title:
Viscous infuences on jetting process from an impacted concave free
  surface

abstract:
  A focused jet is generated from a concave free surface through an impact of a
tube. Experimental and numerical studies were conducted to explore the viscous
influences. The results show that a strong vorticity sheet is generated along
the free surface just after impact, and the liquid bulk is initialized
($t\le$$10^{-4}$s). After the impact, the liquid surface evolves freely and a
jet is ejected ($t\gg$$10^{-4}$s). Pronounced tangential flow is found, and the
material in the jet mainly originates from the viscous penetrating layer below
the free surface. According to the above observations, a vorticity mechanism of
the free surface evolution is proposed. Further, by combining the viscous and
interfacial tension effects, a scaling relation estimating the jetting velocity
is derived, which agrees with the numerical and experimental results well. The
work will be valuable for viscous droplets preparation.


title: Momentum, angular momentum, and spin of waves in plasma

abstract:
  We examine the momentum and angular momentum (including spin) properties of
linear waves, both longitudinal (Langmuir) and transverse (electromagnetic), in
an isotropic nonrelativistic collisionless electron plasma. We focus on
conserved quantities associated with the translational and rotational
invariance of the wave fields with respect to the homogeneous medium; these are
sometimes called pseudo-momenta. There are two types of the momentum and
angular momentum densities: (i) the kinetic ones associated with the energy
flux density and the symmetrized (Belinfante) energy-momentum tensor and (ii)
the canonical ones associated with the conserved Noether currents and canonical
energy-momentum tensor. We find that the canonical momentum and spin densities
of Langmuir waves are similar to those of sound waves in fluids or gases; they
are expressed via the electron velocity field. In turn, the momentum and spin
densities of electromagnetic waves can be written either in the forms known for
free-space electromagnetic fields, involving only the electric field, or in the
dual-symmetric forms involving both electric and magnetic fields, as well as
the effective permittivity of plasma. We derive these properties both within
the phenomenological macroscopic approach and microscopic Lagrangian field
theory for the coupled electromagnetic fields and electrons. Finally, we
explore implications of the canonical momentum and spin densities in transport
and electrodynamic phenomena: the Stokes drift, the wave-induced magnetization
(inverse Faraday effect), etc.


title: Theory of Network Wave (On a Primary Path)

abstract:
  Aiming at the disorder problem (i.e. uncertainty problem) of the utilization
of network resources commonly existing in multi-hop transmission networks, the
paper proposes the idea and the corresponding supporting theory, i.e. theory of
network wave, by constructing volatility information transmission mechanism
between the sending nodes of a primary path, so as to improve the orderliness
of the utilization of network resources. It is proved that the maximum
asymptotic throughput of a primary path depends on its intrinsic period, which
in itself is equal to the intrinsic interference intensity of a primary path.
Based on the proposed theory of network wave, an algorithm for the transmission
of information blocks based on the intrinsic period of a primary path is
proposed, which can maximize the asymptotic throughput of the primary path. The
research results of the paper lay an ideological and theoretical foundation for
further exploring more general methods that can improve the orderly utilization
of network resources.


title:
Conditional Synthetic Data Generation for Personal Thermal Comfort
  Models

abstract:
  Personal thermal comfort models aim to predict an individual's thermal
comfort response, instead of the average response of a large group. Recently,
machine learning algorithms have proven to be having enormous potential as a
candidate for personal thermal comfort models. But, often within the normal
settings of a building, personal thermal comfort data obtained via experiments
are heavily class-imbalanced. There are a disproportionately high number of
data samples for the "Prefer No Change" class, as compared with the "Prefer
Warmer" and "Prefer Cooler" classes. Machine learning algorithms trained on
such class-imbalanced data perform sub-optimally when deployed in the real
world. To develop robust machine learning-based applications using the above
class-imbalanced data, as well as for privacy-preserving data sharing, we
propose to implement a state-of-the-art conditional synthetic data generator to
generate synthetic data corresponding to the low-frequency classes. Via
experiments, we show that the synthetic data generated has a distribution that
mimics the real data distribution. The proposed method can be extended for use
by other smart building datasets/use-cases.


title:
A Closer Look at Debiased Temporal Sentence Grounding in Videos:
  Dataset, Metric, and Approach

abstract:
  Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural
language sentence in an untrimmed video, has drawn widespread attention over
the past few years. However, recent studies have found that current benchmark
datasets may have obvious moment annotation biases, enabling several simple
baselines even without training to achieve SOTA performance. In this paper, we
take a closer look at existing evaluation protocols, and find both the
prevailing dataset and evaluation metrics are the devils that lead to
untrustworthy benchmarking. Therefore, we propose to re-organize the two
widely-used datasets, making the ground-truth moment distributions different in
the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile,
we introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic
recall scores to alleviate the inflating evaluation caused by biased datasets.
New benchmarking results indicate that our proposed evaluation protocols can
better monitor the research progress. Furthermore, we propose a novel
causality-based Multi-branch Deconfounding Debiasing (MDD) framework for
unbiased moment prediction. Specifically, we design a multi-branch deconfounder
to eliminate the effects caused by multiple confounders with causal
intervention. In order to help the model better align the semantics between
sentence queries and video moments, we enhance the representations during
feature encoding. Specifically, for textual information, the query is parsed
into several verb-centered phrases to obtain a more fine-grained textual
feature. For visual information, the positional information has been decomposed
from moment features to enhance representations of moments with diverse
locations. Extensive experiments demonstrate that our proposed approach can
achieve competitive results among existing SOTA approaches and outperform the
base model with great gains.


title:
Experimentally ruling out joint reality based on operational
  completeness

abstract:
  Whether the observables of a physical system admit real values is of
fundamental importance to a deep understanding of nature. In this work, we
report a device-independent experiment to confirm that the joint reality of two
observables on a single two-level system is incompatible with the assumption of
operational completeness, which is strictly weaker than that of preparation
noncontextuality. We implement two observables on a trapped $^{171}{\rm
Yb}^{+}$ ion to test this incompatibility via violation of certain inequalities
derived from both linear and nonlinear criteria. Moreover, by introducing a
highly controllable dephasing channel, we show that the nonlinear criterion is
more robust against noise. Our results push the fundamental limit to delineate
the quantum-classical boundary and pave the way for exploring relevant problems
in other scenarios.


title: Data-driven Control of Unknown Linear Systems via Quantized Feedback

abstract:
  Control using quantized feedback is a fundamental approach to system
synthesis with limited communication capacity. In this paper, we address the
stabilization problem for unknown linear systems with logarithmically quantized
feedback, via a direct data-driven control method. By leveraging a recently
developed matrix S-lemma, we prove a sufficient and necessary condition for the
existence of a common stabilizing controller for all possible dynamics
consistent with data, in the form of a linear matrix inequality. Moreover, we
formulate semi-definite programming to solve the coarsest quantization density.
By establishing its connections to unstable eigenvalues of the state matrix, we
further prove a necessary rank condition on the data for quantized feedback
stabilization. Finally, we validate our theoretical results by numerical
examples.


title: Transient Uniform Electron Gases

abstract:
  The uniform electron gas (UEG), a hypothetical system with finite homogenous
electron density composed by an infinite number of electrons in a box of
infinite volume, is the practical pillar of density-functional theory (DFT) and
the foundation of the most acclaimed approximation of DFT, the local-density
approximation (LDA). In the last thirty years, the knowledge of analytical
parametrizations of the infinite UEG (IUEG) exchange-correlation energy has
allowed researchers to perform a countless number of approximate electronic
structure calculations for atoms, molecules, and solids. Recently, it has been
shown that the traditional concept of the IUEG is not the unique example of
UEGs, and systems, in their lowest-energy state, consisting of electrons that
are confined to the surface of a sphere provide a new family of UEGs with more
customizable properties. Here, we show that, some of the excited states
associated with these systems can be classified as transient UEGs (TUEGs) as
their electron density is only homogenous for very specific values of the
radius of the sphere even though the electronic wave function is not
rotationally invariant. Concrete examples are provided in the case of
two-electron systems.

categories: physics.chem-ph cond-mat.mtrl-sci cond-mat.str-el math-ph math.MP


title:
Non-trivial attenuation of geometric frustration by discretization of
  the degrees of freedom: A study of a Potts-like frustrated spin system

abstract:
  The resolution of geometric frustration in systems with continuous degrees of
freedom (DOF) often involves long-range cooperative response and
super-extensive energy scaling. In contrast, frustrated Ising-like spin systems
show only a local and uniform resolution of the frustration. In this work we
bridge between these two extremes by studying a frustrated $N$-state Potts-like
spin model and varying $N$. The expected cooperative response, observed for
large $N$, is strongly attenuated as $N$ is reduced. Moderate $N$ values show
unique phases that result from the discrete compatibility conditions.


title:
Look Backward and Forward: Self-Knowledge Distillation with
  Bidirectional Decoder for Neural Machine Translation

abstract:
  Neural Machine Translation(NMT) models are usually trained via unidirectional
decoder which corresponds to optimizing one-step-ahead prediction. However,
this kind of unidirectional decoding framework may incline to focus on local
structure rather than global coherence. To alleviate this problem, we propose a
novel method, Self-Knowledge Distillation with Bidirectional Decoder for Neural
Machine Translation(SBD-NMT). We deploy a backward decoder which can act as an
effective regularization method to the forward decoder. By leveraging the
backward decoder's information about the longer-term future, distilling
knowledge learned in the backward decoder can encourage auto-regressive NMT
models to plan ahead. Experiments show that our method is significantly better
than the strong Transformer baselines on multiple machine translation data
sets.


title: On the boundedness of non-standard rough singular integral operators

abstract:
  Let $\Omega$ be homogeneous of degree zero, have vanishing moment of order
one on the unit sphere $\mathbb {S}^{d-1}$($d\ge 2$). In this paper, our object
of investigation is the following rough non-standard singular integral operator
$$T_{\Omega,\,A}f(x)={\rm
p.\,v.}\int_{\mathbb{R}^d}\frac{\Omega(x-y)}{|x-y|^{d+1}}\big(A(x)-A(y)-\nabla
A(y)(x-y)\big)f(y){\rm d}y,$$ where $A$ is a function defined on $\mathbb{R}^d$
with derivatives of order one in ${\rm BMO}(\mathbb{R}^d)$. We show that
$T_{\Omega,\,A}$ enjoys the endpoint $L\log L$ type estimate and is $L^p$
bounded if $\Omega\in L(\log L)^{2}(\mathbb{S}^{d-1})$. These resuts
essentially improve the previous known results given by Hofmann for the $L^p$
boundedness of $T_{\Omega,\,A}$ under the condition $\Omega\in L^{q}(\mathbb
{S}^{d-1})$ $(q>1)$, Hu and Yang for the endpoint weak $L\log L$ type estimates
when $\Omega\in {\rm Lip}_{\alpha}(\mathbb{S}^{d-1})$ for some $\alpha\in
(0,\,1]$. Quantitative weighted strong and endpoint weak $L\log L$ type
inequalities are proved whenever $\Omega\in L^{\infty}(\mathbb {S}^{d-1})$. The
analysis of the weighted results relies heavily on two bilinear sparse
dominations of $T_{\Omega,\,A}$ established herein.


title: On the computational properties of basic mathematical notions

abstract:
  We investigate the computational properties of basic mathematical notions
pertaining to $\mathbb{R}\rightarrow \mathbb{R}$-functions and subsets of
$\mathbb{R}$, like finiteness, countability, (absolute) continuity, bounded
variation, suprema, and regularity. We work in higher-order computability
theory based on Kleene's S1-S9 schemes. We show that the aforementioned
italicised properties give rise to two huge and robust classes of
computationally equivalent operations, the latter based on well-known theorems
from the mainstream mathematics literature. As part of this endeavour, we
develop an equivalent $\lambda$-calculus formulation of S1-S9 that accommodates
partial objects. We show that the latter are essential to our enterprise via
the study of countably based and partial functionals of type $3$. We also
exhibit a connection to \emph{infinite time} Turing machines.


title:
Real-time Scene Text Detection Based on Global Level and Word Level
  Features

abstract:
  It is an extremely challenging task to detect arbitrary shape text in natural
scenes on high accuracy and efficiency. In this paper, we propose a scene text
detection framework, namely GWNet, which mainly includes two modules: Global
module and RCNN module. Specifically, Global module improves the adaptive
performance of the DB (Differentiable Binarization) module by adding k
submodule and shift submodule. Two submodules enhance the adaptability of
amplifying factor k, accelerate the convergence of models and help to produce
more accurate detection results. RCNN module fuses global-level and word-level
features. The word-level label is generated by obtaining the minimum
axis-aligned rectangle boxes of the shrunk polygon. In the inference period,
GWNet only uses global-level features to output simple polygon detections.
Experiments on four benchmark datasets, including the MSRA-TD500, Total-Text,
ICDAR2015 and CTW-1500, demonstrate that our GWNet outperforms the
state-of-the-art detectors. Specifically, with a backbone of ResNet-50, we
achieve an F-measure of 88.6% on MSRA- TD500, 87.9% on Total-Text, 89.2% on
ICDAR2015 and 87.5% on CTW-1500.


title: Finite time effects in single and double Compton scattering

abstract:
  The process of Compton scattering by a free electron with subsequent
reemission of one or two photons is considered in the assumption of finite
interaction time. The corresponding cross sections are obtained in the
framework of relativistic quantum electrodynamics using a modified form of
fermion propagator with complex transmitted momentum. It is shown that finite
time effects can be observable at sufficiently low energies of scattered
photons. The proposed method also regularizes arising infrared divergence in
the cross section of the double Compton effect. Possible experimental
verification of considered theoretical approach is discussed.


title: Productivity within the ETAS seismicity model

abstract:
  The productivity of a magnitude $m$ event can be characterized in term of
triggered events of magnitude above $m-\Delta$: it is the number of direct
"descendants" $\nu_\Delta$ and the number of all "descendants" $V_\Delta$.
There is evidence in favour of the discrete exponential distribution for both
$\nu_\Delta$ and $V_\Delta$ with a dominant magnitude $m$ (the case of
aftershock cluster). We consider the general Epidemic Type Aftershock Sequence
(ETAS) model adapted to any distribution of $\nu_\Delta$. It turns out that the
branching structure of the model excludes the possibility of having exponential
distributions for both productivity characteristics at once. We have
analytically investigated the features of the $V_\Delta$ distribution within a
wide class of ETAS models. We show the fundamental difference in tail behavior
of the $V_\Delta$-distributions for general-type clusters and clusters with a
dominant initial magnitude: the tail is heavy in the former case and light in
the latter. The real data demonstrate the possibilities of this kind. This
result provides theoretical and practical constraints for distributional
analysis of $V_\Delta$.


title:
Regularity of viscosity solutions of the $\sigma_k$-Loewner-Nirenberg
  problem

abstract:
  We study the regularity of the viscosity solution $u$ of the
$\sigma_k$-Loewner-Nirenberg problem on a bounded smooth domain $\Omega \subset
\mathbb{R}^n$ for $k \geq 2$. It was known that $u$ is locally Lipschitz in
$\Omega$. We prove that, with $d$ being the distance function to
$\partial\Omega$ and $\delta > 0$ sufficiently small, $u$ is smooth in $\{0 <
d(x) < \delta\}$ and the first $(n-1)$ derivatives of $d^{\frac{n-2}{2}} u$ are
H\"older continuous in $\{0 \leq d(x) < \delta\}$. Moreover, we identify a
boundary invariant which is a polynomial of the principal curvatures of
$\partial\Omega$ and its covariant derivatives and vanishes if and only if
$d^{\frac{n-2}{2}} u$ is smooth in $\{0 \leq d(x) < \delta\}$. Using a relation
between the Schouten tensor of the ambient manifold and the mean curvature of a
submanifold and related tools from geometric measure theory, we further prove
that, when $\partial\Omega$ contains more than one connected components, $u$ is
not differentiable in $\Omega$.


title:
Revealing universal Majorana fractionalization using differential shot
  noise and conductance in nonequilibrium states controlled by tunneling phases

abstract:
  Universal fractionalization of quantum transport characteristics in Majorana
quantum dot devices is expected to emerge for well separated Majorana bound
states. We show that the Majorana universality of the differential shot noise
$\partial S^>/\partial V$ and conductance $\partial I/\partial V$ at low bias
voltages $V$ arises only in ideal setups with only one Majorana mode entangled
with the quantum dot. In realistic devices, where both Majorana modes are
entangled with the quantum dot, $\partial S^>/\partial V$ and $\partial
I/\partial V$ become very sensitive to tunneling phases and their universal
fractional values are hard to observe even for well separated Majorana bound
states. In contrast, as revealed here, the ratio $(\partial S^>/\partial
V)/(\partial I/\partial V)$ weakly depends on the tunneling phases and is
fractional when the Majorana bound states are well separated or integer when
they significantly overlap. Importantly, for very large $V$ we demonstrate that
this ratio becomes fully independent of the tunneling phases and its universal
fractional Majorana value may be observed in state-of-the-art experiments.


title: Cyber security and the Leviathan

abstract:
  Dedicated cyber-security functions are common in commercial businesses, who
are confronted by evolving and pervasive threats of data breaches and other
perilous security events. Such businesses are enmeshed with the wider societies
in which they operate. Using data gathered from in-depth, semi-structured
interviews with 15 Chief Information Security Officers, as well as six senior
organisational leaders, we show that the work of political philosopher Thomas
Hobbes, particularly Leviathan, offers a useful lens through which to
understand the context of these functions and of cyber security in Western
society. Our findings indicate that cyber security within these businesses
demonstrates a number of Hobbesian features that are further implicated in, and
provide significant benefits to, the wider Leviathan-esque state. These include
the normalisation of intrusive controls, such as surveillance, and the
stimulation of consumption. We conclude by suggesting implications for
cyber-security practitioners, in particular, the reflexivity that these
perspectives offer, as well as for businesses and other researchers.


title: Optical super-resonances in dielectric microsphere particles

abstract:
  Extreme field localization and giant field enhancement are often achieved by
using plasmonic nanostructures and metamaterials such as strongly coupled
silver nanoparticles.Dielectric particles and structures can focus light beyond
the diffraction limit (photonic nanojet effect), but with much weaker
strengths. Recently, we showed that dielectric microspheres could support
high-order Mie resonance modes ("super resonance mode") that can generate
similar level of electric field intensity enhancement as plasmonic structures
on the order of 10^4 - 10^7. In this work, we aim to further advance our
understanding of the super-resonance modes. New results on the effects of size
parameter and refractive index on optical super-resonances across a wide
parameter range and with improved numerical accuracies is presented. The
results suggest that the electric field intensity enhancement could reach a
record high level of 10^9-10^11 at specific conditions that surpass plasmonic
enhancements. Moreover, super-resonance-enabled focusing by microsphere lens
under different lighting sources (e.g., different color LEDs or lasers, halogen
lamps) is investigated and compared for the first time. These results are
important in understanding the super-resolution mechanism for microsphere
nanoscopy and will find numerous potential applications in photonics.


title: von Neumann's information engine without the spectral theorem

abstract:
  von Neumann obtained the formula for the entropy of a quantum state by
assuming the validity of the second law of thermodynamics in a thought
experiment involving semipermeable membranes and an ideal gas of
quantum-labeled particles. Despite being operational in the most part, von
Neumann's argument crucially departs from an operational narrative in its use
of the spectral theorem. In this work we show that the role of the spectral
theorem in von Neumann's argument can be taken over by the operational
assumptions of repeatability and reversibility, and using these we are able to
explore the consequences of the second law also in theories that do not possess
a unique spectral decomposition. As a byproduct, we obtain the
Groenewold--Ozawa information gain as a natural monotone for a suitable
ordering of instruments, providing it with an operational interpretation valid
in quantum theory and beyond.


title:
Non-homothetic convex ancient solutions for flows by high powers of
  curvature

abstract:
  We prove the existence of closed convex ancient solutions to curvature flows
which become more and more oval for large negative times. The speed function is
a general symmetric function of the principal curvatures, homogeneous of degree
greater than one. This generalises previous work on the mean curvature flow and
other one-homogeneous curvature flows. As an auxiliary result, we prove a new
theorem on the convergence to a round point of convex rotationally symmetric
hypersurfaces satisfying a suitable constraint on the curvatures.


title:
Moderate Deviations for the current and Tagged Particle in Symmetric
  Simple Exclusion Processes

abstract:
  We prove moderate deviation principles for the tagged particle position and
current in one-dimensional symmetric simple exclusion processes. There is at
most one particle per site. A particle jumps to one of its two neighbors at
rate $1/2$, and the jump is suppressed if there is already one at the target
site. We distinguish one particular particle which is called the tagged
particle. We first establish a variational formula for the moderate deviation
rate functions of the tagged particle positions based on moderate deviation
principles from hydrodynamic limit proved by Gao and Quastel
\cite{gao2003moderate}. Then we construct a minimizer of the variational
formula and obtain explicit expressions for the moderate deviation rate
functions.


title: Stable Parametrization of Continuous and Piecewise-Linear Functions

abstract:
  Rectified-linear-unit (ReLU) neural networks, which play a prominent role in
deep learning, generate continuous and piecewise-linear (CPWL) functions. While
they provide a powerful parametric representation, the mapping between the
parameter and function spaces lacks stability. In this paper, we investigate an
alternative representation of CPWL functions that relies on local hat basis
functions. It is predicated on the fact that any CPWL function can be specified
by a triangulation and its values at the grid points. We give the necessary and
sufficient condition on the triangulation (in any number of dimensions) for the
hat functions to form a Riesz basis, which ensures that the link between the
parameters and the corresponding CPWL function is stable and unique. In
addition, we provide an estimate of the $\ell_2\rightarrow L_2$ condition
number of this local representation. Finally, as a special case of our
framework, we focus on a systematic parametrization of $\mathbb{R}^d$ with
control points placed on a uniform grid. In particular, we choose hat basis
functions that are shifted replicas of a single linear box spline. In this
setting, we prove that our general estimate of the condition number is optimal.
We also relate our local representation to a nonlocal one based on shifts of a
causal ReLU-like function.


title:
The one-row colored $\mathfrak{sl}_{3}$ Jones polynomials for pretzel
  links

abstract:
  The colored $\mathfrak{sl}_{3}$ Jones polynomial $J_{(n_{1},
n_{2})}^{\mathfrak{sl}_{3}}(L;q)$ are given by a link and an $(n_{1},
n_{2})$-irreducible representation of $\mathfrak{sl}_{3}$. In general, it is
hard to calculate $J_{(n_{1}, n_{2})}^{\mathfrak{sl}_{3}}(L;q)$ for an oriented
link $L$. However, we calculate the one-row $\mathfrak{sl}_{3}$ colored Jones
polynomials $J_{(n, 0)}^{\mathfrak{sl}_{3}}(P(\alpha,\beta,\gamma);q)$ for
three-parameter families of oriented pretzel links $P(\alpha,\beta,\gamma)$ by
using Kuperberg's linear skein theory by setting $n_{2}=0$. Furthermore, we
show the existence of the tails of $J_{(n, 0)}^{\mathfrak{sl}_{3}}(P(2\alpha
+1, 2\beta+1,2\gamma);q)$ for the alternating pretzel knots $P(2\alpha +1,
2\beta+1,2\gamma)$.


title: The Forces outside the static limit in the rotating frame

abstract:
  In this paper we consider the rotating frame of the Minkowski spacetime in
order to describe the inertial forces outside the static limit. We consider the
inertial forces inside the static limit to find the classical analogue
afterwards we find out the expressions for these forces outside the static
limit where we can't consider the limit $\frac{v}{c}\to 0$. We show that in
general case if the angular velocity of the object $\Omega$ is equal to the
angular momentum $\omega$ then the acceleration is equal to zero.


title:
Suppression of Brillouin oscillation in transparent free-standing
  diamond thin films in picosecond ultrasound

abstract:
  Brillouin oscillation appears in picosecond ultrasonics for a transparent
specimen because of backward light scattering by moving strain pulse. Its
amplitude is comparable with those of other responses, such as pulse-echo
signals and through-thickness resonance, obscuring these
non-Brillouin-oscillation responses. We here find that Brillouin oscillation
can be suppressed in a transparent free-standing film by coating both sides
with metallic thin film of appropriate thickness and that this peculiar
behavior is caused by strain pulses generated on both sides with a slight phase
difference. This phenomenon allowed us to fabricate a
Brillouin-oscillation-free diamond free-standing film, which showed high
capability for sensor applications.


title:
Echo-enabled Direction-of-Arrival and range estimation of a mobile
  source in Ambisonic domain

abstract:
  Range estimation of a far field sound source in a reverberant environment is
known to be a notoriously difficult problem, hence most localization methods
are only capable of estimating the source's Direction-of-Arrival (DoA). In an
earlier work, we have demonstrated that, under certain restrictive acoustic
conditions and given the orientation of a reflecting surface, one can exploit
the dominant acoustic reflection to evaluate the DoA \emph{and} the distance to
a static sound source in Ambisonic domain. In this article, we leverage the
recently presented Generalized Time-domain Velocity Vector (GTVV)
representation to estimate these quantities for a moving sound source without
an a priori knowledge of reflectors' orientations. We show that the
trajectories of a moving source and the corresponding reflections are spatially
and temporally related, which can be used to infer the absolute delay of the
propagating source signal and, therefore, approximate the microphone-to-source
distance. Experiments on real sound data confirm the validity of the proposed
approach.


title: EVExchange: A Relay Attack on Electric Vehicle Charging System

abstract:
  To support the increasing spread of Electric Vehicles (EVs), Charging
Stations (CSs) are being installed worldwide. The new generation of CSs employs
the Vehicle-To-Grid (V2G) paradigm by implementing novel standards such as the
ISO 15118. This standard enables high-level communication between the vehicle
and the charging column, helps manage the charge smartly, and simplifies the
payment phase. This novel charging paradigm, which connects the Smart Grid to
external networks (e.g., EVs and CSs), has not been thoroughly examined yet.
Therefore, it may lead to dangerous vulnerability surfaces and new research
challenges.
  In this paper, we present EVExchange, the first attack to steal energy during
a charging session in a V2G communication: i.e., charging the attacker's car
while letting the victim pay for it. Furthermore, if reverse charging flow is
enabled, the attacker can even sell the energy available on the victim's car!
Thus, getting the economic profit of this selling, and leaving the victim with
a completely discharged battery. We developed a virtual and a physical testbed
in which we validate the attack and prove its effectiveness in stealing the
energy. To prevent the attack, we propose a lightweight modification of the ISO
15118 protocol to include a distance bounding algorithm. Finally, we validated
the countermeasure on our testbeds. Our results show that the proposed
countermeasure can identify all the relay attack attempts while being
transparent to the user.


title: Simple Approximative Algorithms for Free-Support Wasserstein Barycenters

abstract:
  Computing Wasserstein barycenters of discrete measures has recently attracted
considerable attention due to its wide variety of applications in data science.
In general, this problem is NP-hard, calling for practical approximative
algorithms. In this paper, we analyze two straightforward algorithms for
approximating barycenters, which produce sparse support solutions and show
promising numerical results. These algorithms require $N-1$ and $N(N-1)/2$
standard two-marginal OT computations between the $N$ input measures,
respectively, so that they are fast, in particular the first algorithm, as well
as memory-efficient and easy to implement. Further, they can be used with any
OT solver as a black box. Based on relations of the barycenter problem to the
multi-marginal optimal transport problem, which are interesting on their own,
we prove sharp upper bounds for the relative approximation error. In the second
algorithm, this upper bound can be evaluated specifically for the given
problem, which always guaranteed an error of at most a few percent in our
numerical experiments.


title: Derivations, local and 2-local derivations of standard operator algebras

abstract:
  Let X be a Banach space over field F (R or C). Denote by B(X) the set of all
bounded linear operators on X and by F(X) the set of all finite rank operators
on X. A subalgebra A of B(X) is called a standard operator algebra if A contain
F(X). We give a brief proof of a well-known result that every derivation from A
into B(X) is inner. There is another classical result that every local
derivation on B(X) is a derivation. We extend the result by proving that every
local derivation from A into B(X) is a derivation. Based on these two results,
we prove that every 2-local derivation from A into B(X) is a derivation.


title: Limit shape phase transitions: a merger of arctic circles

abstract:
  We consider a free fermion formulation of a statistical model exhibiting a
limit shape phenomenon. The model is shown to have a phase transition that can
be visualized as the merger of two liquid regions - arctic circles. We show
that the merging arctic circles provide a space-time resolved picture of the
phase transition in lattice QCD known as Gross-Witten-Wadia transition. The
latter is a continuous phase transition of the third order. We argue that this
transition is universal and is not spoiled by interactions if parity and
time-reversal symmetries are preserved. We refer to this universal transition
as the Merger Transition.


title:
Non-linearity of Vaidya spacetime and forces in the central naked
  singularity

abstract:
  In this paper we consider non-linear Vaidya spacetime i.e. the case when the
mass function has the non-linear form $M(v) \equiv \lambda v^n \,, \lambda >0
\,, n>1 $. We prove that the central naked singularity might form for values
$n>1$ but they are gravitationally weak. Also we investigate the question about
forces in the naked singularity and prove that they might be finite only in the
case of the gravitationally weak naked singularity.


title: The third dimension of ferroelectric domain walls

abstract:
  Ferroelectric domain walls are quasi-2D systems that show great promise for
the development of non-volatile memory, memristor technology and electronic
components with ultra-small feature size. Electric fields, for example, can
change the domain wall orientation relative to the spontaneous polarization and
switch between resistive and conductive states, controlling the electrical
current. Being embedded in a 3D material, however, the domain walls are not
perfectly flat and can form networks, which leads to complex physical
structures. We demonstrate the importance of the nanoscale structure for the
emergent transport properties, studying electronic conduction in the 3D network
of neutral and charged domain walls in ErMnO$_3$. By combining tomographic
microscopy techniques and finite element modelling, we clarify the contribution
of domain walls within the bulk and show the significance of curvature effects
for the local conduction down to the nanoscale. The findings provide insights
into the propagation of electrical currents in domain wall networks, reveal
additional degrees of freedom for their control, and provide quantitative
guidelines for the design of domain wall based technology.


title: Contrastive Boundary Learning for Point Cloud Segmentation

abstract:
  Point cloud segmentation is fundamental in understanding 3D environments.
However, current 3D point cloud segmentation methods usually perform poorly on
scene boundaries, which degenerates the overall segmentation performance. In
this paper, we focus on the segmentation of scene boundaries. Accordingly, we
first explore metrics to evaluate the segmentation performance on scene
boundaries. To address the unsatisfactory performance on boundaries, we then
propose a novel contrastive boundary learning (CBL) framework for point cloud
segmentation. Specifically, the proposed CBL enhances feature discrimination
between points across boundaries by contrasting their representations with the
assistance of scene contexts at multiple scales. By applying CBL on three
different baseline methods, we experimentally show that CBL consistently
improves different baselines and assists them to achieve compelling performance
on boundaries, as well as the overall performance, eg in mIoU. The experimental
results demonstrate the effectiveness of our method and the importance of
boundaries for 3D point cloud segmentation. Code and model will be made
publicly available at https://github.com/LiyaoTang/contrastBoundary.


title:
Study of the Kramers-Fokker-Planck quadratic operator with a constant
  magnetic field

abstract:
  We study the quadratic Kramers-Fokker-Planck operator with a constant
magnetic field and with a quadratic potential. We describe the exact expression
of the norm of the semi-group associated to the operator near the equilibrium.
At this level, explicit and accurate estimates of this norm are shown in small
and long times as well as uniform-in-time estimates when the magnetic parameter
$b$ tends to infinity.

categories: math.AP math-ph math.DS math.FA math.MP math.OA


title:
Scaling limits of Maximal Entropy Random Walks on a non-negative integer
  lattice perturbed at the origin

abstract:
  We investigate random walks on the integer lattice perturbed at the origin
which maximize the entropy along the path or equivalently the entropy rate.
Compared to usual simple random walks which maximize the entropy locally, those
are a complete paradigm shift. For finite graph, they have been introduced as
such by Physicists in [BDLW09] and they enjoy strong localization properties in
heterogeneous environments. We first give an extended definition of such random
walks when the graph is infinite. They are not always uniquely defined contrary
to the finite situation. Then we introduce our model and we show there is a
phase transition phenomenon according with the magnitude of the perturbation.
We obtain either Bessel-like or asymmetric random walks and we study there
scaling limits. In the subcritical situation, we get the classical
three-dimensional Bessel process whereas in the supercritical one we get a
recurrent reflected drifted Brownian motion. We produce a unify proof relying
on the equivalence between submartingale problems and reflected stochastic
differential equations obtained in [KR17]. Finally, we remark that applying our
results to a couple of particles subject to the famous Pauli exclusion
principle, we can recover the electrostatic force assuming only the maximal
entropy principle. Also, we briefly explain how to retrieve those results in
the macroscopic situation without go through the microscopic one by using the
Kullback-Leibler divergence and the Girsanov theorem.


title:
Calculating the ground state energy of benzene under spatial
  deformations with noisy quantum computing

abstract:
  In this manuscript, we calculate the ground state energy of benzene under
spatial deformations by using a state-of-the-art quantum computing methodology
- the variational quantum eigensolver (VQE). The primary goal of the study is
estimating the feasibility of using quantum computing ansatze on near-term
devices for solving problems with large number of orbitals in regions where
classical methods are known to fail. The center of our study are the hardware
efficient and quantum unitary coupled cluster ansatz (qUCC). Our advanced
simulation platform allows us to incorporate a realistic idle noise model -
describing noise in quantum devices with a full Kraus operator formalism. We
find that the hardware efficient ansatz in the presence of realistic noise
outperforms mean-field methods for extreme deformations of benzene. Moreover,
the hardware efficient ansatz yields results that depend on the initial guess
of parameters - both in the noisy and noiseless cases. On the other hand the
qUCC ansatz has deeper circuits and thus the effect of noise is so extreme that
the energies obtained with that method do not outperform mean-field theories.
We therefore foresee that the qUCC method will remain a method for simulators
of quantum computers in the pre-error-correction era, while the hardware
efficient ansatz can be utilized on current day hardware. Furthermore, the
study shows that qUCC method captures the physics of the system and shows that
the qUCC method can be utilized together with the H\"uckel approximation.
Finally, increasing the size of the active space leads to an increasing
probability to converge to a local minimum or in plain words - more qubits does
not mean a better solution.


title:
Constrained Structured Optimization and Augmented Lagrangian Proximal
  Methods

abstract:
  We investigate and develop numerical methods for finite-dimensional
constrained structured optimization problems. Offering a comprehensive yet
simple and expressive language, this problem class provides a modeling
framework for a variety of applications. A general and flexible algorithm is
proposed that interlaces proximal methods and safeguarded augmented Lagrangian
schemes. We provide a theoretical characterization of the algorithm and its
asymptotic properties, deriving convergence results for fully nonconvex
problems. Adopting a proximal gradient method with an oracle as a formal tool,
it is demonstrated how the inner subproblems can be solved by off-the-shelf
methods for composite optimization, without introducing slack variables and
despite the appearance of set-valued projections. Finally, we describe our
open-source matrix-free implementation of the proposed algorithm and test it
numerically. Illustrative examples show the versatility of constrained
structured programs as a modeling tool, expose difficulties arising in this
vast problem class and highlight benefits of the implicit approach developed.


title:
Noise and fluctuations can undermine the efficiency of Majority Rule in
  Group Evaluation problems

abstract:
  Crowdsourcing is a mechanism by means of which groups of people are able to
execute a task by sharing ideas, efforts and resources. Thanks to the online
technologies, crowdsourcing has become in the last decade an even more utilized
process in different and diverse fields. An instance of such process is the
so-called "label aggregation problem": in practice, it is the evaluation of an
item by groups of agents, where each agent gives its own judgment of it.
Starting from the individual evaluations of their members, how can the groups
give their global assessment? In this work, by means of a game-theoretical,
evolutionary approach, we show that in most cases the majority rule (the group
evaluation is the evaluation of the majority of its members) is still the best
way to get a reliable group evaluation, even when the agents are not the best
experts of the topic at stake; on the other hand, we also show that noise
(i.e., fortuitous errors, misunderstanding, or every possible source of
non-deterministic outcomes) can undermine the efficiency of the procedure in
non-trivial situations. Therefore, in order to make the process as reliable as
possible, the presence of noise and its effects should be carefully taken into
account.


title:
Large output voltage to magnetic flux change in nanoSQUIDs based on
  direct-write Focused Ion Beam Induced Deposition technique

abstract:
  NanoSQUIDs are quantum sensors that excel in detecting a small change in
magnetic flux with high sensitivity and high spatial resolution. Here, we
employ resist-free direct-write Ga+ Focused Ion Beam Induced Deposition (FIBID)
techniques to grow W-C nanoSQUIDs, and we investigate their electrical response
to changes in the magnetic flux. Remarkably, FIBID allows the fast
($3~\mathrm{nm}$) growth of $700~\mathrm{nm}\times 300~\mathrm{nm}$
Dayem-bridge nanoSQUIDs based on narrow nanowires ($50~\mathrm{nm}$ wide) that
act as Josephson junctions. The observed transfer coefficient (output voltage
to magnetic flux change) is very high (up to $1301~\mathrm{\mu V/\Phi_0}$),
which correlates with the high resistivity of W-C in the normal state. We
discuss here the potential of this approach to reduce the active area of the
nanoSQUIDs to gain spatial resolution as well as their integration on
cantilevers for scanning-SQUID applications.


title: Thermodynamics of BPS and Near-BPS AdS6 Black Holes

abstract:
  We develop the thermodynamics of BPS and near-BPS AdS6 black holes. We study
the phase diagram of BPS black holes in the grand canonical ensemble. We
highlight two distinct deformations orthogonal to the BPS surface: (i)
increasing the temperature while keeping the charges fixed, (ii) changing the
charges while maintaining extremality such that the BPS constraint is no longer
satisfied. For both these deformations, we show that the considerations of the
BPS entropy function can be extended to describe the near-BPS regime. The
excess entropy together with changes in all potentials are perfectly accounted
for via the extremization principle.


title:
Parameter-Estimation Biases for Eccentric Supermassive Binary Black
  Holes in Pulsar Timing Arrays: Biases Caused by Ignored Pulsar Terms

abstract:
  The continuous nanohertz gravitational waves (GWs) from individual
supermassive binary black holes (SMBBHs) can be encoded in the timing residuals
of pulsar timing arrays (PTAs). For each pulsar, the residuals actually contain
an Earth term and a pulsar term, but usually only the Earth term is considered
as signal and the pulsar term is dropped, which leads to parameter-estimation
biases (PEBs) for the SMBBHs, and currently there are no convenient evaluations
of the PEBs. In this article, we formulate the PEBs for a SMBBH with an
eccentric orbit. In our analyses, the unknown phases of pulsar terms are
treated as random variables obeying the uniform distribution $U[0,2\pi)$, due
to the fact that pulsar distances are generally poorly measured. Our analytical
results are in accordance with the numerical work by Zhu et. al. (2016) at
$1.5\sigma$ level, implying that our formulae are effective in estimating
magnitudes of the PEBs. Additionally, we find that for two parameters -- Earth
term phase $\varphi^E$ and orbital eccentricity $e$, their biases $\Delta
\varphi^E$ and $\Delta e/e$ monotonically decrease as $e$ increases, which
partly confirms a hypothesis in our previous work Chen & Zhang (2018).
Furthermore, we also calculate the PEBs caused by the recently observed
common-spectrum process (CSP), finding that if the strain amplitude of the
continuous GW is significantly stronger ($3$ times larger, in our cases) than
the stochastic GW background, the PEBs from pulsar terms are larger than those
from the CSP. Our formulae of the PEBs can be conveniently applied in the
future PTA data analyses.


title:
Regret-Matching Learning-Based Task Assignment in Vehicular Edge
  Computing

abstract:
  Vehicular edge computing has recently been proposed to support
computation-intensive applications in Intelligent Transportation Systems (ITS)
such as augmented reality and autonomous driving. Despite recent progress in
this area, significant challenges remain to efficiently allocate limited
computation resources to a range of time-critical ITS tasks. Toward this end,
the current paper develops a new task assignment scheme for vehicles in a
highway scenario. Because of the high speed of vehicles and the limited
communication range of road side units (RSUs), the computation tasks of
participating vehicles are to be migrated across multiple servers. We formulate
a binary nonlinear programming (BNLP) problem of assigning computation tasks
from vehicles to RSUs and a macrocell base station. To deal with the
potentially large size of the formulated optimization problem, we develop a
distributed multi-agent regret-matching learning algorithm. Based on the regret
minimization principle, the proposed algorithm employs a forgetting method that
allows the learning process to quickly adapt to and effectively handle the high
mobility feature of vehicle networks. We theoretically prove that it converges
to the correlated equilibrium solutions of the considered BNLP problem.
Simulation results with practical parameter settings show that the proposed
algorithm offers the lowest total delay and cost of processing tasks.
Importantly, our algorithm converges much faster than existing methods as the
problem size grows, demonstrating its clear advantage in large-scale vehicular
networks.


title: The volume of a dislocation network

abstract:
  We derive a simple analytical line integral expression for the relaxation
volume tensor of an arbitrary interconnected dislocation network. This quantity
determines the magnitude of dislocation contribution to the dimensional changes
and volumetric swelling of a material, and highlights the fundamental dual role
of dislocations as sources of internal strain as well as carriers of plastic
deformation. To illustrate applications of the method, we compute the
relaxation volume of a stacking fault tetrahedron, a defect commonly occurring
in fcc metals; the volume of an unusual tetrahedral configuration formed by the
(a/2)<111> and a<001> dislocations in a bcc metal; and estimate the relative
contribution of extended dislocations to the volume relaxation of heavily
irradiated tungsten.


title: Beyond the Badge: Reproducibility Engineering as a Lifetime Skill

abstract:
  Ascertaining reproducibility of scientific experiments is receiving increased
attention across disciplines. We argue that the necessary skills are important
beyond pure scientific utility, and that they should be taught as part of
software engineering (SWE) education. They serve a dual purpose: Apart from
acquiring the coveted badges assigned to reproducible research, reproducibility
engineering is a lifetime skill for a professional industrial career in
computer science. SWE curricula seem an ideal fit for conveying such
capabilities, yet they require some extensions, especially given that even at
flagship conferences like ICSE, only slightly more than one-third of the
technical papers (at the 2021 edition) receive recognition for artefact
reusability. Knowledge and capabilities in setting up engineering environments
that allow for reproducing artefacts and results over decades (a standard
requirement in many traditional engineering disciplines), writing semi-literate
commit messages that document crucial steps of a decision-making process and
that are tightly coupled with code, or sustainably taming dynamic, quickly
changing software dependencies, to name a few: They all contribute to solving
the scientific reproducibility crisis, and enable software engineers to build
sustainable, long-term maintainable, software-intensive, industrial systems. We
propose to teach these skills at the undergraduate level, on par with
traditional SWE topics.


title:
Training and Projecting: A Reduced Basis Method Emulator for Many-Body
  Physics

abstract:
  We present the reduced basis method (RBM) as a tool for developing emulators
for equations with tunable parameters within the context of the nuclear
many-body problem. The RBM uses a basis expansion informed by a set of
solutions for a few values of the model parameters and then projects the
equations over a well-chosen low-dimensional subspace. We connect some of the
results in the eigenvector continuation literature to the formalism of RBMs and
show how RBMs can be applied to a broad set of problems. As we illustrate, the
possible success of the RBM on such problems can be diagnosed beforehand by a
principal component analysis. We apply the RBM to the one-dimensional
Gross-Pitaevskii equation with a harmonic trapping potential and to nuclear
density functional theory for $^{48}$Ca. The outstanding performance of the
approach, together with its straightforward implementation, show promise for
its application to the emulation of computationally demanding calculations,
including uncertainty quantification.

categories: nucl-th hep-ph physics.comp-ph physics.data-an stat.CO


title:
API: Boosting Multi-Agent Reinforcement Learning via
  Agent-Permutation-Invariant Networks

abstract:
  Multi-agent reinforcement learning suffers from poor sample efficiency due to
the exponential growth of the state-action space. Considering a homogeneous
multiagent system, a global state consisting of $m$ homogeneous components has
$m!$ differently ordered representations, thus designing functions satisfying
permutation invariant (PI) can reduce the state space by a factor of
$\frac{1}{m!}$. However, mainstream MARL algorithms ignore this property and
learn over the original state space. To achieve PI, previous works including
data augmentation based methods and embedding-sharing architecture based
methods, suffer from training instability and limited model capacity. In this
work, we propose two novel designs to achieve PI, while avoiding the above
limitations. The first design permutes the same but differently ordered inputs
back to the same order and the downstream networks only need to learn function
mapping over fixed-ordering inputs instead of all permutations, which is much
easier to train. The second design applies a hypernetwork to generate
customized embedding for each component, which has higher representational
capacity than the previous embedding-sharing method. Empirical results on the
SMAC benchmark show that the proposed method achieves 100% win-rates in almost
all hard and super-hard scenarios (never achieved before), and superior
sample-efficiency than the state-of-the-art baselines by up to 400%.


title: Graded $p$-polar rings and the homology of $\Omega^n\Sigma^nX$

abstract:
  As an extension of previous ungraded work, we define a graded $p$-polar ring
to be an analog of a graded commutative ring where multiplication is only
allowed on $p$-tuples (instead of pairs) of elements of equal degree. We show
that the free affine $p$-adic group scheme functor, as well as the free formal
group functor, defined on $k$-algebras for a perfect field $k$ of
characteristic $p$, factors through $p$-polar $k$-algebras. It follows that the
same is true for any affine $p$-adic or formal group functor, in particular for
the functor of $p$-typical Witt vectors. As an application, we show that the
homology of the free $E_n$-algebra $H^*(\Omega^n\Sigma^n X;\mathbf F_p)$, as a
Hopf algebra, only depends on the $p$-polar structure of $H^*(X;\mathbf F_p)$
in a functorial way.


title:
Equivariant $\underline{\mathbb{Z}/\ell}$-modules for the cyclic group
  $C_2$

abstract:
  For the cyclic group $C_2$ we give a complete description of the derived
category of perfect complexes of modules over the constant Mackey ring
$\underline{\mathbb{Z}/\ell}$, for $\ell$ a prime. This is fairly simple for
$\ell$ odd, but for $\ell=2$ depends on a new splitting theorem. As corollaries
of the splitting theorem we compute the associated Picard group and the Balmer
spectrum for compact objects in the derived category, and we obtain a complete
classification of finite modules over the $C_2$-equivariant Eilenberg--MacLane
spectrum $H\underline{\mathbb{Z}/2}$. We also use the splitting theorem to give
new and illuminating proofs of some facts about $RO(C_2)$-graded Bredon
cohomology, namely Kronholm's freeness theorem and the structure theorem of C.
May.


title:
Compliance with restrictive measures during the COVID-19 pandemic in
  Europe: Does political partisanship influence behavioural responses?

abstract:
  The success of public health policies aimed at curtailing the COVID-19
pandemic have relied on large-scale and protracted compliance by the public. A
series of studies have recently argued that previous voting patterns are
important predictors of such compliance. Our research further investigates such
connection by tracking the relationships between parties' vote shares and
mobility in six European countries over an extended period of time. We observe
that while vote shares are occasionally related to variations in mobility
within each country, there is no systematic pattern of decrease or increase in
mobility across all six selected countries depending on party family or
government membership. Over time, the relationships between mobility and vote
shares tend to grow stronger in some but not all countries, again suggesting
that there is no clear connection between vote shares for several party
families and compliance with social distancing measures.


title:
Electrically tunable Berry curvature and strong light-matter coupling in
  birefringent perovskite microcavities at room temperature

abstract:
  The field of spinoptronics is underpinned by good control over photonic
spin-orbit coupling in devices that possess strong optical nonlinearities. Such
devices might hold the key to a new era of optoelectronics where momentum and
polarization degrees-of-freedom of light are interwoven and interfaced with
electronics. However, manipulating photons through electrical means is a
daunting task given their charge neutrality and requires complex electro-optic
modulation of their medium. In this work, we present electrically tunable
microcavity exciton-polariton resonances in a Rashba-Dresselhaus spin-orbit
coupling field at room temperature. We show that a combination of different
spin orbit coupling fields and the reduced cavity symmetry leads to tunable
formation of Berry curvature, the hallmark of quantum geometrical effects. For
this, we have implemented a novel architecture of a hybrid photonic structure
with a two-dimensional perovskite layer incorporated into a microcavity filled
with nematic liquid crystal. Our work interfaces spinoptronic devices with
electronics by combining electrical control over both the strong light-matter
coupling conditions and artificial gauge fields.


title: On the cubic Pell equation over finite fields

abstract:
  The classical Pell equation can be extended to the cubic case considering the
elements of norm one in $Z[\sqrt[3]{r}]$, which satisfy $x^3 + r y^3 + r^2 z^3
- 3 r x y z = 1$. The solution of the cubic Pell equation is harder than the
classical case, indeed a method for solving it as Diophantine equation is still
missing. In this paper, we study the cubic Pell equation over finite fields,
extending the results that hold for the classical one. In particular, we
provide a novel method for counting the number of solutions in all possible
cases depending on the value of r. Moreover, we are also able to provide a
method for generating all the solutions.


title:
On Robustness in Optimization-Based Constrained Iterative Learning
  Control

abstract:
  Iterative learning control (ILC) is a control strategy for repetitive tasks
wherein information from previous runs is leveraged to improve future
performance. Optimization-based ILC (OB-ILC) is a powerful design framework for
constrained ILC where measurements from the process are integrated into an
optimization algorithm to provide robustness against noise and modelling error.
This paper proposes a robust ILC controller for constrained linear processes
based on the forward-backward splitting algorithm. It demonstrates how
structured uncertainty information can be leveraged to ensure constraint
satisfaction and provides a rigorous stability analysis in the iteration domain
by combining concepts from monotone operator theory and robust control.
Numerical simulations of a precision motion stage support the theoretical
results.


title: Reverse Mathematics of the uncountability of $\mathbb{R}$

abstract:
  In his first set theory paper (1874), Cantor establishes the uncountability
of $\mathbb{R}$. We study the latter in Kohlenbach's higher-order Reverse
Mathematics, motivated by the observation that one cannot study concepts like
`arbitrary mappings from $\mathbb{R}$ to $\mathbb{N}$' in second-order Reverse
Mathematics. Now, it was recently shown that the following statement: $$ \text{
NIN: there is no injection from $[0,1]$ to $\mathbb{N}$,} $$ is hard to prove
in terms of conventional comprehension. In this paper, we show that NIN is
robust by establishing equivalences between NIN and NIN restricted to
mainstream function classes, like: bounded variation, semi-continuity, and
Borel. Thus, the aforementioned hardness of NIN is not due to the
quantification over arbitrary $\mathbb{R}\rightarrow \mathbb{N}$-functions in
NIN. Finally, we also study NBI, the restriction of NIN to bijections, and the
connection to Cousin's lemma and Jordan's decomposition theorem.


title:
Hydrodynamic signatures in thermal transport in devices based on 2D
  materials: an ab initio study

abstract:
  We investigate the features arising from hydrodynamic effects in graphene and
phosphorene devices with finite heat sources, using ab initio calculations and
solving the Phonon Boltzmann Transport Equation through energy-based
deviational Monte Carlo methods. We explain the mechanisms that create those
hydrodynamic features, showing that boundary scattering and geometry are
determinant factors, and that the length scales at which they can appear depend
solely on the ability of intrinsic scattering to randomize the heat flux. We
relate this latter point to the non-local lengths and mean free paths,
additionally providing an insight into how the scattering operator must be
treated to obtain a proper description of the hydrodynamic behavior.


title: Domain Generalisation for Object Detection

abstract:
  Domain generalisation aims to promote the learning of domain-invariant
features while suppressing domain specific features, so that a model can
generalise well on previously unseen target domains. This paper studies domain
generalisation in the object detection setting. We propose new terms for
handling both the bounding box detector and domain belonging, and incorporate
them with consistency regularisation. This allows us to learn a domain agnostic
feature representation for object detection, applicable to the problem of
domain generalisation. The proposed approach is evaluated using four standard
object detection datasets with available domain metadata, namely GWHD,
Cityscapes, BDD100K, Sim10K and exhibits consistently superior generalisation
performance over baselines.


title: Electron spectra in double quantum wells of different shapes

abstract:
  We suggest a method for calculating electronic spectra in ordered and
disordered semiconductor structures (superlattices) forming double quantum
wells (QW). In our method, we represent the solution of Schr\"odinger equation
for QW potential with the help of the solution of the corresponding diffusion
equation. This is because the diffusion is the mechanism, which is primarily
responsible for amorphization (disordering) of the QW structure, leading to
so-called interface mixing. We show that the electron spectrum in such a
structure depends on the shape of the quantum well, which, in turn, corresponds
to an ordered or disordered structure. Namely, in a disordered substance, QW
typically has smooth edges, while in ordered one it has an abrupt, rectangular
shape. The present results are relevant for the heterostructures like
GaAs/AlGaAs, GaN/AlGaN, HgCdTe/CdTe, ZnSe/ZnMnSe, Si/SiGe, etc., which may be
used in high-end electronics, flexible electronics, spintronics,
optoelectronics, and energy harvesting applications.


title: A new proof of a theorem of Tietze

abstract:
  We give a new proof of Tietze Theorem on the convergence of infinite
semi-regular continued fractions.


title:
BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for
  Conversational Gestures Synthesis

abstract:
  Achieving realistic, vivid, and human-like synthesized conversational
gestures conditioned on multi-modal data is still an unsolved problem, due to
the lack of available datasets, models and standard evaluation metrics. To
address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i)
76 hours, high-quality, multi-modal data captured from 30 speakers talking with
eight different emotions and in four different languages, ii) 32 millions
frame-level emotion and semantic relevance annotations.Our statistical analysis
on BEAT demonstrates the correlation of conversational gestures with facial
expressions, emotions, and semantics, in addition to the known correlation with
audio, text, and speaker identity. Qualitative and quantitative experiments
demonstrate metrics' validness, ground truth data quality, and baseline's
state-of-the-art performance. To the best of our knowledge, BEAT is the largest
motion capture dataset for investigating the human gestures, which may
contribute to a number of different research fields including controllable
gesture synthesis, cross-modality analysis, emotional gesture recognition. The
data, code and model will be released for research.


title: Synchronizing Boolean networks asynchronously

abstract:
  The {\em asynchronous automaton} associated with a Boolean network
$f:\{0,1\}^n\to\{0,1\}^n$, considered in many applications, is the finite
deterministic automaton where the set of states is $\{0,1\}^n$, the alphabet is
$[n]$, and the action of letter $i$ on a state $x$ consists in either switching
the $i$th component if $f_i(x)\neq x_i$ or doing nothing otherwise. These
actions are extended to words in the natural way. A word is then {\em
synchronizing} if the result of its action is the same for every state. In this
paper, we ask for the existence of synchronizing words, and their minimal
length, for a basic class of Boolean networks called and-or-nets: given an
arc-signed digraph $G$ on $[n]$, we say that $f$ is an {\em and-or-net} on $G$
if, for every $i\in [n]$, there is $a$ such that, for all state $x$, $f_i(x)=a$
if and only if $x_j=a$ ($x_j\neq a$) for every positive (negative) arc from $j$
to $i$; so if $a=1$ ($a=0$) then $f_i$ is a conjunction (disjunction) of
positive or negative literals. Our main result is that if $G$ is strongly
connected and has no positive cycles, then either every and-or-net on $G$ has a
synchronizing word of length at most $10(\sqrt{5}+1)^n$, much smaller than the
bound $(2^n-1)^2$ given by the well known \v{C}ern\'y's conjecture, or $G$ is a
cycle and no and-or-net on $G$ has a synchronizing word. This contrasts with
the following complexity result: it is coNP-hard to decide if every and-or-net
on $G$ has a synchronizing word, even if $G$ is strongly connected or has no
positive cycles.


title: Insight-HXMT Detections of Hard X-ray Tails in Scorpius X-1

abstract:
  Using the observations of the high-energy (HE) detector of the Hard X-ray
Modulation Telescope (Insight-HXMT) for Scorpius X-1 in 2018, we search for
hard X-ray tails in the hard X-ray spectra in ~30-200 keV. The hard X-ray tails
are found throughout the Z-track on the hardness-intensity diagram and they
harden and fade away from the horizontal branch (HB), through the normal branch
(NB), to the flaring branch (FB). Comparing the hard X-ray spectra between Cyg
X-1 and Sco X-1, it is concluded that the hard X-ray spectrum of Cyg X-1 shows
high-energy cutoff, implying a hot corona in it, but the high-energy cutoff
does not reveal in the hard X-ray spectrum of Sco X-1. Jointly fitting the HE
spectrum with the medium-energy and low-energy spectra of Sco X-1 in ~2-200
keV, it is suggested that the upscattering Comptonization of the neutron star
(NS) emission photons by the energetic free-falling electrons onto the NS or by
the hybrid electrons in the boundary layer between the NS and the accretion
disk could be responsible for the hard X-ray tails of Sco X-1 on the HB and NB,
but neither of the two mechanisms can be responsible for the hard X-ray tail on
the FB. Some possible origins for the peculiar hard X-ray tail of FB are
argued.


title:
Connecting Neural Response measurements & Computational Models of
  language: a non-comprehensive guide

abstract:
  Understanding the neural basis of language comprehension in the brain has
been a long-standing goal of various scientific research programs. Recent
advances in language modelling and in neuroimaging methodology promise
potential improvements in both the investigation of language's neurobiology and
in the building of better and more human-like language models. This survey
traces a line from early research linking Event Related Potentials and
complexity measures derived from simple language models to contemporary studies
employing Artificial Neural Network models trained on large corpora in
combination with neural response recordings from multiple modalities using
naturalistic stimuli.


title: Double Constacyclic Codes over Two Finite Commutative Chain Rings

abstract:
  Many kinds of codes which possess two cycle structures over two special
finite commutative chain rings, such as ${\Bbb Z}_2{\Bbb Z}_4$-additive cyclic
codes and quasi-cyclic codes of fractional index etc., were proved
asymptotically good. In this paper we extend the study in two directions: we
consider any two finite commutative chain rings with a surjective homomorphism
from one to the other, and consider double constacyclic structures. We
construct an extensive kind of double constacyclic codes over two finite
commutative chain rings. And, developing a probabilistic method suitable for
quasi-cyclic codes over fields, we prove that the double constacyclic codes
over two finite commutative chain rings are asymptotically good.


title:
A Riemann-Hilbert approach to the modified Camassa-Holm equation with
  step-like boundary conditions

abstract:
  The paper aims at developing the Riemann-Hilbert (RH) approach for the
modified Camassa-Holm (mCH) equation on the line with non-zero boundary
conditions, in the case when the solution is assumed to approach two different
constants at different sides of the line. We present detailed properties of
spectral functions associated with the initial data for the Cauchy problem for
the mCH equation and obtain a representation for the solution of this problem
in terms of the solution of an associated RH problem.


title:
Use of Digital Technologies in Public Health Responses to Tackle
  Covid-19: the Bangladesh Perspective

abstract:
  This paper aims to study the fight against COVID-19 in Bangladesh and digital
intervention initiatives. To achieve the purpose of our research, we conducted
a methodical review of online content. We have reviewed the first digital
intervention that COVID-19 has been used to fight against worldwide. Then we
reviewed the initiatives that have been taken in Bangladesh. Our paper has
shown that while Bangladesh can take advantage of the digital intervention
approach, it will require rigorous collaboration between government
organizations and universities to get the most out of it. Public health can
become increasingly digital in the future, and we are reviewing international
alignment requirements. This exploration also focused on the strategies for
controlling, evaluating, and using digital technology to strengthen epidemic
management and future preparations for COVID-19.


title:
Solving Nonsmooth Resource Allocation Problems with Feasibility
  Constraints through Novel Distributed Algorithms

abstract:
  The distributed non-smooth resource allocation problem over multi-agent
networks is studied in this paper, where each agent is subject to globally
coupled network resource constraints and local feasibility constraints
described in terms of general convex sets. To solve such a problem, two classes
of novel distributed continuous-time algorithms via differential inclusions and
projection operators are proposed. Moreover, the convergence of the algorithms
is analyzed by the Lyapunov functional theory and nonsmooth analysis. We
illustrate that the first algorithm can globally converge to the exact optimum
of the problem when the interaction digraph is weight-balanced and the local
cost functions being strongly convex. Furthermore, the fully distributed
implementation of the algorithm is studied over connected undirected graphs
with strictly convex local cost functions. In addition, to improve the drawback
of the first algorithm that requires initialization, we design the second
algorithm which can be implemented without initialization to achieve global
convergence to the optimal solution over connected undirected graphs with
strongly convex cost functions. Finally, several numerical simulations verify
the results.


title:
How to decide whether two convex octahedra are affinely equivalent using
  their natural developments only

abstract:
  Given two convex octahedra in Euclidean 3-space, we find conditions on their
natural developments which are necessary and sufficient for these octahedra to
be affinely equivalent to each other.


title:
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive
  Recognition of Cereal Grains

abstract:
  Cereal grains are a vital part of human diets and are important commodities
for people's livelihood and international trade. Grain Appearance Inspection
(GAI) serves as one of the crucial steps for the determination of grain quality
and grain stratification for proper circulation, storage and food processing,
etc. GAI is routinely performed manually by qualified inspectors with the aid
of some hand tools. Automated GAI has the benefit of greatly assisting
inspectors with their jobs but has been limited due to the lack of datasets and
clear definitions of the tasks.
  In this paper we formulate GAI as three ubiquitous computer vision tasks:
fine-grained recognition, domain adaptation and out-of-distribution
recognition. We present a large-scale and publicly available cereal grains
dataset called GrainSpace. Specifically, we construct three types of device
prototypes for data acquisition, and a total of 5.25 million images determined
by professional inspectors. The grain samples including wheat, maize and rice
are collected from five countries and more than 30 regions. We also develop a
comprehensive benchmark based on semi-supervised learning and self-supervised
learning techniques. To the best of our knowledge, GrainSpace is the first
publicly released dataset for cereal grain inspection.


title: Saturation of Ordered Graphs

abstract:
  Recently, the saturation problem of $0$-$1$ matrices gained a lot of
attention. This problem can be regarded as a saturation problem of ordered
bipartite graphs. Motivated by this, we initiate the study of the saturation
problem of ordered and cyclically ordered graphs.
  We prove that dichotomy holds also in these two cases, i.e., for a
(cyclically) ordered graph its saturation function is either bounded or linear.
We also determine the order of magnitude for large classes of (cyclically)
ordered graphs, giving infinite many examples exhibiting both possible
behaviours, answering a problem of P\'alv\"olgyi. In particular, in the ordered
case we define a natural subclass of ordered matchings, the class of linked
matchings, and we start their systematic study, concentrating on linked
matchings with at most three links and prove that many of them have bounded
saturation function.
  In both the ordered and cyclically ordered case we also consider the
semisaturation problem, where dichotomy holds as well and we can even fully
characterize the graphs that have bounded semisaturation function.


title:
The evolution of the corona in MAXI J1535-571 through type-C
  quasi-periodic oscillations with Insight-HXMT

abstract:
  Type-C quasi-periodic oscillations (QPOs) in black hole X-ray transients can
appear when the source is in the low-hard and hard-intermediate states. The
spectral-timing evolution of the type-C QPO in MAXI J1535-571 has been recently
studied with Insight-HXMT. Here we fit simultaneously the time-averaged energy
spectrum, using a relativistic reflection model, and the fractional rms and
phase-lag spectra of the type-C QPOs, using a recently developed time-dependent
Comptonization model when the source was in the intermediate state. We show,
for the first time, that the time-dependent Comptonization model can
successfully explain the X-ray data up to 100 keV. We find that in the
hard-intermediate state the frequency of the type-C QPO decreases from 2.6 Hz
to 2.1 Hz, then increases to 3.3 Hz, and finally increases to ~ 9 Hz.
Simultaneously with this, the evolution of corona size and the feedback
fraction (the fraction of photons up-scattered in the corona that return to the
disc) indicates the change of the morphology of the corona. Comparing with
contemporaneous radio observations, this evolution suggests a possible
connection between the corona and the jet when the system is in the
hard-intermediate state and about to transit into the soft-intermediate state.


title: Adaptive Safety for Internet of Things in e-Health

abstract:
  Wireless pervasive computing devices are rapidly penetrating our
environments, where e-Health is among the most critical ones. In the e-Health
environment it is not only important to address security and privacy issues
(which are usually in the focus), but also safety needs for appropriate
treatment. With an increasing proportion of pervasive (smart dust) devices
which lack computing power, security, privacy, and safety provisioning in such
environments is a demanding task - not to mention additional requirement about
their adaptive provisioning. However, some twenty years ago an interesting
research branch in computing domain appeared, called trust management. This
branch is considered as a very handy alternative to support traditional hard
security and privacy approaches. It is, therefore, often referred to as soft
security (privacy) provisioning mechanism. As trust is inherently adaptive and
as security and safety are much related, this paper presents a new approach
where trust management is deployed in e-Health environments to enable adaptive
safety provisioning. This paper also introduces a trustworthiness calculation
framework that extends trust management methods with a comparative analysis of
computational trust in Internet of Things (IoT).


title: Physics of the tau lepton

abstract:
  Within our present knowledge, the tau is the heaviest lepton and the only one
decaying into hadrons, a fact that makes it the source of a very rich
phenomenology. It represents the third family of leptons in the Standard Model,
a feature that helps its classification but whose real meaning is not asserted
yet. The tau lepton provides: i) a clean and unique environment to study both
the hadronization of QCD currents, in an energy region populated by resonances,
and the phenomenological determination of relevant parameters of the Model; ii)
together with the muon, they have a very constrained flavour dynamics (in the
absence of neutrino masses) due to an accidental global symmetry of the
Standard Model. In consequence, the tau lepton brings an excellent benchmark
for the study of QCD at low energies and, at the same time, for the search of
new physics.


title:
Design-Technology Co-Optimization for NVM-based Neuromorphic Processing
  Elements

abstract:
  Neuromorphic hardware platforms can significantly lower the energy overhead
of a machine learning inference task. We present a design-technology tradeoff
analysis to implement such inference tasks on the processing elements (PEs) of
a Non- Volatile Memory (NVM)-based neuromorphic hardware. Through detailed
circuit-level simulations at scaled process technology nodes, we show the
negative impact of technology scaling on the information-processing latency,
which impacts the quality-of-service (QoS) of an embedded ML system. At a finer
granularity, the latency inside a PE depends on 1) the delay introduced by
parasitic components on its current paths, and 2) the varying delay to sense
different resistance states of its NVM cells. Based on these two observations,
we make the following three contributions. First, on the technology front, we
propose an optimization scheme where the NVM resistance state that takes the
longest time to sense is set on current paths having the least delay, and vice
versa, reducing the average PE latency, which improves the QoS. Second, on the
architecture front, we introduce isolation transistors within each PE to
partition it into regions that can be individually power-gated, reducing both
latency and energy. Finally, on the system-software front, we propose a
mechanism to leverage the proposed technological and architectural enhancements
when implementing a machine-learning inference task on neuromorphic PEs of the
hardware. Evaluations with a recent neuromorphic hardware architecture show
that our proposed design-technology co-optimization approach improves both
performance and energy efficiency of machine-learning inference tasks without
incurring high cost-per-bit.


title: Explicit representations for Banach subspaces of Lizorkin distributions

abstract:
  The Lizorkin space is well-suited for studying various operators; e.g.,
fractional Laplacians and the Radon transform. In this paper, we show that the
space is unfortunately not complemented in the Schwartz space. However, we can
show that it is dense in $C_0(\mathbb R^d)$, a property that is shared by the
larger Schwartz space and that turns out to be useful for applications. Based
on this result, we investigate subspaces of Lizorkin distributions that are
Banach spaces and for which a continuous representation operator exists. Then,
we introduce a variational framework involving these spaces and that makes use
of the constructed operator. By investigating two particular cases of this
framework, we are able to strengthen existing results for fractional splines
and 2-layer ReLU networks.


title: On deformations of constrained Hamiltonian systems in BFV-formalism

abstract:
  We study the deformation describing a mapping any dynamical system with
irreducible first-class constraints in the phase space into another dynamical
system with first-class constraints. It is shown that such a deformation
problem can be effectively explored in the framework of the
Batalin-Fradkin-Vilkovisky (BFV) formalism. The basic objects of this formalism
are the BRST-BFV charge and a generalized Hamiltonian that satisfy the defining
equations in the extended phase space in terms of (super)Poisson brackets.
Solution to the deformation problem is found in terms of a (super)canonical
transformation with a special generating function which is explicitly
established. It is proved that this generating function is defined by a single
arbitrary function which depends only on coordinates of initial dynamical
system. To illustrate the developed approach, we have constructed a non-local
deformation of the Abelian gauge theory into a non-local non-Abelian gauge
theory whose local sector coincides with the Yang-Mills theory.


title: SoK: On the Semantic AI Security in Autonomous Driving

abstract:
  Autonomous Driving (AD) systems rely on AI components to make safety and
correct driving decisions. Unfortunately, today's AI algorithms are known to be
generally vulnerable to adversarial attacks. However, for such AI
component-level vulnerabilities to be semantically impactful at the system
level, it needs to address non-trivial semantic gaps both (1) from the
system-level attack input spaces to those at AI component level, and (2) from
AI component-level attack impacts to those at the system level. In this paper,
we define such research space as semantic AI security as opposed to generic AI
security. Over the past 5 years, increasingly more research works are performed
to tackle such semantic AI security challenges in AD context, which has started
to show an exponential growth trend.
  In this paper, we perform the first systematization of knowledge of such
growing semantic AD AI security research space. In total, we collect and
analyze 53 such papers, and systematically taxonomize them based on research
aspects critical for the security field. We summarize 6 most substantial
scientific gaps observed based on quantitative comparisons both vertically
among existing AD AI security works and horizontally with security works from
closely-related domains. With these, we are able to provide insights and
potential future directions not only at the design level, but also at the
research goal, methodology, and community levels. To address the most critical
scientific methodology-level gap, we take the initiative to develop an
open-source, uniform, and extensible system-driven evaluation platform, named
PASS, for the semantic AD AI security research community. We also use our
implemented platform prototype to showcase the capabilities and benefits of
such a platform using representative semantic AD AI attacks.


title:
Investigations of the Systematic Uncertainties in Convolutional Neural
  Network Based Analysis of Atmospheric Cherenkov Telescope Data

abstract:
  Machine learning, through the use of convolutional and recurrent neural
networks is a promising avenue for the improvement of background rejection
performance in imaging atmospheric Cherenkov telescopes. However, it is of
paramount importance for science analysis that their performance remains stable
against a wide range of observing conditions and instrument states.
  We investigate the stability of convolutional recurrent networks by applying
them to background rejection in a toy Monte Carlo simulation of a Cherenkov
telescope array. We then vary a range of observation and instrument parameters
in the simulation. In general, most of the resulting systematics are at a level
not much greater than conventional analyses. However, a strong dependence of
the neural network predictions on the noise level within the camera was found,
with differences of up to 50% in the gamma-ray acceptance rate in very noisy
environments. It is clear from the performance differences seen in these
studies that these observational effects must be considered in the training
step of the final analysis when using such networks for background rejection in
Cherenkov telescope observations.


title:
Tracing Molecular Gas in z $\simeq$ 6 Galaxies with [C${\rm \scriptsize
  II}$]

abstract:
  We investigate the fine-structure [C${\rm \scriptsize II}$] line at
$158\,\mu$m as a molecular gas tracer by analyzing the relationship between
molecular gas mass ($M_{\rm mol}$) and [C${\rm \scriptsize II}$] line
luminosity ($L_{\rm [CII]}$) in 11,125 $z\simeq 6$ star-forming, main sequence
galaxies from the SIMBA simulations, with line emission modeled by S\'IGAME.
Though most ($\sim 50-100\,\%$) of the gas mass in our simulations is ionized,
the bulk ($> 50\,\%$) of the [C${\rm \scriptsize II}$] emission comes from the
molecular phase. We find a sub-linear (slope $0.78\pm 0.01$) $\log L_{\rm
[CII]}-\log M_{\rm mol}$ relation, in contrast with the linear relation derived
from observational samples of more massive, metal-rich galaxies at $z \lesssim
6$. We derive a median [C${\rm \scriptsize II}$]-to-$M_{\rm mol}$ conversion
factor of $\alpha_{\rm [CII]} \simeq 18\,{\rm M_{\rm \odot}/L_{\rm \odot}}$.
This is lower than the average value of $\simeq 30\,{\rm M_{\rm \odot}/L_{\rm
\odot}}$ derived from observations, which we attribute to lower gas-phase
metallicities in our simulations. Thus, a lower, luminosity-dependent,
conversion factor must be applied when inferring molecular gas masses from
[C${\rm \scriptsize II}$] observations of low-mass galaxies. For our
simulations, [C${\rm \scriptsize II}$] is a better tracer of the molecular gas
than CO $J=1-0$, especially at the lowest metallicities, where much of the gas
is 'CO-dark'. We find that $L_{\rm [CII]}$ is more tightly correlated with
$M_{\rm mol}$ than with star-formation rate (${\rm SFR}$), and both the $\log
L_{\rm [CII]}-\log M_{\rm mol}$ and $\log L_{\rm [CII]}-\log {\rm SFR}$
relations arise from the Kennicutt-Schmidt relation. Our findings suggest that
$L_{\rm [CII]}$ is a promising tracer of the molecular gas at the earliest
cosmic epochs.


title: Maximum Likelihood Estimation of a Proportion from a Sample of Triplets

abstract:
  When estimating a proportion and only a sample of triplets is given,
dependencies within the triplets are to be accounted for. Without assuming a
distribution for the success count of the triplet, together with the
proportion, as second and third parameter the correlations of 1st and 2nd order
enter the model. We apply maximum likelihood estimation, and derive consistency
by using that the triplet count is multinomially distributed, combined with the
continuous mapping theorem. The asymptotic normality follows with the
delta-method, resulting in closed-form expressions for the standard errors. As
application we study caries prevalence of pre-school children from a sample to
nursing schools. We compare the standard errors with those for assuming
erroneously independence within the nursing schools. As to be suspected, the
design `inflates' the standard error markedly.


title:
Comment on "Explaining the specific heat of liquids based on
  instantaneous normal modes"

abstract:
  In a recent paper (Phys. Rev. E {\bf 104}, 014103 (2021) ) M. Baggioli and A.
Zaccone formulate a theoretical description of the specific heat of liquids by
using Debye's expression for the specific heat of solids and inserting a
density of states (DOS) which they claim to represent the
instantaneous-normal-mode (INM) spectrum of a liquid. However, the
quantum-mechanical procedure of Debye cannot be used for a classical liquid and
the authors' formula for the INM spectrum does not represent the known INM
spectra of simple liquids. Furthermore, the derivation of this formula from
their model equation of motion is mathematically in error. Finally experimental
test of the teory for the specific heat of {\it liquids} is performed by
fitting the data of {\it supercritical fluids}. \new{To our opinion,} these and
a lot of other inconsistencies render this work not suitable for studying the
specific heat of liquids.


title: Event Weighting vs. Event Counting

abstract:
  Goal of these proceedings is to introduce a method based on event weighting
in particle physics experiments. Weighting means that events are not just
counted as integer numbers but are assigned a weight factor according to their
importance in the analysis. This method has a close connection to the maximum
likelihood method known to reach the smallest statistical error. The purpose of
this document is to give a more educational overview on the subject. As an
example the extraction of a beam polarization from scattered particles is
discussed.


title: Search for $B^{0} \to K_S^{0}K_S^{0}\gamma$ decays at Belle

abstract:
  We report the first search for the penguin-dominated process $B^0 \rightarrow
K_S^0 K_S^0 \gamma$ using the full data sample of $772\times 10^6$ $B\bar{B}$
pairs collected with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$
collider. We do not observe any statistically significant signal yield in the
$K_S^0$-pair invariant mass range 1 GeV/$c^2 < M_{K_S^0 K_S^0} < $ 3 GeV/$c^2$,
and set the following upper limits at 90% confidence level: $\mathcal{B}(B^0
\to K_S^0 K_S^0 \gamma) < 5.8\times10^{-7}$, $\mathcal{B}(B^0 \to f_2
\gamma)\times \mathcal{B}(f_2 (1270) \to K_S^0 K_S^0 ) < 3.1\times10^{-7}$, and
$\mathcal{B}(B^0 \to f_2^{\prime} \gamma)\times \mathcal{B}(f_2^{\prime} (1525)
\to K_S^0 K_S^0 ) < 2.1\times10^{-7}$. Further, 90% confidence upper limits
have also been set in the range of [0.7-2.9]$\times10^{-7}$ on the $B^0
\rightarrow K_S^0 K_S^0 \gamma$ branching fraction in bins of $M_{K_S^0
K_S^0}$.


title: StyleBabel: Artistic Style Tagging and Captioning

abstract:
  We present StyleBabel, a unique open access dataset of natural language
captions and free-form tags describing the artistic style of over 135K digital
artworks, collected via a novel participatory method from experts studying at
specialist art and design schools. StyleBabel was collected via an iterative
method, inspired by `Grounded Theory': a qualitative approach that enables
annotation while co-evolving a shared language for fine-grained artistic style
attribute description. We demonstrate several downstream tasks for StyleBabel,
adapting the recent ALADIN architecture for fine-grained style similarity, to
train cross-modal embeddings for: 1) free-form tag generation; 2) natural
language description of artistic style; 3) fine-grained text search of style.
To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and
cross-modal representation learning, achieving a state of the art accuracy in
fine-grained style retrieval.


title:
Semi-empirical model to determine pre- and post-neutron fission product
  yields and neutron multiplicity

abstract:
  Post-neutron emission fission product mass distributions are calculated by
using pre-neutron emission fission product yields (FPYs) and neutron
multiplicity. A semi-empirical model is used to calculate the pre-neutron FPY,
first. Then the neutron multiplicity for each fission fragment mass is used to
convert the pre-neutron FPY to the post-neutron FPY. In doing so, assumptions
are made for the probability for a pre-emission fission fragment with a mass
number $A^*$ to decay to a post-emission fragment with a mass number $A$. The
resulting post-neutron FPYs are compared with the data available. The systems
where the experimental data of not only the pre- and post-neutron FPY but also
neutron multiplicity are available are the thermal neutron-induced fission of
$^{233}$U, $^{235}$U and $^{239}$Pu. Thus, we applied the model calculations to
these systems and compared the calculation results with those from the GEF and
the data from the ENDF and the EXFOR libraries. Both the pre- and post-neutron
fission product mass distributions calculated by using the semi-empirical model
and the neutron multiplicity reproduce the overall features of the experimental
data.


title:
Exploiting the Potential of Datasets: A Data-Centric Approach for Model
  Robustness

abstract:
  Robustness of deep neural networks (DNNs) to malicious perturbations is a hot
topic in trustworthy AI. Existing techniques obtain robust models given fixed
datasets, either by modifying model structures, or by optimizing the process of
inference or training. While significant improvements have been made, the
possibility of constructing a high-quality dataset for model robustness remain
unexplored. Follow the campaign of data-centric AI launched by Andrew Ng, we
propose a novel algorithm for dataset enhancement that works well for many
existing DNN models to improve robustness. Transferable adversarial examples
and 14 kinds of common corruptions are included in our optimized dataset. In
the data-centric robust learning competition hosted by Alibaba Group and
Tsinghua University, our algorithm came third out of more than 3000 competitors
in the first stage while we ranked fourth in the second stage. Our code is
available at \url{https://github.com/hncszyq/tianchi_challenge}.


title: Shape coexistence in Sr isotopes

abstract:
  Sr isotopes are located in the mass region $A\approx 100$, where a very quick
onset of nuclear deformation exists, being other notable examples of this area
Yb, Zr, and Nb nuclei. The presence of the proton subshell closure $Z=40$
allows the existence of particle-hole excitations that produces low-lying
intruder bands.
  Purpose: The goal of this work is the study of the nuclear structure of the
even-even $^{92-102}$Sr isotopes through the accurate description of excitation
energies, $B(E2)$ transition rates, nuclear radii and two-neutron separation
energies.
  Method: The interacting boson model with configuration mixing will be the
framework to calculate all the observables of the Sr isotopes. Only two types
of configurations will be considered, namely, 0particle-0hole and
2particle-2hole excitations. The parameters of the model are determined using a
least-squares procedure for the excitation energies and the $B(E2)$ transition
rates.
  Results: For the whole chain of isotopes, the value of excitation energies,
$B(E2)$'s, two-neutron separation energies, nuclear radii, and isotope shifts
have been obtained, with a good agreement between theory and experiment. Also,
a detailed analysis of the wave functions have been performed and, finally, the
mean-field energy surfaces and the value of the nuclear deformation, $\beta$,
have been obtained.
  Conclusions: The presence of low-lying intruder states in even-even Sr
isotopes have been confirmed and its connection with the onset of deformation
has been clarified. Lightest Sr isotopes present a spherical structure while
the heaviest ones are clearly deformed. The rapid onset of deformation at
neutron number $60$ is due to the crossing of the regular and intruder
configurations and, moreover, both families of states present an increase of
deformation with the neutron number.


title:
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First --
  Using Relation Extraction to Identify Entities

abstract:
  In this paper, we present an end-to-end joint entity and relation extraction
approach based on transformer-based language models. We apply the model to the
task of linking mathematical symbols to their descriptions in LaTeX documents.
In contrast to existing approaches, which perform entity and relation
extraction in sequence, our system incorporates information from relation
extraction into entity extraction. This means that the system can be trained
even on data sets where only a subset of all valid entity spans is annotated.
We provide an extensive evaluation of the proposed system and its strengths and
weaknesses. Our approach, which can be scaled dynamically in computational
complexity at inference time, produces predictions with high precision and
reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the
domain of physics and math, it achieves high relation extraction macro F1
scores of 95.43% and 79.17%, respectively. The code used for training and
evaluating our models is available at: https://github.com/nicpopovic/RE1st


title: Arc coloring of odd graphs with applications

abstract:
  Coloring the arcs of biregular graphs was recently introduced with possible
applications to industrial chemistry, molecular biology, cellular neuroscience,
etc. In the present work, we deal with arc coloring also in some non-bipartite
graphs. In fact, for $1<k\in\mathbb{Z}$, we find that the odd graph $O_k$ has
an arc factorization with colors $0,1,\ldots,k$ such that the sum of colors of
the two arcs of each edge equals $k$. This is applied to analyzing the
influence of such arc factorizations in recently constructed uniform 2-factors
of $O_k$ and Hamilton cycles of $O_k$ and its double covering graph known as
the middle-levels graph $M_k$.