title:
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object
  Interaction

abstract:
  We present HOI4D, a large-scale 4D egocentric dataset with rich annotations,
to catalyze the research of category-level human-object interaction. HOI4D
consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by
4 participants interacting with 800 different object instances from 16
categories over 610 different indoor rooms. Frame-wise annotations for panoptic
segmentation, motion segmentation, 3D hand pose, category-level object pose and
hand action have also been provided, together with reconstructed object meshes
and scene point clouds. With HOI4D, we establish three benchmarking tasks to
promote category-level HOI from 4D visual signals including semantic
segmentation of 4D dynamic point cloud sequences, category-level object pose
tracking, and egocentric action segmentation with diverse interaction targets.
In-depth analysis shows HOI4D poses great challenges to existing methods and
produces great research opportunities.


title:
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping
  through Continual Learning

abstract:
  While lifelong SLAM addresses the capability of a robot to adapt to changes
within a single environment over time, in this paper we introduce the task of
continual SLAM. Here, a robot is deployed sequentially in a variety of
different environments and has to transfer its knowledge of previously
experienced environments to thus far unseen environments, while avoiding
catastrophic forgetting. This is particularly relevant in the context of
vision-based approaches, where the relevant features vary widely between
different environments. We propose a novel approach for solving the continual
SLAM problem by introducing CL-SLAM. Our approach consists of a dual-network
architecture that handles both short-term adaptation and long-term memory
retention by incorporating a replay buffer. Extensive evaluations of CL-SLAM in
three different environments demonstrate that it outperforms several baselines
inspired by existing continual learning-based visual odometry methods. The code
of our work is publicly available at http://continual-slam.cs.uni-freiburg.de.


title: A Formalisation of Algorithms for Sorting Network

abstract:
  This notes explains how standard algorithms that construct sorting networks
have been formalised and proved correct in the Coq proof assistant using the
SSReflect extension.


title: Weak localisation enhanced ultrathin scattering media

abstract:
  The brilliant white appearance of ultrathin scattering media with low
refractive index contrast and the underlying radiative transport phenomena
fascinate scientists for more than a decade. Examples of such systems are the
scales of beetles of the genus Cyphochilus, photonic network structures or
disordered Bragg stacks (DBS). While previous studies relate the highly
efficient scattering in the scales to the anisotropy of the intra-scale network
and diffusive light transport, the coherent radiation propagation dynamics
remained unaccounted for. Here, we identify different coherent light transport
regimes using time and spatially resolved coherent light scattering
spectroscopy. At least 20% of the collected scattered light originates from
weakly localised random photonic modes, in contrast to solely diffusive light
transport assumed to date. The identification of this significant role of weak
localisation in ultrathin brilliant scattering media establishes a new design
paradigm for efficient scattering optical materials.


title:
A shallow physics-informed neural network for solving partial
  differential equations on surfaces

abstract:
  In this paper, we introduce a mesh-free physics-informed neural network for
solving partial differential equations on surfaces. Based on the idea of
embedding techniques, we write the underlying surface differential equations
using conventional Cartesian differential operators. With the aid of level set
function, the surface geometrical quantities, such as the normal and mean
curvature of the surface, can be computed directly and used in our surface
differential expressions. So instead of imposing the normal extension
constraints used in literature, we take the whole Cartesian differential
expressions into account in our loss function. Meanwhile, we adopt a completely
shallow (one hidden layer) network so the present model is easy to implement
and train. We perform a series of numerical experiments on both stationary and
time-dependent partial differential equations on complicated surface
geometries. The result shows that, with just a few hundred trainable
parameters, our network model is able to achieve high predictive accuracy.


title: On solid cores and hulls of weighted Bergman spaces $A_{\mu}^1$

abstract:
  We consider weighted Bergman spaces $A_\mu^1$ on the unit disc as well as the
corresponding spaces of entire functions, defined using non-atomic Borel
measures with radial symmetry. By extending the techniques from the case of
reflexive Bergman spaces we characterize the solid core of $A_\mu^1$. Also, as
a consequence of a characterization of solid $A_\mu^1$-spaces we show that, in
the case of entire functions, there indeed exist solid $A_\mu^1$-spaces. The
second part of the paper is restricted to the case of the unit disc and it
contains a characterization of the solid hull of $A_\mu^1$, when $\mu $ equals
the weighted Lebesgue measure with weight $v$. The results are based on a
duality relation of weighted $A^1$- and $H^\infty$-spaces, the validity of
which requires the assumption that $- \log v$ belongs to the class
$\mathcal{W}_0$, studied in a number of publications; moreover, $v$ has to
satisfy condition $(b)$, introduced by the authors. The exponentially
decreasing weight $v(z) = \exp( -1 /(1-|z|)$ provides an example satisfying
both assumptions.


title: Towards Universal Backward-Compatible Representation Learning

abstract:
  Conventional model upgrades for visual search systems require offline refresh
of gallery features by feeding gallery images into new models (dubbed as
"backfill"), which is time-consuming and expensive, especially in large-scale
applications. The task of backward-compatible representation learning is
therefore introduced to support backfill-free model upgrades, where the new
query features are interoperable with the old gallery features. Despite the
success, previous works only investigated a close-set training scenario (i.e.,
the new training set shares the same classes as the old one), and are limited
by more realistic and challenging open-set scenarios. To this end, we first
introduce a new problem of universal backward-compatible representation
learning, covering all possible data split in model upgrades. We further
propose a simple yet effective method, dubbed as Universal Backward-Compatible
Training (UniBCT) with a novel structural prototype refinement algorithm, to
learn compatible representations in all kinds of model upgrading benchmarks in
a unified manner. Comprehensive experiments on the large-scale face recognition
datasets MS1Mv3 and IJB-C fully demonstrate the effectiveness of our method.


title:
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for
  Deployed Deep Models

abstract:
  Prioritizing fairness is of central importance in artificial intelligence
(AI) systems, especially for those societal applications, e.g., hiring systems
should recommend applicants equally from different demographic groups, and risk
assessment systems must eliminate racism in criminal justice. Existing efforts
towards the ethical development of AI systems have leveraged data science to
mitigate biases in the training set or introduced fairness principles into the
training process. For a deployed AI system, however, it may not allow for
retraining or tuning in practice. By contrast, we propose a more flexible
approach, i.e., fairness-aware adversarial perturbation (FAAP), which learns to
perturb input data to blind deployed models on fairness-related features, e.g.,
gender and ethnicity. The key advantage is that FAAP does not modify deployed
models in terms of parameters and structures. To achieve this, we design a
discriminator to distinguish fairness-related attributes based on latent
representations from deployed models. Meanwhile, a perturbation generator is
trained against the discriminator, such that no fairness-related features could
be extracted from perturbed inputs. Exhaustive experimental evaluation
demonstrates the effectiveness and superior performance of the proposed FAAP.
In addition, FAAP is validated on real-world commercial deployments
(inaccessible to model parameters), which shows the transferability of FAAP,
foreseeing the potential of black-box adaptation.


title: On Symmetries Of Singular Foliations

abstract:
  This paper shows that a weak symmetry action of a Lie algebra g on a singular
foliation F induces a unique (up to homotopy) Lie $\infty$-morphism from g to
the DGLA of vector fields on a universal N Q-manifold over F. Also, an explicit
formula of such Lie $\infty$-morphism is given for some N Q-manifold of F which
is not necessarily a universal one. We deduce from this general result several
geometric consequences. For instance, we give an example of action of Lie
algebra on a sub-affine variety which cannot be extended to the ambient space.
Last, we introduce the notion of tower of bi-submersions over a singular
foliations and lift symmetries to them.


title: Gaia-DR2 asteroid observations and INPOP planetary ephemerides

abstract:
  We used the INPOP19a planetary ephemerides to perform the orbital adjustment
of 14099 asteroids based on Gaia-DR2 observations, and compare for 23 of them
the resulting orbits to radar data. As Gaia-DR2 has been processed using the
planetary ephemeris INPOP10e, the primary goal of this paper is to confirm the
portability of the data when using an updated version of the solar system
model. In particular, we point out the fact that the Gaia satellite positions
-- provided with respect to the INPOP10e solar system barycenter -- must be
corrected when using another planetary ephemeris . We also present a convenient
least square formalism that only handles small matrices and allows the
adjustment of global parameters, such as masses. In order to check the
consistency of the Gaia observations with other types of observations, we
perform an orbital adjustment in combining Gaia and radar range observations
for 23 objects, together with a careful post-fit analysis including an
estimation of the Gaia systematic errors. Finally, we show that to ensure the
combined use of Gaia angular DR2 observations and radar ranging, a more
developed than firstly proposed dynamical modeling is required together with
the addition of the systematic Gaia bias in the fit procedure.These results
give promising directions for the next Gaia delivery, Gaia-DR3.


title: Multi-Tailed Vision Transformer for Efficient Inference

abstract:
  Recently, Vision Transformer (ViT) has achieved promising performance in
image recognition and gradually serves as a powerful backbone in various vision
tasks. To satisfy the sequential input of Transformer, the tail of ViT first
splits each image into a sequence of visual tokens with a fixed length. Then
the following self-attention layers constructs the global relationship between
tokens to produce useful representation for the downstream tasks. Empirically,
representing the image with more tokens leads to better performance, yet the
quadratic computational complexity of self-attention layer to the number of
tokens could seriously influence the efficiency of ViT's inference. For
computational reduction, a few pruning methods progressively prune
uninformative tokens in the Transformer encoder, while leaving the number of
tokens before the Transformer untouched. In fact, fewer tokens as the input for
the Transformer encoder can directly reduce the following computational cost.
In this spirit, we propose a Multi-Tailed Vision Transformer (MT-ViT) in the
paper. MT-ViT adopts multiple tails to produce visual sequences of different
lengths for the following Transformer encoder. A tail predictor is introduced
to decide which tail is the most efficient for the image to produce accurate
prediction. Both modules are optimized in an end-to-end fashion, with the
Gumbel-Softmax trick. Experiments on ImageNet-1K demonstrate that MT-ViT can
achieve a significant reduction on FLOPs with no degradation of the accuracy
and outperform other compared methods in both accuracy and FLOPs.


title:
Investigation on a bipedal robot: Why do humans need both Soleus
  andGastrocnemius muscles for ankle push-off during walking?

abstract:
  Legged locomotion in humans is influenced by mechanics and neural control.
One mechanism assumed to contribute to the high efficiency of human walking is
the impulsive ankle push-off, which potentially powers the human swing leg
catapult. However, the mechanics of the human's lower leg with its complex
muscle-tendon units spanning over single and multiple joints is not yet
understood. Legged robots allow testing the interaction between complex leg
mechanics, control, and environment in real-world walking gait. We custom
developed a small, 2.2 kg human-like bipedal robot with soleus and
gastrocnemius muscles represented by linear springs, acting as mono- and
biarticular elasticities around the robot's ankle and knee joints. We tested
the influence of three soleus and gastrocnemius spring configurations on the
ankle power curves, on the synchronization of the ankle and knee joint
movements, on the total cost of transport, and on walking speed. We controlled
the robot with a feed-forward central pattern generator, leading to walking
speeds between 0.35 m/s and 0.57 m/s at 1.0 Hz locomotion frequency, at 0.35 m
leg length. We found differences between all three configurations; the soleus
spring supports the robot's speed and energy efficiency by ankle power
amplification, while the GAS spring facilitates the synchronization between
knee and ankle joints during push-off.


title:
Reconfigurable Intelligent Surface Assisted OFDM Relaying: Subcarrier
  Matching with Balanced SNR

abstract:
  Reconfigurable intelligent surface (RIS) is a promising solution to enhance
the performance of wireless communications via reconfiguring the wireless
propagation environment. In this paper, we investigate the joint design of RIS
passive beamforming and subcarrier matching in RIS-assisted orthogonal
frequency division multiplexing (OFDM) dual-hop relaying systems under two
cases, depending on the presence of the RIS reflected link from the source to
the destination in the first hop. Accordingly, we formulate a mixed-integer
nonlinear programming (MINIP) problem to maximize the sum achievable rate over
all subcarriers by jointly optimizing the RIS passive beamforming and
subcarrier matching. To solve this challenging problem, we first develop a
branch-and-bound (BnB)-based alternating optimization algorithm to obtain a
near-optimal solution by alternatively optimizing the subcarrier matching by
the BnB method and the RIS passive beamforming by using semidefinite relaxation
techniques. Then, a low-complexity difference-of-convex penalty-based algorithm
is proposed to reduce the computation complexity in the BnB method. To further
reduce the computational complexity, we utilize the learning-to-optimize
approach to learn the joint design obtained from optimization techniques, which
is more amenable to practical implementations. Lastly, computer simulations are
presented to evaluate the performance of the proposed algorithms in the two
cases. Simulation results demonstrate that the RIS-assisted OFDM relaying
system achieves sustainable achievable rate gain as compared to that without
RIS, and that with random passive beamforming, since RIS passive beamforming
can be leveraged to recast the subcarrier matching among different subcarriers
and balance the signal-to-noise ratio within each subcarrier pair.


title: 5G Network Slice Isolation

abstract:
  This article reveals an adequate comprehension of basic defense, security
challenges, 2 and attack vectors in deploying multi-network slicing. Network
slicing is a revolutionary concept 3 of providing mobile network on-demand and
expanding mobile networking business and services 4 to a new era. The new
business paradigm and service opportunities are encouraging vertical 5
industries to join and develop their own mobile network capabilities for
enhanced performances 6 that are coherent with their applications. However, a
number of security concerns are also raised 7 in this new era. In this article,
we focus on the deployment of multi-network slicing with multi8 tenancy. We
identify the security concerns, and discuss about the defense approaches such
as 9 network slice isolation and insulation in a multi-layer network slicing
security model. Also, we 10 identify the importance to appropriately select the
network slice isolation points, and propose 11 a generic framework to optimize
the isolation policy regarding the implementation cost while 12 guaranteeing
the security and performance requirements.


title:
Plasmon-enhanced polarized single photon source directly coupled to an
  optical fiber

abstract:
  A bright source of fiber-coupled, polarized single photons is an essential
component of any realistic quantum network based on today's existing fiber
infrastructure. Here, we develop a Purcell enhanced, polarized source of single
photons at room temperature by coupling single colloidal quantum dots to the
localized surface plasmon-polariton modes of single gold nanorods, combined on
the surface of an optical nanofiber. A maximum enhancement of photon emission
of 62 times was measured corresponding to a degree of polarization of 86 $\%$,
and a brightness enhancement of four times in the fiber mode. Evanescent
coupling of photons to the nanofiber guided modes ensures automatic coupling to
a single mode fiber. Our technique opens the way to realizing bright sources of
polarized single photons connected to fiber networks using a simple composite
technique.


title: Explosion and non-explosion for the continuous-time frog model

abstract:
  Different sets of conditions are given ensuring the explosion, respectively
non-explosion, of the continuous-time frog model. The proof relies on a certain
type of comparison to a percolation model which we call totally asymmetric
discrete inhomogeneous Boolean percolation.


title: 3D Human Motion Prediction: A Survey

abstract:
  3D human motion prediction, predicting future poses from a given sequence, is
an issue of great significance and challenge in computer vision and machine
intelligence, which can help machines in understanding human behaviors. Due to
the increasing development and understanding of Deep Neural Networks (DNNs) and
the availability of large-scale human motion datasets, the human motion
prediction has been remarkably advanced with a surge of interest among academia
and industrial community. In this context, a comprehensive survey on 3D human
motion prediction is conducted for the purpose of retrospecting and analyzing
relevant works from existing released literature. In addition, a pertinent
taxonomy is constructed to categorize these existing approaches for 3D human
motion prediction. In this survey, relevant methods are categorized into three
categories: human pose representation, network structure design, and
\textit{prediction target}. We systematically review all relevant journal and
conference papers in the field of human motion prediction since 2015, which are
presented in detail based on proposed categorizations in this survey.
Furthermore, the outline for the public benchmark datasets, evaluation
criteria, and performance comparisons are respectively presented in this paper.
The limitations of the state-of-the-art methods are discussed as well, hoping
for paving the way for future explorations.


title:
A Deep Neural Framework for Image Caption Generation Using GRU-Based
  Attention Mechanism

abstract:
  Image captioning is a fast-growing research field of computer vision and
natural language processing that involves creating text explanations for
images. This study aims to develop a system that uses a pre-trained
convolutional neural network (CNN) to extract features from an image,
integrates the features with an attention mechanism, and creates captions using
a recurrent neural network (RNN). To encode an image into a feature vector as
graphical attributes, we employed multiple pre-trained convolutional neural
networks. Following that, a language model known as GRU is chosen as the
decoder to construct the descriptive sentence. In order to increase
performance, we merge the Bahdanau attention model with GRU to allow learning
to be focused on a specific portion of the image. On the MSCOCO dataset, the
experimental results achieve competitive performance against state-of-the-art
approaches.


title:
Diaphragm Ankle Actuation for Efficient Series Elastic Legged Robot
  Hopping

abstract:
  Robots need lightweight legs for agile locomotion, and intrinsic series
elastic compliance has proven to be a major ingredient for energy-efficient
locomotion and robust locomotion control. Animals' anatomy and locomotion
capabilities emphasize the importance of that lightweight legs and integrated,
compact, series elastically actuated for distal leg joints. But unlike robots,
animals achieve series elastic actuation by their muscle-tendon units. So far
no designs are available that feature all characteristics of a perfect distal
legged locomotion actuator; a low-weight and low-inertia design, with high
mechanical efficiency, no stick and sliding friction, low mechanical
complexity, high-power output while being easy to mount. Ideally, such an
actuator can be controlled directly and without mechanical cross-coupling, for
example remotely. With this goal in mind, we propose a low-friction,
lightweight Series ELastic Diaphragm distal Actuator (SELDA) which meets many,
although not all, of the above requirements. We develop, implement, and
characterize a bioinspired robot leg that features a SELDA-actuated foot
segment. We compare two leg configurations controlled by a central pattern
generator that both feature agile forward hopping. By tuning SELDA's activation
timing, we effectively adjust the robot's hopping height by 11% and its forward
velocity by 14%, even with comparatively low power injection to the distal
joint.


title:
Translating the internal climate variability from climate variables to
  hydropower production

abstract:
  Quantifying uncertainties in estimating future hydropower production directly
or indirectly affects India's energy security, planning, and management. The
chaotic and nonlinear nature of atmospheric processes results in considerable
Internal Climate Variability (ICV) for future projections of climate variables.
Multiple Initial Condition Ensembles (MICE) and Multi-Model Ensembles (MME) are
often used to analyze the role of ICV and model uncertainty in precipitation
and temperature. However, there are limited studies focusing on quantifying the
role of internal variability on impact variables, including hydropower
production. In this study, we analyze the role of ICV and model uncertainty on
three prominent hydropower plants of India using MICE of EC-Earth3 and MME from
CMIP6. We estimate the streamflow projections for all ensembles using the
Variable Infiltration Capacity hydrological model for four time periods,
historical, near, mid and far-term. We estimate maximum hydropower production
generated using monthly release and hydraulic head available at the reservoir.
We also analyzed the role of bias correction in hydropower production. The
results show that ICV plays a significant role in estimating streamflow and
hydropower estimation for monsoon and throughout the year, respectively. Model
uncertainty contributes more to total uncertainty than ICV in estimating the
streamflow and potential hydropower. However, ICV is increasing towards the
far-term. We also show that bias correction does not preserve the internal
variability in estimating the streamflow. Although there is an increase in
uncertainty for estimated streamflow, mean hydropower shows the decrease
towards the far-term for February to May, more prominent for MICE than MME. The
results suggest a need to incorporate uncertainty due to internal variability
for addressing power security in changing climate scenarios.


title: Neural Graph Matching for Pre-training Graph Neural Networks

abstract:
  Recently, graph neural networks (GNNs) have been shown powerful capacity at
modeling structural data. However, when adapted to downstream tasks, it usually
requires abundant task-specific labeled data, which can be extremely scarce in
practice. A promising solution to data scarcity is to pre-train a transferable
and expressive GNN model on large amounts of unlabeled graphs or coarse-grained
labeled graphs. Then the pre-trained GNN is fine-tuned on downstream datasets
with task-specific fine-grained labels. In this paper, we present a novel Graph
Matching based GNN Pre-Training framework, called GMPT. Focusing on a pair of
graphs, we propose to learn structural correspondences between them via neural
graph matching, consisting of both intra-graph message passing and inter-graph
message passing. In this way, we can learn adaptive representations for a given
graph when paired with different graphs, and both node- and graph-level
characteristics are naturally considered in a single pre-training task. The
proposed method can be applied to fully self-supervised pre-training and
coarse-grained supervised pre-training. We further propose an approximate
contrastive training strategy to significantly reduce time/memory consumption.
Extensive experiments on multi-domain, out-of-distribution benchmarks have
demonstrated the effectiveness of our approach. The code is available at:
https://github.com/RUCAIBox/GMPT.


title: Hyperbolic inflationary model with nonzero curvature

abstract:
  We consider a cosmological model consisting of two scalar fields defined in
the hyperbolic plane known as hyperbolic inflation. For the background space we
consider a homogeneous and isotropic spacetime with nonzero curvature. We study
the asymptotic behaviour of the dynamics and we search for attractors in the
expanding regime. We prove that the hyperobolic inflation period is an unstable
solution and there are not future attractors which can solve the flatness
problem and describes acceleration.


title:
Uniform Approximations for Randomized Hadamard Transforms with
  Applications

abstract:
  Randomized Hadamard Transforms (RHTs) have emerged as a computationally
efficient alternative to the use of dense unstructured random matrices across a
range of domains in computer science and machine learning. For several
applications such as dimensionality reduction and compressed sensing, the
theoretical guarantees for methods based on RHTs are comparable to approaches
using dense random matrices with i.i.d.\ entries. However, several such
applications are in the low-dimensional regime where the number of rows sampled
from the matrix is rather small. Prior arguments are not applicable to the
high-dimensional regime often found in machine learning applications like
kernel approximation. Given an ensemble of RHTs with Gaussian diagonals,
$\{M^i\}_{i = 1}^m$, and any $1$-Lipschitz function, $f: \mathbb{R} \to
\mathbb{R}$, we prove that the average of $f$ over the entries of $\{M^i v\}_{i
= 1}^m$ converges to its expectation uniformly over $\| v \| \leq 1$ at a rate
comparable to that obtained from using truly Gaussian matrices. We use our
inequality to then derive improved guarantees for two applications in the
high-dimensional regime: 1) kernel approximation and 2) distance estimation.
For kernel approximation, we prove the first \emph{uniform} approximation
guarantees for random features constructed through RHTs lending theoretical
justification to their empirical success while for distance estimation, our
convergence result implies data structures with improved runtime guarantees
over previous work by the authors. We believe our general inequality is likely
to find use in other applications.


title: A theory of cut-restriction: first steps

abstract:
  Cut-elimination is the bedrock of proof theory. It is the algorithm that
eliminates cuts from a sequent calculus proof that leads to cut-free calculi
and applications. Cut-elimination applies to many logics irrespective of their
semantics. Such is its influence that whenever cut-elimination is not provable
in a sequent calculus the invariable response has been a move to a richer proof
system to regain it. In this paper we investigate a radically different
approach to the latter: adapting age-old cut-elimination to restrict the shape
of the cut-formulas when elimination is not possible. We tackle the "first
level" above cut-free: analytic cuts. Our methodology is applied to the sequent
calculi for bi-intuitionistic logic and S5 where analytic cuts are already
known to be required. This marks the first steps in a theory of
cut-restriction.


title: Syntax-Aware Network for Handwritten Mathematical Expression Recognition

abstract:
  Handwritten mathematical expression recognition (HMER) is a challenging task
that has many potential applications. Recent methods for HMER have achieved
outstanding performance with an encoder-decoder architecture. However, these
methods adhere to the paradigm that the prediction is made "from one character
to another", which inevitably yields prediction errors due to the complicated
structures of mathematical expressions or crabbed handwritings. In this paper,
we propose a simple and efficient method for HMER, which is the first to
incorporate syntax information into an encoder-decoder network. Specifically,
we present a set of grammar rules for converting the LaTeX markup sequence of
each expression into a parsing tree; then, we model the markup sequence
prediction as a tree traverse process with a deep neural network. In this way,
the proposed method can effectively describe the syntax context of expressions,
alleviating the structure prediction errors of HMER. Experiments on three
benchmark datasets demonstrate that our method achieves better recognition
performance than prior arts. To further validate the effectiveness of our
method, we create a large-scale dataset consisting of 100k handwritten
mathematical expression images acquired from ten thousand writers. The source
code, new dataset, and pre-trained models of this work will be publicly
available.


title:
First measurement of the $Z\rightarrow \mu^+ \mu^-$ angular coefficients
  in the forward region of $pp$ collisions at $\sqrt{s}=13$ TeV

abstract:
  The first study of the angular distribution of $\mu^+ \mu^-$ pairs produced
in the forward rapidity region via the Drell-Yan reaction $pp \rightarrow
\gamma^{*}/Z +X \rightarrow l^+ l^- + X$ is presented, using data collected
with the LHCb detector at a centre-of-mass energy of 13TeV, corresponding to an
integrated luminosity of 5.1 $\rm{fb}^{-1}$. The coefficients of the five
leading terms in the angular distribution are determined as a function of the
dimuon transverse momentum and rapidity. The results are compared to various
theoretical predictions of the $Z$-boson production mechanism and can also be
used to probe transverse-momentum-dependent parton distributions within the
proton.


title: AdaFamily: A family of Adam-like adaptive gradient methods

abstract:
  We propose AdaFamily, a novel method for training deep neural networks. It is
a family of adaptive gradient methods and can be interpreted as sort of a blend
of the optimization algorithms Adam, AdaBelief and AdaMomentum. We perform
experiments on standard datasets for image classification, demonstrating that
our proposed method outperforms these algorithms.


abstract:
  Generative adversarial network (GAN) is widely used for generalized and
robust learning on graph data. However, for non-Euclidean graph data, the
existing GAN-based graph representation methods generate negative samples by
random walk or traverse in discrete space, leading to the information loss of
topological properties (e.g. hierarchy and circularity). Moreover, due to the
topological heterogeneity (i.e., different densities across the graph
structure) of graph data, they suffer from serious topological distortion
problems. In this paper, we proposed a novel Curvature Graph Generative
Adversarial Networks method, named \textbf{\modelname}, which is the first
GAN-based graph representation method in the Riemannian geometric manifold. To
better preserve the topological properties, we approximate the discrete
structure as a continuous Riemannian geometric manifold and generate negative
samples efficiently from the wrapped normal distribution. To deal with the
topological heterogeneity, we leverage the Ricci curvature for local structures
with different topological properties, obtaining to low-distortion
representations. Extensive experiments show that CurvGAN consistently and
significantly outperforms the state-of-the-art methods across multiple tasks
and shows superior robustness and generalization.


title: Design of the AWAKE Run 2c transfer lines using numerical optimizers

abstract:
  The Advanced Wakefield (AWAKE) Experiment is a proof-of-principle experiment
demonstrating the acceleration of electron beams via proton-driven plasma
wakefield acceleration. AWAKE Run 1 achieved acceleration of electron beams to
2 GeV and the intention for Run 2 is to build on these results by achieving
acceleration to ~10 GeV with a higher beam quality. As part of the upgrade to
Run 2, the existing proton and electron beamlines will be adapted and a second
plasma cell and new 150 MeV electron beamline will be added. This new beamline
will be required to inject electron bunches with micron-level beam size and
stability into the second plasma cell from within the 1 m gap between the two
plasma cells. In this paper we describe the techniques used (e.g. numerical
optimizers and genetic algorithms) to produce the design of the 150 MeV
electron line in order to meet the challenging experimental specifications.
Operational techniques are also studied for both electron transfer lines
including steering and alignment methods utilising numerical optimizers and
beam measurement techniques employing neural networks.


title:
Ensemble Methods for Robust Support Vector Machines using Integer
  Programming

abstract:
  In this work we study binary classification problems where we assume that our
training data is subject to uncertainty, i.e. the precise data points are not
known. To tackle this issue in the field of robust machine learning the aim is
to develop models which are robust against small perturbations in the training
data. We study robust support vector machines (SVM) and extend the classical
approach by an ensemble method which iteratively solves a non-robust SVM on
different perturbations of the dataset, where the perturbations are derived by
an adversarial problem. Afterwards for classification of an unknown data point
we perform a majority vote of all calculated SVM solutions. We study three
different variants for the adversarial problem, the exact problem, a relaxed
variant and an efficient heuristic variant. While the exact and the relaxed
variant can be modeled using integer programming formulations, the heuristic
one can be implemented by an easy and efficient algorithm. All derived methods
are tested on random and realistic datasets and the results indicate that the
derived ensemble methods have a much more stable behaviour when changing the
protection level compared to the classical robust SVM model.


title:
Entanglement quantification from collective measurements processed by
  machine learning

abstract:
  In this paper, we investigate how to reduce the number of measurement
configurations needed for sufficiently precise entanglement quantification.
Instead of analytical formulae, we employ artificial neural networks to predict
the amount of entanglement in a quantum state based on results of collective
measurements (simultaneous measurements on multiple instances of the
investigated state). This approach allows us to explore the precision of
entanglement quantification as a function of measurement configurations. For
the purpose of our research, we consider general two-qubit states and their
negativity as entanglement quantifier. We outline the benefits of this approach
in future quantum communication networks.


title:
Nanopublication-Based Semantic Publishing and Reviewing: A Field Study
  with Formalization Papers

abstract:
  With the rapidly increasing amount of scientific literature,it is getting
continuously more difficult for researchers in different disciplines to be
updated with the recent findings in their field of study.Processing scientific
articles in an automated fashion has been proposed as a solution to this
problem,but the accuracy of such processing remains very poor for extraction
tasks beyond the basic ones.Few approaches have tried to change how we publish
scientific results in the first place,by making articles machine-interpretable
by expressing them with formal semantics from the start.In the work presented
here,we set out to demonstrate that we can formally publish high-level
scientific claims in formal logic,and publish the results in a special issue of
an existing journal.We use the concept and technology of nanopublications for
this endeavor,and represent not just the submissions and final papers in this
RDF-based format,but also the whole process in between,including
reviews,responses,and decisions.We do this by performing a field study with
what we call formalization papers,which contribute a novel formalization of a
previously published claim.We received 15 submissions from 18 authors,who then
went through the whole publication process leading to the publication of their
contributions in the special issue.Our evaluation shows the technical and
practical feasibility of our approach.The participating authors mostly showed
high levels of interest and confidence,and mostly experienced the process as
not very difficult,despite the technical nature of the current user
interfaces.We believe that these results indicate that it is possible to
publish scientific results from different fields with machine-interpretable
semantics from the start,which in turn opens countless possibilities to
radically improve in the future the effectiveness and efficiency of the
scientific endeavor as a whole.


title:
Large deviations of a tracer position in the dense and the dilute limits
  of a single-file diffusion

abstract:
  We apply the macroscopic fluctuation theory to analyze the long-time
statistics of the position of a tracer in the dense and the dilute limits of
diffusive single-file systems. Our explicit results are about the corresponding
large deviation functions for an initial step density profile with the
fluctuating (annealed) and the fixed (quenched) initial conditions. These
hydrodynamic results are applicable for a general single-file system and they
confirm recent exact results obtained by microscopic solutions for specific
model systems.


title: Quantum Proofs of Deletion for Learning with Errors

abstract:
  Quantum information has the property that measurement is an inherently
destructive process. This feature is most apparent in the principle of
complementarity, which states that mutually incompatible observables cannot be
measured at the same time. Recent work by Broadbent and Islam (TCC 2020) builds
on this aspect of quantum mechanics to realize a cryptographic notion called
certified deletion. While this remarkable notion enables a classical verifier
to be convinced that a (private-key) quantum ciphertext has been deleted by an
untrusted party, it offers no additional layer of functionality.
  In this work, we augment the proof-of-deletion paradigm with fully
homomorphic encryption (FHE). This results in a new and powerful cryptographic
notion called fully homomorphic encryption with certified deletion -- an
interactive protocol which enables an untrusted quantum server to compute on
encrypted data and, if requested, to simultaneously prove data deletion to a
client. Our main technical ingredient is an interactive protocol by which a
quantum prover can convince a classical verifier that a sample from the
Learning with Errors (LWE) distribution in the form of a quantum state was
deleted. We introduce an encoding based on Gaussian coset states which is
highly generic and suggests that essentially any LWE-based cryptographic
primitive admits a classically-verifiable quantum proof of deletion. As an
application of our protocol, we construct a Dual-Regev public-key encryption
scheme with certified deletion, which we then extend towards a (leveled) FHE
scheme of the same type. Our construction achieves indistinguishable
ciphertexts in the semi-honest adversarial model, even if the secret key is
later revealed after deletion has taken place.


title:
A Plant-Inspired Multifunctional, Two Way, and Fiberless Soft Gripper
  with Sensorized Kinaesthesia

abstract:
  This work presents a new fiberless soft pneumatic actuator that can work
multifunctional and bidirectional, and its embedded sensors give it a
self-proprioception ability. This actuator works based on the idea of employing
helical pressure channels. Applying the controlled input pressures into these
two channels causes a variety of deformations and actuation. In particular,
single pressure, imbalanced pressures, and balanced pressures applied in the
channels cause bidirectional coilings, opposite bendings, and elongation,
respectively, in a single unit actuator. Also, two U-shaped microchannels are
created, and by injecting a gel-based conductive material, the actuator is
equipped with resistive sensors which are responsive to a vast dynamic range
from a small oscillation to a large elongation. This actuator has so many
promising features as a multifunctional soft gripper, and its embedded soft
sensors enable it to have better controllability in real problems. The
multifunctionality of this actuator has been validated with several
experimental tests, and also we have shown it has excellent potential in
gripping a variety of objects. Finally, the embedded sensors can discriminate
the main functions of actuators, and also they can play the role of independent
sensors as well like a stretch, pressure, or bending sensors.


title:
Magnetic field tuning of the valley population in the Weyl phase of
  Nd$_2$Ir$_2$O$_7$

abstract:
  The frustrated magnet Nd$_2$Ir$_2$O$_7$, where strong correlations together
with spin-orbit coupling play a crucial role, is predicted to be a Weyl
semimetal and to host topological pairs of bulk Dirac-like valleys. Here we use
an external magnetic field to manipulate the localized rare earth 4f moments
coupled to the 5d electronic bands. Low energy optical spectroscopy reveals
that a field of only a few teslas suffices to create charge compensating
pockets of holes and electrons in different regions of momentum space, thus
introducing a valley population shift that can be tuned with the field.


title: Cycles in graphs with geometric property (T)

abstract:
  We show that a sequence of graphs with geometric property (T) has many small
cycles. We also show that when a small part of a sequence of graphs with
geometric property (T) is changed, it still has geometric property (T),
provided that it is still an expander. We use this to give an example of a
sequence of graphs with geometric property (T) that has large cycle-free balls.


title:
Optimal Exploration of an Exhaustible Resource with Stochastic
  Discoveries

abstract:
  The standard Hotelling model assumes that the stock of an exhaustible
resource is known. We expand on the model by Arrow and Chang that introduced
stochastic discoveries and for the first time completely solve such a model
using impulse control. The model has two state variables: the "proven" reserves
as well as a finite unexplored area available for exploration with constant
marginal cost, resulting in a Poisson process of new discoveries. We prove that
a frontier of critical levels of "proven" reserves exists, above which
exploration is stopped, and below which it happens at infinite speed. This
frontier is increasing in the explored area, and higher "proven" reserve levels
along this critical threshold are indicative of more scarcity, not less. In
this stochastic generalization of Hotelling's rule, the expected shadow price
of reserves rises at the rate of interest across exploratory episodes. However,
the actual trajectories of prices realized prior to exhaustion of the
exploratory area may jump up or down upon exploration. Conditional on
non-exhaustion, expected price arises at a rate bounded above by the rate of
interest, consistent with most empirical tests based on observed price
histories.


title:
Lipschitz continuous solutions of the Vlasov-Maxwell systems with a
  conductor boundary condition

abstract:
  We consider relativistic plasma particles subjected to an external
gravitation force in a $3$D half space whose boundary is a perfect conductor.
When the mean free path is much bigger than the variation of electromagnetic
fields, the collision effect is negligible. As an effective PDE, we study the
relativistic Vlasov-Maxwell system and its local-in-time unique solvability in
the space-time locally Lipschitz space, for several basic mesoscopic (kinetic)
boundary conditions: the inflow, diffuse, and specular reflection boundary
conditions. We construct weak solutions to these initial-boundary value
problems and study their locally Lipschitz continuity with the aid of a weight
function depending on the solutions themselves. Finally, we prove the
uniqueness of a solution, by using regularity estimate and realizing the
Gauss's law at the boundary within Lipschitz continuous space.


title:
Physics-Data Driven Machine Learning Based Model: A Hybrid Way for
  Nonlinear, Dynamic, and Open-loop Identification of IPMC Soft Artificial
  Muscles

abstract:
  Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choices
among biocompatible materials for industrial and biomedical applications.
Despite their advantages, some of their drawbacks include non-linear and
hysteretic behavior, which complicates the modeling process. In previous works,
usually autoregressive models were used to predict the behavior of an IPMC
actuator. The main drawback of using an autoregressive model is that it cannot
be used in mobile and real-time applications. In this study, we proposed a
hybrid analytical intelligent model for an IPMC actuator. The most outstanding
feature of this model is its non-autoregressive structure. The hybrid concept
proposed in this study can be generalized to various problems other than IPMCs.
The structure used in this work comprises an analytical model and a deep neural
network, providing a non-linear, dynamic, and non-autoregressive model for the
IPMC actuator. Lastly, the average NMSE achieved using the proposed hybrid
model is 9.5781e-04 showing a significant drop in the error rate compared to
other non-autoregressive structures.


title:
Nuclear-order-induced quantum criticality and heavy-fermion
  superconductivity at ultra-low temperatures in YbRh$_2$Si$_2$

abstract:
  The tetragonal heavy-fermion metal YbRh$_2$Si$_2$ orders
antiferromagnetically at $T_{\rm N} = 70$ mK and exhibits an unconventional
quantum critical point (QCP) of Kondo-destroying type at $B_{\rm N} = 60$ mT,
for the magnetic field applied within the basal ($a,b$) plane.
Ultra-low-temperature magnetization and heat-capacity measurements at very low
fields indicate that the 4$f$-electronic antiferromagnetic (AF) order is
strongly suppressed by a nuclear-dominated hybrid order (`A-phase') at $T_{\rm
A} \le 2.3$ mK, such that quantum critical fluctuations develop at $B \approx
0$ (Schuberth et al., Science \textbf{351}, 485 (2016)). This enables the onset
of heavy-fermion superconductivity ($T_{\rm c} = 2$ mK) which appears to be
suppressed by the primary AF order at elevated temperatures. Measurements of
the Meissner effect reveal bulk superconductivity, with $T_{\rm c}$ decreasing
under applied field to $T_{\rm c} < 1$ mK at $B > 20$ mT. The observation of a
weak but distinct superconducting shielding signal at a temperature as high as
10 mK suggests the formation of insulated random islands with emergent A-phase
order and superconductivity. Upon cooling, the shielding signal increases
almost linearly in temperature, indicating a growth of the islands which
eventually percolate at $T \approx 6.5$ mK. Recent electrical-resistivity
results by Nguyen et al. (Nat. Commun. \textbf{12}, 4341 (2021)) confirm the
existence of superconductivity in YbRh$_2$Si$_2$ at ultra-low temperatures. The
combination of the results of Schuberth et al. and Nguyen et al. at ultra-low
temperatures below $B_{\rm N}$, along with those previously established at
higher temperatures in the paramagnetic state, provide compelling evidence that
the Kondo-destruction quantum criticality robustly drives unconventional
superconductivity.


title:
Multi-scale analysis of global electromagnetic instabilities in ITER
  Pre-Fusion-Power Operation plasmas

abstract:
  Global electromagnetic gyrokinetic simulations are performed with the
Particle-in-Cell code ORB5 for an ITER Pre-Fusion-Power-Operation (PFPO) plasma
scenario, with half-field (2.65 T) and half-current (7.5 MA). We report on a
'multi-scale' analysis of the discharge, considering eigenmodes and instabilies
across three scale-lengths. Although the scenario will nominally have neutral
beam heating with particles injected with 1 MeV, Alfv\'en eigenmodes are
investigated in the absence of such source, and Reversed Shear (RSAE), Toroidal
(TAE) and Elliptical (EAE) Alfv\'en eigenmodes are found with weak damping for
moderately low toroidal mode numbers ($10 \leq n \leq 35$). At higher toroidal
mode numbers ($40 \leq n \leq 70$), unstable Alfv\'enic modes have been
observed close to rational surfaces and are labelled as Beta-induced Alfv\'en
eigenmodes (BAE)/Alfv\'enic Ion Temperature Gradient (AITG) modes, since their
frequency is associated with the BAE gap and they are driven by the bulk plasma
on the Alfv\'enic continuum. These modes are unstable in the absence of
energetic particles, and adding a species of energetic particles (with an
isotropic 1 MeV slowing down distribution) has negligible impact on their
growth rate. At higher toroidal mode numbers ($150 \lessapprox n < 220$), low
frequency microscale instabilities are observed.


title:
Solving Nuclear Structure Problems with the Adaptive Variational Quantum
  Algorithm

abstract:
  We use the Lipkin-Meshkov-Glick (LMG) model and the valence-space nuclear
shell model to examine the likely performance of variational quantum
eigensolvers in nuclear-structure theory. The LMG model exhibits both a phase
transition and spontaneous symmetry breaking at the mean-field level in one of
the phases, features that characterize collective dynamics in medium-mass and
heavy nuclei. We show that with appropriate modifications, the ADAPT-VQE
algorithm, a particularly flexible and accurate variational approach, is not
troubled by these complications. We treat up to 12 particles and show that the
number of quantum operations needed to approach the ground-state energy scales
linearly with the number of qubits. We find similar scaling when the algorithm
is applied to the nuclear shell model with realistic interactions in the $sd$
and $pf$ shells.


title: Linear cuts in Boolean networks

abstract:
  Boolean networks are popular tools for the exploration of qualitative
dynamical properties of biological systems. Several dynamical interpretations
have been proposed based on the same logical structure that captures the
interactions between Boolean components. They reproduce, in different degrees,
the behaviours emerging in more quantitative models. In particular, regulatory
conflicts can prevent the standard asynchronous dynamics from reproducing some
trajectories that might be expected upon inspection of more detailed models. We
introduce and study the class of networks with linear cuts, where linear
components -- intermediates with a single regulator and a single target --
eliminate the aforementioned regulatory conflicts. The interaction graph of a
Boolean network admits a linear cut when a linear component occurs in each
cycle and in each path from components with multiple targets to components with
multiple regulators. Under this structural condition the attractors are in
one-to-one correspondence with the minimal trap spaces, and the reachability of
attractors can also be easily characterized. Linear cuts provide the base for a
new interpretation of the Boolean semantics that captures all behaviours of
multi-valued refinements with regulatory thresholds that are uniquely defined
for each interaction, and contribute a new approach for the investigation of
behaviour of logical models.


title:
Endogenous Security of Computation Offloading in Blockchain-Empowered
  Internet of Things

abstract:
  This paper investigates an endogenous security architecture for computation
offloading in the Internet of Things (IoT), where the blockchain technology
enables the traceability of malicious behaviors, and the task data uploading
link from sensors to small base station (SBS) is protected by intelligent
reflecting surface (IRS)-assisted physical layer security (PLS). After
receiving task data, the SBS allocates computational resources to help sensors
perform the task. The existing computation offloading schemes usually focus on
network performance improvement, such as energy consumption minimization, and
neglect the Gas fee paid by sensors, resulting in the discontent of high Gas
payers. Here, we design a Gas-oriented computation offloading scheme that
guarantees the degree of satisfaction of sensors, while aiming to reduce energy
consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS
transmission that can represent the global secrecy performance to allocate
computational resources. The simulations show that the proposed scheme ensures
that the node paying higher Gas gets stronger computational resources, and just
raises $4\%$ energy consumption in comparison with energy consumption
minimization schemes.


title: Towards European Standards for Quantum Technologies

abstract:
  The Second Quantum Revolution facilitates the engineering of new classes of
sensors, communication technologies, and computers with unprecedented
capabilities. Supply chains for quantum technologies are emerging, some
focussed on commercially available components for enabling technologies and/or
quantum-technologies research infrastructures, others with already higher
technology-readiness levels, near to the market.
  In 2018, the European Commission has launched its large-scale and long-term
Quantum Flagship research initiative to support and foster the creation and
development of a competitive European quantum technologies industry, as well as
the consolidation and expansion of leadership and excellence in European
quantum technology research. One of the measures to achieve an accelerated
development and uptake has been identified by the Quantum Flagship in its
Strategic Research Agenda: the promotion of coordinated, dedicated
standardisation and certification efforts. Standardisation is indeed of
paramount importance to facilitate the growth of new technologies, and the
development of efficient and effective supply chains. The harmonisation of
technologies, methodologies, and interfaces enables interoperable products,
innovation, and competition, all leading to structuring and hence growth of
markets. As quantum technologies are maturing, time has come to start thinking
about further standardisation needs.
  This article presents insights on standardisation for quantum technologies
from the perspective of the CEN-CENELEC Focus Group on Quantum Technologies
(FGQT), which was established in June 2020 to coordinate and support the
development of standards relevant for European industry and research.


title: ETCetera: beyond Event-Triggered Control

abstract:
  We present ETCetera, a Python library developed for the analysis and
synthesis of the sampling behaviour of event triggered control (ETC) systems.
In particular, the tool constructs abstractions of the sampling behaviour of
given ETC systems, in the form of timed automata (TA) or finite-state
transition systems (FSTSs). When the abstraction is an FSTS, ETCetera provides
diverse manipulation tools for analysis of ETC's sampling performance,
synthesis of communication traffic schedulers (when networks shared by multiple
ETC loops are considered), and optimization of sampling strategies.
Additionally, the TA models may be exported to UPPAAL for analysis and
synthesis of schedulers. Several examples of the tool's application for
analysis and synthesis problems with different types of dynamics and
event-triggered implementations are provided.


title: The Impact of Quadratic Biases on Cosmic Shear

abstract:
  In this paper we revisit potential biases in cosmic shear power spectra
caused by bias terms that multiply up to quadratic powers of the shear.
Expanding the multiplicative bias field as a series of independent spin-$s$
fields we find terms $m_s$ that multiply integer and half-integer powers of the
shear. We propagate these biases into shape measurement statistics and the
cosmic shear power spectrum. We find that such biases can be measured by
performing regression on calibration data. We find that for integer powers of
shear the impact of quadratic order terms on the power spectrum is an
additional bispectrum dependency; ignoring quadratic terms can lead to biases
in cosmological parameters of up to $2(m_2+m_{-2}-m_6)0.4\sigma$ for Stage-IV
dark energy experiments, but that the susceptibility to them can be decreased
by using methods to remove small-scale sensitivity. We also find, for
half-integer powers of the shear that, for a Stage-IV experiment, biases are
required to be known to better than approximately
$\sigma[m^R_0+15(m^R_1+m^R_3)+0.1(m^R_{-1}+m^R_5)]\leq 0.01$. In future,
Stage-IV dark energy experiments should seek to measure and minimise such all
such bias terms.


title:
Aspects of differential calculus related to infinite-dimensional vector
  bundles and Poisson vector spaces

abstract:
  We prove various results in infinite-dimensional differential calculus which
relate differentiability properties of functions and associated operator-valued
functions (e.g., differentials). The results are applied in two areas: 1. in
the theory of infinite-dimensional vector bundles, to construct new bundles
from given ones, like dual bundles, topological tensor products, infinite
direct sums, and completions (under suitable hypotheses). 2. in the theory of
locally convex Poisson vector spaces, to prove continuity of the Poisson
bracket and continuity of passage from a function to the associated Hamiltonian
vector field. Topological properties of topological vector spaces are essential
for the studies, which allow hypocontinuity of bilinear mappings to be
exploited. Notably, we encounter $k_{{\mathbb R}}$-spaces and locally convex
spaces $E$ such that $E\times E$ is a $k_{{\mathbb R}}$-space.


title:
Electronic and magnetic properties of the RuX$_3$ (X=Cl, Br, I) family:
  Two siblings -- and a cousin?

abstract:
  Motivated by recent reports of metallic behavior in the newly synthesized
RuI$_3$, in contrast to the Mott-insulating nature of the actively discussed
$\alpha$-RuCl$_3$, as well as RuBr$_3$, we present a detailed comparative
analysis of the electronic and magnetic properties of this family of
trihalides. Using a combination of first-principles calculations and
effective-model considerations, we conclude that RuI$_3$, similarly to the
other two members, is most probably on the verge of a Mott insulator, but with
much smaller magnetic moments and a strong magnetic frustration. We predict the
ideal pristine crystal of RuI$_3$ to have a nearly vanishing conventional
nearest-neighbor Heisenberg interaction and to be a quantum spin liquid
candidate of possibly different kind than the Kitaev spin liquid. In order to
understand the apparent contradiction to the reported resistivity $\rho$, we
analyze the experimental evidence for all three compounds and propose a
scenario for the observed metallicity in existing samples of RuI$_3$.
Furthermore, for the Mott insulator RuBr$_3$ we obtain a magnetic Hamiltonian
of a similar form to that in the much discussed $\alpha$-RuCl$_3$ and show that
this Hamiltonian is in agreement with experimental evidence in RuBr$_3$.


title: Updated Gaia-2MASS 3D maps of Galactic interstellar dust

abstract:
  Three-dimensional (3D) maps of Galactic interstellar dust are a tool for a
wide range of uses. We aim to construct 3D maps of dust extinction in the Local
Arm and surrounding regions. Gaia EDR3 photometric data were combined with
2MASS measurements to derive extinction towards stars with accurate photometry
and relative uncertainties on parallaxes of less than 20%. We applied our
hierarchical inversion algorithm adapted to inhomogeneous spatial distributions
of target stars to this catalogue of extinctions. We present the updated 3D
dust extinction distribution and provide an estimate of the error on integrated
extinctions from the Sun to each area in the 3D map. The computational area is
similar to the one of the previous DR2 map, a 6 kpc x 6 kpc x 0.8
kpcAstrophysics volume around the Sun. Due to the addition of fainter target
stars, the volume in which the clouds can be reconstructed has increased. Due
to the improved accuracy of the parallaxes and photometric data in EDR3,
extinctions among neighbouring targets are more consistent, allowing one to
reach an increased contrast in the dense areas, while cavity contours are more
regular. We show several comparisons with recent results on dust and star
distributions. The wavy pattern around the Plane of the dust concentrations is
better seen and exists over large regions. Its mean vertical peak-to-peak
amplitude is of the order of 300 pc; interestingly, it is similar to the
vertical period of the spectacular snail-shaped stellar kinematical pattern
discovered in Gaia data. The Gaia EDR3 catalogue allows for a significant
improvement of the extinction maps to be made and the hierarchical technique
confirms its efficiency for massive datasets. Future comparisons between 3D
maps of interstellar matter and stellar distributions may help to understand
which mergers or internal perturbations have shaped the Galaxy within the first
3 kpc.


title: Early Time-Series Classification Algorithms: An Empirical Comparison

abstract:
  Early Time-Series Classification (ETSC) is the task of predicting the class
of incoming time-series by observing as few measurements as possible. Such
methods can be employed to obtain classification forecasts in many
time-critical applications. However, available techniques are not equally
suitable for every problem, since differentiations in the data characteristics
can impact algorithm performance in terms of earliness, accuracy, F1-score, and
training time. We evaluate six existing ETSC algorithms on publicly available
data, as well as on two newly introduced datasets originating from the life
sciences and maritime domains. Our goal is to provide a framework for the
evaluation and comparison of ETSC algorithms and to obtain intuition on how
such approaches perform on real-life applications. The presented framework may
also serve as a benchmark for new related techniques.


title:
Continuous Relaxation For The Multivariate Non-Central Hypergeometric
  Distribution

abstract:
  Partitioning a set of elements into a given number of groups of a priori
unknown sizes is an important task in many applications. Due to hard
constraints, it is a non-differentiable problem which prohibits its direct use
in modern machine learning frameworks. Hence, previous works mostly fall back
on suboptimal heuristics or simplified assumptions. The multivariate
hypergeometric distribution offers a probabilistic formulation of how to
distribute a given number of samples across multiple groups. Unfortunately, as
a discrete probability distribution, it neither is differentiable. In this
work, we propose a continuous relaxation for the multivariate non-central
hypergeometric distribution. We introduce an efficient and numerically stable
sampling procedure. This enables reparameterized gradients for the
hypergeometric distribution and its integration into automatic differentiation
frameworks. We highlight the applicability and usability of the proposed
formulation on two different common machine learning tasks.


title: Optimization-based Phase-shift Codebook Design for Large IRSs

abstract:
  In this paper, we focus on large intelligent reflecting surfaces (IRSs) and
propose a new codebook construction method to obtain a set of pre-designed
phase-shift configurations for the IRS unit cells. Since the complexity of
online optimization and the overhead for channel estimation for IRS-assisted
communications scale with the size of the phase-shift codebook, the design of
small codebooks is of high importance. We consider both continuous and discrete
phase shift designs and formulate the codebook construction as optimization
problems. To solve the optimization problems, we propose an optimal algorithm
for the discrete phaseshift design and a low-complexity sub-optimal solution
for the continuous design. Simulation results show that the proposed algorithms
facilitate the construction of codebooks of different sizes and with different
beamwidths. Moreover, the performance of the discrete phase-shift design with
2-bit quantization is shown to approach that of the continuous phase-shift
design. Finally, our simulation results show that the proposed designs enable
large transmit power savings compared to the existing linear and quadratic
codebook designs.


title:
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet
  Transform based on Helgason-Fourier Analysis

abstract:
  Neural network on Riemannian symmetric space such as hyperbolic space and the
manifold of symmetric positive definite (SPD) matrices is an emerging subject
of research in geometric deep learning. Based on the well-established framework
of the Helgason-Fourier transform on the noncompact symmetric space, we present
a fully-connected network and its associated ridgelet transform on the
noncompact symmetric space, covering the hyperbolic neural network (HNN) and
the SPDNet as special cases. The ridgelet transform is an analysis operator of
a depth-2 continuous network spanned by neurons, namely, it maps an arbitrary
given function to the weights of a network. Thanks to the coordinate-free
reformulation, the role of nonlinear activation functions is revealed to be a
wavelet function, and the reconstruction formula directly yields the
universality of the proposed networks.


title:
Stability results of locally coupled wave equations with local
  Kelvin-Voigt damping: Cases when the supports of damping and coupling
  coefficients are disjoint

abstract:
  In this paper, we study the direct/indirect stability of locally coupled wave
equations with local Kelvin-Voigt dampings/damping and by assuming that the
supports of the dampings and the coupling coefficients are disjoint. First, we
prove the well-posedness, strong stability, and polynomial stability for some
one dimensional coupled systems. Moreover, under some geometric control
condition, we prove the well-posedness and strong stability in the
multi-dimensional case.


title:
Numerical method for feasible and approximately optimal solutions of
  multi-marginal optimal transport beyond discrete measures

abstract:
  We propose a numerical algorithm for the computation of multi-marginal
optimal transport (MMOT) problems involving general measures that are not
necessarily discrete. By developing a relaxation scheme in which marginal
constraints are replaced by finitely many linear constraints and by proving a
specifically tailored duality result for this setting, we approximate the MMOT
problem by a linear semi-infinite optimization problem. Moreover, we are able
to recover a feasible and approximately optimal solution of the MMOT problem,
and its sub-optimality can be controlled to be arbitrarily close to 0 under
mild conditions. The developed relaxation scheme leads to a numerical algorithm
which can compute a feasible approximate optimizer of the MMOT problem whose
theoretical sub-optimality can be chosen to be arbitrarily small. Besides the
approximate optimizer, the algorithm is also able to compute both an upper
bound and a lower bound on the optimal value of the MMOT problem. The
difference between the computed bounds provides an explicit upper bound on the
sub-optimality of the computed approximate optimizer. Through a numerical
example, we demonstrate that the proposed algorithm is capable of computing a
high-quality solution of an MMOT problem involving as many as 50 marginals
along with an explicit estimate of its sub-optimality that is much less
conservative compared to the theoretical estimate.


title: License Incompatibilities in Software Ecosystems

abstract:
  Contemporary software is characterized by reuse of components that are
declared as dependencies and that are received from package
managers/registries, such as, NPM, PyPI, RubyGems, Maven Central, etc. Direct
and indirect dependency relations often form opaque dependency networks, that
sometimes lead to conflicting software licenses within these. In this paper, we
study license use and license incompatibilities between all components from
seven package registries (Cargo, Maven, NPM, NuGet, Packagist, PyPI, RubyGems)
with a closer investigation of license incompatibilities caused by the GNU
Affero General Public License (AGPL). We find that the relative amount of used
licenses vary between ecosystems (permissive licenses such as MIT and Apache
are most frequent), that the number of direct license incompatibilities ranges
from low 2.3% in Cargo to a large 20.8% in PyPI, that only a low amount of
direct license incompatibilities are caused by AGPL licenses (max. 0.04% in
PyPI), but that a whopping 6.62% of Maven packages are violating the AGPL
license of an indirect dependency. Our results suggest that it is not too
unlikely that applications that are reusing packages from PyPI or Maven are
confronted with license incompatibilities that could mean that applications
would have to be open-sourced on distribution (PyPI) or as soon as they are
publicly available as web-applications (Maven).


title: Parallel feature selection based on the trace ratio criterion

abstract:
  The growth of data today poses a challenge in management and inference. While
feature extraction methods are capable of reducing the size of the data for
inference, they do not help in minimizing the cost of data storage. On the
other hand, feature selection helps to remove the redundant features and
therefore is helpful not only in inference but also in reducing management
costs. This work presents a novel parallel feature selection approach for
classification, namely Parallel Feature Selection using Trace criterion (PFST),
which scales up to very large datasets. Our method uses trace criterion, a
measure of class separability used in Fisher's Discriminant Analysis, to
evaluate feature usefulness. We analyzed the criterion's desirable properties
theoretically. Based on the criterion, PFST rapidly finds important features
out of a set of features for big datasets by first making a forward selection
with early removal of seemingly redundant features parallelly. After the most
important features are included in the model, we check back their contribution
for possible interaction that may improve the fit. Lastly, we make a backward
selection to check back possible redundant added by the forward steps. We
evaluate our methods via various experiments using Linear Discriminant Analysis
as the classifier on selected features. The experiments show that our method
can produce a small set of features in a fraction of the amount of time by the
other methods under comparison. In addition, the classifier trained on the
features selected by PFST not only achieves better accuracy than the ones
chosen by other approaches but can also achieve better accuracy than the
classification on all available features.


title:
Miniature 120-beam coherent combiner with 3D printed optics for
  multicore fiber based endoscopy

abstract:
  We report high efficiency, miniaturized, ultra-fast coherent beam combining
with 3D printed micro-optics directly on the tip of a multicore fiber bundle.
The highly compact device foot-print (180 micron diameter) facilitates its
incorporation into a minimally invasive ultra-thin nonlinear endoscope to
perform two-photon imaging


title:
Low-rank flat-field correction for artifact reduction in spectral
  computed tomography

abstract:
  Spectral computed tomography has received considerable interest in recent
years since spectral measurements contain much richer information about the
object of interest. In spectral computed tomography, we are interested in the
energy channel-wise reconstructions of the object. However, such
reconstructions suffer from low signal-to-noise ratio and share the challenges
of conventional low-dose computed tomography such as ring artifacts. Ring
artifacts arise from errors in the flat-field correction and can significantly
degrade the quality of the reconstruction. We propose an extended flat-field
model that exploits high correlation in the spectral flat-fields to reduce ring
artifacts in the channel-wise reconstructions. The extended model relies on the
assumption that the spectral flat-fields can be well-approximated by a low-rank
matrix. Our proposed model works directly on the spectral flat-fields and can
be combined with any existing reconstruction model, e.g., filtered back
projection and iterative methods. The proposed model is validated on a neutron
data set. The results show that our method successfully diminishes ring
artifacts and improves the quality of the reconstructions. Moreover, the
results indicate that our method is robust; it only needs a single spectral
flat-field image, whereas existing methods need multiple spectral flat-field
images to reach a similar level of ring reduction.


title: Fabry-Perot microcavity spectra have a fine structure

abstract:
  Optical cavities can support many transverse and longitudinal modes. A
paraxial scalar theory predicts that the resonance frequencies of these modes
cluster in different orders. A non-paraxial vector theory predicts that the
frequency degeneracy within these clusters is lifted, such that each order
acquires a spectral fine structure, comparable to the fine structure observed
in atomic spectra. In this paper, we calculate this fine structure for
microcavities and show how it originates from various non-paraxial effects and
is co-determined by mirror aberrations. The presented theory, which applies
perturbation theory to Maxwell's equations with boundary conditions, proves to
be very powerful. It generalizes the effective 1-dimensional description of
Fabry-Perot cavities to a 3-dimensional multi-transverse-mode description. It
thereby provides new physical insights in several mode-shaping effects and a
detailed prediction of the fine structure in Fabry-Perot spectra.


title: Single $\pi^0$ production in $\mu e$ scattering at MUonE

abstract:
  The recently proposed MUonE experiment at CERN aims at providing a novel
determination of the leading order hadronic contribution to the muon anomalous
magnetic moment through the study of elastic muon-electron scattering at
relatively small momentum transfer. The anticipated accuracy of the order of
10ppm demands for high-precision prediction in radiative corrections to the
$\mu e$ scattering as well as for robust quantitative estimates of all possible
background processes. In this letter, the contribution due to the emission of a
neutral pion through the process $\mu e \to \mu e \pi^0$ is studied and its
numerical impact is discussed in different phase space configurations by means
of the upgraded Monte Carlo event generator MESMER. In fact, single $\pi^0$
production could be a source of reducible background for the measurement of the
QED running coupling constant at MUonE and it could be also an important
background for possible New Physics searches at MUonE involving $2 \to 3$
processes, in phase space regions complementary to the ones characteristic of
the elastic $\mu e$ scattering.


title: Risk-aware Stochastic Shortest Path

abstract:
  We treat the problem of risk-aware control for stochastic shortest path (SSP)
on Markov decision processes (MDP). Typically, expectation is considered for
SSP, which however is oblivious to the incurred risk. We present an alternative
view, instead optimizing conditional value-at-risk (CVaR), an established risk
measure. We treat both Markov chains as well as MDP and introduce, through
novel insights, two algorithms, based on linear programming and value
iteration, respectively. Both algorithms offer precise and provably correct
solutions. Evaluation of our prototype implementation shows that risk-aware
control is feasible on several moderately sized models.


title:
On generating parametrised structural data using conditional generative
  adversarial networks

abstract:
  A powerful approach, and one of the most common ones in structural health
monitoring (SHM), is to use data-driven models to make predictions and
inferences about structures and their condition. Such methods almost
exclusively rely on the quality of the data. Within the SHM discipline, data do
not always suffice to build models with satisfactory accuracy for given tasks.
Even worse, data may be completely missing from one's dataset, regarding the
behaviour of a structure under different environmental conditions. In the
current work, with a view to confronting such issues, the generation of
artificial data using a variation of the generative adversarial network (GAN)
algorithm, is used. The aforementioned variation is that of the conditional GAN
or cGAN. The algorithm is not only used to generate artificial data, but also
to learn transformations of manifolds according to some known parameters.
Assuming that the structure's response is represented by points in a manifold,
part of the space will be formed due to variations in external conditions
affecting the structure. This idea proves efficient in SHM, as it is exploited
to generate structural data for specific values of environmental coefficients.
The scheme is applied here on a simulated structure which operates under
different temperature and humidity conditions. The cGAN is trained on data for
some discrete values of the temperature within some range, and is able to
generate data for every temperature in this range with satisfactory accuracy.
The novelty, compared to classic regression in similar problems, is that the
cGAN allows unknown environmental parameters to affect the structure and can
generate whole manifolds of data for every value of the known parameters, while
the unknown ones vary within the generated manifolds.


title:
Adaptive Path Planning for UAVs for Multi-Resolution Semantic
  Segmentation

abstract:
  Efficient data collection methods play a major role in helping us better
understand the Earth and its ecosystems. In many applications, the usage of
unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly
gaining momentum due to their high mobility, low cost, and flexible deployment.
A key challenge is planning missions to maximize the value of acquired data in
large environments given flight time limitations. This is, for example,
relevant for monitoring agricultural fields. This paper addresses the problem
of adaptive path planning for accurate semantic segmentation of using UAVs. We
propose an online planning algorithm which adapts the UAV paths to obtain
high-resolution semantic segmentations necessary in areas with fine details as
they are detected in incoming images. This enables us to perform close
inspections at low altitudes only where required, without wasting energy on
exhaustive mapping at maximum image resolution. A key feature of our approach
is a new accuracy model for deep learning-based architectures that captures the
relationship between UAV altitude and semantic segmentation accuracy. We
evaluate our approach on different domains using real-world data, proving the
efficacy and generability of our solution.


title: Improving X-ray Diagnostics through Eye-Tracking and XR

abstract:
  There is a growing need to assist radiologists in performing X-ray readings
and diagnoses fast, comfortably, and effectively. As radiologists strive to
maximize productivity, it is essential to consider the impact of reading rooms
in interpreting complex examinations and ensure that higher volume and
reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a
disruptive technology for clinical practice in assessing X-ray images. We argue
that conjugating eye-tracking with VR devices and Machine Learning may overcome
obstacles posed by inadequate ergonomic postures and poor room conditions that
often cause erroneous diagnostics when professionals examine digital images.


title:
UDAAN -- Machine Learning based Post-Editing tool for Document
  Translation

abstract:
  We introduce UDAAN, an open-source post-editing tool that can reduce manual
editing efforts to quickly produce publishable-standard documents in different
languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing
pipeline wherein users can upload a document to obtain raw MT output. Further,
users can edit the raw translations using our tool. UDAAN offers several
advantages: a) Domain-aware, vocabulary-based lexical constrained MT. b)
source-target and target-target lexicon suggestions for users. Replacements are
based on the source and target texts lexicon alignment. c) Suggestions for
translations are based on logs created during user interaction. d)
Source-target sentence alignment visualisation that reduces the cognitive load
of users during editing. e) Translated outputs from our tool are available in
multiple formats: docs, latex, and PDF. Although we limit our experiments to
English-to-Hindi translation for the current study, our tool is independent of
the source and target languages. Experimental results based on the usage of the
tools and users feedback show that our tool speeds up the translation time
approximately by a factor of three compared to the baseline method of
translating documents from scratch.


title: Selective Residual M-Net for Real Image Denoising

abstract:
  Image restoration is a low-level vision task which is to restore degraded
images to noise-free images. With the success of deep neural networks, the
convolutional neural networks surpass the traditional restoration methods and
become the mainstream in the computer vision area. To advance the performanceof
denoising algorithms, we propose a blind real image denoising network (SRMNet)
by employing a hierarchical architecture improved from U-Net. Specifically, we
use a selective kernel with residual block on the hierarchical structure called
M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet
has competitive performance results on two synthetic and two real-world noisy
datasets in terms of quantitative metrics and visual quality. The source code
and pretrained model are available at
https://github.com/TentativeGitHub/SRMNet.


title: On an application of graph neural networks in population based SHM

abstract:
  Attempts have been made recently in the field of population-based structural
health monitoring (PBSHM), to transfer knowledge between SHM models of
different structures. The attempts have been focussed on homogeneous and
heterogeneous populations. A more general approach to transferring knowledge
between structures, is by considering all plausible structures as points on a
multidimensional base manifold and building a fibre bundle. The idea is quite
powerful, since, a mapping between points in the base manifold and their
fibres, the potential states of any arbitrary structure, can be learnt. A
smaller scale problem, but still useful, is that of learning a specific point
of every fibre, i.e. that corresponding to the undamaged state of structures
within a population. Under the framework of PBSHM, a data-driven approach to
the aforementioned problem is developed. Structures are converted into graphs
and inference is attempted within a population, using a graph neural network
(GNN) algorithm. The algorithm solves a major problem existing in such
applications. Structures comprise different sizes and are defined as abstract
objects, thus attempting to perform inference within a heterogeneous population
is not trivial. The proposed approach is tested in a simulated population of
trusses. The goal of the application is to predict the first natural frequency
of trusses of different sizes, across different environmental temperatures and
having different bar member types. After training the GNN using part of the
total population, it was tested on trusses that were not included in the
training dataset. Results show that the accuracy of the regression is
satisfactory even in structures with higher number of nodes and members than
those used to train it.


title:
Parametric complexity analysis for a class of first-order Adagrad-like
  algorithms

abstract:
  A class of algorithms for optimization in the presence of noise is presented,
that does not require the evaluation of the objective function. This class
generalizes the well-known Adagrad method. The complexity of this class is then
analyzed as a function of its parameters, and it is shown that some methods of
the class enjoy a better asymptotic convergence rate than previously known. A
new class of algorithms is then derived with similar characteristics. Initial
numerical experiments suggest that it may have some merits in practice.


title: Boundary points, Minimal $L^{2}$ integrals and Concavity property

abstract:
  In this article, we consider minimal $L^{2}$ integrals on sublevel sets of a
plurisubharmonic function with respect to a module at a boundary point of the
sublevel sets, and establish a concavity property of the minimal $L^{2}$
integrals. As applications, we obtain a sharp effectiveness result related to a
conjecture posed by Jonsson-Musta\c{t}\u{a}, which completes the approach from
the conjecture to the strong openness property; we also obtain a strong
openness property of the module and a lower semi-continuity property with
respect to the module.


title: Classifications of Magnetized $T^4$ and $T^4/Z_2$ Orbifold Models

abstract:
  We study constructions and classifications of three-generation models based
on magnetized $T^4$ and $T^4/{Z}_2$ orbifold as candidates of the compact
space. We focus on chiral fermion zero-mode wave functions in the extra
dimensions. Freedoms of constant gauge fields, called Scherk-Schwarz phases are
taken into account. Infinite number of three-generation models are yielded,
corresponding to the ways in which the magnetic flux can be turned on. We
classify them in a systematic manner, clarifying the relationship between
different models. The Higgs sector is also studied by analyzing possible
assignments of the magnetic flux and Scherk-Schwarz phases, etc. to left- and
right-handed fermions.


title: Euclid preparation. XVIII. The NISP photometric system

abstract:
  Euclid will be the first space mission to survey most of the extragalactic
sky in the 0.95-2.02 $\mu$m range, to a 5$\sigma$ point-source median depth of
24.4 AB mag. This unique photometric data set will find wide use beyond
Euclid's core science. In this paper, we present accurate computations of the
Euclid Y_E, J_E and H_E passbands used by the Near-Infrared Spectrometer and
Photometer (NISP), and the associated photometric system. We pay particular
attention to passband variations in the field of view, accounting among others
for spatially variable filter transmission, and variations of the angle of
incidence on the filter substrate using optical ray tracing. The response
curves' cut-on and cut-off wavelengths - and their variation in the field of
view - are determined with 0.8 nm accuracy, essential for the photometric
redshift accuracy required by Euclid. After computing the photometric
zeropoints in the AB mag system, we present linear transformations from and to
common ground-based near-infrared photometric systems, for normal stars, red
and brown dwarfs, and galaxies separately. A Python tool to compute accurate
magnitudes for arbitrary passbands and spectral energy distributions is
provided. We discuss various factors from space weathering to material
outgassing that may slowly alter Euclid's spectral response. At the absolute
flux scale, the Euclid in-flight calibration program connects the NISP
photometric system to Hubble Space Telescope spectrophotometric white dwarf
standards; at the relative flux scale, the chromatic evolution of the response
is tracked at the milli-mag level. In this way, we establish an accurate
photometric system that is fully controlled throughout Euclid's lifetime.


title: Conformal symmetry in damped Pais-Uhlenbeck oscillator

abstract:
  Two Lagrangian formulations for describing of the damped harmonic oscillator
have been introduced by Bateman. For these models we construct higher
derivative generalization which enjoys the l-conformal Newton-Hooke symmetry.
The dynamics of generalized systems corresponds to the damped Pais-Uhlenbeck
oscillator for a particular choice of its frequencies.


title: Informative Path Planning for Active Learning in Aerial Semantic Mapping

abstract:
  Semantic segmentation of aerial imagery is an important tool for mapping and
earth observation. However, supervised deep learning models for segmentation
rely on large amounts of high-quality labelled data, which is labour-intensive
and time-consuming to generate. To address this, we propose a new approach for
using unmanned aerial vehicles (UAVs) to autonomously collect useful data for
model training. We exploit a Bayesian approach to estimate model uncertainty in
semantic segmentation. During a mission, the semantic predictions and model
uncertainty are used as input for terrain mapping. A key aspect of our pipeline
is to link the mapped model uncertainty to a robotic planning objective based
on active learning. This enables us to adaptively guide a UAV to gather the
most informative terrain images to be labelled by a human for model training.
Our experimental evaluation on real-world data shows the benefit of using our
informative planning approach in comparison to static coverage paths in terms
of maximising model performance and reducing labelling efforts.


title:
Quaternionic $1-$factorizations and complete sets of rainbow spanning
  trees

abstract:
  A $1-$factorization of a complete graph on $2n$ vertices is said to be
$G-$regular if it posseses an automorphism group $G$ acting sharply
transitively on the vertex-set. The problem of determining which groups can
realize such a situation dates back to a result by Hartman and Rosa (1985) on
cyclic groups and, when $n$ is even, the problem is still open. An attempt to
obtain a fairly precise description of groups and $1-$factorizations satisfying
this symmetry constrain can be done by imposing further conditions. It was
recently proved, see Rinaldi (2021) and Mazzuoccolo et al. (2019), that a
$G-$regular $1-$factorization together with a complete set of rainbow spanning
trees exists whenever $n$ is odd, while the existence for each $n$ even was
proved when either $G$ is cyclic and $n$ is not a power of $2$, or when $G$ is
a dihedral group. In this paper we extend this result and prove the existence
also for other classes of groups.


title:
Optimized cost function for demand response coordination of multiple EV
  charging stations using reinforcement learning

abstract:
  Electric vehicle (EV) charging stations represent a substantial load with
significant flexibility. The exploitation of that flexibility in demand
response (DR) algorithms becomes increasingly important to manage and balance
demand and supply in power grids. Model-free DR based on reinforcement learning
(RL) is an attractive approach to balance such EV charging load. We build on
previous research on RL, based on a Markov decision process (MDP) to
simultaneously coordinate multiple charging stations. However, we note that the
computationally expensive cost function adopted in the previous research leads
to large training times, which limits the feasibility and practicality of the
approach. We, therefore, propose an improved cost function that essentially
forces the learned control policy to always fulfill any charging demand that
does not offer any flexibility. We rigorously compare the newly proposed batch
RL fitted Q-iteration implementation with the original (costly) one, using
real-world data. Specifically, for the case of load flattening, we compare the
two approaches in terms of (i) the processing time to learn the RL-based
charging policy, as well as (ii) the overall performance of the policy
decisions in terms of meeting the target load for unseen test data. The
performance is analyzed for different training periods and varying training
sample sizes. In addition to both RL policies performance results, we provide
performance bounds in terms of both (i) an optimal all-knowing strategy, and
(ii) a simple heuristic spreading individual EV charging uniformly over time


title: On partitioning of an SHM problem and parallels with transfer learning

abstract:
  In the current work, a problem-splitting approach and a scheme motivated by
transfer learning is applied to a structural health monitoring problem. The
specific problem in this case is that of localising damage on an aircraft wing.
The original experiment is described, together with the initial approach, in
which a neural network was trained to localise damage. The results were not
ideal, partly because of a scarcity of training data, and partly because of the
difficulty in resolving two of the damage cases. In the current paper, the
problem is split into two sub-problems and an increase in classification
accuracy is obtained. The sub-problems are obtained by separating out the most
difficult-to-classify damage cases. A second approach to the problem is
considered by adopting ideas from transfer learning (usually applied in much
deeper) networks to see if a network trained on the simpler damage cases can
help with feature extraction in the more difficult cases. The transfer of a
fixed trained batch of layers between the networks is found to improve
classification by making the classes more separable in the feature space and to
speed up convergence.


title:
The Exosphere as a Boundary: Origin and Evolution of Airless Bodies in
  the Inner Solar System and Beyond Including Planets with Silicate Atmospheres

abstract:
  In this review we discuss all the relevant solar/stellar radiation and plasma
parameters and processes that act together in the formation and modification of
atmospheres and exospheres that consist of surface-related minerals. Magma
ocean degassed silicate atmospheres or thin gaseous envelopes from planetary
building blocks, airless bodies in the inner Solar System, and close-in
magmatic rocky exoplanets such as CoRot-7b, HD219134b and 55 Cnc e are
addressed. The depletion and fractionation of elements from planetary embryos,
which act as the building blocks for protoplanets are also discussed. In this
context the formation processes of the Moon and Mercury are briefly reviewed.
The Lunar surface modification since its origin by micrometeoroids, plasma
sputtering, plasma impingement as well as chemical surface alteration and the
search of particles from the early Earth's atmosphere that were collected by
the Moon on its surface are also discussed. Finally, we address important
questions on what can be learned from the study of Mercury's environment and
its solar wind interaction by MESSENGER and BepiColombo in comparison with the
expected observations at exo-Mercurys by future space-observatories such as the
JWST or ARIEL and ground-based telescopes and instruments like SPHERE and
ESPRESSO on the VLT, and vice versa.


title:
Monitoring Diversity of AI Conferences: Lessons Learnt and Future
  Challenges in the DivinAI Project

abstract:
  DivinAI is an open and collaborative initiative promoted by the European
Commission's Joint Research Centre to measure and monitor diversity indicators
related to AI conferences, with special focus on gender balance, geographical
representation, and presence of academia vs companies. This paper summarizes
the main achievements and lessons learnt during the first year of life of the
DivinAI project, and proposes a set of recommendations for its further
development and maintenance by the AI community.


title: Magnetism on the stretched diamond lattice in lanthanide orthotantalates

abstract:
  The magnetic Ln$^{3+}$ ions in the fergusonite and scheelite crystal
structures form a distorted or stretched diamond lattice which is predicted to
host exotic magnetic ground states. In this study, polycrystalline samples of
the fergusonite orthotantalates $M$-LnTaO$_4$ (Ln = Nd, Sm, Eu, Gd, Tb, Dy, Ho,
Er) are synthesized and then characterized using powder diffraction and bulk
magnetometry and heat capacity. TbTaO$_4$ orders antiferromagnetically at 2.25
K into a commensurate magnetic cell with $\vec{k}=0$, magnetic space group
14.77 ($P2_1$$'/c$) and Tb moments parallel to the $a$-axis. No magnetic order
was observed in the other materials studied, leaving open the possibility of
exotic magnetic states at $T<2$ K.