title:
Local inversion of maps: A new attack on Symmetric encryption, RSA and
  ECDLP

abstract:
  This paper presents algorithms for local inversion of maps and shows how
several important computational problems such as cryptanalysis of symmetric
encryption algorithms, RSA algorithm and solving the elliptic curve discrete
log problem (ECDLP) can be addressed as local inversion problems. The
methodology is termed as the \emph{Local Inversion Attack}. It utilizes the
concept of \emph{Linear Complexity} (LC) of a recurrence sequence generated by
the map defined by the cryptanalysis problem and the given data. It is shown
that when the LC of the recurrence is bounded by a bound of polynomial order in
the bit length of the input to the map, the local inversion can be accomplished
in polynomial time. Hence an incomplete local inversion algorithm which
searches a solution within a specified bound on computation can estimate the
density of weak cases of cryptanalysis defined by such data causing low LC.
Such cases can happen accidentally but cannot be avoided in practice and are
fatal insecurity flaws of cryptographic primitives which are wrongly assumed to
be secure on the basis of exponential average case complexity. An incomplete
algorithm is proposed for solving problems such as key recovery of symmetric
encryption algorithms, decryption of RSA ciphertext without factoring the
modulus, decrypting any ciphertext of RSA given one plaintext ciphertext pair
created with same public key in chosen ciphertext attack and solving the
discrete logarithm on elliptic curves over finite fields (ECDLP) as local
inversion problems. It is shown that when the LCs of the respective recurrences
for given data are small, solutions of these problems are possible in
practically feasible time and memory resources.


title: Orbital Evolution of Close-in Super-Earths Driven by Atmospheric Escape

abstract:
  The increasing number of super-Earths close to their host stars revealed a
scarcity of close-in small planets with 1.5-2.0$\,R_\oplus$ in the radius
distribution of ${\it Kepler}$ planets. The atmospheric escape of super-Earths
by photoevaporation can explain the origin of the observed "radius gap." Many
theoretical studies considered the in-situ mass loss of a close-in planet.
Planets that undergo the atmospheric escape, however, move outward due to the
change in the orbital angular momentum of their star-planet systems. In this
study, we calculate the orbital evolution of an evaporating super-Earth with a
H$_2$/He atmosphere around FGKM-type stars under a stellar X-ray and extreme UV
irradiation (XUV). The rate of increase in the orbital radius of an evaporating
planet is approximately proportional to that of the atmospheric mass loss
during a high stellar XUV phase. We show that super-Earths with a rocky core of
$\lesssim$ 10$\,M_\oplus$ and a H$_2$/He atmosphere at $\lesssim$
0.03-0.1$\,$au ($\lesssim$ 0.01-0.03$\,$au) around G-type stars (M-type stars)
are prone to the outward migration driven by photoevaporation. Although the
changes in the orbits of the planets would be small, they would rearrange the
orbital configurations of compact, multi-planet systems, such as the TRAPPIST-1
system. We also find that the radius gap and the so-called "Neptune desert" in
the observed population of close-in planets around FGK-type stars still appear
in our simulations. On the other hand, the observed planet population around
M-type stars can be reproduced only by a high stellar XUV luminosity model.


title: Continuum limit of the lattice quantum graph Hamiltonian

abstract:
  We consider the quantum graph Hamiltonian on the square lattice in Euclidean
space, and we show that the spectrum of the Hamiltonian converges to the
corresponding Schr\"odinger operator on the Euclidean space in the continuum
limit, and that the corresponding eigenfunctions and eigenprojections also
converge in some sense. We employ the discrete Schr\"odinger operator as the
intermediate operator, and we use a recent result by the second and third
author on the continuum limit of the discrete Schr\"odinger operator.


title:
Upper bounds on eigenvalue multiplicities for surfaces of genus $0$
  revisited (Work in progress)

abstract:
  We revisit two 1999 papers: [1]~ \emph{M.~Hoffmann-Ostenhof,
T.~Hoffmann-Ostenhof, and N.~Nadirashvili. On the multiplicity of eigenvalues
of the Laplacian on surfaces. Ann. Global Anal. Geom. 17 (1999) 43--48} ~~and~~
[2]~ \emph{T.~Hoffmann-Ostenhof, P.~Michor, and N.~Nadirashvili. Bounds on the
multiplicity of eigenvalues for fixed membranes. Geom. Funct. Anal. 9 (1999)
1169--1188}.\\ The main result of these papers is that the multiplicity
$\mathrm{mult}(\lambda_k(M))$ of the $k$th eigenvalue of the surface $M$ is
bounded from above by $(2k-3)$ provided that $k \geq 3$. Here, $M$ is [1] a
closed surface with genus $0$, or [2] a planar domain with Dirichlet boundary
condition (in both cases, the starting label of eigenvalues is $1$). The proofs
given in [1,2] are not very detailed, and often rely on figures or special
configurations of nodal sets. In the case of closed surfaces with genus $0$, we
provide full details of the proof that $\mathrm{mult}(\lambda_k) \leq (2k-3)$
by introducing and carefully studying the \emph{combinatorial type} (defined in
Subsection~5.2) of the nodal sets involved. In the case of planar domains, we
consider the three boundary conditions, Dirichlet, Neumann, Robin, and we also
carefully study the \emph{combinatorial types} of the nodal sets involved. We
provide full details of the proof of the inequality $\mathrm{mult}(\lambda_k)
\leq (2k-2)$ ($k \geq 3$). Unfortunately, we are so far unable to complete the
proof of the inequality $\mathrm{mult}(\lambda_k) \leq (2k-3)$, even when the
domain is simply connected. In the last section, we explain the difficulties we
met. Our work on the subject is still ongoing.


title: Conditional Generation Net for Medication Recommendation

abstract:
  Medication recommendation targets to provide a proper set of medicines
according to patients' diagnoses, which is a critical task in clinics.
Currently, the recommendation is manually conducted by doctors. However, for
complicated cases, like patients with multiple diseases at the same time, it's
difficult to propose a considerate recommendation even for experienced doctors.
This urges the emergence of automatic medication recommendation which can help
treat the diagnosed diseases without causing harmful drug-drug interactions.Due
to the clinical value, medication recommendation has attracted growing research
interests.Existing works mainly formulate medication recommendation as a
multi-label classification task to predict the set of medicines. In this paper,
we propose the Conditional Generation Net (COGNet) which introduces a novel
copy-or-predict mechanism to generate the set of medicines. Given a patient,
the proposed model first retrieves his or her historical diagnoses and
medication recommendations and mines their relationship with current diagnoses.
Then in predicting each medicine, the proposed model decides whether to copy a
medicine from previous recommendations or to predict a new one. This process is
quite similar to the decision process of human doctors. We validate the
proposed model on the public MIMIC data set, and the experimental results show
that the proposed model can outperform state-of-the-art approaches.


title: Deferred Optical Photon simulation for the JUNO experiment

abstract:
  The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine
the neutrino mass ordering and precisely measure oscillation parameters. It is
being built in South China at a depth of 700~m underground and comprises a
central detector, water Cerenkov detector and top tracker. The central detector
is designed to detect anti-neutrinos with an energy resolution of 3\% at 1~MeV,
using a 20 kt liquid scintillator target with 17,612 20-inch PMTs and 25,600
3-inch PMTs. The scintillator provides a light yield of approximately 10,000
photons per MeV. Monte Carlo simulation is a crucial tool for developing an
understanding of detector performance, requiring the production of large
samples of background processes with optical photons. Simulation of large
numbers of optical photons with Geant4 is computationally challenging for both
processing time and memory resources. In order to optimize resource usage, a
deferred optical photon simulation workflow is proposed and implemented using
Geant4 classes. The key idea is to simulate events initially without optical
photons, only performing the optical photon simulation when user specified
criteria are met.
  In this contribution, the design and the implementation of the deferred
optical photon simulation will be presented.


title:
A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating
  Lymphocytes in Whole Slide Images

abstract:
  Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue
indicate favourable outcomes in many types of cancer. Manual quantification of
immune cells is inaccurate and time consuming for pathologists. Our aim is to
leverage a computational solution to automatically quantify TILs in whole slide
images (WSIs) of standard diagnostic haematoxylin and eosin stained sections
(H&E slides) from lung cancer patients. Our approach is to transfer an open
source machine learning method for segmentation and classification of nuclei in
H&E slides trained on public data to TIL quantification without manual labeling
of our data. Our results show that additional augmentation improves model
transferability when training on few samples/limited tissue types. Models
trained with sufficient samples/tissue types do not benefit from our additional
augmentation policy. Further, the resulting TIL quantification correlates to
patient prognosis and compares favorably to the current state-of-the-art method
for immune cell detection in non-small lung cancer (current standard CD8 cells
in DAB stained TMAs HR 0.34 95% CI 0.17-0.68 vs TILs in HE WSIs: HoVer-Net
PanNuke Aug Model HR 0.30 95% CI 0.15-0.60, HoVer-Net MoNuSAC Aug model HR 0.27
95% CI 0.14-0.53). Moreover, we implemented a cloud based system to train,
deploy and visually inspect machine learning based annotation for H&E slides.
Our pragmatic approach bridges the gap between machine learning research,
translational clinical research and clinical implementation. However,
validation in prospective studies is needed to assert that the method works in
a clinical setting.


title: Maximal acceleration in a Lorentz invariant non-commutative space-time

abstract:
  In this paper, we derive the non-commutative corrections to the maximal
acceleration in the Doplicher-Fredenhagen-Roberts (DFR) space-time and show
that the effect of the non-commutativity is to decrease the magnitude of the
value of the maximal acceleration in the commutative limit. We also obtain an
upper bound on the acceleration along the non-commutative coordinates using the
positivity condition on the magnitude of the maximal acceleration in the
commutative space-time. From the Newtonian limit of the geodesic equation and
Einstein's equation for linearised gravity, we derive the explicit form of
Newton's potential in DFR space-time. By expressing the non-commutative
correction term of the maximal acceleration in terms of Newton's potential and
applying the positivity condition, we obtain a lower bound on the radial
distance between two particles under the gravitational attraction in DFR
space-time. We also derive modified uncertainty relation and commutation
relation between coordinates and its conjugate, due to the existence of maximal
acceleration.


title: Memory Replay with Data Compression for Continual Learning

abstract:
  Continual learning needs to overcome catastrophic forgetting of the past.
Memory replay of representative old training samples has been shown as an
effective solution, and achieves the state-of-the-art (SOTA) performance.
However, existing work is mainly built on a small memory buffer containing a
few original data, which cannot fully characterize the old data distribution.
In this work, we propose memory replay with data compression (MRDC) to reduce
the storage cost of old training samples and thus increase their amount that
can be stored in the memory buffer. Observing that the trade-off between the
quality and quantity of compressed data is highly nontrivial for the efficacy
of memory replay, we propose a novel method based on determinantal point
processes (DPPs) to efficiently determine an appropriate compression quality
for currently-arrived training samples. In this way, using a naive data
compression algorithm with a properly selected quality can largely boost recent
strong baselines by saving more compressed data in a limited storage space. We
extensively validate this across several benchmarks of class-incremental
learning and in a realistic scenario of object detection for autonomous
driving.


title:
Exact Statistical Inference for Time Series Similarity using Dynamic
  Time Warping by Selective Inference

abstract:
  In this paper, we study statistical inference on the similarity/distance
between two time-series under uncertain environment by considering a
statistical hypothesis test on the distance obtained from Dynamic Time Warping
(DTW) algorithm. The sampling distribution of the DTW distance is too
complicated to derive because it is obtained based on the solution of a
complicated algorithm. To circumvent this difficulty, we propose to employ a
conditional sampling distribution for the inference, which enables us to derive
an exact (non-asymptotic) inference method on the DTW distance. Besides, we
also develop a novel computational method to compute the conditional sampling
distribution. To our knowledge, this is the first method that can provide valid
$p$-value to quantify the statistical significance of the DTW distance, which
is helpful for high-stake decision making. We evaluate the performance of the
proposed inference method on both synthetic and real-world datasets.


title:
A formal algebraic approach for the quantitative modeling of connectors
  in architectures

abstract:
  In this paper we propose an algebraic formalization of connectors in the
quantitative setting in order to address the performance issues related with
the architectures of component-based systems. For this, we firstly introduce a
weighted Algebra of Interactions over a set of ports and a commutative and
idempotent semiring. The algebra serves sufficiently for modeling well-known
coordination schemes in the weighted setup. In turn, we introduce and study a
weighted Algebra of Connectors over a set of ports and a commutative and
idempotent semiring, which extends the weighted Algebra of Interactions with
types that express two different modes of synchronization, in particular,
Rendezvous and Broadcast. Moreover, we show the expressiveness of the algebra
by modeling several weighted connectors. Finally, we introduce a congruence
relation for weighted connectors and provide conditions for proving congruence
between distinct weighted connectors.


title: Elementary Constructive Theory of Henselian Local Rings

abstract:
  We give an elementary theory of Henselian local rings and construct the
Henselization of a local ring. All our theorems have an algorithmic content.


title:
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric
  Uncertainty in Physics-informed Temperature Field Reconstruction

abstract:
  For the temperature field reconstruction (TFR), a complex image-to-image
regression problem, the convolutional neural network (CNN) is a powerful
surrogate model due to the convolutional layer's good image feature extraction
ability. However, a lot of labeled data is needed to train CNN, and the common
CNN can not quantify the aleatoric uncertainty caused by data noise. In actual
engineering, the noiseless and labeled training data is hardly obtained for the
TFR. To solve these two problems, this paper proposes a deep Monte Carlo
quantile regression (Deep MC-QR) method for reconstructing the temperature
field and quantifying aleatoric uncertainty caused by data noise. On the one
hand, the Deep MC-QR method uses physical knowledge to guide the training of
CNN. Thereby, the Deep MC-QR method can reconstruct an accurate TFR surrogate
model without any labeled training data. On the other hand, the Deep MC-QR
method constructs a quantile level image for each input in each training epoch.
Then, the trained CNN model can quantify aleatoric uncertainty by quantile
level image sampling during the prediction stage. Finally, the effectiveness of
the proposed Deep MC-QR method is validated by many experiments, and the
influence of data noise on TFR is analyzed.


title: Vulnerability Assessment and Penetration Testing on IP cameras

abstract:
  IP cameras have always been part of the Internet of Things (IoT) and are
among the most widely used devices in both home and professional environments.
Unfortunately, the vulnerabilities of IP cameras have attracted malicious
activities. For example, in 2016, a massive attack resulted in thousands of
cameras and IoT devices being breached and used to create a botnet. Given this
history and the extremely sensitive nature of the data these devices have
access to, it is natural to question what security measures are in place today.
  In this paper, a vulnerability assessment and penetration testing is
performed on a specific model of IP camera, the TP-Link Tapo C200. More in
detail, our findings show that the IP camera in question suffers from three
vulnerabilities such as: denial of service, video eavesdropping and, finally, a
new type of attack called "Motion Oracle". Experiments are not limited to the
offensive part but also propose countermeasures for the camera in question and
for all those that may suffer from the same vulnerabilities. The countermeasure
is based on the use of another IoT device, a Raspberry Pi.


title:
Growth of p-doped 2D-MoS$_2$ on metal oxides from spatial atomic layer
  deposition

abstract:
  In this letter we report on the synthesis of monolayers of MoS$_2$ via
chemical vapor deposition directly on thin films of Al$_2$O$_3$ grown by
spatial atomic layer deposition. The synthesized monolayers are characterized
by atomic force microscopy as well as confocal Raman and photoluminescence
spectroscopies. Our data reveals that the morphology and properties of the 2D
material differ strongly depending on its position on the substrate. Close to
the material source, we find individual flakes with an edge length of several
hundred microns exhibiting a tensile strain of 0.3%, n-doping on the order of
$n_e=0.2 \cdot 10^{13}$ cm$^{-2}$ and a dominant trion contribution to the
photoluminescence signal. In contrast to this, we identify a mm-sized region
downstream, that is made up from densely packed, small MoS$_2$ crystallites
with an edge length of several microns down to the nanometer regime and a
coverage of more than 70%. This nano-crystalline layer shows a significantly
reduced strain of only <0.02%, photoluminescence emission at an energy of 1.86
eV with a reduced trion contribution, and appears to be p-doped with a carrier
density of $n_h=0.1 \cdot 10^{13}$ cm$^{-2}$. The unusual p-type doping
achieved here in a standard CVD process without substitutional doping,
post-processing, or the use of additional chemicals may prove useful for
applications.


title:
Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in
  First Trimester 3D Ultrasound

abstract:
  Segmentation and spatial alignment of ultrasound (US) imaging data acquired
in the in first trimester are crucial for monitoring human embryonic growth and
development throughout this crucial period of life. Current approaches are
either manual or semi-automatic and are therefore very time-consuming and prone
to errors. To automate these tasks, we propose a multi-atlas framework for
automatic segmentation and spatial alignment of the embryo using deep learning
with minimal supervision. Our framework learns to register the embryo to an
atlas, which consists of the US images acquired at a range of gestational age
(GA), segmented and spatially aligned to a predefined standard orientation.
From this, we can derive the segmentation of the embryo and put the embryo in
standard orientation. US images acquired at 8+0 till 12+6 weeks GA were used
and eight pregnancies were selected as atlas images. We evaluated different
fusion strategies to incorporate multiple atlases: 1) training the framework
using atlas images from a single pregnancy, 2) training the framework with data
of all available atlases and 3) ensembling of the frameworks trained per
pregnancy. To evaluate the performance, we calculated the Dice score over the
test set. We found that training the framework using all available atlases
outperformed ensembling and gave similar results compared to the best of all
frameworks trained on a single subject. Furthermore, we found that selecting
images from the four atlases closest in GA out of all available atlases,
regardless of the individual quality, gave the best results with a median Dice
score of 0.72. We conclude that our framework can accurately segment and
spatially align the embryo in first trimester 3D US images and is robust for
the variation in quality that existed in the available atlases. Our code is
publicly available at: https://github.com/wapbastiaansen/multi-atlas-seg-reg.


title:
Research on Dual Channel News Headline Classification Based on ERNIE
  Pre-training Model

abstract:
  The classification of news headlines is an important direction in the field
of NLP, and its data has the characteristics of compactness, uniqueness and
various forms. Aiming at the problem that the traditional neural network model
cannot adequately capture the underlying feature information of the data and
cannot jointly extract key global features and deep local features, a
dual-channel network model DC-EBAD based on the ERNIE pre-training model is
proposed. Use ERNIE to extract the lexical, semantic and contextual feature
information at the bottom of the text, generate dynamic word vector
representations fused with context, and then use the BiLSTM-AT network channel
to secondary extract the global features of the data and use the attention
mechanism to give key parts higher The weight of the DPCNN channel is used to
overcome the long-distance text dependence problem and obtain deep local
features. The local and global feature vectors are spliced, and finally passed
to the fully connected layer, and the final classification result is output
through Softmax. The experimental results show that the proposed model improves
the accuracy, precision and F1-score of news headline classification compared
with the traditional neural network model and the single-channel model under
the same conditions. It can be seen that it can perform well in the
multi-classification application of news headline text under large data volume.


title:
Mode splitting of spin waves in magnetic nanotubes with discrete
  symmetries

abstract:
  We investigate how geometry influences spin dynamics in polygonal magnetic
nanotubes. We find that lowering the rotational symmetry of nanotubes by
decreasing the number of planar facets, splits an increasing number of
spin-wave modes, which are doubly degenerate in cylindrical tubes. This
symmetry-governed splitting is distinct form the topological one recently
observed in cylindrical nanotubes. Doublet modes with half-integer or integer
multiple azimuthal periods of the number of facets, split to singlet pairs with
lateral standing-wave profiles of opposing mirror-plane symmetries.
Furthermore, the polygonal geometry facilitates the hybridization of modes with
different azimuthal periods but the same symmetry, manifested in avoided level
crossings. These phenomena, unimaginable in cylindrical geometry, provide new
tools to control spin dynamics on nanoscale. The presented concept can be
generalized to nano-objects of versatile geometries and order parameters,
offering new routes to engineer dynamic response in nanoscale devices.


title: Neural Re-ranking in Multi-stage Recommender Systems: A Review

abstract:
  As the final stage of the multi-stage recommender system (MRS), re-ranking
directly affects user experience and satisfaction by rearranging the input
ranking lists, and thereby plays a critical role in MRS. With the advances in
deep learning, neural re-ranking has become a trending topic and been widely
applied in industrial applications. This review aims at integrating re-ranking
algorithms into a broader picture, and paving ways for more comprehensive
solutions for future research. For this purpose, we first present a taxonomy of
current methods on neural re-ranking. Then we give a description of these
methods along with the historic development according to their objectives. The
network structure, personalization, and complexity are also discussed and
compared. Next, we provide benchmarks of the major neural re-ranking models and
quantitatively analyze their re-ranking performance. Finally, the review
concludes with a discussion on future prospects of this field. A list of papers
discussed in this review, the benchmark datasets, our re-ranking library
LibRerank, and detailed parameter settings are publicly available at
https://github.com/LibRerank-Community/LibRerank.


title:
Bogomolov-Sommese type vanishing theorem for holomorphic vector bundles
  equipped with positive singular Hermitian metrics

abstract:
  We obtain the Bogomolov-Sommese type vanishing theorem involving multiplier
ideal sheaves for big line bundles. We define a dual Nakano semi-positivity of
singular Hermitian metrics with L2-estimates and prove the vanishing theorem
which is a generalization of the Bogomolov-Sommese type vanishing theorem to
holomorphic vector bundles.


title:
Optimizing the topological properties of stacking
  semiconductor-ferromagnet-superconductor heterostructures

abstract:
  We study the electronic properties of a planar semiconductor-superconductor
heterostructure, in which a thin ferromagnetic insulator layer lies in between
and acts as a spin filtering barrier. We find that in such a system one can
simultaneously enhance the strengths of all the three important induced
physical quantities, i.e., Rashba spin-orbit coupling, exchange coupling, and
superconducting pairing potential, for the hybrid mode by external gating. Our
results show specific advantage of this stacked device geometry compared to
conventional devices. We further discuss how to optimize geometrical parameters
for the heterostructure and complement our numerical simulations with analytic
calculations.


title: Hybrid Soft Robots Incorporating Soft and Stiff Elements

abstract:
  Soft robots are inherently compliant and have a strong potential to realize
human-friendly and safe robots. Despite continued research highlighting the
potential of soft robots, they remain largely confined to laboratory settings.
In this work, inspired by spider monkeys' tails, we propose a hybrid soft robot
(HSR) design. We detail the design objectives and methodology to improve the
controllable stiffness range and achieve independent stiffness and shape
control. We extend the curve parametric approach to We experimentally
demonstrate that the proposed HSR has about a 100% stiffness range increase
than a previous soft robot design with identical physical dimensions. In
addition, we empirically map HSR's bending shape-pressure-stiffness and present
an application example - a soft robotic gripper - to demonstrate the decoupled
nature of stiffness and shape variations. Experimental results show that
proposed HSR can be successfully used in applications where independent
stiffness and shape control is desired.


title: Avenues to generalising Bell inequalities

abstract:
  Characterizing the set of all Bell inequalities is a notably hard task. An
insightful method of solving it in case of Bell correlation inequalities for
scenarios with two dichotomic measurements per site - for arbitrary number of
parties - was given in Refs.[\textit{Phys.~Rev.~A 64, 010102(R) (2001)}] and
[\textit{Phys.~Rev.~A 64, 032112 (2001)}]. In this work we adapt their approach
to more complex scenarios. Although the resulting families of Bell inequalities
are not always tight, their coefficients have an intuitively understandable
structure. Moreover, we identify a similar structure in the CGLMP inequality
expressed in a correlation-based form, which allows us to generalise it to
three parties.


title: Entropy, Ultralimits and the Poisson boundary

abstract:
  In this paper we introduce for a group $G$ the notion of ultralimit of
measure class preserving actions of it, and show that its Poisson boundaries
can be obtained as an ultralimit of actions on itself, when equipped with
appropriately chosen measures. We use this result in embarking on a systematic
quantitative study of the basic question how close to invariant can one find
measures on a $G$-space, particularly for the action of the group on itself. As
applications we show:
  (1) On amenable groups there are always "almost invariant measures" with
respect to the information theoretic Kullback-Leibler divergence (and more
generally, any $f$-divergence).
  (2) For a free group $F$ and a symmetric measure $\lambda$ supported on its
generators, one can compute explicitly the infimum over all measures $\eta$ on
$F$ of the Furstenberg entropy $h_{\lambda}(F,\eta)$.
  Somewhat surprisingly, while in the case of the uniform measure on the
generators, the value in (2) is the same as the Furstenberg entropy of the
Poisson boundary of the same measure $\lambda$, in general it is the
Furstenberg entropy of the Poisson boundary of a measure on $F$ different from
$\lambda$. The proof of (1) makes the use of the existence of measures with
trivial boundary.


title:
UnScenE: Toward Unsupervised Scenario Extraction for Automated Driving
  Systems from Urban Naturalistic Road Traffic Data

abstract:
  Scenario-based testing is a promising approach to solve the challenge of
proving the safe behavior of vehicles equipped with automated driving systems
(ADS). Since an infinite number of concrete scenarios can theoretically occur
in real-world road traffic, the extraction of relevant scenarios that are
sensitive regarding the safety-related behavior of ADS-equipped vehicles is a
key aspect for the successful verification and validation of these systems.
Therefore, this paper provides a method for extracting multimodal urban traffic
scenarios from naturalistic road traffic data in an unsupervised manner for
minimizing the amount of (potentially biased) prior expert knowledge needed.
Rather than an (expensive) rule-based assignment by extracting concrete
scenarios into predefined functional scenarios, the presented method deploys an
unsupervised machine learning pipeline. It includes principal feature analysis,
feature extraction with so-called scenario grids, dimensionality reduction by
principal component analysis, scenario clustering as well as cluster
validation. The approach allows exploring the unknown natures of the data and
interpreting them as scenarios that experts could not have anticipated. The
method is demonstrated and evaluated for naturalistic road traffic data at
urban intersections from the inD and the Silicon Valley dataset. The findings
encourage the use of this type of data as well as unsupervised machine learning
approaches as important pillar for a systematic construction of a relevant
scenario database with sufficient coverage for testing ADS.


title:
Deterministic Covalent Organic Functionalization of Monolayer Graphene
  with 1,3-Dipolar Cycloaddition Via High Resolution Surface Engineering

abstract:
  Spatially-resolved organic functionalization of monolayer graphene is
successfully achieved by combining low-energy electron beam irradiation with
1,3-dipolar cycloaddition of azomethine ylide. Indeed, the modification of the
graphene honeycomb lattice obtained via electron beam irradiation yields to a
local increase of the graphene chemical reactivity. As a consequence, thanks to
the high-spatially resolved generation of structural defects (~ 100 nm),
chemical reactivity patterning has been designed over the graphene surface in a
well-controlled way. Atomic force microscopy and Raman spectroscopy allow to
investigate the two-dimensional spatial distribution of the structural defects
and the new features that arise from the 1,3-dipolar cycloaddition, confirming
the spatial selectivity of the graphene functionalization achieved via defect
engineering. The Raman signature of the functionalized graphene is investigated
both experimentally and via ab initio molecular dynamics simulations, computing
the power spectrum. Furthermore, the organic functionalization is shown to be
reversible thanks to the desorption of the azomethine ylide induced by focused
laser irradiation. The selective and reversible functionalization of high
quality graphene using 1,3-dipolar cycloaddition is a significant step towards
the controlled synthesis of graphene-based complex structures and devices at
the nanoscale.


title:
Stability of the replica-symmetric solution in the
  off-diagonally-disordered Bose-Hubbard model

abstract:
  We study a disordered system of interacting bosons described by the
Bose-Hubbard Hamiltonian with random tunneling amplitudes. We derive the
condition for the stability of the replica-symmetric solution for this model.
Following the scheme of de Almeida and Thouless, we determine if the solution
corresponds to the minimum of free energy by building the respective Hessian
matrix and checking its positive semidefiniteness. Thus, we find the
eigenvalues by postulating the set of eigenvectors based on their expected
symmetry, and require the eigenvalues to be non-negative. We evaluate the
spectrum numerically and identify matrix blocks that give rise to eigenvalues
that are always non-negative. Thus, we find a subset of eigenvalues coming from
decoupled subspaces that is sufficient to be checked as the stability
criterion. We also determine the stability of the phases present in the system,
finding that the disordered phase is stable, the glass phase is unstable, while
the superfluid phase has both stable and unstable parts.


title: Directional distributions and the half-angle principle

abstract:
  Angle halving, or alternatively the reverse operation of angle doubling, is a
useful tool when studying directional distributions. It is especially useful on
the circle where, in particular, it yields an identification between the
wrapped Cauchy distribution and the angular central Gaussian distributions, as
well as a matching of their parameterizations. The operation of angle halving
can be extended to higher dimensions, but its effect on distributions is more
complicated than on the circle. In all dimensions angle halving provides a
simple way to interpret stereographic projection from the sphere to Euclidean
space.


title: Comparison of 2D topological codes and their decoding performances

abstract:
  Topological quantum codes are favored because they allow qubit layouts that
are suitable for practical implementation. An $N$-qubit topological code can be
decoded by minimum-weight perfect matching (MWPM) with complexity
$O(\text{poly}(N))$ if it is of CSS-type. Recently it is shown that various
quantum codes, including non-CSS codes, can be decoded by an adapted belief
propagation with memory effects (denoted MBP) with complexity almost linear in
$N$. In this paper, we show that various two-dimensional topological codes, CSS
or non-CSS, regardless of the layout, can be decoded by MBP, including color
codes and twisted XZZX codes. We will comprehensively compare these codes in
terms of code efficiency and decoding performance, assuming perfect error
syndromes.


title:
Embedded quantitative MRI T1rho mapping using non-linear primal-dual
  proximal splitting

abstract:
  Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical
MRI by providing numerical values on which diagnostic assessment or predictions
of tissue properties can be based. However, qMRI measurements typically take
more time than anatomical imaging due to requiring multiple measurements with
varying contrasts for, e.g., relaxation time mapping. To reduce the scanning
time, undersampled data may be combined with compressed sensing reconstruction
techniques. Typical CS reconstructions first reconstruct a complex-valued set
of images corresponding to the varying contrasts, followed by a non-linear
signal model fit to obtain the parameter maps. We propose a direct, embedded
reconstruction method for T1rho mapping. The proposed method capitalizes on a
known signal model to directly reconstruct the desired parameter map using a
non-linear optimization model. The proposed reconstruction method also allows
directly regularizing the parameter map of interest, and greatly reduces the
number of unknowns in the reconstruction. We test the proposed model using a
simulated radially sampled data from a 2D phantom and 2D cartesian ex vivo
measurements of a mouse kidney specimen. We compare the embedded reconstruction
model to two CS reconstruction models, and in the cartesian test case also
iFFT. The proposed, embedded model outperformed the reference methods on both
test cases, especially with higher acceleration factors.


title:
Accessing Polarized Fragmentation Functions at the Unpolarized EIC and
  BELLE Experiments

abstract:
  We briefly report our recent progress on the study of the polarized
fragmentation functions of $\Lambda$ hyperon in unpolarized semi-inclusive
deep-inelastic scatterings and electron-positron annihilations at low energies.
In particular, we present a simple but practical method on how to measure the
azimuthal-angle-dependent longitudinal polarization and the transverse
polarization inside the production plane and bridge these observables to the
corresponding structure functions and fragmentation functions at the leading
twist. Our work diversifies the high energy reactions that can probe the
polarized fragmentation functions.


title:
Linking boundary conditions for kinetic and hydrodynamic description of
  fermion gas

abstract:
  An approximate analytical solution of the boundary slip problem in magnetic
field is obtained by using the general form of boundary conditions for the
distribution function of fermions with the isotropic energy spectrum. Exact
numerical calculations of the slip length for different models of
angle-dependent specularity parameter and application of the results to the
description of the Poiseuille flow demonstrate the reliability of the
approximate solution for establishing a direct link between the hydrodynamic
and the kinetic approaches to transport in bounded fermion systems.


title: Realization of fast all-microwave CZ gates with a tunable coupler

abstract:
  The development of high-fidelity two-qubit quantum gates is essential for
digital quantum computing. Here, we propose and realize an all-microwave
parametric Controlled-Z (CZ) gates by coupling strength modulation in a
superconducting Transmon qubit system with tunable couplers. After optimizing
the design of the tunable coupler together with the control pulse numerically,
we experimentally realized a 100 ns CZ gate with high fidelity of 99.38%$
\pm$0.34% and the control error being 0.1%. We note that our CZ gates are not
affected by pulse distortion and do not need pulse correction, {providing a
solution for the real-time pulse generation in a dynamic quantum feedback
circuit}. With the expectation of utilizing our all-microwave control scheme to
reduce the number of control lines through frequency multiplexing in the
future, our scheme draws a blueprint for the high-integrable quantum hardware
design.


title: An Application of Online Learning to Spacecraft Memory Dump Optimization

abstract:
  In this paper, we present a real-world application of online learning with
expert advice to the field of Space Operations, testing our theory on real-life
data coming from the Copernicus Sentinel-6 satellite. We show that in
Spacecraft Memory Dump Optimization, a lightweight Follow-The-Leader algorithm
leads to an increase in performance of over $60\%$ when compared to traditional
techniques.


title: KNIFE: Kernelized-Neural Differential Entropy Estimation

abstract:
  Mutual Information (MI) has been widely used as a loss regularizer for
training neural networks. This has been particularly effective when learn
disentangled or compressed representations of high dimensional data. However,
differential entropy (DE), another fundamental measure of information, has not
found widespread use in neural network training. Although DE offers a
potentially wider range of applications than MI, off-the-shelf DE estimators
are either non differentiable, computationally intractable or fail to adapt to
changes in the underlying distribution. These drawbacks prevent them from being
used as regularizers in neural networks training. To address shortcomings in
previously proposed estimators for DE, here we introduce KNIFE, a fully
parameterized, differentiable kernel-based estimator of DE. The flexibility of
our approach also allows us to construct KNIFE-based estimators for conditional
(on either discrete or continuous variables) DE, as well as MI. We empirically
validate our method on high-dimensional synthetic data and further apply it to
guide the training of neural networks for real-world tasks. Our experiments on
a large variety of tasks, including visual domain adaptation, textual fair
classification, and textual fine-tuning demonstrate the effectiveness of
KNIFE-based estimation. Code can be found at
https://github.com/g-pichler/knife.


title: Modeling Population Human Mobility with Dynamic Mode Decomposition

abstract:
  Human mobility research concerns spatiotemporal individual and population
movement. Accurate modeling and prediction of human mobility can provide
opportunities to monitor, manage and optimize human movement for improved
social-economic benefit. In this paper, we adopt the dynamic mode decomposition
algorithm to model population human mobility using visitor flow data between
different states in the United States from 2019 to 2021 [1]. We train multiple
DMD models with different low rank structures, and evaluate their modeling
accuracy and predictability on novel testing data.


title:
What is Next when Sequential Prediction Meets Implicitly Hard
  Interaction?

abstract:
  Hard interaction learning between source sequences and their next targets is
challenging, which exists in a myriad of sequential prediction tasks. During
the training process, most existing methods focus on explicitly hard
interactions caused by wrong responses. However, a model might conduct correct
responses by capturing a subset of learnable patterns, which results in
implicitly hard interactions with some unlearned patterns. As such, its
generalization performance is weakened. The problem gets more serious in
sequential prediction due to the interference of substantial similar candidate
targets.
  To this end, we propose a Hardness Aware Interaction Learning framework
(HAIL) that mainly consists of two base sequential learning networks and mutual
exclusivity distillation (MED). The base networks are initialized differently
to learn distinctive view patterns, thus gaining different training
experiences. The experiences in the form of the unlikelihood of correct
responses are drawn from each other by MED, which provides mutual exclusivity
knowledge to figure out implicitly hard interactions. Moreover, we deduce that
the unlikelihood essentially introduces additional gradients to push the
pattern learning of correct responses. Our framework can be easily extended to
more peer base networks. Evaluation is conducted on four datasets covering
cyber and physical spaces. The experimental results demonstrate that our
framework outperforms several state-of-the-art methods in terms of top-k based
metrics.


title: Measurably Stronger Explanation Reliability via Model Canonization

abstract:
  While rule-based attribution methods have proven useful for providing local
explanations for Deep Neural Networks, explaining modern and more varied
network architectures yields new challenges in generating trustworthy
explanations, since the established rule sets might not be sufficient or
applicable to novel network structures. As an elegant solution to the above
issue, network canonization has recently been introduced. This procedure
leverages the implementation-dependency of rule-based attributions and
restructures a model into a functionally identical equivalent of alternative
design to which established attribution rules can be applied. However, the idea
of canonization and its usefulness have so far only been explored
qualitatively. In this work, we quantitatively verify the beneficial effects of
network canonization to rule-based attributions on VGG-16 and ResNet18 models
with BatchNorm layers and thus extend the current best practices for obtaining
reliable neural network explanations.


title:
An Edge-Cloud based Reference Architecture to support cognitive
  solutions in the Process Industry

abstract:
  Process Industry (PI e.g. Steel, Metals, Chemicals, Cement, Asphalt,
Ceramics) is one of the leading sectors of the world economy, characterized
however by intense environmental impact, and very high energy consumption. In
spite of a traditional low innovation pace in PI, in the recent years a strong
push at worldwide level towards the dual objective of improving the efficiency
of plants and the quality of products, significantly reducing the consumption
of electricity and CO2 emissions has taken momentum. Digital Technologies
(namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies)
are enabling drivers for a Twin Digital-Green Transition, as well as
foundations for human centric, safe, comfortable and inclusive work places.
Currently, digital sensors in plants produce a large amount of data which in
most cases constitutes just a potential and not a real value for Process
Industry, often locked-in in close proprietary systems and seldomly exploited.
Digital technologies, with process modelling-simulation via digital twins, can
build a bridge between the physical and the virtual worlds, bringing innovation
with great efficiency and drastic reduction of waste. In accordance with the
guidelines of Industrie 4.0, the H2020 funded CAPRI project aims to innovate
the process industry, with a modular and scalable Reference Architecture, based
on open source software, which can be implemented both in brownfield and
greenfield scenarios. The ability to distribute processing between the edge,
where the data is created, and the cloud, where the greatest computational
resources are available, facilitates the development of integrated digital
solutions with cognitive capabilities. The reference architecture is being
validated in the asphalt, steel & pharma pilot plants, allowing the development
of integrated planning solutions, with scheduling and control of the plants.


title:
Superballistic boundary conductance and hydrodynamic transport in
  microstructures

abstract:
  It is shown that the ideal boundary between a perfectly conducting electrode
and electron liquid state acts as a contact whose conductance per unit area is
higher than the fundamental Sharvin conductance by a numerical coefficient $2
\alpha$, where $\alpha$ is slightly smaller than unity and depends on the
dimensionality of the system. If the boundary has a finite curvature, an
additional correction to the boundary conductance appears, which is
parametrically small as a product of the curvature by the electron-electron
mean free path length. The relation of the normal current density to the
voltage between the electrode and electron liquid represents itself a
hydrodynamic boundary condition for current-penetrable boundary. Calculations
of the conductance and potential distribution in microstructures by means of
numerical solution of the Boltzmann equation show that the concept of boundary
conductance works very good when the hydrodynamic transport regime is reached.
The superballistic transport, when the device conductance is higher than the
Sharvin conductance, can be realized in Corbino disk devices not only in the
hydrodynamic regime, although requires that the electron-electron scattering
must be stronger than the momentum-relaxing scattering. The theoretical results
for Corbino disks are consistent with recent experimental findings.


title: Routing Schemes and Distance Oracles in the Hybrid Model

abstract:
  The $\mathsf{HYBRID}$ model was introduced as a means for theoretical study
of distributed networks that use various communication modes. Conceptually, it
is a synchronous message passing model with a local communication mode, where
in each round each node can send large messages to all its neighbors in a local
network (a graph), and a global communication mode, where each node is allotted
limited (polylogarithmic) bandwidth per round which it can use to communicate
with any node in the network.
  Prior work has often focused on shortest paths problems in the local network,
as their global nature makes these an interesting case study how combining
communication modes in the $\mathsf{HYBRID}$ model can overcome the individual
lower bounds of either mode. In this work we consider a similar problem, namely
computation of distance oracles and routing schemes. In the former, all nodes
have to compute local tables, which allows them to look up the distance
(estimates) to any target node in the local network when provided with the
label of the target. In the latter, it suffices that nodes give the next node
on an (approximately) shortest path to the target.
  Our goal is to compute these local tables as fast as possible with labels as
small as possible. We show that this can be done exactly in $\widetilde
O(n^{1/3})$ communication rounds and labels of size $\Theta(n^{2/3})$ bits. For
constant stretch approximations we achieve labels of size $O(\log n)$ in the
same time. Further, as our main technical contribution, we provide
computational lower bounds for a variety of problem parameters. For instance,
we show that computing solutions with stretch below a certain constant takes
$\widetilde \Omega(n^{1/3})$ rounds even for labels of size $O(n^{2/3})$.


title: Polyhedron Kernel Computation Using a Geometric Approach

abstract:
  The geometric kernel (or simply the kernel) of a polyhedron is the set of
points from which the whole polyhedron is visible. Whilst the computation of
the kernel for a polygon has been largely addressed in the literature, fewer
methods have been proposed for polyhedra. The most acknowledged solution for
the kernel estimation is to solve a linear programming problem. On the
contrary, we present a geometric approach that extends our previous method,
optimizes it anticipating all calculations in a pre-processing step and
introduces the use of geometric exact predicates. Experimental results show
that our method is more efficient than the algebraic approach on generic
tessellations and in detecting if a polyhedron is not star-shaped. Details on
the technical implementation and discussions on pros and cons of the method are
also provided.


title: MuZero with Self-competition for Rate Control in VP9 Video Compression

abstract:
  Video streaming usage has seen a significant rise as entertainment,
education, and business increasingly rely on online video. Optimizing video
compression has the potential to increase access and quality of content to
users, and reduce energy use and costs overall. In this paper, we present an
application of the MuZero algorithm to the challenge of video compression.
Specifically, we target the problem of learning a rate control policy to select
the quantization parameters (QP) in the encoding process of libvpx, an open
source VP9 video compression library widely used by popular video-on-demand
(VOD) services. We treat this as a sequential decision making problem to
maximize the video quality with an episodic constraint imposed by the target
bitrate. Notably, we introduce a novel self-competition based reward mechanism
to solve constrained RL with variable constraint satisfaction difficulty, which
is challenging for existing constrained RL methods. We demonstrate that the
MuZero-based rate control achieves an average 6.28% reduction in size of the
compressed videos for the same delivered video quality level (measured as PSNR
BD-rate) compared to libvpx's two-pass VBR rate control policy, while having
better constraint satisfaction behavior.


title:
Solution of the Continuous Time Bilinear Quadratic Regulator Problem by
  Krotov's Method

abstract:
  This work contributes to the field of optimal control of bilinear systems. It
concerns a continuous time, finite dimensional, bilinear state equation with a
quadratic performance index to be minimized. The state equation is
non-autonomous and comprises a deterministic, a-priori known excitation. The
control trajectory is constrained to an admissible set without a specific
structure. The performance index is a functional, quadratic in the state
variables and control signals. Krotov's method is used for solving this problem
by means of an improving sequence. To this end, the required sequence of an
improving functions is formulated. Finally, the solution is encapsulated in an
algorithm form and a numerical example of structural control problem is
provided.


title: Notes on Moduli theory, Stacks and 2-Yoneda's Lemma

abstract:
  This note is a survey on the basic aspects of moduli theory along with some
examples. In that respect, one of the purposes of this current document is to
understand how the introduction of stacks circumvents the non-representability
problem of the corresponding moduli functor $\mathcal{F}$ by using the
2-category of stacks. To this end, we shall briefly revisit the basics of
2-category theory and present a 2-categorical version of Yoneda's lemma.


title: Efficiently solving the harmonic model predictive control formulation

abstract:
  Harmonic model predictive control (HMPC) is a model predictive control (MPC)
formulation which displays several benefits over other MPC formulations,
especially when using a small prediction horizon. These benefits, however, come
at the expense of an optimization problem that is no longer the typical
quadratic programming problem derived from most linear MPC formulations due to
the inclusion of a particular class of second order cone constraints. This
article presents a method for efficiently dealing with these constraints in
operator splitting methods, leading to a computation time for solving HMPC in
line with state of the art solvers for linear MPC. We show how to apply this
result to the alternating direction method of multipliers algorithm, presenting
a solver which we compare against other solvers from the literature, including
solvers for other linear MPC formulations. The results show that the proposed
solver, and by extension the HMPC formulation, is suitable for its
implementation in embedded systems.


title:
Improved Finite-Key Security Analysis of Quantum Key Distribution
  Against Trojan-Horse Attacks

abstract:
  Most security proofs of quantum key distribution (QKD) disregard the effect
of information leakage from the users' devices, and, thus, do not protect
against Trojan-horse attacks (THAs). In a THA, the eavesdropper injects strong
light into the QKD apparatuses, and then analyzes the back-reflected light to
learn information about their internal setting choices. Only a few recent works
consider this security threat, but predict a rather poor performance of QKD
unless the devices are strongly isolated from the channel. Here, we derive
finite-key security bounds for decoy-state-based QKD schemes in the presence of
THAs, which significantly outperform previous analyses. Our results constitute
an important step forward to closing the existing gap between theory and
practice in QKD.


title:
Energy dependence of the proton geometry in exclusive vector meson
  production

abstract:
  The gluon radius of the proton is expected to increase at small gluon
momentum fractions $x$, an effect which has hitherto not been considered in the
dipole model framework. We investigate the energy dependence of exclusive
$J/\psi$, $\phi$, and $\rho$ production by introducing three models for $x$
dependence of the gluon thickness function. We allow the transverse width of
the proton to increase as $x$ decreases, using a novel parametrisation in the
spherical proton and the hotspot model. We compare these with a model in which
the number of hotspots increases as $x$ decreases and confront the models with
HERA data. The models exhibit clear differences in the slope of the $t$-spectra
as well as in the cross-section ratios between coherent and incoherent events.
The data shows a preference for models in which the proton's size increases as
$x$ decreases.


title:
Size-dependent thermal stability and optical properties of ultra-small
  nanodiamonds synthesized under high pressure

abstract:
  Diamond properties down to the quantum-size region are still poorly
understood. High-pressure high-temperature (HPHT) synthesis from
chloroadamantane molecules allows precise control of nanodiamond size. Thermal
stability and optical properties of nanodiamonds with sizes spanning range from
$<$1 to 8 nm are investigated. It is shown that hypothesis about enhanced
thermal stability of nanodiamonds smaller than 2 nm is incorrect. The most
striking feature in IR absorption of these samples is the appearance of an
enhanced transmission band near the diamond Raman mode (1332 cm$^{-1}$).
Following previously proposed explanation, we attribute this phenomenon to the
Fano-effect caused by resonance of diamond Raman mode with continuum of
conductive surface states. We assume that these surface states may be formed by
reconstruction of broken bonds on the nanodiamond surfaces. This effect is also
responsible for the observed asymmetry of Raman scattering peak. he mechanism
of nanodiamond formation in HPHT synthesis is proposed, explaining pecularities
of their structure and properties.


title:
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment
  Act Flows

abstract:
  Despite recent progress in open-domain dialogue evaluation, how to develop
automatic metrics remains an open problem. We explore the potential of dialogue
evaluation featuring dialog act information, which was hardly explicitly
modeled in previous methods. However, defined at the utterance level in
general, dialog act is of coarse granularity, as an utterance can contain
multiple segments possessing different functions. Hence, we propose segment
act, an extension of dialog act from utterance level to segment level, and
crowdsource a large-scale dataset for it. To utilize segment act flows,
sequences of segment acts, for evaluation, we develop the first consensus-based
dialogue evaluation framework, FlowEval. This framework provides a
reference-free approach for dialog evaluation by finding pseudo-references.
Extensive experiments against strong baselines on three benchmark datasets
demonstrate the effectiveness and other desirable characteristics of our
FlowEval, pointing out a potential path for better dialogue evaluation.


title:
On the influence of input triggering on the dynamics of the Jansen-Rit
  oscillators network

abstract:
  We investigate the dynamical properties of a network of coupled neural
oscillators with identical Jansen-Rit masses. The connections between nodes
follow the regular Watts-Strogatz topology. Each node receives deterministic
external input plus internal input based on the output signal from the
neighbors. This paper aims to change these two inputs and analyze the generated
results. First, we attempt to analyze the model using the mean-field
approximation, i.e., identical inputs for all nodes. Then, this assumption is
relaxed. A more detailed analysis of these two states is discussed. We use the
Pearson correlation coefficient to measure the amount of synchronization. As a
result of the mean-field approach, we find that despite observed changes in
behavior, there is no phase transition. Surprisingly, both the first
(discontinuous) and second (continuous) phase transition occurs by relaxing the
mean-field assumption. We also demonstrate how changes in input can result in
pathological oscillations similar to those observed in epilepsy. Results show
that coupled Jansen-Rit masses emerge a variety of behaviors influenced by
various external and internal inputs. Moreover, our findings indicate that
delta waves can be generated by altering these inputs in a network of
Jansen-Rit neural mass models, which has not been previously observed in a
single Jansen-Rit neural mass model analysis. \\ Overall, a wide range of
behavioral patterns, including epilepsy, healthy behavior, and the transition
between synchrony, and asynchrony are examined comprehensively in this paper.
Moreover, this work highlights the putative contribution of external and
internal inputs in studying the phase transition and synchronization of neural
mass models.

categories: q-bio.NC cond-mat.dis-nn cond-mat.soft physics.bio-ph


title: Novel prediction methods for virtual drug screening

abstract:
  Drug development is an expensive and time-consuming process where thousands
of chemical compounds are being tested in order to find those possessing
drug-like properties while being safe and effective. One of key parts of the
early drug discovery process has become virtual drug screening -- a method used
to narrow down search for potential drugs by running computer simulations of
drug-target interactions. As these methods are known to demand huge amounts of
computational power to get accurate results, prediction models based on machine
learning techniques became a popular solution requiring less computational
power as well as offering the ability to generate novel chemical structures for
further research. Deep learning is to stay in drug discovery but has a long way
to go. Only in the past few years with increases in computing power have
researchers really started to embrace the potential of neural networks in
various stages of the drug discovery process. While prediction methods promise
great perspective in the future development of drug discovery they open new
questions and challenges that still have to be solved.


title:
Bayesian semi-parametric inference for clustered recurrent events with
  zero-inflation and a terminal event/4163305

abstract:
  Recurrent event data are common in clinical studies when participants are
followed longitudinally, and are often subject to a terminal event. With the
increasing popularity of large pragmatic trials and a heterogeneous source
population, participants are often nested in clinics and can be either
susceptible or structurally unsusceptible to the recurrent process. These
complications require new modeling strategies to accommodate potential
zero-event inflation as well as hierarchical data structures in both the
terminal and non-terminal event processes. In this paper, we develop a Bayesian
semiparametric model to jointly characterize the zero-inflated recurrent event
process and the terminal event process. We use a point mass mixture of
non-homogeneous Poisson processes to describe the recurrent intensity and
introduce shared random effects from different sources to bridge the
non-terminal and terminal event processes. To achieve robustness, we consider
nonparametric Dirichlet processes to model the residual of the accelerated
failure time model for the survival process as well as the cluster-specific
frailty distribution, and develop a Markov Chain Monte Carlo algorithm for
posterior inference. We demonstrate the superiority of our proposed model
compared with competing models via simulations and apply our method to a
pragmatic cluster-randomized trial for fall injury prevention among the
elderly.


title:
Continuous-time stochastic gradient descent for optimizing over the
  stationary distribution of stochastic differential equations

abstract:
  We develop a new continuous-time stochastic gradient descent method for
optimizing over the stationary distribution of stochastic differential equation
(SDE) models. The algorithm continuously updates the SDE model's parameters
using an estimate for the gradient of the stationary distribution. The gradient
estimate is simultaneously updated, asymptotically converging to the direction
of steepest descent. We rigorously prove convergence of our online algorithm
for linear SDE models and present numerical results for nonlinear examples. The
proof requires analysis of the fluctuations of the parameter evolution around
the direction of steepest descent. Bounds on the fluctuations are challenging
to obtain due to the online nature of the algorithm (e.g., the stationary
distribution will continuously change as the parameters change). We prove
bounds for the solutions of a new class of Poisson partial differential
equations, which are then used to analyze the parameter fluctuations in the
algorithm.


title: A characterization of normal $3$-pseudomanifolds with $g_2\leq4$

abstract:
  We characterize normal $3$-pseudomanifolds with $g_2\leq4$. We know that if a
$3$-pseudomanifold with $g_2\leq4$ does not have any singular vertices then it
is a $3$-sphere. We first prove that a normal $3$-pseudomanifold with
$g_2\leq4$ has at most two singular vertices. Then we prove that a normal
$3$-pseudomanifold with $g_2 \leq 4$, which is not a $3$-sphere is obtained
from some boundary of $4$-simplices by a sequence of operations connected sum,
edge expansion and an edge folding. In addition, by using [17], we re-framed
the characterization of normal $3$-pseudomanifolds with $g_2\leq 9$, when it
has no singular vertices.


title:
On the Complexity of Object Detection on Real-world Public
  Transportation Images for Social Distancing Measurement

abstract:
  Social distancing in public spaces has become an essential aspect in helping
to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in
machine learning, there have been many studies in the literature implementing
social distancing via object detection through the use of surveillance cameras
in public spaces. However, to date, there has been no study of social distance
measurement on public transport. The public transport setting has some unique
challenges, including some low-resolution images and camera locations that can
lead to the partial occlusion of passengers, which make it challenging to
perform accurate detection. Thus, in this paper, we investigate the challenges
of performing accurate social distance measurement on public transportation. We
benchmark several state-of-the-art object detection algorithms using real-world
footage taken from the London Underground and bus network. The work highlights
the complexity of performing social distancing measurement on images from
current public transportation onboard cameras. Further, exploiting domain
knowledge of expected passenger behaviour, we attempt to improve the quality of
the detections using various strategies and show improvement over using vanilla
object detection alone.


title:
Exponential BGK Integrator for Multi-Scale Particle-Based Kinetic
  Simulations

abstract:
  Despite the development of an extensive toolbox of multi-scale rarefied flow
simulators, such simulations remain challenging due to the significant
disparity of collisional and macroscopic spatio-temporal scales. Our study
offers a novel and consistent numerical scheme for a coupled treatment of
particles advection and collision governed by the BGK evolution, honouring
positivity of the velocity distribution. Our method shares its framework, in
spirit, with the unified gas kinetic class of multi-scale schemes. Yet it
provides attractive features for particle-based stochastic simulations, readily
implementable to existing direct simulation Monte-Carlo codes. We demonstrate
accuracy and performance of the devised scheme for prototypic gas flows, over a
wide-range of rarefaction parameters. Due to the resulting robustness and
flexibility of the devised exponential BGK integrator, the scheme paves the way
towards more affordable simulations of large- and multi-scale rarefied gas
phenomena.


title: Trajectory, recovery, and orbital history of the Madura Cave meteorite

abstract:
  On the 19th June 2020 at 20:05:07 UTC, a fireball lasting 5.5 s was observed
above Western Australia by three Desert Fireball Network observatories. The
meteoroid entered the atmosphere with a speed of $14.00 \pm 0.17$ km s$^{-1}$
and followed a $58^{\circ}$ slope trajectory from a height of 75 km down to
18.6 km. Despite the poor angle of triangulated planes between observatories
(29$^{\circ}$) and the large distance from the observatories, a well
constrained kilo-size main mass was predicted to have fallen just South of
Madura in Western Australia. However, the search area was predicted to be large
due to the trajectory uncertainties. Fortunately, the rock was rapidly
recovered along the access track during a reconnaissance trip. The 1.072 kg
meteorite called Madura Cave was classified as an L5 ordinary chondrite. The
calculated orbit is of Aten type (mostly contained within the Earth's orbit),
the second time only a meteorite is observed on such an orbit after Bunburra
Rockhole. Dynamical modelling shows that Madura Cave has been in near-Earth
space for a very long time. The NEO dynamical lifetime for the progenitor
meteoroid is predicted to be $\sim87$ Myr. This peculiar orbit also points to a
delivery from the main asteroid belt via the $\nu6$ resonance, and therefore an
origin in the inner belt. This result contributes to drawing a picture for the
existence of a present-day L chondrite parent body in the inner belt.


title: Equivalent widths of Lyman $\alpha$ emitters in MUSE-Wide and MUSE-Deep

abstract:
  The aim of this study is to better understand the connection between the
Lyman $\alpha$ rest-frame equivalent width (EW$_0$) and spectral properties as
well as ultraviolet (UV) continuum morphology by obtaining reliable EW$_0$
histograms for a statistical sample of galaxies and by assessing the fraction
of objects with large equivalent widths. We used integral field spectroscopy
from MUSE combined with broad-band data from the Hubble Space Telescope (HST)
to measure EW$_0$. We analysed the emission lines of $1920$ Lyman $\alpha$
emitters (LAEs) detected in the full MUSE-Wide (one hour exposure time) and
MUSE-Deep (ten hour exposure time) surveys and found UV continuum counterparts
in archival HST data. We fitted the UV continuum photometric images using the
Galfit software to gain morphological information on the rest-UV emission and
fitted the spectra obtained from MUSE to determine the double peak fraction,
asymmetry, full-width at half maximum, and flux of the Lyman $\alpha$ line. The
two surveys show different histograms of Lyman $\alpha$ EW$_0$. In MUSE-Wide,
$20\%$ of objects have EW$_0 > 240$ \r{A}, while this fraction is only $11\%$
in MUSE-Deep and $\approx 16\%$ for the full sample. This includes objects
without HST continuum counterparts (one-third of our sample), for which we give
lower limits for EW$_0$. The object with the highest securely measured EW$_0$
has EW$_0=589 \pm 193$ \r{A} (the highest lower limit being EW$_0=4464$ \r{A}).
We investigate the connection between EW$_0$ and Lyman $\alpha$ spectral or UV
continuum morphological properties. The survey depth has to be taken into
account when studying EW$_0$ distributions. We find that in general, high
EW$_0$ objects can have a wide range of spectral and UV morphological
properties, which might reflect that the underlying causes for high EW$_0$
values are equally varied. (abridged)


title:
Effects of disorder on polaritonic and dark states in a cavity using the
  disordered Tavis-Cummings model

abstract:
  We consider molecules confined to a microcavity whose dimensions are such
that an excitation of the molecule is nearly resonant with a cavity mode. We
investigate the situation where the excitation energies of the molecules are
randomly distributed with a mean value of $\epsilon_a$ and variance $\sigma$.
For this case, we find a solution that approaches the exact result for large
values of the number density $\mathscr{N}$ of the molecules. We find the
conditions for the existence of the polaritonic states, as well as expressions
for their energies. The polaritonic states are quite stable against disorder.
Analytical results are verified by comparison with simulations. When
$\epsilon_a$ is equal to that of the cavity state $\epsilon_c$ (on resonance)
the gap between the two polaritonic states is found to increase with disorder,
the increase being equal to $2 \frac{\sigma^2}{\sqrt{\mathscr{N}}|\tilde{V}|}$
where $\tilde{V}$ is the coupling of a molecular excitation to the cavity
state. An analytic expression is found for the disorder induced width of the
polaritonic peak. We results for various densities of states, and the
absorption spectrum. The dark states that exist in the case $\sigma=0$ turn
"grey" in presence of disorder with their contribution to the absorption
increasing with $\sigma$. We analyze the effect of including lifetimes of the
cavity and molecular states and find that in the strong coupling regime, the
width of the polaritonic peaks is dominated by the lifetime effect and that
disorder plays almost no role, if the Rabi splitting is sufficiently large. We
also consider the case where there is (a) orientational disorder as well as (b)
spatial variation of the cavity field and find that they effectively amount to
a renormlisation of the coupling.


title: Forest-based networks

abstract:
  In evolutionary studies it is common to use phylogenetic trees to represent
the evolutionary history of a set of species. However, in case the transfer of
genes or other genetic information between the species or their ancestors has
occurred in the past, a tree may not provide a complete picture of their
history. In such cases,tree-based phylogenetic networks can provide a useful,
more refined representation of the species evolution. Such a network is
essentially a phylogenetic tree with some arcs added between the tree edges so
as to represent reticulate events such as gene transfer. Even so, this model
does not permit the representation of evolutionary scenarios where reticulate
events have taken place between different subfamilies or lineages of species.
To represent such scenarios, in this paper we introduce the notion of a
forest-based phylogenetic network, that is, a collection of leaf-disjoint
phylogenetic trees on a set of species with arcs added between the edges of
distinct trees within the collection. Forest-based networks include the
recently introduced class of overlaid species forests which are used to model
introgression. As we shall see, even though the definition of forest-based
networks is closely related to that of tree-based networks, they lead to new
mathematical theory which complements that of tree-based networks. As well as
studying the relationship of forest-based networks with other classes of
phylogenetic networks, such as tree-child networks and universal tree-based
networks, we present some characterizations of some special classes of
forest-based networks. We expect that our results will be useful for developing
new models and algorithms to understand reticulate evolution, such as gene
transfer between collections of bacteria that live in different environments.


title:
The Frisch--Parisi formalism for fluctuations of the Schr\"odinger
  equation

abstract:
  We consider the solution of the Schr\"odinger equation $u$ in $\mathbb{R}$
when the initial datum tends to the Dirac comb. Let $h_{\text{p}, \delta}(t)$
be the fluctuations in time of $\int\lvert x\rvert^{2\delta}\lvert
u(x,t)\rvert^2\,dx$, for $0 < \delta < 1$, after removing a smooth background.
We prove that the Frisch--Parisi formalism holds for $H_\delta(t) =
\int_{[0,t]}h_{\text{p}, \delta}(2s)\,ds$, which is morally a simplification of
the Riemann's non-differentiable curve $R$. Our motivation is to understand the
evolution of the vortex filament equation of polygonal filaments, which are
related to $R$.


title: Short-lived Datacenter

abstract:
  Serverless platforms have attracted attention due to their promise of
elasticity, low cost, and fast deployment. Instead of using a fixed virtual
machine (VM) infrastructure, which can incur considerable costs to operate and
run, serverless platforms support short computations, triggered on demand, with
cost proportional to fine-grain function execution time. However, serverless
platforms offer a restricted execution environment. For example, functions have
limited execution times, limited resources, and no support for networking
between functions. In this paper, we explore what it takes to treat serverless
platforms as short-lived, general purpose data-centers which can execute
unmodified existing applications. As a first step in this quest, we have
developed Boxer, a system providing an execution environment on top of existing
functions-as-a-service platforms that allows users to seamlessly migrate
conventional VM-based cloud services to serverless platforms. Boxer allows
generic applications to benefit from the fine-grain elasticity of serverless
platforms without having to modify applications to adopt a restrictive
event-triggered programming model or orchestrate auxiliary systems for data
communication. We implement Boxer on top of AWS Lambda and extend it to
transparently provide standard network interfaces. We describe its
implementation and demonstrate how it can be used to run off-the-shelf cloud
applications with a degree of fine-grained elasticity not available on
traditional VM-based platforms.


title: Fold-Transversal Clifford Gates for Quantum Codes

abstract:
  We generalize the concept of folding from surface codes to CSS codes by
considering certain dualities within them. In particular, this gives a general
method to implement logical operations in suitable LDPC quantum codes using
transversal gates and qubit permutations only.
  To demonstrate our approach, we specifically consider a [[30, 8, 3]]
hyperbolic quantum code called Bring's code. Further, we show that by
restricting the logical subspace of Bring's code to four qubits, we can obtain
the full Clifford group on that subspace.


title: A discrete-time dynamical system of mosquito population

abstract:
  In this paper we study discrete-time dynamical system generated by the
evolution operator of a wild mosquito population with a specific rates of birth
and of emergence from larvae to adults. The death rates of larvae and adults
are assumed to be constant. We find fixed points and under some conditions
parameters we show global attractiveness of a fixed point.


title:
On the Importance of Building High-quality Training Datasets for Neural
  Code Search

abstract:
  The performance of neural code search is significantly influenced by the
quality of the training data from which the neural models are derived. A large
corpus of high-quality query and code pairs is demanded to establish a precise
mapping from the natural language to the programming language. Due to the
limited availability, most widely-used code search datasets are established
with compromise, such as using code comments as a replacement of queries. Our
empirical study on a famous code search dataset reveals that over one-third of
its queries contain noises that make them deviate from natural user queries.
Models trained through noisy data are faced with severe performance degradation
when applied in real-world scenarios. To improve the dataset quality and make
the queries of its samples semantically identical to real user queries is
critical for the practical usability of neural code search. In this paper, we
propose a data cleaning framework consisting of two subsequent filters: a
rule-based syntactic filter and a model-based semantic filter. This is the
first framework that applies semantic query cleaning to code search datasets.
Experimentally, we evaluated the effectiveness of our framework on two
widely-used code search models and three manually-annotated code retrieval
benchmarks. Training the popular DeepCS model with the filtered dataset from
our framework improves its performance by 19.2% MRR and 21.3% Answer@1, on
average with the three validation benchmarks.


title:
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better
  Than Unsupervised?

abstract:
  Keyword extraction is the task of retrieving words that are essential to the
content of a given document. Researchers proposed various approaches to tackle
this problem. At the top-most level, approaches are divided into ones that
require training - supervised and ones that do not - unsupervised. In this
study, we are interested in settings, where for a language under investigation,
no training data is available. More specifically, we explore whether pretrained
multilingual language models can be employed for zero-shot cross-lingual
keyword extraction on low-resource languages with limited or no available
labeled training data and whether they outperform state-of-the-art unsupervised
keyword extractors. The comparison is conducted on six news article datasets
covering two high-resource languages, English and Russian, and four
low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find
that the pretrained models fine-tuned on a multilingual corpus covering
languages that do not appear in the test set (i.e. in a zero-shot setting),
consistently outscore unsupervised models in all six languages.


title:
Performance of quantum heat engines via adiabatic deformation of
  potential

abstract:
  We present a quantum Otto engine model consisting of two isochoric and two
adiabatic strokes, where the adiabatic expansion or compression is realized by
adiabatically changing the shape of the potential. Here we show that such an
adiabatic deformation may alter operation mode and enhance machine performance
by increasing output work and efficiency, even with the advantage of decreasing
work fluctuations. If the heat engine operates under maximal power by
optimizing the control parameter, the efficiency shows certain universal
behavior.


title: A local study of the fiber-full scheme

abstract:
  We study some of the local properties of the fiber-full scheme, which is a
fine moduli space that generalizes the Hilbert scheme by parametrizing closed
subschemes with prescribed cohomological data. As a consequence, we provide
sufficient conditions for cohomology to remain constant under Gr\"obner
degenerations. We also describe a tangent-obstruction theory for the fiber-full
scheme in analogy with the one for the Hilbert scheme.


title:
Recent progress for five-particle two-loop scattering amplitudes with an
  off-shell leg

abstract:
  We report on the advances in the calculation of the two-loop scattering
amplitudes for five-particle processes with one off-shell leg. Focusing on the
production of a Higgs boson in association with a bottom quark pair, we outline
how the newly developed technology allows us to overcome the computational
bottlenecks. In particular, we discuss the use of finite field arithmetic and
elucidate a convenient way to evaluate numerically the special functions
appearing in the amplitudes.


title:
Magnetic APFC modeling and the influence of magneto-structural
  interactions on grain shrinkage

abstract:
  We derive the amplitude expansion for a phase-field-crystal (APFC) model that
captures the basic physics of magneto-structural interactions. We demonstrate
the symmetry breaking due to magnetization and compute the magnetic anisotropy
for a BCC crystal. Using efficient numerics and appropriate computing power we
consider the shrinkage of a spherical grain within a matrix to demonstrate the
influence of different magnetizations. These simulations serve as a proof of
concept for the manipulation of defects and microstructure in ferromagnetic
materials.


title: A Galvin-Hajnal theorem for generalized cardinal characteristics

abstract:
  We prove that a variety of generalized cardinal characteristics, including
meeting numbers, the reaping number, and the dominating number, satisfy an
analogue of the Galvin-Hajnal theorem, and hence also of Silver's theorem, at
singular cardinals of uncountable cofinality.


title: Forming Point Patterns by a Probabilistic Cellular Automata Rule

abstract:
  The objective is to find a Cellular Automata rule that can form a 2D point
pattern with a maximum number of points (1-cells). Points are not allowed to
touch each other, they have to be separated by 0-cells, and every 0-cell can
find at least one point in its Moore-neighborhood. Probabilistic rules are
designed that can solve this task with asynchronous updating and cyclic
boundary condition. The task is considered as a tiling problem, where point
tiles are used to cover the space with overlaps. A point tile consists of a
center pixel (the kernel with value 1) and 8 surrounding pixels forming the
hull with value 0. The term pixel is used to distinguish the cells of a tile
from the cells of a cellular automaton. For each of the 9 tile pixels a
so-called template is defined by a shift of the point tile. In the rule
application, the 9 templates are tested at the actual cell position. If all
template pixels (except the central reference pixel) of a template match with
the corresponding neighbors of the actual cell under consideration, the cell's
state is adjusted to the reference pixel's value. Otherwise the cell is set to
the random value 0 or 1 with a certain probability. The hull pixels are allowed
to overlap. In order to evolve a maximum of points, the overlap between tiles
has to be maximized. To do that, the number of template hits is counted.
Depending on the hit-number, additional noise is injected with certain
probabilities. Thereby optimal patterns with the maximum number of points can
be evolved. The behavior and performance of the designed rules is evaluated for
different parameter settings.


title: The Impact of Batch Learning in Stochastic Linear Bandits

abstract:
  We consider a special case of bandit problems, named batched bandits, in
which an agent observes batches of responses over a certain time period. Unlike
previous work, we consider a practically relevant batch-centric scenario of
batch learning. That is to say, we provide a policy-agnostic regret analysis
and demonstrate upper and lower bounds for the regret of a candidate policy.
Our main theoretical results show that the impact of batch learning can be
measured proportional to the regret of online behavior. Primarily, we study two
settings of the problem: instance-independent and instance-dependent. While the
upper bound is the same for both settings, the worst-case lower bound is more
comprehensive in the former case and more accurate in the latter one. Also, we
provide a more robust result for the 2-armed bandit problem as an important
insight. Finally, we demonstrate the consistency of theoretical results by
conducting empirical experiments and reflect on the optimal batch size choice.


title: PFGE: Parsimonious Fast Geometric Ensembling of DNNs

abstract:
  Ensemble methods have been widely used to improve the performance of machine
learning methods in terms of generalization and uncertainty calibration, while
they struggle to use in deep learning systems, as training an ensemble of deep
neural networks (DNNs) and then deploying them for online prediction incur an
extremely higher computational overhead of model training and test-time
predictions. Recently, several advanced techniques, such as fast geometric
ensembling (FGE) and snapshot ensemble, have been proposed. These methods can
train the model ensembles in the same time as a single model, thus getting
around the hurdle of training time. However, their overhead of model recording
and test-time computations remains much higher than their single model based
counterparts. Here we propose a parsimonious FGE (PFGE) that employs a
lightweight ensemble of higher-performing DNNs generated by several
successively-performed procedures of stochastic weight averaging. Experimental
results across different advanced DNN architectures on different datasets,
namely CIFAR-$\{$10,100$\}$ and Imagenet, demonstrate its performance. Results
show that, compared with state-of-the-art methods, PFGE achieves better
generalization performance and satisfactory calibration capability, while the
overhead of model recording and test-time predictions is significantly reduced.
Our code is available at https://github.com/ZJLAB-AMMI/PFGE.


title: Contractibility of moduli spaces of RCD(0,2)-structures

abstract:
  This paper focuses on RCD(0,2)-spaces, which can be thought of as possibly
non-smooth metric measure spaces with non-negative Ricci curvature and
dimension less than 2. First, we establish a list of the compact topological
spaces admitting an RCD(0,2)-structure. Then, we describe the associated moduli
space of RCD(0,2)-structures for each of them. In particular, we show that all
these moduli spaces are contractible.


title:
Non-Obvious Manipulability for Single-Parameter Agents and Bilateral
  Trade

abstract:
  A recent line of work in mechanism design has focused on guaranteeing
incentive compatibility for agents without contingent reasoning skills:
obviously strategyproof mechanisms guarantee that it is "obvious" for these
imperfectly rational agents to behave honestly, whereas non-obviously
manipulable (NOM) mechanisms take a more optimistic view and ensure that these
agents will only misbehave when it is "obvious" for them to do so. Technically,
obviousness requires comparing certain extrema (defined over the actions of the
other agents) of an agent's utilities for honest behaviour against dishonest
behaviour.
  We present a technique for designing NOM mechanisms in settings where
monetary transfers are allowed based on cycle monotonicity, which allows us to
disentangle the specification of the mechanism's allocation from the payments.
By leveraging this framework, we completely characterise both allocation and
payment functions of NOM mechanisms for single-parameter agents. We then look
at the classical setting of bilateral trade and study whether and how much
subsidy is needed to guarantee NOM, efficiency, and individual rationality. We
prove a stark dichotomy; no finite subsidy suffices if agents look only at
best-case extremes, whereas no subsidy at all is required when agents focus on
worst-case extremes. We conclude the paper by characterising the NOM mechanisms
that require no subsidies whilst satisfying individual rationality.


title: Eternalism and the Problem of Hyperplanes

abstract:
  Eternalism is the view that the past, the present and the future exist
simpliciter. A typical argument in favor of this view leans on the relativity
of simultaneity. The 'equally real with' relation is assumed to be transitive
between spacelike separated events connected by hyperplanes of simultaneity.
This reasoning is in tension with the conventionality of simultaneity.
Conventionality indicates that, even within a specific frame, simultaneity is
based on the choice of the synchronization parameter. Hence the argument for
eternalism is compromised. This paper lays out alternative eternalist
strategies which do not hinge on hyperplanes. While we lack a rigorous proof
for eternalism, there are still cogent reasons to prefer eternalism over
presentism.


title:
Magnetic Field Evolution in the Crust of Neutron Stars: Crust Failure
  and Plastic Flow

abstract:
  The evolution of the magnetic field in neutron star crusts because of the
Hall effect has received significant attention over the last two decades, which
is strongly justified because of the dominance of this effect in highly
magnetised neutron stars. However, the applicability of the Hall effect is
based on the assumption that the crust does not fail and sustains its rigidity.
This assumption can be violated for substantially strong magnetic fields. If
this is the case, the evolution of the magnetic field is described by a
different set of equations, which include the effects of a non-rigid crust. In
this talk, after a brief review of the main characteristic of the Hall
evolution, I will discuss the impact a plastic flow of the crust has on the
magnetic field, studying axisymmetric models. Moreover, the way the crust fails
impacts the overall evolution, with major differences appearing if the failure
is local, intermediate or global. Quite remarkably, crustal failure and
plasticity do not annul the Hall effect, and under certain circumstances they
may even lead to a more dramatic evolution. I will discuss the impact of these
effects in the context of neutron star timing behaviour, with special focus on
timing noise, outbursts and glitches.


title:
Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided
  Multi-user MIMO Systems

abstract:
  Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology
for future wireless communications, enabling programmable radio propagation
environments. However, the passive reflecting feature of RISs induces notable
challenges on channel estimation, making coherent symbol detection a
challenging task. In this paper, we consider the uplink of RIS-aided multi-user
Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning
(ML) approach to jointly design the multi-antenna receiver and configure the
RIS reflection coefficients, which does not require explicit full knowledge of
the channel input-output relationship. Our approach devises a ML-based
receiver, while the configurations of the RIS reflection patterns affecting the
underlying propagation channel are treated as hyperparameters. Based on this
system design formulation, we propose a Bayesian ML framework for optimizing
the RIS hyperparameters, according to which the transmitted pilots are directly
used to jointly tune the RIS and the multi-antenna receiver. Our simulation
results demonstrate the capability of the proposed approach to provide reliable
communications in non-linear channel conditions corrupted by Gaussian noise.


title: The Daugavet property in spaces of vector-valued Lipschitz functions

abstract:
  We prove that if a metric space $M$ has the finite CEP then $\mathcal
F(M)\widehat{\otimes}_{\pi} X$ has the Daugavet property for every non-zero
Banach space $X$. This applies, for instance, if $M$ is a Banach space whose
dual is isometrically an $L_1(\mu)$ space. If $M$ has the CEP then $L(\mathcal
F(M),X)=\Lip(M,X)$ has the Daugavet property for every non-zero Banach space
$X$, showing that this is the case when $M$ is an injective Banach space or a
convex subset of a Hilbert space.


title: Magnetic orderings from spin-orbit coupled electrons on kagome lattice

abstract:
  We investigate magnetic orderings on kagome lattice numerically from the
tight-binding Hamiltonian of electrons, governed by the filling factor and
spin-orbit coupling (SOC) of electrons. We find that even a simple kagome
lattice model can host both ferromagnetic and noncollinear antiferromagnetic
orderings depending on the electron filling, reflecting gap structures in the
Dirac and flat bands characteristic to the kagome lattice. Kane--Mele- or
Rashba-type SOC tends to stabilize noncollinear orderings, such as magnetic
spirals and 120-degree antiferromagnetic orderings, due to the effective
Dzyaloshinskii--Moriya interaction from SOC. The obtained phase structure helps
qualitative understanding of magnetic orderings in various kagome-layered
materials with Weyl or Dirac electrons.


title:
Two is better than one: Regularized shrinkage of large minimum variance
  portfolio

abstract:
  In this paper we construct a shrinkage estimator of the global minimum
variance (GMV) portfolio by a combination of two techniques: Tikhonov
regularization and direct shrinkage of portfolio weights. More specifically, we
employ a double shrinkage approach, where the covariance matrix and portfolio
weights are shrunk simultaneously. The ridge parameter controls the stability
of the covariance matrix, while the portfolio shrinkage intensity shrinks the
regularized portfolio weights to a predefined target. Both parameters
simultaneously minimize with probability one the out-of-sample variance as the
number of assets $p$ and the sample size $n$ tend to infinity, while their
ratio $p/n$ tends to a constant $c>0$. This method can also be seen as the
optimal combination of the well-established linear shrinkage approach of Ledoit
and Wolf (2004, JMVA) and the shrinkage of the portfolio weights by Bodnar et
al. (2018, EJOR). No specific distribution is assumed for the asset returns
except of the assumption of finite $4+\varepsilon$ moments. The performance of
the double shrinkage estimator is investigated via extensive simulation and
empirical studies. The suggested method significantly outperforms its
predecessor (without regularization) and the nonlinear shrinkage approach in
terms of the out-of-sample variance, Sharpe ratio and other empirical measures
in the majority of scenarios. Moreover, it obeys the most stable portfolio
weights with uniformly smallest turnover.


title:
Kagom\'e network of chiral miniband-edge states in double-aligned
  graphene-hexagonal boron nitride structures

abstract:
  Twistronic heterostructures have recently emerged as a new class of quantum
electronic materials with properties determined by the twist angle between the
adjacent two-dimensional materials. Here we study moir\'e superlattice
minibands in graphene (G) encapsulated in hexagonal boron nitride (hBN) with an
almost perfect alignment with both the top and bottom hBN crystals. We show
that, for such an orientation of the unit cells of the hBN layers that locally
breaks inversion symmetry of the graphene lattice, the hBN/G/hBN structure
features a Kagom\'e network of topologically protected chiral states with
energies near the miniband edge, propagating along the lines separating the
areas with different miniband Chern numbers.