title:
Enhanced Image Reconstruction From Quarter Sampling Measurements Using
  An Adapted Very Deep Super Resolution Network

abstract:
  Quarter sampling is a novel sensor concept that enables the acquisition of
higher resolution images without increasing the number of pixels. This is
achieved by covering three quarters of each pixel of a low-resolution sensor
such that only one quadrant of the sensor area of each pixel is sensitive to
light. By randomly masking different parts, effectively a non-regular sampling
of a higher resolution image is performed. Combining a properly designed mask
and a high-quality reconstruction algorithm, a higher image quality can be
achieved than using a low-resolution sensor and subsequent upsampling. For the
latter case, the image quality can be enhanced using super resolution
algorithms. Recently, algorithms based on machine learning such as the Very
Deep Super Resolution network (VDSR) proofed to be successful for this task. In
this work, we transfer the concepts of VDSR to the special case of quarter
sampling. Besides adapting the network layout to take advantage of the case of
quarter sampling, we introduce a novel data augmentation technique enabled by
quarter sampling. Altogether, using the quarter sampling sensor, the image
quality in terms of PSNR can be increased by +0.67 dB for the Urban 100 dataset
compared to using a low-resolution sensor with VDSR.


title: MIRRAX: A Reconfigurable Robot for Limited Access Environments

abstract:
  The development of mobile robot platforms for inspection has gained traction
in recent years with the rapid advancement in hardware and software. However,
conventional mobile robots are unable to address the challenge of operating in
extreme environments where the robot is required to traverse narrow gaps in
highly cluttered areas with restricted access. This paper presents MIRRAX, a
robot that has been designed to meet these challenges with the capability of
re-configuring itself to both access restricted environments through narrow
ports and navigate through tightly spaced obstacles. Controllers for the robot
are detailed, along with an analysis on the controllability of the robot given
the use of Mecanum wheels in a variable configuration. Characterisation on the
robot's performance identified suitable configurations for operating in narrow
environments. The minimum lateral footprint width achievable for stable
configuration ($<2^\text{o}$~roll) was 0.19~m. Experimental validation of the
robot's controllability shows good agreement with the theoretical analysis. A
further series of experiments shows the feasibility of the robot in addressing
the challenges above: the capability to reconfigure itself for restricted entry
through ports as small as 150mm diameter, and navigating through cluttered
environments. The paper also presents results from a deployment in a Magnox
facility at the Sellafield nuclear site in the UK -- the first robot to ever do
so, for remote inspection and mapping.


title: Applications of the quantification of super weak compactness

abstract:
  We introduce a measure of super weak noncompactness $\Gamma$ defined for
bounded linear operators and subsets in Banach spaces that allows to state and
prove a characterization of the Banach spaces which are subspaces of a Hilbert
generated space. The use of super weak compactness and $\Gamma$ casts light on
the structure of these Banach spaces and complements the work of Argyros,
Fabian, Farmaki, Godefroy, H\'ajek, Montesinos,\linebreak Troyanski and Zizler
on this subject. A particular kind of relatively super weakly compact sets,
namely uniformly weakly null sets, plays an important role and exhibits
connections with Banach-Saks type properties.


title:
Synchronization in the Kuramoto model in presence of stochastic
  resetting

abstract:
  What happens when a system of interacting oscillators of distributed natural
frequencies and showing spontaneous collective synchronization in the
stationary state is subject to random and repeated interruptions of its
dynamics with a reset to the initial condition? Within the ambit of the
paradigmatic Kuramoto model of coupled oscillators, we unveil how such a
protocol of stochastic resetting dramatically modifies the phase diagram of the
bare model, allowing in particular for the emergence of a synchronized phase
even in parameter regimes for which the bare model does not support such a
phase. Our results are based on an exact analysis invoking the celebrated
Ott-Antonsen ansatz for the case of Lorentzian distribution of natural
frequencies, and numerical results for Gaussian frequency distribution. Our
work provides a simple protocol to induce global synchrony in the system
through stochastic resetting.


title:
A posteriori error analysis for approximations of time-fractional
  subdiffusion problems

abstract:
  In this paper we consider a sub-diffusion problem where the fractional time
derivative is approximated either by the L1 scheme or by Convolution
Quadrature. We propose new interpretations of the numerical schemes which lead
to a posteriori error estimates. Our approach is based on appropriate pointwise
representations of the numerical schemes as perturbed evolution equations and
on stability estimates for the evolution equation. A posteriori error estimates
in $L^2(H)$ and $L^\infty (H)$ norms of optimal order are derived. Extensive
numerical experiments indicate the reliability and the optimality of the
estimators for the schemes considered, as well as their efficiency as error
indicators driving adaptive mesh selection locating singularities of the
problem.


title:
Christensen-Evans theorem and extensions of GNS-symmetric quantum Markov
  semigroups

abstract:
  In this note we prove a refined version of the Christensen-Evans theorem for
generators of uniformly continuous GNS-symmetric quantum Markov semigroups. We
use this result to show the existence of GNS-symmetric extensions of
GNS-symmetric quantum Markov semigroups. In particular, this implies that the
generators of GNS-symmetric quantum Markov semigroups on finite-dimensional von
Neumann algebra can be written in the form specified by Alicki's theorem.

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


title:
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And
  Upper Error Bound

abstract:
  The study of first-order optimization is sensitive to the assumptions made on
the objective functions. These assumptions induce complexity classes which play
a key role in worst-case analysis, including the fundamental concept of
algorithm optimality. Recent work argues that strong convexity and smoothness,
popular assumptions in literature, lead to a pathological definition of the
condition number (Guille-Escuret et al., 2021). Motivated by this result, we
focus on the class of functions satisfying a lower restricted secant inequality
and an upper error bound. On top of being robust to the aforementioned
pathological behavior and including some non-convex functions, this pair of
conditions displays interesting geometrical properties. In particular, the
necessary and sufficient conditions to interpolate a set of points and their
gradients within the class can be separated into simple conditions on each
sampled gradient. This allows the performance estimation problem (PEP, Drori
and Teboulle (2012)) to be solved analytically, leading to a lower bound on the
convergence rate that proves gradient descent to be exactly optimal on this
class of functions among all first-order algorithms.


title:
Read before Generate! Faithful Long Form Question Answering with Machine
  Reading

abstract:
  Long-form question answering (LFQA) aims to generate a paragraph-length
answer for a given question. While current work on LFQA using large pre-trained
model for generation are effective at producing fluent and somewhat relevant
content, one primary challenge lies in how to generate a faithful answer that
has less hallucinated content. We propose a new end-to-end framework that
jointly models answer generation and machine reading. The key idea is to
augment the generation model with fine-grained, answer-related salient
information which can be viewed as an emphasis on faithful facts.
State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate
the effectiveness of our method, in comparison with strong baselines on
automatic and human evaluation metrics. A detailed analysis further proves the
competency of our methods in generating fluent, relevant, and more faithful
answers.


title:
A Semi-Markov Decision Process (SMDP)-based Channel Allocation Model for
  Unreliable Terahertz (THz) Reconfigurable Intelligent Surfaces (RIS)

abstract:
  Terahertz (THz) communications and reconfigurable intelligent surfaces (RISs)
have been recently proposed to enable various powerful indoor applications,
such as wireless virtual reality (VR). For an efficient servicing of VR users,
an efficient THz channel allocation solution becomes a necessity. Assuming that
RIS component is the most critical one in enabling the service, we investigate
the impact of RIS hardware failure on channel allocation performance. To this
end, we study a THz network that employs THz operated RISs acting as base
stations, servicing VR users. We propose a Semi-Markov decision Process
(SMDP)-based channel allocation model to ensure the reliability of THz
connection, while maximizing the total long-term expected system reward,
considering the system gains, costs of channel utilization, and the penalty of
RIS failure. The SMDP-based model of the RIS system is formulated by defining
the state space, action space, reward model, and transition probability
distribution. We propose an optimal iterative algorithm for channel allocation
that decides the next action at each system state. The results show the average
reward and VR service blocking probability under different scenarios and with
various VR service arrivals and RIS failure rates, as first step towards
feasible VR services over unreliable THz RIS.


title:
The role of polymer structure on water confinement in
  poly(N-isopropylacrylamide) dispersions

abstract:
  Poly(N-isopropylacrylamide) (PNIPAM) is a synthetic polymer that is widely
studied for its thermoresponsive character. However, recent works also reported
evidence of a low temperature (protein-like) dynamical transition around 225 K
in concentrated PNIPAM suspensions, independently of the polymer architecture,
i.e., both for linear chains and for microgels. In this work, we investigate
water-polymer interactions by extensive differential scanning calorimetry (DSC)
measurements of both systems, in order to understand the effect of the
different topological structures on the solution behaviour, in particular
regarding crystallization and melting processes. In addition, we compare
protiated and deuterated microgels, in both water and deuterated water. The DSC
results are complemented by dynamic light scattering experiments, which confirm
that the selective isotopic substitution differently affects the solution
behaviour. Our findings highlight the important role played by the polymer
architecture on the solution behaviour: indeed, microgels turn out to be more
efficient confining agents, able to avoid water crystallization in a wider
concentration range with respect to linear chains. Altogether, the present data
will be valuable to interpret future low-temperature investigations of PNIPAM
dispersions, particularly by neutron scattering experiments.


title:
Statistical properties of partonic configurations and diffractive
  dissociation in high-energy electron-nucleus scattering

abstract:
  In this thesis, we study the detailed partonic content of the quantum states
of a quark-antiquark color dipole subject to high-energy evolution, which are
represented by a set of dipoles generated by a stochastic binary branching
process, in the scattering off a large nucleus, and produce predictions for
diffractive dissociation in electron-ion collisions, based on the dipole
picture of QCD. Our main results are as follows. First, the scattering events
of a color dipole, when parameters are set in such a way that the total cross
section is small, are triggered by rare partonic fluctuations, which look
different as seen from different reference frames. It turns out that the
freedom to select a frame allows to deduce an asymptotic expression for the
rapidity distribution of the first branching of the slowest parent dipole of
the set of those which scatter. In another aspect, such study implies the
importance of the characterization of particle distribution in the frontier
region in the states generated by the QCD dipole branching, and more generally,
by any one-dimensional branching random walk model. To this aim, we develop a
Monte Carlo algorithm to generate the frontier region of a branching random
walk. Furthermore, we are able to calculate the diffractive cross section
demanding a minimal rapidity gap $Y_0$ and the distribution of rapidity gaps
$Y_{gap}$ in the diffractive dissociation of a small dipole off a large
nucleus, in a well-defined parametric region. They are the asymptotic solutions
to the so-called Kovchegov-Levin equation, which describes the diffractive
dissociation at high energy. Finally, we present predictions for the rapidity
gap distributionin realistic kinematics of future electron-ion machines, based
on the numerical solutions of the original Kovchegov-Levin equation and of its
next-to-leading extension taking into account the running of the strong
coupling.


title:
Study of curling mechanism by precision kinematic measurements of
  curling stone's motion

abstract:
  Why do curling stones curl? That is a question physicists are often asked,
yet no answer has been established. Stones rotating clockwise curl right,
contrary to our naive expectations. After a century of debate between
contradicting hypotheses, this paper provides the answer based on experimental
evidence. A digital image analysis technique was used to perform precision
kinematic measurements of a curling stone's motion to identify the curling
mechanism. We observed a significant left-right asymmetric friction due to
velocity dependence on the friction constant. Combined with the discrete
point-like nature of the friction between ice and stone, swinging around
slow-side friction points has been concluded as the dominant origin of the
curling. Many new angular momentum transfer phenomena have been found,
supporting this conclusion.


title:
Shaping Nanoscale Ribbons into Micro-Helices of Controllable Radius and
  Pitch

abstract:
  We report fabrication of highly flexible micron-sized helices from
nanometer-thick ribbons. Building upon the helical coiling of such ultra-thin
ribbons mediated by surface tension, we demonstrate that the enhanced creep
properties of highly confined materials can be leveraged to shape helices into
the desired geometry with full control of the final shape. The helical radius,
total length and pitch angle are all freely and independently tunable within a
wide range: radius within $\sim$ 1-100 micrometer, length within $\sim$
100-3000 micrometer, and pitch angle within $\sim$ 0-70{\deg}. This fabrication
method is validated for three different materials: poly(methyl methacrylate),
poly(dimethylaminoethyl methacrylate), and transition metal chalcogenide
quantum dots, each corresponding to a different solid-phase structure:
respectively a polymer glass, a crosslinked hydrogel, and a nanoparticle array.
This demonstrates excellent versatility with respect to material selection,
enabling further control of the helix mechanical properties.


title: Minimax Risk in Estimating Kink Threshold and Testing Continuity

abstract:
  We derive a risk lower bound in estimating the threshold parameter without
knowing whether the threshold regression model is continuous or not. The bound
goes to zero as the sample size $ n $ grows only at the cube root rate.
Motivated by this finding, we develop a continuity test for the threshold
regression model and a bootstrap to compute its \textit{p}-values. The validity
of the bootstrap is established, and its finite sample property is explored
through Monte Carlo simulations.


title: Results Merging in the Patent Domain

abstract:
  In this paper, we test machine learning methods for results merging in patent
document retrieval. Specifically, we examine random forest, decision tree,
support vector machine (SVR), linear regression, polynomial regression, and
deep neural networks (DNNs). We use two different methods for results merging,
the multiple models (MM) method and the global model method (GM). Furthermore,
we examine whether the ranking of the document's scores is linearly
explainable. The CLEF-IP 2011 standard test collection was used in our
experiments. The random forest produces the best results in comparison to all
other models, and it fits the data better than linear and polynomial
approaches.


title:
Blockchain-based RBAC Model with Separation of Duties constraint in
  Cloud Environment

abstract:
  In recent years, cloud computing has been developing rapidly and is widely
used in various fields such as commerce and scientific research. However,
security issues, including access control, are a very important problem in
popularizing cloud computing and this has influenced its wide application of
cloud computing. As one of the solutions to these problems, we have proposed a
blockchain-based role-based access control model with the separation of duties
constraints in a cloud environment. In the model, we used Hyperledger Fabric as
a blockchain platform for storing the access control policies and provided
several functions for effective role management. In addition, we presented an
access control scheme for cloud storage data by combining the proposed model
and the verification mechanism for the user's ownership of a role and analyzed
the security properties of the scheme. Finally, we deployed Hyperledger Fabric
test network, implemented an online test system that performs access control
using the proposed scheme in the Ali cloud environment, and evaluated the model
performance in this scenario.


title: Affordance Learning from Play for Sample-Efficient Policy Learning

abstract:
  Robots operating in human-centered environments should have the ability to
understand how objects function: what can be done with each object, where this
interaction may occur, and how the object is used to achieve a goal. To this
end, we propose a novel approach that extracts a self-supervised visual
affordance model from human teleoperated play data and leverages it to enable
efficient policy learning and motion planning. We combine model-based planning
with model-free deep reinforcement learning (RL) to learn policies that favor
the same object regions favored by people, while requiring minimal robot
interactions with the environment. We evaluate our algorithm, Visual
Affordance-guided Policy Optimization (VAPO), with both diverse simulation
manipulation tasks and real world robot tidy-up experiments to demonstrate the
effectiveness of our affordance-guided policies. We find that our policies
train 4x faster than the baselines and generalize better to novel objects
because our visual affordance model can anticipate their affordance regions.


title: Diffusive shock acceleration at oblique high Mach number shocks

abstract:
  The current paradigm of cosmic ray (CR) origin states that the most part of
galactic CRs is produced by supernova remnants. The interaction of supernova
ejecta with the interstellar medium after supernova's explosions results in
shocks responsible for CR acceleration via diffusive shock acceleration (DSA).
We use particle-in-cell (PIC) simulations and a combined
PIC-magnetohydrodynamic (PIC-MHD) technique to investigate whether DSA can
occur in oblique high Mach number shocks. Using the PIC method, we follow the
formation of the shock and determine the fraction of the particles that gets
involved in DSA. Then, with this result, we use PIC-MHD simulations to model
the large-scale structure of the plasma and the magnetic field surrounding the
shock and find out whether or not the reflected particles can generate the
upstream turbulence and trigger DSA. We find that the feasibility of this
process in oblique shocks depends strongly on the Alfvenic Mach number, and the
DSA process is more likely triggered at high Mach number shocks.


title:
Improving Performance of Automated Essay Scoring by using
  back-translation essays and adjusted scores

abstract:
  Automated essay scoring plays an important role in judging students' language
abilities in education. Traditional approaches use handcrafted features to
score and are time-consuming and complicated. Recently, neural network
approaches have improved performance without any feature engineering. Unlike
other natural language processing tasks, only a small number of datasets are
publicly available for automated essay scoring, and the size of the dataset is
not sufficiently large. Considering that the performance of a neural network is
closely related to the size of the dataset, the lack of data limits the
performance improvement of the automated essay scoring model. In this paper, we
proposed a method to increase the number of essay-score pairs using
back-translation and score adjustment and applied it to the Automated Student
Assessment Prize dataset for augmentation. We evaluated the effectiveness of
the augmented data using models from prior work. In addition, performance was
evaluated in a model using long short-term memory, which is widely used for
automated essay scoring. The performance of the models was improved by using
augmented data to train the models.


title:
Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI
  Segmentation

abstract:
  Assessing the structure and function of the right ventricle (RV) is important
in the diagnosis of several cardiac pathologies. However, it remains more
challenging to segment the RV than the left ventricle (LV). In this paper, we
focus on segmenting the RV in both short (SA) and long-axis (LA) cardiac MR
images simultaneously. For this task, we propose a new multi-input/output
architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature
pyRAmid (Tempera). Our feature pyramid extends current designs by allowing not
only a multi-scale feature output but multi-scale SA and LA input images as
well. Tempera transfers learned features between SA and LA images via layer
weight sharing and incorporates a geometric target transformer to map the
predicted SA segmentation to LA space. Our model achieves an average Dice score
of 0.836 and 0.798 for the SA and LA, respectively, and 26.31 mm and 31.19 mm
Hausdorff distances. This opens up the potential for the incorporation of RV
segmentation models into clinical workflows.


title: Indoor Localization for Quadrotors using Invisible Projected Tags

abstract:
  Augmented reality (AR) technology has been introduced into the robotics field
to narrow the visual gap between indoor and outdoor environments. However,
without signals from satellite navigation systems, flight experiments in these
indoor AR scenarios need other accurate localization approaches. This work
proposes a real-time centimeter-level indoor localization method based on
psycho-visually invisible projected tags (IPT), requiring a projector as the
sender and quadrotors with high-speed cameras as the receiver. The method
includes a modulation process for the sender, as well as demodulation and pose
estimation steps for the receiver, where screen-camera communication technology
is applied to hide fiducial tags using human vision property. Experiments have
demonstrated that IPT can achieve accuracy within ten centimeters and a speed
of about ten FPS. Compared with other localization methods for AR robotics
platforms, IPT is affordable by using only a projector and high-speed cameras
as hardware consumption and convenient by omitting a coordinate alignment step.
To the authors' best knowledge, this is the first time screen-camera
communication is utilized for AR robot localization.


title: MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning

abstract:
  Logical reasoning is of vital importance to natural language understanding.
Previous studies either employ graph-based models to incorporate prior
knowledge about logical relations, or introduce symbolic logic into neural
models through data augmentation. These methods, however, heavily depend on
annotated training data, and thus suffer from over-fitting and poor
generalization problems due to the dataset sparsity. To address these two
problems, in this paper, we propose MERIt, a MEta-path guided contrastive
learning method for logical ReasonIng of text, to perform self-supervised
pre-training on abundant unlabeled text data. Two novel strategies serve as
indispensable components of our method. In particular, a strategy based on
meta-path is devised to discover the logical structure in natural texts,
followed by a counterfactual data augmentation strategy to eliminate the
information shortcut induced by pre-training. The experimental results on two
challenging logical reasoning benchmarks, i.e., ReClor and LogiQA, demonstrate
that our method outperforms the SOTA baselines with significant improvements.


title: Safe Control with Minimal Regret

abstract:
  As we move towards safety-critical cyber-physical systems that operate in
non-stationary and uncertain environments, it becomes crucial to close the gap
between classical optimal control algorithms and adaptive learning-based
methods. In this paper, we present an efficient optimization-based approach for
computing a finite-horizon robustly safe control policy that minimizes dynamic
regret, in the sense of the loss relative to the optimal sequence of control
actions selected in hindsight by a clairvoyant controller. By leveraging the
system level synthesis framework (SLS), our method extends recent results on
regret minimization for the linear quadratic regulator to optimal control
subject to hard safety constraints, and allows competing against a safety-aware
clairvoyant policy with minor modifications. Numerical experiments confirm
superior performance with respect to finite-horizon constrained $\mathcal{H}_2$
and $\mathcal{H}_\infty$ control laws when the disturbance realizations poorly
fit classical assumptions.


title:
Low Born effective Charges, High Covalency and Strong Optical Activity
  in $X_3^{2+}$Bi$^{3-}$N$^{3-}$ ($X$=Ca,Sr,Ba) $inverse$-perovskites

abstract:
  We compute for the first time a complete charge analysis (Bader and Born
effective) on $X_3^{2+}$Bi$^{3-}$N$^{3-}$ ($X$=Ca,Sr,Ba). The crystals show a
great electron sharing with little possibility of ferroelectricity. $Inverse$
perovskites have been a center of attraction in the recent years and not much
is known on the systems under this study. This research addressed some key
missing components and decomponents like the hardness and optical spectrum in
$X_3^{2+}$Bi$^{3-}$N$^{3-}$ ($X$=Ca,Sr,Ba). The computed lattices slightly
deviated from the parent perovskites indicating a future interfacing under a
proper substrate. We also found out that all the crystals under this study were
semiconducting with direct band gaps but plastic in nature due to strong
covalency. The optical spectrum revealed very strong activity in these crystals
in the ultraviolet regime. The information herein will definitely guide the
experimentalist in fabrication of these materials for novel functionalities.


title:
Non-linear manifold ROM with Convolutional Autoencoders and Reduced
  Over-Collocation method

abstract:
  Non-affine parametric dependencies, nonlinearities and advection-dominated
regimes of the model of interest can result in a slow Kolmogorov n-width decay,
which precludes the realization of efficient reduced-order models based on
linear subspace approximations. Among the possible solutions, there are purely
data-driven methods that leverage autoencoders and their variants to learn a
latent representation of the dynamical system, and then evolve it in time with
another architecture. Despite their success in many applications where standard
linear techniques fail, more has to be done to increase the interpretability of
the results, especially outside the training range and not in regimes
characterized by an abundance of data. Not to mention that none of the
knowledge on the physics of the model is exploited during the predictive phase.
In order to overcome these weaknesses, we implement the non-linear manifold
method introduced by Carlberg et al [37] with hyper-reduction achieved through
reduced over-collocation and teacher-student training of a reduced decoder. We
test the methodology on a 2d non-linear conservation law and a 2d shallow water
models, and compare the results obtained with a purely data-driven method for
which the dynamics is evolved in time with a long-short term memory network.


title:
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for
  Undersampled MRI Reconstruction

abstract:
  Magnetic resonance imaging is subject to slow acquisition times due to the
inherent limitations in data sampling. Recently, supervised deep learning has
emerged as a promising technique for reconstructing sub-sampled MRI. However,
supervised deep learning requires a large dataset of fully-sampled data.
Although unsupervised or self-supervised deep learning methods have emerged to
address the limitations of supervised deep learning approaches, they still
require a database of images. In contrast, scan-specific deep learning methods
learn and reconstruct using only the sub-sampled data from a single scan.
Current scan-specific approaches require a fully-sampled auto calibration scan
region in k-space that cost additional scan time. Here, we introduce
Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that
does not require an auto calibration scan region. DNLINV utilizes a deep image
prior-type generative modeling approach and relies on approximate Bayesian
inference to regularize the deep convolutional neural network. We demonstrate
our approach on several anatomies, contrasts, and sampling patterns and show
improved performance over existing approaches in scan-specific calibrationless
parallel imaging and compressed sensing.


title: Reasonable Space for the $\lambda$-Calculus, Logarithmically

abstract:
  Can the $\lambda$-calculus be considered as a reasonable computational model?
Can we use it for measuring the time and space consumption of algorithms? While
the literature contains positive answers about time, much less is known about
space. This paper presents a new reasonable space cost model for the
$\lambda$-calculus, based on a variant over the Krivine's abstract machine. For
the first time, this cost model is able to account for logarithmic space.
Moreover, we study the time behavior of our machine and show how to transport
our results to the call-by-value $\lambda$-calculus.


title:
A Multidisciplinary Approach to Optimal Communication and Flight
  Operation of High Altitude Long Endurance Platform

abstract:
  Aerial communication platforms especially stratospheric high altitude
pseudo-satellite (HAPS) has the potential to provide/catalyze advanced mobile
wireless communication services with its ubiquitous connectivity and ultra-wide
coverage radius. Recently, HAPS has gained immense popularity - achieved
primarily through self-sufficient energy systems - to render long endurance
characteristics. The photo voltaic cells mounted on the aircraft harvest solar
energy during the day, which is partially used for communication and station
keeping, whereas, the excess is stored in the rechargeable batteries for the
night time operation. We carried out an adroit power budgeting to ascertain if
the available solar power can simultaneously and efficiently self-sustain the
requisite propulsion and communication power expense. We propose an energy
optimum trajectory for station-keeping flight and non-orthogonal multiple
access (NOMA) for users in multicells served by the directional beams from HAPS
communication system. We design optimal power allocation for downlink (DL) NOMA
users along with the ideal position and speed of flight with the aim to
maximize sum data rate during the day and minimize power expenditure during the
night while ensuring quality of service. Our findings reveal the significance
of joint design of communication and aerodynamics parameters for optimum energy
utilization and resource allocation.


title: Practical Mitigation of Smart Contract Bugs

abstract:
  In spite of their popularity, developing secure smart contracts remains a
challenging task. Existing solutions are either impractical as they do not
support many complex real-world contracts or leave the burden to developers for
fixing bugs. In this paper, we propose the first practical smart contract
compiler, called HCC, which automatically inserts security hardening checks at
the source-code level. HCC develops a code property graph (CPG) to model
control-flows and data-flows of a given smart contract. Due to the CPG
notation, HCC can be applied to various smart contract platforms and
programming languages. We demonstrate the effectiveness of this approach on
Ethereum's Solidity smart contracts and show that it efficiently mitigates
reentrancy and integer bugs. We also show how to integrate HCC within other
blockchain platforms such as Hyperledger Fabric. Our evaluation on 10k
real-world contracts demonstrates that HCC is highly practical and effective.


title:
A constrained proof of the strong version of the Eshelby conjecture for
  the three-dimensional isotropic medium

abstract:
  Eshelby's seminal work on the ellipsoidal inclusion problem leads to the
conjecture that the ellipsoid is the only inclusion possessing the uniformity
property that a uniform eigenstrain is transformed into a uniform elastic
strain. For the three-dimensional isotropic medium, the weak version of the
Eshelby conjecture has been substantiated. The previous work of Ammari et al.
substantiates the strong version of the Eshelby conjecture for the cases when
the three eigenvalues of the eigenstress are distinct or all the same, whereas
the case where two of the eigenvalues of the eigenstress are identical and the
other one is distinct remains a difficult problem. In this work, we study the
latter case. To this end, firstly, we present and prove a necessary condition
for a convex inclusion being capable of transforming a single uniform
eigenstress into a uniform elastic stress field. Since the necessary condition
is not enough to determine the shape of the inclusion, secondly, we introduce a
constraint that is concerned with the material parameters, and prove that there
exist combinations of the elastic tensors and uniform eigenstresses such that
only an ellipsoid can have the Eshelby uniformity property for these
combinations simultaneously. Finally, we provide a more specifically
constrained proof of the conjecture by proving that for the uniform strain
fields constrained to that induced by an ellipsoid from a set of specified
uniform eigenstresses, the strong version of the Eshelby conjecture is true for
a set of isotropic elastic tensors which are associated with the specified
uniform eigenstresses. This work makes some progress towards the complete
solution of the intriguing and longstanding Eshelby conjecture for
three-dimensional isotropic media.


title: Local and global properties of p-Laplace Henon equation

abstract:
  We first give some apriori estimates of positive radial solutions of
$p$-Laplace H\'enon equation. Then we study the local and global properties of
those solutions. Finally, we generalize some radial results to the nonradial
case.


title:
Timing techniques with picosecond-order accuracy for novel gaseous
  detectors

abstract:
  A simulation model is developed to train Artificial Neural Networks (ANN),
for precise timing of PICOSEC Micromegas detector signals. The aim is to
develop fast, online timing algorithms as well as minimising the information to
be saved during data acquisition. PICOSEC waveforms were collected and
digitised by a fast oscilloscope during a femptosecond-laser test beam run. A
data set comprising waveforms collected with attenuated laser beam intensity,
eradicating the emission of more than one photoelectron per light pulse from
the PICOSEC photocathode, was utilised by a simulation algorithm to generate
waveforms to train an ANN. A second data set of multi-photoelectron waveforms
was used to evaluate the ANN performance in determining the PICOSEC Signal
Arrival Time, relative to a fast photodiode time-reference. The ANN timing
performance is the same as the results of a full offline signal processing,
achieving a timing precision of 18.3$\pm$0.6 ps.


title:
Explaining a Deep Reinforcement Learning Docking Agent Using Linear
  Model Trees with User Adapted Visualization

abstract:
  Deep neural networks (DNNs) can be useful within the marine robotics field,
but their utility value is restricted by their black-box nature. Explainable
artificial intelligence methods attempt to understand how such black-boxes make
their decisions. In this work, linear model trees (LMTs) are used to
approximate the DNN controlling an autonomous surface vessel (ASV) in a
simulated environment and then run in parallel with the DNN to give
explanations in the form of feature attributions in real-time. How well a model
can be understood depends not only on the explanation itself, but also on how
well it is presented and adapted to the receiver of said explanation. Different
end-users may need both different types of explanations, as well as different
representations of these. The main contributions of this work are (1)
significantly improving both the accuracy and the build time of a greedy
approach for building LMTs by introducing ordering of features in the splitting
of the tree, (2) giving an overview of the characteristics of the
seafarer/operator and the developer as two different end-users of the agent and
receiver of the explanations, and (3) suggesting a visualization of the docking
agent, the environment, and the feature attributions given by the LMT for when
the developer is the end-user of the system, and another visualization for when
the seafarer or operator is the end-user, based on their different
characteristics.


title:
Approximating a deep reinforcement learning docking agent using linear
  model trees

abstract:
  Deep reinforcement learning has led to numerous notable results in robotics.
However, deep neural networks (DNNs) are unintuitive, which makes it difficult
to understand their predictions and strongly limits their potential for
real-world applications due to economic, safety, and assurance reasons. To
remedy this problem, a number of explainable AI methods have been presented,
such as SHAP and LIME, but these can be either be too costly to be used in
real-time robotic applications or provide only local explanations. In this
paper, the main contribution is the use of a linear model tree (LMT) to
approximate a DNN policy, originally trained via proximal policy
optimization(PPO), for an autonomous surface vehicle with five control inputs
performing a docking operation. The two main benefits of the proposed approach
are: a) LMTs are transparent which makes it possible to associate directly the
outputs (control actions, in our case) with specific values of the input
features, b) LMTs are computationally efficient and can provide information in
real-time. In our simulations, the opaque DNN policy controls the vehicle and
the LMT runs in parallel to provide explanations in the form of feature
attributions. Our results indicate that LMTs can be a useful component within
digital assurance frameworks for autonomous ships.


title:
K2 and Spitzer phase curves of the rocky ultra-short-period planet
  K2-141 b hint at a tenuous rock vapor atmosphere

abstract:
  K2-141 b is a transiting, small (1.5 Re) ultra-short-period (USP) planet
discovered by Kepler orbiting a K-dwarf host star every 6.7 hours. The planet's
high surface temperature makes it an excellent target for thermal emission
observations. Here we present 65 hours of continuous photometric observations
of K2-141 b collected with Spitzer's IRAC Channel 2 at 4.5 micron spanning 10
full orbits of the planet. We measure an infrared eclipse depth of 143 +/- 39
ppm and a peak to trough amplitude variation of 121 +/- 43 ppm. The best fit
model to the Spitzer data shows no significant thermal hotspot offset, in
contrast to the previously observed offset for the well-studied USP planet 55
Cnc e. We also jointly analyze the new Spitzer observations with the photometry
collected by Kepler during two separate K2 campaigns. We model the planetary
emission with a range of toy models that include a reflective and a thermal
contribution. With a two-temperature model, we measure a dayside temperature of
2049 +/- 361 K and a night-side temperature that is consistent with zero (Tp,n
< 1712 K at 2 sigma). Models with a steep dayside temperature gradient provide
a better fit to the data than a uniform dayside temperature (DeltaBIC = 22.2).
We also find evidence for a non-zero geometric albedo Ag = 0.28 +/- 0.07. We
also compare the data to a physically motivated, pseudo-2D rock vapor model and
a 1D turbulent boundary layer model. Both models fit the data well. Notably, we
find that the optical eclipse depth can be explained by thermal emission from a
hot inversion layer, rather than reflected light. A thermal inversion may also
be responsible for the deep optical eclipse observed for another USP, Kepler-10
b. Finally, we significantly improve the ephemerides for K2-141 b and c, which
will facilitate further follow-up observations of this interesting system with
state-of-the-art observatories like JWST.


title: Population games on dynamic community networks

abstract:
  In this letter, we deal with evolutionary game theoretic learning processes
for population games on networks with dynamically evolving communities.
Specifically, we propose a novel mathematical framework in which a
deterministic, continuous-time replicator equation on a community network is
coupled with a closed dynamic flow process between communities that is governed
by an environmental feedback mechanism, resulting in co-evolutionary dynamics.
Through a rigorous analysis of the system of differential equations obtained,
we characterize the equilibria of the coupled dynamical system. Moreover, for a
class of population games with two actions and symmetric rewards a Lyapunov
argument is employed to establish an evolutionary folk theorem that guarantees
convergence to the evolutionary stable states of the game. Numerical
simulations are provided to illustrate and corroborate our findings.

categories: cs.SI cs.GT math.DS physics.soc-ph q-bio.PE


title: Clustering Drives Cooperation on Reputation Networks, All Else Fixed

abstract:
  Reputation-based cooperation on social networks offers a causal mechanism
between graph properties and social trust. Recent papers on the `structural
microfoundations` of the society used this insight to show how demographic
processes, such as falling fertility, urbanisation, and migration, can alter
the logic of human societies. This paper demonstrates the underlying mechanism
in a way that is accessible to scientists not specialising in networks.
Additionally, the paper shows that, when the size and degree of the network is
fixed (i.e., all graphs have the same number of agents, who all have the same
number of connections), it is the clustering coefficient that drives
differences in how cooperative social networks are.


title: On a faithful representation of Sturmian morphisms

abstract:
  The set of morphisms mapping any Sturmian sequence to a Sturmian sequence
forms together with composition the so-called monoid of Sturm. For this monoid,
we defne a faithful representation by $(3\times 3)$-matrices with integer
entries. We find three convex cones in $\mathbb{R}^3$ and show that a matrix $R
\in Sl(\mathbb{Z},3)$ is a matrix representing a Sturmian morphism if the three
cones are invariant under multiplication by $R$ or $R^{-1}$. This property
offers a new tool to study Sturmian sequences. We provide alternative proofs of
four known results on Sturmian sequences fixed by a primitive morphism and a
new result concerning the square root of a Sturmian sequence.


title:
Convergence of the Sasaki-Ricci flow on Sasakian 5-manifolds of general
  type

abstract:
  In this paper, we show that the uniform L^{4}-bound of the transverse Ricci
curvature along the Sasaki-Ricci flow on a compact quasi-regular Sasakian
(2n+1)-manifold M of general type. As an application, any solution of the
normalized Sasaki-Ricci flow converges in the Cheeger-Gromov sense to the
unique singular Sasaki {\eta}-Einstein metric on the transverse canonical model
M_{can} of M if n is less than or equal to 3. In particular for n equal to 2,
M_{can} is a S^{1}-orbibundle over the unique Keahler-Einstein orbifold surface
(Z_{can},{\omega}_{KE}) with finite point orbifold singularities. The floating
foliation (-2)-curves in M will be contracted to orbifold points by the
Sasaki-Ricci flow as t goes to infinite.


title:
Quantum simulations of neutral water clusters and singly-charged water
  cluster anions

abstract:
  We report a computational study of the structural and energetic properties of
water clustersand singly-charged water cluster anions containing from 20 to 573
water molecules. We have used both a classical and a quantum description of the
molecular degrees of freedom. Water intra and inter-molecular interactions have
been modelled through the SPC/F model, while the water-excess electron
interaction has been described via the well-known Turi-Borgis potential. We
find that in general the quantum effects of the water degrees of freedom are
small, but they do influence the cluster-size at which the excess electron
stabilises inside the cluster, which occurs at smaller cluster sizes when
quantum effects are taken into consideration.


title:
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender
  Systems

abstract:
  Multimedia recommender systems suggest media items, e.g., songs, (digital)
books and movies, to users by utilizing concepts of traditional recommender
systems such as collaborative filtering. In this paper, we investigate a
potential issue of such collaborative-filtering based multimedia recommender
systems, namely popularity bias that leads to the underrepresentation of
unpopular items in the recommendation lists. Therefore, we study four
multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList,
that we each split into three user groups differing in their inclination to
popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we
evaluate four collaborative filtering-based algorithms with respect to
popularity bias on the item and the user level. Our findings are three-fold:
firstly, we show that users with little interest into popular items tend to
have large user profiles and thus, are important data sources for multimedia
recommender systems. Secondly, we find that popular items are recommended more
frequently than unpopular ones. Thirdly, we find that users with little
interest into popular items receive significantly worse recommendations than
users with medium or high interest into popularity.


title:
A description of charmonia decays $J/\psi \rightarrow B\bar B$ within
  the QCD factorisation framework

abstract:
  Decays $J/\psi\to B\bar B$ into octet baryon-antibaryons pairs are studied
within the QCD factorisation framework. The next-to-leading power correction to
the leading-order decay amplitude is calculated for the first time. The
phenomenological analysis also includes the interference with the
electromagnetic decay $J/\psi\to \gamma^*\to B\bar B$. The branching fractions
and the angular coefficients $\alpha_B$ are described with an accuracy of about
$20\%$. The obtained results allow us to conclude that the calculated
amplitudes provide a dominant contribution and therefore QCD factorisation
provides a suitable basis for understanding of these decays.


title: Unbounded generalization of the Baker-Campbell-Hausdorff formulae

abstract:
  Based on the operator representation on the module over Banach algebra
$B(X)$, the Campbell-Baker-Hausdorff formula is generalized to the unbounded
situations. In conclusion, by means of the logarithmic representation of
generally-unbounded operators, the Campbell-Baker-Hausdorff formula is
generalized to be applicable to the unbounded operators.


title:
Exploring Wilderness Using Explainable Machine Learning in Satellite
  Imagery

abstract:
  Wilderness areas offer important ecological and social benefits and therefore
warrant monitoring and preservation. Yet, the characteristics of wilderness are
little known, making the detection and monitoring of wilderness areas via
remote sensing techniques a challenging task. We explore the appearance and
characteristics of the vague concept of wilderness via multispectral satellite
imagery. For this, we apply a novel explainable machine learning technique to a
dataset consisting of wild and anthropogenic areas in Fennoscandia. With our
technique, we predict continuous, detailed, and high-resolution sensitivity
maps of unseen remote sensing data for wild and anthropogenic characteristics.
Our neural network provides an interpretable activation space in which regions
are semantically arranged regarding these characteristics and certain land
cover classes. Interpretability increases confidence in the method and allows
for new explanations of the investigated concept. Our model advances
explainable machine learning for remote sensing, offers opportunities for
comprehensive analyses of existing wilderness, and has practical relevance for
conservation efforts.


title:
Global-in-$x$ stability of Prandtl layer expansions for steady
  magnetodynamics flows over a moving plate

abstract:
  In this paper, we obtain the global-in-$x$ Sobolev stability of Prandtl layer
expansions for 2-D steady incompressible MHD flows with shear outer ideal MHD
flows $(1,0,\sigma,0)$ ($\sigma\geq 0$) on a moving plate. It is worth noticing
that in the Sobolev sense, the degeneracy of the tangential magnetic field in
our result is allowed, which is much different from both the unsteady case in
[25,26,27] and the steady case in [3,4].


title:
A fully precessing higher-mode surrogate model of effective-one-body
  waveforms

abstract:
  We present a surrogate model of \texttt{SEOBNRv4PHM}, a fully precessing
time-domain effective-one-body waveform model including subdominant modes. We
follow an approach similar to that used to build recent numerical relativity
surrogate models. Our surrogate is 5000M in duration, covers mass-ratios up to
1:20 and dimensionless spin magnitudes up to 0.8. Validating the surrogate
against an independent test set we find that the bulk of the surrogate errors
is less than $\sim 1\%$ in mismatch, which is similar to the modelling error of
\texttt{SEOBNRv4PHM} itself. At high total mass a few percent of configurations
can exceed this threshold if they are highly precessing and they exceed a
mass-ratio of 1:4. This surrogate is nearly two orders of magnitude faster than
the underlying time-domain \texttt{SEOBNRv4PHM} model and can be evaluated in
$\sim 50$ ms. Bayesian inference analyses with \texttt{SEOBNRv4PHM} are
typically very computationally demanding and can take from weeks to months to
complete. The two order of magnitude speedup attained by our surrogate model
enables practical parameter estimation analyses with this waveform family. This
is \emph{crucial} because Bayesian inference allows us to recover the masses
and spins of binary black hole mergers given a model of the emitted
gravitational waveform along with a description of the noise.


title:
Addressing Randomness in Evaluation Protocols for Out-of-Distribution
  Detection

abstract:
  Deep Neural Networks for classification behave unpredictably when confronted
with inputs not stemming from the training distribution. This motivates
out-of-distribution detection (OOD) mechanisms. The usual lack of prior
information on out-of-distribution data renders the performance estimation of
detection approaches on unseen data difficult. Several contemporary evaluation
protocols are based on open set simulations, which average the performance over
up to five synthetic random splits of a dataset into in- and
out-of-distribution samples. However, the number of possible splits may be much
larger, and the performance of Deep Neural Networks is known to fluctuate
significantly depending on different sources of random variation. We
empirically demonstrate that current protocols may fail to provide reliable
estimates of the expected performance of OOD methods. By casting this
evaluation as a random process, we generalize the concept of open set
simulations and propose to estimate the performance of OOD methods using a
Monte Carlo approach that addresses the randomness.


title: Wave-packet spreading in disordered soft architected structures

abstract:
  We study the dynamical and chaotic behavior of a disordered one-dimensional
elastic mechanical lattice which supports translational and rotational waves.
The model used in this work is motivated by the recent experimental results of
B. Deng et al., Nat. Commun. 9, 3410 (2018). This lattice is characterized by
strong geometrical nonlinearities and the coupling of two degrees-of-freedom
(DoFs) per site. Although the linear limit of the structure consists of a
linear Fermi-Pasta-Ulam-Tsingou lattice and a linear Klein-Gordon (KG) lattice
whose DoFs are uncoupled, by using single site initial excitations on the
rotational DoF, we evoke the nonlinear coupling between the system's
translational and rotational DoFs. Our results reveal that such coupling
induces rich wave-packet spreading behavior in the presence of strong disorder.
In the weakly nonlinear regime, we observe energy spreading only due to the
coupling of the two DoFs (per site) which is in contrast to what is known for
KG lattices with a single DoF per lattice site, where the spreading occurs due
to chaoticity. Additionally, for strong nonlinearities, we show that initially
localized wave-packets attain near ballistic behavior in contrast to other
known models. We also reveal persistent chaos during energy spreading, although
its strength decreases in time as quantified by the evolution of the system's
finite-time maximum Lyapunov exponent. Our results show that flexible,
disordered and strongly nonlinear lattices are a viable platform to study
energy transport in combination with multiple DoFs (per site), also present an
alternative way to control energy spreading in heterogeneous media.


title: Data-efficient learning of object-centric grasp preferences

abstract:
  Grasping made impressive progress during the last few years thanks to deep
learning. However, there are many objects for which it is not possible to
choose a grasp by only looking at an RGB-D image, might it be for physical
reasons (e.g., a hammer with uneven mass distribution) or task constraints
(e.g., food that should not be spoiled). In such situations, the preferences of
experts need to be taken into account.
  In this paper, we introduce a data-efficient grasping pipeline (Latent Space
GP Selector -- LGPS) that learns grasp preferences with only a few labels per
object (typically 1 to 4) and generalizes to new views of this object. Our
pipeline is based on learning a latent space of grasps with a dataset generated
with any state-of-the-art grasp generator (e.g., Dex-Net). This latent space is
then used as a low-dimensional input for a Gaussian process classifier that
selects the preferred grasp among those proposed by the generator.
  The results show that our method outperforms both GR-ConvNet and GG-CNN (two
state-of-the-art methods that are also based on labeled grasps) on the Cornell
dataset, especially when only a few labels are used: only 80 labels are enough
to correctly choose 80% of the grasps (885 scenes, 244 objects). Results are
similar on our dataset (91 scenes, 28 objects).


title: Bianchi Type I model of universe with customized scale factors

abstract:
  According to standard cosmology, the universe is homogeneous and isotropic at
large scales. However, some anisotropies can be observed at the local scale in
the universe through various ways. Here we have studied the Bianchi type I
model with customizing the scale factors to understand the anisotropic nature
of the universe. We have considered two cases with slight modifications of
scale factors in different directions in the generalized Bianchi Type I metric
equation, and compared the results with the $\Lambda$CDM model and also with
available cosmological observational data. Through this study, we also want to
predict the possible degree of anisotropy present in the early universe and its
evolution to current time by calculating the value of density parameter for
anisotropy ($\Omega_{\sigma}$) for both low and high redshift (z) along with
the possible relative anisotropy that exist among different directions. It is
found that there was a significant amount of anisotropy in the early universe
and the anisotropic nature of the universe vanishes at the near past and the
present epochs. Thus at near past and present stages of the universe there is
no effective distinction between this anisotropic model and the standard
$\Lambda$CDM model.


title: CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

abstract:
  Training a text-to-image generator in the general domain (e.g., Dall.e,
CogView) requires huge amounts of paired text-image data, which is too
expensive to collect. In this paper, we propose a self-supervised scheme named
as CLIP-GEN for general text-to-image generation with the language-image priors
extracted with a pre-trained CLIP model. In our approach, we only require a set
of unlabeled images in the general domain to train a text-to-image generator.
Specifically, given an image without text labels, we first extract the
embedding of the image in the united language-vision embedding space with the
image encoder of CLIP. Next, we convert the image into a sequence of discrete
tokens in the VQGAN codebook space (the VQGAN model can be trained with the
unlabeled image dataset in hand). Finally, we train an autoregressive
transformer that maps the image tokens from its unified language-vision
representation. Once trained, the transformer can generate coherent image
tokens based on the text embedding extracted from the text encoder of CLIP upon
an input text. Such a strategy enables us to train a strong and general
text-to-image generator with large text-free image dataset such as ImageNet.
Qualitative and quantitative evaluations verify that our method significantly
outperforms optimization-based text-to-image methods in terms of image quality
while not compromising the text-image matching. Our method can even achieve
comparable performance as flagship supervised models like CogView.


title: Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing

abstract:
  Video snapshot compressive imaging (SCI) utilizes a 2D detector to capture
sequential video frames and compresses them into a single measurement. Various
reconstruction methods have been developed to recover the high-speed video
frames from the snapshot measurement. However, most existing reconstruction
methods are incapable of capturing long-range spatial and temporal
dependencies, which are critical for video processing. In this paper, we
propose a flexible and robust approach based on graph neural network (GNN) to
efficiently model non-local interactions between pixels in space as well as
time regardless of the distance. Specifically, we develop a motion-aware
dynamic GNN for better video representation, i.e., represent each pixel as the
aggregation of relative nodes under the guidance of frame-by-frame motions,
which consists of motion-aware dynamic sampling, cross-scale node sampling and
graph aggregation. Extensive results on both simulation and real data
demonstrate both the effectiveness and efficiency of the proposed approach, and
the visualization clearly illustrates the intrinsic dynamic sampling operations
of our proposed model for boosting the video SCI reconstruction results. The
code and models will be released to the public.


title:
A dissipative Nonlinear Schr\"{o}dinger model for wave propagation in
  sea ice

abstract:
  Sea ice attenuates waves propagating from the open ocean. Here we model the
evolution of energetic unidirectional random waves in sea ice with a nonlinear
Schr\"{o}dinger equation, with a frequency dependent dissipative term
consistent with current model paradigms and recent field observations. The
preferential dissipation of high frequency components results in a concurrent
downshift of the spectral peak that leads to a less than exponential energy
decay, but at a lower rate compared to a corresponding linear model.
Attenuation and downshift contrast nonlinearity, and nonlinear wave statistics
at the edge tend to Gaussianity farther into sea ice.


title: On the number of integral ideals in a number field

abstract:
  We update Sunley's explicit estimate for the ideal-counting function, which
is the number of integral ideals of bounded norm in a number field.


title: Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks

abstract:
  Monte Carlo simulations away from half-filling suffer from a sign problem
that can be reduced by deforming the contour of integration. Such a
transformation, which induces a Jacobian determinant in the Boltzmann weight,
can be implemented using neural networks. This additional determinant cost for
a generic neural network scales cubically with the volume, preventing
large-scale simulations. We implement a new architecture, based on
complex-valued affine coupling layers, which reduces this to linear scaling. We
demonstrate the efficacy of this method by successfully applying it to systems
of different size, the larger of which are intractable by other Monte Carlo
methods due to their severe sign problem.


title:
Directing Monolayer Tungsten Disulfide Photoluminescence using a Bent
  Plasmonic Nanowire on a Mirror Cavity

abstract:
  Designing directional optical antennas without compromising the field
enhancement requires specially designed optical cavities. Herein, we report on
the experimental observations of directional photoluminescence emission from a
monolayer Tungsten Disulfide using a bent-plasmonic nanowire on a mirror
cavity. The geometry provides field enhancement and directivity to
photoluminescence by sandwiching the monolayer between an extended cavity
formed by dropcasting bent silver nanowire and a gold mirror. We image the
photoluminescence emission wavevectors by using the Fourier plane imaging
technique. The cavity out-couples the emission in a narrow range of wavevectors
with a radial and azimuthal spreading of only 11.0{\deg} and 25.1{\deg},
respectively. Furthermore, we performed three dimensional finite difference
time domain based numerical calculations to corroborate and understand the
experimental results. We envisage that the results presented here will be
readily harnessed for on-chip coupling applications and in designing inelastic
optical antennas.

categories: physics.optics cond-mat.mtrl-sci physics.app-ph quant-ph


title:
Spin-Dependent Scattering of Scalar and Vector Dark Matter and an
  Electron

abstract:
  The property of dark matter is unknown so far. However, a model-independent
classification of dark matter candidates can be achieved by using various
symmetries, as done in the Standard Model. Fermionic dark matter has been
researched extremely, one favored candidate is the neutralino in the Minimal
Supersymmetric Standard Model, which are required by fermion-boson symmetry and
preservation of R-parity. Bosonic dark matter has not been studied
sufficiently, especially the scenario of dark matter with mass of sub-GeV. In
this paper, we consider the effect of spin-dependent (SD) on scalar and vector
dark matter, which are mediated by pseudo-scalar and axial-vector, and evaluate
effect on the dark matter-electron scattering cross section. We list all the
interaction and form factor of dark matter-electron SD scattering, and use
XENON10/100/1T experiment data to derive the exclude limit of SD cross section.
We find that the SD scattering of scalar and vector dark matter can be three
orders of magnitude stronger than spin-independent (SI) scattering, due to the
$p$-wave scattering.


title: Deep Potentials for Materials Science

abstract:
  To fill the gap between accurate (and expensive) ab initio calculations and
efficient atomistic simulations based on empirical interatomic potentials, a
new class of descriptions of atomic interactions has emerged and been widely
applied; i.e., machine learning potentials (MLPs). One recently developed type
of MLP is the Deep Potential (DP) method. In this review, we provide an
introduction to DP methods in computational materials science. The theory
underlying the DP method is presented along with a step-by-step introduction to
their development and use. We also review materials applications of DPs in a
wide range of materials systems. The DP Library provides a platform for the
development of DPs and a database of extant DPs. We discuss the accuracy and
efficiency of DPs compared with ab initio methods and empirical potentials.


title: Stable implementation of adaptive IGABEM in 2D in MATLAB

abstract:
  We report on our MATLAB program package IGABEM2D, which provides an easily
accessible implementation of adaptive Galerkin boundary element methods in the
frame of isogeometric analysis.


title:
Metric regularity, pseudo-Jacobians and global inversion theorems on
  Finsler manifolds

abstract:
  Our aim in this paper is to study the global invertibility of a locally
Lipschitz map $f:X \to Y$ between (possibly infinite-dimensional) Finsler
manifolds, stressing the connections with covering properties and metric
regularity of $f$. To this end, we introduce a natural notion of
pseudo-Jacobian $Jf$ in this setting, as is a kind of set-valued differential
object associated to $f$. By means of a suitable index, we study the relations
between properties of pseudo-Jacobian $Jf$ and local metric properties of the
map $f$, which lead to conditions for $f$ to be a covering map, and for $f$ to
be globally invertible. In particular, we obtain a version of Hadamard integral
condition in this context.


title: On Some General Operators of Hypergraphs

abstract:
  Here we introduce connectivity operators, namely, diffusion operators,
general Laplacian operators, and general adjacency operators for hypergraphs.
These operators are generalisations of some conventional notions of apparently
different connectivity matrices associated with hypergraphs. In fact, we
introduce here a unified framework for studying different variations of the
connectivity operators associated with hypergraphs at the same time.
Eigenvalues and corresponding eigenspaces of the general connectivity operators
associated with some classes of hypergraphs are computed. Applications such as
random walks on hypergraphs, dynamical networks, and disease transmission on
hypergraphs are studied from the perspective of our newly introduced operators.
We also derive spectral bounds for the weak connectivity number, degree of
vertices, maximum cut, bipartition width, and isoperimetric constant of
hypergraphs.


title: A Theory of Abstraction in Reinforcement Learning

abstract:
  Reinforcement learning defines the problem facing agents that learn to make
good decisions through action and observation alone. To be effective problem
solvers, such agents must efficiently explore vast worlds, assign credit from
delayed feedback, and generalize to new experiences, all while making use of
limited data, computational resources, and perceptual bandwidth. Abstraction is
essential to all of these endeavors. Through abstraction, agents can form
concise models of their environment that support the many practices required of
a rational, adaptive decision maker. In this dissertation, I present a theory
of abstraction in reinforcement learning. I first offer three desiderata for
functions that carry out the process of abstraction: they should 1) preserve
representation of near-optimal behavior, 2) be learned and constructed
efficiently, and 3) lower planning or learning time. I then present a suite of
new algorithms and analysis that clarify how agents can learn to abstract
according to these desiderata. Collectively, these results provide a partial
path toward the discovery and use of abstraction that minimizes the complexity
of effective reinforcement learning.


title:
Web3: A Decentralized Societal Infrastructure for Identity, Trust,
  Money, and Data

abstract:
  A movement for a more transparent and decentralized Internet is globally
attracting more attention. People are becoming more privacy-aware of their
online identities and data. The Internet is constantly evolving. Web2 focused
on companies that provide services in exchange for personal user data. Web3
commits to user-centricity using decentralization and zero-server
architectures. The current digital society demands a global change to empower
citizens and take back control. Citizens are locked into big-tech for personal
data storage and their for-profit digital identity. Protection of data has
proven to be essential, especially due to increased home Internet traffic
during the COVID pandemic. Citizens do not possess their own travel documents.
The European Commission aims to transition this governmental property towards
self-sovereign identity, introducing many new opportunities. Citizens are
locked into banks with non-portable IBAN accounts and unsustainable legacy
banking infrastructures. Migration to all-digital low-fraud infrastructures and
healthier competitive ecosystems is essential. The overall challenge is to
return the power to citizens and users again. The transition to a more
decentralized Internet is the first crucial step in the realization of
user-centricity. This thesis presents the first exploratory study that
integrates governmental-issued travel documents into a (decentralized) societal
infrastructure. These self-sovereign identities form the authentic base to a
private and secure transfer of money and data, and can effectively provide
trust in authenticity that is currently missing in online conversations. A
fully operational zero-server infrastructure that incorporates all our
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title: Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss

abstract:
  Recent advance on linear support vector machine with the 0-1 soft margin loss
($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly.
However, its theoretical and algorithmic requirements restrict us extending the
linear solving framework to its nonlinear kernel form directly, the absence of
explicit expression of Lagrangian dual function of $L_{0/1}$-SVM is one big
deficiency among of them. In this paper, by applying the nonparametric
representation theorem, we propose a nonlinear model for support vector machine
with 0-1 soft margin loss, called $L_{0/1}$-KSVM, which cunningly involves the
kernel technique into it and more importantly, follows the success on
systematically solving its linear task. Its optimal condition is explored
theoretically and a working set selection alternating direction method of
multipliers (ADMM) algorithm is introduced to acquire its numerical solution.
Moreover, we firstly present a closed-form definition to the support vector
(SV) of $L_{0/1}$-KSVM. Theoretically, we prove that all SVs of $L_{0/1}$-KSVM
are only located on the parallel decision surfaces. The experiment part also
shows that $L_{0/1}$-KSVM has much fewer SVs, simultaneously with a decent
predicting accuracy, when comparing to its linear peer $L_{0/1}$-SVM and the
other six nonlinear benchmark SVM classifiers.


title:
RIS-Assisted Quasi-Static Broad Coverage for Wideband mmWave Massive
  MIMO Systems

abstract:
  Reconfigurable intelligent surfaces (RISs) can establish favorable wireless
environments to combat the severe attenuation and blockages in millimeter-wave
(mmWave) bands. However, to achieve the optimal enhancement of performance, the
instantaneous channel state information (CSI) needs to be estimated at the cost
of a large overhead that scales with the number of RIS elements and the number
of users. In this paper, we design a quasi-static broad coverage at the RIS
with the reduced overhead based on the statistical CSI. We propose a design
framework to synthesize the power pattern reflected by the RIS that meets the
customized requirements of broad coverage. For the communication of broadcast
channels, we generalize the broad coverage of the single transmit stream to the
scenario of multiple streams. Moreover, we employ the quasi-static broad
coverage for a multiuser orthogonal frequency division multiplexing access
(OFDMA) system, and derive the analytical expression of the downlink rate,
which is proved to increase logarithmically with the power gain reflected by
the RIS. By taking into account the overhead of channel estimation, the
proposed quasi-static broad coverage even outperforms the design method that
optimizes the RIS phases using the instantaneous CSI. Numerical simulations are
conducted to verify these observations.


title: Dynamical tilting in halide perovskites

abstract:
  Naturals and synthetic perovskites are widely used functional materials
thanks to their particular physical properties, such as superconductivity,
ferroelectricity and photo-activity. Many of these properties are related to
static or dynamic motion of octahedral units; Yet a full understanding of the
relationships between perovskite crystal structure, chemical bonding and
physical properties is currently missing. Several studies indicate the
existence of dynamic disorder generated by anharmonic motion of octahedral
units e.g. in halide perovskite structures. In this work, we derive all
possible space groups for simple perovskites $\rm ABX_3$ with dynamical
octahedral tilting. The derived space groups extend the well established space
group tables for static tiltings by [Glazer, A. Acta Cryst. B 1972, 28,
3384-3392; Aleksandrov, K. Ferroelectrics 1976, 24, 801-805; Howard, C.J.,
Stokes, H.T. Acta Cryst. B 1998, 54, 782-789]. We demonstrate ubiquity of
dynamical tilting by analysing the structural data for perovskites reported in
recent scientific publications and discuss the signature of dynamic tilting in
the corresponding structures. Finally, we discuss the possible influence of
dynamic disorder on the physical properties of halide perovskites.


title: Muon shower vertex reconstruction with waveform information in JUNO

abstract:
  The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton liquid
scintillator detector currently being built in a dedicated underground
laboratory in China. It is a multi-purpose underground experiment with a
physics program including neutrino mass hierarchy determination, precision
measurement of neutrino oscillation parameters, measurement of solar,
atmospheric, geo-neutrinos and other important neutrino physics searches.
Electron anti-neutrinos are detected via the inverse beta decay by measuring
the correlated positron and neutron signals. In this detection channel cosmic
ray muon induced radioactive isotopes are the main background, especially those
connected to cosmogenic backgrounds ($^{9}$Li/$^{8}$He and fast neutrons). They
are predominantly produced by showing muons which account for about 10\% of all
muons. Considering that the $^{9}$Li/$^{8}$He background is correlated with the
parent muon in time and space, the vertex reconstruction of showers along the
muon track is helpful to reject the backgrounds of $^{9}$Li/$^{8}$He and other
isotopes. Based on the waveform simulation analysis, we know that the
multi-peaks in waveform output by PMTs are mainly caused by these showers.
Waveform analysis of muon events and preliminary results of shower vertex
reconstruction based on detector simulation have been studied.


title:
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint
  Deep Learning for Robotics

abstract:
  Existing Deep Learning (DL) frameworks typically do not provide ready-to-use
solutions for robotics, where very specific learning, reasoning, and embodiment
problems exist. Their relatively steep learning curve and the different
methodologies employed by DL compared to traditional approaches, along with the
high complexity of DL models, which often leads to the need of employing
specialized hardware accelerators, further increase the effort and cost needed
to employ DL models in robotics. Also, most of the existing DL methods follow a
static inference paradigm, as inherited by the traditional computer vision
pipelines, ignoring active perception, which can be employed to actively
interact with the environment in order to increase perception accuracy. In this
paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR
aims at developing an open, non-proprietary, efficient, and modular toolkit
that can be easily used by robotics companies and research institutions to
efficiently develop and deploy AI and cognition technologies to robotics
applications, providing a solid step towards addressing the aforementioned
challenges. We also detail the design choices, along with an abstract interface
that was created to overcome these challenges. This interface can describe
various robotic tasks, spanning beyond traditional DL cognition and inference,
as known by existing frameworks, incorporating openness, homogeneity and
robotics-oriented perception e.g., through active perception, as its core
design principles.


title:
The influence of the scalar unparticle on the Z-production at high
  energy $e^{-}e^{+}$ scattering process

abstract:
  An attempt is made to present the contribution of the scalar unparticle on
the Z-production cross-section at International Linear Colliders (ILC) with
polarized initial beams in the Randall-Sundrum model. The cross-sections depend
strongly on the polarization of $e^{-}$, $e^{+}$ initial beams, the center of
mass energy $\sqrt{s} $, the scaling dimension $d_{U}$ of the unparticle
operator $\mathcal{O}_{U}$ and the energy scale $\Lambda_{U}$. The results
indicate that with the effect of the scalar propagators in the s-channel,
including radion, Higgs and scalar unparticle, the cross-section for the ZZ
production is enhanced and larger than that in the Standard model under the
same conditions.


title: The intermediate orders of a Coxeter group

abstract:
  We define a class of partial orders on a Coxeter group that lie between the
left weak order and the Bruhat order. We prove that these posets are graded by
the length function and that the projections on the right parabolic quotients
are always order preserving. We also introduce the notion of $k$-Bruhat graph,
$k$-absolute length and $k$-absolute order, proposing some related conjectures
and problems.


title:
Affine RSK correspondence and crystals of level zero extremal weight
  modules

abstract:
  We give an affine analogue of the Robison-Schensted-Knuth (RSK)
correspondence, which generalizes the affine Robinson-Schensted correspondence
by Chmutov-Pylyavskyy-Yudovina. The affine RSK map sends a generalized affine
permutation of period $(m,n)$ to a pair of tableaux $(P,Q)$ of the same shape,
where $P$ belongs to a tensor product of level one perfect Kirillov-Reshetikhin
crystals of type $A_{m-1}^{(1)}$, and $Q$ belongs to a crystal of extremal
weight module of type $A_{n-1}^{(1)}$ when $m,n\ge 2$. We consider two affine
crystal structures of types $A_{m-1}^{(1)}$ and $A_{n-1}^{(1)}$ on the set of
generalized affine permutations, and show that the affine RSK map preserves the
crystal equivalence. We also give a dual affine Robison-Schensted-Knuth
correspondence.


title:
A Dynamical Estimation and Prediction for Covid19 on Romania using
  ensemble neural networks

abstract:
  In this paper, we propose an analysis of Covid19 evolution and prediction on
Romania combined with the mathematical model of SIRD, an extension of the
classical model SIR, which includes the deceased as a separate category. The
reason is that, because we can not fully trust the reported numbers of infected
or recovered people, we base our analysis on the more reliable number of
deceased people. In addition, one of the parameters of our model includes the
proportion of infected and tested versus infected. Since there are many factors
which have an impact on the evolution of the pandemic, we decide to treat the
estimation and the prediction based on the previous 7 days of data,
particularly important here being the number of deceased. We perform the
estimation and prediction using neural networks in two steps. Firstly, by
simulating data with our model, we train several neural networks which learn
the parameters of the model. Secondly, we use an ensemble of ten of these
neural networks to forecast the parameters from the real data of Covid19 in
Romania. Many of these results are backed up by a theorem which guarantees that
we can recover the parameters from the reported data.


title:
Relativistic Motions of Spin-Zero Quantum Oscillator Field in Global
  Monopole Space-Time with External Potential

abstract:
  We analyze spin-zero relativistic quantum oscillator fields in the presence
of Aharonov-Bohm (AB) magnetic flux and subsequently with Coulomb-types scalar
and vector potentials in the space-time background produced by a point-like
global monopole (PGM). We solve the generalized Klein-Gordon oscillator
analytically by choosing different types of function f(r) (Coulomb- \&
Cornell-types) and obtain the bound-states solutions in each cases. We then
discuss the influences of topological defect associated with scalar curvature
of the PGM space-time, the magnetic flux and Coulomb-types interaction
potentials on the energy spectrum of the oscillator field


title: A study on edge coloring and edge sum coloring of integral sum graphs

abstract:
  Frank Harary introduced the concept of integral sum graph. A graph $G$ is an
\emph{ integral sum graph} if its vertices can be labeled with distinct
integers so that $e = uv$ is an edge of $G$ if and only if the sum of the
labels on vertices $u$ and $v$ is also a label in $G.$ For any non-empty set of
integers $S$, let $G^+(S)$ denote the integral sum graph on the set $S$. In
$G^+(S)$, we define an \emph{edge-sum class} as the set of all edges each with
same edge sum number and call $G^+(S)$ an \emph{edge sum color graph} if each
edge-sum class is considered as an edge color class of $G^+(S)$. The number of
distinct edge-sum classes of $G^+(S)$ is called its \emph{ edge sum chromatic
number}. The main results of this paper are (i) the set of all edge-sum classes
of an integral sum graph partitions its edge set; (ii) the edge chromatic
number and the edge sum chromatic number are equal for the integral sum graphs
$G_{0,s}$ and $S_n$, Star graph of order $n$, whereas it is not in the case of
$G_{r,s} = G^+([r,s])$, $r < 0 < s$, $n,s \geq 2$, $n,r,s\in\mathbb{N}$. We
also obtain an interesting integral sum labeling of Star graphs.


title:
Markovian Analysis of Coordination Strategies in Tandem Polling Queues
  with Setups

abstract:
  We analyze a network of tandem polling queues with two stations operating
under synchronized polling (SP) and out-of-sync polling (OP) strategies, and
with nonzero setups. We conduct an exact analysis using a decomposition
approach to compare the performance in terms of throughput and mean waiting
times to investigate when one strategy might be preferred over the other. We
also numerically investigate the condition for network stability operating
under the two strategies and show that polling network is unstable when there
is bottleneck at downstream stations. We find that the SP strategy outperforms
the OP strategy in case of product and station symmetric networks while under
certain settings of product and station asymmetry, OP strategy outperforms the
SP strategy.


title: Disentangled Spatiotemporal Graph Generative Models

abstract:
  Spatiotemporal graph represents a crucial data structure where the nodes and
edges are embedded in a geometric space and can evolve dynamically over time.
Nowadays, spatiotemporal graph data is becoming increasingly popular and
important, ranging from microscale (e.g. protein folding), to middle-scale
(e.g. dynamic functional connectivity), to macro-scale (e.g. human mobility
network). Although disentangling and understanding the correlations among
spatial, temporal, and graph aspects have been a long-standing key topic in
network science, they typically rely on network processing hypothesized by
human knowledge. This usually fit well towards the graph properties which can
be predefined, but cannot do well for the most cases, especially for many key
domains where the human has yet very limited knowledge such as protein folding
and biological neuronal networks. In this paper, we aim at pushing forward the
modeling and understanding of spatiotemporal graphs via new disentangled deep
generative models. Specifically, a new Bayesian model is proposed that
factorizes spatiotemporal graphs into spatial, temporal, and graph factors as
well as the factors that explain the interplay among them. A variational
objective function and new mutual information thresholding algorithms driven by
information bottleneck theory have been proposed to maximize the
disentanglement among the factors with theoretical guarantees. Qualitative and
quantitative experiments on both synthetic and real-world datasets demonstrate
the superiority of the proposed model over the state-of-the-arts by up to 69.2%
for graph generation and 41.5% for interpretability.


title: Interpretable Molecular Graph Generation via Monotonic Constraints

abstract:
  Designing molecules with specific properties is a long-lasting research
problem and is central to advancing crucial domains such as drug discovery and
material science. Recent advances in deep graph generative models treat
molecule design as graph generation problems which provide new opportunities
toward the breakthrough of this long-lasting problem. Existing models, however,
have many shortcomings, including poor interpretability and controllability
toward desired molecular properties. This paper focuses on new methodologies
for molecule generation with interpretable and controllable deep generative
models, by proposing new monotonically-regularized graph variational
autoencoders. The proposed models learn to represent the molecules with latent
variables and then learn the correspondence between them and molecule
properties parameterized by polynomial functions. To further improve the
intepretability and controllability of molecule generation towards desired
properties, we derive new objectives which further enforce monotonicity of the
relation between some latent variables and target molecule properties such as
toxicity and clogP. Extensive experimental evaluation demonstrates the
superiority of the proposed framework on accuracy, novelty, disentanglement,
and control towards desired molecular properties. The code is open-source at
https://anonymous.4open.science/r/MDVAE-FD2C.


title:
Flat LRW Universe in logarithmic symmetric teleparallel gravity with
  observational constraints

abstract:
  In this paper, we study the homogeneous and isotropic flat LRW Universe in
the logarithmic form of $f\left( Q\right) $ gravity (where $Q$ is the
non-metricity scalar) i.e. $f\left( Q\right) =\alpha +\beta \log \left(Q\right)
$ where $\alpha $ and $\beta $ are the model constant parameters. In this
study, we consider a parametrization of the Hubble parameter as $H\left(
z\right) =\eta \left[ 1+\frac{1}{\left( 1+z\right) ^{\gamma }}\right] $, where
$% \gamma $ and $\eta $ are model/free parameters which are constrained by an
$R^{2}$-test from 57 data points of the Hubble data set in the redshift range
$% 0.07<z<2.36 $. The physical properties of the model are discussed. We study
the energy conditions to check the compatibility of the model. We found the SEC
is violated for the logarithmic form of $f\left( Q\right) $ gravity due to the
reality that the Universe in an accelerating phase. Finally, we discuss some
important parameters in this context to compare our model with dark energy
models such as jerk parameter, statefinder parameters.


title:
Frequency combs with parity-protected cross-correlations from
  dynamically modulated qubit arrays

abstract:
  We develop a general theoretical framework to dynamically engineer quantum
correlations in the frequency-comb emission from an array of superconducting
qubits in a waveguide, rigorously accounting for the temporal modulation of the
qubit resonance frequencies. We demonstrate, that when the resonance
frequencies of the two qubits are periodically modulated with a $\pi$ phase
shift, it is possible to realize simultaneous bunching and antibunching in
cross-correlations of the scattered photons from different sidebands. Our
approach, based on the dynamical conversion between the quantum excitations
with different parity symmetry, is quite universal. It can be used to control
two-particle correlations in generic dynamically modulated dissipative quantum
systems.


title:
The CARMENES search for exoplanets around M dwarfs: Benchmarking the
  impact of activity in high-precision radial velocity measurements

abstract:
  Current exoplanet surveys using the radial velocity (RV) technique are
targeting M dwarfs because any habitable zone terrestrial-mass planets will
induce a high RV and orbit on shorter periods than for more massive stars. One
of the main caveats is that M dwarfs show a wide range of activity levels from
inactive to very active, which can induce an asymmetry in the line profiles
and, consequently, a spurious RV measurement. We aim to benchmark the impact of
stellar activity on high-precision RV measurements using regular-cadence
CARMENES visible and near-infrared observations of the active M3.5 dwarf EV
Lac. We used the newly developed technique of low-resolution Doppler imaging to
determine the centre-of-light, or spot-induced RV component, for eight
observational epochs. We confirm a statistically significant and strong
correlation between the independently measured centre-of-light and the
chromatic index, which is a measure of the amplitude variation with wavelength
of the RVs. We also find circular closed-loop relations of several activity
indices with RV for a subset of data that covers only several rotation periods.
We also investigate the implications of large phase gaps in the periodograms of
activity indicators. Finally, by removing the spot-induced RV component we
improve the planet-mass sensitivity by a factor of at least three. We conclude
that for active M stars, a regular-cadence observing strategy is the most
efficient way to identify and eliminate sources of correlated noise.


title:
Extended Schur's $Q$-functions and the full Kostant--Toda hierarchy on
  the Lie algebra of type $D$

abstract:
  The full Kostant--Toda hierarchy on a semisimple Lie algebra is a system of
Lax equations, in which the flows are determined by the gradients of the
Chevalley invariants.This paper is concerned with the full Kostant--Toda
hierarchy on the even orthogonal Lie algebra. By using a Pfaffian of the Lax
matrix as one of the Chevalley invariants, we construct an explicit form of the
flow associated to this invariant. As a main result, we introduce an extension
of the Schur's $Q$-functions in the time variables, and use them to give
explicit formulas for the polynomial $\tau$-functions of the hierarchy.


title:
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in
  Reflection Geometry in the 0.25-6 THz Range

abstract:
  The increasing need of restoring high-resolution Hyper-Spectral (HS) images
is determining a growing reliance on Computer Vision-based processing to
enhance the clarity of the image content. HS images can, in fact, suffer from
degradation effects or artefacts caused by instrument limitations. This paper
focuses on a procedure aimed at reducing the degradation effects,
frequency-dependent blur and noise, in Terahertz Time-Domain Spectroscopy
(THz-TDS) images in reflection geometry. It describes the application of a
joint deblurring and denoising approach that had been previously proved to be
effective for the restoration of THz-TDS images in transmission geometry, but
that had never been tested in reflection modality. This mode is often the only
one that can be effectively used in most cases, for example when analyzing
objects that are either opaque in the THz range, or that cannot be displaced
from their location (e.g., museums), such as those of cultural interest.
Compared to transmission mode, reflection geometry introduces, however, further
distortion to THz data, neglected in existing literature. In this work, we
successfully implement image deblurring and denoising of both uniform-shape
samples (a contemporary 1 Euro cent coin and an inlaid pendant) and samples
with the uneven reliefs and corrosion products on the surface which make the
analysis of the object particularly complex (an ancient Roman silver coin). The
study demonstrates the ability of image processing to restore data in the 0.25
- 6 THz range, spanning over more than four octaves, and providing the
foundation for future analytical approaches of cultural heritage using the
far-infrared spectrum still not sufficiently investigated in literature.


title:
Recovery of Missing Sensor Data by Reconstructing Time-varying Graph
  Signals

abstract:
  Wireless sensor networks are among the most promising technologies of the
current era because of their small size, lower cost, and ease of deployment.
With the increasing number of wireless sensors, the probability of generating
missing data also rises. This incomplete data could lead to disastrous
consequences if used for decision-making. There is rich literature dealing with
this problem. However, most approaches show performance degradation when a
sizable amount of data is lost. Inspired by the emerging field of graph signal
processing, this paper performs a new study of a Sobolev reconstruction
algorithm in wireless sensor networks. Experimental comparisons on several
publicly available datasets demonstrate that the algorithm surpasses multiple
state-of-the-art techniques by a maximum margin of 54%. We further show that
this algorithm consistently retrieves the missing data even during massive data
loss situations.


title:
Using the Energy probability distribution zeros to obtain the critical
  properties of the two-dimensional anisotropic Heisenberg model

abstract:
  In this paper we present a Monte Carlo study of the critical behavior of the
easy axis anisotropic Heisenberg spin model in two dimensions. Based on the
partial knowledge of the zeros of the energy probability distribution we
determine with good precision the phase diagram of the model obtaining the
critical temperature and exponents for several values of the anisotropy. Our
results indicate that the model is in the Ising universality class for any
anisotropy.


title:
Mode transition ($\alpha-\gamma$) and hysteresis in microwave-driven
  low-temperature plasmas

abstract:
  We discovered a hysteresis in a microwave-driven low-pressure argon plasma
during gas pressure change across the transition region between $\alpha$ and
$\gamma$ discharge modes. The hysteresis is manifested in that the critical
pressure of mode transition depends on the direction of pressure change. As a
corollary, the plasma would attain different discharge properties under the
same operating parameters (pressure, power, and gas composition), suggesting a
bi-stability or existence of memory effect. Analysis of the rotational and
vibrational temperatures measured from the OH (A-X) line emissions shows that
the hysteresis is mainly due to the fast gas heating in the $\gamma$-mode
leading to a smaller neutral density than that of the $\alpha$-mode. When
increasing the gas pressure, the $\gamma$-mode discharge maintains a relatively
higher temperature and lower neutral density, and thus, it requires a higher
operating pressure to reach the $\alpha$-mode. On the other hand, decreasing
the pressure while maintaining $\alpha$-mode, the transition to $\gamma$-mode
occurs at a lower pressure than the former case due to a relatively higher
neutral density of $\alpha$-mode discharge. This interpretation is supported by
the fact that the hysteresis disappears when the plasma properties are
presented with respect to the neutral gas density instead of pressure.


title: Regularity results for free L\'{e}vy processes

abstract:
  Given a free additive convolution semigroup $\left(\mu_t\right)_{t\geq 0}$
and a probability measure $\nu$ on $\mathbb{R}$, we find the necessary and
sufficient conditions for the process $\mu_t \boxplus \nu$ to be Lebesgue
absolutely continuous with a positive and analytic density throughout
$\mathbb{R}$ at all time $t>0$. For semigroups without this property, we find
the necessary and sufficient conditions for the density of $\mu_t \boxplus \nu$
to be analytic at its zeros. These results are quantified by the L\'{e}vy
measure of the semigroup, making it fairly easy to construct many concrete
examples. Finally, we show that $\mu_t \boxplus \nu$ has a finite number of
connected components in its support if both the L\'{e}vy measure of
$\left(\mu_t\right)_{t \geq 0}$ and the initial law $\nu$ do.