title: Nonlocal Thermoelectric Resistance in Vortical Viscous Transport

abstract:
  The pursuit for clearly identifiable signatures of viscous electron flow in
the solid state systems has been a paramount task in the search of the
hydrodynamic electron transport behavior. In this work, we investigate
theoretically the nonlocal electric and thermal resistances for the generic
non-Galilean-invariant electron liquids in the multiterminal Hall-bar devices
in the hydrodynamic regime. The role of the device inhomogeneity is carefully
addressed in the model of the disorder potential with the long-range
correlation radius. We obtain analytic expressions for the thermoelectric
resistances that are applicable in the full crossover regime from charge
neutrality to high electron density. We show that the vortical component of the
electron flow manifests in the thermal transport mode close to the charge
neutrality where vorticity is already suppressed by the intrinsic conductivity
in the electric current. This behavior can be tested by the high-resolution
thermal imaging probes.


title: Quantum Operations on Conformal Nets

abstract:
  On a conformal net $\mathcal{A}$, one can consider collections of unital
completely positive maps on each local algebra $\mathcal{A}(I)$, subject to
natural compatibility, vacuum preserving and conformal covariance conditions.
We call \emph{quantum operations} on $\mathcal{A}$ the subset of extreme such
maps. The usual automorphisms of $\mathcal{A}$ (the vacuum preserving
invertible unital *-algebra morphisms) are examples of quantum operations, and
we show that the fixed point subnet of $\mathcal{A}$ under all quantum
operations is the Virasoro net generated by the stress-energy tensor of
$\mathcal{A}$. Furthermore, we show that every irreducible conformal subnet
$\mathcal{B}\subset\mathcal{A}$ is the fixed points under a subset of quantum
operations.
  When $\mathcal{B}\subset\mathcal{A}$ is discrete (or with finite Jones
index), we show that the set of quantum operations on $\mathcal{A}$ that leave
$\mathcal{B}$ elementwise fixed has naturally the structure of a compact (or
finite) hypergroup, thus extending some results of [Bis17]. Under the same
assumptions, we provide a Galois correspondence between intermediate conformal
nets and closed subhypergroups. In particular, we show that intermediate
conformal nets are in one-to-one correspondence with intermediate subfactors,
extending a result of Longo in the finite index/completely rational conformal
net setting [Lon03].


title:
Self-consistent solution of magnetic and friction energy losses of a
  magnetic nano-particle

abstract:
  We present a simple simulation model for analysing magnetic and frictional
losses of magnetic nano-particles in viscous fluids subject to alternating
magnetic fields. Assuming a particle size below the single-domain limit, we use
a macrospin approach and solve the Landau-Lifshitz-Gilbert equation coupled to
the mechanical torque equation. Despite its simplicity the presented model
exhibits surprisingly rich physics and enables a detailed analysis of the
different loss processes depending on field parameters and initial arrangement
of the particle and the field. Depending on those parameters regions of
different steady states emerge: a region with dominating N\'eel relaxation and
high magnetic losses and another region region with high frictional losses at
low fields or low frequencies. The energy increases continuously even across
regime boundaries up to frequencies above the Brownian relaxation limit. At
those higher frequencies the steady state can also depend on the initial
orientation of the particle in the external field. The general behavior and
special cases and their specific absorption rates are compared and discussed.


title: Strategy Synthesis for Global Window PCTL

abstract:
  Given a Markov decision process (MDP) $M$ and a formula $\Phi$, the strategy
synthesis problem asks if there exists a strategy $\sigma$ s.t. the resulting
Markov chain $M[\sigma]$ satisfies $\Phi$. This problem is known to be
undecidable for the probabilistic temporal logic PCTL. We study a class of
formulae that can be seen as a fragment of PCTL where a local, bounded horizon
property is enforced all along an execution. Moreover, we allow for linear
expressions in the probabilistic inequalities. This logic is at the frontier of
decidability, depending on the type of strategies considered. In particular,
strategy synthesis is decidable when strategies are deterministic while the
general problem is undecidable.


title: Motivating Data Science Students to Participate and Learn

abstract:
  Data science education is increasingly involving human subjects and societal
issues such as privacy, ethics, and fairness. Data scientists need to be
equipped with skills to tackle the complexities of the societal context
surrounding their data science work. In this paper, we offer insights into how
to structure our data science classes so that they motivate students to deeply
engage with material about societal context and lean into the types of
conversations that will produce long lasting growth in critical thinking
skills. In particular, we describe a novel assessment tool called participation
portfolios, which is motivated by a framework that promotes student autonomy,
self reflection, and the building of a learning community. We compare student
participation before and after implementing this assessment tool, and our
results suggest that this tool increased student participation and helped them
move towards course learning objectives.


title: TEMOS: Generating diverse human motions from textual descriptions

abstract:
  We address the problem of generating diverse 3D human motions from textual
descriptions. This challenging task requires joint modeling of both modalities:
understanding and extracting useful human-centric information from the text,
and then generating plausible and realistic sequences of human poses. In
contrast to most previous work which focuses on generating a single,
deterministic, motion from a textual description, we design a variational
approach that can produce multiple diverse human motions. We propose TEMOS, a
text-conditioned generative model leveraging variational autoencoder (VAE)
training with human motion data, in combination with a text encoder that
produces distribution parameters compatible with the VAE latent space. We show
that TEMOS framework can produce both skeleton-based animations as in prior
work, as well more expressive SMPL body motions. We evaluate our approach on
the KIT Motion-Language benchmark and, despite being relatively
straightforward, demonstrate significant improvements over the state of the
art. Code and models are available on our project page.


title:
Seeing without Looking: Analysis Pipeline for Child Sexual Abuse
  Datasets

abstract:
  The online sharing and viewing of Child Sexual Abuse Material (CSAM) are
growing fast, such that human experts can no longer handle the manual
inspection. However, the automatic classification of CSAM is a challenging
field of research, largely due to the inaccessibility of target data that is -
and should forever be - private and in sole possession of law enforcement
agencies. To aid researchers in drawing insights from unseen data and safely
providing further understanding of CSAM images, we propose an analysis template
that goes beyond the statistics of the dataset and respective labels. It
focuses on the extraction of automatic signals, provided both by pre-trained
machine learning models, e.g., object categories and pornography detection, as
well as image metrics such as luminance and sharpness. Only aggregated
statistics of sparse signals are provided to guarantee the anonymity of
children and adolescents victimized. The pipeline allows filtering the data by
applying thresholds to each specified signal and provides the distribution of
such signals within the subset, correlations between signals, as well as a bias
evaluation. We demonstrated our proposal on the Region-based annotated Child
Pornography Dataset (RCPD), one of the few CSAM benchmarks in the literature,
composed of over 2000 samples among regular and CSAM images, produced in
partnership with Brazil's Federal Police. Although noisy and limited in several
senses, we argue that automatic signals can highlight important aspects of the
overall distribution of data, which is valuable for databases that can not be
disclosed. Our goal is to safely publicize the characteristics of CSAM
datasets, encouraging researchers to join the field and perhaps other
institutions to provide similar reports on their benchmarks.


title:
Efficient and Automated Inversions of Magnetically-Sensitive Forbidden
  Coronal Lines: CLEDB -- The Coronal Line Emission DataBase Magnetic Field
  Inversion Algorithm

abstract:
  We present CLEDB, a "single point inversion" algorithm for inferring magnetic
parameters using I,Q,U, and V Stokes parameters of forbidden magnetic dipole
lines formed in the solar corona. We select lines of interest and construct
databases of Stokes parameters for combinations of plasma thermal and magnetic
configurations. The size and complexity of such databases are drastically
reduced by taking advantage of symmetries. Using wavelength-integrated line
profiles, each of which might be decomposed beforehand into several
line-of-sight components, we search for nearest matches to observed Stokes
parameters computed for the elongation corresponding to the observed region.
The method is intended to be applied to two or more lines observed
simultaneously. The solutions initially yield magnetic orientation, thermal
properties, and the spatial position of the emitting plasma in three
dimensions. Multiple possible solutions for each observation are returned,
including irreducible degeneracies, where usually sets of two solutions are
compatible with the two input I,Q,U, and V measurements. In solving for the
scattering geometry, this method avoids an additional degeneracy pointed out by
Dima & Schad (2020). The magnetic field strength is separately derived from the
simple ratio of observed to database Stokes V data, after the thermal
properties and scattering geometry solutions have been determined.


title:
Multiscale Partial Information Decomposition of Dynamic Processes with
  Short and Long-range correlations: Theory and Application to Cardiovascular
  Control

abstract:
  Heart rate variability results from the combined activity of several
physiological systems, including the cardiac, vascular, and respiratory systems
which have their own internal regulation, but also interact with each other to
preserve the homeostatic function. These control mechanisms operate across
multiple temporal scales, resulting in the simultaneous presence of short-term
dynamics and long-range correlations. The Network Physiology framework provides
statistical tools based on information theory able to quantify structural
aspects of multivariate and multiscale interconnected mechanisms driving the
dynamics of complex physiological networks. In this work, the multiscale
representation of Transfer Entropy from Systolic Arterial Pressure (S) and
Respiration (R) to Heart Period (H) and of its decomposition into unique,
redundant and synergistic contributions is obtained using a Vector
AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian
processes. This novel approach allows to quantify the directed information flow
accounting for the simultaneous presence of short-term dynamics and long-range
correlations among the analyzed processes. The approach is first illustrated in
simulated VARFI processes and then applied to H, S and R time series measured
in healthy subjects monitored at rest and during mental and postural stress.
Our results highlight the dependence of the information transfer on the balance
between short-term and long-range correlations in coupled dynamical systems,
which cannot be observed using standard methods that do not consider long-range
correlations. The proposed methodology shows that postural stress induces
larger redundant effects at short time scales and mental stress induces larger
cardiovascular information transfer at longer time scales.


title:
Quantitative Comparison of Planar Coded Aperture Imaging Reconstruction
  Methods

abstract:
  Imaging distributions of radioactive sources plays a substantial role in
nuclear medicine as well as in monitoring nuclear waste and its deposit. Coded
Aperture Imaging has been proposed as an alternative to parallel or pinhole
collimators, but requires image reconstruction as an extra step. Multiple
reconstruction methods with varying run time and computational complexity have
been proposed. Yet, no quantitative comparison between the different
reconstruction methods has been carried out so far. This paper focuses on a
comparison based on three sets of hot-rod phantom images captured with an
experimental Gamma-camera consisting of a Tungsten-based MURA mask with a 2mm
thick 256x256 pixelated CdTe semiconductor detector coupled to a Timepix
readout circuit. Analytical reconstruction methods, MURA Decoding, Wiener
Filter and a convolutional Maximum Likelihood Expectation Maximization (MLEM)
algorithm were compared to data-driven Convolutional Encoder-Decoder (CED)
approaches. The comparison is based on the contrast-to-noise ratio as it has
been previously used to assess reconstruction quality. For the given set-up,
MURA Decoding, the most commonly used CAI reconstruction method, provides
robust reconstructions despite the assumption of a linear model. For single
image reconstruction, however, MLEM performed best among analytical
reconstruction methods, but took the longest with an average of 13s run time.
The fastest reconstruction method is the Wiener Filter with 67ms and mediocre
quality. The CED with a specifically tailored training set was able to succeed
the most commonly used MURA decoding on average by a factor between 1.37 and
2.60 and a run time of around 300ms.


title: Developmental Negation Processing in Transformer Language Models

abstract:
  Reasoning using negation is known to be difficult for transformer-based
language models. While previous studies have used the tools of
psycholinguistics to probe a transformer's ability to reason over negation,
none have focused on the types of negation studied in developmental psychology.
We explore how well transformers can process such categories of negation, by
framing the problem as a natural language inference (NLI) task. We curate a set
of diagnostic questions for our target categories from popular NLI datasets and
evaluate how well a suite of models reason over them. We find that models
perform consistently better only on certain categories, suggesting clear
distinctions in how they are processed.


title: The TAP equation: evaluating combinatorial innovation

abstract:
  We investigate solutions to the TAP equation, a phenomenological
implementation of the Theory of the Adjacent Possible. Several implementations
of TAP are studied, with potential applications in a range of topics including
economics, social sciences, environmental change, evolutionary biological
systems, and the nature of physical laws. The generic behaviour is an extended
plateau followed by a sharp explosive divergence. We find accurate analytic
approximations for the blow-up time that we validate against numerical
simulations, and explore the properties of the equation in the vicinity of
equilibrium between innovation and extinction. A particular variant, the
two-scale TAP model, replaces the initial plateau with a phase of exponential
growth, a widening of the TAP equation phenomenology that may enable it to be
applied in a wider range of contexts.


title: SATfeatPy -- A Python-based Feature Extraction System for Satisfiability

abstract:
  Feature extraction is a fundamental task in the application of machine
learning methods to SAT solving. It is used in algorithm selection and
configuration for solver portfolios and satisfiability classification. Many
approaches have been proposed to extract meaningful attributes from CNF
instances. Most of them lack a working/updated implementation, and the limited
descriptions lack clarity affecting the reproducibility. Furthermore, the
literature misses a comparison among the features. This paper introduces
SATfeatPy, a library that offers feature extraction techniques for SAT problems
in the CNF form. This package offers the implementation of all the structural
and statistical features from there major papers in the field. The library is
provided in an up-to-date, easy-to-use Python package alongside a detailed
feature description. We show the high accuracy of SAT/UNSAT and problem
category classification, using five sets of features generated using our
library from a dataset of 3000 SAT and UNSAT instances, over ten different
classes of problems. Finally, we compare the usefulness of the features and
importance for predicting a SAT instance's original structure in an ablation
study.


title:
A Comparative Study of Meter Detection Methods for Automated
  Infrastructure Inspection

abstract:
  In order to read meter values from a camera on an autonomous inspection robot
with positional errors, it is necessary to detect meter regions from the image.
In this study, we developed shape-based, texture-based, and background
information-based methods as meter area detection techniques and compared their
effectiveness for meters of different shapes and sizes. As a result, we
confirmed that the background information-based method can detect the farthest
meters regardless of the shape and number of meters, and can stably detect
meters with a diameter of 40px.


title: On the Optimization of Margin Distribution

abstract:
  Margin has played an important role on the design and analysis of learning
algorithms during the past years, mostly working with the maximization of the
minimum margin. Recent years have witnessed the increasing empirical studies on
the optimization of margin distribution according to different statistics such
as medium margin, average margin, margin variance, etc., whereas there is a
relative paucity of theoretical understanding. In this work, we take one step
on this direction by providing a new generalization error bound, which is
heavily relevant to margin distribution by incorporating ingredients such as
average margin and semi-variance, a new margin statistics for the
characterization of margin distribution. Inspired by the theoretical findings,
we propose the MSVMAv, an efficient approach to achieve better performance by
optimizing margin distribution in terms of its empirical average margin and
semi-variance. We finally conduct extensive experiments to show the superiority
of the proposed MSVMAv approach.


title: Zeta-function and $\mu^*$-Zariski pairs of surfaces

abstract:
  A Zariski pair of surfaces is a pair of complex polynomial functions in
$\mathbb{C}^3$ which is obtained from a classical Zariski pair of projective
curves $f_0(z_1,z_2,z_3)=0$ and $f_1(z_1,z_2,z_3)=0$ of degree $d$ in
$\mathbb{P}^2$ by adding a same term of the form $z_i^{d+m}$ ($m\geq 1$) to
both $f_0$ and $f_1$ so that the corresponding affine surfaces of
$\mathbb{C}^3$ -- defined by $g_0:=f_0+z_i^{d+m}$ and $g_1:=f_1+z_i^{d+m}$ --
have an isolated singularity at the origin and the same zeta-function for the
monodromy associated with their Milnor fibrations (so, in particular, $g_0$ and
$g_1$ have the same Milnor number). In the present paper, we show that if $f_0$
and $f_1$ are "convenient" with respect to the coordinates $(z_1,z_2,z_3)$ and
if the singularities of the curves $f_0=0$ and $f_1=0$ are Newton
non-degenerate in some suitable local coordinates, then $(g_0,g_1)$ is a
$\mu^*$-Zariski pair of surfaces, that is, a Zariski pair of surfaces whose
polynomials $g_0$ and $g_1$ have the same Teissier's $\mu^*$-sequence but lie
in different path-connected components of the $\mu^*$-constant stratum. To this
end, we prove a new general formula that gives, under appropriate conditions,
the Milnor number of functions of the above type, and we show (in a general
setting) that two polynomials functions lying in the same path-connected
component of the $\mu^*$-constant stratum can always be joined by a "piecewise
complex-analytic path".


title: Persistent current oscillations in a double-ring quantum gas

abstract:
  Vorticity in closed quantum fluid circuits is known to arise in the form of
persistent currents. In this work, we develop a method to engineer transport of
the quantized vorticity between density-coupled ring-shaped atomic
Bose-Einstein condensates in experimentally accessible regimes. Introducing a
tunable weak link between the rings, we observe and characterize the
controllable periodic transfer of the current, and investigate the role of
temperature on suppressing these oscillations via a range of complementary
state-of-the-art numerical methods. Our set-up paves the way for precision
measurements of local acceleration and rotation.


title:
Inverse Probability Weighting: the Missing Link between Survey Sampling
  and Evidence Estimation

abstract:
  We consider the class of inverse probability weight (IPW) estimators,
including the popular Horwitz-Thompson and Hajek estimators used routinely in
survey sampling, causal inference and evidence estimation for Bayesian
computation. We focus on the 'weak paradoxes' for these estimators due to two
counterexamples by Basu (1988) and Wasserman (2004) and investigate the two
natural Bayesian answers to this problem: one based on binning and smoothing :
a 'Bayesian sieve' and the other based on a conjugate hierarchical model that
allows borrowing information via exchangeability. We show that the two Bayesian
estimators achieve lower mean squared errors in Wasserman's example compared to
simple IPW estimators via simulation studies on a broad range of parameter
configurations. We prove posterior consistency for the Bayes estimator and show
how it requires fewer assumptions on the inclusion probabilities. We also
revisit the connection between the different problems where improved or
adaptive IPW estimators will be useful, including survey sampling, evidence
estimation strategies such as Conditional Monte Carlo, Riemannian sum,
Trapezoidal rules and vertical likelihood, as well as average treatment effect
estimation in causal inference.


title:
Path integral in position-deformed Heisenberg algebra with strong
  quantum gravitational measurement

abstract:
  Position-deformed Heisenberg algebra with maximal length uncertainty has
recently been proven to induce strong quantum gravitational fields at the
Planck scale (2022 J. Phys. A: Math. Theor.55 105303). In the present study, we
use the position space representation on the one hand and the Fourier transform
and its inverse representations on the other to construct propagators of path
integrals within this deformed algebra. The propagators and the corresponding
actions of a free particle and a simple harmonic oscillator are discussed as
examples. Since the effects of quantum gravity are strong in this Euclidean
space, we show that the actions which describe the classical trajectories of
both systems are bounded by the ordinary ones of classical mechanics. This
indicates that quantum gravity bends the paths of particles, allowing them to
travel quickly from one point to another. It is numerically observed by the
decrease in values of classical actions as one increases the quantum
gravitational effects.


title:
Towards Optimal Tradeoff Between Data Freshness and Update Cost in
  Information-update Systems

abstract:
  In this paper, we consider a discrete-time information-update system, where a
service provider can proactively retrieve information from the information
source to update its data and users query the data at the service provider. One
example is crowdsensing-based applications. In order to keep users satisfied,
the application desires to provide users with fresh data, where the freshness
is measured by the Age-of-Information (AoI). However, maintaining fresh data
requires the application to update its database frequently, which incurs an
update cost (e.g., incentive payment). Hence, there exists a natural tradeoff
between the AoI and the update cost at the service provider who needs to make
update decisions. To capture this tradeoff, we formulate an optimization
problem with the objective of minimizing the total cost, which is the sum of
the staleness cost (which is a function of the AoI) and the update cost. Then,
we provide two useful guidelines for the design of efficient update policies.
Following these guidelines and assuming that the aggregated request arrival
process is Bernoulli, we prove that there exists a threshold-based policy that
is optimal among all online policies and thus focus on the class of
threshold-based policies. Furthermore, we derive the closed-form formula for
computing the long-term average cost under any threshold-based policy and
obtain the optimal threshold. Finally, we perform extensive simulations using
both synthetic data and real traces to verify our theoretical results and
demonstrate the superior performance of the optimal threshold-based policy
compared with several baseline policies.


title:
Characterization of modulation spaces by symplectic representations and
  applications to Schr\"{o}dinger equations

abstract:
  In the last twenty years modulation spaces, introduced by H. G. Feichtinger
in 1983, have been successfully addressed to the study of signal analysis,
PDE's, pseudodifferential operators, quantum mechanics, by hundreds of
contributions. In 2011 M. de Gosson showed that the time-frequency
representation Short-time Fourier Transform (STFT), which is the tool to define
modulation spaces, can be replaced by the Wigner distribution. This idea was
further generalized to $\tau$-Wigner representations in [9]. In this paper
time-frequency representations are viewed as images of symplectic matrices via
metaplectic operators. This new perspective highlights that the protagonists of
time-frequency analysis are metaplectic operators and symplectic matrices
$\mathcal{A} \in Sp(2d,\mathbb{R})$. We find conditions on $\mathcal{A}$ for
which the related symplectic time-frequency representation $W_\mathcal{A}$ can
replace the STFT and give equivalent norms for weighted modulation spaces. In
particular, we study the case of covariant matrices $\mathcal{A}$, i.e., their
corresponding $W_\mathcal{A}$ are members of the Cohen class.
  Finally, we show that symplectic time-frequency representations
$W_\mathcal{A}$ can be efficiently employed in the study of Schr\"{o}dinger
equations. This new approach may have further applications in quantum mechanics
and PDE's.


title: Crisis and catharsis in atomic physics

abstract:
  Precise measurement of an atomic hydrogen transition resolves the proton size
puzzle.


title: Wide and Deep Neural Networks Achieve Optimality for Classification

abstract:
  While neural networks are used for classification tasks across domains, a
long-standing open problem in machine learning is determining whether neural
networks trained using standard procedures are optimal for classification,
i.e., whether such models minimize the probability of misclassification for
arbitrary data distributions. In this work, we identify and construct an
explicit set of neural network classifiers that achieve optimality. Since
effective neural networks in practice are typically both wide and deep, we
analyze infinitely wide networks that are also infinitely deep. In particular,
using the recent connection between infinitely wide neural networks and Neural
Tangent Kernels, we provide explicit activation functions that can be used to
construct networks that achieve optimality. Interestingly, these activation
functions are simple and easy to implement, yet differ from commonly used
activations such as ReLU or sigmoid. More generally, we create a taxonomy of
infinitely wide and deep networks and show that these models implement one of
three well-known classifiers depending on the activation function used: (1)
1-nearest neighbor (model predictions are given by the label of the nearest
training example); (2) majority vote (model predictions are given by the label
of the class with greatest representation in the training set); or (3) singular
kernel classifiers (a set of classifiers containing those that achieve
optimality). Our results highlight the benefit of using deep networks for
classification tasks, in contrast to regression tasks, where excessive depth is
harmful.


title: Horizontal and Straight Triangulations on Heisenberg Groups

abstract:
  This paper aims to show that there exists a triangulation of the Heisenberg
group $\mathbb{H}^n$ into singular simplexes with regularity properties on both
the low-dimensional and high-dimensional layers. For low dimensions, we request
our simplexes to be horizontal while, for high dimensions, we define a notion
of straight simplexes using exponential and logarithmic maps and we require our
simplexes to have high-dimensional straight layers. A triangulation with such
simplexes is first constructed on a general polyhedral structure and then
extended to the whole Heisenberg group. In this paper we also provide some
explicit examples of grid and triangulations.


title: Riesz spaces with generalized Orlicz growth

abstract:
  We consider a Riesz $\phi$-variation for functions $f$ defined on the real
line when $\varphi:\Omega\times[0,\infty)\to[0,\infty)$ is a generalized
$\Phi$-function. We show that it generates a quasi-Banach space and derive an
explicit formula for the modular when the function $f$ has bounded variation.
The resulting $BV$-type energy has previously appeared in image restoration
models. We generalize and improve previous results in the variable exponent and
Orlicz cases and answer a question regarding the Riesz--Medvedev variation by
Appell, Bana\'s and Merentes [\emph{Bounded Variation and Around}, Studies in
Nonlinear Analysis and Applications, Vol. 17, De Gruyter, Berlin/Boston, 2014].


title:
MET: Model Checking-Driven Explorative Testing of CRDT Designs and
  Implementations

abstract:
  Internet-scale distributed systems often replicate data at multiple
geographic locations to provide low latency and high availability. The
Conflict-free Replicated Data Type (CRDT) is a framework that provides a
principled approach to maintaining eventual consistency among data replicas.
CRDTs have been notoriously difficult to design and implement correctly. Subtle
deep bugs lie in the complex and tedious handling of all possible cases of
conflicting data updates. We argue that the CRDT design should be formally
specified and model-checked to uncover deep bugs. The implementation further
needs to be systematically tested. On the one hand, the testing needs to
inherit the exhaustive nature of the model checking and ensures the coverage of
testing. On the other hand, the testing is expected to find coding errors which
cannot be detected by design level verification.
  Towards the challenges above, we propose the Model Checking-driven
Explorative Testing (MET) framework. At the design level, MET uses TLA+ to
specify and model check CRDT designs. At the implementation level, MET conducts
model checking-driven explorative testing, in the sense that the test cases are
automatically generated from the model checking traces. The system execution is
controlled to proceed deterministically, following the model checking trace.
The explorative testing systematically controls and permutes all
nondeterministic message reorderings.
  We apply MET in our practical development of CRDTs. The bugs in both designs
and implementations of CRDTs are found. As for bugs which can be found by
traditional testing techniques, MET greatly reduces the cost of fixing the
bugs. Moreover, MET can find subtle deep bugs which cannot be found by existing
techniques at a reasonable cost. We further discuss how MET provides us with
sufficient confidence in the correctness of our CRDT designs and
implementations.


title:
Reliability in Time: Evaluating the Web Sources of Information on
  COVID-19 in Wikipedia across Various Language Editions from the Beginning of
  the Pandemic

abstract:
  There are over a billion websites on the Internet that can potentially serve
as sources of information on various topics. One of the most popular examples
of such an online source is Wikipedia. This public knowledge base is co-edited
by millions of users from all over the world. Information in each language
version of Wikipedia can be created and edited independently. Therefore, we can
observe certain inconsistencies in the statements and facts described therein -
depending on language and topic. In accordance with the Wikipedia content
authoring guidelines, information in Wikipedia articles should be based on
reliable, published sources. So, based on data from such a collaboratively
edited encyclopedia, we should also be able to find important sources on
specific topics. This effect can be potentially useful for people and
organizations.
  The reliability of a source in Wikipedia articles depends on the context. So
the same source (website) may have various degrees of reliability in Wikipedia
depending on topic and language version. Moreover, reliability of the same
source can change over the time. The purpose of this study is to identify
reliable sources on a specific topic - the COVID-19 pandemic. Such an analysis
was carried out on real data from Wikipedia within selected language versions
and within a selected time period.


title: Compound conditionals as random quantities and Boolean algebras

abstract:
  Conditionals play a key role in different areas of logic and probabilistic
reasoning, and they have been studied and formalized from different angles. In
this paper we focus on the de Finetti's notion of conditional as a three-valued
object, with betting-based semantics, and its related approach as random
quantity as mainly developed by two of the authors. Compound conditionals have
been studied in the literature, but not in full generality. In this paper we
provide a natural procedure to explicitly attach conditional random quantities
to arbitrary compound conditionals that also allows us to compute their
previsions. By studying the properties of these random quantities, we show
that, in fact, the set of compound conditionals can be endowed with a Boolean
algebraic structure. In doing so, we pave the way to build a bridge between the
long standing tradition of three-valued conditionals and a more recent proposal
of looking at conditionals as elements from suitable Boolean algebras.


title: Shear-induced droplet mobility within porous surfaces

abstract:
  Droplet mobility under shear flows is important in a wide range of
engineering applications, e.g., fog collection, and self-cleaning surfaces. For
structured surfaces to achieve superhydrophobicity, the removal of stains
adhered within the microscale surface features strongly determines the
functional performance and durability. In this study, we numerically
investigate the shear-induced mobility of the droplet trapped within porous
surfaces. Through simulations covering a wide range of flow conditions and
porous geometries, three droplet mobility modes are identified, i.e., the
stick-slip, crossover, and slugging modes. To quantitatively characterise the
droplet dynamics, we propose a droplet-scale capillary number that considers
the driving force and capillary resistance. By comparing against the simulation
results, the proposed dimensionless number presents a strong correlation with
the leftover volume. The dominating mechanisms revealed in this study provide a
basis for further research on enhancing surface cleaning and optimising design
of anti-fouling surfaces.


title: Network Topology Optimization via Deep Reinforcement Learning

abstract:
  Topology impacts important network performance metrics, including link
utilization, throughput and latency, and is of central importance to network
operators. However, due to the combinatorial nature of network topology, it is
extremely difficult to obtain an optimal solution, especially since topology
planning in networks also often comes with management-specific constraints. As
a result, local optimization with hand-tuned heuristic methods from human
experts are often adopted in practice. Yet, heuristic methods cannot cover the
global topology design space while taking into account constraints, and cannot
guarantee to find good solutions.
  In this paper, we propose a novel deep reinforcement learning (DRL)
algorithm, called Advantage Actor Critic-Graph Searching (A2C-GS), for network
topology optimization. A2C-GS consists of three novel components, including a
verifier to validate the correctness of a generated network topology, a graph
neural network (GNN) to efficiently approximate topology rating, and a DRL
actor layer to conduct a topology search. A2C-GS can efficiently search over
large topology space and output topology with satisfying performance. We
conduct a case study based on a real network scenario, and our experimental
results demonstrate the superior performance of A2C-GS in terms of both
efficiency and performance.


title: Quantum Wasserstein isometries on the qubit state space

abstract:
  We describe Wasserstein isometries of the quantum bit state space with
respect to distinguished cost operators. We derive a Wigner-type result for the
cost operator involving all the Pauli matrices: in this case, the isometry
group consists of unitary or anti-unitary conjugations. In the Bloch sphere
model, this means that the isometry group coincides with the classical symmetry
group $\mathbf{O}(3).$ On the other hand, for the cost generated by the qubit
"clock" and "shift" operators, we discovered non-surjective and non-injective
isometries as well, beyond the regular ones. This phenomenon mirrors certain
surprising properties of the quantum Wasserstein distance.

categories: math-ph math.FA math.MG math.MP quant-ph


title:
Topological Shadowing Methods in Arnold Diffusion: Weak Torsion and
  Multiple Time Scales

abstract:
  Consider a symplectic map which possesses a normally hyperbolic invariant
manifold of any even dimension with transverse homoclinic channels. We develop
a topological shadowing argument to prove the existence of Arnold diffusion
along the invariant manifold, shadowing some iterations of the inner dynamics
carried by the invariant manifold and the outer dynamics induced by the stable
and unstable foliations. In doing so, we generalise an idea of Gidea and de la
Llave in [26], based on the method of correctly aligned windows and a so-called
transversality-torsion argument. Our proof permits that the dynamics on the
invariant manifold satisfy only a non-uniform twist condition, and, most
importantly for applications, that the splitting of separatrices be small in
certain directions and thus the associated drift in actions very slow;
diffusion occurs in the directions of the manifold having non-small splitting.
Furthermore we provide estimates for the diffusion time.


title:
Towards Automatic Parsing of Structured Visual Content through the Use
  of Synthetic Data

abstract:
  Structured Visual Content (SVC) such as graphs, flow charts, or the like are
used by authors to illustrate various concepts. While such depictions allow the
average reader to better understand the contents, images containing SVCs are
typically not machine-readable. This, in turn, not only hinders automated
knowledge aggregation, but also the perception of displayed in-formation for
visually impaired people. In this work, we propose a synthetic dataset,
containing SVCs in the form of images as well as ground truths. We show the
usage of this dataset by an application that automatically extracts a graph
representation from an SVC image. This is done by training a model via common
supervised learning methods. As there currently exist no large-scale public
datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC)
dataset comprising 12,000 images with respective bounding box annotations and
detailed graph representations. Our dataset enables the development of strong
models for the interpretation of SVCs while skipping the time-consuming dense
data annotation. We evaluate our model on both synthetic and manually annotated
data and show the transferability of synthetic to real via various metrics,
given the presented application. Here, we evaluate that this proof of concept
is possible to some extend and lay down a solid baseline for this task. We
discuss the limitations of our approach for further improvements. Our utilized
metrics can be used as a tool for future comparisons in this domain. To enable
further research on this task, the dataset is publicly available at
https://bit.ly/3jN1pJJ


title: Improved Sublinear-Time Edit Distance for Preprocessed Strings

abstract:
  We study the problem of approximating the edit distance of two strings in
sublinear time, in a setting where one or both string(s) are preprocessed, as
initiated by Goldenberg, Rubinstein, Saha (STOC '20). Specifically, in the $(k,
K)$-gap edit distance problem, the goal is to distinguish whether the edit
distance of two strings is at most $k$ or at least $K$. We obtain the following
results:
  * After preprocessing one string in time $n^{1+o(1)}$, we can solve $(k, k
\cdot n^{o(1)})$-gap edit distance in time $(n/k + k) \cdot n^{o(1)}$.
  * After preprocessing both strings separately in time $n^{1+o(1)}$, we can
solve $(k, k \cdot n^{o(1)})$-gap edit distance in time $k \cdot n^{o(1)}$.
  Both results improve upon some previously best known result, with respect to
either the gap or the query time or the preprocessing time.
  Our algorithms build on the framework by Andoni, Krauthgamer and Onak (FOCS
'10) and the recent sublinear-time algorithm by Bringmann, Cassis, Fischer and
Nakos (STOC '22). We replace many complicated parts in their algorithm by
faster and simpler solutions which exploit the preprocessing.


title: FRANCIS: Fast Reaction Algorithms for Network Coordination In Switches

abstract:
  Distributed protocols are widely used to support network functions such as
clock synchronization and multicast. As the network gets larger and faster, it
is increasingly challenging for these protocols to react quickly to network
events. The theory community has made significant progress in developing
distributed message passing algorithms with improved convergence times. With
the emerging programmability at switches, it now becomes feasible to adopt and
adapt these theoretical advances for networking functions. In this paper, we
propose FRANCIS, a new framework for running message passing algorithms on
programmable switches to enable fast reactions to network events in large
networks. We introduce an execution engine with computing and communication
primitives for supporting message passing algorithms in P4 switches. We
exemplify the framework's usefulness by improving the resiliency and reaction
times of clock synchronization and source-routed multicast. In particular, our
approach allows lower clock drift than Sundial and PTP, quickly recovers from
multiple failures, and reduces the time uncertainty bound by up to 5x. Compared
with state-of-the-art multicast solutions, our approach uses packet headers up
to 33\% smaller and has an order of magnitude faster reaction time.


title: A study of natural frequencies in a dynamic corona-disk system

abstract:
  Black-hole X-ray Binaries (BHXRBs) in the hard and hard-intermediate spectral
(and temporal) states exhibit in their power spectra characteristic
frequencies, which are called type-C Quasi Periodic Oscillations (QPOs).
Various models have been proposed that can explain them with various degrees of
success, but a definitive answer is still missing. The interacting hot
Comptonizing corona with the cold accretion disk, which are central in
understanding BHXRBs, are essentially a dynamical system. Our aim is to
investigate if the radiative coupling between the two components can produce
QPOs. We write and solve the time-dependent equations that describe energy
conservation in the system corona - accretion disk. We examine both constant
and variable mass accretion rates. By necessity, in this first investigation,
we use a simple model, which however contains all the essential ingredients.
For constant mass accretion rate and certain justifiable conditions, the
dynamic corona - disk system exhibits oscillations, which die out after a few
cycles. The characteristic frequencies of these oscillations are similar to the
ones observed in the power spectra of BHXRBs. For most parameters, the natural
frequencies persist even in the case of variable accretion rates. We argue that
type-C QPOs in BHXRBs could, in principle, arise from the interaction of the
hot Comptonizing corona with the much colder accretion disk. If this picture is
correct, it has immediate implications for other systems containing the above
constituents, like Active Galactic Nuclei.


title:
Runaway electron velocity-space observation regions of bremsstrahlung
  hard X-ray spectroscopy

abstract:
  The reconstruction of the distribution function of runaway electrons (RE) in
magnetically confined fusion plasmas gives insights on the runaway electron
beam dynamics during plasma disruptions. In view of enabling a two-dimensional,
energy-pitch reconstruction of the RE velocity space, in this work we present a
calculation of the weight functions for the bremsstrahlung emission by the REs.
The weight functions allow bridging the bremsstrahlung spectrum with the RE
velocity space, as they tell the region of the velocity space that contributes
to a particular spectral measurement. The results are applied to investigate
the RE velocity-space sensitivity of the hard X-ray diagnostic installed at the
Joint European Torus.


title:
Learning Anisotropic Interaction Rules from Individual Trajectories in a
  Heterogeneous Cellular Population

abstract:
  Interacting particle system (IPS) models have proven to be highly successful
for describing the spatial movement of organisms. However, it has proven
challenging to infer the interaction rules directly from data. In the field of
equation discovery, the Weak form Sparse Identification of Nonlinear Dynamics
(WSINDy) methodology has been shown to be very computationally efficient for
identifying the governing equations of complex systems, even in the presence of
substantial noise. Motivated by the success of IPS models to describe the
spatial movement of organisms, we develop WSINDy for second order IPSs to model
the movement of communities of cells. Specifically, our approach learns the
directional interaction rules that govern the dynamics of a heterogeneous
population of migrating cells. Rather than aggregating cellular trajectory data
into a single best-fit model, we learn the models for each individual cell.
These models can then be efficiently classified according to the active classes
of interactions present in the model. From these classifications, aggregated
models are constructed hierarchically to simultaneously identify different
species of cells present in the population and determine best-fit models for
each species. We demonstrate the efficiency and proficiency of the method on
several test scenarios, motivated by common cell migration experiments.


title:
A Framework for Constructing Machine Learning Models with Feature Set
  Optimisation for Evapotranspiration Partitioning

abstract:
  A deeper understanding of the drivers of evapotranspiration and the modelling
of its constituent parts (evaporation and transpiration) could be of
significant importance to the monitoring and management of water resources
globally over the coming decades. In this work, we developed a framework to
identify the best performing machine learning algorithm from a candidate set,
select optimal predictive features as well as ranking features in terms of
their importance to predictive accuracy. Our experiments used 3 separate
feature sets across 4 wetland sites as input into 8 candidate machine learning
algorithms, providing 96 sets of experimental configurations. Given this high
number of parameters, our results show strong evidence that there is no
singularly optimal machine learning algorithm or feature set across all of the
wetland sites studied despite their similarities. A key finding discovered when
examining feature importance is that methane flux, a feature whose relationship
with evapotranspiration is not generally examined, may contribute to further
biophysical process understanding.


title: Quartic Kerr cavity combs: Bright and dark solitons

abstract:
  We theoretically investigate the dynamics, bifurcation structure and
stability of localized states in Kerr cavities driven at the pure fourth-order
dispersion point. Both the normal and anomalous group velocity dispersion
regimes are analysed, highlighting the main differences with the standard
second order dispersion case. In the anomalous regime, single and multi-peak
localised states exist and are stable over a much wider region of the parameter
space. In the normal dispersion regime, stable narrow bright solitons exist.
Some of our findings can be understood by a new scenario reported here for the
spatial eigenvalues, which imposes oscillatory tails to all localised states.
To be published in Optics Letters.


title:
Splitting of topological charge pumping in an interacting two-component
  fermionic Rice-Mele Hubbard model

abstract:
  A Thouless pump transports an integer amount of charge when pumping
adiabatically around a singularity. We study the splitting of such a critical
point into two separate critical points by adding a Hubbard interaction.
Furthermore, we consider extensions to a spinful Rice-Mele model, namely a
staggered magnetic field or an Ising-type spin coupling, further reducing the
spin symmetry. The resulting models additionally allow for the transport of a
single charge in a two-component system of spinful fermions, whereas in the
absence of interactions, zero or two charges are pumped. In the SU(2)-symmetric
case, the ionic Hubbard model is visited once along pump cycles that enclose a
single singularity. Adding a staggered magnetic field additionally transports
an integer amount of spin while the Ising term realizes a pure charge pump. We
employ real-time simulations in finite and infinite systems to calculate the
adiabatic charge and spin transport, complemented by the analysis of gaps and
the many-body polarization to confirm the adiabatic nature of the pump. The
resulting charge pumps are expected to be measurable in finite-pumping speed
experiments in ultra-cold atomic gases, for which the SU(2) invariant version
is the most promising path. We discuss the implications of our results for a
related quantum-gas experiment by Walter et al. [arXiv:2204.06561].


title:
Efficient solution of robust optimal control problems using local
  reduction

abstract:
  Robust optimal control enables ensuring correct operation of a system subject
to uncertainty. Existing methods for nonlinear robust control often use
scenario-based approaches to formulate the control problem as nonlinear
optimisation problems. Solving the resulting optimisation problems is a
challenge due to the size of the problem. Mitigating the size of the problem by
reducing the number of scenarios requires knowledge about how the uncertainty
affects the system. This paper draws from local reduction methods used in
semi-infinite optimisation to propose a new method to solve robust optimal
control problems without a priori knowledge of the system or the uncertainty.
By iteratively adding interim worst-case scenarios to the problem, we ensure
robustness as well as provide control over the total number of scenarios. The
method is validated in two case studies: temperature control in a residential
building and flow control in a centrifugal compressor. In the building case
study, with 590 uncertain parameters, the usual approach based on boundary
values for each parameter would give $2^{590}$ scenarios. Our method only
needed to include 68 scenarios in order to provide constraint satisfaction when
tested for random realisations of uncertainties. In the compressor case study,
the number of scenarios was reduced from 512 to nine, facilitating the
numerical solution of the nonlinear optimisation problem while satisfying
strict safety constraints.


title: Training Language Models with Natural Language Feedback

abstract:
  Pretrained language models often do not perform tasks in ways that are in
line with our preferences, e.g., generating offensive text or factually
incorrect summaries. Recent work approaches the above issue by learning from a
simple form of human evaluation: comparisons between pairs of model-generated
task outputs. Comparison feedback conveys limited information about human
preferences per human evaluation. Here, we propose to learn from natural
language feedback, which conveys more information per human evaluation. We
learn from language feedback on model outputs using a three-step learning
algorithm. First, we condition the language model on the initial output and
feedback to generate many refinements. Second, we choose the refinement with
the highest similarity to the feedback. Third, we finetune a language model to
maximize the likelihood of the chosen refinement given the input. In synthetic
experiments, we first evaluate whether language models accurately incorporate
feedback to produce refinements, finding that only large language models (175B
parameters) do so. Using only 100 samples of human-written feedback, our
learning algorithm finetunes a GPT-3 model to roughly human-level
summarization.


title: Quantum Dense Coding Network using Multimode Squeezed States of Light

abstract:
  We present a framework of a multimode dense coding network with multiple
senders and a single receiver using continuous variable systems. The protocol
is scalable to arbitrary numbers of modes with the encoding being displacements
while the decoding involves homodyne measurements of the modes after they are
combined in a pairwise manner by a sequence of beam splitters, thereby
exhibiting its potentiality to implement in laboratories with currently
available resources. We compute the closed form expression of the dense coding
capacity for the cases of two and three senders that involve sharing of three-
and four-mode states respectively. The dense coding capacity is calculated with
the constraint of fixed average energy transmission when the modes of the
sender are transferred to the receiver after the encoding operation. In both
the cases, we demonstrate the quantum advantage of the protocol using
paradigmatic classes of three- and four-mode states. The quantum advantage
increases with the increase in the amount of energy that is allowed to be
transmitted from the senders to the receiver.


title: Forward-fitting STIX visibilities

abstract:
  Aima. To determine to what extent the problem of forward fitting visibilities
measured by the Spectrometer/Telescope Imaging X-rays (STIX) on-board Solar
Orbiter is more challenging with respect to the same problem in the case of
previous hard X-ray solar imaging missions; to identify an effective
optimization scheme for parametric imaging for STIX. Methods. This paper
introduces a Particle Swarm Optimization (PSO) algorithm for forward fitting
STIX visibilities and compares its effectiveness with respect to the standard
simplex-based optimization algorithm used so far for the analysis of
visibilities measured by the Reuven Ramaty High Energy Solar Spectroscopic
Imager (RHESSI). This comparison is made by considering experimental
visibilities measured by both RHESSI and STIX, and synthetic visibilities
generated by accounting for the STIX signal formation model. Results. We found
out that the parametric imaging approach based on PSO is as reliable as the one
based on the simplex method in the case of RHESSI visibilities. However, PSO is
significantly more robust when applied to STIX simulated and experimental
visibilities. Conclusions. Standard deterministic optimization is not effective
enough for forward-fitting the few visibilities sampled by STIX in the angular
frequency plane. Therefore a more sofisticated optimization scheme must be
introduced for parametric imaging in the case of the Solar Orbiter X-ray
telescope. The forward-fitting routine based on PSO we introduced in this paper
proved to be significantly robust and reliable, and could be considered as an
effective candidate tool for parametric imaging in the STIX context.


title: On the shape of the first fractional eigenfunction

abstract:
  We show that the first eigenfunction of the fractional Laplacian
${(-\Delta)}^s$, $s\in(1/2,1)$, is superharmonic in the unitary ball up to
dimension $11$. To this aim, we also rely on a computer-assisted step to
estimate a rather complicated constant depending on the dimension and the power
$s$.


title:
Relations of Wiener index and (revised) Szeged index on cacti, and the
  revised Szeged index as vertex sum

abstract:
  We show that on cactus graphs the Szeged index is bounded above by twice the
Wiener index. For the revised Szeged index the situation is reversed if the
graph class is further restricted. Namely, if all blocks of a cactus graph are
cycles, then its revised Szeged index is bounded below by twice its Wiener
index. Additionally, we show that these bounds are sharp and examine the cases
of equality. Along the way, we provide a formulation of the revised Szeged
index as a sum over vertices, which proves very helpful, and may be interesting
in other contexts.


title:
Ill-posedness for the two component Degasperis-Procesi equation in
  critical Besov space

abstract:
  In this paper, we study the Cauchy problem for the two component
Degasperis-Procesi equation in critical Besov space $B^1_{\infty,1}(\mathbb
R)$. By presenting a new construction of initial data, we proved the norm
inflation of the corresponding solutions in $B^1_{\infty,1}(\mathbb R)$ and
hence ill-posedness. This is quite different from the local well-posedness
result for the Degasperis-Procesi equation in critical Besov space
$B^1_{\infty,1}(\mathbb R)$ due to the coupled structure of density function.


title:
Performance Analysis of Crosslink Radiometric Measurement based
  Autonomous Orbit Determination for Cislunar Small Satellite Formations

abstract:
  Recent advances in space technology provide an opportunity for small
satellites to be launched in cislunar space. However, tracking these small
satellites still depends on ground-based operations. Autonomous navigation
could be a possible solution considering challenges presented by costly ground
operations and limited onboard power available for small satellites. There have
been various studies on autonomous navigation methods for cislunar missions.
One of them, LiAISON, provides an autonomous orbit determination solution
solely using inter-satellite measurements. This study aims at providing a
detailed performance analysis of crosslink radiometric measurements based on
autonomous orbit determination for cislunar small satellite formations
considering the effects of measurement type, measurement accuracy, bias,
formation geometry, and network topology. This study shows that range
observations provide better state estimation accuracy than range-rate
observations for the autonomous navigation system in cislunar space.
Line-of-sight angle measurements derived from radiometric measurements do not
improve the overall system performance. In addition, less precise crosslink
measurement methods could be an option for formations in the high observable
orbital configurations. It was found that measurement biases and measurements
with high intervals reduce the overall system performance. In case there are
more than two spacecraft in the formation, the navigation system in the mesh
topology provided better overall state estimation than the centralized
topology.


title: Guarded Kleene Algebra with Tests: Automata Learning

abstract:
  Guarded Kleene Algebra with Tests (GKAT) is the fragment of Kleene Algebra
with Tests (KAT) that arises by replacing the union and iteration operations of
KAT with predicate-guarded variants. GKAT is more efficiently decidable than
KAT and expressive enough to model simple imperative programs, making it
attractive for applications to e.g. network verification. In this paper, we
further explore GKAT's automata theory, and present GL*, an algorithm for
learning the GKAT automaton representation of a black-box, by observing its
behaviour. A complexity analysis shows that it is more efficient to learn a
representation of a GKAT program with GL* than with Angluin's existing L*
algorithm. We implement GL* and L* in OCaml and compare their performances on
example programs.


title:
Outage Performance of Uplink Rate Splitting Multiple Access with
  Randomly Deployed Users

abstract:
  Rate splitting multiple access (RSMA) is a promising solution to improve
spectral efficiency and provide better fairness for the upcoming
sixth-generation (6G) networks. In this paper, the outage performance of uplink
RSMA transmission with randomly deployed users is investigated, taking both
user scheduling schemes and power allocation strategies into consideration.
Specifically, the greedy user scheduling (GUS) and cumulative distribution
function (CDF) based user scheduling (CUS) schemes are considered, which could
maximize the rate performance and guarantee access fairness, respectively.
Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and
propose a new rate-fairness oriented power allocation (FPA) strategy to enhance
the scheduled users rate fairness. By employing order statistics and stochastic
geometry, an analytical expression of the outage probability for each
scheduling scheme combining power allocation is derived to characterize the
performance. To get more insights, the achieved diversity order of each scheme
is also derived. Theoretical results demonstrate that both GUS and CUS schemes
applying CPA or FPA strategy can achieve full diversity orders, and the
application of CPA strategy in RSMA can effectively eliminate the secondary
user's diversity order constraint from the primary user. Simulation results
corroborate the accuracy of the analytical expressions, and show that the
proposed FPA strategy can achieve excellent rate fairness performance in high
signal-to-noise ratio region.


title: Autonomous Crosslink Radionavigation for a Lunar CubeSat Mission

abstract:
  This study presents an autonomous orbit determination system based on
crosslink radiometric measurements applied to a future lunar CubeSat mission to
clearly highlight its advantages with respect to existing ground-based
navigation strategies. This work is based on the Linked Autonomous
Interplanetary Satellite Orbit Navigation (LiAISON) method which provides an
autonomous navigation solution solely using satellite-to-satellite
measurements, such as range and/or range-rate, to estimate absolute spacecraft
states when at least one of the involved spacecraft has an orbit with a unique
size, shape, and orientation. The lunar vicinity is a perfect candidate for
this type of application due to the asymmetrical gravity field: the selected
lunar mission, an Earth-Moon L2 (EML2) Halo orbiter, has an inter-satellite
link between a lunar elliptical frozen orbiter. Simulation results show that,
even in case of high-measurement errors (in the order of 100 m, 1 sigma, the
navigation filter estimates the true states of spacecraft at EML2 with an error
in the order of 500 m for position, and 2 mm/s for velocity, respectively and
the elliptical lunar frozen orbiter states can be estimated in the order of 100
m for position and 1 cm/s for velocity, respectively. This study shows that
range-only measurements provide better state estimation than range-rate-only
measurements for this specific situation. Different bias handling strategies
are also investigated. It has been found that even a less accurate ranging
method, such as data-aided ranging, provides a sufficient orbit determination
solution. This would simplify the communication system design for the selected
CubeSat mission. The most observable states are found to be position states of
the lunar orbiter via the observability analysis. In addition, the best
tracking windows are also investigated for the selected mission scenario.


title:
Redefining Populations of Inference for Generalizations from Small
  Studies

abstract:
  With the growth in experimental studies in education, policymakers and
practitioners are interested in understanding not only what works, but for whom
an intervention works. This interest in the generalizability of a study's
findings has benefited from advances in statistical methods that aim to improve
generalizations, particularly when the original study sample is not randomly
selected. A challenge, however, is that generalizations are frequently based on
small study samples. Limited data affects both the precision and bias of
treatment impact estimates, calling into question the validity of
generalizations. This study explores the extent to which redefining the
inference population is a useful tool to improve generalizations from small
studies. We discuss two main frameworks for redefining populations and apply
the methods to an empirical example based on a completed cluster randomized
trial in education. We discuss the implications of various methods to redefine
the population and conclude with guidance and some recommendations for
practitioners interested in using redefinition.


title:
Time- and angle-resolved photoelectron spectroscopy of strong-field
  light-dressed solids: prevalence of the adiabatic band picture

abstract:
  In recent years, strong-field physics in condensed-matter was pioneered as a
novel approach for controlling material properties through laser-dressing, as
well as for ultrafast spectroscopy via nonlinear light-matter interactions
(e.g. harmonic generation). A potential controversy arising from these
advancements is that it is sometimes vague which band-picture should be used to
interpret strong-field experiments: the field-free bands, the adiabatic
(instantaneous) field-dressed bands, Floquet bands, or some other intermediate
picture. We here try to resolve this issue by performing 'theoretical
experiments' of time- and angle-resolved photoelectron spectroscopy (Tr-ARPES)
for a strong-field laser-pumped solid, which should give access to the actual
observable bands of the irradiated material. To our surprise, we find that the
adiabatic band-picture survives quite well, up to high field intensities
(~10^12 W/cm^2), and in a wide frequency range (driving wavelengths of 4000 to
800nm, with Keldysh parameters ranging up to ~7). We conclude that to first
order, the adiabatic instantaneous bands should be the standard blueprint for
interpreting ultrafast electron dynamics in solids when the field is highly
off-resonant with characteristic energy scales of the material. We then discuss
weaker effects of modifications of the bands beyond this picture that are
non-adiabatic, showing that by using bi-chromatic fields the deviations from
the standard picture can be probed with enhanced sensitivity. Our work outlines
a clear band picture for the physics of strong-field interactions in solids,
which should be useful for designing and analyzing strong-field experimental
observables and also to formulate simpler semi-empirical models.


title:
Optimal regularity for degenerate Kolmogorov equations with rough
  coefficients

abstract:
  We consider a class of degenerate equations satisfying a parabolic
H\"ormander condition, with coefficients that are measurable in time and
H\"older continuous in the space variables. By utilizing a generalized notion
of strong solution, we establish the existence of a fundamental solution and
its optimal H\"older regularity, as well as Gaussian estimates. These results
are key to study the backward Kolmogorov equations associated to a class of
Langevin-type diffusions.


title: Symbolic analysis meets federated learning to enhance malware identifier

abstract:
  Over past years, the manually methods to create detection rules were no
longer practical in the anti-malware product since the number of malware
threats has been growing. Thus, the turn to the machine learning approaches is
a promising way to make the malware recognition more efficient. The traditional
centralized machine learning requires a large amount of data to train a model
with excellent performance. To boost the malware detection, the training data
might be on various kind of data sources such as data on host, network and
cloud-based anti-malware components, or even, data from different enterprises.
To avoid the expenses of data collection as well as the leakage of private
data, we present a federated learning system to identify malwares through the
behavioural graphs, i.e., system call dependency graphs. It is based on a deep
learning model including a graph autoencoder and a multi-classifier module.
This model is trained by a secure learning protocol among clients to preserve
the private data against the inference attacks. Using the model to identify
malwares, we achieve the accuracy of 85\% for the homogeneous graph data and
93\% for the inhomogeneous graph data.


title:
Efficient, High-purity, Robust Sound Frequency Conversion with a Linear
  Metasurface

abstract:
  The intrinsic limitation of the material nonlinearity inevitably results in
the poor mode purity, conversion efficiency and real-time reconfigurability of
the generated harmonic waves, both in optics and acoustics. Rotational Doppler
effect provides us an intuitive paradigm to shifting the frequency in a linear
system, which needs to be facilitated by a spiraling phase change upon the wave
propagation. Here we numerically and experimentally present a rotating linear
vortex metasurface and achieve close-to-unity mode purity (above 95%) and
conversion efficiency (above 65%) in audible sound frequency as low as 3000 Hz.
The topological charge of the transmitted sound is almost immune from the
rotational speed and transmissivity, demonstrating the mechanical robustness
and stability in adjusting the high-performance frequency conversion in situ.
These features enable us to cascade multiple vortex metasurfaces to further
enlarge and diversify the extent of sound frequency conversion, which are
experimentally verified. Our strategy takes a step further towards the
freewheeling sound manipulation at acoustic frequency domain, and may have
far-researching impacts in various acoustic communications, signal processing,
and contactless detection.


title: First cryogenic tests on BINGO innovations

abstract:
  Neutrinoless double-beta decay ($0\nu2\beta$) is a hypothetical rare nuclear
transition. Its observation would provide an important insight about the nature
of neutrinos (Dirac or Majorana particle) demonstrating that the lepton number
is not conserved. BINGO (Bi-Isotope $0\nu2\beta$ Next Generation Observatory)
aims to set the technological grounds for future bolometric $0\nu2\beta$
experiments. It is based on a dual heat-light readout, i.e. a main
scintillating absorber embedding the double-beta decay isotope accompanied by a
cryogenic light detector. BINGO will study two of the most promising isotopes:
$^{100}$Mo embedded in Li$_2$MoO$_4$ (LMO) crystals and $^{130}$Te embedded in
TeO$_2$. BINGO technology will reduce dramatically the background in the region
of interest, thus boosting the discovery sensitivity of $0\nu2\beta$. The
proposed solutions will have a high impact on next-generation bolometric
tonne-scale experiments, like CUPID. In this contribution, we present the
results obtained during the first tests performed in the framework of BINGO
R&D.


title:
Crossing of phantom divide line in model \\of interacting Tsallis
  holographic dark energy

abstract:
  We consider a Tsallis holographic dark energy model with interaction between
dark energy and matter. The density of dark energy is taken as $\rho_d \sim
3C^2/L^{4-2\gamma}$, where $C$, $\gamma$ are constants. The event horizon is
chosen as the characteristic scale $L$. The cosmological dynamics of the
universe are analyzed, with special attention paid to the possibility of
crossing the phantom line $w_{eff}=-1$. It is shown that for certain values of
parameters this may occur not only once, but also twice.


title:
Planetesimals Around Stars with TESS (PAST): II. An M Dwarf "Dipper"
  Star with a Long-Lived Disk in the TESS Continuous Viewing Zone

abstract:
  Studies of T Tauri disks inform planet formation theory; observations of
variability due to occultation by circumstellar dust are a useful probe of
unresolved, planet-forming inner disks, especially around faint M dwarf stars.
We report observations of 2M0632, an M dwarf member of the Carina young moving
group that was observed by TESS over two one-year intervals. The combined light
curve contains >300 dimming events, each lasting a few hours, and as deep as
40% (0.55 magnitudes). These stochastic events are correlated with a distinct,
stable 1.86-day periodic signal that could be stellar rotation. Concurrent
ground-based, multi-band photometry show reddening consistent with ISM-like
dust. The star's excess emission in the infrared and emission lines in optical
and infrared spectra, reveal a T Tauri-like accretion disk around the star. We
confirm membership of 2M0632 in the Carina group by a Bayesian analysis of its
Galactic space motion and position. We combine stellar evolution models with
Gaia photometry and constraints on Teff, luminosity, and the absence of
detectable lithium in the photosphere to constrain the age of the group and
2M0632 to 40-60 Myr, consistent with earlier estimates. 2M0632 joins a handful
of long-lived disks which challenge the canon that disk lifetimes are <10 Myr.
All known examples surround M dwarfs, suggesting that lower X-ray/UV
irradiation and slower photoevaporation by these stars can dramatically affect
disk evolution. The multi-planet systems spawned by long-lived disks probably
experienced significant orbital damping and migration into close-in, resonant
orbits, and perhaps represented by the TRAPPIST-1 system.


title:
Physical properties of MnSi at extreme doping with Co: Quantum
  criticality

abstract:
  The samples of (Mn$_{1-x}$Co$_x$)Si with $x=0.15$ and $x=0.17$ were grown and
their physical properties: magnetization and magnetic susceptibility,
resistivity and heat capacity were studied. The data analysis included also the
previous results at $x=0.057, 0.063, 0.09$. The indicated doping MnSi with Co
completely destroys the helical phase transition whereas basically saves the
helical fluctuation area normally situated slightly above the phase transition
temperature. This area spreading from $\sim$5 to 0 K is not changed much with
doping and forms some sort of helical fluctuation cloud revealing the quantum
critical properties: $C_p/T\rightarrow\infty$ at $T\rightarrow0$.


title: Distributed Inference for Spatial Extremes Modeling in High Dimensions

abstract:
  Extreme environmental events frequently exhibit spatial and temporal
dependence. These data are often modeled using max stable processes (MSPs).
MSPs are computationally prohibitive to fit for as few as a dozen observations,
with supposed computationally-efficient approaches like the composite
likelihood remaining computationally burdensome with a few hundred
observations. In this paper, we propose a spatial partitioning approach based
on local modeling of subsets of the spatial domain that delivers
computationally and statistically efficient inference. Marginal and dependence
parameters of the MSP are estimated locally on subsets of observations using
censored pairwise composite likelihood, and combined using a modified
generalized method of moments procedure. The proposed distributed approach is
extended to estimate spatially varying coefficient models to deliver
computationally efficient modeling of spatial variation in marginal parameters.
We demonstrate consistency and asymptotic normality of estimators, and show
empirically that our approach leads to a surprising reduction in bias of
parameter estimates over a full data approach. We illustrate the flexibility
and practicability of our approach through simulations and the analysis of
streamflow data from the U.S. Geological Survey.


title: OPERA:Operation-Pivoted Discrete Reasoning over Text

abstract:
  Machine reading comprehension (MRC) that requires discrete reasoning
involving symbolic operations, e.g., addition, sorting, and counting, is a
challenging task. According to this nature, semantic parsing-based methods
predict interpretable but complex logical forms. However, logical form
generation is nontrivial and even a little perturbation in a logical form will
lead to wrong answers. To alleviate this issue, multi-predictor -based methods
are proposed to directly predict different types of answers and achieve
improvements. However, they ignore the utilization of symbolic operations and
encounter a lack of reasoning ability and interpretability. To inherit the
advantages of these two types of methods, we propose OPERA, an
operation-pivoted discrete reasoning framework, where lightweight symbolic
operations (compared with logical forms) as neural modules are utilized to
facilitate the reasoning ability and interpretability. Specifically, operations
are first selected and then softly executed to simulate the answer reasoning
procedure. Extensive experiments on both DROP and RACENum datasets show the
reasoning ability of OPERA. Moreover, further analysis verifies its
interpretability.


title:
Modeling Electrokinetic Flows with the Discrete Ion Stochastic Continuum
  Overdamped Solvent Algorithm

abstract:
  In this article we develop an algorithm for the efficient simulation of
electrolytes in the presence of physical boundaries. In previous work the
Discrete Ion Stochastic Continuum Overdamped Solvent (DISCOS) algorithm was
derived for triply periodic domains, and was validated through ion-ion pair
correlation functions and Debye-H{\"u}ckel-Onsager theory for conductivity,
including the Wien effect for strong electric fields. In extending this
approach to include an accurate treatment of physical boundaries we must
address several important issues. First, the modifications to the spreading and
interpolation operators necessary to incorporate interactions of the ions with
the boundary are described. Next we discuss the modifications to the
electrostatic solver to handle the influence of charges near either a fixed
potential or dielectric boundary. An additional short-ranged potential is also
introduced to represent interaction of the ions with a solid wall. Finally, the
dry diffusion term is modified to account for the reduced mobility of ions near
a boundary, which introduces an additional stochastic drift correction. Several
validation tests are presented confirming the correct equilibrium distribution
of ions in a channel. Additionally, the methodology is demonstrated using
electro-osmosis and induced charge electro-osmosis, with comparison made to
theory and other numerical methods. Notably, the DISCOS approach achieves
greater accuracy than a continuum electrostatic simulation method. We also
examine the effect of under-resolving hydrodynamic effects using a `dry
diffusion' approach, and find that considerable computational speedup can be
achieved with a negligible impact on accuracy.


title:
DePa: Simple, Provably Efficient, and Practical Order Maintenance for
  Task Parallelism

abstract:
  A number of problems in parallel computing require reasoning about the
dependency structure in parallel programs. For example, dynamic race detection
relies on efficient "on-the-fly" determination of dependencies between
sequential and parallel tasks (e.g. to quickly determine whether or not two
memory accesses occur in parallel). Several solutions to this "parallel order
maintenance" problem has been proposed, but they all have several drawbacks,
including lack of provable bounds, high asymptotic or practical overheads, and
poor support for parallel execution.
  In this paper, we present a solution to the parallel order maintenance
problem that is provably efficient, fully parallel, and practical. Our
algorithm -- called DePa -- represents a computation as a graph and encodes
vertices in the graph with two components: a dag-depth and a fork-path. In this
encoding, each query requires $O(f/\omega)$ work, where $f$ is the minimum
dynamic nesting depth of the two vertices compared, and $\omega$ is the
word-size. In the common case (where $f$ is small, e.g., less than 100), each
query requires only a single memory lookup and a small constant number of
bitwise instructions. Furthermore, graph maintenance at forks and joins
requires only constant work, resulting in no asymptotic impact on overall work
and span. DePa is therefore work-efficient and fully parallel.


title: Fast data-driven model reduction for nonlinear dynamical systems

abstract:
  We present a fast method for nonlinear data-driven model reduction of
dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs).
We use observed data to locate a low-dimensional, attracting slow SSM and
compute a maximally sparse approximation to the reduced dynamics on it. The
recently released SSMLearn algorithm uses implicit optimization to fit a
spectral submanifold to data and reduce the dynamics to the normal form. Here,
we present two simplified algorithms, which reformulate manifold fitting and
normal form computation as explicit problems under certain assumptions. We show
on both numerical and experimental datasets that these algorithms yield
accurate and sparse rigorous models for essentially nonlinear (or
non-linearizable) dynamics. The new algorithms are significantly simplified and
provide a speedup of several orders of magnitude.


title: Tractable Uncertainty for Structure Learning

abstract:
  Bayesian structure learning allows one to capture uncertainty over the causal
directed acyclic graph (DAG) responsible for generating given data. In this
work, we present Tractable Uncertainty for STructure learning (TRUST), a
framework for approximate posterior inference that relies on probabilistic
circuits as the representation of our posterior belief. In contrast to
sample-based posterior approximations, our representation can capture a much
richer space of DAGs, while being able to tractably answer a range of useful
inference queries. We empirically show how probabilistic circuits can be used
as an augmented representation for structure learning methods, leading to
improvement in both the quality of inferred structures and posterior
uncertainty. Experimental results also demonstrate the improved
representational capacity of TRUST, outperforming competing methods on
conditional query answering.


title: Quantum Universally Composable Oblivious Linear Evaluation

abstract:
  Oblivious linear evaluation is a generalization of oblivious transfer,
whereby two distrustful parties obliviously compute a linear function, $f (x) =
ax + b$, i.e., each one provides their inputs that remain unknown to the other,
in order to compute the output $f (x)$ that becomes known to only one of them.
From both a structural and a security point-of-view, oblivious linear
evaluation is fundamental for arithmetic-based secure multi-party computation
protocols. In the classical case it is known that oblivious linear evaluation
can be generated based on oblivious transfer, and quantum counterparts of these
protocols can, in principle, be constructed as straightforward extensions based
on quantum oblivious transfer. Here, we present the first, to the best of our
knowledge, quantum protocol for oblivious linear evaluation that, furthermore,
does not rely on quantum oblivious transfer. We start by presenting a
semi-honest protocol and then we extend it to the malicious setting employing a
commit-and-open strategy. Our protocol uses high-dimensional quantum states to
obliviously compute the linear function, $f (x)$, on Galois Fields of prime
dimension or prime-power dimension. These constructions utilize the existence
of a complete set of mutually unbiased bases in prime-power dimension Hilbert
spaces and their linear behaviour upon the Heisenberg-Weyl operators. We also
generalize our protocol to achieve vector oblivious linear evaluation, where
several instances of oblivious linear evaluation are generated, thus making the
protocol more efficient. We prove the protocols to have static security in the
framework of quantum universal composability.


title: Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies

abstract:
  We study ELI queries (ELIQs) in the presence of ontologies formulated in the
description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a
frontier (set of least general generalizations) that is of polynomial size and
can be computed in polynomial time. In the dialect DL-LiteF, in contrast,
frontiers may be infinite. We identify a natural syntactic restriction that
enables the same positive results as for DL-LiteH. We use out results on
frontiers to show that ELIQs are learnable in polynomial time in the presence
of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact
learning with only membership queries.


title: Evolutionary Approach to Security Games with Signaling

abstract:
  Green Security Games have become a popular way to model scenarios involving
the protection of natural resources, such as wildlife. Sensors (e.g. drones
equipped with cameras) have also begun to play a role in these scenarios by
providing real-time information. Incorporating both human and sensor defender
resources strategically is the subject of recent work on Security Games with
Signaling (SGS). However, current methods to solve SGS do not scale well in
terms of time or memory. We therefore propose a novel approach to SGS, which,
for the first time in this domain, employs an Evolutionary Computation
paradigm: EASGS. EASGS effectively searches the huge SGS solution space via
suitable solution encoding in a chromosome and a specially-designed set of
operators. The operators include three types of mutations, each focusing on a
particular aspect of the SGS solution, optimized crossover and a local coverage
improvement scheme (a memetic aspect of EASGS). We also introduce a new set of
benchmark games, based on dense or locally-dense graphs that reflect real-world
SGS settings. In the majority of 342 test game instances, EASGS outperforms
state-of-the-art methods, including a reinforcement learning method, in terms
of time scalability, nearly constant memory utilization, and quality of the
returned defender's strategies (expected payoffs).


title: Explainable AI via Learning to Optimize

abstract:
  Indecipherable black boxes are common in machine learning (ML), but
applications increasingly require explainable artificial intelligence (XAI).
The core of XAI is to establish transparent and interpretable data-driven
algorithms. This work provides concrete tools for XAI in situations where prior
knowledge must be encoded and untrustworthy inferences flagged. We use the
"learn to optimize" (L2O) methodology wherein each inference solves a
data-driven optimization problem. Our L2O models are straightforward to
implement, directly encode prior knowledge, and yield theoretical guarantees
(e.g. satisfaction of constraints). We also propose use of interpretable
certificates to verify whether model inferences are trustworthy. Numerical
examples are provided in the applications of dictionary-based signal recovery,
CT imaging, and arbitrage trading of cryptoassets.


title: Segmentation of kidney stones in endoscopic video feeds

abstract:
  Image segmentation has been increasingly applied in medical settings as
recent developments have skyrocketed the potential applications of deep
learning. Urology, specifically, is one field of medicine that is primed for
the adoption of a real-time image segmentation system with the long-term aim of
automating endoscopic stone treatment. In this project, we explored supervised
deep learning models to annotate kidney stones in surgical endoscopic video
feeds. In this paper, we describe how we built a dataset from the raw videos
and how we developed a pipeline to automate as much of the process as possible.
For the segmentation task, we adapted and analyzed three baseline deep learning
models -- U-Net, U-Net++, and DenseNet -- to predict annotations on the frames
of the endoscopic videos with the highest accuracy above 90\%. To show clinical
potential for real-time use, we also confirmed that our best trained model can
accurately annotate new videos at 30 frames per second. Our results demonstrate
that the proposed method justifies continued development and study of image
segmentation to annotate ureteroscopic video feeds.


title:
The uncertainty principle for the short-time Fourier transform on finite
  cyclic groups: cases of equality

abstract:
  A well-known version of the uncertainty principle on the cyclic group
$\mathbb{Z}_N$ states that for any couple of functions
$f,g\in\ell^2(\mathbb{Z}_N)\setminus\{0\}$, the short-time Fourier transform
$V_g f$ has support of cardinality at least $N$. This result can be regarded as
a time-frequency version of the celebrated Donoho-Stark uncertainty principle
on $\mathbb{Z}_N$. Unlike the Donoho-Stark principle, however, a complete
identification of the extremals is still missing. In this note we provide an
answer to this problem by proving that the support of $V_g f$ has cardinality
$N$ if and only if it is a coset of a subgroup of order $N$ of
$\mathbb{Z}_N\times \mathbb{Z}_N$. Also, we completely identify the
corresponding extremal functions $f,g$. Besides translations and modulations,
the symmetries of the problem are encoded by certain metaplectic operators
associated with elements of ${\rm SL}(2,\mathbb{Z}_{N/a})$, where $a$ is a
divisor of $N$. Partial generalizations are given to finite Abelian groups.


title:
Obstructed insulators and flat bands in topological phase-change
  materials

abstract:
  Phase-change materials are ubiquitous in technology because of their ability
to transition between amorphous and crystalline phases fast and reversibly,
upon shining light or passing a current. Here we argue that to fully understand
their electronic properties it is necessary to define a novel electronic phase:
the amorphous obstructed insulator. It differs from an obstructed insulator
crystal in that it presents localized edge or surface states irrespective of
the sample termination. Consequently, we show that obstructed amorphous
insulators in three-dimensions host a surface two-dimensional flat band,
detectable using ARPES. Our work establishes basic models for materials where
topological and obstructed properties can be switched on and off externally,
including two-dimensional surface flatbands.


title:
Increasing the degree of a possible counterexample to the Jacobian
  Conjecture from 100 to 108

abstract:
  We list all the pairs $(deg(P),deg(Q))$ with $\max\{deg(P),deg(Q)\}< 125$ for
any hypothetical counterexample to the plane Jacobian Conjecture and discard
them all, except the pair $(72,108)$ (and the symmetric pair $(108,72)$), thus
we confirm the lower bound of 100 obtained by Moh and raise it up to 108.


title:
Dust masses for a large sample of core-collapse supernovae from optical
  emission line asymmetries: dust formation on 30-year timescales

abstract:
  Modelling the red-blue asymmetries seen in the broad emission lines of
core-collapse supernovae (CCSNe) is a powerful technique to quantify total dust
mass formed in the ejecta at late times ($>5$ years after outburst) when ejecta
dust temperatures become too low to be detected by mid-IR instruments.
Following our success in using the Monte Carlo radiative transfer code DAMOCLES
to measure the dust mass evolution in SN~1987A and other CCSNe, we present the
most comprehensive sample of dust mass measurements yet made with DAMOCLES, for
CCSNe aged between four and sixty years after outburst. Our sample comprises of
multi-epoch late-time optical spectra taken with the Gemini GMOS and VLT
X-Shooter spectrographs, supplemented by archival spectra. For the fourteen
CCSNe that we have modelled, we confirm a dust mass growth with time that can
be fit by a sigmoid curve which is found to saturate beyond an age of $\sim30$
years, at a mass of 0.23$^{+0.17}_{-0.12}$ M$_\odot$. An expanded sample
including dust masses found in the literature for a further eleven CCSNe and
six CCSN remnants, the dust mass at saturation is found to be
0.42$^{+0.09}_{-0.05}$~M$_\odot$. Uncertainty limits for our dust masses were
determined from a Bayesian analysis using the affine invariant Markov Chain
Monte Carlo ensemble sampler emcee with DAMOCLES. The best-fitting line profile
models for our sample all required grain radii between 0.1 and 0.5 $\mu$m. Our
results are consistent with CCSNe forming enough dust in their ejecta to
significantly contribute to the dust budget of the Universe.


title:
Industry-academia research collaboration and knowledge co-creation:
  Patterns and anti-patterns

abstract:
  Increasing the impact of software engineering research in the software
industry and the society at large has long been a concern of high priority for
the software engineering community. The problem of two cultures, research
conducted in a vacuum (disconnected from the real world), or misaligned time
horizons are just some of the many complex challenges standing in the way of
successful industry-academia collaborations. This paper reports on the
experience of research collaboration and knowledge co-creation between industry
and academia in software engineering as a way to bridge the research-practice
collaboration gap. Our experience spans 14 years of collaboration between
researchers in software engineering and the European and Norwegian software and
IT industry. Using the participant observation and interview methods we have
collected and afterwards analyzed an extensive record of qualitative data.
Drawing upon the findings made and the experience gained, we provide a set of
14 patterns and 14 anti-patterns for industry-academia collaborations, aimed to
support other researchers and practitioners in establishing and running
research collaboration projects in software engineering.


title: Solitons in overdamped Brownian dynamics

abstract:
  Solitons are commonly known as waves that propagate without dispersion. Here
we show that they can occur for driven overdamped Brownian dynamics of hard
spheres in periodic potentials at high densities. The solitons manifest
themselves as periodic sequences of different assemblies of particles moving in
the limit of zero noise, where transport of single particles is not possible.
They give rise to particle currents at even low temperature that appear in
band-like structures around certain hard-sphere diameters. At high
temperatures, the band-like structures are washed out by the noise, but the
particle transport is still dominated by the solitons. All these predicted
features should occur in a broad class of periodic systems and are amenable to
experimental tests.


title: On non-counital Frobenius algebras

abstract:
  A Frobenius algebra is a finite-dimensional algebra $A$ which comes equipped
with a coassociative, counital comultiplication map $\Delta$ that is an
$A$-bimodule map. Here, we examine comultiplication maps for generalizations of
Frobenius algebras: finite-dimensional self-injective (quasi-Frobenius)
algebras. We show that large classes of such algebras, including
finite-dimensional weak Hopf algebras, come equipped with a map $\Delta$ as
above that is not necessarily counital. We also conjecture that this
comultiplicative structure holds for self-injective algebras in general.


title: Symmetry results for multiple $t$-values

abstract:
  For a composition $I$ whose first part exceeds 1, we can define the multiple
$t$-value $t(I)$ as the sum of all the terms in the series for the multiple
zeta value $\zeta(I)$ whose denominators are odd. In this paper we show that if
$I$ is composition of $n\ge 3$, then $t(I)=(-1)^{n-1}t(\bar I)$ mod products,
where $\bar I$ is the reverse of $I$, and both sides are suitably regularized
when $I$ ends in 1. This result is not true for multiple zeta values, though
there is an argument-reversal result that does hold for them (and for multiple
$t$-values as well). We actually prove a more general version of this result,
and then use it to establish explicit formulas for several classes of multiple
$t$-values and interpolated multiple $t$-values.


title:
Modeling Ride-Sourcing Matching and Pickup Processes based on Additive
  Gaussian Process Models

abstract:
  Matching and pickup processes are core features of ride-sourcing services.
Previous studies have adopted abundant analytical models to depict the two
processes and obtain operational insights; while the goodness of fit between
models and data was dismissed. To simultaneously consider the fitness between
models and data and analytically tractable formations, we propose a data-driven
approach based on the additive Gaussian Process Model (AGPM) for ride-sourcing
market modeling. The framework is tested based on real-world data collected in
Hangzhou, China. We fit analytical models, machine learning models, and AGPMs,
in which the number of matches or pickups are used as outputs and spatial,
temporal, demand, and supply covariates are utilized as inputs. The results
demonstrate the advantages of AGPMs in recovering the two processes in terms of
estimation accuracy. Furthermore, we illustrate the modeling power of AGPM by
utilizing the trained model to design and estimate idle vehicle relocation
strategies.


title: Accreting strongly magnetised neutron stars: X-ray Pulsars

abstract:
  X-ray pulsars (XRPs) are accreting strongly magnetised neutron stars (NSs) in
binary systems with, as a rule, massive optical companions. Very reach
phenomenology and high observed flux put them into the focus of observational
and theoretical studies since the first X-ray instruments were launched into
space. The main attracting characteristic of NSs in this kind of system is the
magnetic field strength at their surface, about or even higher than
$10^{12}\,{\rm G}$, that is about six orders of magnitude stronger than what is
attainable in terrestrial laboratories. Although accreting XRPs were discovered
about 50 years ago, the details of the physical mechanisms responsible for
their properties are still under debate. Here we review recent progress in
observational and theoretical investigations of XRPs as a unique laboratory for
studies of fundamental physics (plasma physics, QED and radiative processes)
under extreme conditions of ultra-strong magnetic field, high temperature, and
enormous mass density.