (Ar) and N isotopes as tracers. The partitioning model, constrained by
Ar, shows that only about 10% of the total N content can be trapped in the
solidified mantle due to N's low solubility in magma and low partitioning
coefficients in minerals in oxidized conditions supported from geophysical and
geochemical studies. A possible solution for the primordial origin is that
Earth had about 10 times more N at the time of magma ocean solidification. We
show that the excess N could be removed by impact erosion during late
accretion. The cycling model, constrained by N isotopes, shows that mantle N
can originate from efficient N subduction, if the sedimentary N burial rate on
early Earth is comparable to that of modern Earth. Such a high N burial rate
requires biotic processing. Finally, our model provides a methodology to
distinguish the two possible origins with future analysis of the surface and
mantle N isotope record.


title:
Stabilizing Spherical Energy Shells with Angular Momentum in
  Gravitational Backgrounds

abstract:
  Spherical energy shells in General Relativity tend to collapse due to
gravitational effects and/or due to tension effects. Shell stabilization may be
achieved by modifying the gravitational properties of the background spacetime.
Thus, gravastars consist of stiff matter shells with an interior deSitter space
and an exterior Schwarzshild spacetime whose attractive gravity balances the
interior repulsive gravity of the interior deSitter spacetime leading to a
stable stiff matter shell. Similar stabilization effects may be achieved by
considering rotating shells. Here we study the stability of slowly rotating
fluid shells. We show that the angular velocity of the shell has stabilizing
properties analogous to the repulsive deSitter gravity of the interior of a
gravastar. We thus use the Israel junction conditions and the fluid equation of
state of the rotating shell to construct the dynamical equations that determine
the evolution of the rotating shell radius. These dynamical equations depend on
the parameters of the background spacetime and on the angular velocity of the
shell. Assuming a rotating interior and a Schwarzschild exterior spacetime we
show that the angular velocity of the shell has interesting stabilizing
properties on the evolution of its radius R. Thus rotating matter (or vacuum)
shells can imitate black holes while avoiding the presence of a singularity and
without the presence of an interior deSitter space.


title: A simple algorithm for uniform sampling on the surface of a hypersphere

abstract:
  We propose a simple method for uniform sampling of points on the surface of a
hypersphere in arbitrarily many dimensions. By avoiding the evaluation of
computationally expensive functions like logarithms, sines, cosines, or higher
order roots the new method is faster than alternative techniques.


title:
Precision bound in periodically modulated continuous quantum thermal
  machines

abstract:
  We use Floquet formalism to study fluctuations in periodically modulated
continuous thermal machines. We present a generic theory for such machines,
followed by specific examples of continuous machines driven by sinusoidal and
circular modulations. The thermodynamic uncertainty relations hold for both
kind of thermal machines. Interestingly, in case of sinusoidal modulation, the
ratio assumes a minimum at the heat engine to refrigerator transition point.
Moreover, the noise to signal ratio for the hot and the cold currents are
equal, and less than that for the output power for low modulation frequencies.
We also study bounds in fluctuations in the efficiencies of such machines; our
results suggest that while fluctuations in efficiencies are bounded from below
for the heat engine regime, the same is not true in case of a refrigerator.


title:
No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option
  Tracing for Better Student Assessment

abstract:
  Student assessment is one of the most fundamental tasks in the field of AI
Education (AIEd). One of the most common approach to student assessment is
Knowledge Tracing (KT), which evaluates a student's knowledge state by
predicting whether the student will answer a given question correctly or not.
However, in the context of multiple choice (polytomous) questions, conventional
KT approaches are limited in that they only consider the binary (dichotomous)
correctness label (i.e., correct or incorrect), and disregard the specific
option chosen by the student. Meanwhile, Option Tracing (OT) attempts to model
a student by predicting which option they will choose for a given question, but
overlooks the correctness information. In this paper, we propose
Dichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learning
framework that combines KT and OT for more precise student assessment. In
particular, we show that the KT objective acts as a regularization term for OT
in the DP-MTL framework, and propose an appropriate architecture for applying
our method on top of existing deep learning-based KT models. We experimentally
confirm that DP-MTL significantly improves both KT and OT performances, and
also benefits downstream tasks such as Score Prediction (SP).


title:
Searching for Efficient Neural Architectures for On-Device ML on Edge
  TPUs

abstract:
  On-device ML accelerators are becoming a standard in modern mobile
system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for
efficiently utilizing the high compute throughput offered by these
accelerators. However, existing NAS frameworks have several practical
limitations in scaling to multiple tasks and different target platforms. In
this work, we provide a two-pronged approach to this challenge: (i) a
NAS-enabling infrastructure that decouples model cost evaluation, search space
design, and the NAS algorithm to rapidly target various on-device ML tasks, and
(ii) search spaces crafted from group convolution based inverted bottleneck
(IBN) variants that provide flexible quality/performance trade-offs on ML
accelerators, complementing the existing full and depthwise convolution based
IBNs. Using this approach we target a state-of-the-art mobile platform, Google
Tensor SoC, and demonstrate neural architectures that improve the
quality-performance pareto frontier for various computer vision
(classification, detection, segmentation) as well as natural language
processing tasks.


title:
Biologically-inspired neuronal adaptation improves learning in neural
  networks

abstract:
  Since humans still outperform artificial neural networks on many tasks,
drawing inspiration from the brain may help to improve current machine learning
algorithms. Contrastive Hebbian Learning (CHL) and Equilibrium Propagation (EP)
are biologically plausible algorithms that update weights using only local
information (without explicitly calculating gradients) and still achieve
performance comparable to conventional backpropagation. In this study, we
augmented CHL and EP with Adjusted Adaptation, inspired by the adaptation
effect observed in neurons, in which a neuron's response to a given stimulus is
adjusted after a short time. We add this adaptation feature to multilayer
perceptrons and convolutional neural networks trained on MNIST and CIFAR-10.
Surprisingly, adaptation improved the performance of these networks. We discuss
the biological inspiration for this idea and investigate why Neuronal
Adaptation could be an important brain mechanism to improve the stability and
accuracy of learning.


title:
Effective Ion Mobility and Long-Time Dark Current of Metal-Halide
  Perovskites of Different Crystallinity and Composition

abstract:
  Ion transport properties in metal-halide perovskite still constitute a
subject of intense research because of the evident connection between mobile
defects and device performance and operation degradation. In the specific case
of X-ray detectors, dark current level and instability is regarded to be
connected to the ion migration upon bias application. Different compositions
(MAPbBr3 and MAPbI3) and structures (single- and micro-crystalline) are checked
by the analysis of long-time dark current evolution. In all cases, electronic
current increases with time before reaching a steady-state value within a
response time (from 10.000 s down to 10 s) that strongly depends on the applied
bias. Our findings corroborate the existence of a coupling between electronic
transport and ion kinetics that ultimately establishes the time scale of
electronic current. Effective ion mobility mui is extracted that exhibits
applied electrical field E dependence that varies on the perovskite
composition. While ion mobility results field-independent in the case of
MAPbI3, a clear field-enhancement is observed for MAPbBr3 (dmui/dE>0),
irrespective of the crystallinity. Both perovskite compounds present effective
ion mobility in the range of mui = 10-7-10-6 cm-2 V-1 s-1, in accordance with
previous analyses. The E-dependence of the ion mobility is related to the lower
ionic concentration of the bromide compound. Slower-migrating defect drift is
suppressed in the case of MAPbBr3, in opposition to that observed here for
MAPbI3.


title:
Entangling mechanical vibrations of two massive ferrimagnets by fully
  exploiting the nonlinearity of magnetostriction

abstract:
  Quantum entanglement in the motion of macroscopic objects is of significance
to both fundamental studies and quantum technologies. Here we show how to
entangle the mechanical vibration modes of two massive ferrimagnets that are
placed in the same microwave cavity. Each ferrimagnet supports a magnon mode
and a low-frequency vibration mode coupled by the magnetostrictive force. The
two magnon modes are, respectively, coupled to the microwave cavity by the
magnetic dipole interaction. We first generate a stationary nonlocal entangled
state between the vibration mode of the ferrimagnet-1 and the magnon mode of
the ferrimagnet-2. This is realized by continuously driving the ferrimagnet-1
with a strong red-detuned microwave field and the entanglement is achieved by
exploiting the magnomechanical parametric down-conversion and the cavity-magnon
state-swap interaction. We then switch off the pump on the ferrimagnet-1 and,
simultaneously, turn on a red-detuned pulsed drive on the ferrimagnet-2. The
latter drive is used to activate the magnomechanical beamsplitter interaction,
which swaps the magnonic and mechanical states of the ferrimagnet-2.
Consequently, the previously generated phonon-magnon entanglement is
transferred to the mechanical modes of two ferrimagnets. The work provides a
scheme to prepare entangled states of mechanical motion of two massive objects,
which may find applications in various studies exploiting macroscopic entangled
states.


title:
An approximation of the Bayesian state observer with Markov chain Monte
  Carlo propagation stage

abstract:
  The state estimation problem for nonlinear systems with stochastic
uncertainties can be formulated in the Bayesian framework, where the objective
is to replace the state completely by its probability density function. Without
the restriction to selected system classes and disturbance properties, the
Bayesian estimator is particularly interesting for highly nonlinear systems
with non-Gaussian noise. The main limitations of Bayesian filters are the
significant computational costs and the implementation problems for higher
dimensional systems. The present paper introduces a piecewise linear
approximation of the Bayesian state observer with Markov chain Monte Carlo
propagation stage and kernel density estimation. These methods are suitable for
the prediction of multivariate probability density functions. The piecewise
linear approximation and the proposed algorithms can increase the estimation
performance at reasonable computational cost. The estimation performance is
demonstrated in a benchmark comparing the Bayesian state observer with an
extended Kalman filter and a particle filter.


title: Local Explanation of Dimensionality Reduction

abstract:
  Dimensionality reduction (DR) is a popular method for preparing and analyzing
high-dimensional data. Reduced data representations are less computationally
intensive and easier to manage and visualize, while retaining a significant
percentage of their original information. Aside from these advantages, these
reduced representations can be difficult or impossible to interpret in most
circumstances, especially when the DR approach does not provide further
information about which features of the original space led to their
construction. This problem is addressed by Interpretable Machine Learning, a
subfield of Explainable Artificial Intelligence that addresses the opacity of
machine learning models. However, current research on Interpretable Machine
Learning has been focused on supervised tasks, leaving unsupervised tasks like
Dimensionality Reduction unexplored. In this paper, we introduce LXDR, a
technique capable of providing local interpretations of the output of DR
techniques. Experiment results and two LXDR use case examples are presented to
evaluate its usefulness.


title: Additive decomposition of signed prime

abstract: Under the prime-tuple hypothesis, the set of signed primes is a sumset.


title:
A few entanglement criterion for two-qubit and two-qudit system based on
  realignment operation

abstract:
  It is known that realignment crierion is necessary but not a sufficient
criterion for lower as well as higher dimensional system. In this work, we
first consider a two-qubit system and derived the necessary and sufficient
condition based on realignment operation for a particular class of two-qubit
system. Thus we solved the problem of if and only if condition partially for a
particular class of two-qubit state. We have shown that the derived necessary
and sufficient condition detects two-qubit entangled states, which are not
detected by the realignment criterion. Next, we discuss the higher dimensional
system and obtained the necessary condition on the minimum singular value of
the realigned matrix of $d\otimes d$ dimensional separable states. Moreover, we
provide the geometrical interpretation of the derived separability criterion
for $d\otimes d$ dimensional system. Furthermore, we show that our criterion
may also detect bound entangled state. The entanglement detection criterion
studied here is beneficial in the sense that it requires to calculate only
minimum singular value of the realigned matrix while on the other hand
realignment criterion requires all singular values of the realigned matrix.
Thus, our criterion has computational advantage over the realignment criterion.


title: Precision measurements of W properties at LHC

abstract:
  Exploiting the large number of collected events and the symmetries in the W
production and decay at LHC, it is possible to measure the W double
differential cross section of transverse momentum and rapidity and the angular
coefficients. With the proposed method a determination of the integrated
spectrum of transverse momentum of W bosons is possible with an unprecedented
granularity and increased precision with respect to the state-of-the-art
generators. This enables the possibility to perform a theory-agnostic
measurement of the W mass with competitive uncertainty by constraining the W
production directly on data. This paper shows preliminary results derived on a
subset of events collected by CMS during Run 2, for an integrated luminosity of
36 fb-1.


title:
Nuclear equation of state for arbitrary proton fraction and temperature
  based on chiral effective field theory and a Gaussian process emulator

abstract:
  We calculate the equation of state of asymmetric nuclear matter at finite
temperature based on chiral effective field theory interactions to
next-to-next-to-next-to-leading order. Our results assess the theoretical
uncertainties from the many-body calculation and the chiral expansion. Using a
Gaussian process emulator for the free energy, we derive the thermodynamic
properties of matter through consistent derivatives and use the Gaussian
process to access arbitrary proton fraction and temperature. This enables a
first nonparametric calculation of the equation of state in beta equilibrium,
and of the speed of sound and the symmetry energy at finite temperature.
Moreover, our results show that the thermal part of the pressure decreases with
increasing densities.


title:
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient
  Ensembling

abstract:
  Recent advances in federated learning have demonstrated its promising
capability to learn on decentralized datasets. However, a considerable amount
of work has raised concerns due to the potential risks of adversaries
participating in the framework to poison the global model for an adversarial
purpose. This paper investigates the feasibility of model poisoning for
backdoor attacks through \textit{rare word embeddings of NLP models} in text
classification and sequence-to-sequence tasks. In text classification, less
than 1\% of adversary clients suffices to manipulate the model output without
any drop in the performance of clean sentences. For a less complex dataset, a
mere 0.1\% of adversary clients is enough to poison the global model
effectively. We also propose a technique specialized in the federated learning
scheme called gradient ensemble, which enhances the backdoor performance in all
experimental settings.


title: Compact stellar model in higher torsion gravitational theory

abstract:
  In this study, we address the issue of a spherically symmetrical interior
solution to the quadratic form of $f\mathcal{(T)}=\mathcal{T}+\epsilon
\mathcal{T}^2$ gravitational theory using a physical tetrad that provides
vanishing components of the off-components of the field equation, in contrast
to what exists in the current literature. To be able to formulate the resulting
differential equation in a closed form, we employ the Krori-Barua (KB) ansatz.
Using the KB spacetime form, we derive the analytic form of the energy-density,
radial, and tangential pressures and the anisotropic form. All of these
quantities are affected by the dimensional parameter $\epsilon$, which causes
them to have a noted difference from those given in the frame of Einstein
general relativity. The derived model of this study exhibits a non-trivial form
of torsion scalar, and it also contains three constants that we drew from the
matching of the boundary condition with a line element that also features a
non-trivial form of torsion scalar. Having established the physical conditions
that are needed for any real stellar, we check our model and show in detail
that it bypasses all of these. Finally, we analyze the model's stability
utilizing the Tolman-Oppenheimer-Volkoff equation and adiabatic index and show
that our model satisfies these.


title:
High energy emission component, population, and contribution to the
  extragalactic gamma-ray background of gamma-ray emitting radio galaxies

abstract:
  In this study, we systematically studied the X-ray to GeV gamma-ray spectra
of 61 {\it Fermi} Large Area Telescope (LAT) detected radio galaxies. We found
an anticorrelation between peak frequency and peak luminosity in the
high-energy spectral component of radio galaxies, similar to blazars. With this
sample, we also constructed a gamma-ray luminosity function (GLF) of
gamma-ray-loud radio galaxies. We found that blazar-like GLF shapes can
reproduce their redshift and luminosity distribution, but the log$N$-log$S$
relation prefers models with more low-$z$ radio galaxies. This indicates many
low-$z$ gamma-ray-loud radio galaxies. By utilizing our latest GLF, the
contribution of radio galaxies to the extragalactic gamma-ray background is
found to be 1--10\%. We further investigated the nature of gamma-ray-loud radio
galaxies. Compared to radio or X-ray flux-limited radio galaxy samples, the
gamma-ray selected sample tends to lack high radio power galaxies like FR-II
radio galaxies. We also found that only $\sim$10\% of radio galaxies are GeV
gamma-ray loud. Radio galaxies may contribute to the cosmic MeV gamma-ray
background comparable to blazars if gamma-ray-quiet radio galaxies have X-ray
to gamma-ray spectra like Cen A, with a small gamma-ray to X-ray flux ratio.


title:
Exploration and Exploitation in Federated Learning to Exclude Clients
  with Poisoned Data

abstract:
  Federated Learning (FL) is one of the hot research topics, and it utilizes
Machine Learning (ML) in a distributed manner without directly accessing
private data on clients. However, FL faces many challenges, including the
difficulty to obtain high accuracy, high communication cost between clients and
the server, and security attacks related to adversarial ML. To tackle these
three challenges, we propose an FL algorithm inspired by evolutionary
techniques. The proposed algorithm groups clients randomly in many clusters,
each with a model selected randomly to explore the performance of different
models. The clusters are then trained in a repetitive process where the worst
performing cluster is removed in each iteration until one cluster remains. In
each iteration, some clients are expelled from clusters either due to using
poisoned data or low performance. The surviving clients are exploited in the
next iteration. The remaining cluster with surviving clients is then used for
training the best FL model (i.e., remaining FL model). Communication cost is
reduced since fewer clients are used in the final training of the FL model. To
evaluate the performance of the proposed algorithm, we conduct a number of
experiments using FEMNIST dataset and compare the result against the random FL
algorithm. The experimental results show that the proposed algorithm
outperforms the baseline algorithm in terms of accuracy, communication cost,
and security.


title:
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear
  Dynamical Systems

abstract:
  Low sampling frequency challenges the exact identification of the
continuous-time (CT) dynamical system from sampled data, even when its model is
identifiable. The necessary and sufficient condition is proposed -- which is
built from Koopman operator -- to the exact identification of the CT system
from sampled data. The condition gives a Nyquist-Shannon-like critical
frequency for exact identification of CT nonlinear dynamical systems with
Koopman invariant subspaces: 1) it establishes a sufficient condition for a
sampling frequency that permits a discretized sequence of samples to discover
the underlying system and 2) it also establishes a necessary condition for a
sampling frequency that leads to system aliasing that the underlying system is
indistinguishable; and 3) the original CT signal does not have to be
band-limited as required in the Nyquist-Shannon Theorem. The theoretical
criterion has been demonstrated on a number of simulated examples, including
linear systems, nonlinear systems with equilibria, and limit cycles.


title:
Tip-induced excitonic luminescence nanoscopy of an atomically-resolved
  van der Waals heterostructure

abstract:
  Low-temperature scanning tunneling microscopy is used to probe, with
atomic-scale spatial resolution, the intrinsic luminescence of a van der Waals
heterostructure, made of a transition metal dichalcogenide monolayer stacked
onto a few-layer graphene flake supported by an Au(111) substrate. Sharp
emission lines arising from neutral, charged and localised excitons are
reported. Their intensities and emission energies vary as a function of the
nanoscale environment of the van der Waals heterostructure, explaining the
variability of the emission properties observed with diffraction-limited
approaches. Our work paves the way towards understanding and control of
optoelectronic phenomena in moir\'e superlattices with atomic-scale resolution.


title:
Enumeration of Generalized Dyck Paths Based on the Height of Down-Steps
  Modulo $k$

abstract:
  For fixed non-negative integers $k$, $t$, and $n$, with $t < k$, a $k_t$-Dyck
path of length $(k+1)n$ is a lattice path that starts at $(0, 0)$, ends at
$((k+1)n, 0)$, stays weakly above the line $y = -t$, and consists of steps from
the step-set $\{(1, 1), (1, -k)\}$. We enumerate the family of $k_t$-Dyck paths
by considering the number of down-steps at a height of $i$ modulo $k$. Given a
tuple $(a_1, a_2, \ldots, a_k)$ we find an exact enumeration formula for the
number of $k_t$-Dyck paths of length $(k+1)n$ with $a_i$ down-steps at a height
of $i$ modulo $k$, $1 \leq i \leq k$. The proofs given are done via bijective
means or with generating functions.