title:
Short-term passenger flow prediction for multi-traffic modes: A residual
  network and Transformer based multi-task learning method

abstract:
  With the prevailing of mobility as a service (MaaS), it becomes increasingly
important to manage multi-traffic modes simultaneously and cooperatively. As an
important component of MaaS, short-term passenger flow prediction for
multi-traffic modes has thus been brought into focus. It is a challenging
problem because the spatiotemporal features of multi-traffic modes are
critically complex. To solve the problem, this paper proposes a multi-task
learning-based model, called Res-Transformer, for short-term passenger flow
prediction of multi-traffic modes (subway, taxi, and bus). Each traffic mode is
treated as a single task in the model. The Res-Transformer consists of three
parts: (1) several modified transformer layers comprising 2D convolutional
neural networks (CNN) and multi-head attention mechanism, which helps to
extract the spatial and temporal features of multi-traffic modes, (2) a
residual network architecture used to extract the inner pattern of different
traffic modes and enhance the passenger flow features of multi-traffic modes.
The Res-Transformer model is evaluated on two large-scale real-world datasets
from Beijing, China. One is the region of a traffic hub and the other is the
region of a residential area. Experiments are conducted to compare the
performance of the proposed model with several state-of-the-art models to prove
the effectiveness and robustness of the proposed method. This paper can give
critical insights into the short-tern passenger flow prediction for
multi-traffic modes.


title:
Integration of bounded monotone functions: Revisiting the nonsequential
  case, with a focus on unbiased Monte Carlo (randomized) methods

abstract:
  In this article we revisit the problem of numerical integration for monotone
bounded functions, with a focus on the class of nonsequential Monte Carlo
methods. We first provide new a lower bound on the maximal $L^p$ error of
nonsequential algorithms, improving upon a theorem of Novak when p > 1. Then we
concentrate on the case p = 2 and study the maximal error of two unbiased
methods-namely, a method based on the control variate technique, and the
stratified sampling method.


title:
Resolving the information loss paradox from the five-dimensional minimal
  supergravity black hole

abstract:
  In this paper, we study the Hawking tunneling radiation of the charged
rotating black hole in five-dimensional minimal supergravity theory by using
the semiclassical Hamilton-Jacobi equation. By using two separated ways we
obtain the corrected entropy of the black hole. Equality of results gives us a
special condition that may solve the information loss paradox. Then, we focus
on the phase transitions, and the results show that if the effects of thermal
fluctuations are incorporated in the entropy, the black hole is unstable, while
there are phase transitions according to the sign-changing behavior of the
black hole specific heat. We find that, in presence of thermal fluctuations,
the black holes of five-dimensional minimal supergravity behave like the black
holes in Horava-Lifshitz gravity hence the second-order phase transition is
possible.


title: A remark on the Half wave Schr\"odinger equation in the energy space

abstract:
  We investigate the Cauchy problem for the half wave Schr\"odinger equation in
the energy space. We derive the local well-posedness in the energy space for
the odd power type nonlinearities under certain additional assumption for the
initial data, namely $\hat{u}_0 \in L^1_{\xi, \eta}(\mathbb{R}^2)$.


title: A safe Hosmer-Lemeshow test

abstract:
  This technical report proposes an alternative to the Hosmer-Lemeshow (HL)
test for evaluating the calibration of probability forecasts for binary events.
The approach is based on e-values, a new tool for hypothesis testing. An
e-value is a random variable with expected value less or equal to 1 under a
null hypothesis. Large e-values give evidence against the null hypothesis, and
the multiplicative inverse of an e-value is a p-value. In a simulation study,
the proposed e-values detect even slight miscalibration for larger sample
sizes, but with a reduced power compared to the original HL test.


title: Making use of supercomputers in financial machine learning

abstract:
  This article is the result of a collaboration between Fujitsu and Advestis.
This collaboration aims at refactoring and running an algorithm based on
systematic exploration producing investment recommendations on a
high-performance computer of the Fugaku, to see whether a very high number of
cores could allow for a deeper exploration of the data compared to a cloud
machine, hopefully resulting in better predictions. We found that an increase
in the number of explored rules results in a net increase in the predictive
performance of the final ruleset. Also, in the particular case of this study,
we found that using more than around 40 cores does not bring a significant
computation time gain. However, the origin of this limitation is explained by a
threshold-based search heuristic used to prune the search space. We have
evidence that for similar data sets with less restrictive thresholds, the
number of cores actually used could very well be much higher, allowing
parallelization to have a much greater effect.


title: Precision-dissipation trade-off for driven stochastic systems

abstract:
  In this paper, we derive a closed expression for how precisely a small-scaled
system can follow a pre-defined trajectory, while keeping its dissipation below
a fixed limit. In the high-precision limit, the total dissipation is inversely
proportional to the expected deviation from the pre-defined trajectory. We also
find several general properties for the optimal protocol. The results are
verified on a simple electrical circuit.


title:
Colliding-probe bi-atomic magnetometers via energy circulation: Breaking
  symmetry-enforced magneto-optical rotation blockade

abstract:
  We have developed an inelastic wave scattering based colliding-probe
bi-atomic magnetometer theory. We show a propagation growth blockade in single
probe based magnetic field sensing schemes, revealing the root cause of strong
suppression of nonlinear magneto-optical rotation effect (NMORE) in single
probe based atomic magnetometers. We further show, both experimentally and
theoretically, a colliding probe bi-atomic magnetometer that lifts this NMORE
blockade. The directional energy circulation in this new atomic magnetometry
technique results in more than two orders of magnitude increase in NMORE signal
as well as greater than 6dB increase of magnetic field detection sensitivity.
The new technique may have broad applications in photon gates and switching
operations.


title:
The Impact of IT Projects Complexity on Cost Overruns and Schedule
  Delays

abstract:
  This study aims to assist Company leaders and management team in providing
more accurate time and cost estimations for future software projects. This
paper focuses on analyzing the relationship between project complexity and cost
and time overrun. The sample data of around 50 projects are collected from the
Changepoint database. The two research hypotheses were defined and tested using
statistical techniques. The quantitative analysis includes descriptive analysis
and regression modelling. Experimental results show a strong positive linear
relationship between project complexity and cost and time overrun. Based on the
study outcome, there is a need to focus on the planning phase of complex
projects and allocate more time and budget to estimate the completion date and
the initial budget of this type of project more accurately.


title:
Identifying charge density and dielectric environment of graphene using
  Raman spectroscopy and deep learning

abstract:
  The impact of the environment on graphene's properties such as strain, charge
density, and dielectric environment can be evaluated by Raman spectroscopy.
These environmental interactions are not trivial to determine, since they
affect the spectra in overlapping ways. Data preprocessing such as background
subtraction and peak fitting is typically used. Moreover, collected
spectroscopic data vary due to different experimental setups and environments.
Such variations, artifacts, and environmental differences pose a challenge in
accurate spectral analysis. In this work, we developed a deep learning model to
overcome the effects of such variations and classify graphene Raman spectra
according to different charge densities and dielectric environments. We
consider two approaches: deep learning models and machine learning algorithms
to classify spectra with slightly different charge density or dielectric
environment. These two approaches show similar success rates for high
Signal-to-Noise data. However, deep learning models are less sensitive to
noise. To improve the accuracy and generalization of all models, we use data
augmentation through additive noise and peak shifting. We demonstrated the
spectra classification with 99% accuracy using a convolutional neural net (CNN)
model. The CNN model is able to classify Raman spectra of graphene with
different charge doping levels and even subtle variation in the spectra between
graphene on SiO$_2$ and graphene on silanized SiO$_2$. Our approach has the
potential for fast and reliable estimation of graphene doping levels and
dielectric environments. The proposed model paves the way for achieving
efficient analytical tools to evaluate the properties of graphene.


title: Towards Practices for Human-Centered Machine Learning

abstract:
  "Human-centered machine learning" (HCML) is a term that describes machine
learning that applies to human-focused problems. Although this idea is
noteworthy and generates scholarly excitement, scholars and practitioners have
struggled to clearly define and implement HCML in computer science. This
article proposes practices for human-centered machine learning, an area where
studying and designing for social, cultural, and ethical implications are just
as important as technical advances in ML. These practices bridge between
interdisciplinary perspectives of HCI, AI, and sociotechnical fields, as well
as ongoing discourse on this new area. The five practices include ensuring HCML
is the appropriate solution space for a problem; conceptualizing problem
statements as position statements; moving beyond interaction models to define
the human; legitimizing domain contributions; and anticipating sociotechnical
failure. I conclude by suggesting how these practices might be implemented in
research and practice.


title: Quantum teleportation of quantum causal structures

abstract:
  Quantum teleportation is a very helpful information-theoretic protocol that
allows to transfer an unknown arbitrary quantum state from one location to
another without having to transmit the quantum system through the intermediate
region. Quantum states, quantum channels, and indefinite causal structures are
all examples of quantum causal structures that not only enable advanced quantum
information processing functions, but can also model causal structures in
nonclassical spacetimes. In this letter, we develop quantum teleportation of
arbitrary quantum causal structures, as formalized by the process matrix
framework. Instead of teleporting all the physical degrees of freedom that
implement the causal structure, the central idea is to just teleport the inputs
to and outputs from the operations of agents. The communication of outcomes of
Bell state measurements, which is necessary for deterministic quantum
teleportation, is not possible for all causal structures that one might wish to
investigate. To avoid this problem, we propose partially and fully
post-selected teleportation protocols. We prove that our partially
post-selected teleportation protocol is compatible with all quantum causal
structures, including those that involve indefinite causal order.


title:
Parametrization by Horizontal Constraints in the Study of Algorithmic
  Properties of $\mathbb{Z}^2$-Subshift of Finite Type

abstract:
  The non-emptiness, called the Domino Problem, and the characterization of the
possible entropies of $\mathbb{Z}^2$-subshifts of finite type are standard
problems of symbolic dynamics. In this article we study these questions with
horizontal constraints fixed beforehand as a parameter. We determine for which
horizontal constraints the Domino Problem is undecidable and when all
right-recursively enumerable numbers can be obtained as entropy, with two
approaches: either the additional local rules added to the horizontal
constraints can be of any shape, or they can only be vertical rules.


title: se-Shweshwe Inspired Fashion Generation

abstract:
  Fashion is one of the ways in which we show ourselves to the world. It is a
reflection of our personal decisions and one of the ways in which people
distinguish and represent themselves. In this paper, we focus on the fashion
design process and expand computer vision for fashion beyond its current focus
on western fashion. We discuss the history of Southern African se-Shweshwe
fabric fashion, the collection of a se-Shweshwe dataset, and the application of
sketch-to-design image generation for affordable fashion-design. The
application to fashion raises both technical questions of training with small
amounts of data, and also important questions for computer vision beyond
fairness, in particular ethical considerations on creating and employing
fashion datasets, and how computer vision supports cultural representation and
might avoid algorithmic cultural appropriation.


title:
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic
  Segmentation

abstract:
  Image semantic segmentation aims at the pixel-level classification of images,
which has requirements for both accuracy and speed in practical application.
Existing semantic segmentation methods mainly rely on the high-resolution input
to achieve high accuracy and do not meet the requirements of inference time.
Although some methods focus on high-speed scene parsing with lightweight
architectures, they can not fully mine semantic features under low computation
with relatively low performance. To realize the real-time and high-precision
segmentation, we propose a new method named Boundary Corrected Multi-scale
Fusion Network, which uses the designed Low-resolution Multi-scale Fusion
Module to extract semantic information. Moreover, to deal with boundary errors
caused by low-resolution feature map fusion, we further design an additional
Boundary Corrected Loss to constrain overly smooth features. Extensive
experiments show that our method achieves a state-of-the-art balance of
accuracy and speed for the real-time semantic segmentation.


title:
Semi-overlap Functions and Novel Fuzzy Reasoning Algorithms with
  Applications

abstract:
  It is worth noticing that a fuzzy conjunction and its corresponding fuzzy
implication can form a residual pair if and only if it is left-continuous. In
order to get a more general result related on residual implications that
induced by aggregation functions, we relax the definition of general overlap
functions, more precisely, removing its right-continuous, and then introduce a
new kind of aggregation functions, which called semi-overlap functions.
Subsequently, we study some of their related algebraic properties and
investigate their corresponding residual implications. Moreover, serval
scholars have provided kinds of methods for fuzzy modus ponens (FMP,for short)
problems so far, such as Zadeh's compositional rule of inference (CRI, for
short), Wang's triple I method (TIM, for short) and the quintuple implication
principle (QIP, for short). Compared with CRI and TIM methods, QIP method has
some advantages in solving FMP problems, in this paper, we further consider the
QIP method for FMP problems and prove that it satisfies the reducibility of
multiple-rules fuzzy reasoning. Finally, we propose a new classification
algorithm that based on semi-overlap functions and QIP method, which called
SO5I-FRC algorithm. Through the comparative tests, the average accuracy of
SO5I-FRC algorithm is higher than FARC-HD algorithm. The experimental results
indicate that semi-overlap functions and QIP method have certain advantages and
a wide range of applications in classification problems.


title:
An Analytical Approach to Compute the Exact Preimage of Feed-Forward
  Neural Networks

abstract:
  Neural networks are a convenient way to automatically fit functions that are
too complex to be described by hand. The downside of this approach is that it
leads to build a black-box without understanding what happened inside. Finding
the preimage would help to better understand how and why such neural networks
had given such outputs. Because most of the neural networks are noninjective
function, it is often impossible to compute it entirely only by a numerical
way. The point of this study is to give a method to compute the exact preimage
of any Feed-Forward Neural Network with linear or piecewise linear activation
functions for hidden layers. In contrast to other methods, this one is not
returning a unique solution for a unique output but returns analytically the
entire and exact preimage.


title: Active learning with binary models for real time data labelling

abstract:
  Machine learning (ML) and Deep Learning (DL) tasks primarily depend on data.
Most of the ML and DL applications involve supervised learning which requires
labelled data. In the initial phases of ML realm lack of data used to be a
problem, now we are in a new era of big data. The supervised ML algorithms
require data to be labelled and of good quality. Labelling task requires a
large amount of money and time investment. Data labelling require a skilled
person who will charge high for this task, consider the case of the medical
field or the data is in bulk that requires a lot of people assigned to label
it. The amount of data that is well enough for training needs to be known,
money and time can not be wasted to label the whole data. This paper mainly
aims to propose a strategy that helps in labelling the data along with oracle
in real-time. With balancing on model contribution for labelling is 89 and 81.1
for furniture type and intel scene image data sets respectively. Further with
balancing being kept off model contribution is found to be 83.47 and 78.71 for
furniture type and flower data sets respectively.


title: The self-adjoint toroidal dipole operator in nanostructures

abstract:
  The parity violation in nuclear reactions led to the discovery of the new
class of toroidal multipoles. Since then, it was observed that toroidal
multipoles are present in the electromagnetic structure of systems at all
scales, from elementary particles, to solid state systems and metamaterials.
The toroidal dipole ${\bf T}$ (the lowest order multipole) is the most common.
In quantum systems, this corresponds to the toroidal dipole operator $\hat{\bf
T}$, with the projections $\hat{T}_i$ ($i=1,2,3$) on the coordinate axes. Here
we analyze a quantum particle in a system with cylindrical symmetry, which is a
typical system in which toroidal moments appear. We find the expressions for
the Hamiltonian, momenta, and toroidal dipole operators in adequate curvilinear
coordinates, which allow us to find analytical expressions for the
eigenfunctions of the momentum operators. While the toroidal dipole is
hermitian, it is not self-adjoint, but in the new set of coordinates the
operator $\hat{T}_3$ splits into two components, one of which is (only)
hermitian, whereas the other one is self-adjoint. The self-adjoint component is
the one that is physically significant and represents an observable.
Furthermore, we numerically diagonalize the Hamiltonian and the toroidal dipole
operator and find their eigenfunctions and eigenvalues. We write the partition
function and calculate the thermodynamic quantities for a system of ideal
particles on a torus. Besides proving that the toroidal dipole is self-adjoint
and therefore an observable (a finding of fundamental relevance) such systems
open up the possibility of making metamaterials that exploit the quantization
and the quantum properties of the toroidal dipoles.


title:
Bridge the Gap between Supervised and Unsupervised Learning for
  Fine-Grained Classification

abstract:
  Unsupervised learning technology has caught up with or even surpassed
supervised learning technology in general object classification (GOC) and
person re-identification (re-ID). However, it is found that the unsupervised
learning of fine-grained visual classification (FGVC) is more challenging than
GOC and person re-ID. In order to bridge the gap between unsupervised and
supervised learning for FGVC, we investigate the essential factors (including
feature extraction, clustering, and contrastive learning) for the performance
gap between supervised and unsupervised FGVC. Furthermore, we propose a simple,
effective, and practical method, termed as UFCL, to alleviate the gap. Three
key issues are concerned and improved: First, we introduce a robust and
powerful backbone, ResNet50-IBN, which has an ability of domain adaptation when
we transfer ImageNet pre-trained models to FGVC tasks. Next, we propose to
introduce HDBSCAN instead of DBSCAN to do clustering, which can generate better
clusters for adjacent categories with fewer hyper-parameters. Finally, we
propose a weighted feature agent and its updating mechanism to do contrastive
learning by using the pseudo labels with inevitable noise, which can improve
the optimization process of learning the parameters of the network. The
effectiveness of our UFCL is verified on CUB-200-2011, Oxford-Flowers,
Oxford-Pets, Stanford-Dogs, Stanford-Cars and FGVC-Aircraft datasets. Under the
unsupervised FGVC setting, we achieve state-of-the-art results, and analyze the
key factors and the important parameters to provide a practical guidance.


title: 3D particle simulations of positive air-methane streamers for combustion

abstract:
  Streamer discharges can be used as a primary source of reactive species for
plasma-assisted combustion. In this research we investigate positive streamers
in a stoichiometric air-methane mixture at 1 bar and 300 K with a
three-dimensional particle-in-cell model for the electrons. We first discuss
suitable electron scattering cross sections and an extension of the
photoionization mechanism to air-methane mixtures. We discuss that the addition
of 9.5% methane leaves electron transport and reaction coefficients essentially
unchanged, but it largely suppresses photoionization and shortens the photon
mean free path. This leads to (1) accelerated streamer branching, (2) higher
electric field enhancement at the streamer head, (3) lower internal electric
fields, and (4) higher electron densities in the streamer channel. We also
calculate the time-integrated energy density deposited during the evolution of
positive streamers in background electric fields of 12.5 and 20 kV/cm. We find
typical values of the deposited energy density in the range of 0.5-2.5
kJ/m$^{3}$ within the ionized interior of streamers with a length of 5 mm; this
value is rather independent of the electric fields applied here. Finally we
find that the energy deposited in the inelastic electron scattering processes
mainly produces reactive nitrogen species: N$_2$ triplet states and N, but also
O and H radicals. The production of H$_2$ and O$_2$ singlet states also occurs
albeit less pronounced. Our calculation of the primary production of reactive
species can for example be used in global chemistry models.


title: Quantum correlation measurement of laser power noise below shot noise

abstract:
  In this Letter, the quantum correlation measurement technique as a method of
power noise monitoring is investigated. Its principal idea of correlating two
photodetector signals is introduced and contrasted to the conventional
approach, which uses only a single photodetector. We discuss how this scheme
can be used to obtain power noise information below the shot noise of the
detected beam and also below the electronic dark noise of the individual
photodetectors, both of which is not possible with the conventional approach.
Furthermore, experimental results are presented, that demonstrate a detection
of technical laser power noise one order of magnitude below the shot noise of
the detected beam.


title: Parameter-free Mirror Descent

abstract:
  We develop a modified online mirror descent framework that is suitable for
building adaptive and parameter-free algorithms in unbounded domains. We
leverage this technique to develop the first unconstrained online linear
optimization algorithm achieving an optimal dynamic regret bound, and we
further demonstrate that natural strategies based on
Follow-the-Regularized-Leader are unable to achieve similar results. We also
apply our mirror descent framework to build new parameter-free implicit
updates, as well as a simplified and improved unconstrained scale-free
algorithm.


title: Interblock redundancy reduction using QUADTREES

abstract:
  This paper applies the quadtree structure for image coding. The goal is to
adapt the block size and thus to increase the compression ratio (without
reducing SNR). Also, the computational time is not significatively increased.
It has been applied to Block Truncation Coding of still images, and motion
vector coding (interframe). An inter/intraframe application is also discussed.


title:
Propagation of chaos: a review of models, methods and applications. I.
  Models and methods

abstract:
  The notion of propagation of chaos for large systems of interacting particles
originates in statistical physics and has recently become a central notion in
many areas of applied mathematics. The present review describes old and new
methods as well as several important results in the field. The models
considered include the McKean-Vlasov diffusion, the mean-field jump models and
the Boltzmann models. The first part of this review is an introduction to
modelling aspects of stochastic particle systems and to the notion of
propagation of chaos. The second part presents concrete applications and a more
detailed study of some of the important models in the field.


title: Technological evaluation of two AFIS systems

abstract:
  This paper provides a technological evaluation of two Automatic Fingerprint
Identification Systems (AFIS) used in forensic applications. Both of them are
installed and working in Spanish police premises. The first one is a Printrak
AFIS 2000 system with a database of more than 450,000 fingerprints, while the
second one is a NEC AFIS 21 SAID NT-LEXS Release 2.4.4 with a database of more
than 15 million fingerprints. Our experiments reveal that although both systems
can manage inkless fingerprints, the latest one offers better experimental
results


title: Memory Planning for Deep Neural Networks

abstract:
  We study memory allocation patterns in DNNs during inference, in the context
of large-scale systems. We observe that such memory allocation patterns, in the
context of multi-threading, are subject to high latencies, due to
\texttt{mutex} contention in the system memory allocator. Latencies incurred
due to such \texttt{mutex} contention produce undesirable bottlenecks in
user-facing services. Thus, we propose a "memorization" based technique,
\texttt{MemoMalloc}, for optimizing overall latency, with only moderate
increases in peak memory usage. Specifically, our technique consists of a
runtime component, which captures all allocations and uniquely associates them
with their high-level source operation, and a static analysis component, which
constructs an efficient allocation "plan". We present an implementation of
\texttt{MemoMalloc} in the PyTorch deep learning framework and evaluate memory
consumption and execution performance on a wide range of DNN architectures. We
find that \texttt{MemoMalloc} outperforms state-of-the-art general purpose
memory allocators, with respect to DNN inference latency, by as much as 40\%.


title:
Deep Learning based Prediction of MSI using MMR Markers in Colorectal
  Cancer

abstract:
  The accurate diagnosis and molecular profiling of colorectal cancers are
critical for planning the best treatment options for patients. Microsatellite
instability (MSI) or mismatch repair (MMR) status plays a vital role in
appropriate treatment selection, has prognostic implications and is used to
investigate the possibility of patients having underlying genetic disorders
(Lynch syndrome). NICE recommends that all CRC patients should be offered
MMR/MSI testing. Immunohistochemistry is commonly used to assess MMR status
with subsequent molecular testing performed as required. This incurs
significant extra costs and requires additional resources. The introduction of
automated methods that can predict MSI or MMR status from a target image could
substantially reduce the cost associated with MMR testing. Unlike previous
studies on MSI prediction involving training a CNN using coarse labels (MSI vs
Microsatellite Stable (MSS)), we have utilised fine-grain MMR labels for
training purposes. In this paper, we present our work on predicting MSI status
in a two-stage process using a single target slide either stained with CK8/18
or H&E. First, we trained a multi-headed convolutional neural network model
where each head was responsible for predicting one of the MMR protein
expressions. To this end, we performed the registration of MMR stained slides
to the target slide as a pre-processing step. In the second stage, statistical
features computed from the MMR prediction maps were used for the final MSI
prediction. Our results demonstrated that MSI classification can be improved by
incorporating fine-grained MMR labels in comparison to the previous approaches
in which only coarse labels were utilised.


title:
Artificial neural network assisted inverse design of metasurfaces for
  microwave absorption

abstract:
  We show that a tandem neural network can perform the inverse design of
microwave absorbers. After training, the network can give the structural and
material parameters of a metamaterial that satisfies some pre-specified
requirements. As an example, we use the neural network to design a metamaterial
absorber that has a reflection loss of 20 dB from 4.5-31.5 GHz and the
thickness is close to the causality limit. The same network can design
dual-band absorbers with a pre-specified absorption profile.


title: Physics-Informed Neural Networks for Quantum Eigenvalue Problems

abstract:
  Eigenvalue problems are critical to several fields of science and
engineering. We expand on the method of using unsupervised neural networks for
discovering eigenfunctions and eigenvalues for differential eigenvalue
problems. The obtained solutions are given in an analytical and differentiable
form that identically satisfies the desired boundary conditions. The network
optimization is data-free and depends solely on the predictions of the neural
network. We introduce two physics-informed loss functions. The first, called
ortho-loss, motivates the network to discover pair-wise orthogonal
eigenfunctions. The second loss term, called norm-loss, requests the discovery
of normalized eigenfunctions and is used to avoid trivial solutions. We find
that embedding even or odd symmetries to the neural network architecture
further improves the convergence for relevant problems. Lastly, a patience
condition can be used to automatically recognize eigenfunction solutions. This
proposed unsupervised learning method is used to solve the finite well,
multiple finite wells, and hydrogen atom eigenvalue quantum problems.


title:
Long-Tailed Classification with Gradual Balanced Loss and Adaptive
  Feature Generation

abstract:
  The real-world data distribution is essentially long-tailed, which poses
great challenge to the deep model. In this work, we propose a new method,
Gradual Balanced Loss and Adaptive Feature Generator (GLAG) to alleviate
imbalance. GLAG first learns a balanced and robust feature model with Gradual
Balanced Loss, then fixes the feature model and augments the under-represented
tail classes on the feature level with the knowledge from well-represented head
classes. And the generated samples are mixed up with real training samples
during training epochs. Gradual Balanced Loss is a general loss and it can
combine with different decoupled training methods to improve the original
performance. State-of-the-art results have been achieved on long-tail datasets
such as CIFAR100-LT, ImageNetLT, and iNaturalist, which demonstrates the
effectiveness of GLAG for long-tailed visual recognition.


title:
A genetic algorithm for straight-line embedding of a cycle onto a given
  set of points inside the general simple polygons

abstract:
  In this paper, we have examined the problem of embedding a cycle of n
vertices onto a given set of n points inside a simple polygon. The goal of the
problem is that the cycle must be embedded without bends and does not intersect
itself and the polygon. This problem is a special case of the problem of
finding a (s, X, t) - path inside a simple polygon with the minimum number of
bends and intersections. The complexity of the problem is examined in this
paper is open, but it has been proved that similar problems are NP-complete. We
have presented a metaheuristic algorithm based on a genetic algorithm for
straight-line embedding of a cycle with the minimum numbers of intersections,
onto a given set of points inside the general simple polygons. The efficiency
of the proposed genetic algorithm is due to the definition of the mutation
operation, which removes it if there is an intersection between the embedded
edges of the cycle. The experimental results show that the results of the
version of the algorithm that uses this mutation operation are much more
efficient than the version that uses only the usual two-points mutation
operation.


title: Deep learning study on the Dirac eigenvalue spectrum of staggered quarks

abstract:
  We study the chirality of staggered quarks on the Dirac eigenvalue spectrum
using deep learning (DL) techniques. The Kluberg-Stern method to construct
staggered bilinear operators conserves continuum property such as recursion
relations, uniqueness of chirality, and Ward identities, which leads to a
unique and characteristic pattern (we call it "leakage pattern (LP)") in the
matrix elements of the chirality operator sandwiched between two quark
eigenstates of staggered Dirac operator. DL analysis gives $99.4(2)\%$ accuracy
on normal gauge configurations and $0.998$ AUC (Area Under ROC Curve) for
classifying non-zero mode octets in the Dirac eigenvalue spectrum. It confirms
that the leakage pattern is universal on normal gauge configurations. The
multi-layer perceptron (MLP) method turns out to be the best DL model for our
study on the LP.


title:
Correlation of powers of H\"usler-Reiss vectors and Brown-Resnick
  fields, and application to insured wind losses

abstract:
  H\"usler-Reiss vectors and Brown-Resnick fields are popular models in
multivariate and spatial extreme-value theory, respectively, and are widely
used in applications. We provide analytical formulas for the correlation
between powers of the components of the bivariate H\"usler-Reiss vector, extend
these to the case of the Brown-Resnick field, and thoroughly study the
properties of the resulting dependence measure. The use of correlation is
justified by spatial risk theory, while power transforms are insightful when
taking correlation as dependence measure, and are moreover very suited damage
functions for weather events such as wind extremes or floods. This makes our
theoretical results worthwhile for, e.g., actuarial applications. We finally
perform a case study involving insured losses from extreme wind speeds in
Germany, and obtain valuable conclusions for the insurance industry.


title: WEMAC: Women and Emotion Multi-modal Affective Computing dataset

abstract:
  Among the seventeen Sustainable Development Goals (SDGs) proposed within the
2030 Agenda and adopted by all the United Nations member states, the Fifth SDG
is a call for action to turn Gender Equality into a fundamental human right and
an essential foundation for a better world. It includes the eradication of all
types of violence against women. Within this context, the UC3M4Safety research
team aims to develop Bindi. This is a cyber-physical system which includes
embedded Artificial Intelligence algorithms, for user real-time monitoring
towards the detection of affective states, with the ultimate goal of achieving
the early detection of risk situations for women. On this basis, we make use of
wearable affective computing including smart sensors, data encryption for
secure and accurate collection of presumed crime evidence, as well as the
remote connection to protecting agents. Towards the development of such system,
the recordings of different laboratory and into-the-wild datasets are in
process. These are contained within the UC3M4Safety Database. Thus, this paper
presents and details the first release of WEMAC, a novel multi-modal dataset,
which comprises a laboratory-based experiment for 47 women volunteers that were
exposed to validated audio-visual stimuli to induce real emotions by using a
virtual reality headset while physiological, speech signals and self-reports
were acquired and collected. We believe this dataset will serve and assist
research on multi-modal affective computing using physiological and speech
information.


title:
Observation of an ultra-low $Q$-value electron-capture channel decaying
  to $^{75}$As via high-precision mass measurement

abstract:
  A precise determination of the atomic mass of $^{75}$As has been performed
utilizing the double Penning trap mass spectrometer, JYFLTRAP. The mass excess
is measured to be -73035.519(42) keV/c$^2$, which is a factor of 21 more
precise and 1.3(9) keV/c$^2$ lower than the adopted value in the newest Atomic
Mass Evaluation (AME2020). This value has been used to determine the
ground-state-to-ground-state electron-capture decay $Q$ value of $^{75}$Se and
$\beta^-$ decay $Q$ value of $^{75}$Ge, which are derived to be 866.041(81) keV
and 1178.561(65) keV, respectively. Using the nuclear energy-level data of
860.00(40) keV, 865.40(50) keV (final states of electron capture) and
1172.00(60) keV (final state of $\beta^-$ decay) for the excited states of
$^{75}$As$^*$, we have determined the ground-state-to-excited-state $Q$ values
for two transitions of $^{75}$Se $\rightarrow$ $^{75}$As$^*$ and one transition
of $^{75}$Ge $\rightarrow$ $^{75}$As$^*$. The ground-state-to-excited-state $Q$
values are determined to be 6.04(41) keV, 0.64(51) keV and 6.56(60) keV,
respectively, thus confirming that the three low $Q$-value transitions are all
energetically valid and one of them is a possible candidate channel for
antineutrino mass determination. Furthermore, the ground-state-to-excited-state
$Q$ value of transition $^{75}$Se $\rightarrow$ $^{75}$As$^*$ (865.40(50) keV)
is revealed to be ultra-low (< 1 keV) and the first-ever confirmed EC
transition possessing an ultra-low $Q$ value from direct measurements.


title:
A hybrid model-based evolutionary optimization with passive boundaries
  for physical human-robot interaction

abstract:
  The field of physical human-robot interaction has dramatically evolved in the
last decades. As a result, the robotic system's requirements have become more
challenging, including personalized behavior for different tasks and users.
Various machine learning techniques have been proposed to give the robot such
adaptability features. This paper proposes a model-based evolutionary
optimization algorithm to tune the apparent impedance of a wrist rehabilitation
device. We used passivity to define boundaries for the possible controller
outcomes, limiting the shared autonomy of the robot and ensuring the coupled
system stability. The experiment consists of a hardware-in-the-loop
optimization and a one-degree-of-freedom robot used for wrist rehabilitation.
Experimental results showed that the proposed technique could generate
customized passive impedance controllers for three subjects. Furthermore, when
compared with a constant impedance controller, the method suggested decreased
in 20\% the root mean square of interaction torques while maintaining stability
during optimization.


title:
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform
  for Efficient and Accurate Radar Odometry

abstract:
  Masking By Moving (MByM), provides robust and accurate radar odometry
measurements through an exhaustive correlative search across discretised pose
candidates. However, this dense search creates a significant computational
bottleneck which hinders real-time performance when high-end GPUs are not
available. Utilising the translational invariance of the Fourier Transform, in
our approach, f-MByM, we decouple the search for angle and translation. By
maintaining end-to-end differentiability a neural network is used to mask scans
and trained by supervising pose prediction directly. Training faster and with
less memory, utilising a decoupled search allows f-MByM to achieve significant
run-time performance improvements on a CPU (168%) and to run in real-time on
embedded devices, in stark contrast to MByM. Throughout, our approach remains
accurate and competitive with the best radar odometry variants available in the
literature -- achieving an end-point drift of 2.01% in translation and
6.3deg/km on the Oxford Radar RobotCar Dataset.


title: Sensitivity Measures Based on Scoring Functions

abstract:
  We propose a holistic framework for constructing sensitivity measures for any
elicitable functional $T$ of a response variable. The sensitivity measures,
termed score-based sensitivities, are constructed via scoring functions that
are (strictly) consistent for $T$. These score-based sensitivities quantify the
relative improvement in predictive accuracy when available information, e.g.,
from explanatory variables, is used ideally. We establish intuitive and
desirable properties of these sensitivities and discuss advantageous choices of
scoring functions leading to scale-invariant sensitivities. Since elicitable
functionals typically possess rich classes of (strictly) consistent scoring
functions, we demonstrate how Murphy diagrams can provide a picture of all
score-based sensitivity measures. We discuss the family of score-based
sensitivities for the mean functional (of which the Sobol indices are a special
case) and risk functionals such as Value-at-Risk, and the pair Value-at-Risk
and Expected Shortfall. The sensitivity measures are illustrated using numerous
examples, including the Ishigami--Homma test function. In a simulation study,
estimation of score-based sensitivities for a non-linear insurance portfolio is
performed using neural nets.


title:
JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup
  Segmentation and Fovea Detection

abstract:
  Fundus photography has been routinely used to document the presence and
severity of various retinal degenerative diseases such as age-related macula
degeneration, glaucoma, and diabetic retinopathy, for which the fovea, optic
disc (OD), and optic cup (OC) are important anatomical landmarks.
Identification of those anatomical landmarks is of great clinical importance.
However, the presence of lesions, drusen, and other abnormalities during
retinal degeneration severely complicates automatic landmark detection and
segmentation. Most existing works treat the identification of each landmark as
a single task and typically do not make use of any clinical prior information.
In this paper, we present a novel method, named JOINED, for prior guided
multi-task learning for joint OD/OC segmentation and fovea detection. An
auxiliary branch for distance prediction, in addition to a segmentation branch
and a detection branch, is constructed to effectively utilize the distance
information from each image pixel to landmarks of interest. Our proposed JOINED
pipeline consists of a coarse stage and a fine stage. At the coarse stage, we
obtain the OD/OC coarse segmentation and the heatmap localization of fovea
through a joint segmentation and detection module. Afterwards, we crop the
regions of interest for subsequent fine processing and use predictions obtained
at the coarse stage as additional information for better performance and faster
convergence. Experimental results reveal that our proposed JOINED outperforms
existing state-of-the-art approaches on the publicly-available GAMMA, PALM, and
REFUGE datasets of fundus images. Furthermore, JOINED ranked the 5th on the
OD/OC segmentation and fovea detection tasks in the GAMMA challenge hosted by
the MICCAI2021 workshop OMIA8.


title:
On the convergence of second-order in time numerical discretizations for
  the evolution Navier-Stokes equations

abstract:
  We prove the convergence of certain second-order numerical methods to weak
solutions of the Navier-Stokes equations satisfying in addition the local
energy inequality, and therefore suitable in the sense of Scheffer and
Caffarelli-Kohn-Nirenberg. More precisely, we treat the space-periodic case in
three space-dimensions and we consider a full discretization in which the the
classical Crank-Nicolson method ($\theta$-method with $\theta=1/2$) is used to
discretize the time variable, while in the space variables we consider finite
elements. The convective term is discretized in several implicit,
semi-implicit, and explicit ways. In particular, we focus on proving (possibly
conditional) convergence of the discrete solutions towards weak solutions
(satisfying a precise local energy balance), without extra regularity
assumptions on the limit problem. We do not prove orders of convergence, but
our analysis identifies some numerical schemes providing also alternate proofs
of existence of "physically relevant" solutions in three space dimensions.


title: Constraints on future analysis metadata systems in High Energy Physics

abstract:
  In High Energy Physics (HEP), analysis metadata comes in many forms -- from
theoretical cross-sections, to calibration corrections, to details about file
processing. Correctly applying metadata is a crucial and often time-consuming
step in an analysis, but designing analysis metadata systems has historically
received little direct attention. Among other considerations, an ideal metadata
tool should be easy to use by new analysers, should scale to large data volumes
and diverse processing paradigms, and should enable future analysis
reinterpretation. This document, which is the product of community discussions
organised by the HEP Software Foundation, categorises types of metadata by
scope and format and gives examples of current metadata solutions. Important
design considerations for metadata systems, including sociological factors,
analysis preservation efforts, and technical factors, are discussed. A list of
best practices and technical requirements for future analysis metadata systems
is presented. These best practices could guide the development of a future
cross-experimental effort for analysis metadata tools.


title: Controlled doping of electrocatalysts through engineering impurities

abstract:
  Fuel cells recombine water from H2 and O2 thereby powering e.g. cars or
houses with no direct carbon emission. In anion-exchange membrane fuel cells
(AEMFCs), to reach high power densities, operating at high pH is an alternative
to using large volumes of noble metals catalysts at the cathode, where the
oxygen-reduction reaction occurs. However, the sluggish kinetics of the
hydrogen-oxidation reaction (HOR) hinders upscaling despite promising
catalysts. Here, we observe an unexpected ingress of B into Pd nano-catalysts
synthesised by wet-chemistry, gain control over this B-doping, and report on
its influence on the HOR activity in alkaline conditions. We rationalize our
findings using ab-initio calculations of both H- and OH-adsorption on B-doped
Pd. Using this "impurity engineering" approach, we thus design Pt-free
catalysts as required in electrochemical energy conversion devices, e.g. next
generations of AEMFCs, that satisfy the economic and environmental constraints,
i.e. reasonable operating costs and long-term stability, to enable the hydrogen
economy.


title:
Privacy-Friendly Flexible IoT Health Data Processing with User-Centric
  Access Control

abstract:
  This paper proposes a novel Single and Multiple user(s) data Aggregation
(SAMA) scheme designed to support privacy preserving aggregation of health data
collected from users' IoT wearables. SAMA also deploys a user-centric approach
to support flexible fine-grain access control. It achieves this by deploying
two key ideas. First, it uses multi-key Homomorphic cryptosystem (variant
Paillier) to allow flexibility in accommodating both single and multi-user data
processing as well as preserving the privacy of users while processing their
IoT health data. Second, it uses ciphertext-policy attribute-based encryption
to support flexible access control, which ensures users are able to grant data
access securely and selectively. Formal security and privacy analyses have
shown that SAMA supports data confidentiality and authorisation. The scheme has
also been analysed in terms of computational and communication overheads to
demonstrate that it is more efficient than the relevant state-of-the-art
solutions.


title: Modeling the Energy Consumption of the HEVC Decoding Process

abstract:
  In this paper, we present a bit stream feature based energy model that
accurately estimates the energy required to decode a given HEVC-coded bit
stream. Therefore, we take a model from literature and extend it by explicitly
modeling the inloop filters, which was not done before. Furthermore, to prove
its superior estimation performance, it is compared to seven different energy
models from literature. By using a unified evaluation framework we show how
accurately the required decoding energy for different decoding systems can be
approximated. We give thorough explanations on the model parameters and explain
how the model variables are derived. To show the modeling capabilities in
general, we test the estimation performance for different decoding software and
hardware solutions, where we find that the proposed model outperforms the
models from literature by reaching frame-wise mean estimation errors of less
than 7% for software and less than 15% for hardware based systems.


title:
Belief propagation for supply networks: Efficient clustering of their
  factor graphs

abstract:
  We consider belief propagation (BP) as an efficient and scalable tool for
state estimation and optimization problems in supply networks such as power
grids. BP algorithms make use of factor graph representations, whose assignment
to the problem of interest is not unique. It depends on the state variables and
their mutual interdependencies. Many short loops in factor graphs may impede
the accuracy of BP. We propose a systematic way to cluster loops of naively
assigned factor graphs such that the resulting transformed factor graphs have
no additional loops as compared to the original network. They guarantee an
accurate performance of BP with only slightly increased computational effort,
as we demonstrate by a concrete and realistic implementation for power grids.
The method outperforms existing alternatives to handle the loops. We point to
other applications to supply networks such as gas-pipeline or other flow
networks that share the structure of constraints in the form of analogues to
Kirchhoff's laws. Whenever small and abundant loops in factor graphs are
systematically generated by constraints between variables in the original
network, our factor-graph assignment in BP complements other approaches. It
provides a fast and reliable algorithm to perform marginalization in tasks like
state determination, estimation, or optimization issues in supply networks.


title:
Discovery of non-equilibrium ionization plasma associated with the North
  Polar Spur and Loop I

abstract:
  We investigated the detailed plasma condition of the North Polar Spur
(NPS)/Loop I using archival $Suzaku$ data. In previous research collisional
ionization equilibrium (CIE) have been assumed for X-ray plasma state, but we
also assume non-equilibrium ionization (NEI) to check the plasma condition in
more detail. We found that most of the plasma in the NPS/Loop I favors the
state of NEI, and has the density-weighted ionization timescale of $n_e
t\sim10^{11-12}$ s cm$^{-3}$ and the electron number density $n_e\sim$ a few
$\times$ 10$^{-3}$ cm$^{-3}$. The plasma shock age, $t$, or the time elapsed
after the shock front passed through the plasma, is estimated to be on the
order of a few $\rm{Myr}$ for the NPS/Loop I, which puts a strict lower limit
to the age of the whole NPS/Loop I structure. We found that NEI results in
significantly higher temperature and lower emission measure than those
currently derived under CIE assumption. The electron temperature under NEI is
estimated to be as high as 0.5 keV toward the brightest X-ray NPS ridge at
$\Delta\theta=-20^\circ$, which decreases to 0.3 keV at $-10^\circ$, and again
increases to $\sim 0.6$ keV towards the outer edge of Loop I at
$\Delta\theta\sim0^\circ$, about twice the currently estimated temperatures.
Here, $\Delta \theta$ is the angular distance from the outer edge of Loop I. We
discuss the implication of introducing NEI for the research in plasma states in
astrophysical phenomena.


title:
Compliance Challenges in Forensic Image Analysis Under the Artificial
  Intelligence Act

abstract:
  In many applications of forensic image analysis, state-of-the-art results are
nowadays achieved with machine learning methods. However, concerns about their
reliability and opaqueness raise the question whether such methods can be used
in criminal investigations. So far, this question of legal compliance has
hardly been discussed, also because legal regulations for machine learning
methods were not defined explicitly. To this end, the European Commission
recently proposed the artificial intelligence (AI) act, a regulatory framework
for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for
use in law enforcement are permitted but subject to compliance with mandatory
requirements. In this paper, we review why the use of machine learning in
forensic image analysis is classified as high-risk. We then summarize the
mandatory requirements for high-risk AI systems and discuss these requirements
in light of two forensic applications, license plate recognition and deep fake
detection. The goal of this paper is to raise awareness of the upcoming legal
requirements and to point out avenues for future research.


title:
Analytic Ax-Schanuel Theorem for semi-abelian varieties and Nevanlinna
  theory

abstract:
  The purpose of this paper is to explore Nevanlinna theory of the entire curve
$\exh_A f:=(\exp_Af,f):\C \to A \times \Lie(A)$ associated with an entire curve
$f: \C \to \Lie(A)$, where $\exp_A:\Lie(A)\to A$ is an exponential map of a
semi-abelian variety $A$. Firstly we give a Nevanlinna theoretic proof to the
{\em analytic Ax-Schanuel Theorem} for semi-abelian varieties, which was proved
by J. Ax 1972 in the case of formal power series (Ax-Schanuel Theorem). We
assume some non-degeneracy condition for $f$ such that the elements of the
vector-valued function $f(z)-f(0) \in \Lie(A)\iso \C^n$ are $\Q$-linearly
independent in the case of $A=(\C^*)^n$. Then by making use of the Log
Bloch-Ochiai Theorem and a key estimate which we show, we prove that $\td_\C\,
\exh_A f \geq n+ 1$.
  Our next aim is to establish a {\em 2nd Main Theorem} for $\exh_A f$ and its
$k$-jet lifts with truncated counting functions at level one.


title:
Large Interferometer For Exoplanets (LIFE): II. Signal simulation,
  signal extraction and fundamental exoplanet parameters from single epoch
  observations

abstract:
  The Large Interferometer For Exoplanets (LIFE) initiative is developing the
science and a technology roadmap for an ambitious space mission featuring a
space-based mid-infrared (MIR) nulling interferometer in order to detect the
thermal emission of hundreds of exoplanets and characterize their atmospheres.
In order to quantify the science potential of such a mission, in particular in
the context of technical trade-offs, an instrument simulator is required. In
addition, signal extraction algorithms are needed to verify that exoplanet
properties (e.g., angular separation, spectral flux) contained in simulated
exoplanet datasets can be accurately retrieved. We present LIFEsim, a software
tool developed for simulating observations of exoplanetary systems with an MIR
space-based nulling interferometer. It includes astrophysical noise sources
(i.e., stellar leakage and thermal emission from local zodiacal and
exo-zodiacal dust) and offers the flexibility to include instrumental noise
terms in the future. LIFEsim provides an accessible way for predicting the
expected SNR of future observations as a function of various key instrument and
target parameters. The SNRs of the extracted spectra are photon-noise
dominated, as expected from our current simulations. From single epoch
observations in our mock survey of small ($R < 1.5 R_\mathrm{Earth}$) planets
orbiting within the habitable zones of their stars, we find that typical
uncertainties in the estimated effective temperature of the exoplanets are
$\lesssim$10%, for the exoplanet radius $\lesssim$20%, and for the separation
from the host star $\lesssim$2%. SNR values obtained in the signal extraction
process deviate less than 10% from purely photon-counting statistics based
SNRs. (abridged)


title:
DMF-Net: A decoupling-style multi-band fusion model for real-time
  full-band speech enhancement

abstract:
  Full-band speech enhancement based on deep neural networks is still
challenging for the difficulty of modeling more frequency bands and real-time
implementation. Previous studies usually adopt compressed full-band speech
features in Bark and ERB scale with relatively low frequency resolution,
leading to degraded performance, especially in the high-frequency region. In
this paper, we propose a decoupling-style multi-band fusion model to perform
full-band speech denoising and dereverberation. Instead of optimizing the
full-band speech by a single network structure, we decompose the full-band
target into multi sub bands and then employ a multi-stage chain optimization
strategy to estimate clean spectrum stage by stage. Specifically, the low- (0-8
kHz), middle- (8-16 kHz), and high-frequency (16-24 kHz) regions are mapped by
three separate sub-networks and are then fused to obtain the full-band clean
target STFT spectrum. Comprehensive experiments on two public datasets
demonstrate that the proposed method outperforms previous advanced systems and
yields promising performance in terms of speech quality and intelligibility in
real complex scenarios.


title:
Low-luminosity supernovae: SN 2005cs and SN 2020cxd as very low-energy
  iron core-collapse explosions

abstract:
  SN 2020cxd is a representative of the family of low-energy, underluminous
Type IIP supernovae (SNe), whose observations and analysis were recently
reported by Yang et al. (2021). Here we re-evaluate the observational data for
the diagnostic SN properties by employing the hydrodynamic explosion model of a
9 MSun red supergiant progenitor with an iron core and a pre-collapse mass of
8.75 Msun. The explosion of the star was obtained by the neutrino-driven
mechanism in a fully self-consistent simulation in three dimensions (3D).
Multi-band light curves and photospheric velocities for the plateau phase are
computed with the one-dimensional radiation-hydrodynamics code STELLA, applied
to the spherically averaged 3D explosion model as well as spherisized radial
profiles in different directions of the 3D model. We find that the overall
evolution of the bolometric light curve, duration of the plateau phase, and
basic properties of the multi-band emission can be well reproduced by our SN
model with its explosion energy of only 0.7x10^50 erg and an ejecta mass of 7.4
Msun. These values are considerably lower than the previously reported numbers,
but they are compatible with those needed to explain the fundamental
observational properties of the prototype low-luminosity SN 2005cs. Because of
the good compatibility of our photospheric velocities with line velocities
determined for SN 2005cs, we conclude that the line velocities of SN 2020cxd
are probably overestimated by up to a factor of about 3. The evolution of the
line velocities of SN 2005cs compared to photospheric velocities in different
explosion directions might point to intrinsic asymmetries in the SN ejecta.


title:
On the sample complexity of stabilizing linear dynamical systems from
  data

abstract:
  Learning controllers from data for stabilizing dynamical systems typically
follows a two step process of first identifying a model and then constructing a
controller based on the identified model. However, learning models means
identifying generic descriptions of the dynamics of systems, which can require
large amounts of data and extracting information that are unnecessary for the
specific task of stabilization. The contribution of this work is to show that
if a linear dynamical system has dimension (McMillan degree) $n$, then there
always exist $n$ states from which a stabilizing feedback controller can be
constructed, independent of the dimension of the representation of the observed
states and the number of inputs. By building on previous work, this finding
implies that any linear dynamical system can be stabilized from fewer observed
states than the minimal number of states required for learning a model of the
dynamics. The theoretical findings are demonstrated with numerical experiments
that show the stabilization of the flow behind a cylinder from less data than
necessary for learning a model.


title:
A simple model for an internal wave spectrum dominated by non-linear
  interactions

abstract:
  Ocean motions at frequencies of the internal wave band are generally
associated with freely propagating waves that are supported by stable vertical
stratification in density. Previous analyses of yearlong current observations
from the Bay of Biscay showed that a finestructure of semidiurnal tidal and
near-inertial higher harmonics fills the spectrum. Here, a simple model is
presented of forced nondispersive motions with forward energy cascade. The
model fits the spectral shape of higher harmonics well within statistical
significance and shows that such interactions imply maximum wave steepness in a
balance between forcing and turbulent mixing. The single fitting parameter is
related to the barotropic tidal flow speed, which thereby sets a nonlinear
limit to baroclinic current scales without generating non-linear higher
harmonics directly.


title:
Large deviations for uniform projections of $p$-radial distributions on
  $\ell_p^n$-balls

abstract:
  We consider products of uniform random variables from the Stiefel manifold of
orthonormal $k$-frames in $\mathbb{R}^n$, $k \le n$, and random vectors from
the $n$-dimensional $\ell_p^n$-ball $\mathbb{B}_p^n$ with certain $p$-radial
distributions, $p\in[1,\infty)$. The distribution of this product geometrically
corresponds to the projection of the $p$-radial distribution on
$\mathbb{B}^n_p$ onto a random $k$-dimensional subspace. We derive large
deviation principles (LDPs) on the space of probability measures on
$\mathbb{R}^k$ for sequences of such projections.


title:
Generation of electron vortex beams with over 1000 orbital angular
  momentum quanta using a tuneable electrostatic spiral phase plate

abstract:
  We report the use of an electrostatic MEMS-based device to produce high
quality electron vortex beams with more than 1000 quanta of orbital angular
momentum (OAM). Diffraction and off-axis electron holography experiments are
used to show that the diameter of the vortex in the diffraction plane increases
linearly with OAM, thereby allowing the angular momentum content of the vortex
to be calibrated. The realization of electron vortex beams with even larger
values of OAM is currently limited by the breakdown voltage of the device.
Potential solutions to overcome this problem are discussed.


title:
Long-term properties of finite-correlation time isotropic stochastic
  systems

abstract:
  We consider finite-dimensional systems of linear stochastic differential
equations ${\partial_t}{x_k}\left( t \right) = {A_{kp}}\left( t
\right){x_p}\left( t \right)$, ${\bf A}(t)$ being a stationary continuous
statistically isotropic stochastic process with values in real $d \times d$
matrices. We suppose also that the laws of ${\bf A}(t)$ satisfy the large
deviation principle. For these systems, we find exact expressions for the
Lyapunov and generalized Lyapunov exponents and show that they are determined
in a precise way only by the rate function of the diagonal elements of ${\bf
A}$.


title: A Probabilistic Deep Image Prior for Computational Tomography

abstract:
  Existing deep-learning based tomographic image reconstruction methods do not
provide accurate estimates of reconstruction uncertainty, hindering their
real-world deployment. To address this limitation, we construct a Bayesian
prior for tomographic reconstruction, which combines the classical total
variation (TV) regulariser with the modern deep image prior (DIP).
Specifically, we use a change of variables to connect our prior beliefs on the
image TV semi-norm with the hyper-parameters of the DIP network. For the
inference, we develop an approach based on the linearised Laplace method, which
is scalable to high-dimensional settings. The resulting framework provides
pixel-wise uncertainty estimates and a marginal likelihood objective for
hyperparameter optimisation. We demonstrate the method on synthetic and
real-measured high-resolution $\mu$CT data, and show that it provides superior
calibration of uncertainty estimates relative to previous probabilistic
formulations of the DIP.


title:
Search for new particles in an extended Higgs sector with four b quarks
  in the final state at $\sqrt{s}$ = 13 TeV

abstract:
  A search for an extended Higgs sector, characterized by a massive resonance X
decaying to a pair of spin-0 bosons $\phi$ that themselves decay to pairs of
bottom quarks, is presented. The analysis is restricted to the mass ranges
$m_\phi$ from 25 to 100 GeV and $m_\mathrm{X}$ from 1 to 3 TeV. For these mass
ranges, the decay products of each $\phi$ boson are expected to merge into a
single large-radius jet. Jet substructure and flavor identification techniques
are used to identify these jets. The search is based on CERN LHC proton-proton
collision data at $\sqrt{s}$ = 13 TeV, collected with the CMS detector in
2016-2018, corresponding to an integrated luminosity of 138 fb$^{-1}$.
Model-specific limits are set on the product of the production cross section
and branching fraction for X $\to \phi\phi \to
(\mathrm{b\bar{b}})(\mathrm{b\bar{b}})$ as a function of mass, where both the X
$\to \phi\phi$ and $\phi \to \mathrm{b\bar{b}}$ branching fractions are assumed
to be 100%. These limits are the first of their kind on this process, ranging
between 30 and 1 fb at 95% confidence level for the considered mass ranges.


title:
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion
  Attacks

abstract:
  Collaborative machine learning settings like federated learning can be
susceptible to adversarial interference and attacks. One class of such attacks
is termed model inversion attacks, characterised by the adversary
reverse-engineering the model to extract representations and thus disclose the
training data. Prior implementations of this attack typically only rely on the
captured data (i.e. the shared gradients) and do not exploit the data the
adversary themselves control as part of the training consortium. In this work,
we propose a novel model inversion framework that builds on the foundations of
gradient-based model inversion attacks, but additionally relies on matching the
features and the style of the reconstructed image to data that is controlled by
an adversary. Our technique outperforms existing gradient-based approaches both
qualitatively and quantitatively, while still maintaining the same
honest-but-curious threat model, allowing the adversary to obtain enhanced
reconstructions while remaining concealed.


title: A retrieval challenge exercise for the Ariel mission

abstract:
  The Ariel mission, due to launch in 2029, will obtain spectroscopic
information for 1000 exoplanets, providing an unprecedented opportunity for
comparative exoplanetology. Retrieval codes - parameteric atmospheric models
coupled with an inversion algorithm - represent the tool of choice for
interpreting Ariel data. Ensuring that reliable and consistent results can be
produced by these tools is a critical preparatory step for the mission. Here,
we present the results of a retrieval challenge. We use five different
exoplanet retrieval codes to analyse the same synthetic datasets, and test a)
the ability of each to recover the correct input solution and b) the
consistency of the results. We find that generally there is very good agreement
between the five codes, and in the majority of cases the correct solutions are
recovered. This demonstrates the reproducibility of retrievals for transit
spectra of exoplanets, even when codes are not previously benchmarked against
each other.


title:
A Survey on How Test Flakiness Affects Developers and What Support They
  Need To Address It

abstract:
  Non-deterministically passing and failing test cases, so-called flaky tests,
have recently become a focus area of software engineering research. While this
research focus has been met with some enthusiastic endorsement from industry,
prior work nevertheless mostly studied flakiness using a code-centric approach
by mining software repositories. What data extracted from software repositories
cannot tell us, however, is how developers perceive flakiness: How prevalent is
test flakiness in developers' daily routine, how does it affect them, and most
importantly: What do they want us researchers to do about it? To answer these
questions, we surveyed 335 professional software developers and testers in
different domains. The survey respondents confirm that flaky tests are a common
and serious problem, thus reinforcing ongoing research on flaky test detection.
Developers are less worried about the computational costs caused by re-running
tests and more about the loss of trust in the test outcomes. Therefore, they
would like to have IDE plugins to detect flaky code as well as better
visualizations of the problem, particularly dashboards showing test outcomes
over time; they also wish for more training and information on flakiness. These
important aspects will require the attention of researchers as well as tool
developers.


title:
Experimental identification of the second-order non-Hermitian skin
  effect with physics-graph-informed machine learning

abstract:
  Topological phases of matter are conventionally characterized by the
bulk-boundary correspondence in Hermitian systems: The topological invariant of
the bulk in $d$ dimensions corresponds to the number of $(d-1)$-dimensional
boundary states. By extension, higher-order topological insulators reveal a
bulk-edge-corner correspondence, such that $n$-th order topological phases
feature $(d-n)$-dimensional boundary states. The advent of non-Hermitian
topological systems sheds new light on the emergence of the non-Hermitian skin
effect (NHSE) with an extensive number of boundary modes under open boundary
conditions. Still, the higher-order NHSE remains largely unexplored,
particularly in the experiment. We introduce an unsupervised approach --
physics-graph-informed machine learning (PGIML) -- to enhance the data mining
ability of machine learning with limited domain knowledge. Through PGIML, we
experimentally demonstrate the second-order NHSE in a two-dimensional
non-Hermitian topolectrical circuit. The admittance spectra of the circuit
exhibit an extensive number of corner skin modes and extreme sensitivity of the
spectral flow to the boundary conditions. The violation of the conventional
bulk-boundary correspondence in the second-order NHSE implies that modification
of the topological band theory is inevitable in higher dimensional
non-Hermitian systems.


title: Two Classes of Power Mappings with Boomerang Uniformity 2

abstract:
  Let $q$ be an odd prime power. Let $F_1(x)=x^{d_1}$ and $F_2(x)=x^{d_2}$ be
power mappings over $\mathrm{GF}(q^2)$, where $d_1=q-1$ and
$d_2=d_1+\frac{q^2-1}{2}=\frac{(q-1)(q+3)}{2}$. In this paper, we study the the
boomerang uniformity of $F_1$ and $F_2$ via their differential properties. It
is shown that, the boomerang uniformity of $F_i$ ($i=1,2$) is 2 with some
conditions on $q$.


title: Control of the Schr\"odinger equation by slow deformations of the domain

abstract:
  The aim of this work is to study the controllability of the Schr\"odinger
equation \begin{equation}\label{eq_abstract} i\partial_t u(t)=-\Delta
u(t)~~~~~\text{ on }\Omega(t) \tag{$\ast$} \end{equation} with Dirichlet
boundary conditions, where $\Omega(t)\subset\mathbb{R}^N$ is a time-varying
domain. We prove the global approximate controllability of \eqref{eq_abstract}
in $L^2(\Omega)$, via an adiabatic deformation $\Omega(t)\subset\mathbb{R}$
($t\in[0,T]$) such that $\Omega(0)=\Omega(T)=\Omega$. This control is strongly
based on the Hamiltonian structure of \eqref{eq_abstract} provided by [18],
which enables the use of adiabatic motions. We also discuss several explicit
interesting controls that we perform in the specific framework of rectangular
domains.


title:
Labor Market Integration of Refugees: RCT Evidence from an Early
  Intervention Program in Sweden

abstract:
  This study uses a randomized control trial to evaluate a new program for
increased labor market integration of refugees. The program introduces highly
intensive assistance immediately after the residence permit is granted. The
early intervention strategy contrasts previous integration policies, which
typically constitute low-intensive help over long periods of time. We find
positive effects on employment of the program. The magnitude of the effect is
substantial, corresponding to around 15 percentage points. Our cost estimates
suggest that the new policy is less expensive than comparable labor market
programs used in the past.


title:
Strategies for modelling open-loop saccade control of a cable-driven
  biomimetic robot eye

abstract:
  In human-robot interactions, eye movements play an important role in
non-verbal communication. However, controlling the motions of a robotic eye
that display similar performance as the human oculomotor system is still a
major challenge. In this paper, we study how to control a realistic model of
the human eye, with a cable-driven actuation system that mimicks the 6
extra-ocular muscles. We have built a robotic prototype and developed a
non-linear simulation model, for which we compared different techniques to
control its gaze behavior to match the main characteristics of saccade eye
movements. In the first approach, we linearized the six degrees of freedom
nonlinear model, using a local derivative technique, and designed
linear-quadratic optimal controllers to optimize a cost function that accounts
for accuracy, energy and duration. The second method learns a dynamic
neural-network that matches the system dynamics, trained from sample
trajectories of the system, and a non-linear trajectory optimization solver
optimized a similar cost function. We focused on the generation of rapid
saccadic eye movements with fully unconstrained kinematics, and the generation
of control signals for the six cables that simultaneously satisfied several
dynamic optimization criteria. The model faithfully mimicked the
three-dimensional rotational kinematics and dynamics observed for human
saccades. Our experimental results indicate that while the linear model
provides a more accurate eye movement, the nonlinear model simulate eye dynamic
properties in a better way faithful approximation to the properties of the
human saccadic system than the linearized model, at the cost of larger training
and optimization time.


title:
Attention-based Contextual Multi-View Graph Convolutional Networks for
  Short-term Population Prediction

abstract:
  Short-term future population prediction is a crucial problem in urban
computing. Accurate future population prediction can provide rich insights for
urban planners or developers. However, predicting the future population is a
challenging task due to its complex spatiotemporal dependencies. Many existing
works have attempted to capture spatial correlations by partitioning a city
into grids and using Convolutional Neural Networks (CNN). However, CNN merely
captures spatial correlations by using a rectangle filter; it ignores urban
environmental information such as distribution of railroads and location of
POI. Moreover, the importance of those kinds of information for population
prediction differs in each region and is affected by contextual situations such
as weather conditions and day of the week. To tackle this problem, we propose a
novel deep learning model called Attention-based Contextual Multi-View Graph
Convolutional Networks (ACMV-GCNs). We first construct multiple graphs based on
urban environmental information, and then ACMV-GCNs captures spatial
correlations from various views with graph convolutional networks. Further, we
add an attention module to consider the contextual situations when leveraging
urban environmental information for future population prediction. Using
statistics population count data collected through mobile phones, we
demonstrate that our proposed model outperforms baseline methods. In addition,
by visualizing weights calculated by an attention module, we show that our
model learns an efficient way to utilize urban environment information without
any prior knowledge.


title:
Render-in-the-loop aerial robotics simulator: Case Study on Yield
  Estimation in Indoor Agriculture

abstract:
  Inspired by recent promising results in sim-to-real transfer in deep learning
we built a realistic simulation environment combining a Robot Operating System
(ROS)-compatible physics simulator (Gazebo) with Cycles, the realistic
production rendering engine from Blender. The proposed simulator pipeline
allows us to simulate near-realistic RGB-D images. To showcase the capabilities
of the simulator pipeline we propose a case study that focuses on indoor
robotic farming. We developed a solution for sweet pepper yield estimation
task. Our approach to yield estimation starts with aerial robotics control and
trajectory planning, combined with deep learning-based pepper detection, and a
clustering approach for counting fruit. The results of this case study show
that we can combine real time dynamic simulation with near realistic rendering
capabilities to simulate complex robotic systems.


title:
Accuracy of Garmin GPS Running Watches over Repetitive Trials on the
  Same Route

abstract:
  Many runners use watches incorporating Global Positioning System technology
to track their workouts. These devices can be valuable training aids, but they
have limitations. For several reasons including variations in satellite
position, environmental factors, and design decisions made by the manufacturer,
GPS-enabled watches can produce position measurement errors. These can result
in incorrect estimations of total distance covered as well as running pace.
This study examined the accuracy of three Garmin running watches of different
technological generations using repetitive trials, over several years, by the
same runner over the same route. The older watches, a Forerunner 205 and a
Forerunner 220, showed similar accuracy when traversing the route. The newer
generation watch, a Forerunner 45S, was found to be significantly less accurate
in terms of both the trueness and precision of its distance measurements. This
may indicate that Garmin, in competition with other manufacturers of similar
devices, has chosen in recent years to prioritize device miniaturization and
battery life over accuracy.


title: Interacting Tsallis agegraphic dark energy in DGP Braneworld Cosmology

abstract:
  The purpose of this paper is to study the Tsallis agegraphic dark energy with
an interaction term between dark energy and dark matter in the DGP brane-world
scenario. For this, we assume some initial conditions to obtain the dark energy
density, deceleration, dark energy EoS, and total EoS parameters. Then, we
analyze the statefinder parameters, $\omega'{}_{DE}-\omega_{DE}$ plots, and
classical stability features of the model. The results state that the
deceleration parameter provides the phase transition from decelerated to
accelerated phase. The $\omega_{DE}$ graphs show the phantom behavior, while
the $\omega_{tot}$ exhibits the quintessence and phantom during the evolution
of the Universe. Following the graphs, the Statefinder analysis shows the
quintessence behavior of the model for the past and present. However, it tends
to the $\Lambda CDM$ in the following era. The $\omega'{}_{DE}-\omega_{DE}$
plot indicates the thawing or freezing area depending on the type of era and
different values of $b^{2}$, $\delta$, and $m$. By the square of the sound
speed, we see the model is stable in the past, stable or unstable at the
current time, and unstable in the future for selected values of $b^{2}$,
$\delta$, and $m$. To test the model, we use the recent Hubble data. We also
employ Akaike Information Criterion (AIC) and Bayesian Information Criterion
(BIC) to compare the model with the $\Lambda CDM$ as the reference model. In
addition, we test the model using the $H-z$ plot, and we see a turning point in
the future time. The results from the best fit values for the $\omega_{tot}$
plot emphasize that the Universe is in the quintessence region in the current
time. It will enter the phantom phase, and then it will approach the $\Lambda$
state in the future. But, the $\omega_{DE}$ always stays on the phantom region.
The model is unstable in the present and progressive era.


title:
Superconductivity arising from pressure induced Lifshitz transition in
  Rb$_2$Pd$_3$Se$_4$ with kagome lattice

abstract:
  According to the Bardeen-Cooper-Schrieffer (BCS) theory, superconductivity
usually needs well defined Fermi surface(s) with strong electron-phonon
coupling and moderate quasiparticle density of states (DOS). A kagome lattice
can host flat bands and topological Dirac bands; meanwhile, due to the parallel
Fermi surfaces and the saddle points, many interesting orders are expected.
Here, we report the observation of superconductivity by pressurizing a kagome
compound Rb$_2$Pd$_3$Se$_4$ using a DAC anvil. The parent compound shows an
insulating behavior; however, it gradually becomes metallic and turns to a
superconducting state when a high pressure is applied. High pressure
synchrotron measurements show that there is no structural transition occurring
during this transition. The density-functional-theory (DFT) calculations
illustrate that the insulating behavior of the parent phase is due to the
crystalline field splitting of the partial Pd-4d $t_{2g}$ bands and the
Se-derivative 4$p$-band. However, the threshold of metallicity and
superconductivity are reached when the Lifshitz transition occurs, leading to
the emergence of tiny Fermi surface at $\Gamma$ point. Our results point to an
unconventional superconductivity and shed new light on understanding the
electronic evolution of a kagome material.


title:
DreamingV2: Reinforcement Learning with Discrete World Models without
  Reconstruction

abstract:
  The present paper proposes a novel reinforcement learning method with world
models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming.
DreamerV2 is a cutting-edge model-based reinforcement learning from pixels that
uses discrete world models to represent latent states with categorical
variables. Dreaming is also a form of reinforcement learning from pixels that
attempts to avoid the autoencoding process in general world model training by
involving a reconstruction-free contrastive learning objective. The proposed
DreamingV2 is a novel approach of adopting both the discrete representation of
DreamingV2 and the reconstruction-free objective of Dreaming. Compared to
DreamerV2 and other recent model-based methods without reconstruction,
DreamingV2 achieves the best scores on five simulated challenging 3D robot arm
tasks. We believe that DreamingV2 will be a reliable solution for robot
learning since its discrete representation is suitable to describe
discontinuous environments, and the reconstruction-free fashion well manages
complex vision observations.


title:
Evaluation of direct inversion of proton radiographs in the context of
  cylindrical implosions

abstract:
  Direct inversion of deflectometry data, such as proton radiographs and
shadowgraphs, is a well-posed problem with a unique solution for the transverse
deflection of each particle or ray if their trajectories do not cross. When
trajectories cross, there exists an infinite set of solutions. In proton
radiography direct inversion determines the line-integrated transverse Lorentz
force. We have tested five publicly available direct inversion routines with a
view to analyzing proton radiographs of cylindrical implosions on the OMEGA
laser; four Monge-Amp\`{e}re solvers [github.com/flash-center/PRaLine,
github.com/flash-center/PROBLEM,
github.com/mfkasim1/invert-shadowgraphy/tree/fast-inverse,
github.com/OxfordHED/proton-radiography-no-source], and a power-diagram method
[github.com/mfkasim1/invert-shadowgraphy]. Test problems were generated using
four field profiles, three cylindrical and one spherical, with varying field
amplitudes in proton-tracing routines. Two Monge-Amp\`{e}re solvers did not
run, the other two failed to reproduce radiographs when trajectories crossed,
although for one field profile the solutions only diverged from the original at
the boundary. The power-diagram method was successful even when proton
trajectories crossed, giving a solution that minimized proton deflection, but
failed for profiles that produced a single, sufficiently sharp peak. For cases
where trajectories do not cross, the Monge-Amp\`{e}re solvers have the
advantage of being considerably faster than the power-diagram routine, up to
$1000$ times in our tests. The test problems are provided in the supplementary
information in pradformat [github.com/phyzicist/pradformat].


title: Lifting recollements of abelian categories and model structures

abstract:
  We use Quillen model structures to show a systematic method to lift
recollements of hereditary abelian model categories to recollements of their
associated homotopy categories. To that end, we use the notion of Quillen
adjoint triples and we investigate transfers of abelian model structures along
adjoint pairs. Applications include liftings of recollements of module
categories to their derived counterpart, liftings to homotopy categories that
provide models for stable categories of Gorenstein projective and injective
modules and liftings to homotopy categories of n-morphism categories over
Iwanaga-Gorenstein rings.


title:
A predictive analytics approach for stroke prediction using machine
  learning and neural networks

abstract:
  The negative impact of stroke in society has led to concerted efforts to
improve the management and diagnosis of stroke. With an increased synergy
between technology and medical diagnosis, caregivers create opportunities for
better patient management by systematically mining and archiving the patients'
medical records. Therefore, it is vital to study the interdependency of these
risk factors in patients' health records and understand their relative
contribution to stroke prediction. This paper systematically analyzes the
various factors in electronic health records for effective stroke prediction.
Using various statistical techniques and principal component analysis, we
identify the most important factors for stroke prediction. We conclude that
age, heart disease, average glucose level, and hypertension are the most
important factors for detecting stroke in patients. Furthermore, a perceptron
neural network using these four attributes provides the highest accuracy rate
and lowest miss rate compared to using all available input features and other
benchmarking algorithms. As the dataset is highly imbalanced concerning the
occurrence of stroke, we report our results on a balanced dataset created via
sub-sampling techniques.


title:
Eigenfunctions of the van Diejen model generated by gauge and integral
  transformations

abstract:
  We present how explicit eigenfunctions of the principal Hamiltonian for the
$BC_{m}$ relativistic Calogero-Moser-Sutherland model, due to van Diejen, can
be constructed using gauge and integral transformations. In particular, we find
that certain $BC$-type elliptic hypergeometric integrals, including elliptic
Selberg integrals, of both Type I and Type II arise as eigenfunctions of the
van Diejen model under some parameter restrictions. Among these are also joint
eigenfunctions of so-called modular pairs of van Diejen operators. Furthermore,
these transformations are related to reflections of the $E_{8}$ Weyl group
acting on the space of model parameters.


title: Mode Recognition by Shape Morphing for Maxwell's Eigenvalue Problem

abstract:
  In electrical engineering, for example during the design of superconducting
radio-frequency cavities, eigenmodes must be identified based on their field
patterns. This allows to understand the working principle, optimize the
performance of a device and distinguish desired from parasitic modes. For
cavities with simple shapes, the eigenmodes are easily classified according to
the number of nodes and antinodes in each direction as is obvious from
analytical formulae. For cavities with complicated shapes, the eigenmodes are
determined numerically. Thereby, the classification is cumbersome, if not
impossible. In this paper, we propose a new recognition method by morphing the
cavity geometry to a pillbox and tracking its eigenmodes during the
deformation.


title:
Tight bounds for augmented KL divergence in terms of augmented total
  variation distance

abstract:
  We provide optimal variational upper and lower bounds for the augmented
Kullback-Leibler divergence in terms of the augmented total variation distance
between two probability measures defined on two Euclidean spaces having
different dimensions.


title: Keeping Fun Alive: an Experience Report on Running Online Coding Camps

abstract:
  The outbreak of the COVID-19 pandemic prohibited radically the collocation
and face-to-face interactions of participants in coding bootcamps and similar
experiences, which are key characteristics that help participants to advance
technical work. Several specific issues are faced and need to be solved when
running online coding camps, which can achieve the same level of positive
outcomes for participants. One of such issues is how to keep the same level of
fun that participants obtained through physical activities and interactions in
the face-to-face settings. In this paper, we report on our experience and
insights gained from designing and running a fully remote coding camp that
exposes high school students to Agile-based Software Engineering practices to
enhance their ability to develop high-quality software. To design the online
coding camp, we adapted the face-to-face version of the coding camp to keep the
same "level of fun", i.e., adaptations aimed at increasing communication,
engaging participants, and introducing fun items to reduce fatigue due to
prolonged computer use, while preserving the technical curriculum that enables
students to attain the learning goals originally planned. The comparison with
the results of the face-to-face coding camp shows that we succeeded in keeping
the fun alive in the online edition, and the participants of online camp were
able to produce the results at the same level of quality in terms of product
and process as in the face-to-face edition. From our experience, we synthesize
lessons learned, and we sketch some guidelines for educators.


title:
Sensor technologies in cancer research for new directions in diagnosis
  and treatment: and exploratory analysis

abstract:
  The goal of this study is an exploratory analysis concerning main sensor
technologies applied in cancer research to detect new directions in diagnosis
and treatments. The study focused on types of cancer having a high incidence
and mortality worldwide: breast, lung, colorectal and prostate. Data of the Web
of Science (WOS) core collection database are used to retrieve articles related
to sensor technologies and cancer research over 1991-2021 period. We utilized
Gephi software version 0.9.2 to visualize the co-word networks of the
interaction between sensor technologies and cancers under study. Results show
main clusters of interaction per typology of cancer. Biosensor is the only type
of sensor that plays an essential role in all types of cancer: breast cancer,
lung cancer, prostate cancer, and colorectal cancer. Electrochemical sensor is
applied in all types of cancer under study except lung cancer. Electrochemical
biosensor is used in breast cancer, lung cancer, and prostate cancer research
but not colorectal cancer. Optical sensor can also be considered one of the
sensor technologies that significantly is used in breast cancer, prostate
cancer, and colorectal cancer. This study shows that this type of sensor is
applied in more diversified approaches. Moreover, the oxygen sensor is mostly
studied in lung cancer and breast cancer due to the usage in breath analysis
for the treatment process. Finally, Cmos sensor is a technology used mainly in
lung cancer and colorectal cancer. Results here suggest new directions for the
evolution of science and technology of sensors in cancer research to support
innovation and research policy directed to new technological trajectories
having a potential of accelerated growth and positive social impact for
diagnosis and treatments of cancer.


title:
Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models
  through a Single Waist-worn Wearable Sensor

abstract:
  Elderly gait is a source of rich information about their physical and mental
health condition. As an alternative to the multiple sensors on the lower body
parts, a single sensor on the pelvis has a positional advantage and an
abundance of information acquirable. This study aimed to explore a way of
improving the accuracy of gait event detection in the elderly using a single
sensor on the waist and deep learning models. Data was gathered from elderly
subjects equipped with three IMU sensors while they walked. The input was taken
only from the waist sensor was used to train 16 deep-learning models including
CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention
mechanism. The groundtruth was extracted from foot IMU sensors. Fairly high
accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the
tolerance window of $\pm$6TS ($\pm$6ms) and $\pm$1TS ($\pm$1ms) respectively.
Advancing from the previous studies exploring gait event detection, the model
showed a great improvement in terms of its prediction error having an MAE of
6.239ms and 5.24ms for HS and TO events respectively at the tolerance window of
$\pm$1TS. The results showed that the use of CNN-RNN hybrid models with
Attention and Bidirectional mechanisms is promising for accurate gait event
detection using a single waist sensor. The study can contribute to reducing the
burden of gait detection and increase its applicability in future wearable
devices that can be used for remote health monitoring (RHM) or diagnosis based
thereon.


title:
Analysis of Digitalized ECG Signals Based on Artificial Intelligence and
  Spectral Analysis Methods Specialized in ARVC

abstract:
  Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart
muscle disease that appears between the second and forth decade of a patient's
life, being responsible for 20% of sudden cardiac deaths before the age of 35.
The effective and punctual diagnosis of this disease based on
Electrocardiograms (ECGs) could have a vital role in reducing premature
cardiovascular mortality. In our analysis, we firstly outline the
digitalization process of paper-based ECG signals enhanced by a spatial filter
aiming to eliminate dark regions in the dataset's images that do not correspond
to ECG waveform, producing undesirable noise. Next, we propose the utilization
of a low-complexity convolutional neural network for the detection of an
arrhythmogenic heart disease, that has not been studied through the usage of
deep learning methodology to date, achieving high classification accuracy on a
disease the major identification criterion of which are infinitesimal millivolt
variations in the ECG's morphology, in contrast with other arrhythmogenic
abnormalities. Finally, by performing spectral analysis we investigate
significant differentiations in the field of frequencies between normal ECGs
and ECGs corresponding to patients suffering from ARVC. The overall research
carried out in this article highlights the importance of integrating
mathematical methods into the examination and effective diagnosis of various
diseases, aiming to a substantial contribution to their successful treatment.


title:
Tunable crossed Andreev reflection in a heterostructure consisting of
  ferromagnets, normal metal and superconductors

abstract:
  Crossed Andreev reflection (CAR) is a nonlocal transport phenomenon in a
system of two normal metal (NM) leads connected to a superconductor (SC) that
converts the electron like excitations in one metallic lead into hole like
excitations in the other metallic lead. The scattering phenomena viz. electron
tunneling (ET), electron reflection (ER) and Andreev reflection (AR) compete
with CAR and reduce the probability of CAR generically. One of the
experimentally realized proposals to observe CAR is to employ two ferromagnetic
(FM) leads in antiparallel configuration connected to the SC by suppressing ET
and AR. But CAR probability cannot be tuned in this setup. We propose a setup
consisting of a gate tunable NM region connected to two superconducting regions
on either side which are connected to FM leads further away in antiparallel
configuration, in which probabilities of CAR and ER can be changed from $0$ to
$1$ by a changing the gate voltage applied to the NM region. The gate voltage
applied to the NM region dictates the chemical potential of the NM region and
gives a handle on the Fabry-P\'erot interference of the electron and hole modes
in the NM region. We calculate differential transconductance for the proposed
setup which can be tuned across the range $0$ to $-e^2/h$.