title:
Decorated Linear Relations: Extending Gaussian Probability with
  Uninformative Priors

abstract:
  We introduce extended Gaussian distributions as a precise and principled way
of combining Gaussian probability uninformative priors, which indicate complete
absence of information. To give an extended Gaussian distribution on a
finite-dimensional vector space $X$ is to give a subspace $D$, along which no
information is known, together with a Gaussian distribution on the quotient
$X/D$. We show that the class of extended Gaussians remains closed under taking
conditional distributions. We then introduce decorated linear maps and
relations as a general framework to combine probability with nondeterminism on
vector spaces, which includes extended Gaussians as a special case. This
enables us to apply methods from categorical logic to probability, and make
connections to the semantics of probabilistic programs with exact conditioning.


title:
Data+Shift: Supporting visual investigation of data distribution shifts
  by data scientists

abstract:
  Machine learning on data streams is increasingly more present in multiple
domains. However, there is often data distribution shift that can lead machine
learning models to make incorrect decisions. While there are automatic methods
to detect when drift is happening, human analysis, often by data scientists, is
essential to diagnose the causes of the problem and adjust the system. We
propose Data+Shift, a visual analytics tool to support data scientists in the
task of investigating the underlying factors of shift in data features in the
context of fraud detection. Design requirements were derived from interviews
with data scientists. Data+Shift is integrated with JupyterLab and can be used
alongside other data science tools. We validated our approach with a
think-aloud experiment where a data scientist used the tool for a fraud
detection use case.


title: Semi-Assisted Signal Authentication based on Galileo ACAS

abstract:
  A GNSS signal authentication concept named semi-assisted authentication is
proposed. It is based on the re-encryption and publication of keystream
sequences of some milliseconds from an already existing encrypted signal. Some
seconds after the keystreams are transmitted in the signal-in-space, the signal
broadcasts the key allowing to decrypt the sequences and the a-posteriori
correlation at the receiver. The concept is particularized as Galileo Assisted
Commercial Authentication Service, or ACAS, for Galileo E1-B, with OSNMA used
for the decryption keys, and E6C, assumed to be encrypted in the near future.
This work proposes the ACAS cryptographic operations and a model for signal
processing and authentication verification. Semi-assisted authentication can be
provided without any modification to the signal plan of an existing GNSS,
without the disclosure of signal encryption keys, and for several days of
receiver autonomy, depending on its storage capabilities.


title: Geophysical Inversion and Optimal Transport

abstract:
  We propose a new approach to measuring the agreement between two oscillatory
time series, such as seismic waveforms, and demonstrate that it can be employed
effectively in inverse problems. Our approach is based on Optimal Transport
theory and the Wasserstein distance, with a novel transformation of the time
series to ensure that necessary normalisation and positivity conditions are
met. Our measure is differentiable, and can readily be employed within an
optimization framework. We demonstrate performance with a variety of synthetic
examples, including seismic source inversion, and observe substantially better
convergence properties than achieved with conventional $L_2$ misfits. We also
briefly discuss the relationship between Optimal Transport and Bayesian
inference.


title:
Quantum Computing for Power Flow Algorithms: Testing on real Quantum
  Computers

abstract:
  Quantum computing has the potential to solve many computational problems
exponentially faster than classical computers. The high shares of renewables
and the wide deployment of converter-interfaced resources require new tools
that shall drastically accelerate power system computations, including
optimization and security assessment, which can benefit from quantum computing.
To the best of our knowledge, this is the first paper that goes beyond quantum
computing simulations and performs an experimental application of Quantum
Computing for power systems on a real quantum computer. We use four different
quantum computers, apply the HHL quantum algorithm, and examine the impact of
current noisy quantum hardware on the accuracy and speed of an AC power flow
algorithm. Using the insights from our real experiments on a 3-bus system, we
perform the same studies on a 5-bus system with a simulated quantum computer to
identify challenges and open research questions related with the scalability of
these algorithms.


title: All-fibre wavefront sensor

abstract:
  We report on a tapered three-core optical fibre that can be used as a
tip-tilt wavefront sensor. In this device, a coupled region of a few
millimetres at the sensing tip of the fibre converts fragile phase information
from an incoming wavefront into robust intensity information within each of the
cores. The intensity information can be easily converted to linear wavefront
error over small ranges, making it ideal for closed loop systems. The sensor
uses minimal information to infer tip-tilt and is compatible with remote
detector arrays. We explore its application within adaptive optics and present
a validation case to show its applicability to astronomy


title:
Neural Implicit Representations for Physical Parameter Inference from a
  Single Video

abstract:
  Neural networks have recently been used to analyze diverse physical systems
and to identify the underlying dynamics. While existing methods achieve
impressive results, they are limited by their strong demand for training data
and their weak generalization abilities to out-of-distribution data. To
overcome these limitations, in this work we propose to combine neural implicit
representations for appearance modeling with neural ordinary differential
equations (ODEs) for modelling physical phenomena to obtain a dynamic scene
representation that can be identified directly from visual observations. Our
proposed model combines several unique advantages: (i) Contrary to existing
approaches that require large training datasets, we are able to identify
physical parameters from only a single video. (ii) The use of neural implicit
representations enables the processing of high-resolution videos and the
synthesis of photo-realistic images. (iii) The embedded neural ODE has a known
parametric form that allows for the identification of interpretable physical
parameters, and (iv) long-term prediction in state space. (v) Furthermore, the
photo-realistic rendering of novel scenes with modified physical parameters
becomes possible.


title:
Complete calculation of exclusive heavy vector meson production at
  next-to-leading order in the dipole picture

abstract:
  Exclusive production of transversely polarized heavy vector mesons in deep
inelastic scattering at high energy is calculated at next-to-leading order
accuracy in the Color Glass Condensate framework. In addition to the first QCD
correction proportional to the strong coupling constant $\alpha_s$, we
systematically also include the first relativistic correction proportional to
the heavy quark velocity squared $v^2$. When combined with our previously
published results for longitudinal vector meson production at next-to-leading
order accuracy, these results make phenomenological calculations of heavy
vector meson production possible at the order $\mathcal{O}(\alpha_s v^0,
\alpha_s^0 v^2)$. When applied to $\mathrm{J}/\psi$ and $\Upsilon$ production
at HERA and at the LHC, a good agreement between the next-to-leading order
calculations and experimental data is found. Additionally, we demonstrate that
vector meson production can provide additional constraints compared to
structure function analyses when the nonperturbative initial condition for the
Balitsky-Kovchegov evolution equation is extracted.


title:
$\rho$ meson generalized parton distributions in the Nambu--Jona-Lasinio
  model

abstract:
  In this paper, both the unpolarized and the polarized $\rho$ meson
generalized parton distributions are investigated in the framework of the
Nambu--Jona-Lasinio model using proper time regularization scheme. The symmetry
properties of $\rho$ meson generalized parton distributions are checked. The
three independent distribution functions in deep inelastic scattering,
$F_1(x)$, $b_1(x)$ and $g_1(x)$, and the Sachs-like charge, magnetic, and
quadruple form factors $G_C(t)$, $G_M(t)$ and $G_Q(t)$, which are the first
Mellin moments of unpolarized generalized parton distributions are obtained. In
addition, the $u$ quark axial vector form factors $\tilde{F}_1^u(t)$ and
$\tilde{F}_2^u(t)$ related to the axial currents from the polarized generalized
parton distributions are studied. The impact parameter dependent parton
distribution functions, which are the two-dimensional Fourier transform of
generalized parton distributions are studied, too. The obtained $\rho$ meson
generalized parton distributions satisfy the required properties well.


title: Light Dark Matter Axion Detection with Static Electric Field

abstract:
  We explore the axionic dark matter search sensitivity with a narrow-band
detection scheme aiming at the axion-photon conversion by the static electric
field inside a cylindrical capacitor. An alternating magnetic field signal is
induced by effective currents as the axion dark matter flows perpendicularly
through the electric field. At low axion masses, like in a KKLT scenario,
front-end narrow band filtering is provided by using LC resonance with a high
$Q$ factor, which enhances the detectability of the tiny magnetic field signal
and also leads to a thermal noise as the major background that can be reduced
at cryogenic conditions. We demonstrate that high $g_{a\gamma}$ sensitivity can
be achieved by using a strong electric field. The QCD axion theoretical
parameter space can be reached with high $E\sim$ GVm$^{-1}$ field strength.
Using the static electric field scheme essentially avoids exposing the
sensitive superconducting pickup to an applied laboratory magnetic field.


title: A Challenging Benchmark of Anime Style Recognition

abstract:
  Given two images of different anime roles, anime style recognition (ASR) aims
to learn abstract painting style to determine whether the two images are from
the same work, which is an interesting but challenging problem. Unlike
biometric recognition, such as face recognition, iris recognition, and person
re-identification, ASR suffers from a much larger semantic gap but receives
less attention. In this paper, we propose a challenging ASR benchmark. Firstly,
we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of
190 anime works and each work at least has ten different roles. In addition to
the large-scale, LSASRD contains a list of challenging factors, such as complex
illuminations, various poses, theatrical colors and exaggerated compositions.
Secondly, we design a cross-role protocol to evaluate ASR performance, in which
query and gallery images must come from different roles to validate an ASR
model is to learn abstract painting style rather than learn discriminative
features of roles. Finally, we apply two powerful person re-identification
methods, namely, AGW and TransReID, to construct the baseline performance on
LSASRD. Surprisingly, the recent transformer model (i.e., TransReID) only
acquires a 42.24% mAP on LSASRD. Therefore, we believe that the ASR task of a
huge semantic gap deserves deep and long-term research. We will open our
dataset and code at https://github.com/nkjcqvcpi/ASR.


title: The effect of stretching on nerve excitability

abstract:
  Nerves are frequently stretched during movement. We investigate here the
effect of stretch on nerve excitability within the framework of the soliton
theory. This thermodynamic theory for nerve pulse propagation relies on the
presence of a melting transition in the nerve membrane. In this transition, the
area of the nerve membrane and the nerve thickness change. It depends on
thermodynamic variables including temperature, the chemical potentials of
anesthetics and on hydrostatic pressure. A further variable relevant for
movement science is the the stretching of nerves, i.e., a tension in the nerve
caused by muscle contraction, the bending of joints and the pulling on
extremities. We show here that the soliton theory predicts a decrease in nerve
excitability upon stretching. This becomes evident in a reduction of the
amplitude of compound action potentials and in the suppression of reflexes. We
compare these predictions with medical findings.


title: Distributed Software Evolution: a Survey

abstract:
  Distribution can be a feature of the software evolution process. In other
words, temporally and spatially distributed teams and organizations can develop
and work on a software application. The simplest case is to outsource
production and employ workforce at distributed sites so that multiple
distributed teams can work on a project within a parallel framework. If this
distribution is global, it will be called the global software evolution or
development. A higher level of distribution is defined as decentralization and
decentralized software evolution, which means that software development can be
independent of the initial provider. It also means that software execution is
independent of the initial provider and the initial system so that the software
application can easily be reused in different and new projects. However, the
high level architecture is managed within a practically centralized framework
in the decentralized software evolution. Most of the large scale open-source
projects are exemplars of this level. In terms of distribution, there is a
higher level of decentralized software evolution called "distributed cognition
and leadership". At this level of distribution, all system levels evolve within
a distributed framework, and there are no centralized points in the project
network and its evolution process. Some open-source software applications are
the exemplars of this last level. Not only is the distributed software
evolution faced with certain challenges and opportunities to reach its goals,
but it has also caused some challenges and opportunities in other fields. This
paper conducts a general review of the distributed software evolution. For this
purpose, the paper first addresses the importance of the distributed software
evolution, and then introduces its noteworthy paradigms.


title: Discretisation-adaptive regularisation of statistical inverse problems

abstract:
  We consider linear inverse problems under white noise. These types of
problems can be tackled with, e.g., iterative regularisation methods and the
main challenge is to determine a suitable stopping index for the iteration.
Convergence results for popular adaptive methods to determine the stopping
index often come along with restrictions, e.g. concerning the type of
ill-posedness of the problem, the unknown solution or the error distribution.
In the recent work \cite{jahn2021optimal} a modification of the discrepancy
principle, one of the most widely used adaptive methods, applied to spectral
cut-off regularisation was presented which provides excellent convergence
properties in general settings. Here we investigate the performance of the
modified discrepancy principle with other filter based regularisation methods
and we hereby focus on the iterative Landweber method. We show that the method
yields optimal convergence rates and present some numerical experiments
confirming that it is also attractive in terms of computational complexity. The
key idea is to incorporate and modify the discretisation dimension in an
adaptive manner.


title:
Phase-free ZX diagrams are CSS codes (...or how to graphically grok the
  surface code)

abstract:
  In this paper, we demonstrate a direct correspondence between phase-free ZX
diagrams, a graphical notation for representing and manipulating a certain
class of linear maps on qubits, and Calderbank-Shor-Steane (CSS) codes, a large
family of quantum error correcting codes constructed from classical codes,
including for example the Steane code, surface codes, and colour codes. The
stabilisers of a CSS code have an especially nice structure arising from a pair
of orthogonal $\mathbb F_2$-linear subspaces, or in the case of maximal CSS
codes, a single subspace and its orthocomplement. On the other hand, phase-free
ZX diagrams can always be efficiently reduced to a normal form given by the
basis elements of an $\mathbb F_2$-linear subspace. Here, we will show that
these two ways of describing a quantum state by an $\mathbb F_2$-linear
subspace $S$ are in fact the same. Namely, the maximal CSS code generated by
$S$ fixes the quantum state whose ZX normal form is also given by $S$.
  This insight gives us an immediate translation from stabilisers of a maximal
CSS code into a ZX diagram describing its associated state. We show that we can
extend this translation to stabilisers and logical operators of any (possibly
non-maximal) CSS code by "bending wires". To demonstrate the utility of this
translation, we give a simple picture of the surface code and a fully graphical
derivation of the action of physical lattice surgery operations on the space of
logical qubits, completing the ZX presentation of lattice surgery initiated by
de Beudrap and Horsman.


title: Perturbative QCD and the Neutron Star Equation of State

abstract:
  We construct a physics-agnostic approach based on the sound speed model,
which connects both low-density information from nuclear theory and
high-density constraints from perturbative QCD (pQCD). Using this approach, we
study the impact of pQCD constraints on neutron star (NS) equations of state
(EoS) that have been constrained by astrophysical observations. We find that
the pQCD constraints affect the EoS mainly beyond the densities realized in NS.
Furthermore, we observe an interesting interplay between pQCD and astrophysical
constraints, with pQCD preferring softer EoS for the heaviest NS while recent
NICER observations suggest the EoS to be stiffer. We explore the sensitivity of
our findings to pQCD uncertainties and we study the constraining power of pQCD
if future observations of heavy NS were to suggest radii larger than 13 km for
heavy stars.


title:
Convex Grid Drawings of Planar Graphs\\with Constant Edge-Vertex
  Resolution

abstract:
  We continue the study of the area requirement of convex straight-line grid
drawings of 3-connected plane graphs, which has been intensively investigated
in the last decades. Motivated by applications, such as graph editors, we
additionally require the obtained drawings to have bounded edge-vertex
resolution, that is, the closest distance between a vertex and any non-incident
edge is lower bounded by a constant that does not depend on the size of the
graph. We present a drawing algorithm that takes as input a 3-connected plane
graph with n vertices and f internal faces and computes a convex straight-line
drawing with edge-vertex resolution at least 1/2 on an integer grid of size
(n-2+a)x(n-2+a), where a=min{n-3,f}. Our result improves the previously
best-known area bound of (3n-7)x(3n-7)/2 by Chrobak, Goodrich and Tamassia.


title:
The Need for Knowledge Organization. Introduction to the book Linking
  Knowledge: Linked Open Data for Knowledge Organization

abstract:
  This book is not restricted to semantic web (SW) technologies. An aspiration
was to contribute to the awakening of a dialogue between information and
documentation concerned with knowledge organization systems (KOSs), and
branches in computer science with an emphasis on machines, algorithms and
ontologies. The technological evolution of the last decades has not only
fostered the emergence of ever more KOSs but also semantic web technologies.
Both the actions of 'making a KOS' and 'applying existing KOSs' represent
research. The design of an information layer for a knowledge domain and the
design of a domain specific research process are intrinsically interwoven. We
extended our intervention to KOS practices into education, by presenting a
translation of existing standards and recommendations about linked open data
(LOD) publishing for non-experts. The chapters describe the state of the art in
providing KOSs as semantic artefacts; how the state of the art is applied in
new fields; how the state of the art is pushed towards new technological
solutions by being confronted with new applications; how best practices need to
be tailored towards specific solutions; and what challenges occur when merging
new and old ways of expressing KOSs. The linked data (LD) ecosystem represents
a source of knowledge generation, acquisition, production and dissemination.
The underlying discourse shows historical vision alongside the promise of
linking knowledge for interaction. The already maturing ecosystems of the SW
are interlocking information institutions clearly devoted to the expansion of
human experience through the growth of knowledge interaction.


title:
Gathering on an Infinite Triangular Grid with Limited Visibility under
  Asynchronous Scheduler

abstract:
  This work deals with the problem of gathering of $n$ oblivious mobile
entities, called robots, with limited visibility, at a point (not known
beforehand) placed on an infinite triangular grid. Earlier works of gathering
mostly considered the robots either on a plane or on a circle or on a
rectangular grid under both full and limited visibility. In the triangular
grid, there are two works to the best of our knowledge. The first one is
arbitrary pattern formation where full visibility is considered (\cite{C21}).
The other one considers seven robots with 2- hop visibility that form a hexagon
with one robot in the center of the hexagon in a collision-less environment
under a fully synchronous scheduler (\cite{ShibataOS00K21}).
  In this work, we first show that gathering on a triangular grid with 1-hop
vision of robots is not possible even under a fully synchronous scheduler if
the robots do not agree on any axes. So one axis agreement has been considered
in this work (i.e., the robots agree on a direction and its orientation). With
this capability of robots, this work shows that a swarm of any number of robots
with 1-hop visibility can gather within a finite time even when the scheduler
is asynchronous.


title:
Ultra-Broadband Visible and Infrared Light Generation Driven by Far
  Infrared Light in the Broad Region from 8{\mu}m to 240{\mu}m

abstract:
  The most commonly used nonlinear optical process is the conversion of
infrared light at 1064nm to green light at 532nm, as performed in common laser
pointers. However, more relevant for future applications are nonlinear optical
processes that generate a broad spectrum, a so called supercontinuum. A
desirable goal is generating a spectrum that covers the whole visible range
(400 -900nm), i.e., white light. Nowadays, white-light generation is usually
achieved in specially designed photonic fibres requiring high laser
intensities. However, in previous studies we showed that amorphous powders of
$(PhSn)_4S_6$ cluster-molecules generate white light when they are irradiated
by low-intensity near-infrared light. In this study, we use the mid- and
far-infrared radiation of a free-electron laser to investigate the same
molecules. White-light generation is observed for excitation with wavelength
between 8 and 240$\mu$ m. While the emitted radiation shows only slight
variations, its intensity strongly depends on the excitation wavelength. We
then match the wavelength dependent efficiency with the infrared absorption
spectra of the material. This comparison shows: whenever the excitation can
introduce molecular vibrations, less white light is generated. For all other
wavelengths the excitation interacts mostly with the electron system. This
shows that the electron system and the molecular backbone are decoupled to a
large extent. Our work contributes to the understanding of the nonlinear
process that underlies white-light generation in $(PhSn)_4S_6$ cluster
molecules. Additionally, it shows the high potential of this material in
applications where a broad laser spectrum is desired.


title: C3-STISR: Scene Text Image Super-resolution with Triple Clues

abstract:
  Scene text image super-resolution (STISR) has been regarded as an important
pre-processing task for text recognition from low-resolution scene text images.
Most recent approaches use the recognizer's feedback as clues to guide
super-resolution. However, directly using recognition clue has two problems: 1)
Compatibility. It is in the form of probability distribution, has an obvious
modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains
wrong information, thus will mislead the main task and degrade super-resolution
performance. In this paper, we present a novel method C3-STISR that jointly
exploits the recognizer's feedback, visual and linguistical information as
clues to guide super-resolution. Here, visual clue is from the images of texts
predicted by the recognizer, which is informative and more compatible with the
STISR task; while linguistical clue is generated by a pre-trained
character-level language model, which is able to correct the predicted texts.
We design effective extraction and fusion mechanisms for the triple cross-modal
clues to generate a comprehensive and unified guidance for super-resolution.
Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA
methods in fidelity and recognition performance. Code is available in
https://github.com/zhaominyiz/C3-STISR.


title:
Radon Measure Solutions to Riemann Problems for Isentropic Compressible
  Euler Equations of Polytropic Gases

abstract:
  We solve the Riemann problems for isentropic compressible Euler equations of
polytropic gases in the class of Radon measures, and the solutions admit the
concentration of mass. It is found that, under the requirement of satisfying
the over-compressing entropy condition: (i) there is a unique delta shock
solution, corresponding to the case that has two strong classical Lax shocks;
(ii) for the initial data that the classical Riemann solution contains a shock
wave and a rarefaction wave, or two shocks with one being weak, there are
infinitely many solutions, each consists of a delta shock and a rarefaction
wave; (iii) there is no delta shocks for the case that the classical entropy
weak solutions consist only of rarefaction waves. These solutions are
self-similar. Furthermore, for the generalized Riemann problem with mass
concentrated initially at the discontinuous point of initial data, there always
exists a unique delta shock for at least a short time. It could be prolonged to
a global solution. Not all the solutions are self-similar due to the initial
velocity of the concentrated point-mass (particle). Whether the delta shock
solutions constructed satisfy the over-compressing entropy condition is
clarified. This is the first result on the construction of singular measure
solutions to the compressible Euler system of polytropic gases, that is
strictly hyperbolic, and whose characteristics are both genuinely nonlinear. We
also discuss possible physical interpretations and applications of these new
solutions.


title:
Who will stay? Using Deep Learning to predict engagement of citizen
  scientists

abstract:
  Citizen science and machine learning should be considered for monitoring the
coastal and ocean environment due to the scale of threats posed by climate
change and the limited resources to fill knowledge gaps. Using data from the
annotation activity of citizen scientists in a Swedish marine project, we
constructed Deep Neural Network models to predict forthcoming engagement. We
tested the models to identify patterns in annotation engagement. Based on the
results, it is possible to predict whether an annotator will remain active in
future sessions. Depending on the goals of individual citizen science projects,
it may also be necessary to identify either those volunteers who will leave or
those who will continue annotating. This can be predicted by varying the
threshold for the prediction. The engagement metrics used to construct the
models are based on time and activity and can be used to infer latent
characteristics of volunteers and predict their task interest based on their
activity patterns. They can estimate if volunteers can accomplish a given
number of tasks in a certain amount of time, identify early on who is likely to
become a top contributor or identify who is likely to quit and provide them
with targeted interventions. The novelty of our predictive models lies in the
use of Deep Neural Networks and the sequence of volunteer annotations. A
limitation of our models is that they do not use embeddings constructed from
user profiles as input data, as many recommender systems do. We expect that
including user profiles would improve prediction performance.


title:
A Deep Learning based No-reference Quality Assessment Model for UGC
  Videos

abstract:
  Quality assessment for User Generated Content (UGC) videos plays an important
role in ensuring the viewing experience of end-users. Previous UGC video
quality assessment (VQA) studies either use the image recognition model or the
image quality assessment (IQA) models to extract frame-level features of UGC
videos for quality regression, which are regarded as the sub-optimal solutions
because of the domain shifts between these tasks and the UGC VQA task. In this
paper, we propose a very simple but effective UGC VQA model, which tries to
address this problem by training an end-to-end spatial feature extraction
network to directly learn the quality-aware spatial feature representation from
raw pixels of the video frames. We also extract the motion features to measure
the temporal-related distortions that the spatial features cannot model. The
proposed model utilizes very sparse frames to extract spatial features and
dense frames (i.e. the video chunk) with a very low spatial resolution to
extract motion features, which thereby has low computational complexity. With
the better quality-aware features, we only use the simple multilayer perception
layer (MLP) network to regress them into the chunk-level quality scores, and
then the temporal average pooling strategy is adopted to obtain the video-level
quality score. We further introduce a multi-scale quality fusion strategy to
solve the problem of VQA across different spatial resolutions, where the
multi-scale weights are obtained from the contrast sensitivity function of the
human visual system. The experimental results show that the proposed model
achieves the best performance on five popular UGC VQA databases, which
demonstrates the effectiveness of the proposed model. The code will be publicly
available.


title:
Topological Data Analysis in Time Series: Temporal Filtration and
  Application to Single-Cell Genomics

abstract:
  The absence of a conventional association between the cell-cell cohabitation
and its emergent dynamics into cliques during development has hindered our
understanding of how cell populations proliferate, differentiate, and compete,
i.e. the cell ecology. With the recent advancement of the single-cell
RNA-sequencing (RNA-seq), we can potentially describe such a link by
constructing network graphs that characterize the similarity of the gene
expression profiles of the cell-specific transcriptional programs, and
analyzing these graphs systematically using the summary statistics informed by
the algebraic topology. We propose the single-cell topological simplicial
analysis (scTSA). Applying this approach to the single-cell gene expression
profiles from local networks of cells in different developmental stages with
different outcomes reveals a previously unseen topology of cellular ecology.
These networks contain an abundance of cliques of single-cell profiles bound
into cavities that guide the emergence of more complicated habitation forms. We
visualize these ecological patterns with topological simplicial architectures
of these networks, compared with the null models. Benchmarked on the
single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25
cell types and 12 time steps, our approach highlights the gastrulation as the
most critical stage, consistent with consensus in developmental biology. As a
nonlinear, model-independent, and unsupervised framework, our approach can also
be applied to tracing multi-scale cell lineage, identifying critical stages, or
creating pseudo-time series.


title:
Distributed Learning for Principle Eigenspaces without Moment
  Constraints

abstract:
  Distributed Principal Component Analysis (PCA) has been studied to deal with
the case when data are stored across multiple machines and communication cost
or privacy concerns prohibit the computation of PCA in a central location.
However, the sub-Gaussian assumption in the related literature is restrictive
in real application where outliers or heavy-tailed data are common in areas
such as finance and macroeconomic. In this article, we propose a distributed
algorithm for estimating the principle eigenspaces without any moment
constraint on the underlying distribution. We study the problem under the
elliptical family framework and adopt the sample multivariate Kendall'tau
matrix to extract eigenspace estimators from all sub-machines, which can be
viewed as points in the Grassman manifold. We then find the "center" of these
points as the final distributed estimator of the principal eigenspace. We
investigate the bias and variance for the distributed estimator and derive its
convergence rate which depends on the effective rank and eigengap of the
scatter matrix, and the number of submachines. We show that the distributed
estimator performs as if we have full access of whole data. Simulation studies
show that the distributed algorithm performs comparably with the existing one
for light-tailed data, while showing great advantage for heavy-tailed data. We
also extend our algorithm to the distributed learning of elliptical factor
models and verify its empirical usefulness through real application to a
macroeconomic dataset.


title:
Polarimetric imaging for the detection of synthetic models of
  SARS-CoV-2: a proof of concept

abstract:
  Objective: To conduct a proof-of-concept study of the detection of two
synthetic models of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) using polarimetric imaging. Methods: Two SARS-CoV-2 models were
prepared as engineered lentiviruses pseudotyped with the G protein of the
vesicular stomatitis virus, and with the characteristic Spike protein of
SARS-CoV-2. Samples were preparations in two biofluids (saline solution and
artificial saliva), in four concentrations, and deposited as 5-{\mu}L droplets
on a supporting plate. The angles of maximal degree of linear polarization
(DLP) of light diffusely scattered from dry residues were determined using
Mueller polarimetry of 87 samples at 405 nm and 514 nm. A polarimetric camera
was used for simultaneous imaging of several samples under 380-420 nm
illumination at angles similar to those of maximal DLP. A per-pixel image
analysis included quantification and combination of polarization feature
descriptors in other 475 samples. Results: The angles (from sample surface) of
maximal DLP were 3 degrees for 405 nm and 6 degrees for 514 nm. Similar viral
particles that differ only in the characteristic spike protein of the
SARS-CoV-2, their corresponding negative controls, fluids, and the sample
holder were discerned from polarimetric image analysis at 10-degree and
15-degree configurations. Conclusion: Polarimetric imaging in the visible
spectrum has the potential for non-contact, reagent-free detection of viruses
in multiple dry fluid residues simultaneously. Further analysis including real
SARS-CoV-2 in human samples -- particularly, fresh saliva -- are required.
Significance: Polarimetric imaging under visible light could contribute to
fast, cost-effective screening of SARS-CoV-2 and other pathogens.


title: Contests to Incentivize a Target Group

abstract:
  We study how to incentivize agents in a target group to produce a higher
output in the context of incomplete information, by means of rank-order
allocation contests. We describe a symmetric Bayes--Nash equilibrium for
contests that have two types of rank-based prizes: prizes that are accessible
only to the agents in the target group; prizes that are accessible to everyone.
We also specialize this equilibrium characterization to two important
sub-cases: (i) contests that do not discriminate while awarding the prizes,
i.e., only have prizes that are accessible to everyone; (ii) contests that have
prize quotas for the groups, and each group can compete only for prizes in
their share. For these models, we also study the properties of the contest that
maximizes the expected total output by the agents in the target group.


title: Massive merging cluster PSZ2G091 as seen by the NIKA2 camera

abstract:
  PSZ2 G091.83+26.11 is a galaxy cluster with M500 = 7.43 x 10^14 Msun at z =
0.822 1. This object exhibits a complex morphology with a clear bimodality
observed in X-rays. However, it was detected and analysed in the Planck sample
as a single, spherical cluster following a universal profile 2. This model can
lead to miscalculations of thermodynamical quantities, like the pressure
profile. As future multiwavelength cluster experiments will detect more and
more objects at high redshifts, it is crucial to quantify this systematic
effect. In this work, we use high-resolution observations of the NIKA2
camera3,4,5,6 to integrate the morphological characteristics of the cluster in
our modelling. This is achieved by fitting a two-halo model to the SZ image and
then by reconstruction of the resulting projected pressure profile. We then
compare these results with the spherical assumption.


title:
Derivation and analysis of a phase field crystal model for a mixture of
  active and passive particles

abstract:
  We discuss an active phase field crystal (PFC) model that describes a mixture
of active and passive particles. First, a microscopic derivation from dynamical
density functional theory (DDFT) is presented that includes a systematic
treatment of the relevant orientational degrees of freedom. Of particular
interest is the construction of the nonlinear and coupling terms. This allows
for interesting insights into the microscopic justification of phenomenological
constructions used in PFC models for active particles and mixtures, the
approximations required for obtaining them, and possible generalizations.
Second, the derived model is investigated using linear stability analysis and
nonlinear methods. It is found that the model allows for a rich nonlinear
behavior with states ranging from steady periodic and localized states to
various time-periodic states. The latter include standing, traveling, and
modulated waves corresponding to spatially periodic and localized traveling,
wiggling, and alternating peak patterns and their combinations.

categories: cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech nlin.PS


title:
Simulation of indirect 13C-13C J-coupling tensors in diamond clusters
  hosting the NV center

abstract:
  Using the ORCA 5.0.2 software package we have simulated for a first time the
full tensors nJKL (K,L=X,Y,Z), describing n-bond J-coupling of nuclear spins
13C in H-terminated diamond-like clusters C10H16 (adamantane) and C35H36 as
well as in the cluster C33[NV-]H36 hosting the negatively charged NV- center.
We found that, in addition to usually considered isotropic scalar nJ-coupling
constant the anisotropic contributions to the nJ-coupling tensor are essential.
We have also shown that the presence of the NV center affects the J-coupling
characteristics, especially in the case of 13C-13C pairs located near the
vacancy of the NV center.


title: Accelerating, to some extent, the $p$-spin dynamics

abstract:
  We consider a detailed-balance violating dynamics whose stationary state is a
prescribed Boltzmann distribution. Such dynamics have been shown to be faster
than any equilibrium counterpart. We quantify the gain in convergence speed for
a system whose energy landscape displays one, and then an infinite number of,
energy barriers. In the latter case, we work with the mean-field disordered
$p$-spin, and show that the convergence to equilibrium or to the nonergodic
phase is accelerated, both during the $\beta$ and $\alpha$-relaxation stages.
An interpretation in terms of trajectories in phase space and of an accidental
fluctuation-dissipation theorem is provided.


title: Ultra-chaos in the ABC flow and its relationships to turbulence

abstract:
  It is well-known that three-dimensional steady-state
Arnold-Beltrami-Childress (ABC) flow often has a chaotic Lagrangian structure
besides satisfies the Navier-Stokes (NS) equations. Although trajectories of a
chaotic system have the sensitive dependence on initial conditions, i.e. the
famous "butterfly-effect", their statistical properties are normally not
sensitive to small disturbances. Such kind of chaos (such as those governed by
Lorenz equation) is called normal-chaos. However, a kind of new concept, i.e.
ultra-chaos, has been reported recently, whose statistics are sensitive to tiny
disturbances. Here, we illustrate that ultra-chaos widely exists in
trajectories of fluid-particles (in Lagrangian viewpoint) in the unstable ABC
flow, which represents a higher disorder than the normal-chaos. Besides, using
the ABC flow with small disturbance as initial condition, it is found that
trajectories of nearly all fluid-particles become ultra-chaotic after the
transition from laminar to turbulence occurs. Our results highly suggest that
ultra-chaos should have a close relationship with turbulence.


title:
Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype
  Contrast

abstract:
  We present an approach to learn voice-face representations from the talking
face videos, without any identity labels. Previous works employ cross-modal
instance discrimination tasks to establish the correlation of voice and face.
These methods neglect the semantic content of different videos, introducing
false-negative pairs as training noise. Furthermore, the positive pairs are
constructed based on the natural correlation between audio clips and visual
frames. However, this correlation might be weak or inaccurate in a large amount
of real-world data, which leads to deviating positives into the contrastive
paradigm. To address these issues, we propose the cross-modal prototype
contrastive learning (CMPC), which takes advantage of contrastive methods and
resists adverse effects of false negatives and deviate positives. On one hand,
CMPC could learn the intra-class invariance by constructing semantic-wise
positives via unsupervised clustering in different modalities. On the other
hand, by comparing the similarities of cross-modal instances from that of
cross-modal prototypes, we dynamically recalibrate the unlearnable instances'
contribution to overall loss. Experiments show that the proposed approach
outperforms state-of-the-art unsupervised methods on various voice-face
association evaluation protocols. Additionally, in the low-shot supervision
setting, our method also has a significant improvement compared to previous
instance-wise contrastive learning.


title: Delta Shock as Free Piston in Pressureless Euler Flows

abstract:
  We establish the equivalence of free piston and delta shock, for the
one-space-dimensional pressureless compressible Euler equations. The delta
shock appearing in the singular Riemann problem is exactly the piston that may
move freely forward or backward in a straight tube, driven by the pressureless
Euler flows on two sides of it in the tube. This result not only helps to
understand the physics of the somewhat mysterious delta shocks, but also
provides a way to reduce the fluid-solid interaction problem, which consists of
several initial-boundary value problems coupled with moving boundaries, to a
simpler Cauchy problem. We show the equivalence from three different
perspectives. The first one is from the sticky particles, and derives the
ordinary differential equation (ODE) of the trajectory of the piston by a
straightforward application of conservation law of momentum, which is
physically simple and clear. The second one is to study a coupled
initial-boundary value problem of pressureless Euler equations, with the piston
as a moving boundary following the Newton's second law. It depends on a concept
of Radon measure solutions of initial-boundary value problems of the
compressible Euler equations which enables us to calculate the force on the
piston given by the flow. The last one is to solve directly the singular
Riemann problem and obtain the ODE of delta shock by the generalized
Rankine-Hugoniot conditions. All the three methods lead to the same ODE.


title:
Improving the estimation of directional area scattering factor (DASF)
  from canopy reflectance: theoretical basis and validation

abstract:
  Directional area scattering factor (DASF) is a critical canopy structural
parameter for vegetation monitoring. It provides an efficient tool for
decoupling of canopy structure and leaf optics from canopy reflectance. Current
standard approach to estimate DASF from canopy bidirectional reflectance factor
(BRF) is based on the assumption that in the weakly absorbing 710 to 790 nm
spectral interval, leaf scattering does not change much with the concentration
of dry matter and thus its variation can be neglected. This results in biased
estimates of DASF and consequently leads to uncertainty in DASF-related
applications. This study proposes a new approach to account for variations in
concentrations of this biochemical constituent, which additionally uses the
canopy BRF at 2260 nm. In silico analysis of the proposed approach suggests
significant increase in accuracy over the standard technique by a relative root
mean square error (rRMSE) of 49% and 34% for one- and three dimensional scenes,
respectively. When compared with indoor multi-angular hyperspectral
measurements reported in literature, the mean absolute error has reduced by 68%
for needle leaf and 20% for broadleaf canopies. Thus, the proposed DASF
estimation approach outperforms the current one and can be used more reliably
in DASF-related applications, such as vegetation monitoring of functional
traits, dynamics, and radiation budget.


title: How does a heavy symmetric top rise?

abstract:
  The rise of the top is studied by considering slipping friction. For this
case, equations of motion are obtained by using Euler equations for a heavy
symmetric top with a hemispherical peg. Different situations are considered to
see how the rise takes place, which can help understand the rise of the top.


title:
A Collection of Quality Diversity Optimization Problems Derived from
  Hyperparameter Optimization of Machine Learning Models

abstract:
  The goal of Quality Diversity Optimization is to generate a collection of
diverse yet high-performing solutions to a given problem at hand. Typical
benchmark problems are, for example, finding a repertoire of robot arm
configurations or a collection of game playing strategies. In this paper, we
propose a set of Quality Diversity Optimization problems that tackle
hyperparameter optimization of machine learning models - a so far underexplored
application of Quality Diversity Optimization. Our benchmark problems involve
novel feature functions, such as interpretability or resource usage of models.
To allow for fast and efficient benchmarking, we build upon YAHPO Gym, a
recently proposed open source benchmarking suite for hyperparameter
optimization that makes use of high performing surrogate models and returns
these surrogate model predictions instead of evaluating the true expensive
black box function. We present results of an initial experimental study
comparing different Quality Diversity optimizers on our benchmark problems.
Furthermore, we discuss future directions and challenges of Quality Diversity
Optimization in the context of hyperparameter optimization.


title:
Multimodal Transformer-based Model for Buchwald-Hartwig and
  Suzuki-Miyaura Reaction Yield Prediction

abstract:
  Predicting the yield percentage of a chemical reaction is useful in many
aspects such as reducing wet-lab experimentation by giving the priority to the
reactions with a high predicted yield. In this work we investigated the use of
multiple type inputs to predict chemical reaction yield. We used simplified
molecular-input line-entry system (SMILES) as well as calculated chemical
descriptors as model inputs. The model consists of a pre-trained bidirectional
transformer-based encoder (BERT) and a multi-layer perceptron (MLP) with a
regression head to predict the yield. We experimented on two high throughput
experimentation (HTE) datasets for Buchwald-Hartwig and Suzuki-Miyaura
reactions. The experiments show improvements in the prediction on both datasets
compared to systems using only SMILES or chemical descriptors as input. We also
tested the model's performance on out-of-sample dataset splits of
Buchwald-Hartwig and achieved comparable results with the state-of-the-art. In
addition to predicting the yield, we demonstrated the model's ability to
suggest the optimum (highest yield) reaction conditions. The model was able to
suggest conditions that achieves 94% of the optimum reported yields. This
proves the model to be useful in achieving the best results in the wet lab
without expensive experimentation.


title:
greed: An R Package for Model-Based Clustering by Greedy Maximization of
  the Integrated Classification Likelihood

abstract:
  The greed package implements the general and flexible framework of
arXiv:2002.11577 for model-based clustering in the R language. Based on the
direct maximization of the exact Integrated Classification Likelihood with
respect to the partition, it allows jointly performing clustering and selection
of the number of groups. This combinatorial problem is handled through an
efficient hybrid genetic algorithm, while a final hierarchical step allows
accessing coarser partitions and extract an ordering of the clusters. This
methodology is applicable in a wide variety of latent variable models and,
hence, can handle various data types as well as heterogeneous data. Classical
models for continuous, count, categorical and graph data are implemented, and
new models may be incorporated thanks to S4 class abstraction. This paper
introduces the package, the design choices that guided its development and
illustrates its usage on practical use-cases.


title: An Integrated Air Quality Monitoring System for COVID-19 Virus Detection

abstract:
  The pandemic of coronavirus disease 2019 (COVID-19) caused by
syndrome-coronavirus-2 (SARS-CoV-2) has been found rapid and large-scale
diagnosis to spread across the communities. The risk of infectious airborne
aerosol transmission has been an area of increasing concern. In this regard, a
selective optic detection of SARS-CoV-2 aerosol is highly desirable, which can
actively collect the surrounding air samples and provide real-time feedbacks to
users. In this work, we developed an integrated Air Quality Monitoring System
(iAQMS) that enables real-time SARS-CoV-2 aerosol detection with high
sensitivity. The detection device, inspired by localized surface plasmon
resonance (LSPR), was designed to measure the intensity changes of light going
through our biochips in which gold nanoparticles (AuNP) combined with
antibodies were exposed to airflow containing virus aerosols. We found that the
variation of the measured photocurrent after 350 seconds of exposure to virus
aerosols is a function of virus particle concentrations. By incorporating the
air collection device and the optic detection device into a handheld sized
device that can communicate with a smart-phone through Bluetooth serial, we
demonstrate real-time and sensitive detection of SARS-CoV-2 virus aerosols with
a concentration dynamic range of 0.00001 - 0.1 pfu/uL. Because of its
sensitivity and reliability, our device can also be used for detection of other
virus aerosols by simply switching the antibodies combined to the AuNP in the
biochips.


title:
Holistic Determination of Optoelectronic Properties using
  High-Throughput Spectroscopy of Surface-Guided CsPbBr$_3$ Nanowires

abstract:
  Optoelectronic micro- and nanostructures have a vast parameter space to
explore for modification and optimisation of their functional performance. This
paper reports on a data-led approach using high-throughput single nanostructure
spectroscopy to probe > 8,000 structures, allowing for holistic analysis of
multiple material and optoelectronic parameters with statistical confidence.
The methodology is applied to surface-guided CsPbBr$_3$ nanowires, which have
complex and interrelated geometric, structural and electronic properties.
Photoluminescence-based measurements, studying both the surface and embedded
interfaces, exploits the natural inter-nanowire geometric variation to show
that increasing the nanowire width reduces the optical bandgap, increases the
recombination rate in the nanowire bulk and reduces the rate at the surface
interface. A model of carrier recombination and diffusion is developed which
ascribes these trends to carrier density and strain effects at the interfaces
and self-consistently retrieves values for carrier mobility, trap densities,
bandgap, diffusion length and internal quantum efficiency. The model predicts
parameter trends, such as the variation of internal quantum efficiency with
width, which is confirmed by experimental verification. As this approach
requires minimal a-priori information, it is widely applicable to nano- and
micro-scale materials.


title: Classifications as Linked Open Data. Challenges and Opportunities

abstract:
  Linked Data (LD) as a web--based technology enables in principle the
seamless, machine--supported integration, interplay and augmentation of all
kinds of knowledge, into what has been labeled a huge knowledge graph. Despite
decades of web technology and, more recently, the LD approach, the task to
fully exploit these new technologies in the public domain is only commencing.
One specific challenge is to transfer techniques developed preweb to order our
knowledge into the realm of Linked Open Data (LOD) This paper illustrates two
different models in which a general analytico--synthetic classification can be
published and made available as LD. In both cases, an LD solution deals with
the intricacies of a pre--coordinated indexing language.


title:
Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through
  Convex Lifting

abstract:
  This work proposes a rapid global solver for nonconvex low-rank matrix
factorization (MF) problems that we name MF-Global. Through convex lifting
steps, our method efficiently escapes saddle points and spurious local minima
ubiquitous in noisy real-world data, and is guaranteed to always converge to
the global optima. Moreover, the proposed approach adaptively adjusts the rank
for the factorization and provably identifies the optimal rank for MF
automatically in the course of optimization through tools of manifold
identification, and thus it also spends significantly less time on parameter
tuning than existing MF methods, which require an exhaustive search for this
optimal rank. On the other hand, when compared to methods for solving the
lifted convex form only, MF-Global leads to significantly faster convergence
and much shorter running time. Experiments on real-world large-scale
recommendation system problems confirm that MF-Global can indeed effectively
escapes spurious local solutions at which existing MF approaches stuck, and is
magnitudes faster than state-of-the-art algorithms for the lifted convex form.


title:
Controlled Generation of Unseen Faults for Partial and OpenSet&Partial
  Domain Adaptation

abstract:
  New operating conditions can result in a performance drop of fault
diagnostics models due to the domain gap between the training and the testing
data distributions. While several domain adaptation approaches have been
proposed to overcome such domain shifts, their application is limited if the
label spaces of the two domains are not congruent. To improve the
transferability of the trained models, particularly in setups where only the
healthy data class is shared between the two domains, we propose a new
framework based on a Wasserstein GAN for Partial and OpenSet&Partial domain
adaptation. The main contribution is the controlled fault data generation that
enables to generate unobserved fault types and severity levels in the target
domain by having only access to the healthy samples in the target domain and
faulty samples in the source domain. To evaluate the ability of the proposed
method to bridge domain gaps in different domain adaption settings, we conduct
Partial as well as OpenSet&Partial domain adaptation experiments on two bearing
fault diagnostics case studies. The results show the versatility of the
framework and that the synthetically generated fault data helps bridging the
domain gaps, especially in instances where the domain gap is large.


title:
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence
  Modeling in CTR prediction

abstract:
  The exposure sequence is being actively studied for user interest modeling in
Click-Through Rate (CTR) prediction. However, the existing methods for exposure
sequence modeling bring extensive computational burden and neglect noise
problems, resulting in an excessively latency and the limited performance in
online recommenders. In this paper, we propose to address the high latency and
noise problems via Gating-adapted wavelet multiresolution analysis (Gama),
which can effectively denoise the extremely long exposure sequence and
adaptively capture the implied multi-dimension user interest with linear
computational complexity. This is the first attempt to integrate non-parametric
multiresolution analysis technique into deep neural networks to model user
exposure sequence. Extensive experiments on large scale benchmark dataset and
real production dataset confirm the effectiveness of Gama for exposure sequence
modeling, especially in cold-start scenarios. Benefited from its low latency
and high effecitveness, Gama has been deployed in our real large-scale
industrial recommender, successfully serving over hundreds of millions users.


title: Methods pressure control apparatus for lithium metal battery

abstract:
  Lithium (Li) metal anodes are essential for developing next-generation
high-energy-density batteries. However, Li dendrite/whisker formation caused
short-circuiting issue and short cycle life have prevented lithium metal from
being viably used in rechargeable batteries. Numerous works have been done to
study how to regulate the Li growth in electrochemical cycling by using
external stacking forces. While it is widely agreed that stack pressure
positively affects the lithium plating/stripping process, the optimized
pressure range provided by different works varies greatly because of the
difference in the pressure control setup. In this work, a pressure control
apparatus is designed for Li metal batteries with liquid and solid-state
electrolytes (SSE). With considerations of minimizing cell to cell variation, a
reusable split cell and pressure load cell are made for testing electrochemical
cells with high precision pressure control. The capability of the designed
setup is demonstrated by studying the pressure effect on the Li
plating/stripping process.


title:
Dislocation healing during hydrogen absorption and desorption in
  palladium

abstract:
  An in-situ neutron diffraction investigation of the annealing and healing of
dislocations in the bulk Pd-D2 system was carried out. Lattice misfit between
the alpha and beta hydride phases produces dislocations during the phase
transition in either direction, relieving elastic strain, which is reflected in
reduced pressure hysteresis compared to the spinodal hysteresis. The effects on
the dislocation density of annealing the metal under vacuum, of annealing in
the beta hydride phase, and of the phase transformation itself were
investigated by measuring diffraction peak breadths during annealing and
hydrogen cycling. During annealing under vacuum the dislocations were removed
at a lower temperature than was previously reported, but annealing in the beta
phase gave nearly the same result. However, when cycling hydrogen in and out of
the sample, the dislocation density decreased much faster with increasing
temperature compared to annealing. In other words the process of phase
transformation allows for healing of dislocations at lower temperatures than
would be required to anneal them purely by heating. This healing effect was
observed during both absorption and desorption. This result illuminates the
mechanism by which misfit dislocations can be healed at the same rate that they
are created in a sample undergoing absorption-desorption cycling, as proposed
in theoretical models of the origin of pressure hysteresis.


title: PCrystalX -- Web Application

abstract:
  PCrystalX-Web is a web application developed to be easy and fast to use. The
purpose of the software is that researchers from any area are able to use two
methods to analyze an X-ray diffraction pattern more straightforwardly. With
the full width at half maximum (FWHM) as a function of 2$\theta$ for each peak,
it is possible to estimate the crystallite size by the Scherrer equation and
apply the Williamson-Hall methods. PCrystalX-Web is a simplified version of the
PCrystalX Software [1] program, which is still under development.


title:
Non-invasive lipid quantification of living microalgal cultures with
  digital holographic microscopy

abstract:
  Some microalgae store large amounts of neutral lipids inside lipid droplets.
Since these lipids can be used to produce biodiesel in a sustainable way,
research is developing on fast non-destructive methods to quantify and monitor
the amount of lipids in microalgal cultures. In this paper, we have developed
with digital holographic microscopy a fast quantitative method to assess the
evolution of the lipid content inside the diatom Phaeodactylum tricornutum
living cells. The method uses a specific processing of recorded hologram
sequences based on the refocusing capability of digital holographic microscopy.
Each lipid droplet volume is evaluated inside the cells on representative
samples of the culture. We have validated the method thanks to correlative
quantitative phase contrast-fluorescence imaging and extrapolated it to larger
calibrated spherical refractive particles, to demonstrate the flexibility of
the method.


title: The "Null-A" superintegrability for monomial matrix models

abstract:
  We find that superintegrability (character expansion) property persists in
the exotic sector of the monomial non-Gaussian matrix model, with potential
$\Tr X^r$, in pure phase, where the naive partition function $\langle 1
\rangle$ vanishes. The role of the (anomaly-corrected) partition function is
played by $\left\langle\chi_\rho\right\rangle$ -- the Schur average of the
suitably chosen \textit{square} partiton $\rho$; such partitions are well-known
to correspond to singular vectors of the Virasoro algebra. Further, non-zero
are only Schur averages $\left\langle \chi_\mu\right\rangle$ for such $\mu$
that have $\rho$ as their $r$-core, and superintegrability formula features the
value of the \textit{skew} Schur function $\chi_{\mu/\rho}$ at special point.
The associated topological recursion and Harer-Zagier formula generalizations
so far remain obscure.


title:
Lattice study of a magnetic contribution to heavy quark momentum
  diffusion

abstract:
  Heavy quarks placed within a hot QCD medium undergo Brownian motion,
characterized by specific transport coefficients. Their determination can be
simplified by expanding them in $T/M$, where $T$ is the temperature and $M$ is
a heavy quark mass. The leading term in the expansion originates from the
colour-electric part of a Lorentz force, whereas the next-to-leading order
involves the colour-magnetic part. We measure a colour-magnetic 2-point
correlator in quenched QCD at $T \sim 1.2 ... 2.0 T_{\rm c}^{ }$. Employing
multilevel techniques and non-perturbative renormalization, a good signal is
obtained, and its continuum extrapolation can be estimated. Modelling the shape
of the corresponding spectral function, we subsequently extract the momentum
diffusion coefficient, $\kappa$. For charm (bottom) quarks, the magnetic
contribution adds $\sim 30\%$ ($10\%$) to the electric one. The same increases
apply also to the drag coefficient, $\eta$. As an aside, the colour-magnetic
spectral function is computed at NLO.


title:
Dynamic Noises of Multi-Agent Environments Can Improve Generalization:
  Agent-based Models meets Reinforcement Learning

abstract:
  We study the benefits of reinforcement learning (RL) environments based on
agent-based models (ABM). While ABMs are known to offer microfoundational
simulations at the cost of computational complexity, we empirically show in
this work that their non-deterministic dynamics can improve the generalization
of RL agents. To this end, we examine the control of an epidemic SIR
environments based on either differential equations or ABMs. Numerical
simulations demonstrate that the intrinsic noise in the ABM-based dynamics of
the SIR model not only improve the average reward but also allow the RL agent
to generalize on a wider ranges of epidemic parameters.


title:
A Babcock-Leighton-type Solar Dynamo Operating in the Bulk of the
  Convection Zone

abstract:
  The toroidal magnetic field is assumed to be generated in the tachocline in
most Babcock-Leighton (BL)-type solar dynamo models, in which the poloidal
field is produced by the emergence and subsequent dispersal of sunspot groups.
However, magnetic activity of fully convective stars and MHD simulations of
global stellar convection have recently raised serious doubts regarding the
importance of the tachocline in the generation of the toroidal field. In this
study, we aim to develop a new BL-type dynamo model, in which the dynamo
operates mainly within the bulk of the convection zone. Our 2D model includes
the effect of solar-like differential rotation, one-cell meridional flow,
near-surface radial pumping, strong turbulent diffusion, BL-type poloidal
source, and nonlinear back-reaction of the magnetic field on its source with a
vertical outer boundary condition. The model leads to a simple dipolar
configuration of the poloidal field that has the dominant latitudinal
component, which is wound up by the latitudinal shear within the bulk of the
convection zone to generate the toroidal flux. As a result, the tachocline
plays a negligible role in the model. The model reproduces the basic properties
of the solar cycle, including (a) approximately 11 yr cycle period and 18 yr
extended cycle period; (b) equatorward propagation of the antisymmetric
toroidal field starting from high latitudes; and (c) polar field evolution that
is consistent with observations. Our model opens the possibility for a paradigm
shift in understanding the solar cycle to transition from the classical flux
transport dynamo.


title:
GC Insights: Space sector careers resources in the UK need a greater
  diversity of roles

abstract:
  Educational research highlights that improved careers education is needed to
increase participation in science, technology, engineering, and mathematics
(STEM). Current UK careers resources concerning the space sector, however, are
found to perhaps not best reflect the diversity of roles present and may in
fact perpetuate misconceptions about the usefulness of science. We, therefore,
compile a more diverse set of space-related jobs, which will be used in the
development of a new space careers resource.


title:
Fix the Noise: Disentangling Source Feature for Transfer Learning of
  StyleGAN

abstract:
  Transfer learning of StyleGAN has recently shown great potential to solve
diverse tasks, especially in domain translation. Previous methods utilized a
source model by swapping or freezing weights during transfer learning, however,
they have limitations on visual quality and controlling source features. In
other words, they require additional models that are computationally demanding
and have restricted control steps that prevent a smooth transition. In this
paper, we propose a new approach to overcome these limitations. Instead of
swapping or freezing, we introduce a simple feature matching loss to improve
generation quality. In addition, to control the degree of source features, we
train a target model with the proposed strategy, FixNoise, to preserve the
source features only in a disentangled subspace of a target feature space.
Owing to the disentangled feature space, our method can smoothly control the
degree of the source features in a single model. Extensive experiments
demonstrate that the proposed method can generate more consistent and realistic
images than previous works.


title: Intersection of parabolic subgroups in even Artin groups of FC-type

abstract:
  We show that the intersection of parabolic subgroups of an even finitely
generated FC-type Artin group is again a parabolic subgroup.


title: Few-shot learning for medical text: A systematic review

abstract:
  Objective: Few-shot learning (FSL) methods require small numbers of labeled
instances for training. As many medical topics have limited annotated textual
data in practical settings, FSL-based natural language processing (NLP) methods
hold substantial promise. We aimed to conduct a systematic review to explore
the state of FSL methods for medical NLP. Materials and Methods: We searched
for articles published between January 2016 and August 2021 using
PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. To
identify the latest relevant methods, we also searched other sources such as
preprint servers (eg., medRxiv) via Google Scholar. We included all articles
that involved FSL and any type of medical text. We abstracted articles based on
data source(s), aim(s), training set size(s), primary method(s)/approach(es),
and evaluation method(s). Results: 31 studies met our inclusion criteria-all
published after 2018; 22 (71%) since 2020. Concept extraction/named entity
recognition was the most frequently addressed task (13/31; 42%), followed by
text classification (10/31; 32%). Twenty-one (68%) studies reconstructed
existing datasets to create few-shot scenarios synthetically, and MIMIC-III was
the most frequently used dataset (7/31; 23%). Common methods included FSL with
attention mechanisms (12/31; 39%), prototypical networks (8/31; 26%), and
meta-learning (6/31; 19%). Discussion: Despite the potential for FSL in
biomedical NLP, progress has been limited compared to domain-independent FSL.
This may be due to the paucity of standardized, public datasets, and the
relative underperformance of FSL methods on biomedical topics. Creation and
release of specialized datasets for biomedical FSL may aid method development
by enabling comparative analyses.


title: Rydberg Atomic Antenna in Strongly Driven Multi-Electron Atoms

abstract:
  We study the role of intermediate excitations of Rydberg states as an example
of Kuchiev's "atomic antenna" in above-threshold ionization of xenon, in
particular their effect on the coherence between the spin-orbit-split states of
the ion. We focus on the case of a laser frequency close to resonant with the
spin-orbit splitting, where a symmetry (parity) argument would preclude any
coherence being directly generated by strong-field ionization. Using ab initio
simulations of coupled multielectron spin-orbit dynamics in strong laser
fields, we show how field-driven rescattering of the trapped Rydberg electrons
introduces efficient coupling between the spin-orbit-split channels, leading to
substantial coherences, exceeding 10 % for some photon energies.


title:
Anomalous Josephson Coupling and High-Harmonics in Non-Centrosymmetric
  Superconductors with $S$-wave Spin-Triplet Pairing

abstract:
  We study the Josephson effects arising in junctions made of
non-centrosymmetric superconductors with spin-triplet pairing having $s$-wave
orbital-singlet symmetry. We demonstrate that the orbital dependent character
of the spin-triplet order parameter determines its non-trivial texture in the
momentum space due to the inversion symmetry breaking and spin-orbit
interactions. The emergence of this pattern is responsible for the occurrence
of an anomalous Josephson coupling and a dominance of high-harmonics in the
current phase relation. Remarkably, due to the spin-orbital couplings,
variations in the electronic structure across the heterostructure can generally
turn the ground state of the junction from 0- to a generic value of the
Josephson phase, thus realizing the so-called $\varphi$-junction. Hallmarks of
the resulting Josephson behavior, apart from non-standard current-phase
relation, are provided by an unconventional temperature and magnetic field
dependence of the critical current. These findings indicate novel paths for the
design of superconducting orbitronics devices and account for several observed
anomalies of the supercurrent in oxide interface superconductors.


title:
FPGA Acceleration of Image Reconstruction for Real-Time Photoacoustic
  Tomography

abstract:
  Photoacoustic (PA) imaging has been widely applied in both preclinical and
clinical applications. With a significantly increasing number of data
acquisition channels, fast and high-quality image reconstruction for real-time
PA imaging is an open challenge in this community. In this paper, we propose a
FPGA-accelerated method to achieve a much faster image reconstruction speed by
20~60 times compared with using CPU, with much-reduced system cost and power
budget, from dozens of Watt (CPU) to 1~2 Watt (FPGA). Equivalently, the energy
efficiency ratio (EER) is improved by ~1000 times. This FPGA acceleration
method can be easily adapted to the most widely used algorithms, such as
delay-and-sum (DAS) and its variants (e.g. DMAS, DAS-CF). We have performed
in-vivo human finger experiments to demonstrate the feasibility and potential
of the proposed method. To our best knowledge, this is the first study of
accelerating PA image reconstruction based on FPGA platform.


title: Bohr-Rogosinski type inequalities for concave univalent functions

abstract:
  In this paper, we generalize and investigate Bohr-Rogosinski's inequalities
and the Bohr-Rogosinski phenomenon for the subfamilies of univalent (i.e.,
one-to-one) functions defined on unit disk $\mathbb{D}:=\{z\in \mathbb{C}:|z|<1
\}$ which maps to the concave domain, i.e., the domain whose complement is a
convex set. All the results are proved to be sharp.


title:
Deterministic Distributed Sparse and Ultra-Sparse Spanners and
  Connectivity Certificates

abstract:
  This paper presents efficient distributed algorithms for a number of
fundamental problems in the area of graph sparsification:
  We provide the first deterministic distributed algorithm that computes an
ultra-sparse spanner in $\textrm{polylog}(n)$ rounds in weighted graphs.
Concretely, our algorithm outputs a spanning subgraph with only $n+o(n)$ edges
in which the pairwise distances are stretched by a factor of at most $O(\log n
\;\cdot\; 2^{O(\log^* n)})$.
  We provide a $\textrm{polylog}(n)$-round deterministic distributed algorithm
that computes a spanner with stretch $(2k-1)$ and $O(nk + n^{1 + 1/k} \log k)$
edges in unweighted graphs and with $O(n^{1 + 1/k} k)$ edges in weighted
graphs.
  We present the first $\textrm{polylog}(n)$-round randomized distributed
algorithm that computes a sparse connectivity certificate. For an $n$-node
graph $G$, a certificate for connectivity $k$ is a spanning subgraph $H$ that
is $k$-edge-connected if and only if $G$ is $k$-edge-connected, and this
subgraph $H$ is called sparse if it has $O(nk)$ edges. Our algorithm achieves a
sparsity of $(1 + o(1))nk$ edges, which is within a $2(1 + o(1))$ factor of the
best possible.


title:
Dynamics of a Pair of Overlapping Polar Bright Solitons in Spin-1
  Bose-Einstein Condensates

abstract:
  We analyze the dynamics of both population and spin densities, emerging from
the spatial overlap between two distinct polar bright solitons in Spin-1 Spinor
Condensates. The dynamics of overlapping solitons in scalar condensates
exhibits soliton fusion, atomic switching from one soliton to another and
repulsive dynamics depending on the extent of overlap and the relative phase
between the solitons. The scalar case also helps us understand the dynamics of
the vector solitons. In the spinor case, non-trivial dynamics emerge in spatial
and spin degrees of freedom. In the absence of spin changing collisions, we
observe Josephson-like oscillations in the population dynamics of each spin
component. In this case, the population dynamics is independent of the relative
phase, but the dynamics of the spin-density vector depends on it. The latter
also witnesses the appearance of oscillating domain walls. The pair of
overlapping polar solitons emerge as four ferromagnetic solitons irrespective
of the initial phase difference for identical spin-dependent and
spin-independent interaction strengths. But the dynamics of final solitons
depends explicitly on the relative phase. Depending on the ratio of
spin-dependent and spin-independent interaction strengths, a pair of
oscillatons can also emerge as the final state. Then, increasing the extent of
overlap may lead to the simultaneous formation of both a stationary
ferromagnetic soltion and a pair of oscillatons depending on the relative
phase.


title:
Energetically Trapped Triplet Excitons and their Generation Pathways in
  Organic Solar Cell Blends based on (Non-) Halogenated PBDB-T and Y-Series

abstract:
  The great progress in organic photovoltaics (OPV) over the past few years has
been largely achieved by the development of non-fullerene acceptors (NFAs),
with power conversion efficiencies now approaching 20%. To further improve
device performance, loss mechanisms must be identified and minimized. Triplet
states are known to adversely affect device performance, since they can form
energetically trapped excitons on low-lying states that are responsible for
non-radiative losses or even device degradation. Halogenation of OPV materials
has long been employed to tailor energy levels and to enhance open circuit
voltage. Yet, the influence on recombination to triplet excitons has been
largely unexplored. Using the complementary spin-sensitive methods of
photoluminescence detected magnetic resonance (PLDMR) and transient electron
paramagnetic resonance (trEPR) corroborated by transient absorption and
quantum-chemical calculations, we unravel exciton pathways in OPV blends
employing the polymer donors PBDB-T, PM6 and PM7 together with NFAs Y6 and Y7.
All blends reveal triplet excitons on the NFA populated via non-geminate hole
back transfer and, in blends with halogenated donors, also by spin-orbit
coupling driven intersystem crossing. Identifying these triplet formation
pathways in all tested solar cell absorber films highlights the untapped
potential for improved charge generation to further increase plateauing OPV
efficiencies.


title:
Recovery by discretization corrected particle strength exchange (DC PSE)
  operators

abstract:
  A new recovery technique based on discretization corrected particle strength
exchange (DC PSE) operators is developed in this paper. DC PSE is a collocation
method that can be used to compute derivatives directly at nodal points,
instead of by projection from Gauss points as is done in many finite
element-based recovery techniques. The proposed method is truly meshless and
does not require patches of elements to be defined, which makes it generally
applicable to point clouds and arbitrary element topologies. Numerical examples
show that the proposed method is accurate and robust.


title:
Searching for flaring star-planet interactions in AU Mic TESS
  observations

abstract:
  Planets that closely orbit magnetically active stars are thought to be able
to interact with their magnetic fields in a way that modulates stellar
activity. This modulation in phase with the planetary orbit, such as enhanced
X-ray activity, chromospheric spots, radio emission, or flares, is considered
the clearest sign of magnetic star-planet interaction (SPI). However, the
magnitude of this interaction is poorly constrained, and the intermittent
nature of the interaction is a challenge for observers. AU Mic is an early M
dwarf, and the most actively flaring planet host detected to date. Its
innermost companion, AU Mic b, is a promising target for magnetic SPI
observations. We used optical light curves of AU Mic obtained by the Transiting
Exoplanet Survey Satellite to search for signs of flaring SPI with AU Mic b
using a customized Anderson-Darling test. In the about $50$ days of
observations, the flare distributions with orbital, rotational, and synodic
periods were generally consistent with intrinsic stellar flaring. We found the
strongest deviation ($p=0.07,\;n=71$) from intrinsic flaring with the orbital
period of AU Mic b, in the high energy half of our sample ($ED>1$ s). If it
reflects the true SPI signal from AU Mic b, extending the observing time by a
factor of $2-3$ will yield a $>3\sigma$ detection. Continued monitoring of AU
Mic may therefore reveal flaring SPI with orbital phase, while rotational
modulation will smear out due to the star's strong differential rotation.


title: Saturation of turbulent helical dynamos

abstract:
  The presence of large scale magnetic fields in nature is often attributed to
the inverse cascade of magnetic helicity driven by turbulent helical dynamos.
In this work we show that in turbulent helical dynamos the inverse flux of
magnetic helicity towards the large scales $\Pi_{\mathcal{H}}$ is bounded by
$|\Pi_{\mathcal{H}}|\le \epsilon k_\eta^{-1}$, where $\epsilon$ is the energy
injection rate and $k_\eta$ is the Kolmogorov magnetic dissipation wavenumber.
Assuming the classical isotropic turbulence scaling for which $k_\eta\propto
\ell_f^{-1} Rm^{3/4}\max[1,Pr]^{-1/4} $, with $\ell_f$ the forcing lengthscale,
implies that the inverse flux of magnetic helicity decreases with $Rm$ at least
as $\Pi_{\mathcal{H}} \le c \epsilon \ell_f Rm^{-3/4}\max[Pr,1]^{1/4}$, where
$c$ is an order one constant. We demonstrate this scaling with $Rm$ using
direct numerical simulations of turbulent dynamos forced at intermediate
scales. The results further indicate that nonlinear saturation is achieved by a
balance between the inverse cascade and dissipation at domain size scales $L$
and the saturation values of magnetic energy are bounded by
${\mathcal{E}}_\text{m}\lesssim c L (\epsilon \ell_f)^{2/3}
Rm^{1/4}\max[1,Pr]^{1/4}$. Numerical simulations also demonstrate this bound.


title:
Three invariants of strange attractors derived through hypergeometric
  entropy

abstract:
  A new description of strange attractor systems through three geometrical and
dynamical invariants is provided. They are the correlation dimension
($\mathcal{D}$) and the correlation entropy ($\mathcal{K}$), both having
attracted attention over the past decades, and a new invariant called the
correlation concentration ($\mathcal{A}$) introduced in the present study. The
correlation concentration is defined as the normalised mean distance between
the reconstruction vectors $\{\boldsymbol{X}_{i}\}$, evaluated by the
underlying probability measure on the infinite-dimensional embedding space.
These three invariants determine the scaling behaviour of the system's
R\'{e}nyi-type extended entropy, which involves a kernel function of the form
$e^{-\rho\|\boldsymbol{X}_{i}-\boldsymbol{X}_{j}\|}$ with $\rho$ a positive
spectral parameter. The entropy function is modelled by Kummer's confluent
hypergeometric function, which reproduces the known scaling behaviours of
$\mathcal{D}$ and $\mathcal{K}$ in the "microscopic" limit $\rho\to\infty$
while exhibiting a new scaling behaviour of $\mathcal{A}$ in the other,
"macroscopic" limit $\rho\to 0$. The three invariants are estimated
simultaneously via nonlinear regression analysis without needing separate
estimations for each invariant. The proposed method is verified through
simulations in both discrete (logistic and H\'{e}non maps) and continuous
(Lorenz, R\"{o}ssler, Duffing-Ueda and Langford attractors) systems. These
results provide an improved understanding of the complex spatiotemporal
properties of the chaotic system.

categories: nlin.CD cond-mat.stat-mech math-ph math.MP physics.data-an


title:
Learning Localization-aware Target Confidence for Siamese Visual
  Tracking

abstract:
  Siamese tracking paradigm has achieved great success, providing effective
appearance discrimination and size estimation by the classification and
regression. While such a paradigm typically optimizes the classification and
regression independently, leading to task misalignment (accurate prediction
boxes have no high target confidence scores). In this paper, to alleviate this
misalignment, we propose a novel tracking paradigm, called SiamLA. Within this
paradigm, a series of simple, yet effective localization-aware components are
introduced, to generate localization-aware target confidence scores.
Specifically, with the proposed localization-aware dynamic label (LADL) loss
and localization-aware label smoothing (LALS) strategy, collaborative
optimization between the classification and regression is achieved, enabling
classification scores to be aware of location state, not just appearance
similarity. Besides, we propose a separate localization branch, centered on a
localization-aware feature aggregation (LAFA) module, to produce location
quality scores to further modify the classification scores. Consequently, the
resulting target confidence scores, are more discriminative for the location
state, allowing accurate prediction boxes tend to be predicted as high scores.
Extensive experiments are conducted on six challenging benchmarks, including
GOT-10k, TrackingNet, LaSOT, TNL2K, OTB100 and VOT2018. Our SiamLA achieves
state-of-the-art performance in terms of both accuracy and efficiency.
Furthermore, a stability analysis reveals that our tracking paradigm is
relatively stable, implying the paradigm is potential to real-world
applications.


title: Single-Peaked Opinion Updates

abstract:
  We consider opinion diffusion for undirected networks with sequential updates
when the opinions of the agents are single-peaked preference rankings. Our
starting point is the study of preserving single-peakedness. We identify voting
rules that, when given a single-peaked profile, output at least one ranking
that is single peaked w.r.t. a single-peaked axis of the input. For such voting
rules we show convergence to a stable state of the diffusion process that uses
the voting rule as the agents' update rule. Further, we establish an efficient
algorithm that maximises the spread of extreme opinions.


title:
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model
  Pretraining

abstract:
  Large-scale vision-language pre-training has achieved promising results on
downstream tasks. Existing methods highly rely on the assumption that the
image-text pairs crawled from the Internet are in perfect one-to-one
correspondence. However, in real scenarios, this assumption can be difficult to
hold: the text description, obtained by crawling the affiliated metadata of the
image, often suffer from semantic mismatch and mutual compatibility. To address
these issues, here we introduce PyramidCLIP, which constructs an input pyramid
with different semantic levels, and aligns visual elements and linguistic
elements in the form of hierarchy via intra-level semantics alignment and
cross-level relation alignment. Furthermore, we adjust the objective function
by softening the loss of negative samples (unpaired samples) so as to weaken
the strict constraint during the pre-training stage, thus mitigating the risk
of the model being over-confident. Experiments on three downstream tasks,
including zero-shot image classification, zero-shot image-text retrieval and
image object detection, verify the effectiveness of the proposed PyramidCLIP.
In particular, with the same amount of pre-training data of 15 millions
image-text pairs, PyramidCLIP exceeds CLIP by 19.2%/18.5%/19.6% respectively,
with the image encoder being ResNet-50/ViT-B32/ViT-B16 on ImageNet zero-shot
classification top-1 accuracy. When scaling to larger datasets, the results of
PyramidCLIP only trained for 8 epochs using 128M image-text pairs are very
close to that of CLIP trained for 32 epochs using 400M training data.


title: Bayesian Information Criterion for Event-based Multi-trial Ensemble data

abstract:
  Transient recurring phenomena are ubiquitous in many scientific fields like
neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models
(VAR) may be used to characterize peri-event system dynamics associated with
such phenomena, and can be learned by exploiting multi-dimensional data
gathering samples of the evolution of the system in multiple time windows
comprising, each associated with one occurrence of the transient phenomenon,
that we will call "trial". However, optimal VAR model order selection methods,
commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are
typically not designed for multi-trial data. Here we derive the BIC methods for
multi-trial ensemble data which are gathered after the detection of the events.
We show using simulated bivariate AR models that the multi-trial BIC is able to
recover the real model order. We also demonstrate with simulated transient
events and real data that the multi-trial BIC is able to estimate a
sufficiently small model order for dynamic system modeling.


title: Clustering free-falling paper motion with complexity and entropy

abstract:
  Many simple natural phenomena are characterized by complex motion that
appears random at first glance, but that often displays underlying patterns and
behavior that can be clustered in groups. The movement of small pieces of paper
falling through the air is one of these systems whose complete mathematical
description seems unworkable. Understanding these types of motion thus demands
automated experimentation capable of producing large datasets covering
different behaviors -- a task that has become feasible only recently with
advances in computer vision and machine learning methods. Here we use one of
these datasets related to the motion of free-falling paper with different
shapes to propose an information-theoretical approach that automatically
clusters different types of behavior. We evaluate the permutation entropy and
statistical complexity from time series related to the observable area of
free-falling paper pieces captured by a video camera. We find that chaotic and
tumbling motions have a distinct average degree of entropy and complexity,
allowing us to accurately discriminate between these two types of behavior with
a simple unsupervised machine learning algorithm. Our method has a performance
comparable to other approaches based on physical quantities but does not depend
on reconstructing the three-dimensional falling trajectory.


title: Characterization of Orthogonal Polynomials on lattices

abstract:
  We consider two sequences of orthogonal polynomials $(P_n)_{n\geq 0}$ and
$(Q_n)_{n\geq 0}$ such that $$ \sum_{j=1} ^{M} a_{j,n}\mathrm{S}_x\mathrm{D}_x
^k P_{k+n-j} (z)=\sum_{j=1} ^{N} b_{j,n}\mathrm{D}_x ^{m} Q_{m+n-j} (z)\;, $$
with $k,m,M,N \in \mathbb{N}$, $a_{j,n}$ and $b_{j,n}$ are sequences of complex
numbers, $$2\mathrm{S}_xf(x(s))=(\triangle +2\,\mathrm{I})f(z),~~
\mathrm{D}_xf(x(s))=\frac{\triangle}{\triangle x(s-1/2)}f(z),$$ $z=x(s-1/2)$,
$\mathrm{I}$ is the identity operator, $x$ defines a lattice, and $\triangle
f(s)=f(s+1)-f(s)$. We show that under some natural conditions, both involved
orthogonal polynomials sequences $(P_n)_{n\geq 0}$ and $(Q_n)_{n\geq 0}$ are
semiclassical whenever $k=m$. Some particular cases are studied closely where
we characterize the continuous dual Hahn and Wilson polynomials for quadratic
lattices. This work generalizes and extends a previous result
[arXiv:2202.10167] concerning a characterization of Askey-Wilson polynomials.


title:
Climate and Weather: Inspecting Depression Detection via Emotion
  Recognition

abstract:
  Automatic depression detection has attracted increasing amount of attention
but remains a challenging task. Psychological research suggests that depressive
mood is closely related with emotion expression and perception, which motivates
the investigation of whether knowledge of emotion recognition can be
transferred for depression detection. This paper uses pretrained features
extracted from the emotion recognition model for depression detection, further
fuses emotion modality with audio and text to form multimodal depression
detection. The proposed emotion transfer improves depression detection
performance on DAIC-WOZ as well as increases the training stability. The
analysis of how the emotion expressed by depressed individuals is further
perceived provides clues for further understanding of the relationship between
depression and emotion.


title: Adversarial Distortion Learning for Medical Image Denoising

abstract:
  We present a novel adversarial distortion learning (ADL) for denoising two-
and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists
of two auto-encoders: a denoiser and a discriminator. The denoiser removes
noise from input data and the discriminator compares the denoised result to its
noise-free counterpart. This process is repeated until the discriminator cannot
differentiate the denoised data from the reference. Both the denoiser and the
discriminator are built upon a proposed auto-encoder called Efficient-Unet.
Efficient-Unet has a light architecture that uses the residual blocks and a
novel pyramidal approach in the backbone to efficiently extract and re-use
feature maps. During training, the textural information and contrast are
controlled by two novel loss functions. The architecture of Efficient-Unet
allows generalizing the proposed method to any sort of biomedical data. The 2D
version of our network was trained on ImageNet and tested on biomedical
datasets whose distribution is completely different from ImageNet; so, there is
no need for re-training. Experimental results carried out on magnetic resonance
imaging (MRI), dermatoscopy, electron microscopy and X-ray datasets show that
the proposed method achieved the best on each benchmark. Our implementation and
pre-trained models are available at https://github.com/mogvision/ADL.


title:
Ramsey theory over partitions II: Negative Ramsey relations and pump-up
  theorems

abstract:
  In this series of papers we advance Ramsey theory of colorings over
partitions. In this part, we concentrate on anti-Ramsey relations, or, as they
are better known, strong colorings, and in particular solve two problems from
[CKS21].
  It is shown that for every infinite cardinal $\lambda$, a strong coloring on
$\lambda^+$ by $\lambda$ colors over a partition can be stretched to one with
$\lambda^{+}$ colors over the same partition. Also, a sufficient condition is
given for when a strong coloring witnessing $Pr_1(\ldots)$ over a partition may
be improved to witness $Pr_0(\ldots)$.
  Since the classical theory corresponds to the special case of a partition
with just one cell, the two results generalize pump-up theorems due to Eisworth
and Shelah, respectively.


title: The possible formation of Jupiter from supersolar gas

abstract:
  More than two decades ago, the Galileo probe performed in situ measurements
of the composition of Jupiter's atmosphere and found that the abundances of C,
N, S, P, Ar, Kr and Xe were all enriched by factors of 1.5--5.4 times their
protosolar value. Juno's measurements recently confirmed the supersolar N
abundance and also found that the O abundance was enriched by a factor 1--5
compared to its protosolar value. Here, we aim at determining the radial and
temporal evolution of the composition of gases and solids in the protosolar
nebula (hereafter, PSN) to assess the possibility that Jupiter's current
composition was acquired via the direct accretion of supersolar gases. To do
so, we model the evolution of a 1D $\alpha-$viscous accretion disk that
includes the radial transport of dust and ice particles and their vapors, with
their sublimation and condensation rates, to compute the composition of the
PSN. We find that the composition of Jupiter's envelope can be explained only
from its accretion from PSN gas ($\alpha \le 10^{-3}$), or from a mixture of
vapors and solids ($\alpha>10^{-3}$). The composition of the PSN at 4 AU,
namely between the locations of the H$_2$O and CO$_2$ icelines, reproduces the
one measured in Jupiter between 100 and 300 kyr of disk evolution. Our results
are found compatible with both the core accretion model, where Jupiter would
acquire its metallicity by late accretion of volatile-rich planetesimals, and
the gravitational collapse scenario, where the composition of proto-Jupiter
would be similar to that of the PSN.


title:
Reducing Neural Architecture Search Spaces with Training-Free Statistics
  and Computational Graph Clustering

abstract:
  The computational demands of neural architecture search (NAS) algorithms are
usually directly proportional to the size of their target search spaces. Thus,
limiting the search to high-quality subsets can greatly reduce the
computational load of NAS algorithms. In this paper, we present
Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS
search spaces. C-BRED reduces a NAS space by clustering the computational
graphs associated with its architectures and selecting the most promising
cluster using proxy statistics correlated with network accuracy. When
considering the NAS-Bench-201 (NB201) data set and the CIFAR-100 task, C-BRED
selects a subset with 70% average accuracy instead of the whole space's 64%
average accuracy.