title:
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear
  Periodically Parameter-Varying Models

abstract:
  Limit cycle oscillations are phenomena arising in nonlinear dynamical systems
and characterized by periodic, locally-stable, and self-sustained state
trajectories. Systems controlled in a closed loop along a periodic trajectory
can also be modelled as systems experiencing limit cycle behavior. The goal of
this work is to identify from data, the local dynamics around the limit cycle
using linear periodically parameter-varying models. Using a coordinate
transformation onto transversal surfaces, the dynamics are decomposed into two
parts: one along the limit cycle, and one on the transversal surfaces. Then,
the model is identified from trajectory data using kernel-based methods with a
periodic kernel design. The kernel-based model is extended to also account for
variations in system parameters associated with different operating conditions.
The performance of the proposed identification method is demonstrated on a
benchmark nonlinear system and on a simplified airborne wind energy model. The
method provides accurate model parameter estimation, compared to the analytical
linearization, and good prediction capability.


title:
Information spreading and scrambling in disorder-free multiple-spin
  interacting models

abstract:
  Tripartite mutual information (TMI) is an efficient observable to quantify
the ability of scrambler for unitary time-evolution operator with quenched
many-body Hamiltonian. In this paper, we give numerical demonstrations of the
TMI in disorder-free (translational invariant) spin models with 3-body and
4-body multiple-spin interactions. The dynamical behavior of the TMI of these
models does {\it not} exhibit linear light-cone. In early-time evolution, the
TMI displays distinct negative increase behavior fitted by a logarithmic-like
function. This is in contrast to the conventional linear light-cone behavior
present in the XXZ model. The late-time evolution of the TMI in finite-size
systems is also numerically investigated. The results indicate that the
saturation values of the TMI have correlations with nearly integrable
properties of the system estimated by the level spacing analysis. These
multiple-spin interactions make the system nearly integrable and weakly
suppress the spread of information and scrambling.


title: The Alon-Tarsi number of planar graphs -- a simple proof

abstract:
  This paper gives a simple proof of the result that every planar graph $G$ has
Alon-Tarsi number at most 5, and has a matching $M$ such that $G-M$ has
Alon-Tarsi number at most 4.


title: Zero-shot meta-learning for small-scale data from human subjects

abstract:
  While developments in machine learning led to impressive performance gains on
big data, many human subjects data are, in actuality, small and sparsely
labeled. Existing methods applied to such data often do not easily generalize
to out-of-sample subjects. Instead, models must make predictions on test data
that may be drawn from a different distribution, a problem known as
\textit{zero-shot learning}. To address this challenge, we develop an
end-to-end framework using a meta-learning approach, which enables the model to
rapidly adapt to a new prediction task with limited training data for
out-of-sample test data. We use three real-world small-scale human subjects
datasets (two randomized control studies and one observational study), for
which we predict treatment outcomes for held-out treatment groups. Our model
learns the latent treatment effects of each intervention and, by design, can
naturally handle multi-task predictions. We show that our model performs the
best holistically for each held-out group and especially when the test group is
distinctly different from the training group. Our model has implications for
improved generalization of small-size human studies to the wider population.


title:
Hybrid magnonics for short-wavelength spin waves facilitated by a
  magnetic heterostructure

abstract:
  Recent research on hybrid magnonics has been restricted by the long magnon
wavelengths of the ferromagnetic resonance modes. We present an experiment on
the hybridization of 250-nm wavelength magnons with microwave photons in a
multimode magnonic system consists of a planar cavity and a magnetic bilayer.
The coupling between magnon modes in the two magnetic layers, i.e., the uniform
mode in Permalloy (Py) and the perpendicular standing spin waves (PSSWs) in
YIG, serves as an effective means for exciting short-wavelength PSSWs, which is
further hybridized with the photon mode of the microwave resonator. The
demonstrated magnon-photon coupling approaches the superstrong coupling regime,
and can even be achieved near zero bias field.


title:
When to Go, and When to Explore: The Benefit of Post-Exploration in
  Intrinsic Motivation

abstract:
  Go-Explore achieved breakthrough performance on challenging reinforcement
learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that
successful exploration requires an agent to first return to an interesting
state ('Go'), and only then explore into unknown terrain ('Explore'). We refer
to such exploration after a goal is reached as 'post-exploration'. In this
paper we present a systematic study of post-exploration, answering open
questions that the Go-Explore paper did not answer yet. First, we study the
isolated potential of post-exploration, by turning it on and off within the
same algorithm. Subsequently, we introduce new methodology to adaptively decide
when to post-explore and for how long to post-explore. Experiments on a range
of MiniGrid environments show that post-exploration indeed boosts performance
(with a bigger impact than tuning regular exploration parameters), and this
effect is further enhanced by adaptively deciding when and for how long to
post-explore. In short, our work identifies adaptive post-exploration as a
promising direction for RL exploration research.


title: One Dimensional Wormhole Corrosion in Metals

abstract:
  Corrosion is a ubiquitous failure mode of materials in extreme environments.
The more localized it is, the more difficult it is to detect and more
deleterious its effects. Often, the progression of localized corrosion is
accompanied by the evolution of porosity in materials, creating internal
void-structures that facilitate the ingress of the external environment into
the interior of the material, further accelerating the internal corrosion.
Previously, the dominant morphology of such void-structures has been reported
to be either three-dimensional (3D) or two-dimensional (2D). Here, we report a
more localized form of corrosion, which we call 1D wormhole corrosion. Using
electron tomography, we show multiple examples of this 1D and percolating
morphology that manifests a significantly high aspect ratio differentiable from
2D and 3D corrosion. To understand the origin of this mechanism in a Ni-Cr
alloy corroded by molten salt, we combined energy-filtered four-dimensional
scanning transmission electron microscopy (EF-4D-STEM) and ab initio density
functional theory (DFT) calculations to develop a vacancy mapping method with
nanometer-resolution, identifying a remarkably high vacancy concentration in
the diffusion-induced grain boundary migration (DIGM) zone, up to 100 times the
equilibrium value at the melting point. These vacancy supersaturation regions
act as the precursors of wormholes, and lead to the asymmetrical growth of
voids along GBs. We show that similar 1D penetrating corrosion morphologies
could also occur in other materials or corrosion conditions, implying the broad
impact of this extremely localized corrosion mechanism. Deciphering the origins
of 1D corrosion is an important step towards designing structural materials
with enhanced corrosion resistance, and also offers new pathways to create
ordered-porous materials for functional applications.


title:
Transmission laser welding of similar and dissimilar semiconductor
  materials

abstract:
  Laser micro-welding is an advanced manufacturing method today applied in
various domains. However, important physical limitations have prevented so far
to demonstrate its applicability in silicon and other technology-essential
semiconductors. Concentrating on circumventing the optical limits on the
deliverable energy density at interfaces between narrow-gap materials with
intense infrared light, we make the first feasibility demonstration of
transmission laser welding between silicon workpieces using nanosecond laser
pulses. We obtain a shear joining strength of 32$\pm$10 MPa which compares very
favorably to the complex process alternatives. Supported by experiments
repeated on different material combinations including gallium arsenide, we
confirm that this remarkable performance level is achievable for similar and
dissimilar semiconductors. The demonstrations rely on a small footprint fiber
laser, an aspect that holds great promises for the advent of a high-efficiency
flexible process beneficial for important technology developments including
lab-on-a-chip and hybrid semiconductor systems.


title: List $4$-colouring of planar graphs

abstract:
  This paper proves the following result: If $G$ is a planar graph and $L$ is a
$4$-list assignment of $G$ such that $|L(x) \cap L(y)| \le 2$ for every edge
$xy$, then $G$ is $L$-colourable. This answers a question asked by
Kratochv\'{i}l, Tuza and Voigt in [Journal of Graph Theory, 27(1):43--49,
1998].


title:
On the analytical construction of halo orbits and halo tubes in the
  elliptic restricted three-body problem

abstract:
  The halo orbits of the spatial circular restricted three-body problem are
largely considered in space-flight dynamics to design low-energy transfers
between celestial bodies. A very efficient analytical method for the
computation of halo orbits, and the related transfers, has been obtained from
the high-order resonant Birkhoff normal forms defined at the Lagrangian points
L1-L2. In this paper, by implementing a non-linear Floquet-Birkhoff resonant
normal form, we provide the definition of orbits, as well as their manifold
tubes, which exist in a large order approximation of the elliptic three-body
problem and generalize the halo orbits of the circular problem. Since the
libration amplitude of such halo orbits is large (comparable to the distance of
L1-L2 to the secondary body), and the Birkhoff normal forms are obtained
through series expansions at the Lagrangian points, we provide also an error
analysis of the method with respect to the orbits of the genuine elliptic
restricted three-body problem.


title: Some New Views on Product Space and Related Diversification

abstract:
  We aim to contribute to the literature on product space and diversification
by proposing a number of extensions of the current literature: (1) we propose
that the alternative but related idea of a country space also has empirical and
theoretical appeal; (2) we argue that the loss of comparative advantage should
be an integral part of (testing the empirical relevance of) the product space
idea; (3) we propose several new indicators for measuring relatedness in
product space; and (4) we propose a non-parametric statistical test based on
bootstrapping to test the empirical relevance of the product space idea.


title:
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised
  Object Detection

abstract:
  In this paper, we delve into two key techniques in Semi-Supervised Object
Detection (SSOD), namely pseudo labeling and consistency training. We observe
that these two techniques currently neglect some important properties of object
detection, hindering efficient learning on unlabeled data. Specifically, for
pseudo labeling, existing works only focus on the classification score yet fail
to guarantee the localization precision of pseudo boxes; For consistency
training, the widely adopted random-resize training only considers the
label-level consistency but misses the feature-level one, which also plays an
important role in ensuring the scale invariance. To address the problems
incurred by noisy pseudo boxes, we design Noisy Pseudo box Learning (NPL) that
includes Prediction-guided Label Assignment (PLA) and Positive-proposal
Consistency Voting (PCV). PLA relies on model predictions to assign labels and
makes it robust to even coarse pseudo boxes; while PCV leverages the regression
consistency of positive proposals to reflect the localization quality of pseudo
boxes. Furthermore, in consistency training, we propose Multi-view
Scale-invariant Learning (MSL) that includes mechanisms of both label- and
feature-level consistency, where feature consistency is achieved by aligning
shifted feature pyramids between two images with identical content but varied
scales. On COCO benchmark, our method, termed PSEudo labeling and COnsistency
training (PseCo), outperforms the SOTA (Soft Teacher) by 2.0, 1.8, 2.0 points
under 1%, 5%, and 10% labelling ratios, respectively. It also significantly
improves the learning efficiency for SSOD, e.g., PseCo halves the training time
of the SOTA approach but achieves even better performance.


title:
Near-Field Communications for 6G: Fundamentals, Challenges, Potentials,
  and Future Directions

abstract:
  Extremely large antenna array (ELAA) is a common feature of several key
candidate technologies for 6G, such as ultra-massive
multiple-input-multiple-output (UM-MIMO), cell-free massive MIMO,
reconfigurable intelligent surface (RIS), and terahertz communications. Since
the number of antennas is very large for ELAA, near-field communications will
become essential in 6G wireless networks. In this article, we systematically
investigate the emerging near-field communication techniques. Firstly, the
fundamental of near-field communications is explained, and the metric to
determine the near-field ranges in typical communication scenarios is
introduced. Then, we investigate recent studies on near-field communication
techniques by classifying them into two categories, i.e., techniques addressing
the challenges and those exploiting the potentials in near-field regions. Their
principles, recent progress, pros and cons are discussed. More importantly,
several open problems and future research directions for near-field
communications are pointed out. We believe that this article would inspire more
innovations for this important research topic of near-field communications for
6G.


title: Multi-Robot Active Mapping via Neural Bipartite Graph Matching

abstract:
  We study the problem of multi-robot active mapping, which aims for complete
scene map construction in minimum time steps. The key to this problem lies in
the goal position estimation to enable more efficient robot movements. Previous
approaches either choose the frontier as the goal position via a myopic
solution that hinders the time efficiency, or maximize the long-term value via
reinforcement learning to directly regress the goal position, but does not
guarantee the complete map construction. In this paper, we propose a novel
algorithm, namely NeuralCoMapping, which takes advantage of both approaches. We
reduce the problem to bipartite graph matching, which establishes the node
correspondences between two graphs, denoting robots and frontiers. We introduce
a multiplex graph neural network (mGNN) that learns the neural distance to fill
the affinity matrix for more effective graph matching. We optimize the mGNN
with a differentiable linear assignment layer by maximizing the long-term
values that favor time efficiency and map completeness via reinforcement
learning. We compare our algorithm with several state-of-the-art multi-robot
active mapping approaches and adapted reinforcement-learning baselines.
Experimental results demonstrate the superior performance and exceptional
generalization ability of our algorithm on various indoor scenes and unseen
number of robots, when only trained with 9 indoor scenes.


title:
Theoretical analysis of surface brightness-colour relations for
  late-type stars using MARCS model atmospheres

abstract:
  Surface brightness-colour relations (SBCRs) are largely used for general
studies in stellar astrophysics and for determining extragalactic distances.
Based on simulated spectra of late-type stars using MARCS model atmospheres,
our aim is to analyse the effect of stellar fundamental parameters on the
surface brightness. We also compare theoretical and recent empirical SBCRs. We
used MARCS model atmospheres to compute spectra and the surface brightness of
stars. We first explored the parameter space of MARCS (i.e. effective
temperature, $\log g$, $\mathrm{[Fe/H]}$, microturbulence, and mass) in order
to quantify their impact on the surface brightness. Then we considered a
relation between the effective temperature and $\log g$ for late dwarfs and
giants, as well as a solar metallicity, in order to allow a consistent
comparison of theoretical and empirical SBCRs. We find that the SBCR is not
sensitive to the microturbulence and mass. The effect of metallicity on the
SBCR is found to be larger for dwarfs than for giants. It is also larger when
considering larger $V-K_s$ values. We also find that a difference of 0.5 dex in
metallicity between Galactic and LMC SBCRs does not affect the recent LMC
distance determination, based on eclipsing binaries, by more than 0.4%. By
comparing theoretical with empirical SBCRs, we find a good agreement of less
than 2$\sigma$ for F5-K7 dwarfs and giants stars, while a larger discrepancy is
found for M dwarfs and giants (about 4-6$\sigma$). The surface gravity
properties, as modelled in MARCS, explain the differences in the empirical
SBCRs in terms of class. We finally find that theoretical and empirical SBCRs
for Cepheids are consistent. Carefully considering metallicity and $\log g$ is
mandatory when calibrating or using SBCRs.


title:
Excitation and transport of bound magnon clusters in frustrated
  ferromagnetic chain

abstract:
  We investigate magnetic excitations and spin transport properties of a
frustrated spin chain with ferromagnetic nearest-neighbor and antiferromagnetic
next-nearest-neighbor exchange couplings in a magnetic field by density-matrix
renormalization group methods. In the field-induced quadrupole and octupole
regimes, the low-energy excitation is governed by two- and three-magnon bound
states, respectively, so that bound magnon clusters would contribute to the
spin transport. We show that spin current correlations decrease when the system
goes from the quadrupole regime into the octupole regime. This indicates that
the spin transport is suppressed in the octupole regime, although bound
three-magnon clusters carry a larger amount of angular momentum than bound
two-magnon clusters.


title:
Self-tuning of the cosmological constant in brane-worlds with
  $P(X,\phi)$

abstract:
  We revisit the idea of self-tuning the observed cosmological constant to a
vanishing value and promote it to a selection criterion of brane-world models,
in which our Universe is described by a 3-brane embedded in a 5d bulk. As a
concrete setup, we consider a bulk scalar field $\phi$ described by a general
Lagrangian $P(X,\phi)$ with $X=-(\partial\phi)^2/2$. By requiring that the
model enforces the 4d curvature of the maximally symmetric 3-brane world-volume
to vanish independently of the 4d effective vacuum energy, only two
possibilities remain: one with a canonical bulk kinetic term and the other with
an unconventional bulk kinetic term similar to a Cuscuton field. Further
demanding the absence of bulk singularity, the latter is selected as a unique
possibility within the class of models. The solution can accommodate any warp
factor profile free from bulk singularity and with a finite effective 4d Planck
mass. In a cosmological context, our solution would describe our (almost) flat
Universe at late times, with a bulk warp factor profile expected to be
determined by the evolution of the Universe before dilution of the matter
fields by cosmic expansion.


title:
Existence of free boundary disks with constant mean curvature in
  $\mathbb{R}^3$

abstract:
  Given a surface $\Sigma$ in $\mathbb{R}^3$ diffeomorphic to $S^2$, Struwe
(Acta Math., 1988) proved that for almost every $H$ below the mean curvature of
the smallest sphere enclosing $\Sigma$, there exists a branched immersed disk
which has constant mean curvature $H$ and boundary meeting $\Sigma$
orthogonally. We reproduce this result using a different approach and improve
it under additional convexity assumptions on $\Sigma$. Specifically, when
$\Sigma$ itself is convex and has mean curvature bounded below by $H_0$, we
obtain existence for all $H \in (0, H_0)$. Instead of the heat flow used by
Struwe, we use a Sacks-Uhlenbeck type perturbation. As in previous joint work
with Zhou (arXiv:2012.13379), a key ingredient for extending existence across
the measure zero set of $H$'s is a Morse index upper bound.


title:
A thermoelectric generation system using waste heat recovery from
  petrochemical pipeline to power wireless sensor

abstract:
  Wireless monitoring sensor gradually replaces wired equipment for data
support in petrochemical industry production. And wireless monitoring sensor
with continuous energy supply is necessary and still faces great challenges.
Thermoelectric generator makes it possible to power wireless monitoring sensor
by continuously generating thermal energy from hot fluids in a collection
petrochemical pipeline in any environment. The inability to obtain a larger
temperature difference under TEG's high-temperature condition results in low
energy efficiency, limiting the scheme's application. This work describes a
thermoelectric generation system with an optimized heat sink for wireless
monitoring sensor power supply at high temperature. The system is simple to
install and use and is based on a high-performance passive heat sink with no
power consumption and micro-vibration. We establish a practical prototype
system to demonstrate performance and theoretical verification after analyzing
the system's power loss to achieve high power and high efficiency. The
experimental results show that the system proposed in this work can generate
enough power to power the wireless monitoring sensor for 60 seconds. The
minimum output power is 24.7"mW" at the hot-side of thermoelectric generation
(TEG) temperature of 100 {\deg}C- 190 {\deg}C. The maximum power conversion
efficiency in the considered input voltage range can reach an ideal value.


title: A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification

abstract:
  Existing deep learning based methods effectively prompt the performance of
aerial scene classification. However, due to the large amount of parameters and
computational cost, it is rather difficult to apply these methods to multiple
real-time remote sensing applications such as on-board data preception on
drones and satellites. In this paper, we address this task by developing a
light-weight ConvNet named multi-stage duplex fusion network (MSDF-Net). The
key idea is to use parameters as little as possible while obtaining as strong
as possible scene representation capability. To this end, a residual-dense
duplex fusion strategy is developed to enhance the feature propagation while
re-using parameters as much as possible, and is realized by our duplex fusion
block (DFblock). Specifically, our MSDF-Net consists of multi-stage structures
with DFblock. Moreover, duplex semantic aggregation (DSA) module is developed
to mine the remote sensing scene information from extracted convolutional
features, which also contains two parallel branches for semantic description.
Extensive experiments are conducted on three widely-used aerial scene
classification benchmarks, and reflect that our MSDF-Net can achieve a
competitive performance against the recent state-of-art while reducing up to
80% parameter numbers. Particularly, an accuracy of 92.96% is achieved on AID
with only 0.49M parameters.


title:
The origin of correlations between mass, metallicity and morphology in
  galaxies from the EAGLE simulation

abstract:
  Observed and simulated galaxies exhibit correlations between stellar mass,
metallicity and morphology. We use the EAGLE cosmological simulation to examine
the origin of these correlations for galaxies in the stellar mass range
$10^9~\rm{M_\odot} \leqslant\ M_\star \leqslant 10^{10}~\rm{M_\odot}$, and the
extent to which they contribute to the scatter in the mass-metallicity
relation. We find that rotationally supported disc galaxies have lower
metallicity than dispersion supported spheroidal galaxies at a given mass, in
agreement with previous findings. In EAGLE, this correlation arises because
discs form stars at later times, redshift $z\leqslant 1$, from the accretion of
low-metallicity gas, whereas spheroidal galaxies galaxies typically form stars
earlier, mainly by consumption of their gas reservoir. The different behaviour
reflects the growth of their host dark matter halo: at a given stellar mass,
disc galaxies inhabit dark matter haloes with lower mass that formed later
compared to the haloes of spheroidal galaxies. Halo concentration plays a
secondary role.


title:
Deformed Special Relativity with an invariant minimum speed as an
  explanation of the cosmological constant

abstract:
  This paper shows the need of the emergence of a universal minimum speed in
the space-time by means of a more thorough investigation of Dirac's large
number hypothesis (LNH). We will realize that there should be a minimum speed
$V$ with the same status of the invariance of the speed of light $c$, however
$V$ has gravitational origin. Hence, such a minimum speed forms a new kinematic
basis in the space-time, thus leading to a new Deformed Special Relativity
(DSR) for the quantum world so-called Symmetrical Special Relativity (SSR).
Furthermore, we will show that such a new structure of space-time (SSR) reveals
a connection between $V$ and a preferred reference frame $S_V$ of background
field that leads to the cosmological constant $\Lambda$, which can be
associated with a cosmological anti-gravity. We will also investigate the
effect of the number of baryons $N$ (Eddington number) of the observable
universe on the Hydrogen atom. Finally, we will show that SSR-metric plays the
role of a de-Sitter (dS)-metric with a positive cosmological constant, which
could assume a tiny value.


title:
Smooth Robust Tensor Completion for Background/Foreground Separation
  with Missing Pixels: Novel Algorithm with Convergence Guarantee

abstract:
  The objective of this study is to address the problem of
background/foreground separation with missing pixels by combining the video
acquisition, video recovery, background/foreground separation into a single
framework. To achieve this, a smooth robust tensor completion (SRTC) model is
proposed to recover the data and decompose it into the static background and
smooth foreground, respectively. Specifically, the static background is modeled
by the low-rank tucker decomposition and the smooth foreground (moving objects)
is modeled by the spatiotemporal continuity, which is enforced by the total
variation regularization. An efficient algorithm based on tensor proximal
alternating minimization (tenPAM) is implemented to solve the proposed model
with global convergence guarantee under very mild conditions. Extensive
experiments on real data demonstrate that the proposed method significantly
outperforms the state-of-the-art approaches for background/foreground
separation with missing pixels.


title: Parameter-efficient Fine-tuning for Vision Transformers

abstract:
  In computer vision, it has achieved great success in adapting large-scale
pretrained vision models (e.g., Vision Transformer) to downstream tasks via
fine-tuning. Common approaches for fine-tuning either update all model
parameters or leverage linear probes. In this paper, we aim to study
parameter-efficient fine-tuning strategies for Vision Transformers on vision
tasks. We formulate efficient fine-tuning as a subspace training problem and
perform a comprehensive benchmarking over different efficient fine-tuning
methods. We conduct an empirical study on each efficient fine-tuning method
focusing on its performance alongside parameter cost. Furthermore, we also
propose a parameter-efficient fine-tuning framework, which first selects
submodules by measuring local intrinsic dimensions and then projects them into
subspace for further decomposition via a novel Kronecker Adaptation method. We
analyze and compare our method with a diverse set of baseline fine-tuning
methods (including state-of-the-art methods for pretrained language models).
Our method performs the best in terms of the tradeoff between accuracy and
parameter efficiency across three commonly used image classification datasets.


title:
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and
  Mobility during the First Year of the COVID-19 Pandemic in the United States

abstract:
  Correlation between weather and the transmission of SARS-CoV-2 may suggest
its seasonality. Cointegration analysis can avoid spurious correlation among
time series data. We examined the cointegration of virus transmission with
daily temperature, dewpoint, and confounding factors of mobility measurements
during the first year of the pandemic in the United States. We examined the
cointegration of the effective reproductive rate, Rt, of the virus with the
dewpoint at two meters, the temperature at two meters, Apple driving mobility,
and Google workplace mobility measurements. We found that dewpoint and Apple
driving mobility are the best factors to cointegrate with Rt, although
temperature and Google workplace mobility also cointegrate with Rt at
substantial levels. We found that the optimal lag is two days for cointegration
between Rt and weather variables, and three days for Rt and mobility. We
observed clusters of states that share similar cointegration results of Rt,
weather, and mobility, suggesting regional patterns. Our results support the
correlation of weather with the spread of SARS-CoV-2 and its potential
seasonality.


title:
FlexFringe: Modeling Software Behavior by Learning Probabilistic
  Automata

abstract:
  We present the efficient implementations of probabilistic deterministic
finite automaton learning methods available in FlexFringe. These implement
well-known strategies for state-merging including several modifications to
improve their performance in practice. We show experimentally that these
algorithms obtain competitive results and significant improvements over a
default implementation. We also demonstrate how to use FlexFringe to learn
interpretable models from software logs and use these for anomaly detection.
Although less interpretable, we show that learning smaller more convoluted
models improves the performance of FlexFringe on anomaly detection,
outperforming an existing solution based on neural nets.


title: The interplay between mass-loss and binarity

abstract:
  Most stars with birth masses larger than that of our Sun belong to binary or
higher order multiple systems. Similarly, most stars have stellar winds.
Radiation pressure and multiplicity create outflows of material that remove
mass from the primary star and inject it into the interstellar medium or
transfer it to a companion. Both have strong impact on the subsequent evolution
of the stars, yet they are often studied separately. In this short review, I
will sketch part of the landscape of the interplay between stellar winds and
binarity. I will present several examples where binarity shapes the stellar
outflows, providing new opportunities to understand and measure mass loss
properties. Stellar winds spectral signatures often help clearly identifying
key stages of stellar evolution. The multiplicity properties of these stages
then shed a new light onto evolutionary connections between the different
categories of evolved stars.


title: On some nested floor functions and their jump discontinuities

abstract:
  This paper investigates some particular limits involving nested floor
functions. We'll prove some cases and then we'll show a more general result.
Then we'll count the discontinuity points of those functions, and we'll prove a
method to find them all. Surprisingly the set of the jump discontinuities of
$f_n$ is a subset of the set of the jump discontinuities of $f_{n+1}$, $\forall
n\in\mathbb{Z^{+}}$ where:
  \[
  f_n(x)=\underbrace{\Biggl\lfloor x\Bigl\lfloor x
\lfloor\dots\rfloor\Bigr\rfloor\Biggr\rfloor}_{\text{$n$ times}}
  \]
  Furthermore we'll give some generalizations of the result and lots of
considerations; for example we'll prove that the cardinality of the set of the
discontinuities of $f_n$ in a given limited interval approaches infinity as
$n\to\infty$.


title:
Instantaneous Frequency Estimation In Multi-Component Signals Using
  Stochastic EM Algorithm

abstract:
  This paper addresses the problem of estimating the modes of an observed
non-stationary mixture signal in the presence of an arbitrary distributed
noise. A novel Bayesian model is introduced to estimate the model parameters
from the spectrogram of the observed signal, by resorting to the stochastic
version of the EM algorithm to avoid the computationally expensive joint
parameters estimation from the posterior distribution. The proposed method is
assessed through comparative experiments with state-of-the-art methods. The
obtained results validate the proposed approach by highlighting an improvement
of the modes estimation performance.


title: Rapid Scalable Distributed Power Flow with Open-Source Implementation

abstract:
  This paper introduces a new method for solving the distributed AC power flow
(PF) problem by further exploiting the problem formulation. We propose a new
variant of the ALADIN algorithm devised specifically for this type of problem.
This new variant is characterized by using a reduced modelling method of the
distributed AC PF problem, which is reformulated as a zero-residual
least-squares problem with consensus constraints. This PF is then solved by a
Gauss-Newton based inexact ALADIN algorithm presented in the paper. An
open-source implementation of this algorithm, called rapidPF+, is provided.
Simulation results, for which the power system's dimension varies from 53 to
10224 buses, show great potential of this combination in the aspects of both
the computing.


title: TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography

abstract:
  Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram
(sEMG) signals has recently shown significant potential for development of
advanced myoelectric-controlled prosthesis. Existing deep learning approaches,
typically, include only one model as such can hardly maintain acceptable
generalization performance in changing scenarios. In this paper, we aim to
address this challenge by capitalizing on the recent advances of hybrid models
and transformers. In other words, we propose a hybrid framework based on the
transformer architecture, which is a relatively new and revolutionizing deep
learning model. The proposed hybrid architecture, referred to as the
Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel
paths followed by a linear layer that acts as a fusion center to integrate the
advantage of each module and provide robustness over different scenarios. We
evaluated the proposed architecture TraHGR based on the commonly used second
Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset
are measured in the real-life conditions from 40 healthy users, each performing
49 gestures. We have conducted extensive set of experiments to test and
validate the proposed TraHGR architecture, and have compared its achievable
accuracy with more than five recently proposed HGR classification algorithms
over the same dataset. We have also compared the results of the proposed TraHGR
architecture with each individual path and demonstrated the distinguishing
power of the proposed hybrid architecture. The recognition accuracies of the
proposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are
2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance for
DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9
gestures), respectively.


title:
On handwriting pressure normalization for interoperability of different
  acquisition stylus

abstract:
  In this paper, we present a pressure characterization and normalization
procedure for online handwritten acquisition. Normalization process has been
tested in biometric recognition experiments (identification and verification)
using online signature database MCYT, which consists of the signatures from 330
users. The goal is to analyze the real mismatch scenarios where users are
enrolled with one stylus and then, later on, they produce some testing samples
using a different stylus model with different pressure response. Experimental
results show: 1) a saturation behavior in pressure signal 2) different dynamic
ranges in the different stylus studied 3) improved biometric recognition
accuracy by means of pressure signal normalization as well as a performance
degradation in mismatched conditions 4) interoperability between different
stylus can be obtained by means of pressure normalization. Normalization
produces an improvement in signature identification rates higher than 7%
(absolute value) when compared with mismatched scenarios.


title: Stack operation of tensor networks

abstract:
  The tensor network, as a facterization of tensors, aims at performing the
operations that are common for normal tensors, such as addition, contraction
and stacking. However, due to its non-unique network structure, only the tensor
network contraction is so far well defined. In this paper, we propose a
mathematically rigorous definition for the tensor network stack approach, that
compress a large amount of tensor networks into a single one without changing
their structures and configurations. We illustrate the main ideas with the
matrix product states based machine learning as an example. Our results are
compared with the for loop and the efficient coding method on both CPU and GPU.


title: Robust and Energy-efficient PPG-based Heart-Rate Monitoring

abstract:
  A wrist-worn PPG sensor coupled with a lightweight algorithm can run on a MCU
to enable non-invasive and comfortable monitoring, but ensuring robust
PPG-based heart-rate monitoring in the presence of motion artifacts is still an
open challenge. Recent state-of-the-art algorithms combine PPG and inertial
signals to mitigate the effect of motion artifacts. However, these approaches
suffer from limited generality. Moreover, their deployment on MCU-based edge
nodes has not been investigated. In this work, we tackle both the
aforementioned problems by proposing the use of hardware-friendly Temporal
Convolutional Networks (TCN) for PPG-based heart estimation. Starting from a
single "seed" TCN, we leverage an automatic Neural Architecture Search (NAS)
approach to derive a rich family of models. Among them, we obtain a TCN that
outperforms the previous state-of-the-art on the largest PPG dataset available
(PPGDalia), achieving a Mean Absolute Error (MAE) of just 3.84 Beats Per Minute
(BPM). Furthermore, we tested also a set of smaller yet still accurate (MAE of
5.64 - 6.29 BPM) networks that can be deployed on a commercial MCU (STM32L4)
which require as few as 5k parameters and reach a latency of 17.1 ms consuming
just 0.21 mJ per inference.


title: Optimization for Classical Machine Learning Problems on the GPU

abstract:
  Constrained optimization problems arise frequently in classical machine
learning. There exist frameworks addressing constrained optimization, for
instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU
support is limited. Here, we extend the GENO framework to also solve
constrained optimization problems on the GPU. The framework allows the user to
specify constrained optimization problems in an easy-to-read modeling language.
A solver is then automatically generated from this specification. When run on
the GPU, the solver outperforms state-of-the-art approaches like CVXPY combined
with a GPU-accelerated solver such as cuOSQP or SCS by a few orders of
magnitude.


title: Primality Tests and Prime Certificate

abstract:
  This note presents a formalisation done in Coq of Lucas-Lehmer test and
Pocklington certificate for prime numbers. They both are direct consequences of
Fermat little theorem. Fermat little theorem is proved using elementary group
theory and in particular Lagrange theorem.


title: Hopf cocycles associated to pointed and copointed deformations over S3

abstract:
  We present an explicit description of the Hopf 2-cocycles involved in the
classification of finite dimensional pointed and copointed Hopf algebras over
the symmetric group on three letters. We determine which cocycles are
exponentials of Hochschild 2-cocycles and which ones are pure.


title:
AlgebraicSystems: Compositional Verification for Autonomous System
  Design

abstract:
  Autonomous systems require the management of several model views to assure
properties such as safety and security among others. A crucial issue in
autonomous systems design assurance is the notion of emergent behavior; we
cannot use their parts in isolation to examine their overall behavior or
performance. Compositional verification attempts to combat emergence by
implementing model transformation as structure-preserving maps between model
views. AlgebraicDynamics relies on categorical semantics to draw relationships
between algebras and model views. We propose AlgebraicSystems, a conglomeration
of algebraic methods to assign semantics and categorical primitives to give
computational meaning to relationships between models so that the formalisms
and resulting tools are interoperable through vertical and horizontal
composition.


title: Formalizing the Ring of Ad\`eles of a Global Field

abstract:
  The ring of ad\`eles of a global field and its group of units, the group of
id\`eles, are fundamental objects in modern number theory. We discuss a
formalization of their definitions in the Lean 3 theorem prover. As a
prerequisite, we formalized adic valuations on Dedekind domains. We present
some applications, including the statement of the main theorem of global class
field theory and a proof that the ideal class group of a number field is
isomorphic to an explicit quotient of its id\`ele class group.


title: An Algebraic Framework for Structured Epidemic Modeling

abstract:
  Pandemic management requires that scientists rapidly formulate and analyze
epidemiological models in order to forecast the spread of disease and the
effects of mitigation strategies. Scientists must modify existing models and
create novel ones in light of new biological data and policy changes such as
social distancing and vaccination. Traditional scientific modeling workflows
detach the structure of a model -- its submodels and their interactions -- from
its implementation in software. Consequently, incorporating local changes to
model components may require global edits to the code-base through a manual,
time-intensive, and error-prone process. We propose a compositional modeling
framework that uses high-level algebraic structures to capture domain-specific
scientific knowledge and bridge the gap between how scientists think about
models and the code that implements them. These algebraic structures, grounded
in applied category theory, simplify and expedite modeling tasks such as model
specification, stratification, analysis, and calibration. With their structure
made explicit, models also become easier to communicate, criticize, and refine
in light of stakeholder feedback.


title:
Enhanced Spreadsheet Computing with Finite-Domain Constraint
  Satisfaction

abstract:
  The spreadsheet application is among the most widely used computing tools in
modern society. It provides excellent usability and usefulness, and it easily
enables a non-programmer to perform programming-like tasks in a visual tabular
"pen and paper" approach. However, spreadsheets are mostly limited to
bookkeeping-like applications due to their mono-directional data flow. This
paper shows how the spreadsheet computing paradigm is extended to break this
limitation for solving constraint satisfaction problems. We present an enhanced
spreadsheet system where finite-domain constraint solving is well supported in
a visual environment. Furthermore, a spreadsheet-specific constraint language
is constructed for general users to specify constraints among data cells in a
declarative and scalable way. The new spreadsheet system significantly
simplifies the development of many constraint-based applications using a visual
tabular interface. Examples are given to illustrate the usability and
usefulness of the extended spreadsheet paradigm.
  KEYWORDS: Spreadsheet computing, Finite-domain constraint satisfaction,
Constraint logic programming


title: The Symbiotic X-ray binaries Sct X-1, 4U 1700+24 and IGR J17329-2731

abstract:
  Symbiotic X-ray binaries are systems hosting a neutron star accreting form
the wind of a late type companion. These are rare objects and so far only a
handful of them are known. One of the most puzzling aspects of the symbiotic
X-ray binaries is the possibility that they contain strongly magnetized neutron
stars. These are expected to be evolutionary much younger compared to their
evolved companions and could thus be formed through the (yet poorly known)
accretion induced collapse of a white dwarf. In this paper, we perform a
broad-band X-ray and soft $\gamma$-ray spectroscopy of two known symbiotic
binaries, Sct X-1 and 4U 1700+24, looking for the presence of cyclotron
scattering features that could confirm the presence of strongly magnetized NSs.
We exploited available Chandra, Swift, and NuSTAR data. We find no evidence of
cyclotron resonant scattering features (CRSFs) in the case of Sct X-1 but in
the case of 4U 1700+24 we suggest the presence of a possible CRSF at $\sim$16
keV and its first harmonic at $\sim$31 keV, although we could not exclude
alternative spectral models for the broad-band fit. If confirmed by future
observations, 4U 1700+24 could be the second symbiotic X-ray binary with a
highly magnetized accretor. We also report about our long-term monitoring of
the last discovered symbiotic X-ray binary IGR J17329-2731 performed with
Swift/XRT. The monitoring revealed that, as predicted, in 2017 this object
became a persistent and variable source, showing X-ray flares lasting for a few
days and intriguing obscuration events that are interpreted in the context of
clumpy wind accretion.


title: Quantum Control via Landau-Zener-St\"{u}ckelberg-Majorana Transitions

abstract:
  Since the pioneering works by Landau, Zener, St\"{u}ckelberg, and Majorana
(LZSM), it has been known that driving a quantum two-level system results in
tunneling between its states. Even though the interference between these
transitions is known to be important, it is only recently that it became both
accessible, controllable, and useful for engineering quantum systems. Here, we
study systematically various aspects of LZSM physics and review the relevant
literature, significantly expanding the review article in [Shevchenko, S. N.,
S. Ashhab, and F. Nori (2010), "Landau-Zener-St\"{u}ckelberg interferometry,"
Phys. Rep. 492, 1.].


title:
The Block-based Mobile PDE Systems Are Not Secure -- Experimental
  Attacks

abstract:
  Nowadays, mobile devices have been used broadly to store and process
sensitive data. To ensure confidentiality of the sensitive data, Full Disk
Encryption (FDE) is often integrated in mainstream mobile operating systems
like Android and iOS. FDE however cannot defend against coercive attacks in
which the adversary can force the device owner to disclose the decryption key.
To combat the coercive attacks, Plausibly Deniable Encryption (PDE) is
leveraged to plausibly deny the very existence of sensitive data. However, most
of the existing PDE systems for mobile devices are deployed at the block layer
and suffer from deniability compromises.
  Having observed that none of existing works in the literature have
experimentally demonstrated the aforementioned compromises, our work bridges
this gap by experimentally confirming the deniability compromises of the
block-layer mobile PDE systems. We have built a mobile device testbed, which
consists of a host computing device and a flash storage device. Additionally,
we have deployed both the hidden volume PDE and the steganographic file system
at the block layer of the testbed and performed disk forensics to assess
potential compromises on the raw NAND flash. Our experimental results confirm
it is indeed possible for the adversary to compromise the block-layer PDE
systems by accessing the raw NAND flash in practice. We also discuss potential
issues when performing such attacks in real world.


title: Density Matrix Renormalization Group Algorithm For Mixed Quantum States

abstract:
  Density Matrix Renormalization Group (DMRG) algorithm has been extremely
successful for computing the ground states of one-dimensional quantum many-body
systems. For problems concerned with mixed quantum states, however, it is less
successful in that either such an algorithm does not exist yet or that it may
return unphysical solutions. Here we propose a positive matrix product ansatz
for mixed quantum states which preserves positivity by construction. More
importantly, it allows to build a DMRG algorithm which, the same as the
standard DMRG for ground states, iteratively reduces the global optimization
problem to local ones of the same type, with the energy converging
monotonically in principle. This algorithm is applied for computing both the
equilibrium states and the non-equilibrium steady states, and its advantages
are numerically demonstrated.


title: Actegories for the Working Amthematician

abstract:
  Actions of monoidal categories on categories, also known as actegories, have
been familiar to category theorists for a long time, and yet a comprehensive
overview of this topic seems to be missing from the literature. Recently,
actegories have been increasingly employed in applied category theory, thereby
encouraging an effort to fill this gap according to the new needs of these
applications. This work started as an investigation of the notion of monoidal
actegory, a compatible pair of monoidal and actegorical structures, and ended
up including a sizable reference on the elementary theory of actegories. We
cover basic definitions and results on actegories and biactegories, spelling
out explicitly many folkloric definitions. Motivated by the use of actegories
in the theory of optics, we focus particularly on the way actegories combine
and interact with monoidal structure by describing "distributive laws" between
the two. In the last section, we provide three Cayley-like classification
results for these situations.


title: Heavy quarks and jets as probes of the QGP

abstract:
  The Quark-Gluon Plasma (QGP), a QCD state of matter created in
ultra-relativistic heavy-ion collisions, has remarkable properties as a low
shear viscosity over entropy ratio. Through the detection of multi-particle
production, the bulk debris of the collision, these so-called soft probes have
provided quantitative insight into the created matter. However, its fast
evolution and thermalization properties remain elusive to the soft sector. Only
the usage of high momentum objects as probes of the QGP can unveil its
constituents at the different wavelengths. In this review, we attempt to
provide a comprehensive picture of what was, so far, possible to withdraw given
our current theoretical understanding of jets, heavy-flavor, and quarkonia. We
will bridge the resulting qualitative picture to the experimental observations
done at both the LHC and RHIC. We will focus on the phenomenological
description of experimental observations, provide a brief analytical summary of
the description of hard probes, and an outlook towards the main difficulties we
will need to surpass in the following years. To benchmark QGP-related effects,
we will also address nuclear modifications to the initial state and
hadronization effects.


title:
Disconnected entanglement entropy as a marker of edge modes in a
  periodically driven Kitaev chain

abstract:
  We study the disconnected entanglement entropy (DEE) of a Kitaev chain in
which the chemical potential is periodically driven in various ways, namely,
$\delta$-pulses, square pulse and sinusoidal modulation. In all these cases,
the DEE of a sufficiently large system with open boundary conditions turns out
to be integer-quantized, with the integer being equal to the number of Majorana
edge modes generated by the periodic driving. Thus, the DEE can be considered
as a marker for detecting Majorana edge modes in a periodically driven Kitaev
chain. Interestingly, we find that the DEE may, in some cases, also detect the
anomalous edge modes which can be generated by periodic driving of the
nearest-neighbor hopping, even though such modes have no topological
significance.


title: Learning multiobjective rough terrain traversability

abstract:
  We present a method that uses high-resolution topography data of rough
terrain, and ground vehicle simulation, to predict traversability.
Traversability is expressed as three independent measures: the ability to
traverse the terrain at a target speed, energy consumption, and acceleration.
The measures are continuous and reflect different objectives for planning that
go beyond binary classification. A deep neural network is trained to predict
the traversability measures from the local heightmap and target speed. To
produce training data, we use an articulated vehicle with wheeled bogie
suspensions and procedurally generated terrains. We evaluate the model on
laser-scanned forest terrains, previously unseen by the model. The model
predicts traversability with an accuracy of 90%. Predictions rely on features
from the high-dimensional terrain data that surpass local roughness and slope
relative to the heading. Correlations show that the three traversability
measures are complementary to each other. With an inference speed 3000 times
faster than the ground truth simulation and trivially parallelizable, the model
is well suited for traversability analysis and optimal path planning over large
areas.


title:
Generative Adversarial Networks for the fast simulation of the Time
  Projection Chamber responses at the MPD detector

abstract:
  The detailed detector simulation models are vital for the successful
operation of modern high-energy physics experiments. In most cases, such
detailed models require a significant amount of computing resources to run.
Often this may not be afforded and less resource-intensive approaches are
desired. In this work, we demonstrate the applicability of Generative
Adversarial Networks (GAN) as the basis for such fast-simulation models for the
case of the Time Projection Chamber (TPC) at the MPD detector at the NICA
accelerator complex. Our prototype GAN-based model of TPC works more than an
order of magnitude faster compared to the detailed simulation without any
noticeable drop in the quality of the high-level reconstruction characteristics
for the generated data. Approaches with direct and indirect quality metrics
optimization are compared.


title:
Instanton effects in Euclidean vacuum, real time production and in the
  light front wave functions

abstract:
  Nontrivial topological structures of non-Abelian gauge fields were discovered
in the 1970's. Instanton solutions, describing vacuum tunneling through
topological barriers, have fermionic zero modes which are at the origin of
$^\prime$t Hooft effective Lagrangian. In the 1980's instanton ensembles have
been used to explain chiral symmetry breaking. In the 1990's a large set of
numerical simulations were performed deriving Euclidean correlation functions.
The special role of scalar diquarks in nucleons, and color superconductivity in
dense quark matter have been elucidated.
  In these lectures, we discuss further developments of physics related to
gauge topology. We show that the instanton-antiinstanton "streamline"
configurations describe "sphaleron transitions" in high energy collisions,
which result in production of hadronic clusters with nontrivial
topological/chiral charges. (They are not yet observed, but discussions of
dedicated experiments at LHC and RHIC are ongoing.)
  Another new direction is instanton effects in hadronic spectroscopy, both in
the rest frame and on the light front. We will discuss their role in central
and spin-dependent potentials, formfactors and antiquark nuclear "sea".
Finally, we summarize the advances in the semiclassical theory of
deconfinement, and chiral phase transitions at finite temperature, in QCD and
in some of its "deformed" versions.


title:
Practical Learned Lossless JPEG Recompression with Multi-Level
  Cross-Channel Entropy Model in the DCT Domain

abstract:
  JPEG is a popular image compression method widely used by individuals, data
center, cloud storage and network filesystems. However, most recent progress on
image compression mainly focuses on uncompressed images while ignoring
trillions of already-existing JPEG images. To compress these JPEG images
adequately and restore them back to JPEG format losslessly when needed, we
propose a deep learning based JPEG recompression method that operates on DCT
domain and propose a Multi-Level Cross-Channel Entropy Model to compress the
most informative Y component. Experiments show that our method achieves
state-of-the-art performance compared with traditional JPEG recompression
methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this
is the first learned compression method that losslessly transcodes JPEG images
to more storage-saving bitstreams.


title: Floquet States in Open Quantum Systems

abstract:
  In Floquet engineering, periodic driving is used to realize novel phases of
matter which are inaccessible in thermal equilibrium. For this purpose, the
Floquet theory provides us a recipe of obtaining a static effective
Hamiltonian. Although many existing works have treated closed systems, it is
important to consider the effect of dissipation, which is ubiquitous in nature.
Understanding interplay of periodic driving and dissipation is not only a
fundamental problem of nonequilibrium statistical physics, but also receiving
growing interest because of the fact that experimental advances have allowed us
to engineer dissipation in a controllable manner. In this review, we give a
detailed exposition on the formalism of quantum master equations for open
Floquet systems and highlight recent works investigating whether equilibrium
statistical mechanics applies to Floquet states.

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


title: On local antimagic chromatic number of lexicographic product graphs

abstract:
  Let $G = (V,E)$ be a connected simple graph of order $p$ and size $q$. A
graph $G$ is called local antimagic if $G$ admits a local antimagic labeling. A
bijection $f : E \to \{1,2,\ldots,q\}$ is called a local antimagic labeling of
$G$ if for any two adjacent vertices $u$ and $v$, we have $f^+(u) \ne f^+(v)$,
where $f^+(u) = \sum_{e\in E(u)} f(e)$, and $E(u)$ is the set of edges incident
to $u$. Thus, any local antimagic labeling induces a proper vertex coloring of
$G$ if vertex $v$ is assigned the color $f^+(v)$. The local antimagic chromatic
number, denoted $\chi_{la}(G)$, is the minimum number of induced colors taken
over local antimagic labeling of $G$. Let $G$ and $H$ be two vertex disjoint
graphs. The {\it lexicographic product} of $G$ and $H$, denoted $G[H]$, is the
graph with vertex set $V(G) \times V(H)$, and $(u,u')$ is adjacent to $(v,v')$
in $G[H]$ if $(u,v)\in E(G)$ or if $u=v$ and $u'v'\in E(H)$. In this paper, we
obtained sharp upper bound of $\chi_{la}(G[O_n])$ where $O_n$ is a null graph
of order $n\ge 1$. Sufficient conditions for even regular bipartite and
tripartite graphs $G$ to have $\chi_{la}(G)=3$ are also obtained. Consequently,
we successfully determined the local antimagic chromatic number of infinitely
many (connected and disconnected) regular graphs that partially support the
existence of $r$-regular graph $G$ of order $p$ such that (i)
$\chi_{la}(G)=\chi(G)=k$, and (ii) $\chi_{la}(G)=\chi(G)+1=k$ for each possible
$r,p,k$.


title: The Sard problem in step 2 and in filiform Carnot groups

abstract:
  We study the Sard problem for the endpoint map in some well-known classes of
Carnot groups. Our first main result deals with step 2 Carnot groups, where we
provide lower bounds (depending only on the algebra of the group) on the
codimension of the abnormal set; it turns out that our bound is always at least
3, which improves the result proved in arXiv:1503.03610 and settles a question
emerged in arXiv:1709.02854. In our second main result we characterize the
abnormal set in filiform groups and show that it is either a horizontal line,
or a 3-dimensional algebraic variety.


title:
Rainbow Keywords: Efficient Incremental Learning for Online Spoken
  Keyword Spotting

abstract:
  Catastrophic forgetting is a thorny challenge when updating keyword spotting
(KWS) models after deployment. This problem will be more challenging if KWS
models are further required for edge devices due to their limited memory. To
alleviate such an issue, we propose a novel diversity-aware incremental
learning method named Rainbow Keywords (RK). Specifically, the proposed RK
approach introduces a diversity-aware sampler to select a diverse set from
historical and incoming keywords by calculating classification uncertainty. As
a result, the RK approach can incrementally learn new tasks without forgetting
prior knowledge. Besides, the RK approach also proposes data augmentation and
knowledge distillation loss function for efficient memory management on the
edge device. Experimental results show that the proposed RK approach achieves
4.2% absolute improvement in terms of average accuracy over the best baseline
on Google Speech Command dataset with less required memory. The scripts are
available on GitHub.


title:
The scaled-invariant Planckian metal and quantum criticality in
  Ce$_{1-x}$Nd$_x$CoIn$_5$

abstract:
  Perfect $T$-linear resistivity associated with universal scattering rate:
$1/\tau =\alpha k_B T/\hbar$ with $\alpha \sim 1$, so-called Planckian metal
state, has been observed in the normal state of a variety of strongly
correlated superconductors close to a quantum critical point. However, the
microscopic origin of this intriguing phenomena and its link to quantum
criticality still remains an outstanding open problem. In this work, we observe
the quantum-critical $T/B$-scaling of the Planckian metal state in the
resistivity and heat capacity of heavy-electron superconductor
Ce$_{1-x}$Nd$_x$CoIn$_5$ in magnetic fields near the edge of
antiferromagnetism, driven by critical Kondo hybridization at the critical
doping $x_c \sim 0.03$. We further provide the first microscopic mechanism to
account for the Planckian state in a quantum critical system based on the
critical charge fluctuations near Kondo breakdown transition at $x_c$ within
the quasi-two-dimensional Kondo-Heisenberg lattice model. This mechanism
simultaneously captures the observed universal Planckian scattering rate as
well as the quantum-critical scaling and power-law divergence in thermodynamic
observables near criticality. Our mechanism is generic to Planckian metal
states in a variety of quantum critical superconductors near Kondo destruction.


title:
Radio luminosity of GLEAM-X J162759.5-523504.3: does it really exceed
  the spin-down power of the pulsar?

abstract:
  The recently discovered radio pulsar GLEAM-X J162759.5-523504.3 with an
extremely long spin period was reported to have a radio luminosity that exceeds
by orders of magnitude the spin-down power of the pulsar. In this Letter, we
rigorously calculate the radio luminosity of the source taking into account the
dependence of the opening angle of the pulsar-emission cone first on the spin
period alone and then on both the spin parameters and the observing frequency.
We also revise the value of the spin-down power reported previously. Our
analysis is based on the description of the spectral data in terms of two
power-law indices as well as a single power-law index. Even if the pulsar's
opening angle is treated as a frequency-independent parameter in line with the
usual assumption, the period dependence of this parameter implies relatively
small opening angles and therefore radio luminosities well below the spin-down
power. Although we estimate higher radio luminosities in the physically more
plausible case of a frequency-dependent opening angle, the spin-down power is
again not exceeded by the highest possible radio luminosity. The radio
efficiency of GLEAM-X J162759.5-523504.3 can therefore not be used in favor of
a magnetar hypothesis.


title: The Chemical Composition of Extreme-Velocity Stars

abstract:
  Little is known about the origin of the fastest stars in the Galaxy. Our
understanding of the Milky Way and surrounding dwarf galaxies chemical
evolution history allows us to use the chemical composition of a star to
investigate its origin, and say whether a star was formed in-situ or was
accreted. However, the fastest stars, the hypervelocity stars, are young and
massive and their chemical composition has not yet been analyzed. Though it is
difficult to analyze the chemical composition of a massive young star, we are
well versed in the analysis of late-type stars. We have used high-resolution
ARCES/3.5m Apache Point Observatory, MIKE/Magellan spectra to study the
chemical details of 15 late-type hypervelocity stars candidates. With Gaia EDR3
astrometry and spectroscopically determined radial velocities we found total
velocities with a range of $274$ - $520$ km s$^{-1}$ and mean value of $381$ km
s$^{-1}$. Therefore, our sample stars are not fast enough to be classified as
Hypervelocity stars, and are what is known as extreme-velocity stars. Our
sample has a wide iron abundance range of $-2.5 \le \mathrm{[Fe/H]} \le -0.9$.
Their chemistry indicate that at least 50\% of them are accreted extragalactic
stars, with iron-peak elements consistent with prior sub-Chandrasekhar mass
type Ia supernova enrichment. Without indication of binary companions, their
chemical abundances and orbital parameters are indicative that they are the
accelerated tidal debris of disrupted dwarf galaxies.


title:
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network
  Intrusion Detection on UNSW-NB15 Dataset

abstract:
  The effectiveness of machine learning models is significantly affected by the
size of the dataset and the quality of features as redundant and irrelevant
features can radically degrade the performance. This paper proposes IGRF-RFE: a
hybrid feature selection method tasked for multi-class network anomalies using
a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature
reduction technique based on both the filter feature selection method and the
wrapper feature selection method. In our proposed method, we use the filter
feature selection method, which is the combination of Information Gain and
Random Forest Importance, to reduce the feature subset search space. Then, we
apply recursive feature elimination(RFE) as a wrapper feature selection method
to further eliminate redundant features recursively on the reduced feature
subsets. Our experimental results obtained based on the UNSW-NB15 dataset
confirm that our proposed method can improve the accuracy of anomaly detection
while reducing the feature dimension. The results show that the feature
dimension is reduced from 42 to 23 while the multi-classification accuracy of
MLP is improved from 82.25% to 84.24%.


title: Subcritical bootstrap percolation via Toom contours

abstract:
  In this note we provide an alternative proof of the fact that subcritical
bootstrap percolation models have a positive critical probability in any
dimension. The proof relies on a recent extension of the classical framework of
Toom. This approach is not only simpler than the original multi-scale
renormalisation proof of the result in two and more dimensions, but also gives
significantly better bounds. As a byproduct, we improve the best known bounds
for the stability threshold of Toom's North-East-Center majority rule cellular
automaton.


title:
Nonequilibrium work distributions in controlled system-bath mixing
  processes

abstract:
  The fluctuation theorem provides a powerful tool to investigate the
thermodynamics out of equilibrium, where the central quantity is the work
distribution. In this work, we establish an exact formalism to study the work
distributions in isothermal system-bath mixing processes, under certain
controllable protocols denoted as $\lambda$. This formalism extends the
original framework of $\lambda$-dissipaton-equation-of-motion ($\lambda$-DEOM)
from equilibrium to nonequilibrium scenarios. Using this nonequilibrium
$\lambda$-DEOM, we accurately verify Jarzynski equality and Crooks relation,
two foundational components of the fluctuation theorem.


title:
Derivation of the Maxwell-Schr\"odinger Equations: A note on the
  infrared sector of the radiation field

abstract:
  We slightly extend prior results about the derivation of the
Maxwell-Schr\"odinger equations from the bosonic Pauli-Fierz Hamiltonian. More
concretely, we show that the findings from [SIAM J. Math. Anal. 52(5),
4900-4936 (2020)] about the coherence of the quantized electromagnetic field
also hold for soft photons with small energies. This is achieved with the help
of an estimate from [arXiv:2202.05015] which proves that the domain of the
number of photon operator is invariant during the time evolution generated by
the Pauli-Fierz Hamiltonian.


title:
Incorporating Dynamic Semantics into Pre-Trained Language Model for
  Aspect-based Sentiment Analysis

abstract:
  Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a
specific aspect in the given sentence. While pre-trained language models such
as BERT have achieved great success, incorporating dynamic semantic changes
into ABSA remains challenging. To this end, in this paper, we propose to
address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method
designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we
first take the Stack-BERT layers as a primary encoder to grasp the overall
semantic of the sentence and then fine-tune it by incorporating a lightweight
Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention
to a small region of the sentences at each step and re-weigh the vitally
important words for better aspect-aware sentiment understanding. Finally,
experimental results on three benchmark datasets demonstrate the effectiveness
and the rationality of our proposed model and provide good interpretable
insights for future semantic modeling.


title:
$crypto_{lib}$: Comparing and selecting cryptography libraries (long
  version of EICC 2022 publication)

abstract:
  Selecting a library out of numerous candidates can be a laborious and
resource-intensive task. We present the $crypto_{lib}$ index, a tool for
decision-makers to choose the best fitting cryptography library for a given
context. To define our index, 15 library attributes were synthesized from
findings based on a literature review and interviews with decision-makers.
These attributes were afterwards validated and weighted via an online survey.
In order to create the index value for a given library, the individual
attributes are assessed using given evaluation criteria associated with the
respective attribute. As a proof of concept and to give a practical usage
example, the derivation of the $crypto_{lib}$ values for the libraries Bouncy
Castle and Tink are shown in detail. Overall, by tailoring the weighting of the
$crypto_{lib}$ attributes to their current use case, decision-makers are
enabled to systematically select a cryptography library fitting best to their
software project at hand in a guided, repeatable and reliable way.


title: Operation of an ambulance fleet under uncertainty

abstract:
  We introduce two new optimization models for the dispatch of ambulances. The
first model, called the ambulance selection problem, is used when an emergency
call arrives to decide whether an ambulance should be dispatched for that call,
and if so, which ambulance should be dispatched, or whether the request should
be put in a queue of waiting requests. The second model, called the ambulance
reassignment problem, is used when an ambulance finishes its current task to
decide whether the ambulance should be dispatched to a request waiting in
queue, and if so, which request, or whether the ambulance should be dispatched
to an ambulance staging location, and if so, which ambulance staging location.
These decisions affect not only the emergency call and ambulance under
consideration, but also the ability of the ambulance fleet to service future
calls. There is uncertainty regarding the locations, arrival times, and types
of future calls. We propose a rolling horizon approach that combines the
current decisions to be made as first-stage decisions with second-stage models
that represent the ability of the ambulance fleet to service future calls. The
second-stage optimization problems can be formulated as large-scale
deterministic integer linear programs. We propose a column generation algorithm
to solve the continuous relaxation of these second-stage problems. The optimal
objective values of these second-stage continuous relaxations are used to make
approximately optimal first-stage decisions. We compare our resulting dispatch
policy with popular decision rules for Rio de Janeiro emergency medical
service, based on data of more than 2 years of emergency calls for that
service. These tests show that our proposed policy results in smaller response
times than the popular decision rules.


title: Reliability of Open Public Electric Vehicle Direct Current Fast Chargers

abstract:
  In order to achieve a rapid transition to electric vehicle driving, a highly
reliable and easy to use charging infrastructure is critical to building
confidence as consumers shift from using familiar gas vehicles to unfamiliar
electric vehicles (EV). This study evaluated the functionality of the charging
system for 657 EVSE (electric vehicle service equipment) CCS connectors
(combined charging system) on all 181 open, public DCFC (direct current fast
chargers) charging stations in the Greater Bay Area. An EVSE was evaluated as
functional if it charged an EV for 2 minutes or was charging an EV at the time
the station was evaluated. Overall, 72.5% of the 657 EVSEs were functional. The
cable was too short to reach the EV inlet for 4.9% of the EVSEs. Causes of
22.7% of EVSEs that were non-functioning were unresponsive or unavailable
screens, payment system failures, charge initiation failures, network failures,
or broken connectors. A random evaluation of 10% of the EVSEs, approximately 8
days after the first evaluation, demonstrated no overall change in
functionality. This level of functionality appears to conflict with the 95 to
98% uptime reported by the EV service providers (EVSPs) who operate the EV
charging stations. The findings suggest a need for shared, precise definitions
of and calculations for reliability, uptime, downtime, and excluded time, as
applied to open public DCFCs, with verification by third-party evaluation.


title:
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery
  Remaining Useful Life Estimation

abstract:
  Capsule network (CapsNet) acts as a promising alternative to the typical
convolutional neural network, which is the dominant network to develop the
remaining useful life (RUL) estimation models for mechanical equipment.
Although CapsNet comes with an impressive ability to represent the entities'
hierarchical relationships through a high-dimensional vector embedding, it
fails to capture the long-term temporal correlation of run-to-failure time
series measured from degraded mechanical equipment. On the other hand, the
slow-varying dynamics, which reveals the low-frequency information hidden in
mechanical dynamical behaviour, is overlooked in the existing RUL estimation
models, limiting the utmost ability of advanced networks. To address the
aforementioned concerns, we propose a Slow-varying Dynamics assisted Temporal
CapsNet (SD-TemCapsNet) to simultaneously learn the slow-varying dynamics and
temporal dynamics from measurements for accurate RUL estimation. First, in
light of the sensitivity of fault evolution, slow-varying features are
decomposed from normal raw data to convey the low-frequency components
corresponding to the system dynamics. Next, the long short-term memory (LSTM)
mechanism is introduced into CapsNet to capture the temporal correlation of
time series. To this end, experiments conducted on an aircraft engine and a
milling machine verify that the proposed SD-TemCapsNet outperforms the
mainstream methods. In comparison with CapsNet, the estimation accuracy of the
aircraft engine with four different scenarios has been improved by 10.17%,
24.97%, 3.25%, and 13.03% concerning the index root mean squared error,
respectively. Similarly, the estimation accuracy of the milling machine has
been improved by 23.57% compared to LSTM and 19.54% compared to CapsNet.


title:
The excitatory-inhibitory branching process: a parsimonious view of
  cortical asynchronous states, excitability, and criticality

abstract:
  The branching process is the minimal model for propagation dynamics,
avalanches and criticality, broadly used in neuroscience. A simple extension of
it, adding inhibitory nodes, induces a much-richer phenomenology, including, an
intermediate phase, between quiescence and saturation, that exhibits the key
features of asynchronous states in cortical networks. Remarkably, in the
inhibition-dominated case, it also captures a wealth of non-trivial features of
spontaneous brain activity, such as collective excitability, hysteresis, tilted
avalanche shapes, and partial synchronization, allowing us to rationalize
striking empirical findings within a common parsimonious framework.


title: UGR tests with atomic clocks and atom interferometers

abstract:
  Atomic interference experiments test the universality of the coupling between
matter-energy and gravity at different spacetime points, thus being in
principle able to probe possible violations of the universality of the
gravitational redshift (UGR). In this contribution, we introduce a UGR
violation model and then discuss UGR tests performed by atomic clocks and atom
interferometers on the same footing. We present a large class of
atom-interferometric geometries which are sensitive to violations of UGR.


title: Anomaly-free ALP from non-Abelian flavor symmetry

abstract:
  Motivated by the Xenon1T excess in electron-recoil measurements, we
investigate the prospects of probing axion-like particles (ALP) in lepton
flavor violation experiments. In particular, we identify such ALP as a
pseudo-Goldstone from the spontaneous breaking of the flavor symmetries that
explain the mixing structure of the Standard Model leptons. We present the case
of the flavor symmetries being a non-Abelian U(2) and the ALP originating from
its U(1) subgroup, which is anomaly-free with the Standard Model group. We
build two explicit realistic examples that reproduce leptonic masses and
mixings and show that the ALP which is consistent with Xenon1T anomaly could be
probed by the proposed LFV experiments.


title:
A barrier function approach to constrained Pontryagin-based Nonlinear
  Model Predictive Control

abstract:
  A Pontryagin-based approach to solve a class of constrained Nonlinear Model
Predictive Control problems is proposed which employs the method of barrier
functions for dealing with the state constraints. Unlike the existing works in
literature the proposed method is able to cope with nonlinear input and state
constraints without any significant modification of the optimization algorithm.
A stability analysis of the closed-loop system is carried out by using the L-2
norm of the predicted state tracking error as a Lyapunov function. Theoretical
results are tested and confirmed by numerical simulations on the Lotka-Volterra
prey/predator system.


title: Wavefunctionals/S-matrix techniques in de Sitter

abstract:
  Flat-space physics is highly constrained by basic principles such as Lorentz
invariance, locality, unitarity and causality. This is neatly seen in the
structure of scattering amplitudes. For processes occurring in an expanding
background we do not have the same level of understanding, not even in the case
of de Sitter space. In this talk I provide a brief account of some of the
recent efforts which aim to fill this gap. I will discuss some novel results in
the understanding of the analytic structure of the Bunch-Davies
wavefunction(al) of the universe in the perturbative regime, some fundamental
constraints that it ought to satisfy as well as some general insights coming
from an underlying combinatorial structure.


title: Why are we still using 3D masses for cluster cosmology?

abstract:
  The abundance of clusters of galaxies is highly sensitive to the late-time
evolution of the matter distribution, since clusters form at the highest
density peaks. However, the 3D cluster mass cannot be inferred without
deprojecting the observations, introducing model-dependent biases and
uncertainties due to the mismatch between the assumed and the true cluster
density profile and the neglected matter along the sightline. Since projected
aperture masses can be measured directly in simulations and observationally
through weak lensing, we argue that they are better suited for cluster
cosmology. Using the Mira-Titan suite of gravity-only simulations, we show that
aperture masses correlate strongly with 3D halo masses, albeit with large
intrinsic scatter due to the varying matter distribution along the sightline.
Nonetheless, aperture masses can be measured $\approx 2-3$ times more precisely
from observations, since they do not require assumptions about the density
profile and are only affected by the shape noise in the weak lensing
measurements. We emulate the cosmology dependence of the aperture mass function
directly with a Gaussian process. Comparing the cosmology sensitivity of the
aperture mass function and the 3D halo mass function for a fixed survey solid
angle and redshift interval, we find the aperture mass sensitivity is higher
for $\Omega_\mathrm{m}$ and $w_a$, similar for $\sigma_8$, $n_\mathrm{s}$, and
$w_0$, and slightly lower for $h$. With a carefully calibrated aperture mass
function emulator, cluster cosmology analyses can use cluster aperture masses
directly, reducing the sensitivity to model-dependent mass calibration biases
and uncertainties.


title: The p-process in exploding rotating massive stars

abstract:
  The p-process nucleosynthesis can explain proton-rich isotopes that are
heavier than iron, which are observed in the Solar System, but discrepancies
still persist and important questions concerning the astrophysical site(s) of
the p-process remain unanswered. We investigate how the p-process operates in
exploding rotating massive stars that have experienced an enhanced s-process
nucleosynthesis during their life through rotational mixing. We computed 25
$M_{\odot}$ stellar models at a metallicity of $Z=10^{-3}$ with different
initial rotation velocities and rates for the uncertain
$^{17}$O($\alpha$,$\gamma$)$^{21}$Ne reaction. The nucleosynthesis calculation,
followed with a network of 737 isotopes, was coupled to stellar evolution, and
the p-process nucleosynthesis was calculated in post-processing during both the
final evolutionary stages and spherical explosions of various energies. In our
models, the p-nuclides are mainly synthesized during the explosion, but not
much during the ultimate hydrostatic burning stages. The p-process yields
mostly depend on the initial number of trans-iron seeds, which in turn depend
on the initial rotation. We found that the impact of rotation on the p-process
is comparable to the impact of rotation on the s-process. From no to fast
rotation, the s-process yields of nuclides with mass number $A<140$ increase by
$3-4$ dex, and so do the p-process yields. Fast rotation with a lower
$^{17}$O($\alpha,\gamma$) rate significantly produces s- and p-nuclides with
$A\geq140$. Our results suggest that the contribution of core-collapse
supernovae from massive stars to the solar (and Galactic) p-nuclei has been
underestimated in the past, and more specifically, that the contribution from
massive stars with sub-solar metallicities may even dominate. A more detailed
study including stellar models with a wide range of masses and metallicities
remains to be performed.


title:
CardioID: Mitigating the Effects of Irregular Cardiac Signals for
  Biometric Identification

abstract:
  Cardiac patterns are being used to obtain hard-to-forge biometric signatures
and have led to high accuracy in state-of-the-art (SoA) identification
applications. However, this performance is obtained under controlled scenarios
where cardiac signals maintain a relatively uniform pattern, facilitating the
identification process. In this work, we analyze cardiac signals collected in
more realistic (uncontrolled) scenarios and show that their high signal
variability (i.e., irregularity) makes it harder to obtain stable and distinct
user features. Furthermore, SoA usually fails to identify specific groups of
users, rendering existing identification methods futile in uncontrolled
scenarios. To solve these problems, we propose a framework with three novel
properties. First, we design an adaptive method that achieves stable and
distinct features by tailoring the filtering spectrum to each user. Second, we
show that users can have multiple cardiac morphologies, offering us a much
bigger pool of cardiac signals and users compared to SoA. Third, we overcome
other distortion effects present in authentication applications with a
multi-cluster approach and the Mahalanobis distance. Our evaluation shows that
the average balanced accuracy (BAC) of SoA drops from above 90% in controlled
scenarios to 75% in uncontrolled ones, while our method maintains an average
BAC above 90% in uncontrolled scenarios.


title: Topological Burning Glass Effect

abstract:
  We reveal a topological burning glass effect, where the local response of a
system exhibits a topological quantization that is enhanced by an integer due
to its environmental coupling. As a paradigmatic platform for this intriguing
phenomenon, we study a central spin that is quasiperiodically driven by two
incommensurate frequencies, and statically coupled to $N-1$ surrounding spins.
In the strong coupling regime, the adiabatic dynamics of the total system is
readily understood to imprint on the central spin an $N$-fold enhanced
topological frequency conversion between the two driving frequencies. We argue
that the topological burning glass effect is induced by the non-unitary
dynamics of the central spin coupled to the surrounding spins. Our results are
derived in the framework of adiabatic perturbation theory and fully
corroborated by exact numerical simulations.


title:
Tangent-point energies and ropelength as Gamma-limit of discrete
  tangent-point energies on biarc curves

abstract:
  Using interpolation with biarc curves we prove $\Gamma$-convergence of
discretized tangent-point energies to the continuous tangent-point energies in
the $C^1$-topology, as well as to the ropelength functional. As a consequence
discrete almost minimizing biarc curves converge to ropelength minimizers, and
to minimizers of the continuous tangent-point energies. In addition, taking
point-tangent data from a given $C^{1,1}$-curve $\gamma$, we establish
convergence of the discrete energies evaluated on biarc curves interpolating
these data, to the continuous tangent-point energy of $\gamma$, together with
an explicit convergence rate.


title:
A consensus-based algorithm for multi-objective optimization and its
  mean-field description

abstract:
  We present a multi-agent algorithm for multi-objective optimization problems,
which extends the class of consensus-based optimization methods and relies on a
scalarization strategy. The optimization is achieved by a set of interacting
agents exploring the search space and attempting to solve all scalar
sub-problems simultaneously. We show that those dynamics are described by a
mean-field model, which is suitable for a theoretical analysis of the algorithm
convergence. Numerical results show the validity of the proposed method.


title:
Direct parameter estimations from machine-learning enhanced quantum
  state tomography

abstract:
  With the capability to find the best fit to arbitrarily complicated data
patterns, machine-learning (ML) enhanced quantum state tomography (QST) has
demonstrated its advantages in extracting complete information about the
quantum states. Instead of using the reconstruction model in training a
truncated density matrix, we develop a high-performance, lightweight, and
easy-to-install supervised characteristic model by generating the target
parameters directly. Such a characteristic model-based ML-QST can avoid the
problem of dealing with large Hilbert space, but keep feature extractions with
high precision. With the experimentally measured data generated from the
balanced homodyne detectors, we compare the degradation information about
quantum noise squeezed states predicted by the reconstruction and
characteristic models, both give agreement to the empirically fitting curves
obtained from the covariance method. Such a ML-QST with direct parameter
estimations illustrates a crucial diagnostic toolbox for applications with
squeezed states, including advanced gravitational wave detectors, quantum
metrology, macroscopic quantum state generation, and quantum information
process.


title:
Nodal heterogeneity may induce ghost triadic effects in relational event
  models

abstract:
  Temporal network data often encode time-stamped interaction events between
senders and receivers, such as co-authoring a scientific article or sending an
email. A number of relational event frameworks have been proposed to address
specific issues raised by modelling time-stamped data with complex temporal and
spatial dependencies. These models attempt to quantify how individuals'
behaviour, external factors and interaction with other individuals change the
network structure over time. It is often of interest to determine whether
changes in the network can be attributed to endogenous mechanisms reflecting
natural relational tendencies, such as reciprocity or triadic effects, with the
latter thought to represent the inherent complexity of human interaction.
  The propensity to form (or receive) ties can also be related to individual
actors' attributes. Nodal heterogeneity in the network is often modelled by
including actor-specific or dyadic covariates, such as age, gender, shared
neighbourhood, etc. However, capturing personality traits such as popularity or
expansiveness is difficult, if not impossible. A failure to account for
unobserved heterogeneity may confound the substantive effect of key variables
of interest. This research shows how node level popularity in terms of sender
and receiver effects may mask ghost triadic effects. These results suggest that
unobserved nodal heterogeneity plays a substantial role in REM estimation
procedure and influences the conclusions drawn from real-world networks.


title:
Dynamical Casimir effects with atoms: from the emission of photon pairs
  to geometric phases

abstract:
  The coupling between a moving ground-state atom and the quantum
electromagnetic field is at the origin of several intriguing phenomena ranging
from the dynamical Casimir emission of photons to Sagnac-like geometric phase
shifts in atom interferometers. Recent progress in this emerging field reveals
unprecedented connections between non-trivial aspects of modern physics such as
electrodynamic retardation, non-unitary evolution in open quantum systems,
geometric phases, non-locality and inertia.


title: Topological gauge actions on the lattice as Overlap fermion determinants

abstract:
  Overlap fermion on the lattice has been shown to properly reproduce
topological aspects of gauge fields. In this paper, we review the derivation of
Overlap fermion formalism in a torus of three space-time dimensions. Using the
formalism, we show how to use the Overlap fermion determinants in the massless
and infinite mass limits to construct different continuum topological gauge
actions, such as the level-$k$ Chern-Simons action, "half-CS" term and the BF
coupling, in a gauge-invariant lattice UV regulated manner. Taking special
Abelian and non-Abelian background fields, we demonstrate numerically how the
lattice formalism beautifully reproduces the continuum expectations, such as
the flow of action under large gauge transformations.


title: Revisiting in-depth chemical analysis for alkali ions-exchanged glass

abstract:
  The upcoming flexible ultra-thin glass for foldable displays has attracted
widespread attention as an alternative to rigid electronic smartphones.
However, the detailed compositional effect of the chemical strengthening of the
glass is not well understood. The spatially resolved chemistry and the depth of
compression layer of tempered glass is far from being clear. In this study,
commonly-used X-ray spectroscopy techniques and atom probe tomography (APT)
were used comparatively to investigate the distribution of constituent elements
in two representative smartphone glass samples: non-tempered and
chemically-tempered glasses. Using APT, analysing the alkalis (Li, Na, K, Ca)
that could impact the failure of the tempered glass was feasible at
sub-nanoscale resolution; demonstrating that APT can be regarded as an
alternative technique for imaging chemical distribution in glass for mobile
applications.


title: Proper conflict-free coloring of sparse graphs

abstract:
  A {\it proper conflict-free $c$-coloring} of a graph is a proper $c$-coloring
such that each non-isolated vertex has a color appearing exactly once on its
neighborhood. This notion was formally introduced by Fabrici et al., who proved
that planar graphs have a proper conflict-free 8-coloring and constructed a
planar graph with no proper conflict-free 5-coloring. Caro, Petru\v{s}evski,
and \v{S}krekovski investigated this coloring concept further, and in
particular studied upper bounds on the maximum average degree that guarantees a
proper conflict-free $c$-coloring for $c\in\{4,5,6\}$.
  Along these lines, we completely determine the threshold on the maximum
average degree of a graph $G$, denoted $mad(G)$, that guarantees a proper
conflict-free $c$-coloring for all $c$ and also provide tightness examples.
Namely, for $c\geq 5$ we prove that a graph $G$ with $mad(G)\leq
\frac{4c}{c+2}$ has a proper conflict-free $c$-coloring, unless $G$ contains a
$1$-subdivision of the complete graph on $c+1$ vertices. When $c=4$, we show
that a graph $G$ with $mad(G)<\frac{12}{5}$ has a proper conflict-free
$4$-coloring, unless $G$ contains an induced $5$-cycle. In addition, we show
that a planar graph with girth at least 5 has a proper conflict-free
$7$-coloring.


title: Independence complexes of $(n \times 4)$ and $(n \times 5)$-grid graphs

abstract:
  We determine the homotopy types of the independence complexes of $(n \times
4)$ and $(n \times 5)$-square grid graphs. In fact, they are homotopy
equivalent to wedges of spheres.


title:
On learning adaptive acquisition policies for undersampled multi-coil
  MRI reconstruction

abstract:
  Most current approaches to undersampled multi-coil MRI reconstruction focus
on learning the reconstruction model for a fixed, equidistant acquisition
trajectory. In this paper, we study the problem of joint learning of the
reconstruction model together with acquisition policies. To this end, we extend
the End-to-End Variational Network with learnable acquisition policies that can
adapt to different data points. We validate our model on a coil-compressed
version of the large scale undersampled multi-coil fastMRI dataset using two
undersampling factors: $4\times$ and $8\times$. Our experiments show on-par
performance with the learnable non-adaptive and handcrafted equidistant
strategies at $4\times$, and an observed improvement of more than $2\%$ in SSIM
at $8\times$ acceleration, suggesting that potentially-adaptive $k$-space
acquisition trajectories can improve reconstructed image quality for larger
acceleration factors. However, and perhaps surprisingly, our best performing
policies learn to be explicitly non-adaptive.


title: Online Motion Style Transfer for Interactive Character Control

abstract:
  Motion style transfer is highly desired for motion generation systems for
gaming. Compared to its offline counterpart, the research on online motion
style transfer under interactive control is limited. In this work, we propose
an end-to-end neural network that can generate motions with different styles
and transfer motion styles in real-time under user control. Our approach
eliminates the use of handcrafted phase features, and could be easily trained
and directly deployed in game systems. In the experiment part, we evaluate our
approach from three aspects that are essential for industrial game design:
accuracy, flexibility, and variety, and our model performs a satisfying result.


title:
A general approach for running Python codes in OpenFOAM using an
  embedded pybind11 Python interpreter

abstract:
  As the overlap between traditional computational mechanics and machine
learning grows, there is an increasing demand for straight-forward approaches
to interface Python-based procedures with C++-based OpenFOAM. This article
introduces one such general methodology, allowing the execution of Python code
directly within an OpenFOAM solver without the need for Python code
translation. The proposed approach is based on the lightweight library
pybind11, where OpenFOAM data is transferred to an embedded Python interpreter
for manipulation, and results are returned as needed. Following a review of
related approaches, the article describes the approach, with a particular focus
on data transfer between Python and OpenFOAM, executing Python scripts and
functions, and practical details about the implementation in OpenFOAM. Three
complementary test cases are presented to highlight the functionality and
demonstrate the effect of different data transfer approaches: a Python-based
velocity profile boundary condition; a Python-based solver for prototyping; and
a machine learning mechanical constitutive law class for solids4foam which
performs field calculations.