title: The BEHOMO project: $\Lambda$LTB $N$-body simulations

abstract:
  Our Universe may feature large-scale inhomogeneities and anisotropies which
cannot be explained by the standard model of cosmology, that is, the
homogeneous and isotropic FLRW metric, on which the $\Lambda$CDM model is
built, may not describe accurately observations. Currently, there is not a
satisfactory understanding of the evolution of the large-scale structure on an
inhomogeneous background. We start the cosmology beyond homogeneity and
isotropy (BEHOMO) project and study the inhomogeneous $\Lambda$LTB model with
the methods of numerical cosmology. Understanding the evolution of the
large-scale structure is a necessary step to constrain inhomogeneous models
with present and future observables and place the standard model on more solid
grounds. We perform Newtonian $N$-body simulations, whose accuracy in
describing the background evolution is checked against the general relativistic
solution. The large-scale structure of the corresponding $\Lambda$CDM
simulation is also validated. We obtain the first set of simulations of the
$\Lambda$LTB model ever produced. The data products consist of 11 snapshots
between redshift 0 and 3.7 for each of the 68 simulations that have been
performed, together with halo catalogs and lens planes relative to 21
snapshots, between redshift 0 and 4.2, for a total of approximately 180 TB of
data. We plan to study the growth of perturbations at the linear and nonlinear
level, gravitational lensing, cluster abundances and proprieties. Data can be
obtained upon request. Further information is available at
valerio-marra.github.io/BEHOMO-project .


title: A nonlinear bending theory for nematic LCE plates

abstract:
  In this paper, we study an elastic bilayer plate composed of a nematic liquid
crystal elastomer in the top layer and a nonlinearly elastic material in the
bottom layer. While the bottom layer is assumed to be stress-free in the flat
reference configuration, the top layer features an eigenstrain that depends on
the local liquid crystal orientation. As a consequence, the plate shows
non-flat deformations in equilibrium with a geometry that non-trivially depends
on the relative thickness and shape of the plate, material parameters, boundary
conditions for the deformation, and anchorings of the liquid crystal
orientation. We focus on thin plates in the bending regime and derive a
two-dimensional bending model that combines a nonlinear bending energy for the
deformation, with a surface Oseen-Frank energy for the director field that
describes the local orientation of the liquid crystal elastomer. Both energies
are nonlinearly coupled by means of a spontaneous curvature term that
effectively describes the nematic-elastic coupling. We rigorously derive this
model as a {\Gamma}-limit from three-dimensional, nonlinear elasticity. We also
devise a new numerical algorithm to compute stationary points of the
two-dimensional model. We conduct numerical experiments and present simulation
results that illustrate the practical properties of the proposed scheme as well
as the rich mechanical behavior of the system.


title: Evolutionary Neural Cascade Search across Supernetworks

abstract:
  To achieve excellent performance with modern neural networks, having the
right network architecture is important. Neural Architecture Search (NAS)
concerns the automatic discovery of task-specific network architectures. Modern
NAS approaches leverage supernetworks whose subnetworks encode candidate neural
network architectures. These subnetworks can be trained simultaneously,
removing the need to train each network from scratch, thereby increasing the
efficiency of NAS. A recent method called Neural Architecture Transfer (NAT)
further improves the efficiency of NAS for computer vision tasks by using a
multi-objective evolutionary algorithm to find high-quality subnetworks of a
supernetwork pretrained on ImageNet. Building upon NAT, we introduce ENCAS -
Evolutionary Neural Cascade Search. ENCAS can be used to search over multiple
pretrained supernetworks to achieve a trade-off front of cascades of different
neural network architectures, maximizing accuracy while minimizing FLOPs count.
We test ENCAS on common computer vision benchmarks (CIFAR-10, CIFAR-100,
ImageNet) and achieve Pareto dominance over previous state-of-the-art NAS
models up to 1.5 GFLOPs. Additionally, applying ENCAS to a pool of 518 publicly
available ImageNet classifiers leads to Pareto dominance in all computation
regimes and to increasing the maximum accuracy from 88.6% to 89.0%, accompanied
by an 18\% decrease in computation effort from 362 to 296 GFLOPs. Our code is
available at https://github.com/AwesomeLemon/ENCAS


title: Machine learning of Ising criticality with spin-shuffling

abstract:
  We investigate the performance of neural networks in identifying critical
behaviour in the 2D Ising model with next-to-nearest neighbour interactions. We
train DNN and CNN based classifiers on the Ising model configurations with
nearest neighbour interactions and test the ability of our models to identify
the critical (cross-over) region, as well as the phases in the presence of
next-to-nearest neighbour interactions. Our main objective is to investigate
whether our models can learn the notion of criticality and universality, in
contrast to merely identifying the phases by simply learning the value of the
order parameter, magnetization in this case. We design a simple adversarial
training process which forces our models to ignore learning about the order
parameter while training. We find that such models are able to identify
critical region with reasonable success, implying that it has learned to
identify the structure of spin-spin correlations near criticality.


title:
Mutual Contrastive Learning to Disentangle Whole Slide Image
  Representations for Glioma Grading

abstract:
  Whole slide images (WSI) provide valuable phenotypic information for
histological assessment and malignancy grading of tumors. The WSI-based
computational pathology promises to provide rapid diagnostic support and
facilitate digital health. The most commonly used WSI are derived from
formalin-fixed paraffin-embedded (FFPE) and frozen sections. Currently, the
majority of automatic tumor grading models are developed based on FFPE
sections, which could be affected by the artifacts introduced by tissue
processing. Here we propose a mutual contrastive learning scheme to integrate
FFPE and frozen sections and disentangle cross-modality representations for
glioma grading. We first design a mutual learning scheme to jointly optimize
the model training based on FFPE and frozen sections. Further, we develop a
multi-modality domain alignment mechanism to ensure semantic consistency in the
backbone model training. We finally design a sphere normalized
temperature-scaled cross-entropy loss (NT-Xent), which could promote
cross-modality representation disentangling of FFPE and frozen sections. Our
experiments show that the proposed scheme achieves better performance than the
model trained based on each single modality or mixed modalities. The sphere
NT-Xent loss outperforms other typical metrics loss functions.


title: The Virgo O3 run and the impact of the environment

abstract:
  Sources of geophysical noise (such as wind, sea waves and earthquakes) or of
anthropogenic noise impact ground-based gravitational-wave interferometric
detectors, causing transient sensitivity worsening and gaps in data taking.
During the one year-long third Observing Run (O3: from April 01, 2019 to March
27, 2020), the Virgo Collaboration collected a statistically significant
dataset, used in this article to study the response of the detector to a
variety of environmental conditions. We correlated environmental parameters to
global detector performance, such as observation range, duty cycle and control
losses. Where possible, we identified weaknesses in the detector that will be
used to elaborate strategies in order to improve Virgo robustness against
external disturbances for the next data taking period, O4, currently planned to
start at the end of 2022. The lessons learned could also provide useful
insights for the design of the next generation of ground-based interferometers.


title:
A Compilation Flow for the Generation of CNN Inference Accelerators on
  FPGAs

abstract:
  We present a compilation flow for the generation of CNN inference
accelerators on FPGAs. The flow translates a frozen model into OpenCL kernels
with the TVM compiler and uses the Intel OpenCL SDK to compile to an FPGA
bitstream. We improve the quality of the generated hardware with optimizations
applied to the base OpenCL kernels generated by TVM. These optimizations
increase parallelism, reduce memory access latency, increase concurrency and
save on-chip resources. We automate these optimizations in TVM and evaluate
them by generating accelerators for LeNet-5, MobileNetV1 and ResNet-34 on an
Intel Stratix~10SX. We show that the optimizations improve the performance of
the generated accelerators by up to 846X over the base accelerators. The
performance of the optimized accelerators is up to 4.57X better than TensorFlow
on CPU, 3.83X better than single-threaded TVM and is only 0.34X compared to TVM
with 56 threads. Our optimized kernels also outperform ones generated by a
similar approach (that also uses high-level synthesis) while providing more
functionality and flexibility. However, it underperforms an approach that
utilizes hand-optimized designs. Thus, we view our approach as useful in
pre-production environments that benefit from increased performance and fast
prototyping, realizing the benefits of FPGAs without hardware design expertise.


title: A mean-field analysis of a network behavioural-epidemic model

abstract:
  The spread of an epidemic disease and the population's collective behavioural
response are deeply intertwined, influencing each other's evolution. Such a
co-evolution typically has been overlooked in mathematical models, limiting
their real-world applicability. To address this gap, we propose and analyse a
behavioural-epidemic model, in which a susceptible-infected-susceptible
epidemic model and an evolutionary game-theoretic decision-making mechanism
concerning the use of self-protective measures are coupled. Through a
mean-field approach, we characterise the asymptotic behaviour of the system,
deriving conditions for global convergence to a disease-free equilibrium and
characterising the endemic equilibria of the system and their (local)
stability. Interestingly, for a certain range of the model parameters, we prove
global convergence to a limit cycle, characterised by periodic epidemic
outbreaks.


title: Effect of dilute impurities on short graphene Josephson junctions

abstract:
  We study the effect of a dilute homogeneous spatial distribution of
non-magnetic impurities on the equilibrium supercurrent sustained by a
ballistic graphene Josephson junction in the short junction limit. Within the
Dirac Bogoliubov-de Gennes approach and modeling impurities by the Anderson
model we derive the supercurrent and its equilibrium power spectrum. We find a
modification of the current-phase relation with a reduction of the skewness
induced by disorder, and a nonmonotonic temperature dependence of the critical
current. The potentialities of the supercurrent power spectrum for accurate
spectroscopy of the hybridized Andreev bound states-impurities spectrum are
highlighted. In the low temperature limit, the supercurrent zero frequency
thermal noise directly probes the spectral function at the Fermi energy.


title:
Interplay between nonlinear spectral shift and nonlinear damping of spin
  waves in ultrathin YIG waveguides

abstract:
  We use the phase-resolved imaging to directly study the nonlinear
modification of the wavelength of spin waves propagating in 100-nm thick,
in-plane magnetized YIG waveguides. We show that, by using moderate microwave
powers, one can realize spin waves with large amplitudes corresponding to
precession angles in excess of 10 degrees and nonlinear wavelength variation of
up to 18 percent in this system. We also find that, at large precession angles,
the propagation of spin waves is strongly affected by the onset of nonlinear
damping, which results in a strong spatial dependence of the wavelength. This
effect leads to a spatially dependent controllability of the wavelength by the
microwave power. Furthermore, it leads to the saturation of nonlinear spectral
shift's effects several micrometers away from the excitation point. These
findings are important for the development of nonlinear, integrated spin-wave
signal processing devices and can be used to optimize their characteristics.


title: Smooth points of the space of plane foliations with a center

abstract:
  We prove that logarithmic foliations formed by $d+1,\ d\geq 2$ lines in the
complex plane and with pairwise coprime and distinct residues around such lines
form smooth points of the space $M(d)$ of foliations with a center singularity.
This also proves that the space $\L(1^{d+1})$ of such logarithmic foliations
form an irreducible component of $M(d)$. For our purpose, we develop the
Picard-Lefschetz theory of polynomials with non-reduced fibers.


title:
Mini-batch stochastic three-operator splitting for distributed
  optimization

abstract:
  We consider a network of agents, each with its own private cost consisting of
a sum of two possibly nonsmooth convex functions, one of which is composed with
a linear operator. At every iteration each agent performs local calculations
and can only communicate with its neighbors. The challenging aspect of our
study is that the smooth part of the private cost function is given as an
expected value and agents only have access to this part of the problem
formulation via a heavy-tailed stochastic oracle. To tackle such sampling-based
optimization problems, we propose a stochastic extension of the triangular
pre-conditioned primal-dual algorithm. We demonstrate almost sure convergence
of the scheme and validate the performance of the method via numerical
experiments.


title:
EXOSMOOTH: Test of Innovative EXOskeleton Control for SMOOTH Assistance,
  With and Without Ankle Actuation

abstract:
  This work presents a description of the EXOSMOOTH project, oriented to the
benchmarking of lower limb exoskeletons performance. In the field of assisted
walking by powered lower limb exoskeletons, the EXOSMOOTH project proposes an
experiment that targets two scientific questions. The first question is related
to the effectiveness of a novel control strategy for smooth assistance. Current
assist strategies are based on controllers that switch the assistance level
based on the gait segmentation provided by a finite state machine. The proposed
strategy aims at managing phase transitions to provide a smoother assistance to
the user, thus increasing the device transparency and comfort for the user. The
second question is the role of the actuation at the ankle joint in assisted
walking. Many novel exoskeletons devised for industrial applications do not
feature an actuated ankle joint. In the EXOSMOOTH project, the ankle joint
actuation will be one experimental factor to have a direct assessment of the
role of an actuated joint in assisted walking. Preliminary results of 15
healthy subjects walking at different speeds while wearing a lower limb
exoskeleton supported the rationale behind this question: having an actuated
ankle joint could potentially reduce the torques applied by the user by a
maximum value of 85 Nm. The two aforementioned questions will be investigated
in a protocol that includes walking on a treadmill and on flat ground, with or
without slope, and with a load applied on the back. In addition, the
interaction forces measured at the exoskeleton harnesses will be used to assess
the comfort of the user and the effectiveness of the control strategy to
improve transparency.


title: On-shell Lagrangian of an ideal gas

abstract:
  In the context of general relativity, both energy and linear momentum
constraints lead to the same equation for the evolution of the speed of free
localized particles with fixed proper mass and structure in a homogeneous and
isotropic Friedmann-Lema\^itre-Robertson-Walker universe. In this paper we
extend this result by considering the dynamics of particles and fluids in the
context of theories of gravity nonminimally coupled to matter. We show that the
equation for the evolution of the linear momentum of the particles may be
obtained irrespective of any prior assumptions regarding the form of the
on-shell Lagrangian of the matter fields. We also find that consistency between
the evolution of the energy and linear momentum of the particles requires that
their volume-averaged on-shell Lagrangian and energy-momentum tensor trace
coincide ($\mathcal L_{\rm on-shell}=T$). We further demonstrate that the same
applies to an ideal gas composed of many such particles. This result implies
that the two most common assumptions in the literature for the on-shell
Lagrangian of a perfect fluid ($\mathcal L_{\rm on-shell}=\mathcal{P}$ and
$\mathcal L_{\rm on-shell}=-\rho$, where $\rho$ and $\mathcal{P}$ are the
proper density and pressure of the fluid, respectively) do not apply to an
ideal gas, except in the case of dust (in which case $T=-\rho$).


title: Residuality in the set of norm attaining operators between Banach spaces

abstract:
  We study the relationship between the residuality of the set of norm
attaining functionals on a Banach space and the residuality and the denseness
of the set of norm attaining operators between Banach spaces. Our first main
result says that if $C$ is a bounded subset of a Banach space $X$ which admit
an LUR renorming satisfying that, for every Banach space $Y$, the operators $T$
from $X$ to $Y$ for which the supremum of $\|Tx\|$ with $x\in C$ is attained
are dense, then the $G_\delta$ set of those functionals which strongly exposes
$C$ is dense in $X^*$. This extends previous results by J.\ Bourgain and K.-S.\
Lau. The particular case in which $C$ is the unit ball of $X$, in which we get
that the norm of $X^*$ is Fr\'{e}chet differentiable at a dense subset,
improves a result by J.\ Lindenstrauss and we even present an example showing
that Lindenstrauss' result was not optimal. In the reverse direction, we obtain
results for the density of the $G_\delta$ set of absolutely strongly exposing
operators from $X$ to $Y$ by requiring that the set of strongly exposing
functionals on $X$ is dense and conditions on $Y$ or $Y^*$ involving RNP and
discreteness on the set of strongly exposed points of $Y$ or $Y^*$. These
results include examples in which even the denseness of norm attaining
operators was unknown. We also show that the residuality of the set of norm
attaining operators implies the denseness of the set of absolutely strongly
exposing operators provided the domain space and the dual of the range space
are separable, extending a recent result for functionals. Finally, our results
find important applications, among which we point out that we solve a proposed
open problem showing that the unique predual of the space of Lipschitz
functions from the Euclidean unit circle fails to have Lindenstrauss property
A.


title:
Metal Wire Manipulation Planning for 3D Curving -- How a Low Payload
  Robot Can Use a Bending Machine to Bend Stiff Metal Wire

abstract:
  This paper presents a combined task and motion planner for a robot arm to
carry out 3D metal wire curving tasks by collaborating with a bending machine.
We assume a collaborative robot that is safe to work in a human environment but
has a weak payload to bend objects with large stiffness, and developed a
combined planner for the robot to use a bending machine. Our method converts a
3D curve to a bending set and generates the feasible bending sequence, machine
usage, robotic grasp poses, and pick-and-place arm motion considering the
combined task and motion level constraints. Compared with previous deformable
linear object shaping work that relied on forces provided by robotic arms, the
proposed method is suitable for the material with high stiffness. We evaluate
the system using different tasks. The results show that the proposed system is
flexible and robust to generate robotic motion to corporate with the designed
bending machine.


title:
A parametric approach for the identification of single-charged isotopes
  with AMS-02

abstract:
  Measurements of the isotopic composition of single-charged cosmic rays
provide important insights in the propagation processes. However, the isotopic
identification is challenging due to the one hundred times greater abundance of
protons when compared to deuterons, the only stable isotope of hydrogen. Taking
advantage of the precise measurements of the velocity and momentum in the Alpha
Magnetic Spectrometer (AMS-02), a particle physics detector operating aboard
the International Space Station since May 2011, we describe a parametric
template fit method, which takes into account systematic uncertainties such as
the fragmentation of particles inside AMS-02 and eventual differences between
data and simulation through the use of nuisance parameters. With this method we
are also able to assess the AMS-02 performance in terms of mass resolution,
showing that it is able to separate the isotopes of hydrogen up to 10 GeV/n.


title: Toward Understanding Deep Learning Framework Bugs

abstract:
  DL frameworks are the basis of constructing all DL programs and models, and
thus their bugs could lead to the unexpected behaviors of any DL program or
model relying on them. Such wide effect demonstrates the necessity and
importance of guaranteeing DL frameworks' quality. Understanding the
characteristics of DL framework bugs is a fundamental step for this quality
assurance task, facilitating to design effective bug detection and debugging
approaches. Hence, in this work we conduct the most large-scale study on 800
bugs from four popular and diverse DL frameworks (i.e., TensorFlow, PyTorch,
MXNet, and DL4J). By analyzing the root causes and symptoms of DL framework
bugs associated with 5 components decomposed from DL frameworks, as well as
measuring test coverage achieved by three state-of-the-art testing techniques
and developers' efforts on fixing those bugs, we obtain 14 major findings for
the comprehensive understanding of DL framework bugs and the current status of
existing DL framework testing and debugging practice, and then provide a series
of actionable guidelines for better DL framework bug detection and debugging.


title:
Data augmentation with mixtures of max-entropy transformations for
  filling-level classification

abstract:
  We address the problem of distribution shifts in test-time data with a
principled data augmentation scheme for the task of content-level
classification. In such a task, properties such as shape or transparency of
test-time containers (cup or drinking glass) may differ from those represented
in the training data. Dealing with such distribution shifts using standard
augmentation schemes is challenging and transforming the training images to
cover the properties of the test-time instances requires sophisticated image
manipulations. We therefore generate diverse augmentations using a family of
max-entropy transformations that create samples with new shapes, colors and
spectral characteristics. We show that such a principled augmentation scheme,
alone, can replace current approaches that use transfer learning or can be used
in combination with transfer learning to improve its performance.


title:
Tunable superluminal and subluminal reflected group delay in an air-Weyl
  semimetal film-Weyl semimetal substrate layered system

abstract:
  In this work, we theoretically investigate the superluminal and subluminal
reflected group delay from an air-Weyl semimetal film-Weyl semimetal substrate
layered structure by using 4*4 magneto-optical matrix. We show a tunable
transition between positive and negative group delays of reflection pulse in
such a layered structure controlled by the properties of the Weyl Semimetal
layer and reveal its mechanism to control the propagation properties of the
light pulse reflected from such structure. It is demonstrated that the
reflected group delays are tunable from positive to negative values and vice
versa, which are adjusted by tuning the incident angle, thickness of Weyl
semimetal film, tilt parameter, and Fermi energy. The control of the magnitude
and the sign of the reflected group delay of light propagation represent a key
point in slow and fast light technologies. Our results are helpful to control
the pulse propagations and are useful for design of Weyl semimetal-based delay
devices.


title:
Kibble-Zurek scaling due to environment temperature quench in the
  transverse field Ising model

abstract:
  The Kibble-Zurek mechanism applies to defect production due to non-adiabatic
passage to or through a quantum critical point (QCP). Here we study its variant
from ramping the temperature to a QCP. This is achieved by studying the
Lindblad equation for the transverse field Ising chain in the presence of
thermalizing bath, with couplings to environment obeying detailed balance. By
ramping the environment temperature with speed $1/\tau$ to the QCP, we find
that the defect density scales as $\tau^{-d\nu}$ with $d$ and $\nu$ the spatial
dimensionality and the critical exponent of the correlation length,
respectively. This reduced defect density, compared to conventional
Kibble-Zurek mechanism, stems from the enhanced relaxation due to bath-system
interaction. By temperature quenching to the gapped phases, the defect density
gets exponentially suppressed with $\tau$. The von-Neumann or the system-bath
entanglement entropy follows the same scaling.


title: Calculating Gromov-Hausdorff Distance by means of Borsuk Number

abstract:
  The purpose of this article is to demonstrate the connection between the
properties of the Gromov--Hausdorff distance and the Borsuk conjecture. The
Borsuk number of a given bounded metric space $X$ is the infimum of cardinal
numbers $n$ such that $X$ can be partitioned into $n$ smaller parts (in the
sense of diameter). An exact formula for the Gromov--Hausdorff distance between
bounded metric spaces is obtained under the assumption that the diameter and
cardinality of one space are less than the diameter and Borsuk number of
another, respectively. Using the results of Bacon's equivalence between the
Lusternik--Schnirelmann and Borsuk problems, several corollaries are obtained.


title:
Stage-Aware Feature Alignment Network for Real-Time Semantic
  Segmentation of Street Scenes

abstract:
  Over the past few years, deep convolutional neural network-based methods have
made great progress in semantic segmentation of street scenes. Some recent
methods align feature maps to alleviate the semantic gap between them and
achieve high segmentation accuracy. However, they usually adopt the feature
alignment modules with the same network configuration in the decoder and thus
ignore the different roles of stages of the decoder during feature aggregation,
leading to a complex decoder structure. Such a manner greatly affects the
inference speed. In this paper, we present a novel Stage-aware Feature
Alignment Network (SFANet) based on the encoder-decoder structure for real-time
semantic segmentation of street scenes. Specifically, a Stage-aware Feature
Alignment module (SFA) is proposed to align and aggregate two adjacent levels
of feature maps effectively. In the SFA, by taking into account the unique role
of each stage in the decoder, a novel stage-aware Feature Enhancement Block
(FEB) is designed to enhance spatial details and contextual information of
feature maps from the encoder. In this way, we are able to address the
misalignment problem with a very simple and efficient multi-branch decoder
structure. Moreover, an auxiliary training strategy is developed to explicitly
alleviate the multi-scale object problem without bringing additional
computational costs during the inference phase. Experimental results show that
the proposed SFANet exhibits a good balance between accuracy and speed for
real-time semantic segmentation of street scenes. In particular, based on
ResNet-18, SFANet respectively obtains 78.1% and 74.7% mean of class-wise
Intersection-over-Union (mIoU) at inference speeds of 37 FPS and 96 FPS on the
challenging Cityscapes and CamVid test datasets by using only a single GTX
1080Ti GPU.


title:
Bayesian Optimisation-Assisted Neural Network Training Technique for
  Radio Localisation

abstract:
  Radio signal-based (indoor) localisation technique is important for IoT
applications such as smart factory and warehouse. Through machine learning,
especially neural networks methods, more accurate mapping from signal features
to target positions can be achieved. However, different radio protocols, such
as WiFi, Bluetooth, etc., have different features in the transmitted signals
that can be exploited for localisation purposes. Also, neural networks methods
often rely on carefully configured models and extensive training processes to
obtain satisfactory performance in individual localisation scenarios. The above
poses a major challenge in the process of determining neural network model
structure, or hyperparameters, as well as the selection of training features
from the available data. This paper proposes a neural network model
hyperparameter tuning and training method based on Bayesian optimisation.
Adaptive selection of model hyperparameters and training features can be
realised with minimal need for manual model training design. With the proposed
technique, the training process is optimised in a more automatic and efficient
way, enhancing the applicability of neural networks in localisation.


title:
Reappraisal of Whitham's 1967 theory for wave-mean flow interaction in
  shallow water

abstract:
  The modulation equations for Stokes waves in shallow water coupled to wave
generated mean flow, derived in Whitham (1967), based on an averaged Lagrangian
are revisited. Firstly, it is shown that they can be recast into two coupled
classical shallow water equations, with modified gravity having the sign of the
Whitham index: sign($\omega_0'' \omega_2$). Secondly, it is shown that the
amplitude of the mean flow and amplitude of the wave are, in general,
independent. Thirdly, the implications of the coalescing characteristics, whose
unfolding is associated with the Benjamin-Feir instability, are studied.


title:
Precision measurement of a brown dwarf mass in a binary system in the
  microlensing event OGLE-2019-BLG-0033/MOA-2019-BLG-035

abstract:
  Context. Brown dwarfs are poorly understood transition objects between stars
and planets, with several competing mechanisms having been proposed for their
formation. Mass measurements are generally difficult for isolated objects but
also for brown dwarfs orbiting low-mass stars, which are often too faint for
spectroscopic follow-up. Aims. Microlensing provides an alternative tool for
the discovery and investigation of such faint systems. Here we present the
analysis of the microlensing event OGLE-2019-BLG-0033/MOA-2019-BLG-035, which
is due to a binary system composed of a brown dwarf orbiting a red dwarf.
Methods. Thanks to extensive ground observations and the availability of space
observations from Spitzer, it has been possible to obtain accurate estimates of
all microlensing parameters, including parallax, source radius and orbital
motion of the binary lens. Results. After accurate modeling, we find that the
lens is composed of a red dwarf with mass $M_1 = 0.149 \pm 0.010M_\odot$ and a
brown dwarf with mass $M_2 = 0.0463 \pm 0.0031M_\odot$, at a projected
separation of $a_\perp = 0.585$ au. The system has a peculiar velocity that is
typical of old metal-poor populations in the thick disk. Percent precision in
the mass measurement of brown dwarfs has been achieved only in a few
microlensing events up to now, but will likely become common with the Roman
space telescope.


title:
Asymptotic behavior of $L^p$ estimates for a class of multipliers with
  homogeneous unimodular symbols

abstract:
  We study Fourier multiplier operators associated with symbols $\xi\mapsto
\exp(i\lambda\phi(\xi/|\xi|))$, where $\lambda$ is a real number and $\phi$ is
a real-valued $C^\infty$ function on the standard unit sphere
$\mathbb{S}^{n-1}\subset\mathbb{R}^n$. For $1<p<\infty$ we investigate
asymptotic behavior of norms of these operators on $L^p(\mathbb{R}^n)$ as
$|\lambda|\to\infty$. We show that these norms are always $O((p^\ast-1)
|\lambda|^{n|1/p-1/2|})$, where $p^\ast$ is the larger number between $p$ and
its conjugate exponent. More substantially, we show that this bound is sharp in
all even-dimensional Euclidean spaces $\mathbb{R}^n$. In particular, this gives
a negative answer to a question posed by Maz'ya.
  Concrete operators that fall into the studied class are the multipliers
forming the two-dimensional Riesz group, given by the symbols $r\exp(i\varphi)
\mapsto \exp(i\lambda\cos\varphi)$. We show that their $L^p$ norms are
comparable to $(p^\ast-1) |\lambda|^{2|1/p-1/2|}$ for large $|\lambda|$,
solving affirmatively a problem suggested in the work of Dragi\v{c}evi\'{c},
Petermichl, and Volberg.


title:
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via
  Pre-trained StyleGAN

abstract:
  One-shot talking face generation aims at synthesizing a high-quality talking
face video from an arbitrary portrait image, driven by a video or an audio
segment. One challenging quality factor is the resolution of the output video:
higher resolution conveys more details. In this work, we investigate the latent
feature space of a pre-trained StyleGAN and discover some excellent spatial
transformation properties. Upon the observation, we explore the possibility of
using a pre-trained StyleGAN to break through the resolution limit of training
datasets. We propose a novel unified framework based on a pre-trained StyleGAN
that enables a set of powerful functionalities, i.e., high-resolution video
generation, disentangled control by driving video or audio, and flexible face
editing. Our framework elevates the resolution of the synthesized talking face
to 1024*1024 for the first time, even though the training dataset has a lower
resolution. We design a video-based motion generation module and an audio-based
one, which can be plugged into the framework either individually or jointly to
drive the video generation. The predicted motion is used to transform the
latent features of StyleGAN for visual animation. To compensate for the
transformation distortion, we propose a calibration network as well as a domain
loss to refine the features. Moreover, our framework allows two types of facial
editing, i.e., global editing via GAN inversion and intuitive editing based on
3D morphable models. Comprehensive experiments show superior video quality,
flexible controllability, and editability over state-of-the-art methods.


title:
Deep Multi-Branch Aggregation Network for Real-Time Semantic
  Segmentation in Street Scenes

abstract:
  Real-time semantic segmentation, which aims to achieve high segmentation
accuracy at real-time inference speed, has received substantial attention over
the past few years. However, many state-of-the-art real-time semantic
segmentation methods tend to sacrifice some spatial details or contextual
information for fast inference, thus leading to degradation in segmentation
quality. In this paper, we propose a novel Deep Multi-branch Aggregation
Network (called DMA-Net) based on the encoder-decoder structure to perform
real-time semantic segmentation in street scenes. Specifically, we first adopt
ResNet-18 as the encoder to efficiently generate various levels of feature maps
from different stages of convolutions. Then, we develop a Multi-branch
Aggregation Network (MAN) as the decoder to effectively aggregate different
levels of feature maps and capture the multi-scale information. In MAN, a
lattice enhanced residual block is designed to enhance feature representations
of the network by taking advantage of the lattice structure. Meanwhile, a
feature transformation block is introduced to explicitly transform the feature
map from the neighboring branch before feature aggregation. Moreover, a global
context block is used to exploit the global contextual information. These key
components are tightly combined and jointly optimized in a unified network.
Extensive experimental results on the challenging Cityscapes and CamVid
datasets demonstrate that our proposed DMA-Net respectively obtains 77.0% and
73.6% mean Intersection over Union (mIoU) at the inference speed of 46.7 FPS
and 119.8 FPS by only using a single NVIDIA GTX 1080Ti GPU. This shows that
DMA-Net provides a good tradeoff between segmentation quality and speed for
semantic segmentation in street scenes.


title: Gait Recognition with Mask-based Regularization

abstract:
  Most gait recognition methods exploit spatial-temporal representations from
static appearances and dynamic walking patterns. However, we observe that many
part-based methods neglect representations at boundaries. In addition, the
phenomenon of overfitting on training data is relatively common in gait
recognition, which is perhaps due to insufficient data and low-informative gait
silhouettes. Motivated by these observations, we propose a novel mask-based
regularization method named ReverseMask. By injecting perturbation on the
feature map, the proposed regularization method helps convolutional
architecture learn the discriminative representations and enhances
generalization. Also, we design an Inception-like ReverseMask Block, which has
three branches composed of a global branch, a feature dropping branch, and a
feature scaling branch. Precisely, the dropping branch can extract fine-grained
representations when partial activations are zero-outed. Meanwhile, the scaling
branch randomly scales the feature map, keeping structural information of
activations and preventing overfitting. The plug-and-play Inception-like
ReverseMask block is simple and effective to generalize networks, and it also
improves the performance of many state-of-the-art methods. Extensive
experiments demonstrate that the ReverseMask regularization help baseline
achieves higher accuracy and better generalization. Moreover, the baseline with
Inception-like Block significantly outperforms state-of-the-art methods on the
two most popular datasets, CASIA-B and OUMVLP. The source code will be
released.


title: Gaussian quasi-information criteria for ergodic L\'{e}vy driven SDE

abstract:
  We consider relative model comparison for the parametric coefficients of a
semiparametric ergodic L\'{e}vy driven model observed at high-frequency. Our
asymptotics is based on the fully explicit two-stage Gaussian quasi-likelihood
function (GQLF) of the Euler-approximation type. For selections of the scale
and drift coefficients, we propose explicit Gaussian quasi-AIC (GQAIC) and
Gaussian quasi-BIC (GQBIC) statistics through the stepwise inference procedure.
In particular, we show that the mixed-rates structure of the joint GQLF, which
does not emerge for the case of diffusions, gives rise to the non-standard
forms of the regularization terms in the selection of the scale coefficient,
quantitatively clarifying the relation between estimation precision and
sampling frequency. Numerical experiments are given to illustrate our
theoretical findings.


title:
When Will Arctic Sea Ice Disappear? Projections of Area, Extent,
  Thickness, and Volume

abstract:
  Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of
global climate change. We provide point, interval, and density forecasts for
four measures of Arctic sea ice: area, extent, thickness, and volume.
Importantly, we enforce the joint constraint that these measures must
simultaneously arrive at an ice-free Arctic. We apply this constrained joint
forecast procedure to models relating sea ice to cumulative carbon dioxide
emissions and models relating sea ice directly to time. The resulting
"carbon-trend" and "time-trend" projections are mutually consistent and predict
an effectively ice-free summer Arctic Ocean by the mid-2030s with an 80%
probability. Moreover, the carbon-trend projections show that global adoption
of a lower emissions path would likely delay the arrival of a seasonally
ice-free Arctic by only a few years.


title: Shape-invariant 3D Adversarial Point Clouds

abstract:
  Adversary and invisibility are two fundamental but conflict characters of
adversarial perturbations. Previous adversarial attacks on 3D point cloud
recognition have often been criticized for their noticeable point outliers,
since they just involve an "implicit constrain" like global distance loss in
the time-consuming optimization to limit the generated noise. While point cloud
is a highly structured data format, it is hard to constrain its perturbation
with a simple loss or metric properly. In this paper, we propose a novel
Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility
of point perturbations. This map reveals the vulnerability of point cloud
recognition models when encountering shape-invariant adversarial noises. These
noises are designed along the shape surface with an "explicit constrain"
instead of extra distance loss. Specifically, we first apply a reversible
coordinate transformation on each point of the point cloud input, to reduce one
degree of point freedom and limit its movement on the tangent plane. Then we
calculate the best attacking direction with the gradients of the transformed
point cloud obtained on the white-box model. Finally we assign each point with
a non-negative score to construct the sensitivity map, which benefits both
white-box adversarial invisibility and black-box query-efficiency extended in
our work. Extensive evaluations prove that our method can achieve the superior
performance on various point cloud recognition models, with its satisfying
adversarial imperceptibility and strong resistance to different point cloud
defense settings. Our code is available at: https://github.com/shikiw/SI-Adv.


title: Abandoning the Bayer-Filter to See in the Dark

abstract:
  Low-light image enhancement - a pervasive but challenging problem, plays a
central role in enhancing the visibility of an image captured in a poor
illumination environment. Due to the fact that not all photons can pass the
Bayer-Filter on the sensor of the color camera, in this work, we first present
a De-Bayer-Filter simulator based on deep neural networks to generate a
monochrome raw image from the colored raw image. Next, a fully convolutional
network is proposed to achieve the low-light image enhancement by fusing
colored raw data with synthesized monochrome raw data. Channel-wise attention
is also introduced to the fusion process to establish a complementary
interaction between features from colored and monochrome raw images. To train
the convolutional networks, we propose a dataset with monochrome and color raw
pairs named Mono-Colored Raw paired dataset (MCR) collected by using a
monochrome camera without Bayer-Filter and a color camera with Bayer-Filter.
The proposed pipeline take advantages of the fusion of the virtual monochrome
and the color raw images and our extensive experiments indicate that
significant improvement can be achieved by leveraging raw sensor data and
data-driven learning.


title:
Elliptic Weingarten surfaces: singularities, rotational examples and the
  halfspace theorem

abstract:
  We show by phase space analysis that there are exactly 17 possible
qualitative behaviors for a rotational surface in $\mathbb{R}^3$ that satisfies
an arbitrary elliptic Weingarten equation $W(\kappa_1,\kappa_2)=0$, and study
the singularities of such examples. As global applications of this
classification, we prove a sharp halfspace theorem for general elliptic
Weingarten equations of finite order, and a classification of peaked elliptic
Weingarten spheres with at most two singularities. In the case that $W$ is not
elliptic, we give a negative answer to a question by Yau regarding the
uniqueness of rotational ellipsoids.


title: Single-trajectory map equation

abstract:
  Community detection is an effective approach in studies of network data. The
map equation is a particularly popular framework and several extensions of this
method have been proposed. Herein, we revisit the original formulation of the
map equation and consider its "raw form," which we refer to as the
single-trajectory map equation. The map equation requires a network as the
input and considers the flow of the random walk on the network, whereas the
single-trajectory map equation requires a set of trajectories that walkers have
traveled along. We study how these formulations are related and show that the
single-trajectory map equation is a community detection method that provides
coarser-resolution partitions, implying that it prevents the issue of
excessively high resolution in the map equation.


title:
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken
  Conversations

abstract:
  Building robust and general dialogue models for spoken conversations is
challenging due to the gap in distributions of spoken and written data. This
paper presents our approach to build generalized models for the
Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations
Challenge of DSTC-10. In order to mitigate the discrepancies between spoken and
written text, we mainly employ extensive data augmentation strategies on
written data, including artificial error injection and round-trip text-speech
transformation. To train robust models for spoken conversations, we improve
pre-trained language models, and apply ensemble algorithms for each sub-task.
Typically, for the detection task, we fine-tune \roberta and ELECTRA, and run
an error-fixing ensemble algorithm. For the selection task, we adopt a
two-stage framework that consists of entity tracking and knowledge ranking, and
propose a multi-task learning method to learn multi-level semantic information
by domain classification and entity selection. For the generation task, we
adopt a cross-validation data process to improve pre-trained generative
language models, followed by a consensus decoding algorithm, which can add
arbitrary features like relative \rouge metric, and tune associated feature
weights toward \bleu directly. Our approach ranks third on the objective
evaluation and second on the final official human evaluation.


title: General approach to stochastic resetting

abstract:
  We address the effect of stochastic resetting on diffusion and subdiffusion
process. For diffusion we find that MSD relaxes to a constant only when the
distribution of reset times possess finite mean and variance. In this case, the
leading order contribution to the PDF of a Gaussian propagator under resetting
exhibits a cusp independent of the specific details of the reset time
distribution. For subdiffusion we derive the PDF in Laplace space for arbitrary
resetting protocol. Resetting at constant rate allows evaluation of the PDF in
terms of H-function. We analyze the steady state and derive the rate function
governing the relaxation behavior. For a subdiffusive process the steady state
could exist even if the distribution of reset times possesses only finite mean.


title:
A Talented Monoid View on Lie Bracket Algebras over Leavitt Path
  Algebras

abstract:
  In this article, we study properties as simplicity, solvability and
nilpotency for Lie bracket algebras arising from Leavitt path algebras, based
on the talented monoid of the underlying graph. We show that graded simplicity
and simplicity of the Leavitt path algebra can be connected via the Lie bracket
algebra. Moreover, we use the Gelfand-Kirillov dimension for the Leavitt path
algebra for a classification of nilpotency and solvability.


title: Mixed commutator lengths, wreath products and general ranks

abstract:
  In the present paper, for a pair $(G,N)$ of a group $G$ and its normal
subgroup $N$, we consider the mixed commutator length $\mathrm{cl}_{G,N}$ on
the mixed commutator subgroup $[G,N]$. We focus on the setting of wreath
products:$ (G,N)=(\mathbb{Z}\wr \Gamma, \bigoplus_{\Gamma}\mathbb{Z})$. Then we
determine mixed commutator lengths in terms of the general rank in the sense of
Malcev. In particular, for an abelian group $\Gamma$ that is not locally
cyclic, we show that the ordinary commutator length $\mathrm{cl}_G$ does not
coincide with $\mathrm{cl}_{G,N}$ on $[G,N]$. On the other hand, we prove that
if $\Gamma$ is locally cyclic, then for every pair $(G,N)$ such that $1\to N\to
G\to \Gamma \to 1$ is exact, $\mathrm{cl}_{G}$ and $\mathrm{cl}_{G,N}$ coincide
on $[G,N]$. We also study the case of permutational wreath products when the
group $\Gamma$ belongs to a certain class related to surface groups.


title: Graph Attention Transformer Network for Multi-Label Image Classification

abstract:
  Multi-label classification aims to recognize multiple objects or attributes
from images. However, it is challenging to learn from proper label graphs to
effectively characterize such inter-label correlations or dependencies. Current
methods often use the co-occurrence probability of labels based on the training
set as the adjacency matrix to model this correlation, which is greatly limited
by the dataset and affects the model's generalization ability. In this paper,
we propose a Graph Attention Transformer Network (GATN), a general framework
for multi-label image classification that can effectively mine complex
inter-label relationships. First, we use the cosine similarity based on the
label word embedding as the initial correlation matrix, which can represent
rich semantic information. Subsequently, we design the graph attention
transformer layer to transfer this adjacency matrix to adapt to the current
domain. Our extensive experiments have demonstrated that our proposed methods
can achieve state-of-the-art performance on three datasets.


title:
BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary
  Camera Rigs

abstract:
  Semantic segmentation in bird's eye view (BEV) is an important task for
autonomous driving. Though this task has attracted a large amount of research
efforts, it is still challenging to flexibly cope with arbitrary (single or
multiple) camera sensors equipped on the autonomous vehicle. In this paper, we
present BEVSegFormer, an effective transformer-based method for BEV semantic
segmentation from arbitrary camera rigs. Specifically, our method first encodes
image features from arbitrary cameras with a shared backbone. These image
features are then enhanced by a deformable transformer-based encoder. Moreover,
we introduce a BEV transformer decoder module to parse BEV semantic
segmentation results. An efficient multi-camera deformable attention unit is
designed to carry out the BEV-to-image view transformation. Finally, the
queries are reshaped according the layout of grids in the BEV, and upsampled to
produce the semantic segmentation result in a supervised manner. We evaluate
the proposed algorithm on the public nuScenes dataset and a self-collected
dataset. Experimental results show that our method achieves promising
performance on BEV semantic segmentation from arbitrary camera rigs. We also
demonstrate the effectiveness of each component via ablation study.


title: Robot Learning of Mobile Manipulation with Reachability Behavior Priors

abstract:
  Mobile Manipulation (MM) systems are ideal candidates for taking up the role
of a personal assistant in unstructured real-world environments. Among other
challenges, MM requires effective coordination of the robot's embodiments for
executing tasks that require both mobility and manipulation. Reinforcement
Learning (RL) holds the promise of endowing robots with adaptive behaviors, but
most methods require prohibitively large amounts of data for learning a useful
control policy. In this work, we study the integration of robotic reachability
priors in actor-critic RL methods for accelerating the learning of MM for
reaching and fetching tasks. Namely, we consider the problem of optimal base
placement and the subsequent decision of whether to activate the arm for
reaching a 6D target. For this, we devise a novel Hybrid RL method that handles
discrete and continuous actions jointly, resorting to the Gumbel-Softmax
reparameterization. Next, we train a reachability prior using data from the
operational robot workspace, inspired by classical methods. Subsequently, we
derive Boosted Hybrid RL (BHyRL), a novel algorithm for learning Q-functions by
modeling them as a sum of residual approximators. Every time a new task needs
to be learned, we can transfer our learned residuals and learn the component of
the Q-function that is task-specific, hence, maintaining the task structure
from prior behaviors. Moreover, we find that regularizing the target policy
with a prior policy yields more expressive behaviors. We evaluate our method in
simulation in reaching and fetching tasks of increasing difficulty, and we show
the superior performance of BHyRL against baseline methods. Finally, we
zero-transfer our learned 6D fetching policy with BHyRL to our MM robot
TIAGo++. For more details and code release, please refer to our project site:
irosalab.com/rlmmbp


title:
Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical
  Multi-Agent Pickup and Delivery framework

abstract:
  The multi-Agent Pickup and Delivery (MAPD) problem is crucial in the realm of
Intelligent Storage Systems (ISSs), where multiple robots are assigned with
time-varying, heterogeneous, and potentially uncertain tasks. When it comes to
Human-Swarm Hybrid System ((HS)$_2$), robots and human workers will accomplish
the MAPD tasks in collaboration. Herein, we propose a Human-Swarm Hybrid System
Pickup and Delivery ((HS)$_2$PD) framework, which is predominant in future
ISSs. A two-layer decision framework based on the prediction horizon window is
established in light of the unpredictability of human behavior and the dynamic
changes of tasks. The first layer is a two-level programming problem to solve
the problems of mode assignment and TA. The second layer is devoted to the
exact path of each agent via solving mixed-integer programming (MIP) problems.
An integrated algorithm for the (HS)$_2$PD problem is summarized. The
practicality and validity of the above algorithm are illustrated via a
numerical simulation example towards (HS)$_2$PD tasks.


title:
Risk sharing in equity-linked insurance products: Stackelberg
  equilibrium between an insurer and a reinsurer

abstract:
  Equity-linked insurance products often have capital guarantees. Common
investment strategies ensuring these guarantees are challenged nowadays by low
interest rates. Thus, we study an alternative strategy when an insurance
company shares financial risk with a reinsurance company. We model this
situation as a Stackelberg game. The reinsurer is the leader in the game and
maximizes its expected utility by selecting its optimal investment strategy and
a safety loading in the reinsurance contract it offers to the insurer. The
reinsurer can assess how the insurer will rationally react on each action of
the reinsurer. The insurance company is the follower and maximizes its expected
utility by choosing its investment strategy and the amount of reinsurance the
company purchases at the price offered by the reinsurer. In this game, we
derive the Stackelberg equilibrium for general utility functions. For power
utility functions, we calculate the equilibrium explicitly and find that the
reinsurer selects the largest reinsurance premium such that the insurer may
still buy the maximal amount of reinsurance. Since in the equilibrium the
insurer is indifferent in the amount of reinsurance, in practice, the reinsurer
should consider charging a smaller reinsurance premium than the equilibrium
one. Therefore, we propose several criteria for choosing such a discount rate
and investigate its wealth-equivalent impact on the utilities of both parties.


title: Isometric rigidity of Wasserstein tori and spheres

abstract:
  We prove isometric rigidity for $p$-Wasserstein spaces over
finite-dimensional tori and spheres for all $p$. We present a unified approach
to proving rigidity that relies on the robust method of recovering measures
from their Wasserstein potentials.


title:
$C^{2,\alpha}$ regularity of free boundaries in parabolic non-local
  obstacle problems

abstract:
  We study the regularity of the free boundary in the parabolic obstacle
problem for the fractional Laplacian $(-\Delta)^s$ (and more general
integro-differential operators) in the regime $s>\frac{1}{2}$. We prove that
once the free boundary is $C^1$ it is actually $C^{2,\alpha}$.
  To do so, we establish a boundary Harnack inequality in $C^1$ and
$C^{1,\alpha}$ (moving) domains, providing that the quotient of two solutions
of the linear equation, that vanish on the boundary, is as smooth as the
boundary.
  As a consequence of our results we also establish for the first time optimal
regularity of such solutions to nonlocal parabolic equations in moving domains.


title: Enhanced charm CP asymmetries from final state interactions

abstract:
  We show that the final state interaction (FSI) within a CPT invariant
two-channel framework is able to enhance the charge-parity (CP) violation
difference between $D^0\to\pi^-\pi^+$ and $D^0\to K^-K^+$ decays up to the
experimental value. The quantitative result relies only on: (i) the dominant
tree level diagram, (ii) the well known experimental values for the $D^
0\to\pi^-\pi^+$ and $D^ 0\to K^-K^+$ branching ratios, and (iii) the $\pi\pi
\to \pi\pi$ and $\pi\pi \to K K $ scattering data to extract the strong phase
difference and inelasticity. Based on well-grounded theoretical properties, we
found the sign and bulk value of the $\Delta A_{CP} $ recently observed by the
LHCb collaboration.


title:
Large Sample Covariance Matrices of Gaussian Observations with Uniform
  Correlation Decay

abstract:
  We derive the Marchenko-Pastur (MP) law for sample covariance matrices of the
form $V_n=\frac{1}{n}XX^T$, where $X$ is a $p\times n$ data matrix and $p/n\to
y\in(0,\infty)$ as $n,p \to \infty$. We assume the data in $X$ stems from a
correlated joint normal distribution. In particular, the correlation acts both
across rows and across columns of $X$, and we do not assume a specific
correlation structure, such as separable dependencies. Instead, we assume that
correlations converge uniformly to zero at a speed of $a_n/n$, where $a_n$ may
grow mildly to infinity. We employ the method of moments tightly: We identify
the exact condition on the growth of $a_n$ which will guarantee that the
moments of the empirical spectral distributions (ESDs) converge to the MP
moments. If the condition is not met, we can construct an ensemble for which
all but finitely many moments of the ESDs diverge. We also investigate the
operator norm of $V_n$ under a uniform correlation bound of $C/n^{\delta}$,
where $C,\delta>0$ are fixed, and observe a phase transition at $\delta=1$. In
particular, convergence of the operator norm to the maximum of the support of
the MP distribution can only be guaranteed if $\delta>1$. The analysis leads to
an example for which the MP law holds almost surely, but the operator norm
remains stochastic in the limit, and we provide its exact limiting
distribution.


title:
A Fast Hardware Pseudorandom Number Generator Based on the xoroshiro128
  LFSR

abstract:
  The Graphcore Intelligent Processing Unit (IPU) contains an original
pseudorandom number generator (PRNG) called xoroshiro128aox that is based on
the xoroshiro128 LFSR. It is designed to be cheap to implement in hardware and
provide a high quality of statistical randomness. In this paper, we present a
rigorous assessment of the quality of our new PRNG using standard statistical
test suites and compare the results with the fast contemporary PRNGs
xoroshiro128+, pcg64 and philox4x32. As a baseline for the analysis, we include
the widely-used Mersenne Twister PRNG. In our experiments, we show that
xoroshiro128aox mitigates the known weakness in the lower order bits of
xoroshiro128+ with our new AOX output function by passing the BigCrush and
PractRand test suites. We extend our testing with the Gjrand test suite and a
Hamming-Weight dependency test to highlight the linear weaknesses of both
xoroshiro128 PRNGs, but conclude that these linearities are hard to detect, and
the xoroshiro128aox PRNG otherwise provides an excellent trade off between
statistical quality and hardware implementation cost.


title: Vortex creation, annihilation and nonlinear dynamics in atomic vapors

abstract:
  We exploit new techniques for generating vortices and controlling their
interactions in an optical beam in a nonlinear atomic vapor. A precise control
of the vortex positions allows us to observe strong interactions leading to
vortex dynamics involving annihilations. With this improved controlled
nonlinear system, we get closer to the pure hydrodynamic regime than in
previous experiments while a wavefront sensor offers us a direct access to the
fluid's density and velocity. Finally, we developed a relative phase shift
method which mimics a time evolution process without changing non-linear
parameters. These observations are an important step toward the experimental
implementation of 2D turbulent state


title:
Classification of Middle Tropospheric Systems over the Arabian Sea and
  Western India

abstract:
  The formation of Middle Tropospheric Cyclones (MTCs) that are responsible for
a large portion of annual precipitation and extreme rainfall events over
western India is studied using an unsupervised machine learning algorithm and
cyclone tracking. Both approaches reveal four dominant weather patterns that
lead to the genesis of these systems; specifically, re-intensification of
westward moving synoptic systems from Bay of Bengal (Type 1, 51%), in-situ
formation with a coexisting cyclonic system over the Bay of Bengal that
precedes (Type 2a, 31%) or follows (Type 2b, 10%) genesis in the Arabian Sea,
and finally in-situ genesis within a northwestward propagating cyclonic anomaly
from the south Bay of Bengal (Type 2c, 8%). Thus, a large fraction of rainy
middle tropospheric synoptic systems in this region form in association with
cyclonic activity in the Bay of Bengal. The four variants identified also show
a marked dependence on large-scale environmental features with Type 1 and Type
2a formation primarily occurring in phases 4 and 5, and Type 2b and Type 2c in
phases 3 and 4 of the Boreal Summer Intraseasonal Oscillation. Further, while
in-situ formation with a Bay of Bengal cyclonic anomaly (Type 2a and 2b) mostly
occurs in June, downstream development is more likely in the core of the
monsoon season. Out of all categories, Type 2a is associated with the highest
rain rate (60 mm/day) and points towards the dynamical interaction between a
low pressure system over the Bay of Bengal and the development of MTCs over
western India and the northeast Arabian Sea. This classification,
identification of precursors, connection with cyclonic activity over the Bay of
Bengal and dependence on large-scale environment provides an avenue for better
understanding and prediction of rain-bearing MTCs over western India.


title: Counting with Adaptive Auxiliary Learning

abstract:
  This paper proposes an adaptive auxiliary task learning based approach for
object counting problems. Unlike existing auxiliary task learning based
methods, we develop an attention-enhanced adaptively shared backbone network to
enable both task-shared and task-tailored features learning in an end-to-end
manner. The network seamlessly combines standard Convolution Neural Network
(CNN) and Graph Convolution Network (GCN) for feature extraction and feature
reasoning among different domains of tasks. Our approach gains enriched
contextual information by iteratively and hierarchically fusing the features
across different task branches of the adaptive CNN backbone. The whole
framework pays special attention to the objects' spatial locations and varied
density levels, informed by object (or crowd) segmentation and density level
segmentation auxiliary tasks. In particular, thanks to the proposed dilated
contrastive density loss function, our network benefits from individual and
regional context supervision in terms of pixel-independent and pixel-dependent
feature learning mechanisms, along with strengthened robustness. Experiments on
seven challenging multi-domain datasets demonstrate that our method achieves
superior performance to the state-of-the-art auxiliary task learning based
counting methods. Our code is made publicly available at:
https://github.com/smallmax00/Counting_With_Adaptive_Auxiliary


title:
On the Grad-Rubin boundary value problem for the two-dimensional
  magneto-hydrostatic equations

abstract:
  In this work, we study the solvability of a boundary value problem for the
magneto-hydrostatic equations originally proposed by Grad and Rubin in the late
50's. The proof relies on a fixed point argument which combines the so-called
current transport method together with H\"older estimates for a class of
non-convolution singular integral operators. The same method allows to solve an
analogous boundary value problem for the steady incompressible Euler equations.


title: Adiabatic and irreversible classical discrete time crystals

abstract:
  We simulate the dynamics of paramagnetic colloidal particles that are placed
above a magnetic hexagonal pattern and exposed to an external field
periodically changing its direction along a control loop. The conformation of
three colloidal particles above one unit cell adiabatically responds with half
the frequency of the external field creating a time crystal at arbitrary low
frequency. The adiabatic time crystal occurs because of the non-trivial
topology of the stationary manifold. When coupling colloidal particles in
different unit cells, many body effects cause the formation of topologically
isolated time crystals and dynamical phase transitions between different
adiabatic reversible and non-adiabatic irreversible space-time-crystallographic
arrangements.


title:
Analyzing General-Purpose Deep-Learning Detection and Segmentation
  Models with Images from a Lidar as a Camera Sensor

abstract:
  Over the last decade, robotic perception algorithms have significantly
benefited from the rapid advances in deep learning (DL). Indeed, a significant
amount of the autonomy stack of different commercial and research platforms
relies on DL for situational awareness, especially vision sensors. This work
explores the potential of general-purpose DL perception algorithms,
specifically detection and segmentation neural networks, for processing
image-like outputs of advanced lidar sensors. Rather than processing the
three-dimensional point cloud data, this is, to the best of our knowledge, the
first work to focus on low-resolution images with 360\textdegree field of view
obtained with lidar sensors by encoding either depth, reflectivity, or
near-infrared light in the image pixels. We show that with adequate
preprocessing, general-purpose DL models can process these images, opening the
door to their usage in environmental conditions where vision sensors present
inherent limitations. We provide both a qualitative and quantitative analysis
of the performance of a variety of neural network architectures. We believe
that using DL models built for visual cameras offers significant advantages due
to the much wider availability and maturity compared to point cloud-based
perception.


title: Honest calibration assessment for binary outcome predictions

abstract:
  Probability predictions from binary regressions or machine learning methods
ought to be calibrated: If an event is predicted to occur with probability $x$,
it should materialize with approximately that frequency, which means that the
so-called calibration curve $p(x)$ should equal the bisector for all $x$ in the
unit interval. We propose honest calibration assessment based on novel
confidence bands for the calibration curve, which are valid only subject to the
natural assumption of isotonicity. Besides testing the classical
goodness-of-fit null hypothesis of perfect calibration, our bands facilitate
inverted goodness-of-fit tests whose rejection allows for the sought-after
conclusion of a sufficiently well specified model. We show that our bands have
a finite sample coverage guarantee, are narrower than existing approaches, and
adapt to the local smoothness and variance of the calibration curve $p$. In an
application to model predictions of an infant having a low birth weight, the
bounds give informative insights on model calibration.


title: Chirality in Affine Spaces and in Spacetime

abstract:
  An object is chiral when its symmetry group contains no indirect isometry. It
can be difficult to classify isometries as direct or indirect, except in the
Euclidean case. We classify them with the help of outer semidirect products of
isometry groups, in particular in the case of an affine space defined over a
finite-dimensional real quadratic space. We also classify as direct or indirect
the isometries of the real Lorentz-Minkowski spacetime and those of the
classical spacetime defined by the Newton-Cartan theory.


title: Lane Detection with Versatile AtrousFormer and Local Semantic Guidance

abstract:
  Lane detection is one of the core functions in autonomous driving and has
aroused widespread attention recently. The networks to segment lane instances,
especially with bad appearance, must be able to explore lane distribution
properties. Most existing methods tend to resort to CNN-based techniques. A few
have a try on incorporating the recent adorable, the seq2seq Transformer
\cite{transformer}. However, their innate drawbacks of weak global information
collection ability and exorbitant computation overhead prohibit a wide range of
the further applications. In this work, we propose Atrous Transformer
(AtrousFormer) to solve the problem. Its variant local AtrousFormer is
interleaved into feature extractor to enhance extraction. Their collecting
information first by rows and then by columns in a dedicated manner finally
equips our network with stronger information gleaning ability and better
computation efficiency. To further improve the performance, we also propose a
local semantic guided decoder to delineate the identities and shapes of lanes
more accurately, in which the predicted Gaussian map of the starting point of
each lane serves to guide the process. Extensive results on three challenging
benchmarks (CULane, TuSimple, and BDD100K) show that our network performs
favorably against the state of the arts.


title:
Finding nontrivial zeros of quadratic forms over rational function
  fields of characteristic 2

abstract:
  We propose polynomial-time algorithms for finding nontrivial zeros of
quadratic forms with four variables over rational function fields of
characteristic 2. We apply these results to find prescribed quadratic subfields
of quaternion division division algebras and zero divisors in $M_2(D)$, the
full matrix algebra over a division algebra, given by structure constants. We
also provide an implementation of our results in MAGMA which shows that the
algorithms are truly practical.


title:
Construction of Boundary Conditions for Hyperbolic Relaxation
  Approximations II: Jin-Xin Relaxation Model

abstract:
  This is our second work in the series about constructing boundary conditions
for hyperbolic relaxation approximations. The present work is concerned with
the one-dimensional linearized Jin-Xin relaxation model, a convenient
approximation of hyperbolic conservation laws, with non-characteristic
boundaries. Assume that proper boundary conditions are given for the
conservation laws. We construct boundary conditions for the relaxation model
with the expectation that the resultant initial-boundary-value problems are
approximations to the given conservation laws with the boundary conditions. The
constructed boundary conditions are highly non-unique. Their satisfaction of
the generalized Kreiss condition is analyzed. The compatibility with initial
data is studied. Furthermore, by resorting to a formal asymptotic expansion, we
prove the effectiveness of the approximations.


title: A Generic Solution of Fermion Sign Problem

abstract:
  The fermion sign problem, the biggest obstacle in quantum Monte Carlo
calculations, is completely solved in this paper. Here, we find a strategy, in
which the contribution from those negative-weighted paths is thoroughly
cancelled or replaced by some positive-weighted paths. The crucial point lies
on the Feynman path integral formula proposed in our group, which allows us to
deeply analyze the Boltzmann weight of each path. Through mathematical proof,
we demonstrate that physical quantities can be exactly calculated within a
specific wavefunction space, in which all paths have positive Boltzmann weight.
With this finding, a new Monte Carlo method is proposed, in which the fermion
sign problem is absent. As an example, the current method is applied to the
two-dimensional Hubbard model, and the results do manifest the correctness.


title: AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch

abstract:
  Current state-of-the-art employs approximate multipliers to address the
highly increased power demands of DNN accelerators. However, evaluating the
accuracy of approximate DNNs is cumbersome due to the lack of adequate support
for approximate arithmetic in DNN frameworks. We address this inefficiency by
presenting AdaPT, a fast emulation framework that extends PyTorch to support
approximate inference as well as approximation-aware retraining. AdaPT can be
seamlessly deployed and is compatible with the most DNNs. We evaluate the
framework on several DNN models and application fields including CNNs, LSTMs,
and GANs for a number of approximate multipliers with distinct bitwidth values.
The results show substantial error recovery from approximate re-training and
reduced inference time up to 53.9x with respect to the baseline approximate
implementation.


title:
Guidelines for cyber risk management in shipboard operational technology
  systems

abstract:
  Over the past few years, we have seen several cyber incidents being reported,
where some of the primary causes were the lack of proper security controls
onboard the ship and crew awareness on cybersecurity. In response to the
growing cyber threat landscape in the maritime sector, we have developed a set
of guidelines for maritime cyber risk management, focusing on four major
shipboard Operational Technology (OT) systems that are crucial for the
day-to-day operation of ships. These four OT systems are: Communication
Systems, Propulsion, Machinery and Power Control Systems, Navigation Systems
and Cargo Management Systems. The guidelines identify the cyber risks in each
of the OT systems and recommend the necessary actions that can be taken to
manage risks in each shipboard OT system. In this paper, we introduce the new
guidelines, which include cyber risks, mitigation measures, cyber risk
assessment, and a checklist to help shipowners and maritime authorities assess
and enhance cyber hygiene of their vessels. Our guidelines have been
disseminated by the Maritime and Port Authority of Singapore (MPA) to owners
and operators of the Singapore Registry of Ships for their reference and use.


title:
Exploiting of flux shadowing effect on In$_{x}$Ga$_{1-x}$As asymmetric
  shell growth for strain and bending engineering in GaAs -
  In$_{x}$Ga$_{1-x}$As core - shell NW arrays

abstract:
  Here we report on non-uniform shell growth of In(x)Ga(1-x)As onto GaAs
nanowire (NW) core by molecular beam epitaxy (MBE). The growth was realized on
pre-patterned silicon substrates with pitch size (p) ranging from 0.1 um to 10
um. Considering the preferable bending direction with respect to the MBE cells
as well as the layout of the substrate pattern, we are able to modify the
strain distribution along the NW growth axis and the subsequent bending
profile. For NW arrays with high number density, the obtained bending profile
of the NWs is composed of straight (barely-strained) and bent (strained)
segments with different lengths which depend on the pitch size. A precise
control of the bent and straight NW segment length provides a recipe to design
NW based devices with length selective strain distribution.


title:
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed
  Instance Segmentation

abstract:
  Contour-based instance segmentation methods have developed rapidly recently
but feature rough and hand-crafted front-end contour initialization, which
restricts the model performance, and an empirical and fixed backend
predicted-label vertex pairing, which contributes to the learning difficulty.
In this paper, we introduce a novel contour-based method, named E2EC, for
high-quality instance segmentation. Firstly, E2EC applies a novel learnable
contour initialization architecture instead of hand-crafted contour
initialization. This consists of a contour initialization module for
constructing more explicit learning goals and a global contour deformation
module for taking advantage of all of the vertices' features better. Secondly,
we propose a novel label sampling scheme, named multi-direction alignment, to
reduce the learning difficulty. Thirdly, to improve the quality of the boundary
details, we dynamically match the most appropriate predicted-ground truth
vertex pairs and propose the corresponding loss function named dynamic matching
loss. The experiments showed that E2EC can achieve a state-of-the-art
performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries
Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for
use in real-time applications, with an inference speed of 36 fps for 512*512
images on an NVIDIA A6000 GPU. Code will be released at
https://github.com/zhang-tao-whu/e2ec.


title: Obstacle Aware Sampling for Path Planning

abstract:
  Many path planning algorithms are based on sampling the state space. While
this approach is very simple, it can become costly when the obstacles are
unknown, since samples hitting these obstacles are wasted. The goal of this
paper is to efficiently identify obstacles in a map and remove them from the
sampling space. To this end, we propose a pre-processing algorithm for space
exploration that enables more efficient sampling. We show that it can boost the
performance of other space sampling methods and path planners.
  Our approach is based on the fact that a convex obstacle can be approximated
provably well by its minimum volume enclosing ellipsoid (MVEE), and a
non-convex obstacle may be partitioned into convex shapes. Our main
contribution is an algorithm that strategically finds a small sample, called
the \emph{active-coreset}, that adaptively samples the space via
membership-oracle such that the MVEE of the coreset approximates the MVEE of
the obstacle. Experimental results confirm the effectiveness of our approach
across multiple planners based on Rapidly-exploring random trees, showing
significant improvement in terms of time and path length.


title: Semantic Distillation Guided Salient Object Detection

abstract:
  Most existing CNN-based salient object detection methods can identify local
segmentation details like hair and animal fur, but often misinterpret the real
saliency due to the lack of global contextual information caused by the
subjectiveness of the SOD task and the locality of convolution layers.
Moreover, due to the unrealistically expensive labeling costs, the current
existing SOD datasets are insufficient to cover the real data distribution. The
limitation and bias of the training data add additional difficulty to fully
exploring the semantic association between object-to-object and
object-to-environment in a given image. In this paper, we propose a semantic
distillation guided SOD (SDG-SOD) method that produces accurate results by
fusing semantically distilled knowledge from generated image captioning into
the Vision-Transformer-based SOD framework. SDG-SOD can better uncover
inter-objects and object-to-environment saliency and cover the gap between the
subjective nature of SOD and its expensive labeling. Comprehensive experiments
on five benchmark datasets demonstrate that the SDG-SOD outperforms the
state-of-the-art approaches on four evaluation metrics, and largely improves
the model performance on DUTS, ECSSD, DUT, HKU-IS, and PASCAL-S datasets.


title: Evidence of trion-libron coupling in chirally adsorbed single molecules

abstract:
  Interplay between motion of nuclei and excited electrons in molecules plays a
key role both in biological and artificial nanomachines. Here we provide a
detailed analysis of coupling between quantized librational modes (librons) and
charged excited states (trions) on single phthalocyanine dyes adsorbed on a
surface. By means of tunnelling electron-induced electroluminescence, we
identify libronic progressions on a {\mu}eV energy range in spectra of chirally
adsorbed phthalocyanines, which are otherwise absent from spectra of
symmetrically adsorbed species. Experimentally measured libronic spectra match
very well the theoretically calculated libron eigenenergies and peak
intensities (Franck-Condon factors) and reveal an unexpected depopulation
channel for the zero libron of the excited state that can be effectively
controlled by tuning the size of the nanocavity. Our results showcase the
possibility of characterizing the dynamics of molecules by their low-energy
molecular modes using {\mu}eV-resolved tip-enhanced spectroscopy.


title: Pressure live loads and the variational derivation of linear elasticity

abstract:
  The rigorous derivation of linear elasticity from finite elasticity by means
of Gamma-convergence is a well-known result, which has been extended to
different models also beyond the elastic regime. However, in these results the
applied forces are usually assumed to be dead loads, that is, their density in
the reference configuration is independent of the actual deformation. In this
paper we begin a study of the variational derivation of linear elasticity in
the presence of live loads. We consider a pure traction problem for a
nonlinearly elastic body subject to a pressure live load and we compute its
linearization for small pressure by Gamma-convergence. We allow for a weakly
coercive elastic energy density and we prove strong convergence of minimizers.


title: A New Fault-Tolerant Synchronization Scheme with Anonymous Pulses

abstract:
  Robust pulse synchronization is fundamental in constructing reliable
synchronous applications in wired and wireless distributed systems. In wired
systems, self-stabilizing Byzantine pulse synchronization aims for
synchronizing fault-prone distributed components with arbitrary initial states
in bounded-delay message-passing systems. In wireless systems, fault-tolerant
synchronization of pulse-coupled oscillators is also built for a similar goal
but often works under specific system restrictions, such as low computation
power, low message complexity, and anonymous physical pulses whose senders
cannot be identified by the receivers. These restrictions often prevent us from
constructing high-reliable wireless synchronous applications. This paper tries
to break barriers between bounded-delay message-passing systems and classical
pulse-coupled oscillators by introducing a new fault-tolerant synchronization
scheme for the so-called anonymous bounded-delay pulsing systems in the
presence of indeterministic communication delays and inconsistent faults. For
low computation power and low message complexity, instead of involving in
consensus-based primitives, the proposed synchronization scheme integrates the
so-called discrete mean-fields, planar random walks, and some additional easy
operations in utilizing only sparsely generated anonymous pulses. For
fault-tolerance, we show that a square-root number of faulty oscillators can be
tolerated by utilizing planar random walks in anonymous pulse synchronization.
For self-stabilization, we show that the stabilization can be reached in an
expected constant number of observing windows in anonymous bounded-delay
pulsing systems with the pulsing-frequency restriction.


title: On Robust Inference in Time Series Regression

abstract:
  Least squares regression with heteroskedasticity and autocorrelation
consistent (HAC) standard errors has proved very useful in cross section
environments. However, several major difficulties, which are generally
overlooked, must be confronted when transferring the HAC estimation technology
to time series environments. First, most economic time series have strong
autocorrelation, which renders HAC regression parameter estimates highly
inefficient. Second, strong autocorrelation similarly renders HAC conditional
predictions highly inefficient. Finally, the structure of most popular HAC
estimators is ill-suited to capture the autoregressive autocorrelation
typically present in economic time series, which produces large size
distortions and reduced power in hypothesis testing, in all but the largest
sample sizes. We show that all three problems are largely avoided by the use of
a simple dynamic regression (DynReg), which is easily implemented and also
avoids possible problems concerning strong exogeneity. We demonstrate the
advantages of DynReg with detailed simulations covering a range of practical
issues.


title:
Holographic representation of the unified early and late universe via a
  viscous dark fluid

abstract:
  In this article we apply the holographic principle for describing in a
unifying way the early and the late-time universe, when the general equation of
state contains a bulk viscosity. We use the idea of a generalized cut-off
holographic dark energy introduced by Nojiri and Odintsov (2006,2017), and
study the evolution of the universe when the equation of state has two
power-law asymptotes. Analytical expressions for the infrared cut-offs in terms
of the particle horizon are obtained. The energy conservation laws as derived
from the holographic point of view, are given for various forms of the
thermodynamic parameters and for various forms of the bulk viscosity. As a
result, we obtain a unifying description of the early and the late-time
universe in the presence of a viscous holographic fluid.


title:
A concise formula for the Hessian determinant of a function
  parameterising a quadratic hypersurface

abstract:
  We will give a concise formula for the Hessian determinant of a smooth
function $y:\mathbb R^n\supseteq\Omega\to\mathbb R$ such that its graph is
contained in a quadratic hypersurface. The proof will make heavy use of matrix
algebra.


title:
Proceedings to the 24rd International Workshop "What Comes Beyond the
  Standard Models", July 5 -- July 11, 2021, Bled, Slovenia, [Virtual Workshop
  -- July 5.--11. 2021]

abstract:
  Contributions of the participants of the 24th International Workshop "What
Comes Beyond the Standard Models", July 5 to July 11, 2021, Bled, Slovenia,
presented either with the arxiv number of each contribution or with the webside
of the Proceedings published by DMFA, ZALOlZNISTVO LJUBLJANA, DECEMBER 2021.


title:
The Effects of Transverse Inclination on Aeroelastic Cantilever Prisms:
  Phenomenology, Unsteady Force, and the Base Intensification Phenomenon

abstract:
  The transverse inclination is a probable scenario when inclined structures
experience an inflow of altered attack angles. This work investigates the
effects of transverse inclination on an aeroelastic prism through
forced-vibration wind tunnel experiments. The aerodynamic characteristics are
tri-parametrically evaluated under different wind speeds, inclination angles,
and oscillation amplitudes. Results show that transverse inclination
fundamentally changes the wake phenomenology by impinging the fix-end horseshoe
vortex and breaking the separation symmetry. The aftermath is a bi-polar,
once-for-all change in the aerodynamics near the prism base. The suppression of
the horseshoe vortex unleashes the Karman vortex, which significantly increases
the unsteady crosswind force. After the initial morphology switch, the
aerodynamics become independent of inclination angle and oscillation amplitude
and depend solely on wind speed. The structure's upper portion does not feel
the effect, so this phenomenon is called Base Intensification. The phenomenon
only projects notable impacts on the low-speed and VIV regime and is
indifferent in the high-speed, quasi-steady Galloping regime. In practice, Base
Intensification will disrupt the pedestrian-level wind environment from the
unleashed Bernard-Karman vortex shedding, making it erratic and gusty.
Moreover, it increases the aerodynamic load at a structure base by as much as
4.3 times. Since fix-end stiffness prevents elastic dissipation, the load
translates to massive stress, making detection trickier and failures, if they
are to occur, more sudden, extreme, and without any warnings. The 4.3-time
amplification also surpasses the safety factor of many standard designs, so
transverse inclination deserves engineering attention.


title:
Urban Vehicle Mobility Characteristic Mining and Trip Generation Based
  on Knowledge Graph

abstract:
  The operation of urban transportation produces massive traffic data, which
contains abundant information and is of great significance for the study of
intelligent transportation systems. In particular, with the improvement of
perception technology, it has become possible to obtain trip data in
individual-level of vehicles. It has finer granularity and greater research
potential, but at the same time requires higher requirements in terms of data
organization and analysis. More importantly it cannot be made public due to
privacy issues. To handle individual-level urban vehicle trip big data better,
we introduce the knowledge graph for the study. For organization of individual
level trip data, we designed and constructed an individual-level trip knowledge
graph which greatly improves the efficiency of obtaining data. Then we used the
trip knowledge graph as the data engine and designed logical rules to mine the
trip characteristics of vehicles by combining the transportation domain
knowledge. Finally, we further propose an individual-level trip synthesis
method based on knowledge graph generation to address the privacy issue of
individual-level traffic data. The experiment shows that the final generated
trip data are similar to the historical one in mobility patterns and vehicle
associations, and have high spatial continuity.


title:
The infinity norm bounds and characteristic polynomial for high order RK
  matrices

abstract:
  This paper shows that $t_m \leq \|\mathbf{A}\|_\infty \leq \sqrt{t_m}$ holds,
when $\mathbf{A} \in \mathbb{R}^{m \times m}$ is a Runge-Kutta matrix which
nodes originating from the Gaussian quadrature that integrates polynomials of
degree $2m-2$ exactly. It can be shown that this is also true for the
Gauss-Lobatto quadrature. Additionally, the characteristic polynomial of
$\mathbf{A}$, when the matrix is nonsingular, is $p_A(\lambda) = m!t^m +
(m-1)!a_{m-1}t^{m-1} + \dots + a_0$, where the coefficients $a_i$ are the
coefficients of the polynomial of nodes $\omega(t) = (t - t_1) \dots (t - t_m)
= t^m + a_{m-1}t^{m-1} + \dots + a_0$.


title: General Methods of Quantum Field Theory

abstract:
  We employ the polar form to investigate all possible classes spinor fields
from a perspective that does not involve the use of plane waves. Results can be
taken as the starting point for a procedure of field quantization that can be
developed in the most general of circumstances.


title:
The role of alcohol outlet visits derived from mobile phone location
  data in enhancing domestic violence prediction at the neighborhood level

abstract:
  Domestic violence (DV) is a serious public health issue, with 1 in 3 women
and 1 in 4 men experiencing some form of partner-related violence every year.
Existing research has shown a strong association between alcohol use and DV at
the individual level. Accordingly, alcohol use could also be a predictor for DV
at the neighborhood level, helping identify the neighborhoods where DV is more
likely to happen. However, it is difficult and costly to collect data that can
represent neighborhood-level alcohol use especially for a large geographic
area. In this study, we propose to derive information about the alcohol outlet
visits of the residents of different neighborhoods from anonymized mobile phone
location data, and investigate whether the derived visits can help better
predict DV at the neighborhood level. We use mobile phone data from the company
SafeGraph, which is freely available to researchers and which contains
information about how people visit various points-of-interest including alcohol
outlets. In such data, a visit to an alcohol outlet is identified based on the
GPS point location of the mobile phone and the building footprint (a polygon)
of the alcohol outlet. We present our method for deriving neighborhood-level
alcohol outlet visits, and experiment with four different statistical and
machine learning models to investigate the role of the derived visits in
enhancing DV prediction based on an empirical dataset about DV in Chicago. Our
results reveal the effectiveness of the derived alcohol outlets visits in
helping identify neighborhoods that are more likely to suffer from DV, and can
inform policies related to DV intervention and alcohol outlet licensing.


title: Associating eHealth Policies and National Data Privacy Regulations

abstract:
  As electronic data becomes the lifeline of modern society, privacy concerns
increase. These concerns are reflected by the European Union's enactment of the
General Data Protection Regulation (GDPR), one of the most comprehensive and
robust privacy regulations globally. This project aims to evaluate and
highlight associations between eHealth systems' policies and personal data
privacy regulations. Using bias-corrected Cramer's V and Thiel's U tests, we
found weak and zero associations between e-health systems' rules and
protections for data privacy. A simple decision tree model is trained, which
validates the association scores obtained


title: Foundations for Grassroots Democratic Metaverse

abstract:
  While the physical lives of many of us are in democracies (one person, one
vote - e.g., the EU and the US), our digital lives are mostly in autocracies
(one person, all votes - e.g., Facebook). Cryptocurrencies promise liberation
but stop short, at plutocracy (one coin, one vote). What would it take for us
to live our digital lives in a digital democracy? This paper offers a vision, a
theoretical framework, and an architecture for a grassroots network of
autonomous, people-owned, people-operated, and people-governed digital
communities, namely a grassroots democratic metaverse. It also charts a roadmap
towards realizing it, and identifies unexplored territory for further research.


abstract:
  This is a write-up of two lectures delivered at COST CA18108 First Training
School. They cover the motivations and some basic technical results in the
field of Doubly Special Relativity and Relative Locality. The energy-dependent
speed of light is a recurring theme here. The soccer ball problem is also
briefly described. The video recording of these and other lectures can be found
at https://www.youtube.com/channel/UChRfzgjTkO9sOkwq358kHZw/videos.


title: Generalizations of Graded $S$-Primary Ideals

abstract:
  The goal of this article is to present the graded weakly $S$-primary ideals
and $g$-weakly $S$-primary ideals which are extensions of graded weakly primary
ideals. Let $R$ be a commutative graded ring, $S\subseteq h(R)$ and $P$ be a
graded ideal of $R$. We state $P$ is a graded weakly $S$-primary ideal of $R$
if there exists $s\in S$ such that for all $x,y \in h(R)$, if $0\neq xy\in P$,
then $sx\in P$ or $sy\in Grad(P)$ (the graded radical of $P$). Several
properties and characteristics of graded weakly $S$-primary ideals as well as
graded $g$-weakly $S$-primary ideals are investigated.


title:
Exploration of Various Deep Learning Models for Increased Accuracy in
  Automatic Polyp Detection

abstract:
  This paper is created to explore deep learning models and algorithms that
results in highest accuracy in detecting polyp on colonoscopy images. Previous
studies implemented deep learning using convolution neural network (CNN)
algorithm in detecting polyp and non-polyp. Other studies used dropout, and
data augmentation algorithm but mostly not checking the overfitting, thus,
include more than four-layer modelss. Rulei Yu et.al from the Institute of
Software, Chinese Academy of Sciences said that transfer learning is better
talking about performance or improving the previous used algorithm. Most
especially in applying the transfer learning in feature extraction. Series of
experiments were conducted with only a minimum of 4 CNN layers applying
previous used models and identified the model that produce the highest
percentage accuracy of 98% among the other models that apply transfer learning.
Further studies could use different optimizer to a different CNN modelsto
increase accuracy.


title:
Status of deep subbarrier $\mathbf{{}^{12}{\rm C}+{}^{12}{\rm C}}$
  fusion and advancing the Trojan horse method

abstract:
  In this paper, I will update the current status of the carbon-carbon fusion
research taking into account that after the latest analysis [Beck {\it et al.}
Eur. Phys. J. A {\bf 56}, 97 (2020), Letter to the Editor] new important
experimental and theoretical results had been published and will discuss how to
advance new THM measurements to extract the low-energy astrophysical
$S$-factors.