title: The influence of funding on the Open Access citation advantage

abstract:
  Some of the citation advantage in open access is likely due to more access
allows more people to read and hence cite articles they otherwise would not.
However, causation is difficult to establish and there are many possible bias.
Several factors can affect the observed differences in citation rates. Funder
mandates can be one of them. Funders are likely to have OA requirement, and
well-funded studies are more likely to receive more citations than poorly
funded studies. In this paper this hypothesis is tested. Thus, we studied the
effect of funding on the publication modality and the citations received in
more than 128 thousand research articles, of which 31% were funded. These
research articles come from 40 randomly selected subject categories in the year
2016, and the citations received from the period 2016-2020 in the Scopus
database. We found open articles published in hybrid journals were considerably
more cited than those in open access journals. Thus, articles under the hybrid
gold modality are cite on average twice as those in the gold modality. This is
the case regardless of funding, so this evidence is strong. Moreover, within
the same publication modality, we found that funded articles generally obtain
50% more citations than unfunded ones. The most cited modality is the hybrid
gold and the least cited is the gold, well below even the paywalled.
Furthermore, the use of open access repositories considerably increases the
citations received, especially for those articles without funding. Thus, the
articles in open access repositories (green) are 50% more cited than the
paywalled ones. This evidence is remarkable and does not depend on funding.
Excluding the gold modality, there is a citation advantage in more than 75% of
the cases and it is considerably greater among unfunded articles. This result
is strong both across fields and over time.


title: Plateau flow or the heat flow for half-harmonic maps

abstract:
  Using the Millot-Sire interpretation of the half-Laplacian on $S^1$ as the
Dirichlet-to-Neumann operator for the Laplace equation on the ball $B$, we
devise a classical approach to the heat flow for half-harmonic maps from $S^1$
to a closed target manifold $N$, recently studied by Wettstein, and for
arbitrary finite-energy data we obtain a result fully analogous to the
classical results for the harmonic map heat flow of surfaces and in similar
generality. When $N$ is a smoothly embedded, oriented closed curve $\Gamma$ the
half-harmonic map heat flow may be viewed as an alternative gradient flow for
the Plateau problem of disc-type minimal surfaces.


title: Structural Analysis of DNA molecule in a confined shell

abstract:
  Recent advances in operating and manipulating DNA have provided unique
experimental possibilities in many fields of DNA research, especially in gene
therapy. Researchers have deployed many techniques, experimental and
theoretical, to study the DNA structure changes due to external perturbation.
It is crucial to understand the structural and dynamical changes in the DNA
molecules in a confined state to understand and control the self-assembly of
DNA confined in a chamber or nano-channel for various applications. In the
current manuscript, we extend the work study the effect of confinement on the
thermal stability and the structural properties of duplex DNA. The present work
is an extension of our previous research works. For our study we have
considered a 1 BNA chain that is confined in a cylindrical geometry. How the
geometry of the confinement affects the opening and other structural parameters
of DNA molecule is the objective of this manuscript. We have used a statistical
model(PBD model) and Molecular dynamics simulations for our purpose.


title: SignSGD: Fault-Tolerance to Blind and Byzantine Adversaries

abstract:
  Distributed learning has become a necessity for training ever-growing models
by sharing calculation among several devices. However, some of the devices can
be faulty, deliberately or not, preventing the proper convergence. As a matter
of fact, the baseline distributed SGD algorithm does not converge in the
presence of one Byzantine adversary. In this article we focus on the more
robust SignSGD algorithm derived from SGD. We provide an upper bound for the
convergence rate of SignSGD proving that this new version is robust to
Byzantine adversaries. We implemented SignSGD along with Byzantine strategies
attempting to crush the learning process. Therefore, we provide empirical
observations from our experiments to support our theory. Our code is available
on GitHub https://github.com/jasonakoun/signsgd-fault-tolerance and our
experiments are reproducible by using the provided parameters.


title: About Code Equivalence -- a Geometric Approach

abstract:
  The equivalence test is a main part in any classification problem. It helps
to prove bounds for the main parameters of the considered combinatorial
structures and to study their properties. In this paper, we present algorithms
for equivalence of linear codes, based on their relation to multisets of points
in a projective geometry.


title:
Spectral analysis of Jacobi operators and asymptotic behavior of
  orthogonal polynomials

abstract:
  We find and discuss asymptotic formulas for orthonormal polynomials
$P_{n}(z)$ with recurrence coefficients $a_{n}, b_{n}$. Our main goal is to
consider the case where off-diagonal elements $a_{n}\to\infty$ as $n\to\infty$.
Formulas obtained are essentially different for relatively small and large
diagonal elements $b_{n}$.
  Our analysis is intimately linked with spectral theory of Jacobi operators
$J$ with coefficients $a_{n}, b_{n}$ and a study of the corresponding second
order difference equations.
  We introduce the Jost solutions $f_{n}(z)$, $n\geq -1$, of such equations by
a condition for $n\to\infty$ and suggest an Ansatz for them playing the role of
the semiclassical Liouville-Green Ansatz for solutions of the Schr\"odinger
equation.
  This allows us to study the spectral structure of Jacobi operators and their
eigenfunctions $P_{n}(z)$ by traditional methods of spectral theory developed
for differential equations. In particular, we express all coefficients in
asymptotic formulas for $P_{n}(z)$ as $n \to\infty$ in terms of the Wronskian
of the solutions $ P_{n} (z) $ and $ f_{n} (z)$. The formulas obtained for
$P_{n}(z)$ generalize the asymptotic formulas for the classical Hermite
polynomials where $a_{n}=\sqrt{(n+1)/2}$ and $b_{n}=0$.


title: Entropy of group actions beyond uniform lattices

abstract:
  Entropy of measure preserving or continuous actions of amenable discrete
groups allows for various equivalent approaches. Among them are the ones given
by the techniques developed by Ollagnier and Pinchon on the one hand and the
Ornstein-Weiss lemma on the other. We extend these two approaches to the
context of actions of amenable topological groups. In contrast to the discrete
setting, our results reveal a remarkable difference between the two concepts of
entropy in the realm of non-discrete groups: while the first quantity collapses
to 0 in the non-discrete case, the second yields a well-behaved invariant for
amenable unimodular groups. Concerning the latter, we moreover study the
corresponding notion of topological pressure, prove a Goodwyn-type theorem, and
establish the equivalence with the uniform lattice approach (for locally
compact groups admitting a uniform lattice). Our study elaborates on a version
of the Ornstein-Weiss lemma due to Gromov.


title: Mahonian and Euler-Mahonian statistics for set partitions

abstract:
  A partition of the set $[n]:=\{1,2,\ldots,n\}$ is a collection of disjoint
nonempty subsets (or blocks) of $[n]$, whose union is $[n]$. In this paper we
consider the following rarely used representation for set partitions: given a
partition of $[n]$ with blocks $B_{1},B_{2},\ldots,B_{m}$ satisfying $\max
B_{1}<\max B_{2}<\cdots<\max B_{m}$, we represent it by a word
$w=w_{1}w_{2}\ldots w_{n}$ such that $i\in B_{w_{i}}$, $1\leq i\leq n$. We
prove that the Mahonian statistics INV, MAJ, MAJ$_{d}$, $r$-MAJ, Z, DEN, MAK,
MAD are all equidistributed on set partitions via this representation, and that
the Euler-Mahonian statistics (des, MAJ), (mstc, INV), (exc, DEN), (des, MAK)
are all equidistributed on set partitions via this representation.


title:
Relating cognition to both brain structure and function: A systematic
  review of methods

abstract:
  Cognitive neuroscience explores the mechanisms of cognition by studying its
structural and functional brain correlates. Here, we report the first
systematic review that assesses how information from structural and functional
neuroimaging methods can be integrated to investigate the brain substrates of
cognition. Web of Science and Scopus databases were searched for studies of
healthy young adult populations that collected cognitive data, and structural
and functional neuroimaging data. Five percent of screened studies met all
inclusion criteria. Next, 54% of included studies related cognitive performance
to brain structure and function without quantitative analysis of the
relationship. Finally, 32% of studies formally integrated structural and
functional brain data. Overall, many studies consider either structural or
functional neural correlates of cognition, and of those that consider both,
they have rarely been integrated. We identified four emergent approaches to the
characterisation of the relationship between brain structure, function and
cognition; comparative, predictive, fusion and complementary. We discuss the
insights provided each approach and how authors can select approaches to suit
their research questions.


title:
Vector vorticity of skyrmionic texture: an internal degree of freedom
  tunable by magnetic field

abstract:
  Different from the skyrmion driven by the Dzyaloshinskii-Moriya interaction
in non-centrosymmetric materials, the skyrmionic texture in centrosymmetric
magnet may possess the extra internal degrees of freedom, which greatly enrich
its morphologies, imply the continuous deformation allowed by topological
protection, and enable flexible tunability and potential functionality. To
describe the internal degree of freedom to full extent, the conventional
integer-valued scalar vorticity is extended into a vector vorticity with
continuous rotation allowed. The further simplified vorticity angle, together
with helicity angle, represents the whole rotation freedom of spin space. The
centrosymmetric magnet with frustration provides a perfect platform for
realizing a controllable manipulation on these internal degrees of freedom,
where the magnetic field can be applied to tune vorticity continuously in both
crystal and isolated forms of skyrmionic texture, and the helicity can be
further controlled by electric field. Moreover, the simulation reveals the
distinctive dynamic effects related to the vorticity modulation, namely, a
straight motion can be generated by rotating magnetic field to tune vorticity,
and the vorticity can also be controlled to modulate the dynamics induced by
the spin polarized current.


title: Couplings and Matchings: Combinatorial notes on Strassen's theorem

abstract:
  Some mathematical theorems represent ideas that are discovered again and
again in different forms. One such theorem is Hall's marriage theorem. This
theorem is equivalent to several other theorems in combinatorics and
optimization theory, in the sense that these results can easily be derived from
each other. In this paper it is shown that this equivalence extends to a finite
version of Strassen's theorem, a celebrated result on couplings of probability
measures. Though this equivalence is known, probabilistic or combinatorial
proofs of this fact are lacking. A novel combinatorial lemma will be introduced
that can be used to deduce both Hall's and Strassen's theorems.


title: Temporal Attention for Language Models

abstract:
  Pretrained language models based on the transformer architecture have shown
great success in NLP. Textual training data often comes from the web and is
thus tagged with time-specific information, but most language models ignore
this information. They are trained on the textual data alone, limiting their
ability to generalize temporally. In this work, we extend the key component of
the transformer architecture, i.e., the self-attention mechanism, and propose
temporal attention - a time-aware self-attention mechanism. Temporal attention
can be applied to any transformer model and requires the input texts to be
accompanied with their relevant time points. It allows the transformer to
capture this temporal information and create time-specific contextualized word
representations. We leverage these representations for the task of semantic
change detection; we apply our proposed mechanism to BERT and experiment on
three datasets in different languages (English, German, and Latin) that also
vary in time, size, and genre. Our proposed model achieves state-of-the-art
results on all the datasets.


title:
Numerical Demonstration of Multiple Actuator Constraint Enforcement
  Algorithm for a Molten Salt Loop

abstract:
  To advance the paradigm of autonomous operation for nuclear power plants, a
data-driven machine learning approach to control is sought. Autonomous
operation for next-generation reactor designs is anticipated to bolster safety
and improve economics. However, any algorithms that are utilized need to be
interpretable, adaptable, and robust.
  In this work, we focus on the specific problem of optimal control during
autonomous operation. We will demonstrate an interpretable and adaptable
data-driven machine learning approach to autonomous control of a molten salt
loop. To address interpretability, we utilize a data-driven algorithm to
identify system dynamics in state-space representation. To address
adaptability, a control algorithm will be utilized to modify actuator setpoints
while enforcing constant, and time-dependent constraints. Robustness is not
addressed in this work, and is part of future work. To demonstrate the
approach, we designed a numerical experiment requiring intervention to enforce
constraints during a load-follow type transient.


title: Fixed-Point Code Synthesis For Neural Networks

abstract:
  Over the last few years, neural networks have started penetrating safety
critical systems to take decisions in robots, rockets, autonomous driving car,
etc. A problem is that these critical systems often have limited computing
resources. Often, they use the fixed-point arithmetic for its many advantages
(rapidity, compatibility with small memory devices.) In this article, a new
technique is introduced to tune the formats (precision) of already trained
neural networks using fixed-point arithmetic, which can be implemented using
integer operations only. The new optimized neural network computes the output
with fixed-point numbers without modifying the accuracy up to a threshold fixed
by the user. A fixed-point code is synthesized for the new optimized neural
network ensuring the respect of the threshold for any input vector belonging
the range [xmin, xmax] determined during the analysis. From a technical point
of view, we do a preliminary analysis of our floating neural network to
determine the worst cases, then we generate a system of linear constraints
among integer variables that we can solve by linear programming. The solution
of this system is the new fixed-point format of each neuron. The experimental
results obtained show the efficiency of our method which can ensure that the
new fixed-point neural network has the same behavior as the initial
floating-point neural network.


title: To Impute or not to Impute? Missing Data in Treatment Effect Estimation

abstract:
  Missing data is a systemic problem in practical scenarios that causes noise
and bias when estimating treatment effects. This makes treatment effect
estimation from data with missingness a particularly tricky endeavour. A key
reason for this is that standard assumptions on missingness are rendered
insufficient due to the presence of an additional variable, treatment, besides
the individual and the outcome. Having a treatment variable introduces
additional complexity with respect to why some variables are missing that is
not fully explored by previous work. In our work we identify a new missingness
mechanism, which we term mixed confounded missingness (MCM), where some
missingness determines treatment selection and other missingness is determined
by treatment selection. Given MCM, we show that naively imputing all data leads
to poor performing treatment effects models, as the act of imputation
effectively removes information necessary to provide unbiased estimates.
However, no imputation at all also leads to biased estimates, as missingness
determined by treatment divides the population in distinct subpopulations,
where estimates across these populations will be biased. Our solution is
selective imputation, where we use insights from MCM to inform precisely which
variables should be imputed and which should not. We empirically demonstrate
how various learners benefit from selective imputation compared to other
solutions for missing data.


title:
Explicit port-Hamiltonian FEM models for geometrically nonlinear
  mechanical systems

abstract:
  In this article, we present the port-Hamiltonian representation, the
structure preserving discretization and the resulting finite-dimensional state
space model of geometrically nonlinear mechanical systems based on a mixed
finite element formulation. This article focuses on St. Venant-Kirchhoff
materials connecting the Green strain and the second Piola-Kirchhoff stress
tensor in a linear relationship which allows a port-Hamiltonian representation
by means of its co-energy (effort) variables. Due to treatment of both
Dirichlet and Neumann boundary conditions in the appropriate variational
formulation, the resulting port-Hamiltonian state space model features both of
them as explicit (control) inputs. Numerical experiments generated with FEniCS
illustrate the properties of the resulting FE models.


title: Supervised Contrastive Learning for Product Matching

abstract:
  Contrastive learning has moved the state of the art for many tasks in
computer vision and information retrieval in recent years. This poster is the
first work that applies supervised contrastive learning to the task of product
matching in e-commerce using product offers from different e-shops. More
specifically, we employ a supervised contrastive learning technique to
pre-train a Transformer encoder which is afterward fine-tuned for the matching
task using pair-wise training data. We further propose a source-aware sampling
strategy that enables contrastive learning to be applied for use cases in which
the training data does not contain product identifiers. We show that applying
supervised contrastive pre-training in combination with source-aware sampling
significantly improves the state-of-the-art performance on several widely used
benchmarks: For Abt-Buy, we reach an F1-score of 94.29 (+3.24 compared to the
previous state-of-the-art), for Amazon-Google 79.28 (+ 3.7). For WDC Computers
datasets, we reach improvements between +0.8 and +8.84 in F1-score depending on
the training set size. Further experiments with data augmentation and
self-supervised contrastive pre-training show that the former can be helpful
for smaller training sets while the latter leads to a significant decline in
performance due to inherent label noise. We thus conclude that contrastive
pre-training has a high potential for product matching use cases in which
explicit supervision is available.


title:
Impact of the Primordial Stellar Initial Mass Function on the 21-cm
  Signal

abstract:
  Properties of the first generation of stars (Pop III), such as their initial
mass function (IMF), are poorly constrained by observations and have yet to
converge between simulations. The cosmological 21-cm signal of neutral hydrogen
is predicted to be sensitive to Lyman band photons produced by these stars,
thus providing a unique way to probe the first stellar population. In this
paper, we investigate the impacts of the Pop III IMF on the cosmic dawn 21-cm
signal via the Wouthysen-Field effect, Lyman-Werner feedback, Ly-alpha heating,
and CMB heating. We calculate the emission spectra of star-forming halos for
different IMFs by integrating over individual metal-free stellar spectra,
computed from a set of stellar evolution histories and stellar atmospheres, and
taking into account variability of the spectra with stellar age. Through this
study, we therefore relax two common assumptions: that the zero-age main
sequence emission rate of a Pop III star is representative of its lifetime mean
emission rate, and that Pop III emission can be treated as instantaneous.
Exploring a bottom-heavy, a top-heavy, and intermediate IMFs, we show that
variations in the 21-cm signal are driven by stars lighter than 20 solar
masses. For the explored models we find maximum relative differences of 68% in
the cosmic dawn global 21-cm signal, and 137% between power spectra. Although
this impact is modest, precise modelling of the first stars and their evolution
is necessary for accurate prediction and interpretation of the 21-cm signal.


title: A Divisor problem for polynomials

abstract:
  We characterize all monic polynomials $f(x) \in \mathbb{Z}[x]$ that have the
property that \[f(p) \mid f(p^{p}),~\text{for all sufficiently large primes }p
\geq N(f). \] We also give necessary conditions and a sufficient condition for
monic polynomials $f(x) \in \mathbb{Z}[x]$ to satisfy $f(p) \mid f(p^{p})$ for
all primes $p$.


title: MeerKAT Primary Beam Measurements in the L Band

abstract:
  Full-polarization primary beam patterns of MeerKAT antennas have been
measured in L-band (856 to 1711 MHz) by means of radio holography using
celestial targets. This paper presents the observed frequency dependent
properties of these beams, and guides users of this 64 antenna radio telescope
that are concerned by its direction dependent polarization effects. In this
work the effects on the primary beams due to modeling simplifications,
bandwidth averaging, gravitational loading and ambient temperature are
quantified within the half power region of the beam. A perspective is provided
on the level of significance of typical use case effects. It is shown that
antenna pointing is a leading cause of inaccuracy for telescope users in the
presumed beam shape, introducing errors exceeding 1% in power near the half
power point of beams, owing to a telescope pointing accuracy of $\sigma\approx
0.6$ arcminutes. Disregarding these pointing errors, variability in the Stokes
I beam shape relative to the array average is most commonly around 0.3% in
power; however, the impact above 1500 MHz is on average triple that of the
lower half of the band. This happens because the proportion of higher order
waveguide modes that are activated and propagate is sensitive to small
manufacturing differences in the orthomode transducer for each receiver.
Primary beam correction verification test results for an off-axis spectral
index measurement experiment are included.

categories: astro-ph.IM astro-ph.CO astro-ph.GA astro-ph.HE


title:
Decision curve analysis for personalized treatment choice between
  multiple options

abstract:
  Decision curve analysis can be used to determine whether a personalized model
for treatment benefit would lead to better clinical decisions. Decision curve
analysis methods have been described to estimate treatment benefit using data
from a single RCT. Our main objective is to extend the decision curve analysis
methodology to the scenario where several treatment options exist and evidence
about their effects comes from a set of trials, synthesized using network
meta-analysis (NMA). We describe the steps needed to estimate the net benefit
of a prediction model using evidence from studies synthesized in an NMA. We
show how to compare personalized versus one-size-fit-all treatment
decision-making strategies, like "treat none" or "treat all patients with a
specific treatment" strategies. The net benefit per strategy can then be
plotted for a plausible range of threshold probabilities to reveal the most
clinically useful strategy. We applied our methodology to an NMA prediction
model for relapsing-remitting multiple sclerosis, which can be used to choose
between Natalizumab, Dimethyl Fumarate, Glatiramer Acetate, and placebo. We
illustrated the extended decision curve analysis methodology using several
threshold values combinations for each available treatment. For the examined
threshold values, the "treat patients according to the prediction model"
strategy performs either better than or close to the one-size-fit-all treatment
strategies. However, even small differences may be important in clinical
decision-making. As the advantage of the personalized model was not consistent
across all thresholds, an improved model may be needed before advocating its
applicability for decision-making. This novel extension of decision curve
analysis can be applied to NMA based prediction models to evaluate their use to
aid treatment decision-making.


title:
The new simplest proof of Ceyley's formula and connections with
  Kirkwood-Salzburg equations

abstract:
  A new very simple proof of the number of labeled rooted forest-graphs with a
given number of vertices is given. As a partial case of this formula we have
Cayley's formula.


title: Brokenwire : Wireless Disruption of CCS Electric Vehicle Charging

abstract:
  We present a novel attack against the Combined Charging System, one of the
most widely used DC rapid charging systems for electric vehicles (EVs). Our
attack, Brokenwire, interrupts necessary control communication between the
vehicle and charger, causing charging sessions to abort. The attack can be
conducted wirelessly from a distance, allowing individual vehicles or entire
fleets to be disrupted stealthily and simultaneously. In addition, it can be
mounted with off-the-shelf radio hardware and minimal technical knowledge. The
exploited behavior is a required part of the HomePlug Green PHY, DIN 70121 &
ISO 15118 standards and all known implementations exhibit it.
  We first study the attack in a controlled testbed and then demonstrate it
against seven vehicles and 18 chargers in real deployments. We find the attack
to be successful in the real world, at ranges up to 47 m, for a power budget of
less than 1 W. We further show that the attack can work between the floors of a
building (e.g., multi-story parking), through perimeter fences, and from
'drive-by' attacks. We present a heuristic model to estimate the number of
vehicles that can be attacked simultaneously for a given output power.
  Brokenwire has immediate implications for many of the around 12 million
battery EVs on the roads worldwide - and profound effects on the new wave of
electrification for vehicle fleets, both for private enterprise and crucial
public services. As such, we conducted a disclosure to the industry and
discussed a range of mitigation techniques that could be deployed to limit the
impact.


title:
Nuclear spin polaron-formation: anisotropy effects and quantum phase
  transition

abstract:
  We study theoretically the formation of the nuclear-spin polaron state in
semiconductor nanosystems within the Lindblad equation approach. To this end,
we derive a general Lindblad equation for the density operator that complies
with the symmetry of the system Hamiltonian and address the nuclear-spin
polaron formation for localized charge carriers subject to an arbitrarily
anisotropic hyperfine interaction when optically cooling the nuclei. The
steady-state solution of the density matrix for an anisotropic central spin
model is presented as a function of the electron and nuclear spin bath
temperature. Results for the electron-nuclear spin correlator as well as data
for the nuclear spin distribution function serve as a measure of
spin-entanglement. The features in both of them clearly indicate the formation
of the nuclear polaron state at low temperatures where the crossover regime
coincides with an enhancement of quantum fluctuations and agrees with the
mean-field prediction of the critical temperature line. We can identify two
distinct polaron states dependent upon the hyperfine anisotropy which are
separated by a quantum phase transition at the isotropic point. These states
are reflected in the temporal spin auto-correlation functions accessible in
experiment via spin-noise measurements.


title:
Chemistry of nebulae around binary post-AGB stars: A molecular survey of
  mm-wave lines

abstract:
  Context. There is a class of binary post-asymptotic giant branch (post-AGB)
stars that exhibit remarkable near-infrared (NIR) excess. Such stars are
surrounded by Keplerian or quasi-Keplerian disks, as well as extended outflows
composed of gas escaping from the disk. This class can be subdivided into disk-
and outflow-dominated sources, depending on whether it is the disk or the
outflow that represents most of the nebular mass, respectively. The chemistry
of this type of source has been practically unknown thus far. Methods. We
focused our observations on the 1.3, 2, 3 mm bands of the 30 m IRAM telescope
and on the 7 and 13 mm bands of the 40 m Yebes telescope. Our observations add
up around 600 hours of telescope time. Results. We present the first
single-dish molecular survey of mm-wave lines in nebulae around binary post-AGB
stars. We conclude that the molecular content is relatively low in nebulae
around binary post-AGB stars, as their molecular lines and abundances are
especially weaker compared with AGB stars. This fact is very significant in
those sources where the Keplerian disk is the dominant component of the nebula.
The study of their chemistry allows us to classify nebulae around AC Her, the
Red Rectangle, AI CMi, R Sct, and IRAS 20056+1834 as O-rich, while that of 89
Her is probably C-rich. The calculated abundances of the detected species other
than CO are particularly low compared with AGB stars. The initial stellar mass
derived from the 17O/18O ratio for the Red Rectangle and 89 Her is compatible
with the central total stellar mass derived from previous mm-wave
interferometric maps. The very low 12CO/13CO ratios found in binary post-AGB
stars reveal a high 13CO abundance compared to AGB and other post-AGB stars.


title:
Implementation of a Type-2 Fuzzy Logic Based Prediction System for the
  Nigerian Stock Exchange

abstract:
  Stock Market can be easily seen as one of the most attractive places for
investors, but it is also very complex in terms of making trading decisions.
Predicting the market is a risky venture because of the uncertainties and
nonlinear nature of the market. Deciding on the right time to trade is key to
every successful trader as it can lead to either a huge gain of money or
totally a loss in investment that will be recorded as a careless trade. The aim
of this research is to develop a prediction system for stock market using Fuzzy
Logic Type2 which will handle these uncertainties and complexities of human
behaviour in general when it comes to buy, hold or sell decision making in
stock trading. The proposed system was developed using VB.NET programming
language as frontend and Microsoft SQL Server as backend. A total of four
different technical indicators were selected for this research. The selected
indicators are the Relative Strength Index, William Average, Moving Average
Convergence and Divergence, and Stochastic Oscillator. These indicators serve
as input variable to the Fuzzy System. The MACD and SO are deployed as primary
indicators, while the RSI and WA are used as secondary indicators. Fibonacci
retracement ratio was adopted for the secondary indicators to determine their
support and resistance level in terms of making trading decisions. The input
variables to the Fuzzy System is fuzzified to Low, Medium, and High using the
Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference
rules were used for combining the trading rules for each input variable to the
fuzzy system. The developed system was tested using sample data collected from
ten different companies listed on the Nigerian Stock Exchange for a total of
fifty two periods. The dataset collected are Opening, High, Low, and Closing
prices of each security.


title: Fluctuations in pedestrian dynamics routing choices

abstract:
  Routing choices of walking pedestrians in geometrically complex environments
are regulated by the interplay of a multitude of factors such as local
crowding, (estimated) time to destination, (perceived) comfort. As individual
choices combine, macroscopic traffic flow patterns emerge. Understanding the
physical mechanisms yielding macroscopic traffic distributions in environments
with complex geometries is an outstanding scientific challenge, with
implications in the design and management of crowded pedestrian facilities. In
this work, we analyze, by means of extensive real-life pedestrian tracking
data, unidirectional flow dynamics in an asymmetric setting, as a prototype for
many common complex geometries. Our environment is composed of a main walkway
and a slightly longer detour. Our measurements have been collected during a
dedicated high-accuracy pedestrian tracking campaign held in Eindhoven (The
Netherlands). We show that the dynamics can be quantitatively modeled by
introducing a collective discomfort function, and that fluctuations on the
behavior of single individuals are crucial to correctly recover the global
statistical behavior. Notably, the observed traffic split substantially departs
from an optimal, transport-wise, partition, as the global pedestrian throughput
is not maximized.


title: The Zariski-Lipman conjecture for toric varieties

abstract:
  We give a short proof of the Zariski-Lipman conjecture for toric varieties:
any complex toric variety with locally free tangent sheaf is smooth.


title:
New Inner and Outer Bounds for 2-User Gaussian Broadcast Channels with
  Heterogeneous Blocklength Constraints

abstract:
  We investigate both a novel inner and outer bound on the rate region of a
2-user Gaussian broadcast channel with finite, heterogeneous blocklength
constraints (HB-GBC). In particular, we introduce a new, modified Sato-type
outer bound that can be applied in the finite blocklength regime and does not
require the same marginal property. We then develop and analyze concatenated
shell codes, which are suitable for the HB-GBC. Especially, to achieve a
smaller decoding latency for the user with shorter blocklength constraint when
successive interference cancellation is used, we derive the number of symbols
needed to successfully early decode the other user's message. We numerically
compare our derived outer bound to the best known achievable rate regions.
Numerical results show that the new early decoding performance is significantly
improved compared to the state of the art, and performs very close to the
asymptotic limit.


title: Complex structures on stratified Lie algebras

abstract:
  This paper investigates some properties of complex structures on Lie
algebras. In particular, we focus on $\textit{nilpotent}$ $\textit{complex
structures}$ that are characterized by a suitable $J$-invariant ascending or
descending central series $\mathfrak{d}^j$ and $\mathfrak{d}_j$ respectively.
In this article, we introduce a new descending series $\mathfrak{p}_j$ and use
it to give proof of a new characterization of nilpotent complex structures. We
examine also whether nilpotent complex structures on stratified Lie algebras
preserve the strata. We find that there exists a $J$-invariant stratification
on a step $2$ nilpotent Lie algebra with a complex structure.


title:
Musical Audio Similarity with Self-supervised Convolutional Neural
  Networks

abstract:
  We have built a music similarity search engine that lets video producers
search by listenable music excerpts, as a complement to traditional full-text
search. Our system suggests similar sounding track segments in a large music
catalog by training a self-supervised convolutional neural network with triplet
loss terms and musical transformations. Semi-structured user interviews
demonstrate that we can successfully impress professional video producers with
the quality of the search experience, and perceived similarities to query
tracks averaged 7.8/10 in user testing. We believe this search tool will make
for a more natural search experience that is easier to find music to soundtrack
videos with.


title:
From Discrimination to Generation: Knowledge Graph Completion with
  Generative Transformer

abstract:
  Knowledge graph completion aims to address the problem of extending a KG with
missing triples. In this paper, we provide an approach GenKGC, which converts
knowledge graph completion to sequence-to-sequence generation task with the
pre-trained language model. We further introduce relation-guided demonstration
and entity-aware hierarchical decoding for better representation learning and
fast inference. Experimental results on three datasets show that our approach
can obtain better or comparable performance than baselines and achieve faster
inference speed compared with previous methods with pre-trained language
models. We also release a new large-scale Chinese knowledge graph dataset
AliopenKG500 for research purpose. Code and datasets are available in
https://github.com/zjunlp/PromptKG/tree/main/GenKGC.


title:
Muscle pre-stimulation tunes viscous-like perturbation rejection in
  legged hopping

abstract:
  Muscle fibres possess unique visco-elastic properties, capable of generating
a stabilising zero-delay response to unexpected perturbations. This
instantaneous response -- termed "preflex" -- is crucial in the presence of
neuro-transmission delays, which are particularly hazardous during fast
locomotion due to the short stance duration. While the elastic contribution to
preflexes has been studied extensively, research on the role of fibre viscosity
due to the force-velocity relation remains unexplored. Moreover, muscle models
predict conditions with saturated force-velocity relations resulting in reduced
viscous-like fibre engagement. The goal of our study is to isolate and quantify
the preflex force produced by the force-velocity relation. We use our approach
to analyse two perturbed vertical hopping conditions, differing in their
viscosity engagement at touch-down. Both cases showed stable hopping patterns
despite the reduced viscous-like response to ground perturbations in the
saturated case. Moreover, the force-velocity relation was not the predominant
factor driving energy adjustment to disturbance intensity. From a robotics
perspective, our results suggest that already a simple, constant damper mounted
in parallel to an actuator could provide stabilising preflexes at and shortly
after impact.


title: Polyphonic pitch detection with convolutional recurrent neural networks

abstract:
  Recent directions in automatic speech recognition (ASR) research have shown
that applying deep learning models from image recognition challenges in
computer vision is beneficial. As automatic music transcription (AMT) is
superficially similar to ASR, in the sense that methods often rely on
transforming spectrograms to symbolic sequences of events (e.g. words or
notes), deep learning should benefit AMT as well. In this work, we outline an
online polyphonic pitch detection system that streams audio to MIDI by
ConvLSTMs. Our system achieves state-of-the-art results on the 2007 MIREX
multi-F0 development set, with an F-measure of 83\% on the bassoon, clarinet,
flute, horn and oboe ensemble recording without requiring any musical language
modelling or assumptions of instrument timbre.


title:
Inverse localization and global approximation for some Schr\"odinger
  operators on hyperbolic spaces

abstract:
  We consider the question of whether the high-energy eigenfunctions of certain
Schr\"odinger operators on the $d$-dimensional hyperbolic space of constant
curvature $-\kappa^2$ are flexible enough to approximate an arbitrary solution
of the Helmholtz equation $\Delta h+h=0$ on $\mathbf{R}^d$, over the natural
length scale $O(\lambda^{-1/2})$ determined by the eigenvalue $\lambda \gg 1$.
This problem is motivated by the fact that, by the asymptotics of the local
Weyl law, approximate Laplace eigenfunctions do have this approximation
property on any compact Riemannian manifold. In this paper we are specifically
interested in the Coulomb and harmonic oscillator operators on the hyperbolic
spaces $\mathbf{H}^d(\kappa)$. As the dimension of the space of bound states of
these operators tends to infinity as $\kappa$ tends to 0, one can hope to
approximate solutions to the Helmholtz equation by eigenfunctions for some
$\kappa > 0$ that is not fixed a priori. Our main result shows that this is
indeed the case, under suitable hypotheses. We also prove a global
approximation theorem with decay for the Helmholtz equation on manifolds that
are isometric to the hyperbolic space outside a compact set, and consider an
application to the study of the heat equation on $\mathbf{H}^d(\kappa)$.
Although global approximation and inverse approximation results are
heuristically related in that both theorems explore flexibility properties of
solutions to elliptic equations on hyperbolic spaces, we will see that the
underlying ideas behind these theorems are very different.


title:
Majorana flat bands at structured surfaces of nodal noncentrosymmetric
  superconductors

abstract:
  Surfaces of nodal noncentrosymmetric superconductors can host flat bands of
Majorana modes, which provide a promising platform for quantum computation if
one can find methods for manipulating localized Majorana wave packets. We study
the fate of such flat bands when part of the surface is subjected to an
exchange field induced by a ferromagnetic insulator. We use exact
diagonalization to find the eigenstates and eigenenergies of the Bogoliubov-de
Gennes Hamiltonian of a model system, for which an exchange field is applied
along a strip on the surface of a slab. We consider different orientations of
the strip and the applied field. If the spin polarization of the field-free
system along the field direction is sufficiently large perturbation theory
predicts that energies of states which are mostly localized on the
exchange-field strip are shifted away from zero energy by an amount
proportional to the field strength. On the other hand, energies corresponding
to states localized on the field-free strip are only weakly affected by the
field. Exact diagonalization confirms this. Moreover, we discuss a setup with a
small exchange field applied to the previously field-free strip with the goal
of introducing a linear dispersion. By switching this dispersion on and off, a
wave packet could be moved in a certain direction. We find that in our model
system, a linear dispersion can indeed be achieved. The qualitative features of
this dispersion can be predicted from the momentum-dependent spin polarization
of the field-free surface.


title: Detection of two additional circumbinary planets around Kepler-451

abstract:
  We announce the detection of two new planetary-mass companions around
Kepler-451 binary system in addition to the one detected previously based on
eclipse timing variation analysis. We found that an inner planet with 43 d
period with a minimum mass of 1.76 Mjup and an outer one with a $\sim$ 1800 d
orbital period with a minimum mass of 1.61 Mjup can explain the periodic
variations in the residuals of the one-planet fit of the eclipse timings. We
updated the orbital period of the middle planet as 406 d, and determined its
eccentricity as 0.33. The newly discovered outer planet is also on an eccentric
orbit (0.29), while the innermost planet was assumed to have a circular orbit.
All three Jovian planets have similar masses, and our dynamical stability test
yields that the system is stable.


title:
The most continuous part of the Plancherel decomposition for a real
  spherical space

abstract:
  In this article we give a precise description of the Plancherel decomposition
of the most continuous part of $L^{2}(Z)$ for a real spherical homogeneous
space $Z$. Our starting point is the recent construction of Bernstein morphisms
by Delorme, Knop, Kr\"otz and Schlichtkrull. The most continuous part
decomposes into a direct integral of unitary principal series representations.
We give an explicit construction of the $H$-invariant functionals on these
principal series. We show that for generic induction data the multiplicity
space equals the full space of $H$-invariant functionals. Finally, we determine
the inner products on the multiplicity spaces by refining the Maass-Selberg
relations.


title:
Unusual dynamics of spin-1/2 antiferromagnets on the triangular lattice
  in magnetic field

abstract:
  We discuss theoretically dynamical properties of spin-$\frac12$ Heisenberg
antiferromagnet on the triangular lattice in magnetic field $\bf H$. We use the
recently proposed bond-operator theory taking into account all spin degrees of
freedom in the magnetic unit cell containing three spins. This technique
describes accurately short-range spin correlations and provides a quantitative
description of elementary excitations which appear in other approaches as bound
states of conventional low-energy quasiparticles (e.g., magnons). In
quantitative agreement with previous numerical and analytical findings, we
observe four phases with coplanar spin arrangements upon the field increasing.
Among them are the fully saturated phase at $H>H_s$ and the collinear
"up-up-down" (UUD) state at $H_1<H<H_2$ showing the magnetization plateau at
1/3 of its saturation value. We demonstrate that apart from magnons (spin-1
quasiparticles) there is a well-defined spin-0 elementary excitation in the UUD
state whose spectrum lies below magnon branches. This mode originates from a
high-energy quasiparticle at $H=0$. It produces anomalies in the longitudinal
spin correlator whose spectral weights, however, are much smaller than spectral
weights of magnons in the transverse spin correlator that hinders its
experimental observation. At $H_2<H<H_s$, we obtain multiple short-wavelength
spin excitations and a highly nontrivial field evolution of quasiparticles
spectra which would be very difficult to find by any conventional analytical
approach.


title: Bipolar Thermoelectric Josephson Engine

abstract:
  Thermoelectric effects in metals are typically small due to the
nearly-perfect particle-hole (PH) symmetry around their Fermi surface [1, 2].
Despite being initially considered paradoxical [3], thermophase effects [4-8]
and linear thermoelectricity [9] in superconducting systems were identified
only when PH symmetry is explicitly broken [10-14]. Here, we experimentally
demonstrate that a superconducting tunnel junction can develop a very large
bipolar thermoelectric effect in the presence of a nonlinear thermal gradient
thanks to spontaneous PH symmetry breaking [15]. Our junctions show a maximum
thermovoltage of $\pm150\; \mu$ V at $\pm650$ mK, directly proportional to the
superconducting gap. Notably, the corresponding Seebeck coefficient of
$\pm300\; \mu$V/K is roughly $10^5$ times larger than the one expected for a
normal metal at the same temperature [16, 17]. Moreover, by integrating our
junctions into a Josephson interferometer, we realize a bipolar thermoelectric
Josephson engine (BTJE) [18] with phase-coherent thermopower control [19]. When
connected to a generic load, the BTJE generates a phase-tunable electric power
up to about 140 mW/m$^2$ at subKelvin temperatures. In addition, our device
implements the prototype for a persistent thermoelectric memory cell, written
or erased by current injection [20]. We expect that our findings will trigger
thermoelectricity in PH symmetric systems, and will lead to a number of
groundbreaking applications in superconducting electronics [21], cutting-edge
quantum technologies [22-24] and sensing [25].


title: Observational appearence of Kaluza-Klein black holes

abstract:
  The optical properties of rotating black holes in Kaluza-Klein theory
described by the total mass, spin, and electric and magnetic charges are
investigated in detail. Using a developed general relativistic ray-tracing code
to calculate the motion of photons, the numerous shadows of Kaluza-Klein black
holes are generated. The properties of the black hole shadow and the light
deflection angle around these black holes are also studied in order to put
constraints on the parameters of Kaluza-Klein black holes using M87* shadow
observations. The possibility of imposing constraints on Kaluza-Klein black
holes using shadow observations is investigated. Moreover, we find that small
charges (electric and magnetic) of the black hole can meet these constraints.
We conclude that with the current precision of the M87* black hole shadow image
observation by the EHT collaboration, the shadow observations of Kaluza-Klein
black holes are indistinguishable from that of the Kerr black hole. Much better
observational accuracy than the current capabilities of the EHT collaboration
are required in order to place verified constraints on the parameters of
modified theories of gravity in the strong field regime.


title: Binary Subgroups of Direct Products

abstract:
  We explore an elementary construction that produces finitely presented groups
with diverse homological finiteness properties -- the {\em binary subgroups},
$B(\Sigma,\mu)<G_1\times\dots\times G_m$. These full subdirect products require
strikingly few generators. If each $G_i$ is finitely presented, $B(\Sigma,\mu)$
is finitely presented. When the $G_i$ are non-abelian limit groups (e.g. free
or surface groups), the $B(\Sigma,\mu)$ provide new examples of finitely
presented, residually-free groups that do not have finite classifying spaces
but are not of Stallings-Bieri type. We answer a question of Minasyan relating
different notions of rank for residually-free groups. And we prove that if
$G_1,\dots,G_m$ are perfect groups, each requiring at most $r$ generators, then
$G_1\times\dots\times G_m$ requires at most $r \lfloor \log_2 m+1 \rfloor$
generators.


title: TIML: Task-Informed Meta-Learning for Agriculture

abstract:
  Labeled datasets for agriculture are extremely spatially imbalanced. When
developing algorithms for data-sparse regions, a natural approach is to use
transfer learning from data-rich regions. While standard transfer learning
approaches typically leverage only direct inputs and outputs, geospatial
imagery and agricultural data are rich in metadata that can inform transfer
learning algorithms, such as the spatial coordinates of data-points or the
class of task being learned. We build on previous work exploring the use of
meta-learning for agricultural contexts in data-sparse regions and introduce
task-informed meta-learning (TIML), an augmentation to model-agnostic
meta-learning which takes advantage of task-specific metadata. We apply TIML to
crop type classification and yield estimation, and find that TIML significantly
improves performance compared to a range of benchmarks in both contexts, across
a diversity of model architectures. While we focus on tasks from agriculture,
TIML could offer benefits to any meta-learning setup with task-specific
metadata, such as classification of geo-tagged images and species distribution
modelling.


title: OntoSeer -- A Recommendation System to Improve the Quality of Ontologies

abstract:
  Building an ontology is not only a time-consuming process, but it is also
confusing, especially for beginners and the inexperienced. Although ontology
developers can take the help of domain experts in building an ontology, they
are not readily available in several cases for a variety of reasons. Ontology
developers have to grapple with several questions related to the choice of
classes, properties, and the axioms that should be included. Apart from this,
there are aspects such as modularity and reusability that should be taken care
of. From among the thousands of publicly available ontologies and vocabularies
in repositories such as Linked Open Vocabularies (LOV) and BioPortal, it is
hard to know the terms (classes and properties) that can be reused in the
development of an ontology. A similar problem exists in implementing the right
set of ontology design patterns (ODPs) from among the several available.
Generally, ontology developers make use of their experience in handling these
issues, and the inexperienced ones have a hard time. In order to bridge this
gap, we propose a tool named OntoSeer, that monitors the ontology development
process and provides suggestions in real-time to improve the quality of the
ontology under development. It can provide suggestions on the naming
conventions to follow, vocabulary to reuse, ODPs to implement, and axioms to be
added to the ontology. OntoSeer has been implemented as a Prot\'eg\'e plug-in.


title: Zero-sum mean-field Dynkin games: characterization and convergence

abstract:
  We introduce a zero-sum game problem of mean-field type as an extension of
the classical zero-sum Dynkin game problem to the case where the payoff
processes might depend on the value of the game and its probability law. We
establish sufficient conditions under which such a game admits a value and a
saddle point. Furthermore, we provide a characterization of the value of the
game in terms of a specific class of doubly reflected backward stochastic
differential equations (BSDEs) of mean-field type, for which we derive an
existence and uniqueness result. We then introduce a corresponding system of
weakly interacting zero-sum Dynkin games and show its well-posedness. Finally,
we provide a propagation of chaos result for the value of the zero-sum
mean-field Dynkin game.


title: Rings generated by idempotents and nilpotents

abstract:
  We present new characterizations of the rings in which every element is the
sum of two idempotents and a nilpotent that commute, and the rings in which
every element is the sum of two tripotents and a nilpotent that commute. We
prove that such rings are completely determined by the additive decompositions
of their square elements.


title:
Bipolar conduction asymmetries lead to ultra-high thermoelectric power
  factor

abstract:
  Low band gap thermoelectric materials suffer from bipolar effects at high
temperatures, with increased electronic thermal conductivity and reduced
Seebeck coefficient, leading to reduced power factor and low ZT figure of
merit. In this work we show that the presence of strong transport asymmetries
between the conduction and valence bands can allow high phonon-limited
electronic conductivity at finite Seebeck coefficient values, leading to
largely enhanced power factors. The power factors that can be achieved can be
significantly larger compared to their maximum unipolar counterparts, allowing
for doubling of the ZT figure of merit. We identify this behavior in low band
gap cases from the half-Heusler materials family. Using both, advanced
electronic Boltzmann transport calculations for realistic material
bandstructures, as well as model parabolic electronic bands, we elaborate on
the parameters that determine this effect. We then develop a series of
descriptors which can guide machine learning studies in identifying such
classes of materials with extraordinary power factors at nearly pristine
conditions. For this we test more than 3000 analytical bandstructures and their
features, and more than 120 possible descriptors, to identify the most
promising ones that contain: i) only band structure features for easy
identification from material databases, and ii) band structure and transport
parameters that provide much higher correlations, but for which parameter
availability can be somewhat scarce.


title: Rapid loss of Arctic sea ice

abstract:
  Several environmental tipping points and self-reinforcing feedback loops are
still disregarded within the frequently used climate models. Thus, existing
climate models are not very representative for providing projections of the
conditions after the actual environmental tipping points are triggered. In this
contribution, we discuss the presence of the Arctic sea ice loss tipping point
and its influence on global warming. Our literature search and PIOMAS sea ice
volume observation data based projection indicate the significant acceleration
of the sea ice loss (which leads to a year around and permanent ice-free
Arctic) in near future.


title:
Computational analysis of interface evolution and droplet pinch-off
  mechanism in two-phase liquid flow through T-junction microfluidic system

abstract:
  This work has explored interface evolution and pinch-off mechanism of the
droplet formation in two-phase flow through cross-flow microfluidic device. The
two-dimensional mathematical model equations have been solved using the finite
element method under the squeezing regime ($Ca_c < 10^{-2}$) for wide range of
flow rates ($Qr = 0.1 - 10$) and fixed contact angle ($\theta=135^o$). The
droplet formation process has been classified into various instantaneous stages
as initial, filling, squeezing, pinch-off and stable droplet through
microscopic visualization of interface evolution in phase profiles. The
dynamics of interface, and point pressure in both phases is further gained and
discussed. Maximum pressure in the continuous phase varied linearly with Qr.
The droplet pinch-off mechanism has been thoroughly elucidated by determining
the local radius of the curvature ($R_{c,min}$) and neck width (2r) during the
squeezing and pinch-off stages. At the pinch-off point, both $R_{c,min}$ and 2r
are non-linearly related to Qr. Further, the topological dynamics of interface
has been explored by analyzing the Laplace pressure ($p_{\text{L}}$), acting on
the interface curvature, evaluated using (a) pressure sensors in both phases,
(b) local radius of curvature, and (c) minimum radius of curvature. The
insights obtained from the present work can reliably be used in designing the
model and prototypes of microfluidic devices for generating monodispersed
droplets in emulsions, and the droplet breakup mechanism would help accurate
prediction of the pinch-off moment. The proposed knowledge provides detailed
insights of the interface evolution and droplet pinch-off to a precision of 10
$\mu$s and resolution of 10 $\mu$m, equivalent to experimental flow
visualization with a high-speed ($10^{5}$ fps) and high-resolution (10 $\mu$m
pixel size) camera.


title:
Interpretability methods of machine learning algorithms with
  applications in breast cancer diagnosis

abstract:
  Early detection of breast cancer is a powerful tool towards decreasing its
socioeconomic burden. Although, artificial intelligence (AI) methods have shown
remarkable results towards this goal, their "black box" nature hinders their
wide adoption in clinical practice. To address the need for AI guided breast
cancer diagnosis, interpretability methods can be utilized. In this study, we
used AI methods, i.e., Random Forests (RF), Neural Networks (NN) and Ensembles
of Neural Networks (ENN), towards this goal and explained and optimized their
performance through interpretability techniques, such as the Global Surrogate
(GS) method, the Individual Conditional Expectation (ICE) plots and the Shapley
values (SV). The Wisconsin Diagnostic Breast Cancer (WDBC) dataset of the open
UCI repository was used for the training and evaluation of the AI algorithms.
The best performance for breast cancer diagnosis was achieved by the proposed
ENN (96.6% accuracy and 0.96 area under the ROC curve), and its predictions
were explained by ICE plots, proving that its decisions were compliant with
current medical knowledge and can be further utilized to gain new insights in
the pathophysiological mechanisms of breast cancer. Feature selection based on
features' importance according to the GS model improved the performance of the
RF (leading the accuracy from 96.49% to 97.18% and the area under the ROC curve
from 0.96 to 0.97) and feature selection based on features' importance
according to SV improved the performance of the NN (leading the accuracy from
94.6% to 95.53% and the area under the ROC curve from 0.94 to 0.95). Compared
to other approaches on the same dataset, our proposed models demonstrated state
of the art performance while being interpretable.


title:
Characterization and dynamics of the peculiar stream Jhelum, A tentative
  role for the Sagittarius dwarf galaxy

abstract:
  Stellar streams are a promising tool to study the Milky Way's dark matter
subhalo population, as interactions with subhalos could produce substructure in
streams. However, other possible causes for substructure first need to be well
understood. Here we study the kinematics and the unusual morphology of the
stellar stream Jhelum. Using a combination of ground-based photometry and Gaia
EDR3 astrometry we characterize the morphology of Jhelum. We combine this new
data with radial velocities from the literature to perform orbit integrations
of the stream in static Galactic potentials. We also explore N-body models in
the presence of large perturbers to explain some of the kinematic and
morphological features observed. The new data reveals a previously unreported
tertiary component in the stream, as well as several gaps and a kink-like
feature in its narrow component. We find that for a range of realistic Galactic
potentials, no single orbit is able to reproduce Jhelum's radial velocity data
entirely. A generic property of the orbital solutions is that they share a
similar orbital plane to Sagittarius and this leads to repeated encounters with
the stream. Using N-body simulations that include a massive Sagittarius, we
show that these encounters are able to reproduce the narrow and broad
components in Jhelum, as well as create a tertiary component in some cases. We
also find evidence that such encounters can result in an apparent increase in
the velocity dispersion of the stream by a factor up to ~ 5 due to overlapping
narrow and broad components. Our findings suggest that the Jhelum stream is
even more complex than once thought, however its morphology and kinematics can
be relatively easily explained via the interactions with Sagittarius. In this
scenario, the formation of Jhelum's narrow and broad components occurs
naturally, yet some of the smaller gap-like features remain to be explained.


title:
Electron Spectrum Topology and Giant Density-of-States Singularities in
  Cubic Lattices

abstract:
  The topology of isoenergetic surfaces in reciprocal space for simple (sc),
body-centered (bcc), and face-centered (fcc) cubic lattices is investigated in
detail in the tight-binding approximation, taking into account the transfer
integrals between the nearest and next neighbors $t$ and $t'$. It is shown
that, for values $\tau = t'/t = \tau_\ast$ corresponding to a change in the
topology of surfaces, lines and surfaces of $\mathbf k$-van Hove points can be
formed. With a small deviation of $\tau$ from these singular values, the
spectrum in the vicinity of the van Hove line (surface) is replaced by a weak
dependence on $\mathbf k$ in the vicinity of several van Hove points that have
a giant mass proportional to $|\tau - \tau_ \ast|^{-1}$. Singular contributions
to the density of states near peculiar $\tau$ values are considered; analytical
expressions for the density of states being obtained in terms of elliptic
integrals. It is shown that in a number of cases the maximum value of the
density of states is achieved at energies corresponding not to
$\mathbf{k}$-points on the Brillouin zone edges, but to its internal points in
highly symmetrical directions. The corresponding contributions to electron and
magnetic properties are treated, in particular, in application to weak
itinerant magnets.


title:
On characteristic ideal of Selmer group associated to Artin
  representations

abstract:
  Selmer group for an Artin representation over totally real fields was studied
by Greenberg and Vatsal. In this paper we study the Selmer groups for an Artin
representation over a totally complex field. We establish an algebraic function
of the characteristic ideal of the Selmer group associated to Artin
representation over the cyclotomic $\Z_p$- extension of the rational numbers
under certain mild hypotheses and construct several examples to illustrate our
result. We also prove that in this situation $\mu$-invariant of the dual Selmer
group is independent of the choice of the lattice.


title: Extracting Software Requirements from Unstructured Documents

abstract:
  Requirements identification in textual documents or extraction is a tedious
and error prone task that many researchers suggest automating. We manually
annotated the PURE dataset and thus created a new one containing both
requirements and non-requirements. Using this dataset, we fine-tuned the BERT
model and compare the results with several baselines such as fastText and ELMo.
In order to evaluate the model on semantically more complex documents we
compare the PURE dataset results with experiments on Request For Information
(RFI) documents. The RFIs often include software requirements, but in a less
standardized way. The fine-tuned BERT showed promising results on PURE dataset
on the binary sentence classification task. Comparing with previous and recent
studies dealing with constrained inputs, our approach demonstrates high
performance in terms of precision and recall metrics, while being agnostic to
the unstructured textual input.


title: Tableau systems for some Ivlev-like (quantified) modal logics

abstract:
  Ivlev's pioneering work started in the 1970's showed a new and promissory way
in the study of modal logic from the perspective of many-valued logics.
Continuing our previous work on Ivlev-like non-normal modal logics with
non-deterministic semantics, we present in this paper tableau systems for Tm,
S4m and S5m, the non-normal versions of T, S4 and S5, respectively, as well as
for their corresponding first-order extensions Tm*, S4m* and S5m*.


title: Two-photon spontaneous emission of an atom in a cosmic string background

abstract:
  It is well known that the vicinities of an atomic system may substantially
affect its radiative properties. In this work, we consider the influence of a
cosmic string background in the spontaneous emission of an excited atom. We
start by computing the one-photon spontaneous emission rate of a quantum
emitter, which is a narrow band process, and then we analyze the more complex
case of the two-photon spontaneous emission, which is a broad band and much
richer phenomenon. In the former case, we analyze not only the behavior of the
decay rate with the distance from the atom to the string, but also with the
deficit angle associated with the cosmic string metric. In the latter case, we
show that the spectral distribution of the emitted photons is substantially
affected by the cosmic string background.


title:
A Practical Guide to the Numerical Implementation of Tensor Networks I:
  Contractions, Decompositions and Gauge Freedom

abstract:
  We present an overview of the key ideas and skills necessary to begin
implementing tensor network methods numerically, which is intended to
facilitate the practical application of tensor network methods for researchers
that are already versed with their theoretical foundations. These skills
include an introduction to the contraction of tensor networks, to optimal
tensor decompositions, and to the manipulation of gauge degrees of freedom in
tensor networks. The topics presented are of key importance to many common
tensor network algorithms such as DMRG, TEBD, TRG, PEPS and MERA.


title:
Multi Objective Resource Optimization of Wireless Network Based on Cross
  Domain Virtual Network Embedding

abstract:
  The rapid development of virtual network architecture makes it possible for
wireless network to be widely used. With the popularity of artificial
intelligence (AI) industry in daily life, efficient resource allocation of
wireless network has become a problem. Especially when network users request
wireless network resources from different management domains, they still face
many practical problems. From the perspective of virtual network embedding
(VNE), this paper designs and implements a multi-objective optimization VNE
algorithm for wireless network resource allocation. Resource allocation in
virtual network is essentially a problem of allocating underlying resources for
virtual network requests (VNRs). According to the proposed objective formula,
we consider the optimization mapping cost, network delay and VNR acceptance
rate. VNE is completed by node mapping and link mapping. In the experiment and
simulation stage, it is compared with other VNE algorithms, the cross domain
VNE algorithm proposed in this paper is optimal in the above three indicators.
This shows the effectiveness of the algorithm in wireless network resource
allocation.


title:
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution
  Neural Network and Reinforcement Learning

abstract:
  Network virtualization (NV) is a technology with broad application prospects.
Virtual network embedding (VNE) is the core orientation of VN, which aims to
provide more flexible underlying physical resource allocation for user function
requests. The classical VNE problem is usually solved by heuristic method, but
this method often limits the flexibility of the algorithm and ignores the time
limit. In addition, the partition autonomy of physical domain and the dynamic
characteristics of virtual network request (VNR) also increase the difficulty
of VNE. This paper proposed a new type of VNE algorithm, which applied
reinforcement learning (RL) and graph neural network (GNN) theory to the
algorithm, especially the combination of graph convolutional neural network
(GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness
value, we set up the objective function of the algorithm implementation,
realized an efficient dynamic VNE algorithm, and effectively reduced the degree
of resource fragmentation. Finally, we used comparison algorithms to evaluate
the proposed method. Simulation experiments verified that the dynamic VNE
algorithm based on RL and GCNN has good basic VNE characteristics. By changing
the resource attributes of physical network and virtual network, it can be
proved that the algorithm has good flexibility.


title:
Incorporating Distributed DRL into Storage Resource Optimization of
  Space-Air-Ground Integrated Wireless Communication Network

abstract:
  Space-air-ground integrated network (SAGIN) is a new type of wireless network
mode. The effective management of SAGIN resources is a prerequisite for
high-reliability communication. However, the storage capacity of space-air
network segment is extremely limited. The air servers also do not have
sufficient storage resources to centrally accommodate the information uploaded
by each edge server. So the problem of how to coordinate the storage resources
of SAGIN has arisen. This paper proposes a SAGIN storage resource management
algorithm based on distributed deep reinforcement learning (DRL). The resource
management process is modeled as a Markov decision model. In each edge physical
domain, we extract the network attributes represented by storage resources for
the agent to build a training environment, so as to realize the distributed
training. In addition, we propose a SAGIN resource management framework based
on distributed DRL. Simulation results show that the agent has an ideal
training effect. Compared with other algorithms, the resource allocation
revenue and user request acceptance rate of the proposed algorithm are
increased by about 18.15\% and 8.35\% respectively. Besides, the proposed
algorithm has good flexibility in dealing with the changes of resource
conditions.


title: Deep invariant networks with differentiable augmentation layers

abstract:
  Designing learning systems which are invariant to certain data
transformations is critical in machine learning. Practitioners can typically
enforce a desired invariance on the trained model through the choice of a
network architecture, e.g. using convolutions for translations, or using data
augmentation. Yet, enforcing true invariance in the network can be difficult,
and data invariances are not always known a piori. State-of-the-art methods for
learning data augmentation policies require held-out data and are based on
bilevel optimization problems, which are complex to solve and often
computationally demanding. In this work we investigate new ways of learning
invariances only from the training data. Using learnable augmentation layers
built directly in the network, we demonstrate that our method is very
versatile. It can incorporate any type of differentiable augmentation and be
applied to a broad class of learning problems beyond computer vision. We
provide empirical evidence showing that our approach is easier and faster to
train than modern automatic data augmentation techniques based on bilevel
optimization, while achieving comparable results. Experiments show that while
the invariances transferred to a model through automatic data augmentation are
limited by the model expressivity, the invariance yielded by our approach is
insensitive to it by design.


title:
Global Stability of a Diffusive SEIR Epidemic Model with Distributed
  Delay

abstract:
  We study the global dynamics of a reaction-diffusion SEIR infection model
with distributed delay and nonlinear incidence rate. The well-posedness of the
proposed model is proved. By means of Lyapunov functionals, we show that the
disease free equilibrium state is globally asymptotically stable when the basic
reproduction number is less or equal than one, and that the disease endemic
equilibrium is globally asymptotically stable when the basic reproduction
number is greater than one. Numerical simulations are provided to illustrate
the obtained theoretical results.


title:
Proceedings 10th International Workshop on Theorem Proving Components
  for Educational Software

abstract:
  This EPTCS volume contains the proceedings of the ThEdu'21 workshop, promoted
on 11 July 2021, as a satellite event of CADE-28. Due to the COVID-19 pandemic,
CADE-28 and all its co-located events happened as virtual events. ThEdu'21 was
a vibrant workshop, with an invited talk by Gilles Dowek (ENS Paris-Saclay),
eleven contributions, and one demonstration. After the workshop an open call
for papers was issued and attracted 10 submissions, 7 of which have been
accepted by the reviewers, and collected in the present post-proceedings
volume.
  The ThEdu series pursues the smooth transition from an intuitive way of doing
mathematics at secondary school to a more formal approach to the subject in
STEM education, while favouring software support for this transition by
exploiting the power of theorem-proving technologies.
  The volume editors hope that this collection of papers will further promote
the development of theorem-proving based software, and that it will collaborate
on improving mutual understanding between computer scientists, mathematicians
and stakeholders in education.


title: Generative Modeling of Complex Data

abstract:
  In recent years, several models have improved the capacity to generate
synthetic tabular datasets. However, such models focus on synthesizing simple
columnar tables and are not useable on real-life data with complex structures.
This paper puts forward a generic framework to synthesize more complex data
structures with composite and nested types. It then proposes one practical
implementation, built with causal transformers, for struct (mappings of types)
and lists (repeated instances of a type). The results on standard benchmark
datasets show that such implementation consistently outperforms current
state-of-the-art models both in terms of machine learning utility and
statistical similarity. Moreover, it shows very strong results on two complex
hierarchical datasets with multiple nesting and sparse data, that were
previously out of reach.


title:
Elastic Gradient Descent and Elastic Gradient Flow: LARS Like Algorithms
  Approximating the Solution Paths of the Elastic Net

abstract:
  The elastic net combines lasso and ridge regression to fuse the sparsity
property of lasso with the grouping property of ridge regression. The
connections between ridge regression and gradient descent and between lasso and
forward stagewise regression have previously been shown. Here we combine
gradient descent and forward stagewise regression into elastic gradient
descent. We investigate the case of infinitesimal step size, obtaining a
piecewise analytical solution, similar to the LARS algorithm. We also compare
elastic gradient descent and the elastic net on real and simulated data, and
show that they provide similar solution paths. Compared to the elastic net,
elastic gradient descent is more robust with respect to penalization as to
which parameters to include in the model. Elastic gradient descent also
provides significantly better sensitivity and mean squared prediction error,
without sacrificing specificity.


title: Structure Property in Cu crystallization

abstract:
  Phase transition is a central topic in condensed matter physics, all the
time. In this paper, as a general representative of phase transition, the Cu
crystallization is discussed. And some physical quantities is defined to
quantificationally describe the structure property in Cu crystallization, such
as diffusion property and symmetry so on. As a result, it is indicated that
there are some interesting changes of structure property in Cu crystallization.


title: Winning Wordle Wisely

abstract:
  In this missive, the author goes about ruining an online word guessing game
called Wordle, by generally trying to mathify the whole thing. On the other
hand, one might consider that the author is trying to spice up mathematics by
applying it to an online word guessing game. Also, a spoiler warning: don't
read past page 9 if you want to retain that Wordle magic.


title: Edge-Selective Feature Weaving for Point Cloud Matching

abstract:
  This paper tackles the problem of accurately matching the points of two 3D
point clouds. Most conventional methods improve their performance by extracting
representative features from each point via deep-learning-based algorithms. On
the other hand, the correspondence calculation between the extracted features
has not been examined in depth, and non-trainable algorithms (e.g. the Sinkhorn
algorithm) are frequently applied. As a result, the extracted features may be
forcibly fitted to a non-trainable algorithm. Furthermore, the extracted
features frequently contain stochastically unavoidable errors, which degrades
the matching accuracy. In this paper, instead of using a non-trainable
algorithm, we propose a differentiable matching network that can be jointly
optimized with the feature extraction procedure. Our network first constructs
graphs with edges connecting the points of each point cloud and then extracts
discriminative edge features by using two main components: a shared set-encoder
and an edge-selective cross-concatenation. These components enable us to
symmetrically consider two point clouds and to extract discriminative edge
features, respectively. By using the extracted discriminative edge features,
our network can accurately calculate the correspondence between points. Our
experimental results show that the proposed network can significantly improve
the performance of point cloud matching. Our code is available at
https://github.com/yanarin/ESFW


title: Correcting Confounding via Random Selection of Background Variables

abstract:
  We propose a method to distinguish causal influence from hidden confounding
in the following scenario: given a target variable Y, potential causal drivers
X, and a large number of background features, we propose a novel criterion for
identifying causal relationship based on the stability of regression
coefficients of X on Y with respect to selecting different background features.
To this end, we propose a statistic V measuring the coefficient's variability.
We prove, subject to a symmetry assumption for the background influence, that V
converges to zero if and only if X contains no causal drivers. In experiments
with simulated data, the method outperforms state of the art algorithms.
Further, we report encouraging results for real-world data. Our approach aligns
with the general belief that causal insights admit better generalization of
statistical associations across environments, and justifies similar existing
heuristic approaches from the literature.


title: Compact quantum distance-based binary classifier

abstract:
  Quantum computing opens exciting opportunities for kernel-based machine
learning methods, which have broad applications in data analysis. Recent works
show that quantum computers can efficiently construct a model of a classifier
by engineering the quantum interference effect to carry out the kernel
evaluation in parallel. For practical applications of these quantum machine
learning methods, an important issue is to minimize the size of quantum
circuits. We present the simplest quantum circuit for constructing a
kernel-based binary classifier. This is achieved by generalizing the
interference circuit to encode data labels in the relative phases of the
quantum state and by introducing compact amplitude encoding, which encodes two
training data vectors into one quantum register. When compared to the simplest
known quantum binary classifier, the number of qubits is reduced by two and the
number of steps is reduced linearly with respect to the number of training
data. The two-qubit measurement with post-selection required in the previous
method is simplified to single-qubit measurement. Furthermore, the final
quantum state has a smaller amount of entanglement than that of the previous
method, which advocates the cost-effectiveness of our method. Our design also
provides a straight-forward way to handle an imbalanced data set, which is
often encountered in many machine learning problems.


title: Character of couple and couple-stress in continuum mechanics

abstract:
  In this paper, the concept of moment and couple in mechanics is examined from
a fundamental perspective. It turns out that although representing a couple by
its moment vector is very useful in rigid body mechanics and strength of
materials, it has been very misleading in continuum mechanics. To specify the
effect of a concentrated couple in continuum mechanics, not only the couple
moment, but also the line of action of its constituent parallel opposite forces
must be specified. However, in the governing equations of equilibrium or motion
of a continuum, only moment of body couple, moment of couple-tractions, and
moment of couple-stresses appear without specifying the line of action of any
couple density forces. This results in non-uniqueness of the state of stresses
and deformation in the continuum, which has shown itself in the indeterminacy
of the couple-stress tensor. Nevertheless, the physical state of stress and
deformation in the continuum is unique and determinate. Therefore, this imposes
some restrictions on the form of body couple, couple-traction and
couple-stresses. Here, the uniqueness of interactions in the continuum is used
to establish that the continuum does not support a distribution of body couple,
and a distribution of surface twisting couple-traction with normal moment. From
this the mechanism of action of the couple-traction as a double layer of shear
force-tractions, and the skew-symmetric character of the couple-stress moment
tensor in continuum mechanics are also established.


title: Eight Equilibrium and continuity equations within the material media

abstract:
  The paper aims to apply the octonions to explore the torques and forces and
so forth in the electromagnetic and gravitational fields, investigating the
influences of material media on the equilibrium and continuity equations. The
contemporary scholars utilize the quaternions and octonions to research the
electromagnetic fields and gravitational fields and so forth. In the paper, the
octonions are capable of surveying the electromagnetic and gravitational fields
within material media, including the octonion field strength, field source,
angular momentum, torque, and force. Further, the octonion field strength and
angular momentum can be combined together to become the octonion composite
field strength, deducing the octonion composite torque and force and others.
When the octonion composite force is equal to zero under certain circumstances,
it is able to achieve eight equations independent of each other, including the
force equilibrium equation, fluid continuity equation, and current continuity
equation and so on. The above reveals that the material media and octonion
field strength can make a contribution to the eight equilibrium and continuity
equations. And it is beneficial to deepen the understanding of equilibrium and
continuity equations within material media.


title: The Relativistic Maxwell-J\"uttner Velocity Distribution Function

abstract:
  In this paper, the expression of the relativistic Maxwell-J\"uttner velocity
distribution was deduced in a detailed and didactic way. This distribution was
compared with the well-known Maxwell-Boltzmann distribution, which does not
take into account relativistic effects. Applications and comparisons between
the two distributions were carried out in three situations: i) protons at solar
surface temperature, ii) electrons at Lawson's criterion temperature, and iii)
protons at quasar temperatures. It was found that the value of the ratio
between the rest mass of the particle considered and the temperature of the
system determines the need to consider or not a relativistic treatment for the
velocity distribution function of the system.


title: Source data selection for out-of-domain generalization

abstract:
  Models that perform out-of-domain generalization borrow knowledge from
heterogeneous source data and apply it to a related but distinct target task.
Transfer learning has proven effective for accomplishing this generalization in
many applications. However, poor selection of a source dataset can lead to poor
performance on the target, a phenomenon called negative transfer. In order to
take full advantage of available source data, this work studies source data
selection with respect to a target task. We propose two source selection
methods that are based on the multi-bandit theory and random search,
respectively. We conduct a thorough empirical evaluation on both simulated and
real data. Our proposals can be also viewed as diagnostics for the existence of
a reweighted source subsamples that perform better than the random selection of
available samples.


title: Generalising Aumann's Agreement Theorem

abstract:
  Aumann's celebrated theorem says that a group of agents who once shared a
common prior probability distribution cannot assign different posteriors to a
given proposition, should these agents have common knowledge about their
posteriors. In other words, rational agents cannot agree to disagree. Aumann's
agreement theorem was one of the first attempts to formalise and explore the
role played by common knowledge in decision theory. Recently, we have seen a
resurfacing of the debate around possible (quantum) extensions of Aumann's
results. This paper contributes to this discussion. First, as expected, we
argue that agreeing to disagree is impossible in quantum theory. Secondly, and
based on the quantum argument, we show that agreeing to disagree is also
forbidden in any generalised probability theory.


title: Chemical exploration of Galactic cold cores

abstract:
  A solar-type system starts from an initial molecular core that acquires
organic complexity as it evolves. The so-called prestellar cores that can be
studied are rare, which has hampered our understanding of how organic chemistry
sets in and grows. Aims. We selected the best prestellar core targets from the
cold core catalogue that represent a diversity in terms of their environment to
explore their chemical complexity: 1390 (in the compressed shell of Lambda
Ori), 869 (in the MBM12 cloud), and 4149 (in the California nebula). We
obtained a spectral survey with the IRAM 30 m telescope in order to explore the
molecular complexity of the cores. We carried out a radiative transfer analysis
of the detected transitions in order to place some constraints on the physical
conditions of the cores and on the molecular column densities. We also used the
molecular ions in the survey to estimate the cosmic-ray ionisation rate and the
S/H initial elemental abundance using a gas-phase chemical model to reproduce
their abundances. We found large differences in the molecular complexity
(deuteration, complex organic molecules, sulphur, carbon chains, and ions) and
compared their chemical properties with a cold core and two prestellar cores.
The chemical diversity we found in the three cores seems to be correlated with
their chemical evolution: two of them are prestellar (1390 and 4149), and one
is in an earlier stage (869). The influence of the environment is likely
limited because cold cores are strongly shielded from their surroundings. The
high extinction prevents interstellar UV radiation from penetrating deeply into
the cores. Higher spatial resolution observations of the cores are therefore
needed to constrain the physical structure of the cores, as well as a
larger-scale distribution of molecular ions to understand the influence of the
environment on their molecular complexity.


title:
Theoretical Insights into Non-Arrhenius Behaviors of Thermal Vacancies
  in Anharmonic Crystals

abstract:
  Vacancies are prevalent point defects in crystals, but their thermal
responses are elusive. Herein, we formulate a simple theoretical model to shed
light on the vacancy evolution during heating. Vibrational excitations are
thoroughly investigated via moment recurrence techniques in quantum statistical
mechanics. On that basis, we carry out numerical analyses for Ag, Cu, and Ni
with the Sutton-Chen many-body potential. Our results reveal that the
well-known Arrhenius law is insufficient to describe the proliferation of
vacancies. Specifically, anharmonic effects lead to a strong nonlinearity in
the Gibbs energy of vacancy formation. Our physical picture is well supported
by previous simulations and experiments.


title:
Non-contact Andreev reflection as a direct probe of superconductivity on
  the atomic scale

abstract:
  Direct detection of superconductivity has long been a key strength of
point-contact Andreev reflection. However, its applicability to atomic-scale
imaging is limited by the mechanical contact of the Andreev probe. To this end,
we present a new method to probe Andreev reflection in a tunnel junction,
leveraging tunneling spectroscopy and junction tunability to achieve
quantitative detection of Andreev scattering. This method enables unambiguous
assignment of superconducting origins of current-carrying excitations as well
as detection of higher order Andreev processes in atomic-scale junctions. We
furthermore revealed distinct sensitivity of Andreev reflection to natural
defects, such as step edges, even in classical superconductors. The methodology
opens a new path to nanoand atomic-scale imaging of superconducting properties,
including disordered superconductors and proximity to phase transitions.


title:
Heat coefficients for magnetic Laplacians on the complex projective
  space $\mathbf{P}^{n}(\mathbb{C})$

abstract:
  Denoting by $\Delta_\nu$ the Fubini-Study Laplacian perturbed by a uniform
magnetic field strength proportional to $\nu$, this operator has a discrete
spectrum consisting on eigenvalues $\beta_m, \ m\in\mathbb{Z}_+$, when acting
on bounded functions of the complex projective $n$-space. For the corresponding
eigenspaces, we give a new proof for their reproducing kernels by using
Zaremba's expansion directly. These kernels are then used to obtain an integral
representation for the heat kernel of $\Delta_\nu$. Using a suitable polynomial
decomposition of the multiplicity of each $\beta_m$, we write down a trace
formula for the heat operator associated with $\Delta_\nu$ in terms of Jacobi's
theta functions and their higher order derivatives. Doing so enables us to
establish the asymptotics of this trace as $t\searrow 0^+$ by giving the
corresponding heat coefficients in terms of Bernoulli numbers and polynomials.
The obtained results can be exploited in the analysis of the spectral zeta
function associated with $\Delta_\nu$.


title:
Structural properties of bosonic He clusters with N=2-10 for different
  potential models at the physical point and at unitarity

abstract:
  Since the $^4$He dimer supports only one weakly bound state with an average
interatomic distance much larger than the van der Waals length and no deeply
bound states, $^4$He$_N$ clusters with $N>2$ are a paradigmatic model system
with which to explore foundational concepts such as large $s$-wave scattering
length universality, van der Waals universality, Efimov physics, and effective
field theories. This work presents structural properties such as the pair and
triple distribution functions, the hyperradial density, the probability to find
the $N$th particle at a given distance from the center of mass of the other
$N-1$ atoms, and selected contacts. The kinetic energy release, which can be
measured via Coulomb explosion in dedicated size-selected molecular beam
experiments -- at least for small $N$ -- , is also presented. The structural
properties are determined for three different realistic $^4$He-$^4$He
interaction potentials and contrasted with those for an effective low-energy
potential model from the literature that reproduces the energies of $^4$He$_N$
clusters in the ground state for $N=2$ to $N=\infty$ at the $\gtrsim
95$~percent level with just four input parameters. The study is extended to
unitarity (infinite $s$-wave scattering length) by artificially weakening the
interaction potentials. In addition to contributing to the characterization of
small bosonic helium quantum droplets, our study provides insights into the
effective low-energy theory's predictability of various structural properties.


title:
Regge phenomenology and coherent photoproduction of charmonium in
  peripheral heavy ion collisions

abstract:
  By using models based on Regge phenomenology we analyse the coherent
photoproduction of charmonium in peripheral heavy-ion collisions at the Large
Hadron Collider (LHC). The centrality dependence is investigated and compared
to the experimental results for coherent $J/\psi$ production in lead-lead LHC
runs at the energies of 2.76 and 5.02 TeV. Theoretical uncertainties and
possible limitations of the formalism are also discussed.


title:
COIL: Constrained Optimization in Learned Latent Space -- Learning
  Representations for Valid Solutions

abstract:
  Constrained optimization problems can be difficult because their search
spaces have properties not conducive to search, e.g., multimodality,
discontinuities, or deception. To address such difficulties, considerable
research has been performed on creating novel evolutionary algorithms or
specialized genetic operators. However, if the representation that defined the
search space could be altered such that it only permitted valid solutions that
satisfied the constraints, the task of finding the optimal would be made more
feasible without any need for specialized optimization algorithms. We propose
Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a
learned latent representation from a dataset comprising samples from the valid
region of the search space according to a constraint, thus enabling the
optimizer to find the objective in the new space defined by the learned
representation. Preliminary experiments show promise: compared to an identical
GA using a standard representation that cannot meet the constraints or find fit
solutions, COIL with its learned latent representation can perfectly satisfy
different types of constraints while finding high-fitness solutions.


title: Group invariant machine learning by fundamental domain projections

abstract:
  We approach the well-studied problem of supervised group invariant and
equivariant machine learning from the point of view of geometric topology. We
propose a novel approach using a pre-processing step, which involves projecting
the input data into a geometric space which parametrises the orbits of the
symmetry group. This new data can then be the input for an arbitrary machine
learning model (neural network, random forest, support-vector machine etc).
  We give an algorithm to compute the geometric projection, which is efficient
to implement, and we illustrate our approach on some example machine learning
problems (including the well-studied problem of predicting Hodge numbers of
CICY matrices), in each case finding an improvement in accuracy versus others
in the literature. The geometric topology viewpoint also allows us to give a
unified description of so-called intrinsic approaches to group equivariant
machine learning, which encompasses many other approaches in the literature.


title: Detecting New Physics as Novelty -- Complementarity Matters

abstract:
  Novelty detection is a task of machine learning that aims at detecting novel
events without a prior knowledge. In particular, its techniques can be applied
to detect unexpected signals from new phenomena at colliders. In this paper, we
develop an analysis scheme that exploits the complementarity, originally
studied in Ref.~\cite{Hajer:2018kqm}, between isolation-based and
clustering-based novelty evaluators. This approach can significantly improve
the performance and overall applicability of novelty detection at colliders,
which we demonstrate using a variety of two dimensional Gaussian samples
mimicking collider events. As a further proof of principle, we subsequently
apply this scheme to the detection of two significantly different signals at
the LHC featuring a $t\bar{t}\gamma\gamma$ final state: $t\bar t h$, giving a
narrow resonance in the diphoton mass spectrum, and gravity-mediated
supersymmetry, which results in broad distributions at high transverse
momentum. Compared to existing dedicated searches at the LHC, the sensitivities
for both signals are found to be encouraging.