title: Hartogs-type theorems in real algebraic geometry, I

abstract:
  Let f:X-->R be a function defined on a connected nonsingular real algebraic
set X in R^n. We prove that regularity of f can be detected on either algebraic
curves or surfaces in X. If dimX>1 and k is a positive integer, then f is a
regular function whenever the restriction f|C is a regular function for every
algebraic curve C in X that is a C^k submanifold homeomorphic to the unit
circle and is either nonsingular or has precisely one singularity. Moreover, in
the latter case, the singularity of C is equivalent to the plane curve
singularity defined by the equation x^p=y^q for some primes p<q. If dimX>2,
then f is a regular function whenever the restriction f|S is a regular function
for every nonsingular algebraic surface S in X that is homeomorphic to the unit
2-sphere. We also have suitable versions of these results for X not necessarily
connected.


title: An eccentric binary blackhole in post-Newtonian theory

abstract:
  Gravitational waves radiated during binary black hole coalescence is a
perfect probe for studying the characteristics of strong gravity. Advanced
techniques for creating numerical relativity substitute models for eccentric
binary black hole systems are presumed to become more crucial in existing and
anticipated gravitational wave detectors. The imprint on the observation data
of the gravitational wave emitted by the binary coalescence enhances the two
body system studies. The aim of this study is to present an overview of the
change in characteristics behaviours of hierarchical massive astrophysical
objects merger, which are the data bank of the early universe. We present
results from numerical relativity simulations of equal-mass and unequal mass
nonspinning inspiral binary-black-hole system in the Post Newtonian framework.
We also consider the time evolution of eccentricity for the initial eccentric
system. The eccentric Post Newtonian equations are expanded in the form of
frequency related variable $x = (M \omega)^{2/3}$. The model is restricted to
the (2, 2) spin-weighted spherical harmonic modes. We conclude that for higher
eccentricity as well as mass ratio there exist higher oscillation in orbital
radius and in eccentricity.


title:
Reconfigurable Intelligent Surface-Aided Spectrum Sharing Coexisting
  with Multiple Primary Networks

abstract:
  In the spectrum sharing system (SSS) coexisting with multiple primary
networks, a well-designed reconfigurable intelligent surface (RIS) is employed
to control the radio environments of wireless channels and relieve the scarcity
of the spectrum resource in this work, which can realize high spectral
efficiency (SE) and energy efficiency (EE). Specifically, the SE enhancement in
the considered SSS is decomposed into two subproblems which are a second-order
programming (SOP) and a fractional programming of the convex quadratic form
(CQFP), respectively, to optimize alternatively the beamforming vector at the
secondary access point and the reflecting coefficients at the RIS. The CQFP
subproblem about optimizing the reflecting coefficients can be solved by the
domain and envelope shrinking algorithm (DES), providing the best SE
performance. Besides, a low-complexity method of gradient-based linearization
with domain (GLD) is proposed for obtaining a sub-optimal reflecting
coefficients for fast deployment. Considering the power consumption in the
practical application of RIS-aided SSS, the EE performance of our proposed GLD
method has significant gain over that of the SSS without RIS. The simulation
results indicate the effectiveness of the DES algorithm and show that the GLD
method improves the SE and EE performance in RIS-aided SSS with multiple
primary networks.


title:
Revisiting N$_2$-N$_2$ collisional linewidth models for S-branch
  rotational Raman scattering

abstract:
  This paper presents an evaluation of two commonly accepted Raman linewidth
models typically used to fit CARS model parameters to data; the Modified
Exponential Gap (MEG) and the Energy Corrected Sudden (ECS) models. The
adjustable parameters in each model have been fit to published experimental
linewidths, and comparisons are made to the various publications that have
already provided similar adjustable constants. The approaches presented in the
literature are discussed as are differences with the findings presented here.


title: Multi-Modal Recurrent Fusion for Indoor Localization

abstract:
  This paper considers indoor localization using multi-modal wireless signals
including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By
formulating the localization as a multi-modal sequence regression problem, a
multi-stream recurrent fusion method is proposed to combine the current hidden
state of each modality in the context of recurrent neural networks while
accounting for the modality uncertainty which is directly learned from its own
immediate past states. The proposed method was evaluated on the large-scale
SPAWC2021 multi-modal localization dataset and compared with a wide range of
baseline methods including the trilateration method, traditional fingerprinting
methods, and convolution network-based methods.


title: Wavelet-Based Multi-Class Seizure Type Classification System

abstract:
  Epilepsy is one of the most common brain diseases that affect more than 1\%
of the world's population. It is characterized by recurrent seizures, which
come in different types and are treated differently. Electroencephalography
(EEG) is commonly used in medical services to diagnose seizures and their
types. The accurate identification of seizures helps to provide optimal
treatment and accurate information to the patient. However, the manual
diagnostic procedures of epileptic seizures are laborious and
highly-specialized. Moreover, EEG manual evaluation is a process known to have
a low inter-rater agreement among experts. This paper presents a novel
automatic technique that involves extraction of specific features from EEG
signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them.
We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2
dataset and compared the performance with existing state-of-the-art techniques
using overall F1-score due to class imbalance seizure types. Our proposed
technique achieved the best results of weighted F1-score of 99.1\% and 74.7\%
for seizure-wise and patient-wise classification respectively, thereby setting
new benchmark results for this dataset.


title:
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification
  with Rejection from ECG Recordings

abstract:
  With the development of deep learning-based methods, automated classification
of electrocardiograms (ECGs) has recently gained much attention. Although the
effectiveness of deep neural networks has been encouraging, the lack of
information given by the outputs restricts clinicians' reexamination. If the
uncertainty estimation comes along with the classification results,
cardiologists can pay more attention to "uncertain" cases. Our study aims to
classify ECGs with rejection based on data uncertainty and model uncertainty.
We perform experiments on a real-world 12-lead ECG dataset. First, we estimate
uncertainties using the Monte Carlo dropout for each classification prediction,
based on our Bayesian neural network. Then, we accept predictions with
uncertainty under a given threshold and provide "uncertain" cases for
clinicians. Furthermore, we perform a simulation experiment using varying
thresholds. Finally, with the help of a clinician, we conduct case studies to
explain the results of large uncertainties and incorrect predictions with small
uncertainties. The results show that correct predictions are more likely to
have smaller uncertainties, and the performance on accepted predictions
improves as the accepting ratio decreases (i.e. more rejections). Case studies
also help explain why rejection can improve the performance. Our study helps
neural networks produce more accurate results and provide information on
uncertainties to better assist clinicians in the diagnosis process. It can also
enable deep-learning-based ECG interpretation in clinical implementation.


title: A comparative study of several parameterizations for speaker recognition

abstract:
  This paper presents an exhaustive study about the robustness of several
parameterizations, in speaker verification and identification tasks. We have
studied several mismatch conditions: different recording sessions, microphones,
and different languages (it has been obtained from a bilingual set of
speakers). This study reveals that the combination of several parameterizations
can improve the robustness in all the scenarios for both tasks, identification
and verification. In addition, two different methods have been evaluated:
vector quantization, and covariance matrices with an arithmetic-harmonic
sphericity measure.


title: Higgs boson decays into a pair of heavy vector quarkonia

abstract:
  Rare Higgs decays into a pair of heavy vector quarkonia, $h\to VV$
($V=J/\Psi$, $\Upsilon$ etc.), have been investigated in the standard model.
Different from the past literature in which these decays are thought to be only
dominated by the longitudinally polarized final states, we also include the
transitions, which proceed through $h\to \gamma^*\gamma^*$/$h\to V\gamma^*$,
followed by $\gamma^*\to V$. The final vector quarkonia via these ways are
dominantly transversely polarized. Our calculation however shows that these
transitions could lead to significant contributions to the decay rate,
especially for the charmonium final states. The total branching ratios of these
processes are predicted to be around $10^{-10}$, far below the current
experimental upper bounds. Hopefully, experimental studies of these very rare
decays in future high-precision experimental facilities might be interesting
both to test the standard model and to look for new physics scenarios.


title: Social Network Extraction Unsupervised

abstract:
  In the era of information technology, the two developing sides are data
science and artificial intelligence. In terms of scientific data, one of the
tasks is the extraction of social networks from information sources that have
the nature of big data. Meanwhile, in terms of artificial intelligence, the
presence of contradictory methods has an impact on knowledge. This article
describes an unsupervised as a stream of methods for extracting social networks
from information sources. There are a variety of possible approaches and
strategies to superficial methods as a starting concept. Each method has its
advantages, but in general, it contributes to the integration of each other,
namely simplifying, enriching, and emphasizing the results.


title: Mental State Classification Using Multi-graph Features

abstract:
  We consider the problem of extracting features from passive, multi-channel
electroencephalogram (EEG) devices for downstream inference tasks related to
high-level mental states such as stress and cognitive load. Our proposed method
leverages recently developed multi-graph tools and applies them to the time
series of graphs implied by the statistical dependence structure (e.g.,
correlation) amongst the multiple sensors. We compare the effectiveness of the
proposed features to traditional band power-based features in the context of
three classification experiments and find that the two feature sets offer
complementary predictive information. We conclude by showing that the
importance of particular channels and pairs of channels for classification when
using the proposed features is neuroscientifically valid.


title:
Multi-task Learning Approach for Modulation and Wireless Signal
  Classification for 5G and Beyond: Edge Deployment via Model Compression

abstract:
  Future communication networks must address the scarce spectrum to accommodate
extensive growth of heterogeneous wireless devices. Wireless signal recognition
is becoming increasingly more significant for spectrum monitoring, spectrum
management, secure communications, among others. Consequently, comprehensive
spectrum awareness on the edge has the potential to serve as a key enabler for
the emerging beyond 5G networks. State-of-the-art studies in this domain have
(i) only focused on a single task - modulation or signal (protocol)
classification - which in many cases is insufficient information for a system
to act on, (ii) consider either radar or communication waveforms (homogeneous
waveform category), and (iii) does not address edge deployment during neural
network design phase. In this work, for the first time in the wireless
communication domain, we exploit the potential of deep neural networks based
multi-task learning (MTL) framework to simultaneously learn modulation and
signal classification tasks while considering heterogeneous wireless signals
such as radar and communication waveforms in the electromagnetic spectrum. The
proposed MTL architecture benefits from the mutual relation between the two
tasks in improving the classification accuracy as well as the learning
efficiency with a lightweight neural network model. We additionally include
experimental evaluations of the model with over-the-air collected samples and
demonstrate first-hand insight on model compression along with deep learning
pipeline for deployment on resource-constrained edge devices. We demonstrate
significant computational, memory, and accuracy improvement of the proposed
model over two reference architectures. In addition to modeling a lightweight
MTL model suitable for resource-constrained embedded radio platforms, we
provide a comprehensive heterogeneous wireless signals dataset for public use.


title:
Adaptive nonparametric estimation in the functional linear model with
  functional output

abstract:
  In this paper, we consider a functional linear regression model, where both
the covariate and the response variable are functional random variables. We
address the problem of optimal nonparametric estimation of the conditional
expectation operator in this model. A collection of projection estimators over
finite dimensional subspaces is first introduce. We provide a non-asymptotic
bias-variance decomposition for the Mean Square Prediction error in the case
where these subspaces are generated by the (empirical) PCA functional basis.
The automatic trade-off is realized thanks to a model selection device which
selects the best projection dimensions: the penalized contrast estimator
satisfies an oracle-type inequality and is thus optimal in an adaptive point of
view. These upper-bounds allow us to derive convergence rates over ellipsoidal
smoothness spaces. The rates are shown to be optimal in the minimax sense: they
match with a lower bound of the minimax risk, which is also proved. Finally, we
conduct a numerical study, over simulated data and over two real-data sets.


title: Entropic Hyper-Connectomes Computation and Analysis

abstract:
  Brain function and connectivity is a pressing mystery in medicine related to
many diseases. Neural connectomes have been studied as graphs with graph theory
methods including topological methods. Work has started on hypergraph models
and methods where the geometry and topology is significantly different. We
define a hypergraph called the hyper-connectome with joint information entropy
and total correlation. We give the pseudocode for computation from finite
samples. We give the theoretic importance of this generalization's topology and
geometry with respect to random variables and then prove the hypergraph can be
necessary for prediction and classification. We confirm with a simulation study
and computation. We prove the approximation for continuous random variables
with finite samples. We compare connectome versus hyper-connectome for
predicting schizophrenia in subjects based on a fMRI dataset using a linear
support vector machine. The hyper-connectome achieves better performance in
accuracy (up to 56\%) and F1 score (up to 0.52) than the connectome. We reject
null hypothesis at 95\% with p-value = 0.00074.


title:
Particle-based Fast Jet Simulation at the LHC with Variational
  Autoencoders

abstract:
  We study how to use Deep Variational Autoencoders for a fast simulation of
jets of particles at the LHC. We represent jets as a list of constituents,
characterized by their momenta. Starting from a simulation of the jet before
detector effects, we train a Deep Variational Autoencoder to return the
corresponding list of constituents after detection. Doing so, we bypass both
the time-consuming detector simulation and the collision reconstruction steps
of a traditional processing chain, speeding up significantly the events
generation workflow. Through model optimization and hyperparameter tuning, we
achieve state-of-the-art precision on the jet four-momentum, while providing an
accurate description of the constituents momenta, and an inference time
comparable to that of a rule-based fast simulation.


title: Causal Structure Learning with Greedy Unconditional Equivalence Search

abstract:
  We consider the problem of characterizing directed acyclic graph (DAG) models
up to unconditional equivalence, i.e., when two DAGs have the same set of
unconditional d-separation statements. Each unconditional equivalence class
(UEC) can be uniquely represented with an undirected graph whose clique
structure encodes the members of the class. Via this structure, we provide a
transformational characterization of unconditional equivalence. Combining these
results, we introduce a hybrid algorithm for learning DAG models from
observational data, called Greedy Unconditional Equivalence Search (GUES),
which first estimates the UEC of the data using independence tests and then
greedily searches the UEC for the optimal DAG. Applying GUES on synthetic data,
we show that it achieves comparable accuracy to existing methods. However, in
contrast to existing methods, since the average UEC is observed to contain few
DAGs, the search space for GUES is drastically reduced.


title:
On the importance of Fe fragmentation for LiBeB analyses: Do we need a
  Li primary source to explain AMS-02 data?

abstract:
  Li, Be, and B high-precision data from AMS-02 provide the best constraints on
Galactic cosmic-ray transport parameters. We re-evaluate the impact of Fe
fragmentation on the Li, Be, and B modelling. We discuss the consequences on
the transport parameter determination and reassess whether a primary source of
Li is needed to match AMS-02 data. We renormalise several cross-section
parametrisations to existing data for the most important reactions producing
Li, Be, and B. We use the \usine{} code with these new cross-section sets to
re-analyse Li/C, Be/C, and B/C AMS-02 data. We build three equally plausible
cross-section sets. Compared to the use of the initial cross-section sets, they
lead to an average enhanced production of Li ($\sim20-50\%$) and Be
($\sim5-15\%$), while leaving the B flux mostly unchanged. In particular, Fe
fragmentation is found to contribute to up to 10\% of the Li and Be fluxes.
Used in the combined analysis of AMS-02 Li/C, Be/C, and B/C data, the fit is
significantly improved, with an enhanced diffusion coefficient ($\sim 20\%)$.
The three updated cross-section sets are found to either slightly undershoot or
overshoot the Li/C and B/C ratios: this strongly disfavours evidences for a
primary source of Li in cosmic rays. We stress that isotopic cosmic-ray ratios
of Li (and to a lesser extent Be), soon to be released by AMS-02, are also
impacted by the use of these updated sets. Almost no nuclear data exist for the
production of Li and B isotopes from Ne, Mg, Si, and Fe, whereas these
reactions are estimated to account for $\sim 20\%$ of the total production.
Some new nuclear measurements are clearly desired to better exploit the
high-precision AMS-02 cosmic-ray data.


title:
Towards Creativity Characterization of Generative Models via Group-based
  Subset Scanning

abstract:
  Deep generative models, such as Variational Autoencoders (VAEs) and
Generative Adversarial Networks (GANs), have been employed widely in
computational creativity research. However, such models discourage
out-of-distribution generation to avoid spurious sample generation, thereby
limiting their creativity. Thus, incorporating research on human creativity
into generative deep learning techniques presents an opportunity to make their
outputs more compelling and human-like. As we see the emergence of generative
models directed toward creativity research, a need for machine learning-based
surrogate metrics to characterize creative output from these models is
imperative. We propose group-based subset scanning to identify, quantify, and
characterize creative processes by detecting a subset of anomalous
node-activations in the hidden layers of the generative models. Our experiments
on the standard image benchmarks, and their "creatively generated" variants,
reveal that the proposed subset scores distribution is more useful for
detecting creative processes in the activation space rather than the pixel
space. Further, we found that creative samples generate larger subsets of
anomalies than normal or non-creative samples across datasets. The node
activations highlighted during the creative decoding process are different from
those responsible for the normal sample generation. Lastly, we assess if the
images from the subsets selected by our method were also found creative by
human evaluators, presenting a link between creativity perception in humans and
node activations within deep neural nets.


title: A Tiny Needle in a Messy Haystack: Chaos in Quadratic Gravity

abstract:
  While recent gravitational wave observations by LIGO and Virgo allow for
tests of general relativity in the extreme gravity regime, these observations
are still blind to a large swath of phenomena outside these instruments'
sensitivity curves. Future gravitational-wave detectors, such as LISA, will
enable probes of longer-duration and lower-frequency events. In particular,
LISA will enable the characterization of the non-linear dynamics of extreme
mass-ratio inspirals, when a small compact object falls into a supermassive
black hole. In this paper, we study the motion of test particles around
spinning black holes in two quadratic gravity theories: scalar Gauss-Bonnet and
dynamical Chern-Simons gravity. We show that geodesic trajectories around
rotating black holes in these theories are likely to not have a fourth constant
of the motion. In particular, we show that Poincar\'e sections of the orbital
phase space present chaotic features that will affect the inspiral of small
compact objects into supermassive black holes in these theories. Nevertheless,
the characteristic size of these chaotic features is tiny and their location in
parameter space is very close to the event horizon of the supermassive black
hole. Therefore, the detection of such chaotic features with LISA is likely
very challenging, at best.


title:
E-LMC: Extended Linear Model of Coregionalization for Predictions of
  Spatial Fields

abstract:
  Physical simulations based on partial differential equations typically
generate spatial fields results, which are utilized to calculate specific
properties of a system for engineering design and optimization. Due to the
intensive computational burden of the simulations, a surrogate model mapping
the low-dimensional inputs to the spatial fields are commonly built based on a
relatively small dataset. To resolve the challenge of predicting the whole
spatial field, the popular linear model of coregionalization (LMC) can
disentangle complicated correlations within the high-dimensional spatial field
outputs and deliver accurate predictions. However, LMC fails if the spatial
field cannot be well approximated by a linear combination of base functions
with latent processes. In this paper, we extend LMC by introducing an
invertible neural network to linearize the highly complex and nonlinear spatial
fields such that the LMC can easily generalize to nonlinear problems while
preserving the traceability and scalability. Several real-world applications
demonstrate that E-LMC can exploit spatial correlations effectively, showing a
maximum improvement of about 40% over the original LMC and outperforming the
other state-of-the-art spatial field models.


title:
Multi-Objective Latent Space Optimization of Generative Molecular Design
  Models

abstract:
  Molecular design based on generative models, such as variational autoencoders
(VAEs), has become increasingly popular in recent years due to its efficiency
for exploring high-dimensional molecular space to identify molecules with
desired properties. While the efficacy of the initial model strongly depends on
the training data, the sampling efficiency of the model for suggesting novel
molecules with enhanced properties can be further enhanced via latent space
optimization. In this paper, we propose a multi-objective latent space
optimization (LSO) method that can significantly enhance the performance of
generative molecular design (GMD). The proposed method adopts an iterative
weighted retraining approach, where the respective weights of the molecules in
the training data are determined by their Pareto efficiency. We demonstrate
that our multi-objective GMD LSO method can significantly improve the
performance of GMD for jointly optimizing multiple molecular properties.


title:
Tensor-Train Split Operator KSL (TT-SOKSL) Method for Quantum Dynamics
  Simulations

abstract:
  Numerically exact simulations of quantum reaction dynamics, including
non-adiabatic effects in excited electronic states, are essential to gain
fundamental insights into ultrafast chemical reactivity and rigorous
interpretations of molecular spectroscopy. Here, we introduce the tensor-train
split-operator KSL (TT-SOKSL) method for quantum simulations in tensor-train
(TT)/matrix product state (MPS) representations. TT-SOKSL propagates the
quantum state as a tensor train using the Trotter expansion of the
time-evolution operator, as in the tensor-train split-operator Fourier
transform (TT-SOFT) method. However, the exponential operators of the Trotter
expansion are applied using a rank adaptive TT-KSL scheme instead of using the
scaling and squaring approach as in TT-SOFT. We demonstrate the accuracy and
efficiency of TT-SOKSL as applied to simulations of the photoisomerization of
the retinal chromophore in rhodopsin, including non-adiabatic dynamics at a
conical intersection of potential energy surfaces. The quantum evolution is
described in full dimensionality by a time-dependent wavepacket evolving
according to a two-state 25-dimensional model Hamiltonian. We find that
TT-SOKSL converges faster than TT-SOFT with respect to the maximally allowed
memory requirement of the tensor-train representation and better preserves the
norm of the time-evolving state. When compared to the corresponding simulations
based on the TT-KSL method, TT-SOKSL has the advantage of avoiding the need of
constructing the matrix product state Laplacian by exploiting the linear
scaling of multidimensional tensor train Fourier transforms.


title:
On genetic programming representations and fitness functions for
  interpretable dimensionality reduction

abstract:
  Dimensionality reduction (DR) is an important technique for data exploration
and knowledge discovery. However, most of the main DR methods are either linear
(e.g., PCA), do not provide an explicit mapping between the original data and
its lower-dimensional representation (e.g., MDS, t-SNE, isomap), or produce
mappings that cannot be easily interpreted (e.g., kernel PCA, neural-based
autoencoder). Recently, genetic programming (GP) has been used to evolve
interpretable DR mappings in the form of symbolic expressions. There exists a
number of ways in which GP can be used to this end and no study exists that
performs a comparison. In this paper, we fill this gap by comparing existing GP
methods as well as devising new ones. We evaluate our methods on several
benchmark datasets based on predictive accuracy and on how well the original
features can be reconstructed using the lower-dimensional representation only.
Finally, we qualitatively assess the resulting expressions and their
complexity. We find that various GP methods can be competitive with
state-of-the-art DR algorithms and that they have the potential to produce
interpretable DR mappings.


title: The Duflo-Serganova functor, vingt ans apr\`es

abstract:
  We review old and new results concerning the $DS$ functor and associated
varieties for Lie superalgebras. These notions were introduced in the
unpublished manuscript arXiv:math/0507198 by Michel Duflo and the third author.
This paper includes the results and proofs of the original manuscript, as well
as a survey of more recent results.


title:
Impact of hypermagnetic fields on relic gravitational waves, neutrino
  oscillations and baryon asymmetry

abstract:
  We study the evolution of random hypermagnetic fields (HMFs) in the symmetric
phase of the early universe before the electroweak phase transition. The
behavior of HMFs is driven by the analog of the chiral magnetic effect
accounting for the asymmetries of leptons and Higgs bosons. These asymmetries
are also dynamical variables of the model and evolve together with HMFs.
Moreover, we account for the contribution of the hyper-MHD turbulence in the
effective diffusion coefficient and the $\alpha$-dynamo parameter. The
realistic spectrum of seed HMFs consists of two branches: Batchelor and
Kolmogorov ones. The impact of HMFs on the production of relic gravitational
waves (GWs) and the baryon asymmetry of the universe (BAU), as well as flavor
oscillations of supernova neutrinos in the GWs generated are considered. We
establish the constraint on the strength of the seed HMF comparing the spectral
density of produced GWs with the observations of the LIGO-Virgo-KAGRA
collaborations. The stronger upper bound on the seed HMF is obtained from the
condition of not exceeding the observed value of BAU.


title:
Towards deep learning-powered IVF: A large public benchmark for
  morphokinetic parameter prediction

abstract:
  An important limitation to the development of Artificial Intelligence
(AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a
public reference benchmark to train and evaluate deep learning (DL) models. In
this work, we describe a fully annotated dataset of 756 videos of developing
embryos, for a total of 337k images. We applied ResNet, LSTM, and ResNet-3D
architectures to our dataset and demonstrate that they overperform algorithmic
approaches to automatically annotate stage development phases. Altogether, we
propose the first public benchmark that will allow the community to evaluate
morphokinetic models. This is the first step towards deep learning-powered IVF.
Of note, we propose highly detailed annotations with 16 different development
phases, including early cell division phases, but also late cell divisions,
phases after morulation, and very early phases, which have never been used
before. We postulate that this original approach will help improve the overall
performance of deep learning approaches on time-lapse videos of embryo
development, ultimately benefiting infertile patients with improved clinical
success rates (Code and data are available at
https://gitlab.univ-nantes.fr/E144069X/bench_mk_pred.git).


title: Orientation of alcoves in affine Weyl groups

abstract:
  Let $W$ be an irreducible Weyl group and $W_a$ its affine Weyl group. In a
previous work the author introduced an affine variety $\widehat{X}_{W_a}$,
called the Shi variety of $W_a$, whose integral points are in bijection with
$W_a$. The set of irreducible components of $\widehat{X}_{W_a}$ provided
results at the intersection of group theory, combinatorics and geometry. In
this article we express the notion of orientation of alcoves in terms of the
first group of cohomogoly of $W$ and in terms of the irreducible components of
the Shi variety. We also provide modular equations in terms of Shi coefficients
that describe efficiently the property of having the same orientation.


title: Numerically Probing the Universal Operator Growth Hypothesis

abstract:
  Recently, a hypothesis on the complexity growth of unitarily evolving
operators was presented. This hypothesis states that in generic, non-integrable
many-body systems the so-called Lanczos coefficients associated with an
autocorrelation function grow asymptotically linear, with a logarithmic
correction in one-dimensional systems. In contrast, the growth is expected to
be slower in integrable or free models. In the paper at hand, we numerically
test this hypothesis for a variety of exemplary systems, including 1d and 2d
Ising models as well as 1d Heisenberg models. While we find the hypothesis to
be practically fulfilled for all considered Ising models, the onset of the
hypothesized universal behavior could not be observed in the attainable
numerical data for the Heisenberg model. The proposed linear bound on operator
growth eventually stems from geometric arguments involving the locality of the
Hamiltonian as well as the lattice configuration. We investigate such a
geometric bound and find that it is not sharply achieved for any considered
model.


title: The SKA as a prebiotic molecule detector

abstract:
  One of the theories for the origin of life proposes that a significant
fraction of prebiotic material could have arrived to Earth from outer space
between 4.1 and 3.8 billion years ago. This suggests that those prebiotic
compounds could have originated in interstellar space, to be later on
incorporated to small Solar-system bodies and planetesimals. The recent
discovery of prebiotic molecules such as hydroxylamine and ethanolamine in the
interstellar medium, strongly supports this hypothesis. However, some species
such as sugars, key for the synthesis of ribonucleotides and for metabolic
processes, remain to be discovered in space. The unmatched sensitivity of the
Square Kilometer Array (SKA) at centimeter wavelengths will be able to detect
even more complex and heavier prebiotic molecules than existing
instrumentation. In this contribution, we illustrate the potential of the SKA
to detect simple sugars with three and four carbon atoms, using a moderate
investment of observing time.


title:
Measurement of the helicity dependence for single $\pi^{0}$
  photoproduction from the deuteron

abstract:
  The helicity-dependent single $\pi^{0}$ photoproduction cross section on the
deuteron and the angular dependence of the double polarisation observable $E$
for the quasi-free single $\pi^0$ production off the proton and the neutron
have been measured for the first time from the threshold region up to the
photon energy 1.4 GeV. The experiment was performed at the tagged photon
facility of the MAMI accelerator and used a circularly polarised photon beam
and longitudinally polarised deuteron target. The reaction products were
detected using the large acceptance Crystal Ball/TAPS calorimeter, which
covered 97% of the full solid angle.
  Comparing the cross section from the deuteron with the sum of free nucleon
cross sections provides a quantitative estimate of the effects of the nuclear
medium on pion production. In contrast, comparison of $E$ helicity asymmetry
data from quasi-free protons off deuterium with data from a free proton target
indicates that nuclear effects do not significantly affect this observable. As
a consequence, it is deduced that the helicity asymmetry $E$ on a free neutron
can be reliably extracted from measurements on a deuteron in quasi-free
kinematics.


title:
Thermal topological phase transition in SnTe from \emph{ab-initio}
  calculations

abstract:
  One of the key issues in the physics of topological insulators is whether the
topologically non-trivial properties survive at finite temperatures and, if
yes, whether they disappear only at the temperature of topological gap closing.
Here we study this problem, using quantum fidelity as a measure, by means of
ab-initio methods supplemented by an effective dissipative theory built on the
top of the ab-initio electron and phonon band structures. We reveal the
presence of a characteristic temperature that can be associated with a change
of curvature of the conduction band. The transition is not present in a purely
electronic system but it appears once we invoke coupling with a dissipative
bosonic bath. Features in the dependence with temperature of the fidelity
susceptibility can be related to changes in the band curvature, but signatures
of a topological phase transition appear in the fidelity only though the
non-adiabatic coupling with soft phonons. Our argument is valid in valley
topological insulators, but in principle can be generalized to the broader
class of topological insulators which host any symmetry-breaking boson.


title:
DynamicRetriever: A Pre-training Model-based IR System with Neither
  Sparse nor Dense Index

abstract:
  Web search provides a promising way for people to obtain information and has
been extensively studied. With the surgence of deep learning and large-scale
pre-training techniques, various neural information retrieval models are
proposed and they have demonstrated the power for improving search (especially,
the ranking) quality. All these existing search methods follow a common
paradigm, i.e. index-retrieve-rerank, where they first build an index of all
documents based on document terms (i.e., sparse inverted index) or
representation vectors (i.e., dense vector index), then retrieve and rerank
retrieved documents based on similarity between the query and documents via
ranking models. In this paper, we explore a new paradigm of information
retrieval with neither sparse nor dense index but only a model. Specifically,
we propose a pre-training model-based IR system called DynamicRetriever. As for
this system, the training stage embeds the token-level and document-level
information (especially, document identifiers) of the corpus into the model
parameters, then the inference stage directly generates document identifiers
for a given query. Compared with existing search methods, the model-based IR
system has two advantages: i) it parameterizes the traditional static index
with a pre-training model, which converts the document semantic mapping into a
dynamic and updatable process; ii) with separate document identifiers, it
captures both the term-level and document-level information for each document.
Extensive experiments conducted on the public search benchmark MS MARCO verify
the effectiveness and potential of our proposed new paradigm for information
retrieval.


title: Capability-based Frameworks for Industrial Robot Skills: a Survey

abstract:
  The research community is puzzled with words like skill, action, atomic unit
and others when trying to describe robot capabilities. However, for giving the
possibility to integrate such in the industrial scenario a standardization of
their description is necessary. This work, through a structured review
approach, tries to identify commonalities in the research community. From this
review it was possible to perceive that most of the industrially focused
research work targets simple capabilities like pick and place, the large amount
of authors agree on a structure consisting of task, skill and primitive, the
Robot Operating System is a common framework both in industrial and
non-industrial domains and skills are a main enabler for high mix - low volume
productions.


title: Morse Theory for Complexes of Groups

abstract:
  We construct an equivariant version of discrete Morse theory for simplicial
complexes endowed with group actions. The key ingredient is a 2-categorical
criterion for making acyclic partial matchings on the quotient space compatible
with an overlaid complex of groups. We use the discrete flow category of any
such compatible matching to build the corresponding Morse complex of groups.
Our main result establishes that the development of the Morse complex of groups
recovers the original simplicial complex up to equivariant homotopy
equivalence.


title:
Space and time correlations for diffusion models with prompt and delayed
  birth-and-death events

abstract:
  Understanding the statistical properties of a collection of individuals
subject to random displacements and birth-and-death events is key to several
applications in physics and life sciences, encompassing the diagnostic of
nuclear reactors and the analysis of epidemic patterns. Previous investigations
of the critical regime, where births and deaths balance on average, have shown
that highly non-Poissonian fluctuations might occur in the population, leading
to spontaneous spatial clustering, and eventually to a critical catastrophe,
where fluctuations can result in the extinction of the population. A milder
behaviour is observed when the population size is kept constant:
thefluctuations asymptotically level off and the critical catastrophe is
averted. In this paper, we shall extend these results by considering the
broader class of models with prompt and delayed birth-and-death events, which
mimic the presence of precursors in nuclear reactor physics or incubation
inepidemics. We shall consider models with and without population control
mechanisms. Analytical or semi-analytical results for the density, the
two-point correlation function and the mean-squared pair distance will be
derived and compared to Monte Carlo simulations, which will be used as a
reference.


title:
Whittaker categories, properly stratified categories and Fock space
  categorification for Lie superalgebras

abstract:
  We study various categories of Whittaker modules over a type I Lie
superalgebra realized as cokernel categories that fit into the framework of
properly stratified categories. These categories are the target of the Backelin
functor $\Gamma_\zeta$. We show that these categories can be described, up to
equivalence, as Serre quotients of the BGG category $\mathcal O$ and of certain
singular categories of Harish-Chandra $(\mathfrak g,\mathfrak g_{\bar
0})$-bimodules. We also show that $\Gamma_\zeta$ is a realization of the Serre
quotient functor. We further investigate a $q$-symmetrized Fock space over a
quantum group of type A and prove that, for general linear Lie superalgebras
our Whittaker categories, the functor $\Gamma_\zeta$ and various realizations
of Serre quotients and Serre quotient functors categorify this $q$-symmetrized
Fock space and its $q$-symmetrizer. In this picture, the canonical and dual
canonical bases in this $q$-symmetrized Fock space correspond to tilting and
simple objects in these Whittaker categories, respectively.


title: Hall effect in doped Mott insulator: DMFT-approximation

abstract:
  In the framework of dynamical mean field theory (DMFT) we analyze Hall effect
in doped Mott insulator as a parent cuprate superconductor. We consider the
partial filling (hole doping) of the lower Hubbard band and calculate the
dependence of Hall coefficient and Hall number on hole doping, determining the
critical concentration for sign change of the Hall coefficient. Significant
temperature dependence of Hall effect is noted. A good agreement is
demonstrated with concentration dependence of Hall number obtained in
experiments in the normal state of YBCO.


title: On the Generalization of Representations in Reinforcement Learning

abstract:
  In reinforcement learning, state representations are used to tractably deal
with large problem spaces. State representations serve both to approximate the
value function with few parameters, but also to generalize to newly encountered
states. Their features may be learned implicitly (as part of a neural network)
or explicitly (for example, the successor representation of
\citet{dayan1993improving}). While the approximation properties of
representations are reasonably well-understood, a precise characterization of
how and when these representations generalize is lacking. In this work, we
address this gap and provide an informative bound on the generalization error
arising from a specific state representation. This bound is based on the notion
of effective dimension which measures the degree to which knowing the value at
one state informs the value at other states. Our bound applies to any state
representation and quantifies the natural tension between representations that
generalize well and those that approximate well. We complement our theoretical
results with an empirical survey of classic representation learning methods
from the literature and results on the Arcade Learning Environment, and find
that the generalization behaviour of learned representations is well-explained
by their effective dimension.


title:
Discovering Opportunities in New York City's Discovery Program: an
  Analysis of Affirmative Action Mechanisms

abstract:
  Discovery program (DISC) is an affirmative action policy used by the New York
City Department of Education (NYC DOE). It has been instrumental in increasing
the number of admissions for disadvantaged students at specialized high
schools. However, our empirical analysis of the student-school matches shows
that about 950 in-group blocking pairs were created each year amongst the
disadvantaged group of students, impacting about 650 disadvantaged students.
Moreover, we find that this program usually benefits lower-performing
disadvantaged students more than the top-performing ones, thus unintentionally
creating an incentive to under-perform.
  In this work, we explore two affirmative action policies that can be used to
minimally modify and improve the discovery program: minority reserve (MR) and
joint-seat allocation (JSA). We show that (i) both MR and JSA result in no
in-group blocking pairs, and (ii) JSA is weakly group strategy-proof, ensures
that at least one disadvantaged is not worse off, and when reservation quotas
are carefully chosen then no disadvantaged student is worse-off. In the general
setting, we show that there is no clear winner in terms of the matchings
provided by DISC, JSA and MR, from the perspective of disadvantaged students.
We however characterize a condition for markets, that we term high
competitiveness, where JSA dominates MR for disadvantaged students. This
condition is verified in markets when there is a higher demand for seats than
supply, and the performances of disadvantaged students are significantly lower
than that of advantaged students. Data from NYC DOE satisfy the high
competitiveness condition, and for this dataset our empirical results
corroborate our theoretical predictions, showing the superiority of JSA. We
believe that the discovery program, and more generally affirmative action
mechanisms, can be changed for the better by implementing JSA.


title:
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for
  Multilingual Named Entity Recognition

abstract:
  The MultiCoNER shared task aims at detecting semantically ambiguous and
complex named entities in short and low-context settings for multiple
languages. The lack of contexts makes the recognition of ambiguous named
entities challenging. To alleviate this issue, our team DAMO-NLP proposes a
knowledge-based system, where we build a multilingual knowledge base based on
Wikipedia to provide related context information to the named entity
recognition (NER) model. Given an input sentence, our system effectively
retrieves related contexts from the knowledge base. The original input
sentences are then augmented with such context information, allowing
significantly better contextualized token representations to be captured. Our
system wins 10 out of 13 tracks in the MultiCoNER shared task.


title: Optimal quantum dataset for learning a unitary transformation

abstract:
  Unitary transformations formulate the time evolution of quantum states. How
to learn a unitary transformation efficiently is a fundamental problem in
quantum machine learning. The most natural and leading strategy is to train a
quantum machine learning model based on a quantum dataset. Although presence of
more training data results in better models, using too much data reduces the
efficiency of training. In this work, we solve the problem on the minimum size
of sufficient quantum datasets for learning a unitary transformation exactly,
which reveals the power and limitation of quantum data. First, we prove that
the minimum size of dataset with pure states is $2^n$ for learning an $n$-qubit
unitary transformation. To fully explore the capability of quantum data, we
introduce a quantum dataset consisting of $n+1$ mixed states that are
sufficient for exact training. The main idea is to simplify the structure
utilizing decoupling, which leads to an exponential improvement on the size
over the datasets with pure states. Furthermore, we show that the size of
quantum dataset with mixed states can be reduced to a constant, which yields an
optimal quantum dataset for learning a unitary. We showcase the applications of
our results in oracle compiling and Hamiltonian simulation. Notably, to
accurately simulate a 3-qubit one-dimensional nearest-neighbor Heisenberg
model, our circuit only uses $48$ elementary quantum gates, which is
significantly less than $4320$ gates in the circuit constructed by the
Trotter-Suzuki product formula.


title: A dual and conjugate system for $q$-Gaussians for all $q$

abstract:
  We provide a concrete formula for a dual system as well as for a conjugate
system of $q$-Gaussians represented on the $q$-deformed Fock space. Moreover,
using this formula, we prove the existence of a free Gibbs potential and that
the non-microstates free Fisher information is finite for any $q$ with
$-1<q<1$, which is an improvement on a previous result of Y. Dabrowski. We also
indicate how our results can be extended to the more general setting of mixed
$q_{ij}$-relations.


title:
An Adaptable and Agnostic Flow Scheduling Approach for Data Center
  Networks

abstract:
  Cloud applications have reshaped the model of services and infrastructure of
the Internet. Search engines, social networks, content delivery and retail and
e-commerce sites belong to this group of applications. An important element in
the architecture of data centers where these applications run is the
communication infrastructure, commonly known as data center networks (DCNs). A
critical challenge DCNs have to address is the processing of the traffic of
cloud applications, which due to its properties is essentially different to the
traffic of other Internet applications. In order to improve the responsiveness
and throughput of applications, DCNs should be able to prioritize short flows
(a few KB) over long flows (several MB). However, given the time and space
variations the traffic presents, the information about flow sizes is not
available in advance in order to plan the flow scheduling. In this paper, we
present an adaptable mechanism called Adaptable Workload-Agnostic Flow
Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic
way the scheduling configuration of DCN forwarding devices. This agnostic
adjustment contributes to reduce the Flow Completion Time (FCT) of those short
flows, representing around 85% of the traffic handled by cloud applications.
Our evaluation results based on simulation show that AWAFS can reduce the
average FCT of short flows between 16.9% and 45.2% when compared to the best
existing agnostic non-adaptable solution, without inducing starvation on long
flows. Indeed, it can provide improvements as high as 39% for long flows.
Additionally, AWAFS can improve the FCT for short flows in scenarios with high
heterogeneity in the traffic present in the network, with a reduction up to 5%
for the average FCT and 15% for the tail FCT.


title:
Embodied Active Domain Adaptation for Semantic Segmentation via
  Informative Path Planning

abstract:
  This work presents an embodied agent that can adapt its semantic segmentation
network to new indoor environments in a fully autonomous way. Because semantic
segmentation networks fail to generalize well to unseen environments, the agent
collects images of the new environment which are then used for self-supervised
domain adaptation. We formulate this as an informative path planning problem,
and present a novel information gain that leverages uncertainty extracted from
the semantic model to safely collect relevant data. As domain adaptation
progresses, these uncertainties change over time and the rapid learning
feedback of our system drives the agent to collect different data. Experiments
show that our method adapts to new environments faster and with higher final
performance compared to an exploration objective, and can successfully be
deployed to real-world environments on physical robots.


title: Multivariate permutation entropy, a Cartesian graph product approach

abstract:
  Entropy metrics are nonlinear measures to quantify the complexity of time
series. Among them, permutation entropy is a common metric due to its
robustness and fast computation. Multivariate entropy metrics techniques are
needed to analyse data consisting of more than one time series. To this end, we
present a multivariate permutation entropy, $MPE_G$, using a graph-based
approach.
  Given a multivariate signal, the algorithm $MPE_G$ involves two main steps:
1) we construct an underlying graph G as the Cartesian product of two graphs G1
and G2, where G1 preserves temporal information of each times series together
with G2 that models the relations between different channels, and 2) we
consider the multivariate signal as samples defined on the regular graph G and
apply the recently introduced permutation entropy for graphs.
  Our graph-based approach gives the flexibility to consider diverse types of
cross channel relationships and signals, and it overcomes with the limitations
of current multivariate permutation entropy.


title:
Bayesian Optimisation for Robust Model Predictive Control under Model
  Parameter Uncertainty

abstract:
  We propose an adaptive optimisation approach for tuning stochastic model
predictive control (MPC) hyper-parameters while jointly estimating probability
distributions of the transition model parameters based on performance rewards.
In particular, we develop a Bayesian optimisation (BO) algorithm with a
heteroscedastic noise model to deal with varying noise across the MPC
hyper-parameter and dynamics model parameter spaces. Typical homoscedastic
noise models are unrealistic for tuning MPC since stochastic controllers are
inherently noisy, and the level of noise is affected by their hyper-parameter
settings. We evaluate the proposed optimisation algorithm in simulated control
and robotics tasks where we jointly infer control and dynamics parameters.
Experimental results demonstrate that our approach leads to higher cumulative
rewards and more stable controllers.


title:
Computation of forces arising from the linear Poisson--Boltzmann method
  in the domain-decomposition paradigm

abstract:
  The Linearized Poisson--Boltzmann (LPB) equation is a popular and widely
accepted model for accounting solvent effects in computational (bio-)
chemistry. In the present article we derive the analytical forces of the
domain-decomposition-based ddLPB-method. We present an efficient strategy to
compute the forces and its implementation, and present numerical tests
illustrating the accuracy and efficiency of the computation of the analytical
forces.


title: Global-Local Regularization Via Distributional Robustness

abstract:
  Despite superior performance in many situations, deep neural networks are
often vulnerable to adversarial examples and distribution shifts, limiting
model generalization ability in real-world applications. To alleviate these
problems, recent approaches leverage distributional robustness optimization
(DRO) to find the most challenging distribution, and then minimize loss
function over this most challenging distribution. Regardless of achieving some
improvements, these DRO approaches have some obvious limitations. First, they
purely focus on local regularization to strengthen model robustness, missing a
global regularization effect which is useful in many real-world applications
(e.g., domain adaptation, domain generalization, and adversarial machine
learning). Second, the loss functions in the existing DRO approaches operate in
only the most challenging distribution, hence decouple with the original
distribution, leading to a restrictive modeling capability. In this paper, we
propose a novel regularization technique, following the veins of
Wasserstein-based DRO framework. Specifically, we define a particular joint
distribution and Wasserstein-based uncertainty, allowing us to couple the
original and most challenging distributions for enhancing modeling capability
and applying both local and global regularizations. Empirical studies on
different learning problems demonstrate that our proposed approach
significantly outperforms the existing regularization approaches in various
domains: semi-supervised learning, domain adaptation, domain generalization,
and adversarial machine learning.


title: Neural Score Matching for High-Dimensional Causal Inference

abstract:
  Traditional methods for matching in causal inference are impractical for
high-dimensional datasets. They suffer from the curse of dimensionality: exact
matching and coarsened exact matching find exponentially fewer matches as the
input dimension grows, and propensity score matching may match highly unrelated
units together. To overcome this problem, we develop theoretical results which
motivate the use of neural networks to obtain non-trivial, multivariate
balancing scores of a chosen level of coarseness, in contrast to the classical,
scalar propensity score. We leverage these balancing scores to perform matching
for high-dimensional causal inference and call this procedure neural score
matching. We show that our method is competitive against other matching
approaches on semi-synthetic high-dimensional datasets, both in terms of
treatment effect estimation and reducing imbalance.


title: DeepNet: Scaling Transformers to 1,000 Layers

abstract:
  In this paper, we propose a simple yet effective method to stabilize
extremely deep Transformers. Specifically, we introduce a new normalization
function (DeepNorm) to modify the residual connection in Transformer,
accompanying with theoretically derived initialization. In-depth theoretical
analysis shows that model updates can be bounded in a stable way. The proposed
method combines the best of two worlds, i.e., good performance of Post-LN and
stable training of Pre-LN, making DeepNorm a preferred alternative. We
successfully scale Transformers up to 1,000 layers (i.e., 2,500 attention and
feed-forward network sublayers) without difficulty, which is one order of
magnitude deeper than previous deep Transformers. Remarkably, on a multilingual
benchmark with 7,482 translation directions, our 200-layer model with 3.2B
parameters significantly outperforms the 48-layer state-of-the-art model with
12B parameters by 5 BLEU points, which indicates a promising scaling direction.


title: Mixed State Entanglement For Holographic Systems With A Scalar Hair

abstract:
  We study the mixed state entanglement of an asymptotic AdS black hole system
with scalar hair. Through numerical calculations, we find that the holographic
entanglement entropy (HEE) presents a non-monotonic behavior with the system
parameter, depending on the size of the subregion. In addition, the mutual
information (MI) also shows non-monotonic behavior in certain ranges of system
parameters. However, the entanglement wedge minimum cross-section (EWCS), which
is a mixed state entanglement measure, increases monotonically with the AdS
radius; meanwhile, shows non-monotonic with the temperature. We also give
analytical understandings of the phenomena above.


title:
MeerTRAP: Twelve Galactic fast transients detected in a real-time,
  commensal MeerKAT survey

abstract:
  MeerTRAP is a real-time untargeted search project using the MeerKAT telescope
to find single pulses from fast radio transients and pulsars. It is performed
commensally with the MeerKAT large survey projects (LSPs), using data from up
to 64 of MeerKAT's 13.96~m dishes to form hundreds of coherent beams on sky,
each of which is processed in real time to search for millisecond-duration
pulses. We present the first twelve Galactic sources discovered by MeerTRAP,
with DMs in the range of 33--381~pc~cm$^{-3}$. One source may be Galactic or
extragalactic depending on the Galactic electron density model assumed.
Follow-up observations performed with the MeerKAT, Lovell, and Parkes radio
telescopes have detected repeat pulses from seven of the twelve sources. Pulse
periods have been determined for four sources. Another four sources could be
localised to the arcsecond-level using a novel implementation of the tied-array
beam localisation method.


title:
Evolution of magnetic stripes under uniaxial stress in
  La$_{1.885}$Ba$_{0.115}$CuO$_4$ studied by neutron scattering

abstract:
  Here we present the effect of uniaxial stress on the magnetic stripes in the
cuprate system La$_{2-x}$Ba$_{x}$CuO$_4$ with $x = 0.115$, previously found to
have a stress-induced enhancement in the superconducting transition
temperature. By means of neutron scattering, we show that the static stripes
are suppressed by stress, pointing towards a trade-off between
superconductivity and static magnetism, in direct agreement with previously
reported $\mu$SR measurements. Additionally, we show that some of the reduced
weight in the elastic channel appears to have moved to the inelastic channel.
Moreover, a stress-induced momentum shift of the fluctuations towards the
typical 1/8 value of commensurability is observed. These results impose a
strong constraint on the theoretical interpretation of stress-enhanced
superconductivity in cuprate systems.


title:
Testing Feedback from Star Clusters in Simulations of the Milky Way
  Formation

abstract:
  We present a suite of galaxy formation simulations that directly model star
cluster formation and disruption. Starting from a model previously developed by
our group, here we introduce several improvements to the prescriptions for
cluster formation and feedback, then test these updates using a large suite of
cosmological simulations of Milky Way mass galaxies. We perform a differential
analysis with the goal of understanding how each of the updates affects star
cluster populations. Two key parameters are the momentum boost of supernova
feedback $f_{\mathrm{boost}}$ and star formation efficiency per freefall time
$\epsilon_{\mathrm{ff}}$. We find that $f_{\mathrm{boost}}$ has a strong
influence on the galactic star formation rate, with higher values leading to
less star formation. The efficiency $\epsilon_{\mathrm{ff}}$ does not have a
significant impact on the global star formation rate, but dramatically changes
cluster properties, with increasing $\epsilon_{\mathrm{ff}}$ leading to a
higher maximum cluster mass, shorter age spread of stars within clusters, and
higher integrated star formation efficiencies. We also explore the redshift
evolution of the observable cluster mass function, finding that most massive
clusters have formed at high redshift $z>4$. Extrapolation of cluster
disruption to $z=0$ produces good agreement with both the Galactic globular
cluster mass function and age-metallicity relation. Our results emphasize the
importance of using small-scale properties of galaxies to calibrate subgrid
models of star cluster formation and feedback.


title:
Classical, semi-classical and quantum optical models for x-ray planar
  cavity with electronic resonance

abstract:
  Here two theoretical models of semi-classical matrix and quantum Green's
function are developed for the system of x-ray thin-film planar cavity with
inner-shell electronic resonances. The semi-classical model is based on the
matrix formalism to treat each layer as the propagating matrix. The crucial
idea is to expand the propagating matrix of the resonant atomic layer under
ultrathin-film approximation, then derive the analytical expression of the
spectral observation, i.e, the cavity reflectance. The typical cavity effects
of cavity enhanced decay rate, cavity induced energy shift and the Fano
interference which were observed in the recent experiments could be
phenomenologically interpreted. The second quantum model employs the analytical
Green's function to solve the cavity system. The system Hamiltonian and the
effective energy-level are derived. The effective energy-level scheme indicates
that the cavity effect acts on the regulation of the intermediated core-hole
state. To test the validity of the semi-classical matrix and quantum Green's
function models, the classical Parratt's formalism and the dispersion
correction of the atomic refractive index are also recalled. Very good
agreements in reflectivity spectra between semi-classical and quantum models
with the Parratt's results are observed. The present semi-classical matrix and
quantum Green's function models will be useful to predict the new phenomena and
optimize the cavity structures for future experiments and promote the emerging
of quantum optical effects with modern x-ray spectroscopy techniques.


title: Stability of ionisation-injection-based laser-plasma accelerators

abstract:
  Laser-plasma acceleration (LPA) is a compact technique to accelerate electron
bunches to highly relativistic energies, making it a promising candidate to
power radiation sources for industrial or medical applications. We report on
the generation of electron beams from an 80 MeV-level LPA setup based on
ionisation injection (II) over a duration of 8 hours at a repetition rate of
2.5 Hz, resulting in 72,000 consecutive shots with charge injection and
acceleration. Over the full operation time the moving averages of the total
beam charge of 14.5 pC and the charge between 70-80 MeV did not drift on a
detectable level. The largest source of shot-to-shot jitter was in the beam
charge (26% standard deviation), which was most strongly correlated with
fluctuations in the plasma density (3.6% standard deviation). Particle-in-cell
simulations demonstrate that this was chiefly caused by stronger laser
self-focusing in higher density plasmas, which significantly increased the
ionised charge along with the emittance of the beam. The nonlinearity of this
process imposes tight constraints on the reproducibility of the laser-plasma
conditions required for a low jitter II-LPA output if self-focusing plays a
role in the laser evolution.


title:
Scaling laws for planetary sediment transport from grain scale-resolved
  numerical simulations

abstract:
  Aeolian sediment transport shapes erodible surfaces and affects the dust
cycles and climates of planetary bodies. For the approximately unidirectional
near-surface winds often temporarily prevailing in planetary atmospheres,
sediment transport approaches an equilibrium state when given enough fetch to
adapt. Here, we use grain scale-resolved numerical sediment transport
simulations to derive scaling laws for equilibrium transport. The simulations
cover seven orders of magnitude in the particle-fluid-density ratio $s$,
ranging from water to extremely rarefied air on Pluto. They suggest that the
threshold fluid shear velocity needed to sustain aeolian transport exhibits a
parabolic dependency on the grain size with a pronounced threshold minimum. We
show that this behavior originates from the equations of motion describing
quasicontinuous saltation driven by Stokes drag in a hydrodynamically smooth
turbulent boundary layer. In particular, the threshold minimum, rescaled using
units of particle density, gravity, and dynamic fluid viscosity, scales as
$s^{1/3}$, contrary to the $s^{1/2}$-scaling measured in nonequilibrium wind
tunnel experiments. Furthermore, the simulations suggest that the aeolian
transport rate and grain impact energy flux, which drives dust emission into a
planetary body's atmosphere, obey scaling laws resembling the classical law by
Ungar and Haff (Sedimentology 34, 289-299, 1987), but with nonconstant scaling
coefficients proportional to $s^{1/3}$ and $s^{1/4}$, respectively. The
simulation-derived semiempirical master curves for the cessation threshold and
the threshold-dependent transport rate and impact flux, supported by their
agreement with terrestrial measurements in air and water, provide a simple
means to make predictions of aeolian processes across a large range of
planetary conditions.

categories: astro-ph.EP cond-mat.soft physics.ao-ph physics.geo-ph


title: Quasitriangular operator algebras

abstract:
  We give characterizations of quasitriangular operator algebras along the line
of Voiculescu's characterization of quasidiagonal $C^*$-algebras.


title: A unified analysis of convex and non-convex lp-ball projection problems

abstract:
  The task of projecting onto $\ell_p$ norm balls is ubiquitous in statistics
and machine learning, yet the availability of actionable algorithms for doing
so is largely limited to the special cases of $p = \left\{ 0, 1,2, \infty
\right\}$. In this paper, we introduce novel, scalable methods for projecting
onto the $\ell_p$ ball for general $p>0$. For $p \geq1 $, we solve the
univariate Lagrangian dual via a dual Newton method. We then carefully design a
bisection approach for $p<1$, presenting theoretical and empirical evidence of
zero or a small duality gap in the non-convex case. The success of our
contributions is thoroughly assessed empirically, and applied to large-scale
regularized multi-task learning and compressed sensing.


title: Topological Data Analysis for Word Sense Disambiguation

abstract:
  We develop and test a novel unsupervised algorithm for word sense induction
and disambiguation which uses topological data analysis. Typical approaches to
the problem involve clustering, based on simple low level features of distance
in word embeddings. Our approach relies on advanced mathematical concepts in
the field of topology which provides a richer conceptualization of clusters for
the word sense induction tasks. We use a persistent homology barcode algorithm
on the SemCor dataset and demonstrate that our approach gives low relative
error on word sense induction. This shows the promise of topological algorithms
for natural language processing and we advocate for future work in this
promising area.


title: Measuring the propagation speed of gravitational waves with LISA

abstract:
  The propagation speed of gravitational waves, $c_T$, has been tightly
constrained by the binary neutron star merger GW170817 and its electromagnetic
counterpart, under the assumption of a frequency-independent $c_T$. Drawing
upon arguments from Effective Field Theory and quantum gravity, we discuss the
possibility that modifications of General Relativity allow for transient
deviations of $c_T$ from the speed of light at frequencies well below the band
of current ground-based detectors. We motivate two representative Ans\"atze for
$c_T(f)$, and study their impact upon the gravitational waveforms of massive
black hole binary mergers detectable by the LISA mission. We forecast the
constraints on $c_T(f)$ obtainable from individual systems and a population of
sources, from both inspiral and a full inspiral-merger-ringdown waveform. We
show that LISA will enable us to place stringent independent bounds on
departures from General Relativity in unexplored low-frequency regimes, even in
the absence of an electromagnetic counterpart.


title: Descriptellation: Deep Learned Constellation Descriptors for SLAM

abstract:
  Current global localization descriptors in Simultaneous Localization and
Mapping (SLAM) often fail under vast viewpoint or appearance changes. Adding
topological information of semantic objects into the descriptors ameliorates
the problem. However, hand-crafted topological descriptors extract limited
information and they are not robust to environmental noise, drastic perspective
changes, or object occlusion or misdetections. To solve this problem, we
formulate a learning-based approach by constructing constellations from
semantically meaningful objects and use Deep Graph Convolution Networks to map
the constellation representation to a descriptor. We demonstrate the
effectiveness of our Deep Learned Constellation Descriptor (Descriptellation)
on the Paris-Rue-Lille and IQmulus datasets. Although Descriptellation is
trained on randomly generated simulation datasets, it shows good generalization
abilities on real-world datasets. Descriptellation outperforms the PointNet and
handcrafted constellation descriptors for global localization, and shows
robustness against different types of noise.


title:
Unified ab initio description of Fr\"ohlich electron-phonon interactions
  in two-dimensional and three-dimensional materials

abstract:
  \textit{Ab initio} calculations of electron-phonon interactions including the
polar Fr\"ohlich coupling have advanced considerably in recent years. The
Fr\"ohlich electron-phonon matrix element is by now well understood in the case
of bulk three-dimensional (3D) materials. In the case of two-dimensional (2D)
materials, the standard procedure to include Fr\"ohlich coupling is to employ
Coulomb truncation, so as to eliminate artificial interactions between periodic
images of the 2D layer. While these techniques are well established, the
transition of the Fr\"ohlich coupling from three to two dimensions has not been
investigated. Furthermore, it remains unclear what error one makes when
describing 2D systems using the standard bulk formalism in a periodic supercell
geometry. Here, we generalize previous work on the \textit{ab initio}
Fr\"ohlich electron-phonon matrix element in bulk materials by investigating
the electrostatic potential of atomic dipoles in a periodic supercell
consisting of a 2D material and a continuum dielectric slab. We obtain a
unified expression for the matrix element, which reduces to the existing
formulas for 3D and 2D systems when the interlayer separation tends to zero or
infinity, respectively. This new expression enables an accurate description of
the Fr\"ohlich matrix element in 2D systems without resorting to Coulomb
truncation. We validate our approach by direct \textit{ab initio}
density-functional perturbation theory calculations for monolayer BN and
MoS$_2$, and we provide a simple expression for the 2D Fr\"ohlich matrix
element that can be used in model Hamiltonian approaches. The formalism
outlined in this work may find applications in calculations of polarons,
quasiparticle renormalization, transport coefficients, and superconductivity,
in 2D and quasi-2D materials.


title:
Inferring the Helium abundance of extragalactic Globular Clusters using
  Integrated Spectra

abstract:
  The leading method for the determination of relevant stellar population
parameters of unresolved extragalactic Globular Clusters is through the study
of their integrated spectroscopy, where Balmer line-strength indices are
considered to be age sensitive. Previously, a splitting in the highly optimised
spectral line-strength index H$\beta_o$ was observed in a sample of Galactic
globular clusters at all metallicities resulting in an apparent "upper branch"
and "lower branch" of globular clusters in the H$\beta_o$ - [MgFe] diagram.
This was suggested to be caused by the presence of hot Blue straggler stars
(BSSs), resulting in an underestimation of 'spectroscopic' ages in the upper
branch. Over a decade on, we look to re-evaluate these findings. We make use of
new, large Galactic Globular Cluster integrated spectroscopy datasets. To
produce a large, homogeneously combined sample we have considered a number of
factors including the radial dependence of Balmer and metal lines. Using this
new sample, in disagreement with previous work, we find the splitting in
H$\beta_o$ only occurs at intermediate to high metallicities ([M/H]$>-1$), and
is not the result of an increased fraction of BSSs, but rather is due to an
increased Helium abundance. We explore the possible impact of varying Helium on
simple stellar population models to provide a theoretical basis for our
hypothesis and then use the relationship between upper branch candidacy and
enhanced Helium to predict the Helium content of three M31 clusters. We discuss
what this can tell us about their mass and fraction of first generation stars.


title:
Towards a unified view of unsupervised non-local methods for image
  denoising: the NL-Ridge approach

abstract:
  We propose a unified view of unsupervised non-local methods for image
denoising that linearily combine noisy image patches. The best methods,
established in different modeling and estimation frameworks, are two-step
algorithms. Leveraging Stein's unbiased risk estimate (SURE) for the first step
and the "internal adaptation", a concept borrowed from deep learning theory,
for the second one, we show that our NL-Ridge approach enables to reconcile
several patch aggregation methods for image denoising. In the second step, our
closed-form aggregation weights are computed through multivariate Ridge
regressions. Experiments on artificially noisy images demonstrate that NL-Ridge
may outperform well established state-of-the-art unsupervised denoisers such as
BM3D and NL-Bayes, as well as recent unsupervised deep learning methods, while
being simpler conceptually.


title:
Finite difference method for stochastic Cahn--Hilliard equation: Strong
  convergence rate and density convergence

abstract:
  This paper presents the strong convergence rate and density convergence of a
spatial finite difference method (FDM) when applied to numerically solve the
stochastic Cahn--Hilliard equation driven by multiplicative space-time white
noises. The main difficulty lies in the control of the drift coefficient that
is neither global Lipschitz nor one-sided Lipschitz. To handle this difficulty,
we first utilize an interpolation approach to derive the discrete
$H^1$-regularity of the numerical solution. This is the key to deriving the
optimal strong convergence order $1$ of the numerical solution. Further, we
propose a novel localization argument to estimate the total variation distance
between the exact and numerical solutions, which along with the existence of
the density of the numerical solution finally yields the convergence of density
in $L^1(\mathbb{R})$ of the numerical solution. This partially answers
positively to the open problem emerged in [9,Section 5] on computing the
density of the exact solution numerically.


title:
Empirical Evidence of Non-Minimally Coupled Dark Matter in the Dynamics
  of Local Spiral Galaxies?

abstract:
  We look for empirical evidence of a non-minimal coupling (NMC) between dark
matter (DM) and gravity in the dynamics of local spiral galaxies. In
particular, we consider a theoretically motivated NMC that may arise
dynamically from the collective behavior of the coarse-grained DM field (e.g.,
via Bose-Einstein condensation) with averaging/coherence length $L$. In the
Newtonian limit, this NMC amounts to modify the Poisson equation by a term
$L^{2} \nabla^{2} \rho$ proportional to the Laplacian of the DM density itself.
We show that such a term, when acting as a perturbation over the standard
Navarro-Frenk-White (NFW) profile of cold DM particles, can substantially alter
the dynamical properties of galaxies, in terms of their total radial
acceleration within the disk and rotation velocity. Specifically, we find that
this NMC model can properly fit the stacked rotation curves of local spiral
galaxies with different velocities at the optical radius, including dwarfs and
low-surface brightness systems, at a level of precision comparable to, and in
some instances even better than, the phenomenological Burkert profile. Finally
we show that, extrapolating down to smaller masses the scaling of $L$ vs. halo
mass found from the above rotation curve analysis, the NMC model can adequately
reproduce the radial acceleration relation (or RAR) in shape and normalization
down to the dwarf spheroidal galaxy range, a task which constitutes a serious
challenge for alternative DM models even inclusive of baryonic effects.


title:
Contrasting random and learned features in deep Bayesian linear
  regression

abstract:
  Understanding how feature learning affects generalization is among the
foremost goals of modern deep learning theory. Here, we study how the ability
to learn representations affects the generalization performance of a simple
class of models: deep Bayesian linear neural networks trained on unstructured
Gaussian data. By comparing deep random feature models to deep networks in
which all layers are trained, we provide a detailed characterization of the
interplay between width, depth, data density, and prior mismatch. We show that
both models display sample-wise double-descent behavior in the presence of
label noise. Random feature models can also display model-wise double-descent
if there are narrow bottleneck layers, while deep networks do not show these
divergences. Random feature models can have particular widths that are optimal
for generalization at a given data density, while making neural networks as
wide or as narrow as possible is always optimal. Moreover, we show that the
leading-order correction to the kernel-limit learning curve cannot distinguish
between random feature models and deep networks in which all layers are
trained. Taken together, our findings begin to elucidate how architectural
details affect generalization performance in this simple class of deep
regression models.


title: Spelling Out Leptonic CP Violation in the Language of Invariant Theory

abstract:
  In terms of flavor invariants, we establish the intimate connection between
leptonic CP violation in the canonical seesaw model for neutrino masses and
that in the seesaw effective field theory (SEFT). For the first time, we
calculate the Hilbert series and explicitly construct the primary flavor
invariants in the SEFT by considering both the dimension-five Weinberg operator
${\cal O}^{\alpha \beta}_5 = \overline{\ell^{}_{\alpha \rm L}} \widetilde{H}
\widetilde{H}^{\rm T} \ell^{\rm C}_{\beta \rm L}$ and the dimension-six
operator ${\cal O}^{\alpha \beta}_6 = \overline{\ell^{}_{\alpha \rm L}}
\widetilde{H} {\rm i} \not\!\partial \widetilde{H}^\dagger \ell^{}_{\beta \rm
L}$ at the tree-level matching. The inclusion of only the Wilson coefficients
$C^{\alpha \beta}_5$ and $C^{\alpha \beta}_6$ already enables the SEFT to
incorporate all physical information about the full seesaw model. Moreover, the
minimal sufficient and necessary conditions for CP conservation both in the
SEFT and in the full theory are clarified, and the matching between the flavor
invariants in both theories is accomplished. Through the matching of flavor
invariants, the CP asymmetries necessary for successful leptogenesis are
directly linked to those in neutrino-neutrino and neutrino-antineutrino
oscillations at low energies. Surprisingly, it is revealed that the precise
measurements of $C^{\alpha \beta}_5$ and $C^{\alpha \beta}_6$ in low-energy
experiments are powerful enough to probe the full seesaw model, including CP
violation for cosmological matter-antimatter asymmetry.


title: Dielectric response of thin water films: A thermodynamic perspective

abstract:
  The surface of a polar liquid presents a special environment for the
solvation and organization of charged solutes, which differ from bulk behaviors
in important ways. These differences have motivated many attempts to understand
electrostatic response at aqueous interfaces in terms of a spatially varying
dielectric permittivity, typically concluding that the dielectric constant of
interfacial water is significantly lower than in the bulk liquid. Such
analyses, however, are complicated by the potentially nonlocal nature of
dielectric response over the short length scales of interfacial heterogeneity.
Here we circumvent this problem for thin water films by adopting a
thermodynamic approach. Using molecular simulations, we calculate the solvent's
contribution to the reversible work of charging a parallel plate capacitor. We
find good agreement with a simple dielectric continuum model that assumes bulk
dielectric permittivity all the way up to the liquid's boundary, even for very
thin ($\sim 1$ nm) films. This comparison requires careful attention to the
placement of dielectric boundaries between liquid and vapor, which also
resolves apparent discrepancies with dielectric imaging experiments.


title: On stable and fixed polynomials

abstract:
  Let $\nu$ be a rank one valuation on $K[x]$ and $\Psi_n$ the set of key
polynomials for $\nu$ of degree $n\in\N$. We discuss the concepts of being
$\Psi_n$-stable and $(\Psi_n,Q)$-fixed. We discuss when these two concepts
coincide. We use this discussion to present a simple proof of Proposition 8.2
of [5] and Theorem 1.2 of [5].


title:
Flares and their echoes can help distinguish photon rings from black
  holes with space-Earth very long baseline interferometry

abstract:
  Photon rings near the edge of a black hole shadow is supposed to be a unique
tool to validate general relativity and provide reliable measurements of
principal black hole parameters: spin and mass. Such measurements are possible
though only for nearby supermassive black holes (SMBH) with Space-Earth Very
Long Baseline Interferometry (S-VLBI) in the submillimeter wavelength range.
For subrings to be distinguished S-EVLBI observations with long baselines at
the Lagrangian Sun-Earth L2 libration point are needed. However, the average
fluxes of nearby SMBH: Sagittarius A$^\ast$ (Sgr A$^\ast$) and M87$^\ast$ --
$F_\nu\sim 1$ Jy, are still insufficient to detect the signal from the photon
rings with even such long baselines. We argue that only manifestations of
flares in the submillimeter waveband in their accretion disks can reveal
observable signals from the photon rings with the S-EVLBI at L2. Such
observations will become possible within the planned join program of the {\it
Event Horizon Telescope} (EHT) and {\it Millimetron Space Observatory} (MSO),
and within the planned {\it next generation EHT} (ngEHT) project. Two different
observational tests for photons rings are discussed. The first one involves
observations of a time series of responds from subsequent subrings as can be
seen in a 1D visibility function within the join EHT-MSO configuration, the
second one -- measurements of an increase of the angle between subsequent
subrings in the 2D VLBI image which can be obtained within the ngEHT project.


title: Silicene for flexible electronics

abstract:
  The outstanding properties of graphene have laid the foundation for exploring
graphene-like two-dimensional systems, commonly referred to as 2D-Xenes.
Amongst them, silicene is a front-runner owing to its compatibility with
current silicon fabrication technologies. Recent works on silicene have
unveiled its useful electronic and mechanical properties. The rapid
miniaturization of silicon devices and the useful electro-mechanical properties
of silicene necessitates the exploration for potential applications of silicene
flexible electronics in the nano electro-mechanical systems. Using a
theoretical model derived from the integration of \textit{ab-initio}
density-functional theory and quantum transport theory, we investigate the
piezoresistance effect of silicene in the nanoscale regime. Like graphene, we
obtain a small value of piezoresistance gauge factor of silicene, which is
sinusoidally dependent on the transport angle. The small gauge factor of
silicene is attributed to its robust Dirac cone and strain-independent valley
degeneracy. Based on the obtained results, we propose to use silicene as an
interconnect in flexible electronic devices and a reference piezoresistor in
strain sensors. This work will hence pave the way for exploring flexible
electronics applications in other 2D-Xene materials.


title:
Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi
  Networks: A State of the Art Review

abstract:
  Multi-Channel Man-in-the-Middle (MitM) attacks are special MitM attacks
capable of manipulating encrypted Wi-Fi wireless frames between two legitimate
endpoints. Since its inception in 2014, attackers have been targeting WPA Wi-Fi
networks to perform different attacks, such as cipher downgrades, denial of
service, key reinstallation Man-in-the-Middle (MitM) attacks (KRACK) in 2017,
and recently FragAttacks in 2021, which widely impacted millions of Wi-Fi
Multi-Channel MitM (MC-MitM) devices, especially IoT devices. To the best of
our knowledge, there are no studies in the literature that KRACK holistically
review the different types of Multi-Channel MitM enabled attacks and analyze
their potential Internet of Things (IoT) impact. To this end, we evaluate the
capabilities of Multi-Channel MitM and review every reported attack in
Encryption the state of the art. We examine practical issues that hamper the
total adoption of protection mechanisms, i.e., Security security patches and
Protected Management Frames (PMF), and review available defense mechanisms in
FragAttacks confronting the Multi-Channel MitM enabled attacks in the IoT
context. Finally, we highlight the potential research problems and identify
future research lines in this field.


title: Ultrafast chirality: the road to efficient chiral measurements

abstract:
  Today we are witnessing the electric-dipole revolution in chiral
measurements. Here we reflect on its lessons and outcomes, such as the
perspective on chiral measurements using the complementary principles of
"chiral reagent" and "chiral observer", the hierarchy of scalar, vectorial and
tensorial observables, the new properties of the chiro-optical response in the
ultrafast and non-linear domains, and the geometrical magnetism associated with
the chiral response in photoionization. The electric-dipole revolution is a
landmark event. It has opened routes to extremely efficient
enantio-discrimination with a family of new methods. These methods are governed
by the same principles but work in vastly different regimes -- from microwaves
to optical light; they address all molecular degrees of freedom -- electronic,
vibrational and rotational, and use flexible detection schemes, i.e. detecting
photons or electrons, making them applicable to different chiral phases, from
gases to liquids to amorphous solids. The electric-dipole revolution has also
enabled enantio-sensitive manipulation of chiral molecules with light. This
manipulation includes exciting and controlling ultrafast helical currents in
vibronic states of chiral molecules, enantio-sensitive control of populations
in electronic, vibronic and rotational molecular states, and opens the way to
efficient enantio-separation and enantio-sensitive trapping of chiral
molecules. The word "perspective" has two meanings: "outlook" and "point of
view". In this perspective article, we have tried to cover both meanings.


title: Resolving structural changes and symmetry lowering in spinel FeCr2S4

abstract:
  The cubic spinel FeCr2S4 has been receiving immense research interest because
of its emergent phases and the interplay of spin, orbital and lattice degrees
of freedom. Despite the intense research, several fundamental questions are yet
to be answered, such as the refinement of the crystal structure in the
different magnetic and orbital ordered phases. Here, using high-resolution
synchrotron powder diffraction on stoichiometric crystals of FeCr2S4 we
resolved the long sought-after cubic to tetragonal transition at ~65 K,
reducing the lattice symmetry to I41/amd. With further lowering the
temperature, at ~9 K, the crystal structure becomes polar, hence the compound
becomes multiferroic. The elucidation of the lattice symmetry throughout
different phases of FeCr2S4 provides a basis for the understanding this
enigmatic system and also highlights the importance of structural deformation
in correlated materials.


title: Algorithm Design and Integration for a Robotic Apple Harvesting System

abstract:
  Due to labor shortage and rising labor cost for the apple industry, there is
an urgent need for the development of robotic systems to efficiently and
autonomously harvest apples. In this paper, we present a system overview and
algorithm design of our recently developed robotic apple harvester prototype.
Our robotic system is enabled by the close integration of several core modules,
including calibration, visual perception, planning, and control. This paper
covers the main methods and advancements in robust extrinsic parameter
calibration, deep learning-based multi-view fruit detection and localization,
unified picking and dropping planning, and dexterous manipulation control.
Indoor and field experiments were conducted to evaluate the performance of the
developed system, which achieved an average picking rate of 3.6 seconds per
apple. This is a significant improvement over other reported apple harvesting
robots with a picking rate in the range of 7-10 seconds per apple. The current
prototype shows promising performance towards further development of efficient
and automated apple harvesting technology. Finally, limitations of the current
system and future work are discussed.


title:
Modeling of electric double layer at solid-liquid interface with spatial
  complexity

abstract:
  Electrical double layer (EDL) is formed when an electrode is in contact with
an electrolyte solution, and is widely used in biophysics, electrochemistry,
polymer solution and energy storage. Poisson-Boltzmann (PB) coupled equations
provides the foundational framework for modeling electrical potential and
charge distribution at EDL. In this work, based on fractional calculus, we
reformulate the PB equations (with and without steric effects) by introducing a
phenomenal parameter $D$ (with a value between 0 and 1) to account for the
spatial complexity due to impurities in EDL. The electrical potential and ion
charge distribution for different $D$ are investigated. At $D$ = 1, the model
recover the classical findings of ideal EDL. The electrical potential decays
slowly at $D <$1, thus suggesting a wider region of saturated layer under fixed
surface potential in the presence of spatial complexity. The fractional-space
generalized model developed here provides a useful tool to account for spatial
complexity effects which are not captured in the classic full-dimensional
models.

categories: cond-mat.mes-hall physics.app-ph physics.chem-ph physics.comp-ph


title: A Two Plus One Dimensional Continuous Wavelet Transform

abstract:
  The group $G_2$ of invertible affine transformations of $\mathbb{R}^2$ has,
up to equivalence, one square--integrable representation. Two new realizations
of this representation are presented and novel continuous wavelet transforms,
acting on functions of two plus one variables, are derived.


title:
Self-Supervised Vision Transformers Learn Visual Concepts in
  Histopathology

abstract:
  Tissue phenotyping is a fundamental task in learning objective
characterizations of histopathologic biomarkers within the tumor-immune
microenvironment in cancer pathology. However, whole-slide imaging (WSI) is a
complex computer vision in which: 1) WSIs have enormous image resolutions with
precludes large-scale pixel-level efforts in data curation, and 2) diversity of
morphological phenotypes results in inter- and intra-observer variability in
tissue labeling. To address these limitations, current efforts have proposed
using pretrained image encoders (transfer learning from ImageNet,
self-supervised pretraining) in extracting morphological features from
pathology, but have not been extensively validated. In this work, we conduct a
search for good representations in pathology by training a variety of
self-supervised models with validation on a variety of weakly-supervised and
patch-level tasks. Our key finding is in discovering that Vision Transformers
using DINO-based knowledge distillation are able to learn data-efficient and
interpretable features in histology images wherein the different attention
heads learn distinct morphological phenotypes. We make evaluation code and
pretrained weights publicly-available at:
https://github.com/Richarizardd/Self-Supervised-ViT-Path.


title: Quantum diffusion as a process of linear quantum dynamics

abstract:
  Quantum diffusion, as developed in the 1990s, could explain how a system,
subject to measurement, goes into an eigenstate of the measured observable.
Here it is shown that quantum diffusion theory can be interpreted as a result
within linear relativistic quantum mechanics. Thus, in contrast to what is
widely believed, quantum measurement can be analyzed within the theory of
quantum mechanics itself.


title:
Search for events with a pair of displaced vertices from long-lived
  neutral particles decaying into hadronic jets in the ATLAS muon spectrometer
  in pp collisions at $\sqrt{s}=13$ TeV

abstract:
  A search for events with two displaced vertices from long-lived particles
(LLP) pairs using data collected by the ATLAS detector at the LHC is presented.
This analysis uses 139~fb$^{-1}$ of proton-proton collision data at
$\sqrt{s}=13$ TeV recorded in 2015-2018. The search employs techniques for
reconstructing vertices of LLPs decaying to jets in the muon spectrometer
displaced between 3 m and 14 m with respect to the primary interaction vertex.
The observed numbers of events are consistent with the expected background and
limits for several benchmark signals are determined. For the Higgs boson with a
mass of 125 GeV, the paper reports the first exclusion limits for branching
fractions into neutral long-lived particles below 0.1%, while branching
fractions above 10% are excluded at 95% confidence level for LLP proper
lifetimes ranging from 4 cm to 72.4 m. In addition, the paper present the first
results for the decay of LLPs into into $t\overline{t}$ in the ATLAS muon
spectrometer.