title: Bayesian inference of the fluctuating proton shape

abstract:
  Using Bayesian inference, we determine probabilistic constraints on the
parameters describing the fluctuating structure of protons at high energy. We
employ the color glass condensate framework supplemented with a model for the
spatial structure of the proton, along with experimental data from the ZEUS and
H1 Collaborations on coherent and incoherent diffractive $\mathrm{J}/\psi$
production in e+p collisions at HERA. This data is found to constrain most
model parameters well. This work sets the stage for future global analyses,
including experimental data from e+p, p+p, and p+A collisions, to constrain the
fluctuating structure of nucleons along with properties of the final state.


title: Neural Dual Contouring

abstract:
  We introduce neural dual contouring (NDC), a new data-driven approach to mesh
reconstruction based on dual contouring (DC). Like traditional DC, it produces
exactly one vertex per grid cell and one quad for each grid edge intersection,
a natural and efficient structure for reproducing sharp features. However,
rather than computing vertex locations and edge crossings with hand-crafted
functions that depend directly on difficult-to-obtain surface gradients, NDC
uses a neural network to predict them. As a result, NDC can be trained to
produce meshes from signed or unsigned distance fields, binary voxel grids, or
point clouds (with or without normals); and it can produce open surfaces in
cases where the input represents a sheet or partial surface. During experiments
with five prominent datasets, we find that NDC, when trained on one of the
datasets, generalizes well to the others. Furthermore, NDC provides better
surface reconstruction accuracy, feature preservation, output complexity,
triangle quality, and inference time in comparison to previous learned (e.g.,
neural marching cubes, convolutional occupancy networks) and traditional (e.g.,
Poisson) methods. Code and data are available at
https://github.com/czq142857/NDC.


title:
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label
  Fusion

abstract:
  Multi-atlas segmentation (MAS) is a promising framework for medical image
segmentation. Generally, MAS methods register multiple atlases, i.e., medical
images with corresponding labels, to a target image; and the transformed atlas
labels can be combined to generate target segmentation via label fusion
schemes. Many conventional MAS methods employed the atlases from the same
modality as the target image. However, the number of atlases with the same
modality may be limited or even missing in many clinical applications. Besides,
conventional MAS methods suffer from the computational burden of registration
or label fusion procedures. In this work, we design a novel cross-modality MAS
framework, which uses available atlases from a certain modality to segment a
target image from another modality. To boost the computational efficiency of
the framework, both the image registration and label fusion are achieved by
well-designed deep neural networks. For the atlas-to-target image registration,
we propose a bi-directional registration network (BiRegNet), which can
efficiently align images from different modalities. For the label fusion, we
design a similarity estimation network (SimNet), which estimates the fusion
weight of each atlas by measuring its similarity to the target image. SimNet
can learn multi-scale information for similarity estimation to improve the
performance of label fusion. The proposed framework was evaluated by the left
ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets,
respectively. Results have shown that the framework is effective for
cross-modality MAS in both registration and label fusion.


title:
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear
  Discriminant Analysis for Event-related Potential Brain-computer Interfaces

abstract:
  Covariance matrices of noisy multichannel electroencephalogram time series
data are hard to estimate due to high dimensionality. In brain-computer
interfaces (BCI) based on event-related potentials and a linear discriminant
analysis (LDA) for classification, the state of the art to address this problem
is by shrinkage regularization. We propose a novel idea to tackle this problem
by enforcing a block-Toeplitz structure for the covariance matrix of the LDA,
which implements an assumption of signal stationarity in short time windows for
each channel. On data of 213 subjects collected under 13 event-related
potential BCI protocols, the resulting 'ToeplitzLDA' significantly increases
the binary classification performance compared to shrinkage regularized LDA (up
to 6 AUC points) and Riemannian classification approaches (up to 2 AUC points).
This translates to greatly improved application level performances, as
exemplified on data recorded during an unsupervised visual speller application,
where spelling errors could be reduced by 81% on average for 25 subjects. Aside
from lower memory and time complexity for LDA training, ToeplitzLDA proved to
be almost invariant even to a twenty-fold time dimensionality enlargement,
which reduces the need of expert knowledge regarding feature extraction.


title: The devil is in the labels: Semantic segmentation from sentences

abstract:
  We propose an approach to semantic segmentation that achieves
state-of-the-art supervised performance when applied in a zero-shot setting. It
thus achieves results equivalent to those of the supervised methods, on each of
the major semantic segmentation datasets, without training on those datasets.
This is achieved by replacing each class label with a vector-valued embedding
of a short paragraph that describes the class. The generality and simplicity of
this approach enables merging multiple datasets from different domains, each
with varying class labels and semantics. The resulting merged semantic
segmentation dataset of over 2 Million images enables training a model that
achieves performance equal to that of state-of-the-art supervised methods on 7
benchmark datasets, despite not using any images therefrom. By fine-tuning the
model on standard semantic segmentation datasets, we also achieve a significant
improvement over the state-of-the-art supervised segmentation on NYUD-V2 and
PASCAL-context at 60% and 65% mIoU, respectively. Based on the closeness of
language embeddings, our method can even segment unseen labels. Extensive
experiments demonstrate strong generalization to unseen image domains and
unseen labels, and that the method enables impressive performance improvements
in downstream applications, including depth estimation and instance
segmentation.


title:
Capturing and incorporating expert knowledge into machine learning
  models for quality prediction in manufacturing

abstract:
  Increasing digitalization enables the use of machine learning methods for
analyzing and optimizing manufacturing processes. A main application of machine
learning is the construction of quality prediction models, which can be used,
among other things, for documentation purposes, as assistance systems for
process operators, or for adaptive process control. The quality of such machine
learning models typically strongly depends on the amount and the quality of
data used for training. In manufacturing, the size of available datasets before
start of production is often limited. In contrast to data, expert knowledge
commonly is available in manufacturing. Therefore, this study introduces a
general methodology for building quality prediction models with machine
learning methods on small datasets by integrating shape expert knowledge, that
is, prior knowledge about the shape of the input-output relationship to be
learned. The proposed methodology is applied to a brushing process with $125$
data points for predicting the surface roughness as a function of five process
variables. As opposed to conventional machine learning methods for small
datasets, the proposed methodology produces prediction models that strictly
comply with all the expert knowledge specified by the involved process
specialists. In particular, the direct involvement of process experts in the
training of the models leads to a very clear interpretation and, by extension,
to a high acceptance of the models. Another merit of the proposed methodology
is that, in contrast to most conventional machine learning methods, it involves
no time-consuming and often heuristic hyperparameter tuning or model selection
step.


title:
Determination of $\alpha$-optical potential for reactions with p-nuclei
  from the study of ($\alpha$,n) reactions in the astrophysically relevant
  energy region

abstract:
  Optical potential parameters in nuclear model calculations are determined by
fitting elastic scattering angular distribution data. Due to the dominance of
Coulomb part, elastic scattering is performed at much higher energies. A
different approach using ($\alpha$,n) reaction is suggested to determine the
alpha optical potentials suitable for astrophysically relevant low energy
regions. Reaction on p-nuclei in the mass region A $\approx$ 92-168 have been
chosen and a modified McFadden-Satchler $\alpha$-optical potential is obtained
from fitting the ($\alpha$,n) reaction data. The effect of level density and
$\gamma$-ray strength function is also studied using this new potential by
analyzing ($\alpha,\gamma$) cross-sections.


title: BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

abstract:
  In this paper, we study the problem of enabling a vision-based robotic
manipulation system to generalize to novel tasks, a long-standing challenge in
robot learning. We approach the challenge from an imitation learning
perspective, aiming to study how scaling and broadening the data collected can
facilitate such generalization. To that end, we develop an interactive and
flexible imitation learning system that can learn from both demonstrations and
interventions and can be conditioned on different forms of information that
convey the task, including pre-trained embeddings of natural language or videos
of humans performing the task. When scaling data collection on a real robot to
more than 100 distinct tasks, we find that this system can perform 24 unseen
manipulation tasks with an average success rate of 44%, without any robot
demonstrations for those tasks.


title:
5G Network on Wings: A Deep Reinforcement Learning Approach to UAV-based
  Integrated Access and Backhaul

abstract:
  Fast and reliable wireless communication has become a critical demand in
human life. When natural disasters strike, providing ubiquitous connectivity
becomes challenging by using traditional wireless networks. In this context,
unmanned aerial vehicle (UAV) based aerial networks offer a promising
alternative for fast, flexible, and reliable wireless communications in
mission-critical (MC) scenarios. Due to the unique characteristics such as
mobility, flexible deployment, and rapid reconfiguration, drones can readily
change location dynamically to provide on-demand communications to users on the
ground in emergency scenarios. As a result, the usage of UAV base stations
(UAV-BSs) has been considered as an appropriate approach for providing rapid
connection in MC scenarios. In this paper, we study how to control a UAV-BS in
both static and dynamic environments. We investigate a situation in which a
macro BS is destroyed as a result of a natural disaster and a UAV-BS is
deployed using integrated access and backhaul (IAB) technology to provide
coverage for users in the disaster area. We present a data collection system,
signaling procedures and machine learning applications for this use case. A
deep reinforcement learning algorithm is developed to jointly optimize the tilt
of the access and backhaul antennas of the UAV-BS as well as its
three-dimensional placement. Evaluation results show that the proposed
algorithm can autonomously navigate and configure the UAV-BS to satisfactorily
serve the MC users on the ground.


title: Arc diagrams on 3-manifold spines

abstract:
  We develop a theory of link projections to trivalent spines of 3-manifolds.
We prove a Reidemeister Theorem providing a set of combinatorial moves
sufficient to relate the projections of isotopic links. We also show that any
link admits a crossingless projection to any special spine and we refine our
theorem to provide a set of combinatorial moves sufficient to relate
crossingless diagrams. Finally, we discuss the connection to Turaev's shadow
world, interpreting our result as a statement about shadow equivalence of a
class of 4-manifolds.


abstract:
  In this paper, we realize C*-algebras of generalized Boolean dynamical
systems as partial crossed products. Reciprocally, we give some sufficient
conditions for a partial crossed product to be isomorphic to a C*-algebra of a
generalized Boolean dynamical system. As an application, we show that
gauge-invariant ideals of C*-algebras of generalized Boolean dynamical systems
are themselves C*-algebras of generalized Boolean dynamical system.


title: Spin Quantum Heat Engine Quantified by Quantum Steering

abstract:
  Following the rising interest in quantum information science, the extension
of a heat engine to the quantum regime by exploring microscopic quantum systems
has seen a boom of interest in the last decade. Although quantum coherence in
the quantum system of the working medium has been investigated to play a
nontrivial role, a complete understanding of the intrinsic quantum advantage of
quantum heat engines remains elusive. We experimentally demonstrate that the
quantum correlation between the working medium and the thermal bath is critical
for the quantum advantage of a quantum Szilard engine, where quantum coherence
in the working medium is naturally excluded. By quantifying the non-classical
correlation through quantum steering, we reveal that the heat engine is quantum
when the demon can truly steer the working medium. The average work obtained by
taking different ways of work extraction on the working medium can be used to
verify the real quantum Szilard engine.


title: Globally Minimal Defensive Alliances: A Parameterized Perspective

abstract:
  A defensive alliance in an undirected graph $G=(V,E)$ is a non-empty set of
vertices $S$ satisfying the condition that every vertex $v\in S$ has at least
as many neighbours (including itself) in $S$ as it has in $V\setminus S$. We
consider the notion of global minimality in this paper. We are interested in
globally minimal defensive alliance of maximum size. This problem is known to
be NP-hard but its parameterized complexity remains open until now. We enhance
our understanding of the problem from the viewpoint of parameterized complexity
by showing that the Globally Minimal Defensive Alliance problem is FPT
parameterized by the neighbourhood diversity of the input graph. The result for
neighborhood diversity implies that the problem is FPT parameterized by vertex
cover number also. We prove that the problem parameterized by the vertex cover
number of the input graph does not admit a polynomial compression unless coNP
$\subseteq$ NP/poly. We show that the problem is W[1]-hard parameterized by a
wide range of fairly restrictive structural parameters such as the feedback
vertex set number, pathwidth, treewidth and treedepth. We also proved that,
given a vertex $r \in V(G)$, deciding if $G$ has a globally minimal defensive
alliance of any size containing vertex $r$ is NP-complete.


title: Bounds in Nonequilibrium Quantum Dynamics

abstract:
  We review various bounds concerning out-of-equilibrium dynamics in few-level
and many-body quantum systems. We primarily focus on closed quantum systems but
will also mention some related results for open quantum systems and classical
stochastic systems. We start from the speed limits, the universal bounds on the
speeds of (either quantum or classical) dynamical evolutions. We then turn to
review the bounds that address how good and how long would a quantum system
equilibrate or thermalize. Afterward, we focus on the stringent constraint set
by locality in many-body systems, rigorously formalized as the Lieb-Robinson
bound. We also review the bounds related to the dynamics of entanglement, a
genuine quantum property. Apart from some other miscellaneous topics, several
notable error bounds for approximated quantum dynamics are discussed. While far
from comprehensive, this topical review covers a considerable amount of recent
progress and thus could hopefully serve as a convenient starting point and
up-to-date guidance for interested readers.

categories: quant-ph cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el math-ph math.MP


title:
Apsidal motion in massive eccentric binaries in NGC 6231: The case of HD
  152219

abstract:
  The measurement of the apsidal motion in close eccentric massive binary
systems provides essential information to probe the internal structure of the
stars that compose the system. Following the determination of the fundamental
stellar and binary parameters, we make use of the tidally induced apsidal
motion to infer constraints on the internal structure of the stars composing
the binary system HD152219. The extensive set of spectroscopic, photometric,
and radial velocity observations allows us to constrain the fundamental
parameters of the stars together with the rate of apsidal motion of the system.
Stellar structure and evolution models are further built with the Cl\'es code
testing different prescriptions for the internal mixing occurring inside the
stars. The effect of stellar rotation axis misalignment with respect to the
normal to the orbital plane on our interpretation of the apsidal motion in
terms of internal structure constants is investigated. Made of an O9.5 III
primary star (M1 = 18.64+/-0.47M${_\odot}$, R1 = 9.40+0.14-0.15R${_\odot}$,
Teff,1 = 30900+/-1000 K) and a B1-2 V-III secondary star (M2 =
7.70+/-0.12M${_\odot}$, R2 = 3.69+/-0.06R${_\odot}$, Teff,2 = 21697+/-1000 K),
the binary system HD152219 displays apsidal motion at a rate
(1.198+/-0.300){\deg}yr-1. The weighted-average mean of the internal structure
constant of the binary system is inferred: k2 = 0.00173+/-0.00052. For the
Cl\'es models to reproduce the k2-value of the primary star, a significant
enhanced mixing is required, notably through the turbulent mixing, but at the
cost that other stellar parameters cannot be reproduced simultaneously. The
difficulty to reproduce the k2-value simultaneously with the stellar parameters
as well as the incompatibility between the age estimates of the primary and
secondary stars are indications that some physics of the stellar interior are
still not completely understood.


title:
A Benchmark Corpus for the Detection of Automatically Generated Text in
  Academic Publications

abstract:
  Automatic text generation based on neural language models has achieved
performance levels that make the generated text almost indistinguishable from
those written by humans. Despite the value that text generation can have in
various applications, it can also be employed for malicious tasks. The
diffusion of such practices represent a threat to the quality of academic
publishing. To address these problems, we propose in this paper two datasets
comprised of artificially generated research content: a completely synthetic
dataset and a partial text substitution dataset. In the first case, the content
is completely generated by the GPT-2 model after a short prompt extracted from
original papers. The partial or hybrid dataset is created by replacing several
sentences of abstracts with sentences that are generated by the Arxiv-NLP
model. We evaluate the quality of the datasets comparing the generated texts to
aligned original texts using fluency metrics such as BLEU and ROUGE. The more
natural the artificial texts seem, the more difficult they are to detect and
the better is the benchmark. We also evaluate the difficulty of the task of
distinguishing original from generated text by using state-of-the-art
classification models.


title:
2D Modeling of HTS Coils with $T$-$A$ Formulation: How to Handle
  Different Coupling Scenarios

abstract:
  Numerical models based on the finite-element method (FEM) are popular tools
for investigating the macroscopic electromagnetic behavior of high-temperature
superconductor (HTS) applications. This article explains how to use the $T$-$A$
formulation for modeling HTS coils in 2D with different coupling scenarios
between the turns. First we consider a racetrack coil wound from one piece of
superconducting tape. Then we consider a coil obtained by winding a cable
composed of different HTS tapes. In the latter case, the tape turns are either
electrically connected along their entire length or just at the two ends of the
coil: in the model, these two different types of electrical connection are
implemented with the help of the electrical circuit module. The current density
distributions and the AC losses of the coils in the different coupling
scenarios are compared and discussed. The limits of applicability of the
presented approach are pointed out. The model is developed for the straight
section of racetrack coils, but can be easily adapted to axisymmetric
geometries.


title:
Energy-Efficient High-Accuracy Spiking Neural Network Inference Using
  Time-Domain Neurons

abstract:
  Due to the limitations of realizing artificial neural networks on prevalent
von Neumann architectures, recent studies have presented neuromorphic systems
based on spiking neural networks (SNNs) to reduce power and computational cost.
However, conventional analog voltage-domain integrate-and-fire (I&F) neuron
circuits, based on either current mirrors or op-amps, pose serious issues such
as nonlinearity or high power consumption, thereby degrading either inference
accuracy or energy efficiency of the SNN. To achieve excellent energy
efficiency and high accuracy simultaneously, this paper presents a low-power
highly linear time-domain I&F neuron circuit. Designed and simulated in a 28nm
CMOS process, the proposed neuron leads to more than 4.3x lower error rate on
the MNIST inference over the conventional current-mirror-based neurons. In
addition, the power consumed by the proposed neuron circuit is simulated to be
0.230uW per neuron, which is orders of magnitude lower than the existing
voltage-domain neurons.


title: Identifiability of Label Noise Transition Matrix

abstract:
  The noise transition matrix plays a central role in the problem of learning
from noisy labels. Among many other reasons, a significant number of existing
solutions rely on access to it. Estimating the transition matrix without using
ground truth labels is a critical and challenging task. When label noise
transition depends on each instance, the problem of identifying the
instance-dependent noise transition matrix becomes substantially more
challenging. Despite recent works proposing solutions for learning from
instance-dependent noisy labels, we lack a unified understanding of when such a
problem remains identifiable, and therefore learnable. This paper seeks to
provide answers to a sequence of related questions: What are the primary
factors that contribute to the identifiability of a noise transition matrix?
Can we explain the observed empirical successes? When a problem is not
identifiable, what can we do to make it so? We will relate our theoretical
findings to the literature and hope to provide guidelines for developing
effective solutions for battling instance-dependent label noise.


title: Flow Redirection for Epidemic Reaction-DIffusion Control

abstract:
  We show we can control an epidemic reaction-diffusion on a directed, and
heterogeneous, network by redirecting the flows, thanks to the optimisation of
well-designed loss functions, in particular the basic reproduction number of
the model. We provide a final size relation linking the basic reproduction
number to the epidemic final sizes, for diffusions around a reference diffusion
with basic reproduction number less than 1. Experimentally, we show control is
possible for different topologies, network heterogeneity levels, and speeds of
diffusion. Our experimental results highlight the relevance of the basic
reproduction number loss, compared to more straightforward losses.


title: Image-to-Image MLP-mixer for Image Reconstruction

abstract:
  Neural networks are highly effective tools for image reconstruction problems
such as denoising and compressive sensing. To date, neural networks for image
reconstruction are almost exclusively convolutional. The most popular
architecture is the U-Net, a convolutional network with a multi-resolution
architecture. In this work, we show that a simple network based on the
multi-layer perceptron (MLP)-mixer enables state-of-the art image
reconstruction performance without convolutions and without a multi-resolution
architecture, provided that the training set and the size of the network are
moderately large. Similar to the original MLP-mixer, the image-to-image
MLP-mixer is based exclusively on MLPs operating on linearly-transformed image
patches. Contrary to the original MLP-mixer, we incorporate structure by
retaining the relative positions of the image patches. This imposes an
inductive bias towards natural images which enables the image-to-image
MLP-mixer to learn to denoise images based on fewer examples than the original
MLP-mixer. Moreover, the image-to-image MLP-mixer requires fewer parameters to
achieve the same denoising performance than the U-Net and its parameters scale
linearly in the image resolution instead of quadratically as for the original
MLP-mixer. If trained on a moderate amount of examples for denoising, the
image-to-image MLP-mixer outperforms the U-Net by a slight margin. It also
outperforms the vision transformer tailored for image reconstruction and
classical un-trained methods such as BM3D, making it a very effective tool for
image reconstruction problems.


title:
Efficient implementation of molecular CCSD gradients with
  Cholesky-decomposed electron repulsion integrals

abstract:
  We present an efficient implementation of ground and excited state CCSD
gradients based on Cholesky-decomposed electron repulsion integrals. Cholesky
decomposition, like density-fitting, is an inner projection method, and thus
similar implementation schemes can be applied for both methods. One well-known
advantage of inner projection methods, which we exploit in our implementation,
is that one can avoid storing large V3O and V4 arrays by instead considering
three-index intermediates. Furthermore, our implementation does not require the
formation and storage of Cholesky vector derivatives. The new implementation is
shown to perform well, with less than 10% of the time spent calculating the
gradients in geometry optimizations. The computational time spent per
optimization cycle are furthermore found to be significantly lower compared to
other implementations based on an inner projection method. We illustrate the
capabilities of the implementation by optimizing the geometry of the retinal
molecule (C20H28O) at the CCSD/aug-cc-pVDZ level of theory.


title: The perfectoid commutant of Lubin-Tate power series

abstract:
  Let LT be a Lubin-Tate formal group attached to a finite extension of Qp. By
a theorem of Lubin-Sarkis, an invertible characteristic p power series that
commutes with the elements of Aut(LT) is itself in Aut(LT). We extend this
result to perfectoid power series, by lifting such a power series to
characteristic zero and using the theory of locally analytic vectors in certain
rings of p-adic periods. This allows us to recover the field of norms of the
Lubin-Tate extension from its completed perfection.


title:
Mutual modulation via charge transfer and unpaired electrons of
  catalyt-ic site for superior intrinsic activity of N2 reduction: from
  high-throughput computations assisted with machine learning perspective

abstract:
  Electrocatalysts of nitrogen reduction reaction (NRR) have attracted
ever-growing attention due to its application for renewable energy alternative
to fossil fuels. However, activation of inert N-N bond requires multiple
complex charge injection which complicates the design of the catalysts. Here
via combining atomic-scale screening and machine learning (ML) methods we
explore the rational design of NRR single-atom catalysts (SACs) supported by
molybdenum disulfide (MoS2). Our work reveals that the activity of NRR SACs is
highly dependent on the number of unpaired d electrons of TM: positive samples
with high activity favoring higher values while negative cases distributing at
lower values, both varying with the doping conditions of the host. We find that
the substitution of sulfur with boron can activate the intrinsic NRR activity
of some TMs such as Ti and V which are otherwise inactive above pristine MoS2.
Importantly, among the various de-scriptors used in ML, the charged state of
adsorbed TM plays a key role in donating electron to pi-anti-bonding orbital of
N2 via the back-donation mechanism. Our work shows a feasible strategy for
rational design of NRR SACs and retrieval of the decisive feature of active
catalysts.


title: $\mathrm{Fe_3GeTe_2}$: A site-differentiated Hund metal

abstract:
  Magnetism in two-dimensional (2D) van der Waals (vdW) materials has lately
attracted considerable attention from the point of view of both fundamental
science and device applications. Obviously, establishing the detailed and solid
understanding of their magnetism is the key first step toward various
applications. Although $\mathrm{Fe_3GeTe_2}$ is a representative ferromagnetic
(FM) metal in this family, many aspects of its magnetic and electronic
behaviors still remain elusive. Here, we report our new finding that
$\mathrm{Fe_3GeTe_2}$ is a special type of correlated metal known as 'Hund
metal'. Furthermore, we demonstrate that Hund metallicity in this material is
quite unique by exhibiting remarkable site-dependence of Hund correlation
strength, hereby dubbed 'site-differentiated Hund metal'. Within this new
picture, many of previous experiments can be clearly understood including the
ones that were seemingly contradictory to one another.


title: Cardinal Functions, Bornologies and Strong Whitney convergence

abstract:
  Let $C(X)$ be the set of all real valued continuous functions on a metric
space $(X,d)$. Caserta introduced the topology of strong Whitney convergence on
bornology for $C(X)$ in [A. Caserta, Strong Whitney convergence, Filomat,
2012], which is a generalization of the topology of strong uniform convergence
on bornology introduced by Beer-Levi in [Beer-Levi, Strong uniform continuity,
J. Math. Anal. Appl., 2009]. The purpose of this paper is to study various
cardinal invariants of the function space $C(X)$ endowed with the topologies of
strong Whitney and Whitney convergence on bornology. In the process, we present
simpler proofs of a number of results from the literature. In the end,
relationships between cardinal invariants of strong Whitney convergence and
strong uniform convergence on $C(X)$ have also been studied.


title:
Topological and Quantum Stability of Low-Dimensional Crystalline
  Lattices with Multiple Nonequivalent Sublattices

abstract:
  The terms of topological and quantum stabilities of low-dimensional
crystalline carbon lattices with multiple non-equivalent sublattices are coined
based on theoretical analysis and multilevel simulations. It is demonstrated
that some planar lattices are prone to symmetry breakdown caused by
non-compensated deformations generated by non-equivalent sublattices. To impose
the limitations on the periodic boundary conditions, the Topology Conservation
Theorem is formulated and proved. It is shown that the lack of perfect filling
of planar 2D active crystalline space by structural units may cause the
formation of i) Structure waves; ii) Nanotubes or rolls; iii) Saddle
structures; iv) Aperiodic ensembles of atomic clusters; v) Stabilization of 2D
lattices by aromatic resonance, correlation effects, or interactions with
substrates. For infinite structural waves, the quasiparticle approach is used
to study the effect of quantum instability and periodicity breakdown with
lattice restructuring. In contrast with infinite structural waves, perfect
finite-sized waves can exist and can be synthesized. It is shown that for
complex low-dimensional lattices which prone to breakdown the translation
invariance (TI), complete active space of structural coordinates cannot be
reduced to a subspace of TI coordinates. As a result, constrained TI
minimization may artificially return a regular point at the potential energy
surface as a global/local minimum/maximum. It is proved that for such lattices,
phonon dispersion cannot be used as solid and final proof of the lattice
stability. A flowchart algorithm for structural analysis of low-dimensional
crystals is proposed and proved to be a powerful tool for theoretical design of
advanced complex nanomaterials.


title: Optimal loading of hydrogel-based drug-delivery systems

abstract:
  Drug-loaded hydrogels provide a means to deliver pharmaceutical agents to
specific sites within the body at a controlled rate. The aim of this paper is
to understand how controlled drug release can be achieved by tuning the initial
distribution of drug molecules in a hydrogel. A mathematical model is presented
for a spherical drug-loaded hydrogel. The model captures the nonlinear
elasticity of the polymer network and thermodynamics of swelling. By assuming
that the drug molecules are dilute, the equations for hydrogel swelling and
drug transport partially decouple. A fast optimisation method is developed to
accurately compute the optimal initial drug concentration by minimising the
error between the numerical drug-release profile and a target profile. By
taking the target drug efflux to be piecewise constant, the optimal initial
configuration consists of a central drug-loaded core with isolated drug packets
near the free boundary of the hydrogel. The optimal initial drug concentration
is highly effective at mitigating the burst effect, where a large amount of
drug is rapidly released into the environment. The hydrogel stiffness can be
used to further tune the rate of drug release. Although stiffer gels lead to
less swelling and hence reduce the drug diffusivity, the drug-release kinetics
are faster than for soft gels due to the decreased distance that drug molecules
must travel to reach the free surface.


title:
Combination of the Perturbation Theory with Configuration Interaction
  Method

abstract:
  Present atomic theory provides accurate and reliable results for atoms with a
small number of valence electrons. However, most current methods of
calculations fail when the number of valence electrons exceeds four or five.
This means that we can not make reliable predictions for more than a half of
the periodic table. Here we suggest a modification of the CI+MBPT
(configuration interaction plus many-body perturbation theory) method, which
may be applicable to atoms and ions with filling d and f shells.


title: Engineering multifunctionality at oxide interfaces by multimode coupling

abstract:
  We employed first-principles density functional theory calculations guided by
group-theoretical analysis and demonstrated the control of
insulator-metal-insulator transition, polarization and two sublattice
magnetization in (LaFeO$_3$)$_1$/(CaFeO$_3$)$_1$ superlattice via. multi
structural mode coupling i.e., 'multimode coupling'. We have discovered a polar
A-type charge disproportionation mode, Q$_{ACD}$ (analogous to the A-type
antiferromagnetic ordering), and found that it couples with the trilinear
coupling, $Q_{Tri}$ mode (common in $Pnma$ perovskite oxides and involves three
structural modes), and lowers the symmetry further. By tuning the strength of
the coupling between the participating modes, the polar metallic phase, polar
zero bandgap semiconducting, and polar insulating phases can be obtained. Here,
$Q_{Tri}$ switches the polarization direction, whereas, Q$_{ACD}$ can trigger
insulator-metal-insulator transition along with the polarization switching. The
mechanism is true for any transition metal superlattices constituted with
$Pnma$ building blocks and with partially filled $e_g$ or $t_{2g}$ electron(s)
at the transition metal sites.


title: Universal constraint on nonlinear population dynamics

abstract:
  Ecological and evolutionary processes show various population dynamics
depending on internal interactions and environmental changes. While crucial in
predicting biological processes, discovering general relations for such
nonlinear dynamics has remained a challenge. Here, we derive a universal
information-theoretical constraint on a broad class of nonlinear dynamical
systems represented as population dynamics. The constraint is interpreted as a
generalization of Fisher's fundamental theorem of natural selection.
Furthermore, the constraint indicates nontrivial bounds for the speed of
critical relaxation around bifurcation points, which we argue are universally
determined only by the type of bifurcation. Our theory is verified for an
evolutionary model and an epidemiological model, which exhibit the
transcritical bifurcation, as well as for an ecological model, which undergoes
limit-cycle oscillation. This work paves a way to predict biological dynamics
in light of information theory, by providing fundamental relations in
nonequilibrium statistical mechanics of nonlinear systems.


title:
First-order integer-valued autoregressive processes with Generalized
  Katz innovations

abstract:
  A new integer-valued autoregressive process (INAR) with Generalised
Lagrangian Katz (GLK) innovations is defined. We show that our GLK-INAR process
is stationary, discrete semi-self-decomposable, infinite divisible, and
provides a flexible modelling framework for count data allowing for under- and
over-dispersion, asymmetry, and excess of kurtosis. A Bayesian inference
framework and an efficient posterior approximation procedure based on Markov
Chain Monte Carlo are provided. The proposed model family is applied to a
Google Trend dataset which proxies the public concern about climate change
around the world. The empirical results provide new evidence of heterogeneity
across countries and keywords in the persistence, uncertainty, and long-run
public awareness level.


title:
On the role of technology in human-dog relationships: a future filled
  with dreams or nightmares?

abstract:
  Digital technologies that help us take care of our dogs are becoming more
widespread. Yet, little research explores what the role of technology in the
human-dog relationship should be. We conducted a mixed-method study
incorporating quantitative and qualitative thematic analysis of 155 UK dog
owners reflecting on their daily routines and technology's role in it,
disentangling the what-where-why of interspecies routines and activities,
technological desires, and rationales for technological support across common
human-dog activities. We found that increasingly entangled daily routines lead
to close multi-species households where dog owners conceptualize technology as
having a role to support them in giving care to their dogs. When confronted
with the role of technology across various activities, only chores like
cleaning up after our dogs lead to largely positive considerations, while
activities that benefit us like walking together lead to largely negative
considerations. For other activities, whether playing, training, or feeding,
attitudes remain diverse. In general, across all activities both a nightmare
scenario of technology taking the human's role and in doing so disentangling
the human-dog bond, as well as a dream scenario of technology augmenting our
abilities arise. We argue that the current trajectory of digital technology for
pets towards allowing dog owners to interact with their dogs while away
(feeding, playing, and so on) is an example of this nightmare scenario becoming
reality, and that it is important to redirect this trajectory to one of
technology predominantly supporting us in becoming better and more informed
caregivers.


title: Complex-to-Real Random Features for Polynomial Kernels

abstract:
  Kernel methods are ubiquitous in statistical modeling due to their
theoretical guarantees as well as their competitive empirical performance.
Polynomial kernels are of particular importance as their feature maps model the
interactions between the dimensions of the input data. However, the
construction time of explicit feature maps scales exponentially with the
polynomial degree and a naive application of the kernel trick does not scale to
large datasets. In this work, we propose Complex-to-Real (CtR) random features
for polynomial kernels that leverage intermediate complex random projections
and can yield kernel estimates with much lower variances than their real-valued
analogs. The resulting features are real-valued, simple to construct and have
the following advantages over the state-of-the-art: 1) shorter construction
times, 2) lower kernel approximation errors for commonly used degrees, 3) they
enable us to obtain a closed-form expression for their variance.


title:
Intrinsic structure perspective for MIPS interfaces in two dimensional
  systems of Active Brownian Particles

abstract:
  Suspensions of Active Brownian Particles (ABP) undergo motility induced phase
separation (MIPS) over a wide range of mean density and activity strength [1],
even in the absence of an explicit attraction. Negative values of the
mechanical surface tension have been reported, from the total forces across the
interface, while the stable fluctuations of the interfacial line would be
interpreted as a positive capillary surface tension [2], while in equilibrium
liquid surfaces these two magnitudes are equal. We present here the analysis of
2D-ABP interfaces in terms of the intrinsic density and force profiles,
calculated with the particle distance to the instantaneous interfacial line.
Our results provide a new insight in the origin of the MIPS from the local
rectification of the random active force on the particles near the interface.
As it had been pointed, that effect acts as an external potential [3] that
produces a pressure gradient across the interface, so that the mechanical
surface tension of the MIPS cannot be described as that of equilibrium
coexisting phases; but our analysis shows that most of that effect comes from
the tightly caged particles at the dense (inner) side of the MIPS interface,
rather than from the free moving particles at the outer side that collide with
the dense cluster. Moreover, a clear correlation appears between the decay of
the hexatic order parameter at the dense slab and the end of the MIPS as the
strength of the active force is lowered. We test that with the strong active
forces required for MIPS, the interfacial structure and properties are very
similar for ABP with purely repulsive (WCA-LJ model truncated at its minimum)
and when the interaction includes a range of the LJ attractive force.


title: Static and Dynamic Melvin Universes

abstract:
  We briefly review the known properties of Melvin's magnetic universe and
study the propagation of test charged matter waves in this static spacetime.
Moreover, the possible correspondence between the wave perturbations on the
background Melvin universe and the motion of charged test particles is
discussed. Next, we explore a simple scenario for turning Melvin's static
universe into one that undergoes gravitational collapse. In the resulting
dynamic gravitational field, the formation of cosmic double-jet configurations
is emphasized.


title: Three-photon excitation of quantum two-level systems

abstract:
  We demonstrate experimentally the long-standing fundamental theoretical
prediction that quantum two-level systems can only be efficiently resonantly
excited via multi-photon pulses involving an odd number of photons. This
prediction can be seen directly from time-dependent Floquet theory that also
allows us to quantify the strength of the multi-photon processes. For the
experimental demonstration, we perform spectroscopy measurements on a single
InGaN quantum dot with a variety of laser detunings, and observe that the
system can be excited in a resonant three-photon process, whilst resonant
two-photon excitation is entirely suppressed. Finally, we exploit this
technique to probe intrinsic properties of InGaN quantum dots. In contrast to
non-resonant excitation, slow relaxation of charge carriers is avoided which
allows us to measure directly the radiative lifetime of the lowest energy
exciton states. Since the emission energy is detuned far from the resonant
driving laser field, polarization filtering is not required and emission with a
greater degree of linear polarization is observed compared to non-resonant
excitation.


title:
Non-Coherent MIMO-OFDM Uplink empowered by the Spatial Diversity in
  Reflecting Surfaces

abstract:
  Reflecting Surfaces (RSs) are being lately envisioned as an energy efficient
solution capable of enhancing the signal coverage in cases where obstacles
block the direct communication from Base Stations (BSs), especially at high
frequency bands due to attenuation loss increase. In the current literature,
wireless communications via RSs are exclusively based on traditional coherent
demodulation, which necessitates the estimation of accurate Channel State
Information (CSI). However, this requirement results in an increased overhead,
especially in time-varying channels, which reduces the resources that can be
used for data communication. In this paper, we consider the uplink between a
single-antenna user and a multi-antenna BS and present a novel RS-empowered
Orthogonal Frequency Division Multiplexing (OFDM) communication system based on
the differential phase shift keying, which is suitable for high noise and/or
mobility scenarios. As a benchmark, analytical expressions for the
Signal-to-Interference and Noise Ratio (SINR) of the proposed system are
presented. Our extensive simulation results verify the accuracy of the
presented analysis and showcase the performance and superiority of the proposed
system over coherent demodulation.


title: Nesterov Acceleration for Riemannian Optimization

abstract:
  In this paper, we generalize the Nesterov accelerated gradient (NAG) method
to solve Riemannian optimization problems in a computationally tractable
manner. The iteration complexity of our algorithm matches that of the NAG
method on the Euclidean space when the objective functions are geodesically
convex or geodesically strongly convex. To the best of our knowledge, the
proposed algorithm is the first fully accelerated method for geodesically
convex optimization problems without requiring strong convexity. Our
convergence rate analysis exploits novel metric distortion lemmas as well as
carefully designed potential functions. We also identify a connection with the
continuous-time dynamics for modeling Riemannian acceleration in Alimisis et
al. [1] to understand the accelerated convergence of our scheme through the
lens of continuous-time flows.


title:
The odd ideal gas: Hall viscosity and thermal conductivity from
  non-Hermitian kinetic theory

abstract:
  The flow of momentum and energy in a fluid is typically associated with
dissipative transport coefficients: viscosity and thermal conductivity. Fluids
that break certain symmetries such as mirror symmetry and time-reversal
invariance can display non-dissipative transport coefficients called odd (or
Hall) viscosities and thermal conductivities. The goal of this paper is to
elucidate the microscopic origin of these dissipationless transport
coefficients using kinetic theory. We show that odd viscosity and odd thermal
conductivity arise when the linearized collision operator is not Hermitian.
This symmetry breaking occurs when collisions are chiral, i.e. not mirror
symmetric. To capture this feature in a minimalistic way, we introduce a
modified relaxation time approximation in which the distribution function is
rotated by an angle characterizing the average chirality of the collisions. In
the limit of an infinitesimal rotation, the effect of the parity-violating
collisions can be described as an emergent effective magnetic field.

categories: cond-mat.stat-mech cond-mat.mes-hall cond-mat.soft physics.flu-dyn


title: Perspectives of Visualization Onboarding and Guidance in VA

abstract:
  A typical problem in Visual Analytics is that users are highly trained
experts in their application domains, but have mostly no experience in using VA
systems. Thus, users often have difficulties interpreting and working with
visual representations. To overcome these problems, user assistance can be
incorporated into VA systems to guide experts through the analysis while
closing their knowledge gaps. Different types of user assistance can be applied
to extend the power of VA, enhance the user's experience, and broaden the
audience for VA. Although different approaches to visualization onboarding and
guidance in VA already exist, there is a lack of research on how to design and
integrate them in effective and efficient ways. Therefore, we aim at putting
together the pieces of the mosaic to form a coherent whole. Based on the
Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of
user assistance for VA by integrating the process of visualization onboarding
and guidance as the two main approaches in this direction. As a result, we
clarify and discuss the commonalities and differences between visualization
onboarding and guidance, and discuss how they benefit from the integration of
knowledge extraction and exploration. Finally, we discuss our descriptive model
by applying it to VA tools integrating visualization onboarding and guidance,
and showing how they should be utilized in different phases of the analysis in
order to be effective and accepted by the user.


title:
Search for type-III seesaw heavy leptons in leptonic final states in
  $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

abstract:
  A search for the pair production of heavy leptons as predicted by the
type-III seesaw mechanism is presented. The search uses proton-proton collision
data at a centre-of-mass energy of 13 TeV, corresponding to 139 fb$^{-1}$ of
integrated luminosity recorded by the ATLAS detector during Run 2 of the Large
Hadron Collider. The analysis focuses on final states with three or four
electrons or muons from the possible decays of new heavy leptons via
intermediate electroweak bosons. No significant deviations above the Standard
Model expectation are observed; upper and lower limits on the heavy lepton
production cross-section and masses are derived respectively. These results are
then combined for the first time with the ones already published by ATLAS using
the channel with two leptons in the final state. The observed lower limit on
the mass of the type-III seesaw heavy leptons combining two, three and four
lepton channels together is 910 GeV at the 95% confidence level.


title:
Formation of ultra-massive carbon-oxygen white dwarfs from the merger of
  carbon-oxygen and helium white dwarf pairs

abstract:
  Ultra-massive white dwarfs (UMWDs) with masses larger than 1.05Msun are
basically believed to harbour oxygen-neon (ONe) cores. Recently, Gaia data
reveals an enhancement of UMWDs on Hertzsprung-Russell diagram (HRD), which
indicates that extra cooling delay mechanism such as crystallization and
elemental sedimentation may exist in the UMWDs. Further studies suggested that
a portion of UMWDs should have experienced pretty long cooling delays, implying
that they are carbon-oxygen (CO) WDs. However, the formation mechanism of these
UMCOWDs is still under debate. In this work, we investigated whether the merges
of massive CO WDs with helium WDs (He WDs) can evolve to UMCOWDs. By employing
stellar evolution code MESA, we construct double WD merger remnants to
investigate their final fates. We found that the post-merger evolution of the
remnants are similar to R CrB stars. The helium burning of the He shell leads
to the mass growing of the CO core at a rate from 2.0*10^-6 to 5.0*10^-6
Msun/yr . The final CO WD mass is influenced by the wind-mass-loss rate during
the post-merger evolution, and cannot exceed about 1.2Msun. The remnants with
core mass larger than 1.2Msun will experience surface carbon ignition, which
may finally end their lives as ONe WDs. Current results implies that at least a
portion of UMWDs which experience extra long cooling delay may stem from
merging of CO WDs and He WDs.


title:
Searching for the origin of the Ehrenreich effect in ultra-hot Jupiters:
  Evidence for strong C/O gradients in the atmosphere of WASP-76b?

abstract:
  Extreme temperature contrasts between the day and nightside of ultra-hot
Jupiters result in significantly asymmetric atmospheres, with a large expansion
occurring over a small range of longitude around the terminator. Over the
course of a transit, WASP-76b rotates by about 30 degree, changing the
observable part of the atmosphere and invoking variations in the appearance of
its constituents. As recently reported, this results in time-variable effects
in the neutral iron signal, which are amplified by its possible condensation on
the nightside. Here, we study the presence of molecular signals during a
transit of WASP-76b observed with the CARMENES spectrograph and compare the
contributions from this planet's morning and evening terminators. The results
are somewhat puzzling, with formal detections of water vapor (5.5$\sigma$) and
hydrogen cyanide (5.2$\sigma$) but at significantly different positions in the
K$_p$-V$_{sys}$ diagram, with a blueshift of -14.3 $\pm$ 2.6 km/s and a
redshift of $+$20.8 $^{+7.8}_{-3.9}$ km/s respectively, and a higher K$_p$ than
expected. The H$_2$O signal also appears stronger later on in the transit, in
contrast to that of HCN, which seems stronger early on. We tentatively explain
this by silicate clouds forming and raining out on the nightside, partially
removing oxygen from the upper atmosphere. For C/O values between 0.7 and 1,
this leads to the formation of HCN at the morning limb. At the evening
terminator, with the sequestered oxygen being returned to the gas phase due to
evaporation, these C/O values lead to formation of H$_2$O instead of HCN. If
confirmed, these results indicate that individual molecules trace different
parts of the atmosphere, as well as nightside condensation, allowing spatial
characterization. As these results are based on a single transit, we advocate
that more data are needed to confirm them and further explore these scenarios.


title: Rigidity of valuative trees under henselization

abstract:
  Let $(K,v)$ be a valued field and let $(K^h,v^h)$ be the henselization
determined by the choice of an extension of $v$ to an algebraic closure of $K$.
Consider an embedding $v(K^*)\hookrightarrow\Lambda$ of the value group into a
divisible ordered abelian group. Let $T(K,\Lambda)$, $T(K^h,\Lambda)$ be the
trees formed by all $\Lambda$-valued extensions of $v$, $v^h$ to the polynomial
rings $K[x]$, $K^h[x]$, respectively. We show that the natural restriction
mapping $T(K^h,\Lambda)\to T(K,\Lambda)$ is an isomorphism of posets.
  As a consequence, the restriction mapping $T_v\to T_{v^h}$ is an isomorphism
of posets too, where $T_v$, $T_{v^h}$ are the trees whose nodes are the
equivalence classes of valuations on $K[x]$, $K^h[x]$ whose restriction to $K$,
$K^h$ are equivalent to $v$, $v^h$, respectively.


title:
With a Little Help from My Friends: Transport Deniability for Instant
  Messaging

abstract:
  Traffic analysis for instant messaging (IM) applications continues to pose an
important privacy challenge. In particular, transport-level data can leak
unintentional information about IM -- such as who communicates with whom.
Existing tools for metadata privacy have adoption obstacles, including the
risks of being scrutinized for having a particular app installed, and
performance overheads incompatible with mobile devices.
  We posit that resilience to traffic analysis must be directly supported by
major IM services themselves, and must be done in a low-cost manner without
breaking existing features. As a first step in this direction, we propose a
hybrid messaging model that combines regular and deniable messages. We present
a novel protocol for deniable instant messaging, which we call DenIM. DenIM is
built on the principle that deniable messages can be made indistinguishable
from regular messages with a little help from a user's friends. Deniable
messages' network traffic can then be explained by a plausible cover story.
DenIM achieves overhead proportional to the messages sent, as opposed to
scaling with time or number of users. To show the effectiveness of DenIM, we
implement a trace simulator, and show that DenIM's deniability guarantees hold
against strong adversaries such as internet service providers.


title:
A framework for nonrelativistic isotropic models based on generalized
  uncertainty principles

abstract:
  The existence of a fundamental length scale in Nature is a common prediction
of distinct quantum gravity models. Discovery of such would profoundly change
current knowledge of quantum phenomena and modifications to the Heisenberg
uncertainty principle may be expected. Despite the attention given to this
possibility in the past decades, there has been no common framework for a
systematic investigation of so called generalized uncertainty principles (GUP).
In this work we provide such framework in the context of nonrelativistic
quantum mechanics. Our approach is based on very few assumptions: there is a
fundamental length scale, space isotropy, invariance under parity and time
reversal transformations, and symmetricity of the position and momentum
operators. We show simple dimensional analysis allows for building a unified
framework containing any isotropic generalized uncertainty principle (iGUP) and
discuss some popular GUP models in this context after elaborating on relevant
theoretical aspects of the framework. At last, we translate current bounds on
three often investigated GUP models into bounds on parameters of such unified
iGUP framework.


title: Radiation of the energy-critical wave equation with compact support

abstract:
  We prove exterior energy lower bounds for (nonradial) solutions to the
energy-critical nonlinear wave equation in space dimensions $3 \le d \le 5$,
with compactly supported initial data. In particular, it is shown that
nontrivial global solutions with compact spatial support must be radiative in
the sense that at least one of the following is true: (1) $\int_{|x|> |t|}
\left( |\partial_t u|^2 + |\nabla u|^2 \right) \mathrm{d}x \ge \eta_1(u) > 0, \
\mathrm{for} \ \mathrm{all} \ t \ge 0 \ \mathrm{or} \ \mathrm{all} \ t \le 0,$
(2) $\int_{|x|> -\varepsilon +|t|} \left( |\partial_t u|^2 + |\nabla u|^2
\right) \mathrm{d}x \ge \eta_2(\varepsilon, u) > 0, \ \mathrm{for} \
\mathrm{all} \ t \in \mathbb{R}, \varepsilon > 0.$ In space dimensions 3 and 4,
a nontrivial soliton background is also considered. As an application, we
obtain partial results on the rigidity conjecture concerning solutions with the
compactness property, including a new proof for the global existence of such
solutions.


title: A Framework for Loop and Path Puzzle Satisfiability NP-Hardness Results

abstract:
  Building on the results published in arxiv:2004.12849 we present a general
framework for demonstrating the NP-hardness of satisfying many genres of loop
and path puzzles using a 'T-metacell' gadget. We then use this to prove the
NP-completeness of a variety of such genres, and discuss some of the
limitations of this gadget.


title:
On the generation and destruction mechanisms of arch vortices in urban
  fluid flows

abstract:
  Studying and interpreting the different flow patterns present in urban areas
is becoming essential since they help develop new approaches to fight climate
change through an improved understanding of the dynamics of the pollutants in
urban environments. This study uses higher order dynamic mode decomposition
(HODMD) to analyze a high-fidelity database of the turbulent flow in various
simplified urban environments. The geometry simulated consists of two buildings
separated by a certain distance. Three different cases have been studied,
corresponding to the three different regimes identified in the bibliography. We
recognize the characteristics of the well-known arch vortex forming on the
leeward side of the first building and document possible generation and
destruction mechanisms of this vortex based on the resulting temporal modes.
These so-called vortex-generating and vortex-breaking modes are further
analyzed via proper-orthogonal decomposition. We show that the arch vortex
plays a prominent role in the dispersion of pollutants in urban environments,
where its generation leads to an increase in their concentration; therefore,
the reported mechanisms are of extreme importance in the context of urban
sustainability.


title:
Differentiability of the diffusion coefficient for a family of
  intermittent maps

abstract:
  It is well known that the Liverani-Saussol-Vaienti map satisfies a central
limit theorem for H\"older observables in the parameter regime where the
correlations are summable. We show that when $C^2$ observables are considered,
the variance of the limiting normal distribution is a $C^1$ function of the
parameter. We first show this for the first return map to the base of the
second branch by studying the Green-Kubo formula, then conclude the result for
the original map using Kac's lemma and relying on linear response.


title: The asymptotic expansion of the Humbert hyper-Bessel function

abstract:
  We consider the asymptotic expansion of the Humbert hyper-Bessel function
expressed in terms of a ${}_0F_2$ hypergeometric function by
\[J_{m,n}(x)=\frac{(x/3)^{m+n}}{m! n!}\,{}_0F_2(-\!\!\!-;m+1, n+1; -(x/3)^3)\]
as $x\to+\infty$, where $m$, $n$ are not necessarily non-negative integers.
Particular attention is paid to the determination of the exponentially small
contribution. The main approach utilised is that described by the author (J.
Comput. Appl. Math. {\bf 234} (2010) 488-504); a leading-order estimate is also
obtained by application of the saddle-point method applied to an integral
representation containing a Bessel function. Numerical results are presented to
demonstrate the accuracy of the resulting compound expansion.


title:
Conjugation Matters. Bioctonionic Veronese Vectors and Cayley-Rosenfeld
  Planes

abstract:
  Motivated by the recent interest in Lie algebraic and geometric structures
arising from tensor products of division algebras and their relevance to high
energy theoretical physics, we analyze generalized bioctonionic projective and
hyperbolic planes. After giving a Veronese representation of the
complexification of the Cayley plane $\mathbb{O}P_{\mathbb{C}}^{2}$, we present
a novel, explicit construction of the bioctonionic Cayley-Rosenfeld plane
$\left( \mathbb{C}\otimes \mathbb{O}\right) P^{2}$, again by exploiting
Veronese coordinates. We discuss the isometry groups of all generalized
bioctonionic planes, recovering all complex and real forms of the exceptional
groups $F_{4}$ and $E_{6}$, and characterizing such planes as symmetric and
Hermitian symmetric spaces. We conclude by discussing some possible physical
applications.


title: Anomaly Detection at the European XFEL using a Parity Space based Method

abstract:
  A novel approach to detect anomalies in superconducting radio-frequency
cavities is presented, based on the parity space method with the goal to detect
quenches and distinguish them from other anomalies. The model-based parity
space method relies on analytical redundancy and generates a residual signal
computed from measurable RF waveforms. The residual is a sensitive indicator of
deviation from the model and provides different signatures for different types
of anomalies. This new method not only helps with detecting faults, but also
provides a catalogue of unique signatures, based on the detected fault. The
method was experimentally verified at the European X-ray Free Electron Laser
(EuXFEL). Various types of anomalies incorrectly detected as quenches by the
current quench detection system are analysed using this new approach.


title: White dwarfs as Physics laboratories: lights and shadows

abstract:
  The evolution of white dwarfs is essentially a gravothermal process of
cooling in which the basic ingredients for predicting their evolution are well
identified, although not always well understood. There are two independent ways
to test the cooling rate. One is the luminosity function of the white dwarf
population, and another is the secular drift of the period of pulsation of
those individuals that experience variations. Both scenarios are sensitive to
the cooling or heating time scales, for which reason, the inclusion of any
additional source or sink of energy will modify these properties and will allow
to set bounds to these perturbations. These studies also require complete and
statistical significant samples for which current large data surveys are
providing an unprecedented wealth of information. In this paper we review how
these techniques are applied to several cases like the secular drift of the
Newton gravitational constant, neutrino magnetic moments, axions and weakly
interacting massive particles (WIMPS).


title:
SummaryLens -- A Smartphone App for Exploring Interactive Use of
  Automated Text Summarization in Everyday Life

abstract:
  We present SummaryLens, a concept and prototype for a mobile tool that
leverages automated text summarization to enable users to quickly scan and
summarize physical text documents. We further combine this with a
text-to-speech system to read out the summary on demand. With this concept, we
propose and explore a concrete application case of bringing ongoing progress in
AI and Natural Language Processing to a broad audience with interactive use
cases in everyday life. Based on our implemented features, we describe a set of
potential usage scenarios and benefits, including support for low-vision,
low-literate and dyslexic users. A first usability study shows that the
interactive use of automated text summarization in everyday life has noteworthy
potential. We make the prototype available as an open-source project to
facilitate further research on such tools.


title:
Yamabe solitons on conformal Sasaki-like almost contact B-metric
  manifolds

abstract:
  A Yamabe soliton is defined on arbitrary almost contact B-metric manifold,
which is obtained by a contact conformal transformation of the Reeb vector
field, its dual contact 1-form, the B-metric, and its associated B-metric. The
cases when the given manifold is cosymplectic or Sasaki-like are studied. In
this way, manifolds from one of the main classes of the studied manifolds are
obtained. The same class contains the conformally equivalent manifolds of
cosymplectic manifolds by the usual conformal transformation of the B-metric.
An explicit 5-dimensional example of a Lie group is given, which is
characterized in relation to the obtained results.


title: Tunable bilayer Hubbard model physics in twisted WSe2

abstract:
  Moir\'e materials with flat electronic bands provide a highly controllable
quantum system for studies of strong-correlation physics and topology. In
particular, angle-aligned heterobilayers of semiconducting transition metal
dichalcogenides (TMDs) with large band offset realize the single-band Hubbard
models. Introduction of a new layer degree of freedom is expected to foster
richer interactions, enabling Hund's physics, interlayer exciton condensation
and new superconducting pairing mechanisms to name a few. Here, we report
competing electronic orders in twisted AB-homobilayer WSe2, which realizes a
bilayer Hubbard model in the weak interlayer hopping limit for holes. We
characterize the charge order, layer polarization and magnetization of the
moir\'e bilayer, subjected to an out-of-plane electric and magnetic field, by
exciton sensing and magneto circular dichroism measurements. By
layer-polarizing holes via the electric field, we observe a crossover from an
excitonic insulator to a charge-transfer insulator at hole density of $\nu$=1
(in unit of moir\'e density), a transition from a paramagnetic to an
antiferromagnetic charge-transfer insulator at $\nu$=2, and evidence for a
layer-selective Mott insulator at 1<$\nu$<2. The unique coupling of charge and
spin to external fields also manifests a giant magneto-electric response. Our
results establish a new solid-state simulator for problems in
strong-correlation physics that are described by bilayer Hubbard models.


title:
Twitter Referral Behaviours on News Consumption with Ensemble Clustering
  of Click-Stream Data in Turkish Media

abstract:
  Click-stream data, which comes with a massive volume generated by the human
activities on the websites, has become a prominent feature to identify readers'
characteristics by the newsrooms after the digitization of the news outlets. It
is essential to have elastic architectures to process the streaming data,
particularly for unprecedented traffic, enabling conducting more comprehensive
analyses such as recommending mostly related articles to the readers. Although
the nature of click-stream data has a similar logic within the websites, it has
inherent limitations to recognize human behaviors when looking from a broad
perspective, which brings the need of limiting the problem in niche areas. This
study investigates the anonymized readers' click activities in the
organizations' websites to identify news consumption patterns following
referrals from Twitter, who incidentally reach but propensity is mainly the
routed news content. The investigation is widened to a broad perspective by
linking the log data with news content to enrich the insights rather than
sticking into the web journey. The methodologies on ensemble cluster analysis
with mixed-type embedding strategies are applied and compared to find similar
reader groups and interests independent from time. Our results demonstrate that
the quality of clustering mixed-type data set approaches to optimal internal
validation scores when embedded by Uniform Manifold Approximation and
Projection (UMAP) and using consensus function as a key to access the most
applicable hyper parameter configurations in the given ensemble rather than
using consensus function results directly. Evaluation of the resulting clusters
highlights specific clusters repeatedly present in the samples, which provide
insights to the news organizations and overcome the degradation of the modeling
behaviors due to the change in the interest over time.


title: Grid-Forming Control Design of Dynamic Virtual Power Plants

abstract:
  We present a novel grid-forming control design approach for dynamic virtual
power plants. We consider a group of heterogeneous grid-forming distributed
energy resources which collectively provide desired dynamic ancillary services
such as fast frequency and voltage control. To achieve that, we employ an
adaptive divide-and-conquer strategy which disaggregates the desired control
specifications of the aggregate DVPP via adaptive dynamic participation factors
to obtain local desired behaviors of each device. We then employ local controls
to realize these desired behaviors. In the process, we ensure that local device
limitations are taken into account. Finally, the control performance is
verified via simulations on a power system testbed.


title:
Equivariant Filter Design for Inertial Navigation Systems with Input
  Measurement Biases

abstract:
  Inertial Navigation Systems (INS) are a key technology for autonomous
vehicles applications. Recent advances in estimation and filter design for the
INS problem have exploited geometry and symmetry to overcome limitations of the
classical Extended Kalman Filter (EKF) approach that formed the mainstay of INS
systems since the mid-twentieth century. The industry standard INS filter, the
Multiplicative Extended Kalman Filter (MEKF), uses a geometric construction for
attitude estimation coupled with classical Euclidean construction for position,
velocity and bias estimation. The recent Invariant Extended Kalman Filter
(IEKF) provides a geometric framework for the full navigation states,
integrating attitude, position and velocity, but still uses the classical
Euclidean construction to model the bias states. In this paper, we use the
recently proposed Equivariant Filter (EqF) framework to derive a novel observer
for biased inertial-based navigation in a fully geometric framework. The
introduction of virtual velocity inputs with associated virtual bias leads to a
full equivariant symmetry on the augmented system. The resulting filter
performance is evaluated with both simulated and real-world data, and
demonstrates increased robustness to a wide range of erroneous initial
conditions, and improved accuracy when compared with the industry standard
Multiplicative EKF (MEKF) approach.


title:
The Hydrogen-Poor Superluminous Supernovae from the Zwicky Transient
  Facility Phase-I Survey: I. Data

abstract:
  During the Zwicky Transient Facility (ZTF) Phase-I operation, 78
hydrogen-poor superluminous supernovae (SLSNe-I) were discovered in less than
three years, making up the largest sample from a single survey. This paper
(Paper I) presents the data, including the optical/ultraviolet light curves and
classification spectra, while Paper II in this series will focus on the
detailed analysis of the light curves and modeling. Our photometry is primarily
taken by the ZTF in the $g,r,i$ bands, and with additional data from other
ground-based facilities and $\it Swift$. The events of our sample cover a
redshift range of $z = 0.06 - 0.67$, with a median and $1\sigma$ error ($16\%$
and $84\%$ percentiles) $z_{med} = 0.265^{+0.143}_{-0.135}$. The peak
luminosity covers $-22.9\,{\rm mag} \leq M_{g,peak} \leq -19.9\,$mag, with a
median value of $-21.54^{+1.12}_{-0.61}\,$mag. Their light curves evolve slowly
with the mean rest-frame rise time of $t_{rise} =42.0\pm17.8\,$days. The
luminosity and time scale distributions suggest that low luminosity SLSNe-I
with peak luminosity $\sim -20\,$mag or extremely fast rising events ($<10 -
15\,$days) exist but are rare. We confirm previous findings that slowly rising
SLSNe-I also tend to fade slowly. The rest-frame color and temperature
evolution show large scatters, suggesting that the SLSN-I population may have
diverse spectral energy distributions. The peak rest-frame color shows a
moderate correlation with the peak absolute magnitude, $\it i.e.$ brighter
SLSNe-I tend to have bluer colors. With optical and ultraviolet photometry, we
construct bolometric luminosity and derive a bolometric correction relation
generally applicable for converting $g,r$-band photometry to bolometric
luminosity for SLSNe-I.


title:
The Hydrogen-Poor Superluminous Supernovae from the Zwicky Transient
  Facility Phase-I Survey: II. Light Curve Modeling and Analysis

abstract:
  We present analysis of the light curves (LCs) of 77 hydrogen-poor
superluminous supernovae (SLSNe-I) discovered during the Zwicky Transient
Facility Phase-I operation. We find that the majority ($67\%$) of the sample
can be fit equally well by both magnetar and ejecta-circumstellar medium (CSM)
interaction plus $^{56}Ni$ decay models. This implies that LCs alone can not
unambiguously constrain the physical power sources for SLSNe-I. However, $20\%$
of the sample show clear signatures such as inverted V-shape or steep declining
LCs, which are better described by the CSM+Ni model. The remaining $13\%$ of
the sample favor the magnetar model. Moreover, our analysis shows that LC
undulations are quite common, observed in $34-62\%$ of the sample, depending on
the strength of the undulations and the quality of the LCs. The majority
($73\%$) of the undulations occur post-peak and "bumps"/"dips" each account for
around half of the undulations. Undulations show a wide range in energy and
duration, with median values and 1$\sigma$ errors of
$1.8\%^{+3.4\%}_{-0.9\%}\,\rm E_{rad,total}$ and $26.2^{+22.6}_{-10.7}\,$days,
respectively. Our analysis of the undulation time scales suggests that
intrinsic temporal variations of the central engine can explain half of the
undulating events, while CSM interaction can account for the majority of the
sample. Finally, all of the well-observed He-rich SLSNe-Ib have either strongly
undulating LCs or the LCs are much better fit by the CSM+Ni model. These
observations imply that their progenitor stars have not had enough time to lose
all of the He-envelopes before supernova explosions, and H-poor CSM are likely
to present in these events.


title: Monoidal Streams for Dataflow Programming

abstract:
  We introduce monoidal streams: a generalization of causal stream functions to
monoidal categories. In the same way that streams provide semantics to dataflow
programming with pure functions, monoidal streams provide semantics to dataflow
programming with theories of processes represented by a symmetric monoidal
category. At the same time, monoidal streams form a feedback monoidal category,
which can be used to interpret signal flow graphs. As an example, we study a
stochastic dataflow language.


title:
Stochastic Effects in Axion Inflation and Primordial Black Holes
  Formation

abstract:
  We revisit the model of axion inflation in the context of stochastic
inflation and investigate the effects of the stochastic noises associated to
the electromagnetic fields. Because of the parity violating interaction, one
polarization of the gauge field is amplified inducing large curvature
perturbation power spectrum. Taking into account the stochastic kicks arising
from the short modes at the time of horizon crossing we obtain the
corresponding Langevin equations for the long modes of the electromagnetic and
axion fields. It is shown that a mean-reverting process governs the dynamics of
the electromagnetic fields such that the tachyonic growth of the gauge fields
is balanced by the diffusion forces. As the instability parameter grows towards
the end of inflation, the large curvature perturbations induced from gauge
field perturbations lead to copious production of small mass primordial black
holes (PBHs). It is shown that the produced PBHs follow a Gaussian statistics.
Imposing the observational constraints on PBHs formation relaxes the previous
bounds on the instability parameter by about fifty percents.


title:
Color Image Inpainting via Robust Pure Quaternion Matrix Completion:
  Error Bound and Weighted Loss

abstract:
  In this paper, we study color image inpainting as a pure quaternion matrix
completion problem. In the literature, the theoretical guarantee for quaternion
matrix completion is not well-established. Our main aim is to propose a new
minimization problem with an objective combining nuclear norm and a quadratic
loss weighted among three channels. To fill the theoretical vacancy, we obtain
the error bound in both clean and corrupted regimes, which relies on some new
results of quaternion matrices. A general Gaussian noise is considered in
robust completion where all observations are corrupted. Motivated by the error
bound, we propose to handle unbalanced or correlated noise via a cross-channel
weight in the quadratic loss, with the main purpose of rebalancing noise level,
or removing noise correlation. Extensive experimental results on synthetic and
color image data are presented to confirm and demonstrate our theoretical
findings.


title:
Comment on "Inferring broken detailed balance in the absence of
  observable currents"

abstract:
  In this reply, we resolve the apparent discrepancy raised in the "Comment on
Inferring broken detailed balance in the absence of observable currents"
[arXiv:2112.08978v1]. We stress that the non-instantaneous transition paths
originate from the choice of a decimation process non-local in time, in
contrast to the decimation process described in our original work, which
commutes with time-reversal. Therefore, the example using such a non-local
decimation procedure, which indeed gives rise to spurious time-irreversibility,
does not invalidate our main result, which was rigorously derived and proven
mathematically. We hope this reply highlights the subtlety of the different
choices for decimating time series data which is pivotal for a correct
understanding of the thermodynamics of small systems.


title:
Measurements of Chromospheric Mg I Emission Lines of Zero-Age
  Main-Sequence Stars

abstract:
  The chromosphere is the active atmosphere in which energetic eruption events,
such as flares, occur. Chromospheric activity is driven by the magnetic field
generated by stellar rotation and convection. The relationship between
chromospheric activity and the Rossby number, the ratio of the rotational
period to the convective turnover time, has been extensively examined for many
types of stars, by using narrow chromospheric emission lines, such as the Ca II
lines and the Mg II h and k lines. However, the stars with small Rossby
numbers, i.e., stars with rapid rotations and/or long convective turnover
times, show constant strengths of such lines against the Rossby number. In this
study, we investigate the infrared Mg I emission lines at 8807 A of 47 zero-age
main-sequence (ZAMS) stars in IC 2391 and IC 2602 using the archive data of the
Anglo-Australian Telescope at the University College London Echelle
Spectrograph. After subtracting the photospheric absorption component, the Mg I
line is detected as an emission line for 45 ZAMS stars, whose equivalent widths
are between 0.02 A and 0.52 A. A total of 42 ZAMS stars show the narrower Mg I
emission lines instead of the Ca II infrared triplet emission lines, suggesting
that they are formed at different depths. The ZAMS stars with smaller Rossby
numbers show stronger Mg I emission lines. The Mg I emission line is not
saturated even in "the saturated regime of the Ca II emission lines," i.e.,
Rossby number < 10^(-1.1). The Mg I emission line is considered to be a good
indicator of chromospheric activity, particularly for active objects.


title: Parabolic carpets

abstract:
  We introduce and study a family of non-conformal and non-uniformly
contracting iterated function systems. We refer to the attractors of such
systems as parabolic carpets. Roughly speaking they may be thought of as
nonlinear analogues of self-affine carpets which are allowed to have parabolic
fixed points. We compute the $L^q$-spectrum of a class of weak Gibbs measures
supported on parabolic carpets as well as the box dimensions of the carpet
itself.


title:
An exponentially convergent discretization for space-time fractional
  parabolic equations using $hp$-FEM

abstract:
  We consider a space-time fractional parabolic problem. Combining a
sinc-quadrature based method for discretizing the Riesz-Dunford integral with
$hp$-FEM in space yields an exponentially convergent scheme for the initial
boundary value problem with homogeneous right-hand side. For the inhomogeneous
problem, an $hp$-quadrature scheme is implemented. We rigorously prove
exponential convergence with focus on small times $t$, proving robustness with
respect to startup singularities due to data incompatibilities.


title:
Well-balanced adaptive compact approximate Taylor methods for systems of
  balance laws

abstract:
  Compact Approximate Taylor (CAT) methods for systems of conservation laws
were introduced by Carrillo and Pares in 2019. These methods, based on a
strategy that allows one to extend high-order Lax-Wendroff methods to nonlinear
systems without using the Cauchy-Kovalevskaya procedure, have arbitrary even
order of accuracy 2p and use (2p + 1)-point stencils, where p is an arbitrary
positive integer. More recently in 2021 Carrillo, Macca, Pares, Russo and Zorio
introduced a strategy to get rid of the spurious oscillations close to
discontinuities produced by CAT methods. This strategy led to the so-called
Adaptive CAT (ACAT) methods, in which the order of accuracy, and thus the width
of the stencils, is adapted to the local smoothness of the solution. The goal
of this paper is to extend CAT and ACAT methods to systems of balance laws. To
do this, the source term is written as the derivative of its indefinite
integral that is formally treated as a flux function. The well-balanced
property of the methods is discussed and a variant that allows in principle to
preserve any stationary solution is presented. The resulting methods are then
applied to a number of systems going from a linear scalar conservation law to
the 2D Euler equations with gravity, passing by the Burgers equations with
source term and the 1D shallow water equations: the order and well-balanced
properties are checked in several numerical tests.


title:
Beware of Greeks bearing entanglement? Quantum covert channels,
  information flow and non-local games

abstract:
  Can quantum entanglement increase the capacity of (classical) covert
channels? To one familiar with Holevo's Theorem it is tempting to think that
the answer is obviously no. However, in this work we show: quantum entanglement
can in fact increase the capacity of a classical covert channel, in the
presence of an active adversary; on the other hand, a zero-capacity channel is
not improved by entanglement, so entanglement cannot create `purely quantum'
covert channels; the problem of determining the capacity of a given channel in
the presence of entanglement is undecidable; but there is an algorithm to bound
the entangled capacity of a channel from above, adapted from the semi-definite
hierarchy from the theory of non-local games, whose close connection to channel
capacity is at the core of all of our results.


title:
CGS-Net: Aggregating Colour, Geometry and Semantic Features for
  Large-Scale Indoor Place Recognition

abstract:
  We describe an approach to large-scale indoor place recognition that
aggregates low-level colour and geometric features with high-level semantic
features. We use a deep learning network that takes in RGB point clouds and
extracts local features with five 3-D kernel point convolutional (KPConv)
layers. We specifically train the KPConv layers on the semantic segmentation
task to ensure that the extracted local features are semantically meaningful.
Then, feature maps from all the five KPConv layers are concatenated together
and fed into the NetVLAD layer to generate the global descriptors. The approach
is trained and evaluated using a large-scale indoor place recognition dataset
derived from the ScanNet dataset, with a test set comprising 3,608 point clouds
generated from 100 different rooms. Comparison with a traditional feature based
method and three state-of-the-art deep learning methods demonstrate that the
approach significantly outperforms all four methods, achieving, for example, a
top-3 average recall rate of 75% compared with 41% for the closest rival
method.


title: Alea-BFT: Practical Asynchronous Byzantine Fault Tolerance

abstract:
  Traditional Byzantine Fault Tolerance (BFT) state machine replication
protocols assume a partial synchrony model, leading to a design where a leader
replica drives the protocol and is replaced after a timeout. Recently, we
witnessed a surge of asynchronous BFT protocols that use randomization to
remove the assumptions of bounds on message delivery times, making them more
resilient to adverse network conditions. However, these protocols still fall
short of being practical across a broad range of scenarios due to their cubic
communication costs, use of expensive primitives, and overall protocol
complexity. In this paper, we present Alea-BFT, the first asynchronous BFT
protocol to achieve quadratic communication complexity, allowing it to scale to
large networks. Alea-BFT brings the key design insight from classical protocols
of concentrating part of the work on a single designated replica, and
incorporates this principle in a two stage pipelined design, with an efficient
broadcast led by the designated replica followed by an inexpensive binary
agreement. We evaluated our prototype implementation across 10 sites in 4
continents, and our results show significant scalability gains from the
proposed design.


title: Signal Shaping for Semantic Communications

abstract:
  Semantic communications target to reliably convey the semantic meaning of
messages. It is different from existing communication systems focusing on
reliable bit transmission. To achieve the goal of semantic communications, we
propose a signal shaping method by minimizing the semantic loss, which is
measured by the pretrained bidirectional encoder representation from
transformers (BERT) model. The signal set optimization problem is transformed
to a vector optimization subject to a power constraint. We propose an efficient
projected gradient descent method to solve the problem and prove its
convergence. Simulation results show that the proposed method outperforms
existing signal shaping methods in minimizing the semantic loss.


title: Microphysics of Relativistic Collisionless Electron-ion-positron Shocks

abstract:
  We perform particle-in-cell simulations to elucidate the microphysics of
relativistic weakly magnetized shocks loaded with electron-positron pairs.
Various external magnetizations $\sigma\lesssim 10^{-4}$ and pair-loading
factors $Z_\pm \lesssim 10$ are studied, where $Z_\pm$ is the number of loaded
electrons and positrons per ion. We find the following. (1) The shock becomes
mediated by the ion Larmor gyration in the mean field when $\sigma$ exceeds a
critical value $\sigma_{\rm L}$ that decreases with $Z_\pm$. At
$\sigma\lesssim\sigma_{\rm L}$ the shock is mediated by particle scattering in
the self-generated microturbulent fields, the strength and scale of which
decrease with $Z_\pm$, leading to lower $\sigma_{\rm L}$. (2) The energy
fraction carried by the post-shock pairs is robustly in the range between 20%
and 50% of the upstream ion energy. The mean energy per post-shock electron
scales as $\overline{E}_{\rm e}\propto (Z_\pm+1)^{-1}$. (3) Pair loading
suppresses nonthermal ion acceleration at magnetizations as low as
$\sigma\approx 5\times 10^{-6}$. The ions then become essentially thermal with
mean energy $\overline{E}_{\rm i}$, while electrons form a nonthermal tail,
extending from $E\sim (Z_\pm + 1)^{-1}\overline{E}_{\rm i}$ to
$\overline{E}_{\rm i}$. When $\sigma = 0$, particle acceleration is enhanced by
the formation of intense magnetic cavities that populate the precursor during
the late stages of shock evolution. Here, the maximum energy of the nonthermal
ions and electrons keeps growing over the duration of the simulation. Alongside
the simulations, we develop theoretical estimates consistent with the numerical
results. Our findings have important implications for models of early gamma-ray
burst afterglows.


title:
Urban Region Profiling via A Multi-Graph Representation Learning
  Framework

abstract:
  Urban region profiling can benefit urban analytics. Although existing studies
have made great efforts to learn urban region representation from multi-source
urban data, there are still three limitations: (1) Most related methods focused
merely on global-level inter-region relations while overlooking local-level
geographical contextual signals and intra-region information; (2) Most previous
works failed to develop an effective yet integrated fusion module which can
deeply fuse multi-graph correlations; (3) State-of-the-art methods do not
perform well in regions with high variance socioeconomic attributes. To address
these challenges, we propose a multi-graph representative learning framework,
called Region2Vec, for urban region profiling. Specifically, except that human
mobility is encoded for inter-region relations, geographic neighborhood is
introduced for capturing geographical contextual information while POI side
information is adopted for representing intra-region information by knowledge
graph. Then, graphs are used to capture accessibility, vicinity, and
functionality correlations among regions. To consider the discriminative
properties of multiple graphs, an encoder-decoder multi-graph fusion module is
further proposed to jointly learn comprehensive representations. Experiments on
real-world datasets show that Region2Vec can be employed in three applications
and outperforms all state-of-the-art baselines. Particularly, Region2Vec has
better performance than previous studies in regions with high variance
socioeconomic attributes.


title: On Vandermonde determinants via $n$-determinants

abstract:
  We use earlier defined notion of $n$- determinant to investigate
sub-determinants of an extended Vandermonde matrix. Firstly, we demonstrate our
method on a number of particular cases. Then we prove that all these results
may be stated in terms of Schur's polynomials. In our main result, we prove
that Schur polynomials are equal to minors of a fixed matrix, which entries are
formed of elementary symmetric polynomials. Such a formula is known as the
second Jaccobi-Trudi identity.


title:
Quantum Central Limit Theorems, Emergence of Classicality and
  Time-dependent Differential Entropy

abstract:
  We derive some Quantum Central Limit Theorems for expectation values of
macroscopically coarse-grained observables, which are functions of
coarse-grained hermitean operators. Thanks to the hermicity constraints, we
obtain positive-definite distribution for the expectation values of
observables. These probability distributions open some pathway for an emergence
of classical behaviours in the limit of infinitely large number of identical
and non-interacting quantum constituents. This is in contradistinction to other
mechanisms of classicality emergence due to environmental decoherence and
consistent histories. The probability distributions so derived also enable us
to evaluate the nontrivial time-dependence of certain differential entropies.


title: Exploring the Feature Space of TSP Instances Using Quality Diversity

abstract:
  Generating instances of different properties is key to algorithm selection
methods that differentiate between the performance of different solvers for a
given combinatorial optimization problem. A wide range of methods using
evolutionary computation techniques has been introduced in recent years. With
this paper, we contribute to this area of research by providing a new approach
based on quality diversity (QD) that is able to explore the whole feature
space. QD algorithms allow to create solutions of high quality within a given
feature space by splitting it up into boxes and improving solution quality
within each box. We use our QD approach for the generation of TSP instances to
visualize and analyze the variety of instances differentiating various TSP
solvers and compare it to instances generated by a $(\mu+1)$-EA for TSP
instance generation.


title: Heed the Noise in Performance Evaluations in Neural Architecture Search

abstract:
  Neural Architecture Search (NAS) has recently become a topic of great
interest. However, there is a potentially impactful issue within NAS that
remains largely unrecognized: noise. Due to stochastic factors in neural
network initialization, training, and the chosen train/validation dataset
split, the performance evaluation of a neural network architecture, which is
often based on a single learning run, is also stochastic. This may have a
particularly large impact if a dataset is small. We therefore propose to reduce
this noise by evaluating architectures based on average performance over
multiple network training runs using different random seeds and
cross-validation. We perform experiments for a combinatorial optimization
formulation of NAS in which we vary noise reduction levels. We use the same
computational budget for each noise level in terms of network training runs,
i.e., we allow less architecture evaluations when averaging over more training
runs. Multiple search algorithms are considered, including evolutionary
algorithms which generally perform well for NAS. We use two publicly available
datasets from the medical image segmentation domain where datasets are often
limited and variability among samples is often high. Our results show that
reducing noise in architecture evaluations enables finding better architectures
by all considered search algorithms.


title:
Ab initio Circular Dichroism with the Yambo code: applications to
  dipeptides

abstract:
  Circular dichroism (CD) spectroscopy is a useful technique for characterizing
chiral molecules. It is more sensitive than total absorption to molecule
conformation, and it is routinely used to identify enantiomers. We present here
a first principles implementation of CD with application to three
cyclo-dipeptides. Our CD approach for molecules has been integrated in the 5.0
release of the Yambo code [1], distributed under GPL.


title: Robust Linear Regression for General Feature Distribution

abstract:
  We investigate robust linear regression where data may be contaminated by an
oblivious adversary, i.e., an adversary than may know the data distribution but
is otherwise oblivious to the realizations of the data samples. This model has
been previously analyzed under strong assumptions. Concretely, $\textbf{(i)}$
all previous works assume that the covariance matrix of the features is
positive definite; and $\textbf{(ii)}$ most of them assume that the features
are centered (i.e. zero mean). Additionally, all previous works make additional
restrictive assumption, e.g., assuming that the features are Gaussian or that
the corruptions are symmetrically distributed.
  In this work we go beyond these assumptions and investigate robust regression
under a more general set of assumptions: $\textbf{(i)}$ we allow the covariance
matrix to be either positive definite or positive semi definite,
$\textbf{(ii)}$ we do not necessarily assume that the features are centered,
$\textbf{(iii)}$ we make no further assumption beyond boundedness
(sub-Gaussianity) of features and measurement noise.
  Under these assumption we analyze a natural SGD variant for this problem and
show that it enjoys a fast convergence rate when the covariance matrix is
positive definite. In the positive semi definite case we show that there are
two regimes: if the features are centered we can obtain a standard convergence
rate; otherwise the adversary can cause any learner to fail arbitrarily.


title: Tracking Discourse Influence in Darknet Forums

abstract:
  This technical report documents our efforts in addressing the tasks set forth
by the 2021 AMoC (Advanced Modelling of Cyber Criminal Careers) Hackathon. Our
main contribution is a joint visualisation of semantic and temporal features,
generating insight into the supplied data on darknet cybercrime through the
aspects of novelty, transience, and resonance, which describe the potential
impact a message might have on the overall discourse in darknet communities.
All code and data produced by us as part of this hackathon is publicly
available.