title: Graphical Evidence

abstract:
  Marginal likelihood, also known as model evidence, is a fundamental quantity
in Bayesian statistics. It is used for model selection using Bayes factors or
for empirical Bayes tuning of prior hyper-parameters. Yet, the calculation of
evidence has remained a longstanding open problem in Gaussian graphical models.
Currently, the only feasible solutions that exist are for special cases such as
the Wishart or G-Wishart, in moderate dimensions. We present an application of
Chib's technique that is applicable to a very broad class of priors under mild
requirements. Specifically, the requirements are: (a) the priors on the
diagonal terms on the precision matrix can be written as gamma or scale
mixtures of gamma random variables and (b) those on the off-diagonal terms can
be represented as normal or scale mixtures of normal. This includes structured
priors such as the Wishart or G-Wishart, and more recently introduced
element-wise priors, such as the Bayesian graphical lasso and the graphical
horseshoe. Among these, the true marginal is known in an analytically closed
form for Wishart, providing a useful validation of our approach. For the
general setting of the other three, and several more priors satisfying
conditions (a) and (b) above, the calculation of evidence has remained an open
question that this article seeks to resolve.


title:
Flood Risk Mitigation and Valve Control in Stormwater Systems:
  State-Space Modeling, Control Algorithms, and Case Studies

abstract:
  The increasing access to non-expensive sensors, computing power, and more
accurate forecasting of storm events provides unique opportunities to shift
flood management practices from static approaches to an optimization-based
real-time control (RTC) of urban drainage systems. Recent studies have
addressed a plethora of strategies for flood control in stormwater reservoirs;
however, advanced control theoretic techniques are not yet fully investigated
and applied to these systems. In addition, there is an absence of a coupled
integrated control model for systems composed of watersheds, reservoirs, and
channels for flood mitigation.
  To this end, we develop a novel state-space model of hydrologic and
hydrodynamic processes in reservoirs and one-dimensional channels. The model is
tested under different types of reservoir control strategies based on real-time
measurements (reactive control), and based on predictions of the future
behavior of the system (predictive control) using rainfall forecastings. We
apply the modeling approach in a system composed by a single watershed,
reservoir, and a channel connected in series, respectively, for the San Antonio
observed rainfall data. Results indicate that for flood mitigation, the
predictive control strategy outperforms the reactive controls not only when
applied for synthetic design storm events, but also for a continuous
simulation. Moreover, the predictive control strategy requires smaller valve
operations, while still guaranteeing efficient hydrological performance. From
the results, we recommend the use of the model predictive control strategy to
control stormwater systems due to the ability to handle different objective
functions, which can be altered according to rainfall forecasting and shift the
reservoir operation from flood-based control to strategies focused on
increasing detention times, depending on the forecasting.


title: Binding of heavy fermions by a single light atom in one dimension

abstract:
  We consider the problem of $N$ identical fermions interacting via a
zero-range attractive potential with a lighter atom in one dimension. Using the
few-body approach based on the Skorniakov and Ter-Martirosian equation, we
determine the energies and the critical mass ratios for the emergence of the
tetramer, pentamer, and hexamer. For large $N$, we solve the problem
analytically by using the mean-field theory based on the Thomas-Fermi
approximation. The system becomes bound when the heavy-to-light mass ratio
exceeds a critical value which grows as $N^3$ at large $N$. We also employ a
more sophisticated Hartree-Fock approach, which turns out to be equivalent to
the Thomas-Fermi approximation for determining the energies, but provides a
better description of the microscopic structure of the clusters.


title: HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation

abstract:
  Sounds, especially music, contain various harmonic components scattered in
the frequency dimension. It is difficult for normal convolutional neural
networks to observe these overtones. This paper introduces a multiple rates
dilated causal convolution (MRDC-Conv) method to capture the harmonic structure
in logarithmic scale spectrograms efficiently. The harmonic is helpful for
pitch estimation, which is important for many sound processing applications. We
propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and
other dilated convolutions in pitch estimation. The results show that this
model outperforms the DeepF0, yields state-of-the-art performance in three
datasets, and simultaneously reduces more than 90% parameters. We also find
that it has stronger noise resistance and fewer octave errors.


title: Reply to arXiv:2203.14555

abstract:
  This comment refutes the claim made by A. Jadczyk and A.M. Schlichtinger in
arXiv:2203.14555 that the photon position operator with commuting components
proposed in 1999 does not have the properties required for a photon position
operator.


title:
Hiding neutrinoless double beta decay in split seesaw model with 2+1
  right-handed neutrinos

abstract:
  In the paper by Asaka, Ishida and Tanaka [Phys. Rev. D {\bf 103} (2021)
015014, arXiv:2012.12564], they proposed a novel possibility that the
neutrinoless double beta decay can be hidden in the minimal seesaw model with
two right-handed neutrinos which have a hierarchical mass structure. The
heavier right-handed neutrino is sufficiently heavy to decouple from the
neutrinoless double beta decay while the lighter one is lighter enough than the
typical Fermi-momentum scale of nuclei. They show that, under some specific
condition on the neutrino Yukawa couplings, the neutrinoless double beta decay
can be hidden. In this paper, we perform a further study of such an interesting
possibility in the complete seesaw model with three right-handed neutrinos. Our
framework is same as theirs except that there are two heavier right-handed
neutrinos so that the baryon-antibaryon asymmetry of the Universe can be
explained via the leptogenesis mechanism. We first give the condition on the
neutrino Yukawa couplings for hiding the $0\nu \beta\beta$ decay, discuss its
realization by employing an Abelian flavor symmetry, and study its implications
for the mixing of the lighter right-handed neutrino with three left-handed
neutrinos. We then successively study the implications for leptogenesis of the
interesting scenarios where $M^{}_{\rm D}$ is a triangular matrix or respects
the $\mu$-$\tau$ reflection symmetry.


title:
Asymptotic Normality for Plug-in Estimators of Generalized Shannon's
  Entropy

abstract:
  Shannon's entropy is one of the building blocks of information theory and an
essential aspect of Machine Learning methods (e.g., Random Forests). Yet, it is
only finitely defined for distributions with fast decaying tails on a countable
alphabet. The unboundedness of Shannon's entropy over the general class of all
distributions on an alphabet prevents its potential utility from being fully
realized. To fill the void in the foundation of information theory, Zhang
(2020) proposed generalized Shannon's entropy, which is finitely defined
everywhere. The plug-in estimator, adopted in almost all entropy-based ML
method packages, is one of the most popular approaches to estimating Shannon's
entropy. The asymptotic distribution for Shannon's entropy's plug-in estimator
was well studied in the existing literature. This paper studies the asymptotic
properties for the plug-in estimator of generalized Shannon's entropy on
countable alphabets. The developed asymptotic properties require no assumptions
on the original distribution. The proposed asymptotic properties allow interval
estimation and statistical tests with generalized Shannon's entropy.


title: A Unified Approach to Discrepancy Minimization

abstract:
  We study a unified approach and algorithm for constructive discrepancy
minimization based on a stochastic process. By varying the parameters of the
process, one can recover various state-of-the-art results. We demonstrate the
flexibility of the method by deriving a discrepancy bound for smoothed
instances, which interpolates between known bounds for worst-case and random
instances.


title:
Mean square values of $L$-functions over subgroups for non primitive
  characters, Dedekind sums and bounds on relative class numbers

abstract:
  An explicit formula for the mean value of $\vert L(1,\chi)\vert^2$ is known,
where $\chi$ runs over all odd primitive Dirichlet characters of prime
conductors $p$. Bounds on the relative class number of the cyclotomic field
${\mathbb Q}(\zeta_p)$ follow. Lately the authors obtained that the mean value
of $\vert L(1,\chi)\vert^2$ is asymptotic to $\pi^2/6$, where $\chi$ runs over
all odd primitive Dirichlet characters of prime conductors $p\equiv 1\pmod{2d}$
which are trivial on a subgroup $H$ of odd order $d$ of the multiplicative
group $({\mathbb Z}/p{\mathbb Z})^*$, provided that $d\ll\frac{\log p}{\log\log
p}$. Bounds on the relative class number of the subfield of degree
$\frac{p-1}{2d}$ of the cyclotomic field ${\mathbb Q}(\zeta_p)$ follow. Here,
for a given integer $d_0>1$ we consider the same questions for the
non-primitive odd Dirichlet characters $\chi'$ modulo $d_0p$ induced by the odd
primitive characters $\chi$ modulo $p$. We obtain new estimates for Dedekind
sums and deduce that the mean value of $\vert L(1,\chi')\vert^2$ is asymptotic
to $\frac{\pi^2}{6}\prod_{q\mid d_0}\left (1-\frac{1}{q^2}\right )$, where
$\chi$ runs over all odd primitive Dirichlet characters of prime conductors $p$
which are trivial on a subgroup $H$ of odd order $d\ll\frac{\log p}{\log\log
p}$. As a consequence we improve the previous bounds on the relative class
number of the subfield of degree $\frac{p-1}{2d}$ of the cyclotomic field
${\mathbb Q}(\zeta_p)$. Moreover, we give a method to obtain explicit formulas
and use Mersenne primes to show that our restriction on $d$ is essentially
sharp.


title:
Wasserstein Asymptotics for the Empirical Measure of Fractional Brownian
  Motion on a Flat Torus

abstract:
  We establish asymptotic upper and lower bounds for the Wasserstein distance
of any order $p\ge 1$ between the empirical measure of a fractional Brownian
motion on a flat torus and the uniform Lebesgue measure. Our inequalities
reveal an interesting interaction between the Hurst index $H$ and the dimension
$d$ of the state space, with a "phase-transition" in the rates when $d=2+1/H$,
akin to the Ajtai-Koml\'os-Tusn\'ady theorem for the optimal matching of i.i.d.
points in two-dimensions. Our proof couples PDE's and probabilistic techniques,
and also yields a similar result for discrete-time approximations of the
process, as well as a lower bound for the same problem on $\mathbb{R}^d$.


title: Safety-Critical Manipulation for Collision-Free Food Preparation

abstract:
  Recent advances allow for the automation of food preparation in
high-throughput environments, yet the successful deployment of these robots
requires the planning and execution of quick, robust, and ultimately
collision-free behaviors. In this work, we showcase a novel framework for
modifying previously generated trajectories of robotic manipulators in highly
detailed and dynamic collision environments using Control Barrier Functions
(CBFs). This method dynamically re-plans previously validated behaviors in the
presence of changing environments -- and does so in a computationally efficient
manner. Moreover, the approach provides rigorous safety guarantees of the
resulting trajectories, factoring in the true underlying dynamics of the
manipulator. This methodology is extensively validated on a full-scale robotic
manipulator in a real-world cooking environment, and has resulted in
substantial improvements in computation time and robustness over re-planning.


title:
The impact of COVID-19, climate change and working from home on travel
  choices

abstract:
  The COVID-19 pandemic changed various aspects of our daily lives, including
the way in which we travel and commute. Working from home has become
significantly more popular since the beginning of the pandemic and is expected
to remain a reality for many people even when COVID-19 no longer poses a
threat. Decreased commuting trips may also have environmental benefits as
people will be able to reduce their overall travel. Working from home may
present an opportunity to accelerate the Scottish Government's 'Mission Zero
for transport', which aims to decarbonise the transport sector by 2045,
however, meeting this target also depends on restoring faith in public
transport, which saw significant decreases in usage during the pandemic, and
increasing other forms of sustainable travel (e.g., walking and cycling). In
this study we investigate various aspects of Scottish residents' climate change
and COVID-19 perceptions using survey data (n=1,050) collected in Scotland
during January 2022. Quota restraints were enforced for age and gender to
ensure the survey sample was approximately representative of the Scottish
population. The survey also included a discrete choice experiment to
investigate mode preferences for commuting trips in different working from home
and COVID-19 risk scenarios. Our findings show that some people still need to
be convinced that their travel choices can have an effect on climate change and
the spread of infectious diseases. The discrete choice experiment showed that
the use of cars for commuting is relatively consistent regardless of the
working from home situation, however, bicycles become more popular in high
working from home scenarios. As expected, the attractiveness of public
transport decreases with increased COVID-19 risk and private modes become more
popular.


title:
Off-resonance artifact correction for magnetic resonance imaging: a
  review

abstract:
  In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field
used for imaging, referred to as off-resonance, can lead to image artifacts
ranging from mild to severe depending on the application. Off-resonance
artifacts, such as signal loss, geometric distortions, and blurring, can
compromise the clinical and scientific utility of MR images. In this review, we
describe sources of off-resonance in MRI, how off-resonance affects images, and
strategies to prevent and correct for off-resonance. Given recent advances and
the great potential of low field and/or portable MRI, we also highlight the
advantages and challenges of imaging at low field with respect to
off-resonance.


title: Verification and Realizability in Finite-Horizon Multiagent Systems

abstract:
  The problems of \emph{verification} and \emph{realizability} are two central
themes in the analysis of reactive systems. When multiagent systems are
considered, these problems have natural analogues of existence (nonemptiness)
of pure-strategy Nash equilibria and verification of pure-strategy Nash
equilibria. Recently, this body of work has begun to include finite-horizon
temporal goals. With finite-horizon temporal goals, there is a natural
hierarchy of goal representation, ranging from deterministic finite automata
(DFA), to nondeterministic finite automata (NFA), and to alternating finite
automata (AFA), with a worst-case exponential gap between each successive
representation. Previous works showed that the realizability problem with DFA
goals was PSPACE-complete, while the realizability problem with temporal logic
goals is in 2EXPTIME. In this work, we study both the realizability and the
verification problems with respect to various goal representations. We first
show that the realizability problem with NFA goals is EXPTIME-complete and with
AFA goals is 2EXPTIME-complete, thus establishing strict complexity gaps
between realizability with respect to DFA, NFA, and AFA goals. We then contrast
these complexity gaps with the complexity of the verification problem, where we
show that verification with respect to DFAs, NFA, and AFA goals is
PSPACE-complete.


title:
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion
  Recognition

abstract:
  Previous electroencephalogram (EEG) emotion recognition relies on single-task
learning, which may lead to overfitting and learned emotion features lacking
generalization. In this paper, a graph-based multi-task self-supervised
learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the
ability to learn more general representations by integrating multiple
self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and
contrastive learning tasks. By learning from multiple tasks simultaneously,
GMSS can find a representation that captures all of the tasks thereby
decreasing the chance of overfitting on the original task, i.e., emotion
recognition task. In particular, the spatial jigsaw puzzle task aims to capture
the intrinsic spatial relationships of different brain regions. Considering the
importance of frequency information in EEG emotional signals, the goal of the
frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG
emotion recognition. To further regularize the learned features and encourage
the network to learn inherent representations, contrastive learning task is
adopted in this work by mapping the transformed data into a common feature
space. The performance of the proposed GMSS is compared with several popular
unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED
datasets show that the proposed model has remarkable advantages in learning
more discriminative and general features for EEG emotional signals.


title:
Auslander-Reiten and Huneke-Wiegand conjectures over quasi-fiber product
  rings

abstract:
  In this paper we explore consequences of the vanishing of ${\rm Ext}$ for
finitely generated modules over a quasi-fiber product ring $R$; that is, $R$ is
a local ring such that $R/(\underline x)$ is a non-trivial fiber product ring,
for some regular sequence $\underline x$ of $R$. Equivalently, the maximal
ideal of $R/(\underline x)$ decomposes as a direct sum of two nonzero ideals.
Gorenstein quasi-fiber product rings are AB-rings and are Ext-bounded. We show
in Theorem 3.31 that quasi-fiber product rings satisfy a sharpened form of the
Auslander-Reiten Conjecture. We also make some observations related to the
Huneke-Wiegand conjecture for quasi-fiber product rings.


title: \'Etale extensions of polynomial rings are faithfully flat

abstract:
  We apply Ohi's criterion for faithfully flatness of extensions of commutative
rings to prove that any \'etale extension $k[Y_1, \ldots, Y_n]\subseteq k[X_1,
\ldots, X_n]$ of polynomial rings (each in $n$ indeterminates) over a
commutative ring $k$ is faithfully flat. In particular, if $k$ is an
algebraically closed field then any \'etale polynomial map $k^{n} \to k^{n}$ is
surjective.


title:
Updated constraints on dark matter (WIMP) annihilation by radio
  observations of M31

abstract:
  The present work derived the robust constraints on annihilating WIMP
parameters utilizing new radio observations of M31, as well as new studies of
its dark matter distribution and other properties. The characteristics of
emission due to DM annihilation were computed in the frame of 2D galactic model
employing GALPROP code adapted specifically for M31. This enabled to refine
various inaccuracies of previous studies on the subject. DM constraints were
obtained for two representative annihilation channels: $\chi\chi \rightarrow
b\bar{b}$ and $\chi\chi \rightarrow \tau^+\tau^-$. A wide variety of radio data
was utilized in the frequency range $\approx$(0.1-10) GHz. As the result the
thermal WIMP lighter than $\approx$ 72 GeV for $b\bar{b}$ channel and $\approx$
39 GeV for $\tau^+\tau^-$ was excluded. The corresponding mass threshold
uncertainty ranges were estimated to be 20-210 GeV and 18-89 GeV. The obtained
exclusions are competitive to those from Fermi-LAT observations of dwarfs and
AMS-02 measurements of antiprotons. Our constraints does not exclude the
explanation of the gamma-ray outer halo of M31 and Galactic center excess by
annihilating DM. The thermal WIMP with $m_x \approx 70$ GeV, which explains the
outer halo, would make a significant contribution into the non-thermal radio
flux in M31 nucleus fitting well both the spectrum and morphology. And finally
we questioned the possibility to robustly constrain heavy thermal WIMP with
$m_x > 100$ GeV by radio data on M31 claimed in other studies.


title:
Gradient Based Reconstruction: Inviscid and viscous flux
  discretizations, shock capturing, and its application to single and
  multicomponent flows

abstract:
  This paper presents a gradient-based reconstruction approach for simulations
of compressible single and multi-species Navier-Stokes equations. The novel
feature of the proposed algorithm is the efficient reconstruction via
derivative sharing between the inviscid and viscous schemes: highly accurate
explicit and implicit gradients are used for the solution reconstruction
expressed in terms of derivatives. The higher-order accurate gradients of the
flow variables (density, pressure and velocity components) are reused to
compute the viscous fluxes for efficiency and significantly improve the
solution and gradient quality, as demonstrated by several viscous-flow test
cases. The viscous schemes are fourth-order accurate and carefully designed
with a high-frequency damping property, which has been identified as a
critically important property for stable compressible-flow simulations with
shock waves [Chamarthi et al., JCP, 2022]. Shocks and material discontinuities
are captured using a monotonicity-preserving (MP) scheme, which is also
improved by reusing the gradients. Several numerical results obtained for
simulations of complex viscous flows are presented to demonstrate the accuracy
and robustness of the proposed methodology.


title: An information-theoretic approach to hypergraph psychometrics

abstract:
  Psychological network approaches propose to see symptoms or questionnaire
items as interconnected nodes, with links between them reflecting pairwise
statistical dependencies evaluated cross-sectional, time-series, or panel data.
These networks constitute an established methodology to assess the interactions
and relative importance of nodes/indicators, providing an important complement
to other approaches such as factor analysis. However, focusing the modelling
solely on pairwise relationships can neglect potentially critical information
shared by groups of three or more variables in the form of higher-order
interdependencies. To overcome this important limitation, here we propose an
information-theoretic framework based on hypergraphs as psychometric models. As
edges in hypergraphs are capable of encompassing several nodes together, this
extension can thus provide a richer representation of the interactions that may
exist among sets of psychological variables. Our results show how psychometric
hypergraphs can highlight meaningful redundant and synergistic interactions on
either simulated or state-of-art, re-analyzed psychometric datasets. Overall,
our framework extends current network approaches while leading to new ways of
assessing the data that differ at their core from other methods, extending the
psychometric toolbox and opening promising avenues for future investigation.


title: Instability of pole singularities for the Chazy equation

abstract:
  We prove that the negative resonances of the Chazy equation (in thesense of
Painlev\'e analysis) can be related directly to it sgroup-invariance
properties. These resonances indicate in this case the instability of pole
singularities. Depending on the value of a parameter in the equation, an
unstable isolated pole may turn into the familiar natural boundary, or split
into several isolated singularities. In the first case, a convergent series
representation involving exponentially small corrections can be given. This
reconciles several earlier approaches to the interpretation of negative
resonances. On the other hand, we also prove that pole singularities with the
maximum number of positive resonances are stable. The proofs rely on general
properties of nonlinear Fuchsian equations.


title: Data Justice in Practice: A Guide for Developers

abstract:
  The Advancing Data Justice Research and Practice project aims to broaden
understanding of the social, historical, cultural, political, and economic
forces that contribute to discrimination and inequity in contemporary ecologies
of data collection, governance, and use. This is the consultation draft of a
guide for developers and organisations, which are producing, procuring, or
using data-intensive technologies.In the first section, we introduce the field
of data justice, from its early discussions to more recent proposals to
relocate understandings of what data justice means. This section includes a
description of the six pillars of data justice around which this guidance
revolves. Next, to support developers in designing, developing, and deploying
responsible and equitable data-intensive and AI/ML systems, we outline the
AI/ML project lifecycle through a sociotechnical lens. To support the
operationalisation data justice throughout the entirety of the AI/ML lifecycle
and within data innovation ecosystems, we then present five overarching
principles of responsible, equitable, and trustworthy data research and
innovation practices, the SAFE-D principles-Safety, Accountability, Fairness,
Explainability, and Data Quality, Integrity, Protection, and Privacy. The final
section presents guiding questions that will help developers both address data
justice issues throughout the AI/ML lifecycle and engage in reflective
innovation practices that ensure the design, development, and deployment of
responsible and equitable data-intensive and AI/ML systems.


title:
Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of
  Demographic Data Collection in the Pursuit of Fairness

abstract:
  Most proposed algorithmic fairness techniques require access to data on a
"sensitive attribute" or "protected category" (such as race, ethnicity, gender,
or sexuality) in order to make performance comparisons and standardizations
across groups, however this data is largely unavailable in practice, hindering
the widespread adoption of algorithmic fairness. Through this paper, we
consider calls to collect more data on demographics to enable algorithmic
fairness and challenge the notion that discrimination can be overcome with
smart enough technical methods and sufficient data alone. We show how these
techniques largely ignore broader questions of data governance and systemic
oppression when categorizing individuals for the purpose of fairer algorithmic
processing. In this work, we explore under what conditions demographic data
should be collected and used to enable algorithmic fairness methods by
characterizing a range of social risks to individuals and communities. For the
risks to individuals we consider the unique privacy risks associated with the
sharing of sensitive attributes likely to be the target of fairness analysis,
the possible harms stemming from miscategorizing and misrepresenting
individuals in the data collection process, and the use of sensitive data
beyond data subjects' expectations. Looking more broadly, the risks to entire
groups and communities include the expansion of surveillance infrastructure in
the name of fairness, misrepresenting and mischaracterizing what it means to be
part of a demographic group or to hold a certain identity, and ceding the
ability to define for themselves what constitutes biased or unfair treatment.
We argue that, by confronting these questions before and during the collection
of demographic data, algorithmic fairness methods are more likely to actually
mitigate harmful treatment disparities without reinforcing systems of
oppression.


title:
Big Tech Companies Impact on Research at the Faculty of Information
  Technology and Electrical Engineering

abstract:
  Artificial intelligence is gaining momentum, ongoing pandemic is fuel to that
with more opportunities in every sector specially in health and education
sector. But with the growth in technology, challenges associated with ethics
also grow (Katharine Schwab, 2021). Whenever a new AI product is developed,
companies publicize that their systems are transparent, fair, and are in
accordance with the existing laws and regulations as the methods and procedures
followed by a big tech company for ensuring AI ethics, not only affect the
trust and perception of public, but it also challenges the capabilities of the
companies towards business strategies in different regions, and the kind of
brains it can attract for their projects. AI Big Tech companies have influence
over AI ethics as many influencing ethical-AI researchers have roots in Big
Tech or its associated labs.


title: A Survey on Uncertainty Toolkits for Deep Learning

abstract:
  The success of deep learning (DL) fostered the creation of unifying
frameworks such as tensorflow or pytorch as much as it was driven by their
creation in return. Having common building blocks facilitates the exchange of,
e.g., models or concepts and makes developments easier replicable. Nonetheless,
robust and reliable evaluation and assessment of DL models has often proven
challenging. This is at odds with their increasing safety relevance, which
recently culminated in the field of "trustworthy ML". We believe that, among
others, further unification of evaluation and safeguarding methodologies in
terms of toolkits, i.e., small and specialized framework derivatives, might
positively impact problems of trustworthiness as well as reproducibility. To
this end, we present the first survey on toolkits for uncertainty estimation
(UE) in DL, as UE forms a cornerstone in assessing model reliability. We
investigate 11 toolkits with respect to modeling and evaluation capabilities,
providing an in-depth comparison for the three most promising ones, namely
Pyro, Tensorflow Probability, and Uncertainty Quantification 360. While the
first two provide a large degree of flexibility and seamless integration into
their respective framework, the last one has the larger methodological scope.


title: The Chaotic State of UK Drone Regulation

abstract:
  In December 2020 the law for drone pilots and unmanned aerial vehicle (UAV)
use went into a transition phase in preparation for new EU international UAV
regulation. That EU regulation comes into full effect as the transition periods
defined in the United Kingdom's Civil Aviation Authority Air Policy CAP722
expire during December 2022 (CAA, 2020). However, international homologation
regulation will not address the patchwork of inconsistent drone use regulations
that exist in the United Kingdom from the layering of local and subordinate
authority byelaws over UK aviation law. We provide an extensive review of local
authority regulation of drone use on public open and green spaces, finding that
many local authorities are unaware of the issues being created through: (i)
inappropriately couched or poorly framed byelaws; (ii) multiple byelaws
covering the same area by virtue of overlapping jurisdictions; or (iii) the
lack readily identifiable policies for drone use on public land.
Overregulation, inconsistent regulation and regulatory disharmony are causing
confusion for recreational drone enthusiasts such that it is never clear which
public or crown-owned open and green spaces they are allowed to, or prohibited
from, flying. While the government and local authorities might like them to,
drones are not going away. Therefore, we conclude, the easiest way to ensure
citizens stay within the bounds of drone law that is intended to ensure public
safety, is to make that law comprehensible, consistent and easy to comply with.


title:
Machine Learning and Artificial Intelligence in Circular Economy: A
  Bibliometric Analysis and Systematic Literature Review

abstract:
  With unorganized, unplanned and improper use of limited raw materials, an
abundant amount of waste is being produced, which is harmful to our environment
and ecosystem. While traditional linear production lines fail to address
far-reaching issues like waste production and a shorter product life cycle, a
prospective concept, namely circular economy (CE), has shown promising
prospects to be adopted at industrial and governmental levels. CE aims to
complete the product life cycle loop by bringing out the highest values from
raw materials in the design phase and later on by reusing, recycling, and
remanufacturing. Innovative technologies like artificial intelligence (AI) and
machine learning(ML) provide vital assistance in effectively adopting and
implementing CE in real-world practices. This study explores the adoption and
integration of applied AI techniques in CE. First, we conducted bibliometric
analysis on a collection of 104 SCOPUS indexed documents exploring the critical
research criteria in AI and CE. Forty papers were picked to conduct a
systematic literature review from these documents. The selected documents were
further divided into six categories: sustainable development, reverse
logistics, waste management, supply chain management, recycle & reuse, and
manufacturing development. Comprehensive research insights and trends have been
extracted and delineated. Finally, the research gap needing further attention
has been identified and the future research directions have also been
discussed.


title: The $L^q$ spectrum of self-affine measures on sponges

abstract:
  In this paper a sponge in $\mathbb{R}^d$ is the attractor of an iterated
function system consisting of finitely many strictly contracting affine maps
whose linear part is a diagonal matrix. A suitable separation condition is
introduced under which a variational formula is proved for the $L^q$ spectrum
of any self-affine measure defined on a sponge for all $q\in\mathbb{R}$. We
give sufficient conditions for the formula to have a closed form. In
particular, this is always the case for the box dimension of the sponge. The
Frostman and box dimension of these measures is also determined. The approach
unifies several existing results and extends them to arbitrary dimensions. The
result is derived from a more general variational principle for calculating box
counting quantities on sponges which resembles the Ledrappier-Young formula for
Hausdorff dimension and could be of interest to further study in its own right.


title: Coding Concepts and Reed-Solomon Codes

abstract:
  The material in this book is presented to graduate students in Information
and Communication theory. The idea is that we give an introduction to
particular applications of information theory and coding in digital
communications. The goal is to bring understanding of the underlying concepts,
both in theory as well as in practice. We mainly concentrate on our own
research results. After showing obtainable performance, we give a specific
implementation using Reed-Solomon (RS) codes. The reason for using RS codes is
that they can be seen as optimal codes with maximum obtainable minimum
distance. Furthermore, the structure of RS codes enables specific applications
that fit perfectly into the developed concepts. We do not intend to develop the
theory of error correcting codes.


title: Geo-located data for better dynamic replication

abstract:
  An increasing number of mobile applications share location-dependent
information, from collaborative applications and social networks to
location-based games. For such applications, peer-to-peer architectures where
mobile devices share information directly may grant lower latency and reduce
server load. In this work, we propose a framework to support these
applications, providing location-dependent replication. Data has an associated
location and is replicated in the mobile devices that show interest and are
close to that location. Our proposal combines peer-to-peer synchronisation
among mobile devices and replication at edge servers. Our preliminary results
are promising.


title: Ungraded matrix factorizations as mirrors of non-orientable Lagrangians

abstract:
  We introduce the notion of ungraded matrix factorization as a mirror of
non-orientable Lagrangian submanifolds. An ungraded matrix factorization of a
polynomial $W$, with coefficients in a field of characteristic 2, is a square
matrix $Q$ of polynomial entries satisfying $Q^2 = W \cdot \mathrm{Id}$. We
then show that non-orientable Lagrangians correspond to ungraded matrix
factorizations under the localized mirror functor and illustrate this
construction in a few instances. Our main example is the Lagrangian submanifold
$\mathbb{R}P^2 \subset \mathbb{C}P^2$ and its mirror ungraded matrix
factorization, which we construct and study. In particular, we prove a version
of Homological Mirror Symmetry in this setting.


title: Minimal hypersurfaces for generic metrics in dimension 8

abstract:
  We show that in an $8$-dimensional closed Riemmanian manifold with
$C^\infty$-generic metrics, every minimal hypersurface is smooth and
nondegenerate. This confirms a full generic regularity conjecture of minimal
hypersurfaces in dimension eight. This also enables us to generalize many
generic geometric properties of (Almgren-Pitts) min-max minimal hypersurfaces,
previously only known in low dimensions, to dimension eight.


title:
Center-of-Mass-based Robust Grasp Pose Adaptation Using RGBD Camera and
  Force/Torque Sensing

abstract:
  Object dropping may occur when the robotic arm grasps objects with uneven
mass distribution due to additional moments generated by objects' gravity. To
solve this problem, we present a novel work that does not require extra wrist
and tactile sensors and large amounts of experiments for learning. First, we
obtain the center-of-mass position of the rod object using the widely fixed
joint torque sensors on the robot arm and RGBD camera. Further, we give the
strategy of grasping to improve grasp stability. Simulation experiments are
performed in "Mujoco". Results demonstrate that our work is effective in
enhancing grasping robustness.


title:
High precision modeling of polarized signals: moment expansion method
  generalized to spin-2 fields

abstract:
  The modeling and removal of foregrounds poses a major challenge to searches
for signals from inflation using the cosmic microwave background (CMB). In
particular, the modeling of CMB foregrounds including various spatial averaging
effects introduces multiple complications that will have to be accounted for in
upcoming analyses. In this work, we introduce the generalization of the
intensity moment expansion to the spin-2 field of linear polarization: the
spin-moment expansion. Within this framework, moments become spin-2 objects
that are directly related to the underlying spectral parameter and polarization
angle distribution functions. In obtaining the required expressions for the
polarization modeling, we highlight the similarities and differences with the
intensity moment methods. A spinor rotation in the complex plane with frequency
naturally arises from the first order moment when the signal contains both SED
distortions and polarization mixing. Additional dependencies are introduced at
higher order, and we demonstrate on several illustrative examples how these can
be accounted for. Our new modeling of the polarized SED reveals to be a
powerful tool to model the frequency dependence of the polarization angle. As
such, it can be immediately applied to numerous astrophysical situations.


title:
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp
  and Lift task

abstract:
  Kinematics decoding from brain activity helps in developing rehabilitation or
power-augmenting brain-computer interface devices. Low-frequency signals
recorded from non-invasive electroencephalography (EEG) are associated with the
neural motor correlation utilised for motor trajectory decoding (MTD). In this
communication, the ability to decode motor kinematics trajectory from
pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy
participants. In particular, two deep learning-based neural decoders called
PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related
neural information existing in the pre-movement EEG data. EEG data segments
with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the
movement onset are utilised for the same. The MTD is presented for
grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken
as input to the neural decoders. The performance of the proposed decoders are
compared with the state-of-the-art multi-variable linear regression (mLR)
model. Pearson correlation coefficient and hand trajectory are utilised as
performance metric. The results demonstrate the viability of decoding 3D hand
kinematics using pre-movement EEG data, enabling better control of BCI-based
external devices such as exoskeleton/exosuit.


title:
RANG: A Residual-based Adaptive Node Generation Method for
  Physics-Informed Neural Networks

abstract:
  Learning solutions of partial differential equations (PDEs) with
Physics-Informed Neural Networks (PINNs) is an attractive alternative approach
to traditional solvers due to its flexibility and ease of incorporating
observed data. Despite the success of PINNs in accurately solving a wide
variety of PDEs, the method still requires improvements in terms of
computational efficiency. One possible improvement idea is to optimize the
generation of training point sets. Residual-based adaptive sampling and
quasi-uniform sampling approaches have been each applied to improve the
training effects of PINNs, respectively. To benefit from both methods, we
propose the Residual-based Adaptive Node Generation (RANG) approach for
efficient training of PINNs, which is based on a variable density nodal
distribution method for RBF-FD. The method is also enhanced by a memory
mechanism to further improve training stability. We conduct experiments on
three linear PDEs and three nonlinear PDEs with various node generation
methods, through which the accuracy and efficiency of the proposed method
compared to the predominant uniform sampling approach is verified numerically.


title: HTTPA/2: a Trusted End-to-End Protocol for Web Services

abstract:
  We received positive feedback and inquiries on the previous work of HTTPA
[10] (HTTPA/1). As a result, we present the major revision of HTTPA protocol
(HTTPA/2) to protect sensitive data in HTTPA transactions from cyber attacks.
Comparatively, the previous work [10] is mainly focused on how to include
Remote Attestation (RA) and secret provisioning to HTTP protocol in assumption
of using Transport Layer Security (TLS) across Internet. In contrast, HTTPA/2
does not need TLS protocol, such as TLS 1.3 [19], for secure communication over
Internet. The design of HTTPA/2 follows SIGMA model [12] to establish an L7
trusted communication, a secure communication between trusted (attested)
endpoints at L7. Different from connection-based protocol, HTTPA/2 is
transaction-based in which TEEs is considered as resources to be requested via
Internet. In addition to protecting sensitive data for TEE-based Services
(TServices), HTTPA/2 can potentially optimize the end-to-end performance of
Internet or cloud backend traffics, thus saving energy and reducing the
operational costs of Cloud Service Providers (CSPs). We envision that HTTPA/2
to further enable confidential web services and trustworthy AI applications in
the future.


title:
Markov Abstractions for PAC Reinforcement Learning in Non-Markov
  Decision Processes

abstract:
  Our work aims at developing reinforcement learning algorithms that do not
rely on the Markov assumption. We consider the class of Non-Markov Decision
Processes where histories can be abstracted into a finite set of states while
preserving the dynamics. We call it a Markov abstraction since it induces a
Markov Decision Process over a set of states that encode the non-Markov
dynamics. This phenomenon underlies the recently introduced Regular Decision
Processes (as well as POMDPs where only a finite number of belief states is
reachable). In all such kinds of decision process, an agent that uses a Markov
abstraction can rely on the Markov property to achieve optimal behaviour. We
show that Markov abstractions can be learned during reinforcement learning. For
these two tasks, any algorithms satisfying some basic requirements can be
employed. We show that our approach has PAC guarantees when the employed
algorithms have PAC guarantees, and we also provide an experimental evaluation.


title: A Change Dynamic Model for the Online Detection of Gradual Change

abstract:
  Changes in the statistical properties of a stochastic process are typically
assumed to occur via change-points, which demark instantaneous moments of
complete and total change in process behavior. In cases where these transitions
occur gradually, this assumption can result in a reduced ability to properly
identify and respond to process change. With this observation in mind, we
introduce a novel change-dynamic model for the online detection of gradual
change in a Bayesian framework, in which change-points are used within a
hierarchical model to indicate moments of gradual change onset or termination.
We apply this model to synthetic data and EEG readings drawn during epileptic
seizure, where we find our change-dynamic model can enable faster and more
accurate identification of gradual change than traditional change-point models
allow.


title: Instabilities Appearing in Effective Field theories: When and How?

abstract:
  Nonlinear partial differential equations appear in many domains of physics,
and we study here a typical equation which one finds in effective field
theories (EFT) originated from cosmological studies. In particular, we are
interested in the equation $\partial_t^2 u(x,t) = \alpha (\partial_x u(x,t))^2
+\beta \partial_x^2 u(x,t)$ in $1+1$ dimensions. It has been known for quite
some time that solutions to this equation diverge in finite time, when $\alpha
>0$. We study the detailed nature of this divergence as a function of the
parameters $\alpha>0 $ and $\beta\ge0$. The divergence does not disappear even
when $\beta $ is very large contrary to what one might believe. But it will
take longer to appear as $\beta$ increases when $\alpha$ is fixed. We note that
there are two types of divergence and we discuss the transition between these
two as a function of parameter choices. The blowup is unavoidable unless the
corresponding equations are modified. Our results extend to $3+1$ dimensions.

categories: math-ph astro-ph.CO cs.NA gr-qc math.AP math.MP math.NA


title: A word-hyperbolic special monoid with undecidable Diophantine problem

abstract:
  The Diophantine problem for a monoid $M$ is the decision problem to decide
whether any given system of equations has a solution in $M$. In this note, we
give a simple example of a context-free, word-hyperbolic, finitely presented,
special monoid $M$ with trivial group of units, and such that the Diophantine
problem is undecidable in $M$. This answers two questions asked by Garreta &
Gray in 2019, and shows that the decidability of the Diophantine problem in
hyperbolic groups, as proved by Dahmani & Guirardel, does not generalise to
word-hyperbolic monoids.


title:
Causal Discovery on the Effect of Antipsychotic Drugs on Delirium
  Patients in the ICU using Large EHR Dataset

abstract:
  Delirium occurs in about 80% cases in the Intensive Care Unit (ICU) and is
associated with a longer hospital stay, increased mortality and other related
issues. Delirium does not have any biomarker-based diagnosis and is commonly
treated with antipsychotic drugs (APD). However, multiple studies have shown
controversy over the efficacy or safety of APD in treating delirium. Since
randomized controlled trials (RCT) are costly and time-expensive, we aim to
approach the research question of the efficacy of APD in the treatment of
delirium using retrospective cohort analysis. We plan to use the Causal
inference framework to look for the underlying causal structure model,
leveraging the availability of large observational data on ICU patients. To
explore safety outcomes associated with APD, we aim to build a causal model for
delirium in the ICU using large observational data sets connecting various
covariates correlated with delirium. We utilized the MIMIC III database, an
extensive electronic health records (EHR) dataset with 53,423 distinct hospital
admissions. Our null hypothesis is: there is no significant difference in
outcomes for delirium patients under different drug-group in the ICU. Through
our exploratory, machine learning based and causal analysis, we had findings
such as: mean length-of-stay and max length-of-stay is higher for patients in
Haloperidol drug group, and haloperidol group has a higher rate of death in a
year compared to other two-groups. Our generated causal model explicitly shows
the functional relationships between different covariates. For future work, we
plan to do time-varying analysis on the dataset.


title: Electronic Laboratory Notebook: A lazy approach

abstract:
  Good research data management is essential in modern-day lab work. Various
solutions exist that are either highly specific or need a significant effort to
be customized appropriately. This paper presents an integrated solution for
individuals and small groups of researchers in data-driven deductive research.
Our electronic lab book generates itself out of notes and files, which are
generated by one or several experiments. The generated electronic lab book is
then presented on a Django-based website. The automated gathering of metadata
significantly reduces the documentation effort for the lab worker and prevents
human error in the repetitive task of manually entering basic meta-data. The
skilled user can quickly adapt the electronic lab book software to his needs
because the software employs widely used open-source software libraries with
active communities and excellent documentation.


title:
AL-PINNs: Augmented Lagrangian relaxation method for Physics-Informed
  Neural Networks

abstract:
  Physics-Informed Neural Networks (PINNs) has become a prominent application
of deep learning in scientific computation, as it is a powerful approximator of
solutions to nonlinear partial differential equations (PDEs). There have been
numerous attempts to facilitate the training process of PINNs by adjusting the
weight of each component of the loss function, called adaptive loss balancing
algorithms. In this paper, we propose an Augmented Lagrangian relaxation method
for PINNs (AL-PINNs). We treat the initial and boundary conditions as
constraints for the optimization problem of the PDE residual. By employing
Augmented Lagrangian relaxation, the constrained optimization problem becomes a
sequential max-min problem so that the learnable parameters $\lambda$'s
adaptively balance each loss component. Our theoretical analysis reveals that
the sequence of minimizers of the proposed loss functions converges to an
actual solution for the Helmholtz, viscous Burgers, and Klein--Gordon
equations. We demonstrate through various numerical experiments that AL-PINNs
yields a much smaller relative error compared with that of state-of-the-art
adaptive loss balancing algorithms.


title:
Investigating the impact of COVID-19 on Online Learning-based Web
  Behavior

abstract:
  COVID-19, a pandemic that the world has not seen in decades, has resulted in
presenting a multitude of unprecedented challenges for student learning across
the globe. The global surge in COVID-19 cases resulted in several schools,
colleges, and universities closing in 2020 in almost all parts of the world and
switching to online or remote learning, which has impacted student learning in
different ways. This has resulted in both educators and students spending more
time on the internet than ever before, which may be broadly summarized as both
these groups investigating, learning, and familiarizing themselves with
information, tools, applications, and frameworks to adapt to online learning.
This paper takes an explorative approach to further investigate and analyze the
impact of COVID-19 on such web behavior data related to online learning to
interpret the associated interests, challenges, and needs. The study
specifically focused on investigating Google Search-based web behavior data as
Google is the most popular search engine globally. The impact of COVID-19
related to online learning-based web behavior on Google was studied for the top
20 worst affected countries in terms of the total number of COVID-19 cases, and
the findings have been published as an open-access dataset. Furthermore, to
interpret the trends in web behavior data related to online learning, the paper
discusses a case study in terms of the impact of COVID-19 on the education
system of one of these countries.


title: Robust inference for matching under rolling enrollment

abstract:
  Matching in observational studies faces complications when units enroll in
treatment on a rolling basis. While each treated unit has a specific time of
entry into the study, control units each have many possible comparison, or
"pseudo-treatment," times. The recent GroupMatch framework (Pimentel et al.,
2020) solves this problem by searching over all possible pseudo-treatment times
for each control and selecting those permitting the closest matches based on
covariate histories. However, valid methods of inference have been described
only for special cases of the general GroupMatch design, and these rely on
strong assumptions. We provide three important innovations to address these
problems. First, we introduce a new design, GroupMatch with instance
replacement, that allows additional flexibility in control selection and proves
more amenable to analysis. Second, we propose a block bootstrap approach for
inference in GroupMatch with instance replacement and demonstrate that it
accounts properly for complex correlations across matched sets. Third, we
develop a permutation-based falsification test to detect possible violations of
the important timepoint agnosticism assumption underpinning GroupMatch, which
requires homogeneity of potential outcome means across time. Via simulation and
a case study of the impact of short-term injuries on batting performance in
major league baseball, we demonstrate the effectiveness of our methods for data
analysis in practice.


title:
Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and
  the resulting failure to machine learn four body interactions"

abstract:
  The "quasi-constant" SOAP and ACSF fingerprint manifolds recently discovered
by Parsaeifard and Goedecker are a direct consequence of the presence of
degenerate pairs of configurations, a known shortcoming of all low-body-order
atom-density correlation representations of molecular structures. Contrary to
the configurations that are rigorously singular -- that we demonstrate can only
occur in finite, discrete sets -- the continuous "quasi-constant" manifolds
exhibit low, but non-zero, sensitivity to atomic displacements. Thus, it is
possible to build interpolative machine-learning models of high-order
interactions along the manifold, even though the numerical instabilities
associated with proximity to the exact singularities affect the accuracy and
transferability of such models, to an extent that depends on numerical details
of the implementation.


title:
Designing thermal radiation metamaterials via hybrid adversarial
  autoencoder and Bayesian optimization

abstract:
  Designing thermal radiation metamaterials is challenging especially for
problems with high degrees of freedom and complex objective. In this letter, we
have developed a hybrid materials informatics approach which combines the
adversarial autoencoder and Bayesian optimization to design narrowband thermal
emitters at different target wavelengths. With only several hundreds of
training data sets, new structures with optimal properties can be quickly
figured out in a compressed 2-dimensional latent space. This enables the
optimal design by calculating far less than 0.001\% of the total candidate
structures, which greatly decreases the design period and cost. The proposed
design framework can be easily extended to other thermal radiation
metamaterials design with higher dimensional features.


title: Meta Transfer Learning for Early Success Prediction in MOOCs

abstract:
  Despite the increasing popularity of massive open online courses (MOOCs),
many suffer from high dropout and low success rates. Early prediction of
student success for targeted intervention is therefore essential to ensure no
student is left behind in a course. There exists a large body of research in
success prediction for MOOCs, focusing mainly on training models from scratch
for individual courses. This setting is impractical in early success prediction
as the performance of a student is only known at the end of the course. In this
paper, we aim to create early success prediction models that can be transferred
between MOOCs from different domains and topics. To do so, we present three
novel strategies for transfer: 1) pre-training a model on a large set of
diverse courses, 2) leveraging the pre-trained model by including meta
information about courses, and 3) fine-tuning the model on previous course
iterations. Our experiments on 26 MOOCs with over 145,000 combined enrollments
and millions of interactions show that models combining interaction data and
course information have comparable or better performance than models which have
access to previous iterations of the course. With these models, we aim to
effectively enable educators to warm-start their predictions for new and
ongoing courses.


title: On the topology of Gaussian random zero sets

abstract:
  We study the asymptotic laws for the number, Betti numbers, and
diffeomorphism types of connected components of zero sets of real smooth
Gaussian random fields defined on either a Euclidean space or a Riemannian
manifold without boundary, where the random zero sets almost surely consist of
submanifolds of high codimensions.


title: Quantifying Health Inequalities Induced by Data and AI Models

abstract:
  AI technologies are being increasingly tested and applied in critical
environments including healthcare. Without an effective way to detect and
mitigate AI induced inequalities, AI might do more harm than good, potentially
leading to the widening of underlying inequalities. This paper proposes a
generic allocation-deterioration framework for detecting and quantifying AI
induced inequality. Specifically, AI induced inequalities are quantified as the
area between two allocation-deterioration curves. To assess the framework's
performance, experiments were conducted on ten synthetic datasets (N>33,000)
generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showing
its ability to accurately detect and quantify inequality proportionally to
controlled inequalities. Extensive analyses were carried out to quantify health
inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI
models trained for two resource allocation scenarios. Results showed that
compared to men, women had up to 33% poorer deterioration in markers of
prognosis when admitted to HiRID ICUs. All four AI models assessed were shown
to induce significant inequalities (2.45% to 43.2%) for non-White compared to
White patients. The models exacerbated data embedded inequalities significantly
in 3 out of 8 assessments, one of which was >9 times worse.
  The codebase is at https://github.com/knowlab/DAindex-Framework.


title:
Analyzing the Adoption Challenges of the Internet of Things (IoT) and
  Artificial Intelligence (AI) for Smart Cities in China

abstract:
  Smart cities play a vital role in the growth of a nation. In recent years,
several countries have made huge investments in developing smart cities to
offer sustainable living. However, there are some challenges to overcome in
smart city development, such as traffic and transportation man-agement, energy
and water distribution and management, air quality and waste management
monitoring, etc. The capabilities of the Internet of Things (IoT) and
artificial intelligence (AI) can help to achieve some goals of smart cities,
and there are proven examples from some cities like Singapore, Copenhagen, etc.
However, the adoption of AI and the IoT in developing countries has some
challenges. The analysis of challenges hindering the adoption of AI and the IoT
are very limited. This study aims to fill this research gap by analyzing the
causal relationships among the challenges in smart city development, and
contains several parts that conclude the previous scholars work, as well as
independent research and investigation, such as data collection and analysis
based on DEMATEL. In this paper, we have reviewed the literature to extract key
chal-lenges for the adoption of AI and the IoT. These helped us to proceed with
the investigation and analyze the adoption status. Therefore, using the PRISMA
method, 10 challenges were identified from the literature review. Subsequently,
determination of the causal inter-relationships among the key challenges based
on expert opinions using DEMATEL is performed. This study explored the driving
and dependent power of the challenges, and causal relationships between the
barriers were established.


title: OPT: Open Pre-trained Transformer Language Models

abstract:
  Large language models, which are often trained for hundreds of thousands of
compute days, have shown remarkable capabilities for zero- and few-shot
learning. Given their computational cost, these models are difficult to
replicate without significant capital. For the few that are available through
APIs, no access is granted to the full model weights, making them difficult to
study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only
pre-trained transformers ranging from 125M to 175B parameters, which we aim to
fully and responsibly share with interested researchers. We show that OPT-175B
is comparable to GPT-3, while requiring only 1/7th the carbon footprint to
develop. We are also releasing our logbook detailing the infrastructure
challenges we faced, along with code for experimenting with all of the released
models.


title: Deep Learning: From Basics to Building Deep Neural Networks with Python

abstract:
  This book is intended for beginners who have no familiarity with deep
learning. Our only expectation from readers is that they already have the basic
programming skills in Python.


title: Five Ps: Leverage Zones Towards Responsible AI

abstract:
  There is a growing debate amongst academics and practitioners on whether
interventions made, thus far, towards Responsible AI would have been enough to
engage with root causes of AI problems. Failure to effect meaningful changes in
this system could see these initiatives to not reach their potential and lead
to the concept becoming another buzzword for companies to use in their
marketing campaigns. We propose that there is an opportunity to improve the
extent to which interventions are understood to be effective in their
contribution to the change required for Responsible AI. Using the notions of
leverage zones adapted from the 'Systems Thinking' literature, we suggest a
novel approach to evaluate the effectiveness of interventions, to focus on
those that may bring about the real change that is needed. In this paper we
argue that insights from using this perspective demonstrate that the majority
of current initiatives taken by various actors in the field, focus on low-order
interventions, such as short-term fixes, tweaking algorithms and updating
parameters, absent from higher-order interventions, such as redefining the
system's foundational structures that govern those parameters, or challenging
the underlying purpose upon which those structures are built and developed in
the first place(high-leverage). This paper presents a conceptual framework
called the Five Ps to identify interventions towards Responsible AI and
provides a scaffold for transdisciplinary question asking to improve outcomes
towards Responsible AI.


title:
Sharing and Caring: Creating a Culture of Constructive Criticism in
  Computational Legal Studies

abstract:
  We introduce seven foundational principles for creating a culture of
constructive criticism in computational legal studies. Beginning by challenging
the current perception of papers as the primary scholarly output, we call for a
more comprehensive interpretation of publications. We then suggest to make
these publications computationally reproducible, releasing all of the data and
all of the code all of the time, on time, and in the most functioning form
possible. Subsequently, we invite constructive criticism in all phases of the
publication life cycle. We posit that our proposals will help form our field,
and float the idea of marking this maturity by the creation of a modern
flagship publication outlet for computational legal studies.


title: The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

abstract:
  Machine Learning (ML) decision-making algorithms are now widely used in
predictive decision-making, for example, to determine who to admit and give a
loan. Their wide usage and consequential effects on individuals led the ML
community to question and raise concerns on how the algorithms differently
affect different people and communities. In this paper, we study fairness
issues that arise when decision-makers use models (proxy models) that deviate
from the models that depict the physical and social environment in which the
decisions are situated (intended models). We also highlight the effect of
obstacles on individual access and utilization of the models. To this end, we
formulate an Equity Framework that considers equal access to the model, equal
outcomes from the model, and equal utilization of the model, and
consequentially achieves equity and higher social welfare than current fairness
notions that aim for equality. We show how the three main aspects of the
framework are connected and provide an equity scoring algorithm and questions
to guide decision-makers towards equitable decision-making. We show how failure
to consider access, outcome, and utilization would exacerbate proxy gaps
leading to an infinite inequity loop that reinforces structural inequities
through inaccurate and incomplete ground truth curation. We, therefore,
recommend a more critical look at the model design and its effect on equity and
a shift towards equity achieving predictive decision-making models.


title:
The thermodynamic soliton theory of the nervous impulse and possible
  medical implications

abstract:
  The textbook picture of nerve activity is that of a propagating voltage pulse
driven by electrical currents through ion channel proteins, which are gated by
changes in voltage, temperature, pressure or by drugs. All function is directly
attributed to single molecules. We show that this leaves out many important
thermodynamic couplings between different variables. A more recent alternative
picture for the nerve pulse is of thermodynamic nature. It considers the nerve
pulse as a soliton, i.e., a macroscopic excited region with properties that are
influenced by thermodynamic variables including voltage, temperature, pressure
and chemical potentials of membrane components. All thermodynamic variables are
strictly coupled. We discuss the consequences for medical treatment in a view
where one can compensate a maladjustment of one variable by adjusting another
variable. For instance, one can explain why anesthesia can be counteracted by
hydrostatic pressure and decrease in pH, suggest reasons why lithium over-dose
may lead to tremor, and how tremor is related to alcohol intoxication. Lithium
action as well as the effect of ethanol and the anesthetic ketamine in bipolar
patients may fall in similar thermodynamic patterns. Such couplings remain
obscure in a purely molecular picture. Other fields of application are the
response of nerve activity to muscle stretching and the possibility of neural
stimulation by ultrasound.


title: Entanglement quantification enhanced by dark count correction

abstract:
  In this letter, we propose a method of dark count correction in quantum state
tomography of entangled photon pairs. The framework is based on a linear model
of dark counts, which is imposed on the measurement formalism. The method is
tested on empirical data derived from an experiment on polarization-entangled
photons. We demonstrate that the numerical approach to dark count correction
guarantees more reliable state reconstruction compared with standard
estimation. Most importantly, however, the conceptually simple theoretical
approach proves to be more efficient at entanglement quantification than
experimental techniques.


title:
Optimization of detector modules for measuring gamma-ray polarization in
  Positron Emission Tomography

abstract:
  Detection of $\gamma$-ray polarization in Positron Emission Tomography (PET)
is as yet an unexploited feature that could be used as an additional handle to
improve signal-to-background ratio in this imaging modality. The $\gamma$
polarization is related to the azimuthal angle in the Compton scattering
process, so the initial correlation of polarizations of the annihilation quanta
translates to the correlation of the azimuthal angles in events where both
annihilation photons undergo Compton scattering. This results in a modulated
distribution of the azimuthal angle difference for true events, while this
modulation is lacking for the background events. We present a comprehensive
experimental study of five detector configurations based on scintillator
matrices and silicon photomultipliers, suitable for measuring the azimuthal
modulation. The modules consist of either GaGG:Ce or LYSO:Ce pixels with sizes
varying from 1.9x1.9x20 $\mathrm{mm^3}$ to 3x3x20 $\mathrm{mm^3}$. The
distinctive feature of the modules is that they can reconstruct the Compton
scattering by detecting the recoil electron and the scattered gamma in a single
detector layer, which simplifies extension to larger systems. The amplitude
modulation of the azimuthal angles' difference is clearly observable in all
configurations ranging from $0.26\pm0.01$ to $0.34\pm 0.02$ depending on the
event selection criteria. The results suggest that finer detector segmentation
plays a leading role in achieving higher modulation factors.


title:
Classification of Buildings' Potential for Seismic Damage by Means of
  Artificial Intelligence Techniques

abstract:
  Developing a rapid, but also reliable and efficient, method for classifying
the seismic damage potential of buildings constructed in countries with regions
of high seismicity is always at the forefront of modern scientific research.
Such a technique would be essential for estimating the pre-seismic
vulnerability of the buildings, so that the authorities will be able to develop
earthquake safety plans for seismic rehabilitation of the highly
earthquake-susceptible structures. In the last decades, several researchers
have proposed such procedures, some of which were adopted by seismic code
guidelines. These procedures usually utilize methods based either on simple
calculations or on the application of statistics theory. Recently, the increase
of the computers' power has led to the development of modern statistical
methods based on the adoption of Machine Learning algorithms. These methods
have been shown to be useful for predicting seismic performance and classifying
structural damage level by means of extracting patterns from data collected via
various sources. A large training dataset is used for the implementation of the
classification algorithms. To this end, 90 3D R/C buildings with three
different masonry infills' distributions are analysed utilizing Nonlinear Time
History Analysis method for 65 real seismic records. The level of the seismic
damage is expressed in terms of the Maximum Interstory Drift Ratio. A large
number of Machine Learning algorithms is utilized in order to estimate the
buildings' damage response. The most significant conclusion which is extracted
is that the Machine Learning methods that are mathematically well-established
and their operations that are clearly interpretable step by step can be used to
solve some of the most sophisticated real-world problems in consideration with
high accuracy.


title:
Catalysis for e-Chemistry: Need and Gaps for a Future De-Fossilized
  Chemical Production, with Focus on the Role of Complex (Direct) Syntheses by
  Electrocatalysis

abstract:
  The prospects, needs and limits in current approaches in catalysis to
accelerate the transition to e-chemistry, where this term indicates a fossil
fuel-free chemical production, are discussed. It is suggested that e-chemistry
is a necessary element of the transformation to meet the targets of net zero
emissions by year 2050 and that this conversion from the current petrochemistry
is feasible. However, the acceleration of the development of catalytic
technologies based on the use of renewable energy sources (indicated as
reactive catalysis) is necessary, evidencing that these are part of a system of
changes and thus should be assessed from this perspective. However, it is
perceived that the current studies in the area are not properly addressing the
needs to develop the catalytic technologies required for e-chemistry,
presenting a series of relevant aspects and directions in which research should
be focused to develop the framework system transformation necessary to
implement e-chemistry.


title: Chemical Bonding in Many Electron Molecules

abstract:
  Chemical bonding is the stabilization of a composite molecular system caused
by different interactions in and between the subsystems, among the strong kinds
of bonding is covalent bonding especially important. Characteristic for
covalent bonding are small atom groups with short distances between the
involved atoms, indicating that covalent bonding is essentially a local effect,
according to Lewis, this is caused by shared electron pairs. However, the
energetic stabilization is an approximately additive one-electron effect, as
was shown by Ruedenberg and coworkers. In systems composed of many-electron
subsystems, the fermionic character of the electrons determines the structure
of the electron distribution in a subsystem, and it is decisive for the local
interactions between the subsystems. Especially important is the Pauli
exclusion principle (PEP), which directs the relative positions of identical
electrons. Spin and charge rearrangements are of utmost importance for chemical
bonding. Quantum chemical methods like CASSCF (complete active space SCF), also
called FORS (fully optimized reaction space), are made to cover all such
processes. The standard building blocks of CASSCF wave functions are
delocalized molecular orbitals, which cannot display local effects. OVB
(orthogonal valence bond) is a method to analyze CASSCF wave functions and to
reveal local processes that are responsible for both the energetic aspects of
bonding and the spatial structure of the stabilized system. This is shown by
analyzing dissociation of ethene, disilene, and silaethene, and the
corresponding reverse reactions. Aspects of diabaticity of the reactions and
entanglement of subsystems are discussed.


title:
Anisotropic cosmic optical background bound for decaying dark matter in
  light of the LORRI anomaly

abstract:
  Recently anomalous flux in the cosmic optical background (COB) is reported by
the New Horizon observations. The COB flux is $16.37\pm1.47\, \rm nW m^{-2}
sr^{-1}$, at the LORRI pivot wavelength of $0.608\,\rm \mu m$, which is $\sim
4\sigma$ level above the expected flux from the Hubble Space Telescope (HST)
galaxy count. It would be great if this were a hint for the eV scale dark
matter decaying into photons. In this paper, we point out that such a decaying
dark matter model predicts a substantial amount of anisotropy in the COB flux,
which is accurately measured by the HST. The data of the HST excludes the decay
rate of the dominant cold dark matter larger than $10^{-24}$-$10^{-23}\,{\rm
s}^{-1}$ in the mass range of $5$-$20\,$eV. As a result, the decaying cold dark
matter explaining the COB excess is excluded by the anisotropy bound. We
discuss some loopholes: e.g. warm/hot dark matter or two-step decay of the dark
matter to explain the COB excess.


title: A Probabilistic Interpretation of Transformers

abstract:
  We propose a probabilistic interpretation of exponential dot product
attention of transformers and contrastive learning based off of exponential
families. The attention sublayer of transformers is equivalent to a gradient
ascent step of the log normalizer, which is the log-sum-exp term in the
Hopfield theory of attention. This ascent step induces a parallel expansion of
points, which is counterbalanced by a contraction from layer normalization. We
also state theoretical limitations of our theory and the Hopfield theory and
suggest directions for resolution.


title:
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
  literature review

abstract:
  The explosion of data volumes generated by an increasing number of
applications is strongly impacting the evolution of distributed digital
infrastructures for data analytics and machine learning (ML). While data
analytics used to be mainly performed on cloud infrastructures, the rapid
development of IoT infrastructures and the requirements for low-latency, secure
processing has motivated the development of edge analytics. Today, to balance
various trade-offs, ML-based analytics tends to increasingly leverage an
interconnected ecosystem that allows complex applications to be executed on
hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC
systems in what is called the Computing Continuum, the Digital Continuum, or
the Transcontinuum.Enabling learning-based analytics on such complex
infrastructures is challenging. The large scale and optimized deployment of
learning-based workflows across the Edge-to-Cloud Continuum requires extensive
and reproducible experimental analysis of the application execution on
representative testbeds. This is necessary to help understand the performance
trade-offs that result from combining a variety of learning paradigms and
supportive frameworks. A thorough experimental analysis requires the assessment
of the impact of multiple factors, such as: model accuracy, training time,
network overhead, energy consumption, processing latency, among others.This
review aims at providing a comprehensive vision of the main state-of-the-art
libraries and frameworks for machine learning and data analytics available
today. It describes the main learning paradigms enabling learning-based
analytics on the Edge-to-Cloud Continuum. The main simulation, emulation,
deployment systems, and testbeds for experimental research on the Edge-to-Cloud
Continuum available today are also surveyed. Furthermore, we analyze how the
selected systems provide support for experiment reproducibility. We conclude
our review with a detailed discussion of relevant open research challenges and
of future directions in this domain such as: holistic understanding of
performance; performance optimization of applications;efficient deployment of
Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures;
and reproducible analysis of experiments on the Computing Continuum.


title: Multipolar theory of bianisotropic response

abstract:
  Bianisotropy of metaatoms is usually associated with their nonlocal response
and the mutual coupling between electric and magnetic dipole moments induced by
the incident field. In this work, we generalize the theory of bianisotropy
beyond the dipole response to the cases of arbitrary high-order multipole
resonances. We demonstrate that bianisotropy is exclusively caused by the
absence of the inversion symmetry of metaatoms. The strength of the
bianisotropy response is normally increased with the size of a meta-atom but
its origin is fully connected to the symmetry of the structure. As an important
example of bianisotropic particle, we consider a triangular prism and show how
accounting for the higher-order multipoles prevents the violation of the
Onsager-Casimir conditions for kinetic coefficients appearing in the dipole
approximation. The developed theory is an important step towards a deeper
insight into the scattering properties of nanoantennas and meta-atoms.


title:
Generalized MPI Multi-Patch Reconstruction using Clusters of similar
  System Matrices

abstract:
  The tomographic imaging method magnetic particle imaging (MPI) requires a
multi-patch approach for capturing large field of views. This approach consists
of a continuous or stepwise spatial shift of a small sub-volume of only few
cubic centimeters size, which is scanned using one or multiple excitation
fields in the kHz range. Under the assumption of ideal magnetic fields, the MPI
system matrix is shift invariant and in turn a single matrix suffices for image
reconstruction significantly reducing the calibration time and reconstruction
effort. For large field imperfections, however, the method can lead to severe
image artifacts. In the present work we generalize the efficient multi-patch
reconstruction to work under non-ideal field conditions, where shift invariance
holds only approximately for small shifts of the sub-volume. Patches are
clustered based on a magnetic-field-based metric such that in each cluster the
shift invariance holds in good approximation. The total number of clusters is
the main parameter of our method and allows to trade off calibration time and
image artifacts. The magnetic-field-based metric allows to perform the
clustering without prior knowledge of the system matrices. The developed
reconstruction algorithm is evaluated on a multi-patch measurement sequence
with 15 patches, where efficient multi-patch reconstruction with a single
calibration measurement leads to strong image artifacts. Analysis reveals that
calibration measurements can be decreased from 15 to 11 with no visible image
artifacts. A further reduction to 9 is possible with only slight degradation in
image quality.


title: Sum of elements in finite Sidon sets II

abstract:
  A set $S\subset\{1,2,...,n\}$ is called a Sidon set if all the sums
$a+b~~(a,b\in S)$ are different. Let $S_n$ be the largest cardinality of the
Sidon sets in $\{1,2,...,n\}$. In a former article, the author proved the
following asymptotic formula $$\sum_{a\in
S,~|S|=S_n}a=\frac{1}{2}n^{3/2}+O(n^{111/80+\varepsilon}),$$ where
$\varepsilon>0$ is an arbitrary small constant. In this note, we give an
extension of the above formula. We show that $$\sum_{a\in
S,~|S|=S_n}a^{\ell}=\frac{1}{\ell+1}n^{\ell+1/2}+O\left(n^{\ell+61/160}\right)$$
for any positive integers $\ell$. Besides, we also consider the asymptotic
formulae of other type summations involving Sidon sets. The proofs are
established in a more general setting, namely we obtain the asymptotic formulae
of the Sidon sets with $t$ elements when $t$ is near the magnitude $n^{1/2}$.


title:
Suppression of Motion Artifacts Caused by Temporally Recurring Tracer
  Distributions in Multi-Patch Magnetic Particle Imaging

abstract:
  Magnetic particle imaging is a tracer based imaging technique to determine
the spatial distribution of superparamagnetic iron oxide nanoparticles with a
high spatial and temporal resolution. Due to physiological constraints, the
imaging volume is restricted in size and larger volumes are covered by shifting
object and imaging volume relative to each other. This results in reduced
temporal resolution, which can lead to motion artifacts when imaging dynamic
tracer distributions. A common source of such dynamic distributions are cardiac
and respiratory motion in in-vivo experiments, which are in good approximation
periodic. We present a raw data processing technique that combines data
snippets into virtual frames corresponding to a specific state of the dynamic
motion. The technique is evaluated on the basis of measurement data obtained
from a rotational phantom at two different rotational frequencies. These
frequencies are determined from the raw data without reconstruction and without
an additional navigator signal. The reconstructed images give reasonable
representations of the rotational phantom frozen in several different states of
motion while motion artifacts are suppressed.


title:
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo
  Languages

abstract:
  We introduce Wav2Seq, the first self-supervised approach to pre-train both
parts of encoder-decoder models for speech data. We induce a pseudo language as
a compact discrete representation, and formulate a self-supervised pseudo
speech recognition task -- transcribing audio inputs into pseudo subword
sequences. This process stands on its own, or can be applied as low-cost
second-stage pre-training. We experiment with automatic speech recognition
(ASR), spoken named entity recognition, and speech-to-text translation. We set
new state-of-the-art results for end-to-end spoken named entity recognition,
and show consistent improvements on 20 language pairs for speech-to-text
translation, even when competing methods use additional text data for training.
Finally, on ASR, our approach enables encoder-decoder methods to benefit from
pre-training for all parts of the network, and shows comparable performance to
highly optimized recent methods.


title:
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection
  Operators

abstract:
  We present incomplete gamma kernels, a generalization of Locally Optimal
Projection (LOP) operators. In particular, we reveal the relation of the
classical localized $ L_1 $ estimator, used in the LOP operator for surface
reconstruction from noisy point clouds, to the common Mean Shift framework via
a novel kernel. Furthermore, we generalize this result to a whole family of
kernels that are built upon the incomplete gamma function and each represents a
localized $ L_p $ estimator. By deriving various properties of the kernel
family concerning distributional, Mean Shift induced, and other aspects such as
strict positive definiteness, we obtain a deeper understanding of the
operator's projection behavior. From these theoretical insights, we illustrate
several applications ranging from an improved Weighted LOP (WLOP) density
weighting scheme and a more accurate Continuous LOP (CLOP) kernel approximation
to the definition of a novel set of robust loss functions. These incomplete
gamma losses include the Gaussian and LOP loss as special cases and can be
applied for reconstruction tasks such as normal filtering. We demonstrate the
effects of each application in a range of quantitative and qualitative
experiments that highlight the benefits induced by our modifications.


title:
Ensemble pruning via an integer programming approach with diversity
  constraints

abstract:
  Ensemble learning combines multiple classifiers in the hope of obtaining
better predictive performance. Empirical studies have shown that ensemble
pruning, that is, choosing an appropriate subset of the available classifiers,
can lead to comparable or better predictions than using all classifiers. In
this paper, we consider a binary classification problem and propose an integer
programming (IP) approach for selecting optimal classifier subsets. We propose
a flexible objective function to adapt to desired criteria of different
datasets. We also propose constraints to ensure minimum diversity levels in the
ensemble. Despite the general case of IP being NP-Hard, state-of-the-art
solvers are able to quickly obtain good solutions for datasets with up to 60000
data points. Our approach yields competitive results when compared to some of
the best and most used pruning methods in literature.


title:
ComPhy: Compositional Physical Reasoning of Objects and Events from
  Videos

abstract:
  Objects' motions in nature are governed by complex interactions and their
properties. While some properties, such as shape and material, can be
identified via the object's visual appearances, others like mass and electric
charge are not directly visible. The compositionality between the visible and
hidden properties poses unique challenges for AI models to reason from the
physical world, whereas humans can effortlessly infer them with limited
observations. Existing studies on video reasoning mainly focus on visually
observable elements such as object appearance, movement, and contact
interaction. In this paper, we take an initial step to highlight the importance
of inferring the hidden physical properties not directly observable from visual
appearances, by introducing the Compositional Physical Reasoning (ComPhy)
dataset. For a given set of objects, ComPhy includes few videos of them moving
and interacting under different initial conditions. The model is evaluated
based on its capability to unravel the compositional hidden properties, such as
mass and charge, and use this knowledge to answer a set of questions posted on
one of the videos. Evaluation results of several state-of-the-art video
reasoning models on ComPhy show unsatisfactory performance as they fail to
capture these hidden properties. We further propose an oracle neural-symbolic
framework named Compositional Physics Learner (CPL), combining visual
perception, physical property learning, dynamic prediction, and symbolic
execution into a unified framework. CPL can effectively identify objects'
physical properties from their interactions and predict their dynamics to
answer questions.


title: Spectral Features for Re-entry Break-up Event Identification

abstract:
  The fragmentation of two aerospace aluminum alloys is investigated in a
ground testing facility including mechanical loads as occurring due to
aerodynamic forces in a real atmospheric entry event at three trajectory
points. The emission spectroscopic analysis shows that these materials fail
after distinct alkali metal features are observed in the spectra. The two
alloys feature characteristic emissions of the different alkali metals. The
presence of lithium lines that have previously been exclusively attributed to
battery failure in observation campaigns may be considered as a marker for
aluminum breakup. This is particularly interesting for future entry
observations because it allows a new insight into the structural failure
processes of the demising spacecraft. The lack of emission of alloying elements
points to these spectra being a candidate for the determination of spacecraft
demise. The identification of such features in ground testing will allow a more
certain identification of specific break-up events


title: Blockchain in a nutshell

abstract:
  Blockchain enables a digital society where people can contribute,
collaborate, and transact without having to second-guess trust and
transparency. It is the technology behind the success of Bitcoin, Ethereum, and
many disruptive applications and platforms that have positive impact in
numerous sectors, including finance, education, health care, environment,
transportation, and philanthropy, to name a few. This chapter provides a
friendly description of essential concepts, mathematics, and algorithms that
lay the foundation for blockchain technology.


title:
Drift parameter estimation for nonlinear reflected stochastic
  differential equations

abstract:
  We study the maximum likehood estimator and least squares estimator for drift
parameters of nonlinear reflected stochastic differential equations based on
continuous observations. Under some regular conditions, we obtain the
consistency and give the asymptotic distributions of the two estimators. We
briefly remark that our methods could be applied the the reflected stochastic
processes with only one-sided reflecting barrier spontaneously. Numerical
studies show that the proposed estimators are adequate for practical use.


title: The Subdifferential Descent Method in a Nonsmooth Variational Problem

abstract:
  The paper is devoted to the classical variational problem with a nonsmooth
integrand of the functional to be minimized. The integrand is supposed to be
subdifferentiable. Under some natural conditions the subdifferentiability of
the functional considered is proved. The problem of finding the subdifferential
descent is being solved and the subdifferential descent method is applied to
solve the original problem. The algorithm developed is demonstrated by
examples.


title:
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools
  Stock Prediction

abstract:
  More and more investors and machine learning models rely on social media
(e.g., Twitter and Reddit) to gather real-time information and sentiment to
predict stock price movements. Although text-based models are known to be
vulnerable to adversarial attacks, whether stock prediction models have similar
vulnerability is underexplored. In this paper, we experiment with a variety of
adversarial attack configurations to fool three stock prediction victim models.
We address the task of adversarial generation by solving combinatorial
optimization problems with semantics and budget constraints. Our results show
that the proposed attack method can achieve consistent success rates and cause
significant monetary loss in trading simulation by simply concatenating a
perturbed but semantically similar tweet.


title: New Tsallis Agegraphic Dark Energy

abstract:
  The proposed model is a study of the nature of dark energy through
non-extensive Tsallis entropy. The method is based on the Karolyhazy relation
which is a combined idea from quantum physics and general relativity. Dark
energy is the energy density of quantum fluctuations in space-time. This is the
key idea behind proposing agegraphic dark energy (ADE) models here. The
parameter $\delta$ is used to measure the quantitative distinction from the
standard scenario. To look at the cosmological implications of the hypothesized
dark energy model, as well as the expansion of the Universe filled with zero
pressure matter and the resulting dark energy alternatives, the role of IR
cutoff is played by age of the universe. The dynamic behavior of dark energy
density parameter is carried out. The expressions for the equation of state
parameter and deceleration parameter are obtained. The analysis is performed by
taking into account a no flow as well as a flow of energy among the dark matter
and dark energy sectors of the universe.


title:
Some cosmological features of 4D Gauss-Bonnet gravity with varying
  cosmological constant

abstract:
  We explore some cosmological features of the newly suggested 4D Gauss-Bonnet
gravity through two different models assuming a varying cosmological constant.
Observational constraints, such as the cosmic transit and the flat curvature,
have been considered in constructing the models. The cosmology in the current
work has been probed using a given scale factor derived from the desired cosmic
behavior which is the inverse of the usual viewpoint. The stability and
cosmography have been studied for the two models.


title: Generalization of the basis theorem for the D-type Coxeter groups

abstract:
  The OGS for non-abelian groups is an interesting generalization of the basis
of finite abelian groups. The definition of OGS states that every element of a
group has a unique presentation as a product of some powers of specific
generators of the group, in a specific given order. In case of the symmetric
groups Sn there is a paper of R. Shwartz, which demonstrates a strong
connection between the OGS and the standard Coxeter presentation of the
symmetric group, which is called the standard OGS of Sn. In this paper we
generalize the standard OGS of Sn to the finite classical Coxeter group Dn. We
describe the exchange laws for the generalized standard OGS of Dn, and we
connect it to the Coxeter length of elements of Dn.


title:
Initial Access for Millimeter-Wave and Terahertz Communications with
  Hybrid Beamforming

abstract:
  In order to achieve terabits-per-second (Tbps) data rates in the
sixth-generation (6G) mobile system, wireless communications are required to
exploit the abundant spectrum in the millimeter-wave (mmWave) and terahertz
(THz) bands. However, high-frequency transmission heavily relies on high
beamforming gain to compensate for severe propagation loss. A beam-based system
faces a barrier in the process of initial access, where a base station must
broadcast synchronization signals and system information to all users within
its coverage. Hence, this paper proposes a novel omnidirectional broadcasting
scheme for mmWave and THz systems with hybrid beamforming. It provides an
instantaneously equal gain over all directions by forming complementary beams
over sub-arrays. Numerical results verify that it can achieve omnidirectional
coverage with a performance that remarkably outperforms the previous scheme.


title:
Fast algorithms for nonlinear and constrained phase retrieval in
  near-field X-ray holography based on Tikhonov regularization

abstract:
  Based on phase retrieval, lensless coherent imaging and in particular
holography offers quantitative phase and amplitude images. This is of
particular importance for spectral ranges where suitable lenses are
challenging, such as for hard x-rays. Here, we propose a phase retrieval
approach for inline x-ray holography based on Tikhonov regularization applied
to the full nonlinear forward model of image formation. The approach can be
seen as a nonlinear generalization of the well-established
contrast-transfer-function (CTF) reconstruction method. While similar methods
have been proposed before, the current work achieves nonlinear, constrained
phase retrieval at competitive computation times. We thus enable
high-throughput imaging of optically strong objects beyond the scope of CTF.
Using different examples of inline holograms obtained from illumination by a
x-ray waveguide-source, we demonstrate superior image quality even for samples
which do not obey the assumption of a weakly varying phase. Since the presented
approach does not rely on linearization, we expect it to be well suited also
for other probes such as visible light or electrons, which often exhibit strong
phase interaction.