title: The solitary route to chimera states

abstract:
  We show how solitary states in a system of globally coupled FitzHugh-Nagumo
oscillators can lead to the emergence of chimera states. By a numerical
bifurcation analysis of a suitable reduced system in the thermodynamic limit we
demonstrate how solitary states, after emerging from the synchronous state,
become chaotic in a period-doubling cascade. Subsequently, states with a single
chaotic oscillator give rise to states with an increasing number of incoherent
chaotic oscillators. In large systems, these chimera states show extensive
chaos. We demonstrate the coexistence of many of such chaotic attractors with
different Lyapunov dimensions, due to different numbers of incoherent
oscillators.


title:
Autoencoder for Synthetic to Real Generalization: From Simple to More
  Complex Scenes

abstract:
  Learning on synthetic data and transferring the resulting properties to their
real counterparts is an important challenge for reducing costs and increasing
safety in machine learning. In this work, we focus on autoencoder architectures
and aim at learning latent space representations that are invariant to
inductive biases caused by the domain shift between simulated and real images
showing the same scenario. We train on synthetic images only, present
approaches to increase generalizability and improve the preservation of the
semantics to real datasets of increasing visual complexity. We show that
pre-trained feature extractors (e.g. VGG) can be sufficient for generalization
on images of lower complexity, but additional improvements are required for
visually more complex scenes. To this end, we demonstrate a new sampling
technique, which matches semantically important parts of the image, while
randomizing the other parts, leads to salient feature extraction and a
neglection of unimportant parts. This helps the generalization to real data and
we further show that our approach outperforms fine-tuned classification models.


title:
DAG-WGAN: Causal Structure Learning With Wasserstein Generative
  Adversarial Networks

abstract:
  The combinatorial search space presents a significant challenge to learning
causality from data. Recently, the problem has been formulated into a
continuous optimization framework with an acyclicity constraint, allowing for
the exploration of deep generative models to better capture data sample
distributions and support the discovery of Directed Acyclic Graphs (DAGs) that
faithfully represent the underlying data distribution. However, so far no study
has investigated the use of Wasserstein distance for causal structure learning
via generative models. This paper proposes a new model named DAG-WGAN, which
combines the Wasserstein-based adversarial loss, an auto-encoder architecture
together with an acyclicity constraint. DAG-WGAN simultaneously learns causal
structures and improves its data generation capability by leveraging the
strength from the Wasserstein distance metric. Compared with other models, it
scales well and handles both continuous and discrete data. Our experiments have
evaluated DAG-WGAN against the state-of-the-art and demonstrated its good
performance.


title:
Experimental genuine tripartite nonlocality in a quantum triangle
  network

abstract:
  Quantum networks are the center of many of the recent advances in quantum
science, not only leading to the discovery of new properties in the foundations
of quantum theory but also allowing for novel communication and cryptography
protocols. It is known that networks beyond that in the paradigmatic Bell's
theorem imply new and sometimes stronger forms of nonclassicality. Due to a
number of practical difficulties, however, the experimental implementation of
such networks remains far less explored. Going beyond what has been previously
tested, here we verify the nonlocality of an experimental triangle network,
consisting of three independent sources of bipartite entangled photon states
interconnecting three distant parties. By performing separable measurements
only and evaluating parallel chained Bell inequalities, we show that such
networks can lead to a genuine form of tripartite nonlocality, where classical
models are unable to mimic the quantum predictions even if some of the parties
are allowed to communicate.


title: Magnetic resonances in EuSn$_2$As$_2$ single crystal

abstract:
  In this work, we report the broad-band ferromagnetic resonance spectroscopy
of EuSn$_2$As$_2$ single crystals at different temperatures in combination with
magnetization measurements and structural characterization. We observe
conventional collective acoustic resonance mode of the A-type antiferromagnetic
spin-flop phase in the Eu sub-lattice, and its transition to the paramagnetic
resonance above the ordering temperature. Furthermore, we observe reproducibly
additional well-defined spectral line. The origin of the additional line
remains unclear. However, its temperature dependence attributes it to magnetism
in the Eu sub-lattice.


title:
Many-quark interactions: Large-$N$ scaling and contribution to baryon
  masses

abstract:
  Starting from an effective Hamiltonian modelling a baryon made of $N$
identical quarks in the large-$N$ approach of QCD, we obtain analytical
formulas allowing to estimate the contributions of multiquark interactions to
the baryon mass. The cases of vanishing (mass spectrum) and non-vanishing
(baryon melting) temperatures are treated.


title:
Automatic Biomedical Term Clustering by Learning Fine-grained Term
  Representations

abstract:
  Term clustering is important in biomedical knowledge graph construction.
Using similarities between terms embedding is helpful for term clustering.
State-of-the-art term embeddings leverage pretrained language models to encode
terms, and use synonyms and relation knowledge from knowledge graphs to guide
contrastive learning. These embeddings provide close embeddings for terms
belonging to the same concept. However, from our probing experiments, these
embeddings are not sensitive to minor textual differences which leads to
failure for biomedical term clustering. To alleviate this problem, we adjust
the sampling strategy in pretraining term embeddings by providing dynamic hard
positive and negative samples during contrastive learning to learn fine-grained
representations which result in better biomedical term clustering. We name our
proposed method as CODER++, and it has been applied in clustering biomedical
concepts in the newly released Biomedical Knowledge Graph named BIOS.


title: ECOTS: Early Classification in Open Time Series

abstract:
  Learning to predict ahead of time events in open time series is challenging.
While Early Classification of Time Series (ECTS) tackles the problem of
balancing online the accuracy of the prediction with the cost of delaying the
decision when the individuals are time series of finite length with a unique
label for the whole time series. Surprisingly, this trade-off has never been
investigated for open time series with undetermined length and with different
classes for each subsequence of the same time series. In this paper, we propose
a principled method to adapt any technique for ECTS to the Early Classification
in Open Time Series (ECOTS). We show how the classifiers must be constructed
and what the decision triggering system becomes in this new scenario. We
address the challenge of decision making in the predictive maintenance field.
We illustrate our methodology by transforming two state-of-the-art ECTS
algorithms for the ECOTS scenario and report numerical experiments on a real
dataset for predictive maintenance that demonstrate the practicality of the
novel approach.


title:
On the Importance of High-Frequency Damping in High-Order Conservative
  Finite-Difference Schemes for Viscous Fluxes

abstract:
  This paper discusses the importance of high-frequency damping in high-order
conservative finite-difference schemes for viscous terms in the Navier-Stokes
equations. Investigating nonlinear instability encountered in a high-resolution
viscous shock-tube simulation, we have discovered that a modification to the
viscous scheme rather than the inviscid scheme resolves a problem with spurious
oscillations around shocks. The modification introduces a term responsible for
high-frequency damping that is missing in a conservative high-order viscous
scheme. The importance of damping has been known for schemes designed for
unstructured grids. However, it has not been recognized well in very high-order
difference schemes, especially in conservative difference schemes. Here, we
discuss how it is easily missed in a conservative scheme and how to improve
such schemes by a suitably designed damping term.


title:
Finding lower bounds on the growth and entropy of subshifts over
  countable groups

abstract:
  We give a lower bound on the growth of a subshift based on a simple condition
on the set of forbidden patterns defining that subshift. Aubrun et Al. showed a
similar result based on the Lov\'asz Local Lemma for subshift over any
countable group and Bernshteyn extended their approach to deduce, amongst other
things, some lower bound on the exponential growth of the subshift. However,
our result has a simpler proof, is easier to use for applications, and provides
better bounds on the applications from their articles (although it is not clear
that our result is stronger in general).
  In the particular case of subshift over $\mathbb{Z}$ a similar but weaker
condition given by Miller was known to imply nonemptiness of the associated
shift. Pavlov used the same approach to provide a condition that implied
exponential growth. We provide a version of our result for this particular
setting and it is provably strictly stronger than the result of Pavlov and the
result of Miller (and, in practice, leads to considerable improvement in the
applications).
  We also apply our two results to a few different problems including strongly
aperiodic subshifts, nonrepetitive subshifts, and Kolmogorov complexity of
subshifts.


title:
Near-Linear Orbit Uncertainty Propagation in the Perturbed Two-Body
  Problem

abstract:
  The paper addresses the problem of minimizing the impact of non-linearities
when dealing with uncertainty propagation in the perturbed two-body problem.
The recently introduced generalized equinoctial orbital element set (GEqOE) is
employed as a means to reduce non-linear effects stemming from J$_2$ and higher
order gravity field harmonics. The uncertainty propagation performance of the
proposed set of elements in different Earth orbit scenarios, including
low-thrust orbit raising, is evaluated using a Cram\'er-von Mises test on the
Mahalanobis distance of the uncertainty distribution. A considerable
improvement compared to all sets of elements proposed so far is obtained.


title: Phenomenology of diphoton photoproduction at next-to-leading order

abstract:
  We develop the analysis of diphoton exclusive photoproduction in the
kinematics where a collinear QCD factorization framework applies, namely nearly
forward large invariant mass diphoton production. We work at the leading twist
level and at the next-to-leading order (NLO) in the strong coupling constant
$\alpha_S$. We compare our predictions for cross-sections with Born order
calculations for the experimental conditions accessible to JLab experiments and
show the interesting sensitivity of our results to various models of
generalized parton distributions (GPDs). The NLO corrections are rather large
and negative but do not prevent the studied reaction from being a promising
tool for the extraction of C-dd GPDs, which do not contribute to either
spacelike or timelike deeply virtual Compton scattering amplitudes.


title: High Density, Localized Quantum Emitters in Strained 2D Semiconductors

abstract:
  Two-dimensional chalcogenide semiconductors have recently emerged as a host
material for quantum emitters of single photons. While several reports on
defect and strain-induced single photon emission from 2D chalcogenides exist, a
bottom-up, lithography-free approach to producing a high density of emitters
remains elusive. Further, the physical properties of quantum emission in the
case of strained 2D semiconductors are far from being understood. Here, we
demonstrate a bottom-up, scalable, and lithography-free approach to creating
large areas of localized emitters with high density (~150 emitters/um2) in a
WSe2 monolayer. We induce strain inside the WSe2 monolayer with high spatial
density by conformally placing the WSe2 monolayer over a uniform array of Pt
nanoparticles with a size of 10 nm. Cryogenic, time-resolved, and gate-tunable
luminescence measurements combined with near-field luminescence spectroscopy
suggest the formation of localized states in strained regions that emit single
photons with a high spatial density. Our approach of using a metal nanoparticle
array to generate a high density of strained quantum emitters opens a new path
towards scalable, tunable, and versatile quantum light sources.

categories: cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.other physics.app-ph physics.optics


title: All-optical valley switch and clock of electronic dephasing

abstract:
  2D materials with broken inversion symmetry posses an extra degree of
freedom, the valley pseudospin, that labels in which of the two
energy-degenerate crystal momenta, $K$ or $K'$, the conducting carriers are
located. It has been shown that shining circularly-polarized light allows to
achieve close to 100% of valley polarization, opening the way to valley-based
transistors. Yet, switching of the valley polarization is still a key challenge
for the practical implementation of such devices due to the short coherence
lifetimes. Recent progress in ultrashort laser technology now allows to produce
trains of attosecond pulses with controlled phase and polarization between the
pulses. Taking advantage of such technology, we introduce a coherent control
protocol to turn on, off and switch the valley polarization at faster
timescales than electronic and valley decoherence, that is, an ultrafast
optical valley switch. We theoretically demonstrate the protocol for hBN and
MoS$_2$ monolayers calculated from first principles. Additionally, using two
time-delayed linearly-polarized pulses with perpendicular polarization, we show
that we can extract the electronic dephasing time $T_2$ from the valley Hall
conductivity.


title:
New Directions in Quantum Music: concepts for a quantum keyboard and the
  sound of the Ising model

abstract:
  We explore ideas for generating sounds and eventually music by using quantum
devices in the NISQ era using quantum circuits. In particular, we first
consider a concept for a "qeyboard", i.e. a quantum keyboard, where the
real-time behaviour of expectation values using a time evolving quantum circuit
can be associated to sound features like intensity, frequency and tone.
  Then, we examine how these properties can be extracted from physical quantum
systems, taking the Ising model as an example. This can be realized by
measuring physical quantities of the quantum states of the system, e.g. the
energies and the magnetization obtained via variational quantum simulation
techniques.


title: Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

abstract:
  Large, pre-trained neural networks consisting of self-attention layers
(transformers) have recently achieved state-of-the-art results on several
speech emotion recognition (SER) datasets. These models are typically
pre-trained in self-supervised manner with the goal to improve automatic speech
recognition performance -- and thus, to understand linguistic information. In
this work, we investigate the extent in which this information is exploited
during SER fine-tuning. Using a reproducible methodology based on open-source
tools, we synthesise prosodically neutral speech utterances while varying the
sentiment of the text. Valence predictions of the transformer model are very
reactive to positive and negative sentiment content, as well as negations, but
not to intensifiers or reducers, while none of those linguistic features impact
arousal or dominance. These findings show that transformers can successfully
leverage linguistic information to improve their valence predictions, and that
linguistic analysis should be included in their testing.


title: CTAB-GAN+: Enhancing Tabular Data Synthesis

abstract:
  While data sharing is crucial for knowledge development, privacy concerns and
strict regulation (e.g., European General Data Protection Regulation (GDPR))
limit its full effectiveness. Synthetic tabular data emerges as alternative to
enable data sharing while fulfilling regulatory and privacy constraints.
State-of-the-art tabular data synthesizers draw methodologies from Generative
Adversarial Networks (GAN). As GANs improve the synthesized data increasingly
resemble the real data risking to leak privacy. Differential privacy (DP)
provides theoretical guarantees on privacy loss but degrades data utility.
Striking the best trade-off remains yet a challenging research question. We
propose CTAB-GAN+ a novel conditional tabular GAN. CTAB-GAN+ improves upon
state-of-the-art by (i) adding downstream losses to conditional GANs for higher
utility synthetic data in both classification and regression domains; (ii)
using Wasserstein loss with gradient penalty for better training convergence;
(iii) introducing novel encoders targeting mixed continuous-categorical
variables and variables with unbalanced or skewed data; and (iv) training with
DP stochastic gradient descent to impose strict privacy guarantees. We
extensively evaluate CTAB-GAN+ on data similarity and analysis utility against
state-of-the-art tabular GANs. The results show that CTAB-GAN+ synthesizes
privacy-preserving data with at least 48.16% higher utility across multiple
datasets and learning tasks under different privacy budgets.


title:
The innovative $^{52g}$Mn for PET imaging: production cross section
  modeling and dosimetric evaluation

abstract:
  Background: Recently, the reaction $^{nat}$V($\alpha$,x)$^{52g}$Mn has been
proposed as a possible alternative to the standard $^{nat}$Cr($p$,x)$^{52g}$Mn
one, but improvements in the modeling were needed to better compare the two
production routes.
  Purpose: This work focuses on the development of precise simulations and
models to compare the $^{52g}$Mn production from both reactions in terms of
amount of activity and radionuclidic purity (RNP), as well as in terms of dose
increase (DI) due to the co-produced radioactive contaminants, with respect to
a pure $^{52g}$MnCl$_2$ injection.
  Methods: The nuclear code Talys has been employed to optimize the
$^{nat}$V($\alpha$,x)$^{52g}$Mn cross section by tuning the parameters of the
microscopic level densities. Dosimetric assessments of [$^{xx}$Mn]Cl$_2$ have
been accomplished with OLINDA software 2.2.0 using female and male phantoms. At
the end, the yield of $^{xx}$Mn radioisotopes estimated for the two production
routes have been combined with the dosimetric results, to assess the DI at
different times after the end of the irradiation.
  Results: Good agreement was obtained between cross sections calculations and
measurements. The comparison of the two reaction channels suggests that
$^{nat}$V($\alpha$,x)$^{52g}$Mn leads to higher yield and higher purity,
resulting in a less harmful impact on patients' health in terms of DI.
  Conclusions: Both $^{nat}$V($\alpha$,x) and $^{nat}$Cr($p$,x) production
routes provide clinically acceptable $^{52g}$MnCl$_2$ for PET imaging. However,
the $^{nat}$V($\alpha$,x)$^{52g}$Mn reaction provides a DI systematically lower
than the one obtainable with $^{nat}$Cr($p$,x)$^{52g}$Mn and a longer time
window in which it can be used clinically (RNP $\ge$ 99\%).


title:
Regulating MDA-MB-231 breast cancer cell adhesion on laser-patterned
  surfaces with micro- and nanotopography

abstract:
  Breast cancer is the most common type of cancer observed in women.
Communication with the tumor microenvironment allows invading breast cancer
cells, such as triple negative breast cancer cells, to adapt to specific
substrates. The substrate topography modulates the cellular behavior, among
other factors. A number of different materials and micro/nanofabrication
techniques have been employed to develop substrates for cell culturing.
Silicon-based substrates present a lot of advantages as they are amenable to a
wide range of processing techniques and they permit rigorous control over the
surface structure. We investigate and compare the response of triple negative
breast cancer cells (MDA-MB-231) on laser-patterned silicon substrates with two
different topographical scales, i.e., the micro- and the nanoscale, in the
absence of any other biochemical modification. We develop silicon surfaces with
distinct morphological characteristics by employing two laser systems with
different pulse durations (nanosecond and femtosecond) and different processing
environments (vacuum, SF6 gas, and water). Our findings demonstrate that
surfaces with micro-topography are repellent, while surfaces with
nano-topography are attractive for MDA-MB-231 cell adherence.


title: Tunable hole spin-photon interaction based on g-matrix modulation

abstract:
  We consider a spin circuit-QED device where a superconducting microwave
resonator is capacitively coupled to a single hole confined in a semiconductor
quantum dot. Thanks to the strong spin-orbit coupling intrinsic to valence-band
states, the gyromagnetic g-matrix of the hole can be modulated electrically.
This modulation couples the photons in the resonator to the hole spin. We show
that the applied gate voltages and the magnetic-field orientation enable a
versatile control of the spin-photon interaction, whose character can be
switched from fully transverse to fully longitudinal. The longitudinal coupling
is actually maximal when the transverse one vanishes and vice-versa. This
"reciprocal sweetness" results from geometrical properties of the g-matrix and
protects the spin against dephasing or relaxation. We estimate coupling rates
reaching ~ 10 MHz in realistic settings and discuss potential circuit-QED
applications harnessing either the transverse or the longitudinal spin-photon
interaction. Furthermore, we demonstrate that the g-matrix curvature can be
used to achieve parametric longitudinal coupling with enhanced coherence.


title:
Should alterations in water viscosity be addressed in soil carbon
  models?

abstract:
  Despite all the efforts, there is no agreement on how temperature affects
soil carbon decay and consequently soil CO2 emission, due to overlapping of
environmental constraints. To gain further insight into the driving forces of
soil microbial processes, we herein examine the abiotic physical environment
and its potential influence on microbial activity. In this work we discuss a
mechanism which is related to temperature sensitivity of soil carbon stability
following a first-order kinetic theory. Soil carbon decomposition is linked to
diffusion and consequently to water viscosity, splitting the effects of
temperature from viscosity, here we suggest that viscosity could be a
controlling factor on bacterial mobility and nutrient diffusion. As a result,
viscosity effect on the potential soil carbon losses is demonstrated and could
be an important influence in the feedbacks of climate change on soil carbon
cycling kinetics.


title:
A Semismooth Newton Stochastic Proximal Point Algorithm with Variance
  Reduction

abstract:
  We develop an implementable stochastic proximal point (SPP) method for a
class of weakly convex, composite optimization problems. The proposed
stochastic proximal point algorithm incorporates a variance reduction mechanism
and the resulting SPP updates are solved using an inexact semismooth Newton
framework. We establish detailed convergence results that take the inexactness
of the SPP steps into account and that are in accordance with existing
convergence guarantees of (proximal) stochastic variance-reduced gradient
methods. Numerical experiments show that the proposed algorithm competes
favorably with other state-of-the-art methods and achieves higher robustness
with respect to the step size selection.


title:
Tuning of Carrier Concentration and Superconductivity in
  High-Entropy-Alloy-Type Metal Telluride (AgSnPbBi)(1-x)/4InxTe

abstract:
  High-entropy-alloy-type (HEA-type) compound superconductors have been drawing
much attention as a new class of exotic superconductors with local structural
inhomogeneity. NaCl-type (Ag,In,Sn,Pb,Bi)Te is a typical HEA-type
superconductor, but the carrier doping mechanism had been unclear. In this
study, we synthesized (Ag,In,Sn,Pb,Bi)Te with various In concentration using
high-pressure synthesis: the studied system is (AgSnPbBi)(1-x)/4InxTe (x =
0-0.4). Single-phase samples were obtained for x = 0-0.3. A semiconductor-like
temperature dependence of resistivity was observed for x = 0, while
superconductivity appeared for the In-doped samples. The highest transition
temperature (Tc) was 3.0 K for x = 0.3. The Seebeck coefficient decreases with
increase of x, which suggests that In3+ generates electron carriers in
(AgSnPbBi)(1-x)/4InxTe. Tuning of carrier concentration and superconducting
properties of (Ag,In,Sn,Pb,Bi)Te would be useful for further investigation of
exotic superconductivity in the HEA-type compound.


title: Structured Pruning Learns Compact and Accurate Models

abstract:
  The growing size of neural language models has led to increased attention in
model compression. The two predominant approaches are pruning, which gradually
removes weights from a pre-trained model, and distillation, which trains a
smaller compact model to match a larger one. Pruning methods can significantly
reduce the model size but hardly achieve large speedups as distillation.
However, distillation methods require large amounts of unlabeled data and are
expensive to train. In this work, we propose a task-specific structured pruning
method CoFi (Coarse- and Fine-grained Pruning), which delivers highly
parallelizable subnetworks and matches the distillation methods in both
accuracy and latency, without resorting to any unlabeled data. Our key insight
is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads
and hidden units) modules, which controls the pruning decision of each
parameter with masks of different granularity. We also devise a layerwise
distillation strategy to transfer knowledge from unpruned to pruned models
during optimization. Our experiments on GLUE and SQuAD datasets show that CoFi
yields models with over 10x speedups with a small accuracy drop, showing its
effectiveness and efficiency compared to previous pruning and distillation
approaches.


title:
Light-cone Sum Rule Analysis of Semileptonic Decays $\Lambda_b^0 \to
  \Lambda_c^+ \ell^- \overline{\nu}_\ell$

abstract:
  In this work, we analyze the semileptonic decay processes of $\Lambda_b \to
\Lambda_c$ in the light-cone sum rule approach. In the calculation of the form
factors of $\Lambda_b$ baryon transition matrix element, the light-cone
distribution amplitudes of $\Lambda_b$ which are obtained from QCD sum rule in
the heavy quark effective field theory framework are used. With the calculation
of the six form factors of $\Lambda_b \to \Lambda_c$ transition matrix element,
the differential decay widths of $\Lambda_b^0 \to \Lambda_c^+ \ell^-
\overline{\nu}_\ell (\ell = e, \mu, \tau)$ and absolute branching fractions are
obtained. Additionally the ratio of $R(\Lambda_c)=\mathcal{B}r(\Lambda_b\to
\Lambda_c e \nu_e)/\mathcal{B}r(\Lambda_b \to \Lambda_c \tau \nu_\tau)$ is
obtained. These results are in accordance with the newest experimental result
and other theoretical calculations and predictions.


title:
The Kauffman bracket skein module of the lens spaces via unoriented
  braids

abstract:
  In this paper we develop a braid theoretic approach for computing the
Kauffman bracket skein module of the lens spaces $L(p,q)$, KBSM($L(p,q)$), for
$q\neq 0$. For doing this, we introduce a new concept, that of an {\it
unoriented braid}. Unoriented braids are obtained from standard braids by
ignoring the natural top-to-bottom orientation of the strands. We first define
the {\it generalized Temperley-Lieb algebra of type B}, $TL_{1, n}$, which is
related to the knot theory of the solid torus ST, and we obtain the universal
Kauffman bracket type invariant, $V$, for knots and links in ST, via a unique
Markov trace constructed on $TL_{1, n}$. The universal invariant $V$ is
equivalent to the KBSM(ST). For passing now to the KBSM($L(p,q)$), we impose on
$V$ relations coming from the band moves (or slide moves), that is, moves that
reflect isotopy in $L(p,q)$ but not in ST, and which reflect the surgery
description of $L(p,q)$, obtaining thus, an infinite system of equations. By
construction, solving this infinite system of equations is equivalent to
computing KBSM($L(p,q)$). We first present the solution for the case $q=1$,
which corresponds to obtaining a new basis, $\mathcal{B}_{p}$, for
KBSM($L(p,1)$) with $(\lfloor p/2 \rfloor +1)$ elements. We note that the basis
$\mathcal{B}_{p}$ is different from the one obtained by Hoste \& Przytycki. For
dealing with the complexity of the infinite system for the case $q>1$, we first
show how the new basis $\mathcal{B}_{p}$ of KBSM($L(p,1)$) can be obtained
using a diagrammatic approach based on unoriented braids, and we finally extend
our result to the case $q>1$.


title: Synthetic Photovoltaic and Wind Power Forecasting Data

abstract:
  Photovoltaic and wind power forecasts in power systems with a high share of
renewable energy are essential in several applications. These include stable
grid operation, profitable power trading, and forward-looking system planning.
However, there is a lack of publicly available datasets for research on machine
learning based prediction methods. This paper provides an openly accessible
time series dataset with realistic synthetic power data. Other publicly and
non-publicly available datasets often lack precise geographic coordinates,
timestamps, or static power plant information, e.g., to protect business
secrets. On the opposite, this dataset provides these. The dataset comprises
120 photovoltaic and 273 wind power plants with distinct sides all over Germany
from 500 days in hourly resolution. This large number of available sides allows
forecasting experiments to include spatial correlations and run experiments in
transfer and multi-task learning. It includes side-specific, power
source-dependent, non-synthetic input features from the ICON-EU weather model.
A simulation of virtual power plants with physical models and actual
meteorological measurements provides realistic synthetic power measurement time
series. These time series correspond to the power output of virtual power
plants at the location of the respective weather measurements. Since the
synthetic time series are based exclusively on weather measurements, possible
errors in the weather forecast are comparable to those in actual power data. In
addition to the data description, we evaluate the quality of
weather-prediction-based power forecasts by comparing simplified physical
models and a machine learning model. This experiment shows that forecasts
errors on the synthetic power data are comparable to real-world historical
power measurements.


title: Multiplicative complements I

abstract:
  In this paper, we study how dense a multiplicative basis of order $h$ for
$\mathbb{Z}^+\!$ can be, improving on earlier results. Upon introducing the
notion of a \textit{multiplicative complement}, we present some tight density
bounds.


title:
Oil reservoir recovery factor assessment using Bayesian networks based
  on advanced approaches to analogues clustering

abstract:
  The work focuses on the modelling and imputation of oil and gas reservoirs
parameters, specifically, the problem of predicting the oil recovery factor
(RF) using Bayesian networks (BNs). Recovery forecasting is critical for the
oil and gas industry as it directly affects a company's profit. However,
current approaches to forecasting the RF are complex and computationally
expensive. In addition, they require vast amount of data and are difficult to
constrain in the early stages of reservoir development. To address this
problem, we propose a BN approach and describe ways to improve parameter
predictions' accuracy. Various training hyperparameters for BNs were
considered, and the best ones were used. The approaches of structure and
parameter learning, data discretization and normalization, subsampling on
analogues of the target reservoir, clustering of networks and data filtering
were considered. Finally, a physical model of a synthetic oil reservoir was
used to validate BNs' predictions of the RF. All approaches to modelling based
on BNs provide full coverage of the confidence interval for the RF predicted by
the physical model, but at the same time require less time and data for
modelling, which demonstrates the possibility of using in the early stages of
reservoirs development. The main result of the work can be considered the
development of a methodology for studying the parameters of reservoirs based on
Bayesian networks built on small amounts of data and with minimal involvement
of expert knowledge. The methodology was tested on the example of the problem
of the recovery factor imputation.


title: Passive Laser Power Stabilization via an Optical Spring

abstract:
  Metrology experiments can be limited by the noise produced by the laser
involved via small fluctuations in the laser's power or frequency. Typically,
active power stabilization schemes consisting of an in-loop sensor and a
feedback control loop are employed. Those schemes are fundamentally limited by
shot noise coupling at the in-loop sensor. In this letter we propose to use the
optical spring effect to passively stabilize the classical power fluctuations
of a laser beam. In a proof of principle experiment, we show that the relative
power noise of the laser is stabilized from approximately $2 \times 10^{-5}$
Hz$^{-1/2}$ to a minimum value of $1.6 \times 10^{-7}$ Hz$^{-1/2}$,
corresponding to the power noise reduction by a factor of $125$. The bandwidth
at which stabilization occurs ranges from $400$ Hz to $100$ kHz. The work
reported in this letter further paves the way for high power laser stability
techniques which could be implemented in optomechanical experiments and in
gravitational wave detectors.


title: Gate lattices

abstract:
  A reversible gate on a subshift on a residually finite group $G$ can be
applied on any sparse enough finite-index subgroup $H$, to obtain what we call
a gate lattice. Gate lattices are automorphisms of the shift action of $H$,
thus generate a subgroup of the Hartman-Kra-Schmieding stabilized automorphism
group. We show that for subshifts of finite type with a gluing property we call
the eventual filling property, the subgroup generated by even gate lattices is
simple. Under some conditions, even gate lattices generate all gate lattices,
and in the case of a one-dimensional mixing SFT, they generate the inert part
of the stabilized automorphism group, thus we obtain that this group is simple.
In the case of a full shift this has been previously shown by Hartman, Kra and
Schmieding.


title: Thermodynamics and the Origin of Life

abstract:
  Modern developments in nonequilibrium thermodynamics have significant
implications for the origins of life. The reasons for this are closely related
to a generalized version of the second law of thermodynamics recently found for
entropy production during irreversible evolution of a given system such as
self-replicating RNA. This paper is intended to serve as an introduction to
these developments.


title: Evolutionary quantization of matter and Universe expansion

abstract:
  In this paper we consider generalization of classical and quantum mechanics
that directly follows from the causality principle and topology of a system
state space. In generalized mechanics, the Hamiltonian/Schrodinger equations
remain the same, but the Hamiltonian may depend on the action and its
canonically conjugated variables as additional dynamical variables. This
extension of quantum mechanics indicates that the quantization of matter could
be an evolutionary process, and in the distant future, even massive bodies may
become entirely quantum objects without well-defined trajectories and shapes.
In the classical limit, the first approximation of the Hamiltonian with respect
to the action explains the accelerated expansion of the Universe, Hubble law,
formation of spiral galaxies with a non-Kepler curve of rotation velocity, and
asymmetry between distributions of matter and antimatter. This theory predicts
that our open universe could have extended pre-history and be preceded by a
long set of closed precursor universes.


title:
Cauchy's Logico-Linguistic Slip, the Heisenberg Uncertainty Principle
  and a Semantic Dilemma Concerning "Quantum Gravity"

abstract:
  The importance of language in physics has gained emphasis in recent times, on
the one hand through Hilbert's views that concern formalism and intuition
applied for outer inquiry, and on the other hand through Brouwer's point of
view that concerns intuition applied for inner inquiry or, as I call,
self-inquiry. It is to demonstrate the essence of such investigations,
especially self-inquiry (inward intuition), I find it compelling to report that
a careful analysis of Cauchy's statements for the definition of derivative, as
applied in physics, unveils the connection to the Heisenberg uncertainty
principle as a condition for the failure of classical mechanics. Such
logico-linguistic, or semantically driven, self-inquiry of physics can provide
new insights to physicists in the pursuit of truth and reality, for example, in
the context of Schroedinger equation. I point out an explicit dilemma that
plagues the semantics of physics, as far as general relativity and quantum
mechanics are concerned, which needs to be taken into account during any
attempt to pen down a theory of "quantum gravity".


title:
Artificial Intelligence and work: a critical review of recent research
  from the social sciences

abstract:
  This review seeks to present a comprehensive picture of recent discussions in
the social sciences of the anticipated impact of AI on the world of work.
Issues covered include technological unemployment, algorithmic management,
platform work an the politics of AI work. The review identifies the major
disciplinary and methodological perspectives on AI's impact on work, and the
obstacles they face in making predictions. Two parameters influencing the
development and deployment of AI in the economy are highlighted, the capitalist
imperative and nationalistic pressures.


title:
Biot-Savart type magnetic field quantization via prime number theory,
  applications to symbolic dynamics

abstract:
  In the present work we propose an algorithm based on the theory of prime
numbers for the estimation of the magnetic field in a device of current
carrying circular rings. Using the proposed algorithm, the magnetic field can
be determined in a very good agreement with that resulting from an algorithm
based on the Biot-Savart law. In addition, the prime-numbers-based algorithm
gives quantized values of the magnetic field and reveals previously unknown
behaviors such as special properties of the distribution of the waiting times
at the quantized magnetic field values. Applications of the proposed
prime-numbers-based algorithm to systems exhibiting symbolic dynamics is
presented, proving its ability to provide a measure of the existence or not of
dynamics in the system.


title: Missed ferroelectricity in methylammonium lead iodide

abstract:
  Methylammonium lead iodide, as related organometal halide perovskites,
emerged recently as a particularly attractive material for photovoltaic
applications. The origin of its appealing properties is sometimes assigned to
its potential ferroelectric character, which remains however a topic of intense
debate. Here, we rationalize from first-principles calculations how the spatial
arrangement of methylammonium polar molecules is progressively constrained by
the subtle interplay between their tendency to bond with the inorganic
framework and the appearance of iodine octahedra rotations inherent to the
perovskite structure. The disordered tetragonal phase observed at room
temperature is paraelectric. We show that it should a priori become
ferroelectric but that iodine octahedra rotations drive the system toward an
antipolar orthorhombic ground state, making it a missed ferroelectric.


title:
Westerlund 1 under the light of Gaia EDR3: Distance, isolation, extent,
  and a hidden population

abstract:
  (Abridged) We have used Gaia EDR3 data, together with spectra of a large
sample of luminous stars in the field surrounding Westerlund 1, to explore the
extent of the cluster. We carry out a non-parametric analysis of proper motions
and membership determination. We investigate the reddening and proper motions
of several dozen OB stars and red supergiants < 1 deg away from Wd 1. We
identify a population of kinematic members of Wd 1 that largely includes the
known spectroscopic members. From their EDR3 parallaxes, we derive a distance
to the cluster of $4.23^{+0.23}_{-0.21}$kpc. Extinction in this direction
increases by a large amount around 2.8 kpc, due to dark clouds associated to
the Scutum-Crux arm. As a consequence, we hardly see any stars at distances
comparable to that of the cluster. The proper motions of Wd 1, however, are
very similar to those of stars in the field surrounding it, but distinct. We
find a second, astrometrically well-defined population in the foreground
($d\approx2\:$kpc), likely connected to the possible open cluster BH197. Wd 1
is very elongated, an effect not driven by the very heavy extinction to the
East and South. We find a low-density halo extending up to 10' from the cluster
centre, mainly in the NW quadrant. A few OB stars at larger distances from the
cluster, most notably the LBV MN48, share its proper motions, suggesting that
Wd 1 has little or no peculiar motion with respect to the field population of
the Norma arm. However, we are unable to find any red supergiant that could
belong to an extended population related to the cluster, although we observe
several dozen such objects in the foreground. We find a substantial population
of luminous OB members obscured by several more magnitudes of extinction than
most known members. These objects, mostly located in the central region of the
cluster, increase the population of OB supergiants by about 25%.


title: Transformers for 1D Signals in Parkinson's Disease Detection from Gait

abstract:
  This paper focuses on the detection of Parkinson's disease based on the
analysis of a patient's gait. The growing popularity and success of Transformer
networks in natural language processing and image recognition motivated us to
develop a novel method for this problem based on an automatic features
extraction via Transformers. The use of Transformers in 1D signal is not really
widespread yet, but we show in this paper that they are effective in extracting
relevant features from 1D signals. As Transformers require a lot of memory, we
decoupled temporal and spatial information to make the model smaller. Our
architecture used temporal Transformers, dimension reduction layers to reduce
the dimension of the data, a spatial Transformer, two fully connected layers
and an output layer for the final prediction. Our model outperforms the current
state-of-the-art algorithm with 95.2\% accuracy in distinguishing a
Parkinsonian patient from a healthy one on the Physionet dataset. A key
learning from this work is that Transformers allow for greater stability in
results. The source code and pre-trained models are released in
https://github.com/DucMinhDimitriNguyen/Transformers-for-1D-signals-in-Parkinson-s-disease-detection-from-gait.git


title:
Comparison of convolutional neural networks for cloudy optical images
  reconstruction from single or multitemporal joint SAR and optical images

abstract:
  With the increasing availability of optical and synthetic aperture radar
(SAR) images thanks to the Sentinel constellation, and the explosion of deep
learning, new methods have emerged in recent years to tackle the reconstruction
of optical images that are impacted by clouds. In this paper, we focus on the
evaluation of convolutional neural networks that use jointly SAR and optical
images to retrieve the missing contents in one single polluted optical image.
We propose a simple framework that ease the creation of datasets for the
training of deep nets targeting optical image reconstruction, and for the
validation of machine learning based or deterministic approaches. These methods
are quite different in terms of input images constraints, and comparing them is
a problematic task not addressed in the literature. We show how space
partitioning data structures help to query samples in terms of cloud coverage,
relative acquisition date, pixel validity and relative proximity between SAR
and optical images. We generate several datasets to compare the reconstructed
images from networks that use a single pair of SAR and optical image, versus
networks that use multiple pairs, and a traditional deterministic approach
performing interpolation in temporal domain.


title: The complete metric study of effective Dirac algebra

abstract:
  Following our work from the previous paper about the study of effective Dirac
algebra and the metric of the simple, special case of relativistic hydrogen
atom, this paper gives the complete metric study defined by the effective Dirac
algebra in the Dirac and Weyl presentation, showing that relativistic
electromagnetic interaction gives the correction of the flat background metric
$\eta_{\mu\nu}$, thus curving spacetime. The curved metric can be nicely broken
down into two parts, the pure correction on the flat spacetime metric and the
projection tensor. We find that the curved metric is independent of the
representation chosen.


title:
A Fast and Efficient Conditional Learning for Tunable Trade-Off between
  Accuracy and Robustness

abstract:
  Existing models that achieve state-of-the-art (SOTA) performance on both
clean and adversarially-perturbed images rely on convolution operations
conditioned with feature-wise linear modulation (FiLM) layers. These layers
require many new parameters and are hyperparameter sensitive. They
significantly increase training time, memory cost, and potential latency which
can prove costly for resource-limited or real-time applications. In this paper,
we present a fast learnable once-for-all adversarial training (FLOAT)
algorithm, which instead of the existing FiLM-based conditioning, presents a
unique weight conditioned learning that requires no additional layer, thereby
incurring no significant increase in parameter count, training time, or network
latency compared to standard adversarial training. In particular, we add
configurable scaled noise to the weight tensors that enables a trade-off
between clean and adversarial performance. Extensive experiments show that
FLOAT can yield SOTA performance improving both clean and perturbed image
classification by up to ~6% and ~10%, respectively. Moreover, real hardware
measurement shows that FLOAT can reduce the training time by up to 1.43x with
fewer model parameters of up to 1.47x on iso-hyperparameter settings compared
to the FiLM-based alternatives. Additionally, to further improve memory
efficiency we introduce FLOAT sparse (FLOATS), a form of non-iterative model
pruning and provide detailed empirical analysis to provide a three way
accuracy-robustness-complexity trade-off for these new class of pruned
conditionally trained models.


title:
General relativity effects in precision spin experimental tests of
  fundamental symmetries

abstract:
  A search for the $P$- and $CP(T)$-violating electric dipole moments (EDM) of
atoms, particles and nuclei with sensitivity up to $10^{-15}$ in units of
magnetic dipole moments, allowed by all discrete symmetries, is one of the
topical problems of modern physics. According to Sakharov, $CP$-violation is
one of the three key criteria of baryogenesis in generally accepted paradigm of
the Big Bang cosmology. All three criteria are supported by the Standard Model
(SM), but it fails to describe the observed baryon asymmetry of the Universe.
This is regarded a strong argument in favor of existence of $CP$-symmetry
breaking mechanisms beyond minimal SM, which can lead to measurable EDMs of
atoms, particles and nuclei. Direct searches for EDM of charged particles and
nuclei are possible only in storage rings (COSY, NICA). After successful
studies by the JEDI collaboration at the COSY synchrotron, at the forefront is
a search for the proton EDM in an electrostatic storage ring with the proton
spin frozen at the magic energy with projected sensitivity $d_p\sim
10^{-29}\,e\cdot$cm. Following a brief introduction to the $CP$-violation
physics and the baryogenesis, the review presents a detailed discussion of
significant contributions to the spin dynamics from the terrestrial gravity
along with the new effects of Earth's rotation in ultrasensitive searches for
the EDM of charged particles and neutrons. Quite remarkably, for the projected
sensitivity to the proton EDM, these false EDM effects can by one to two orders
of magnitude exceed the signal of the proton EDM, and become comparable to an
EDM contribution in experiments with ultracold neutrons. We also discuss the
role of a precessing spin as a detector of the axion-like dark matter, and
consider applications of the quantum gravitational anomalies to the dense
matter hydrodynamics and the spin phenomena in the non-central nuclear
collisions.

categories: hep-th gr-qc hep-ph nucl-th physics.acc-ph


title: Generalized Harish-Chandra theory for Dade's Conjecture

abstract:
  We prove new results in generalized Harish-Chandra theory providing a
description of the so-called Brauer--Lusztig blocks in terms of the information
encoded in the $\ell$-adic cohomology of Deligne--Lusztig varieties.
Furthermore, we apply these results to obtain new progress towards the
verification of the inductive condition for Dade's Conjecture in the case of
groups of Lie type in non-defining characteristic.


title: Sense disambiguation of compound constituents

abstract:
  In distributional semantic accounts of the meaning of noun-noun compounds
(e.g. starfish, bank account, houseboat) the important role of constituent
polysemy remains largely unaddressed(cf. the meaning of star in starfish vs.
star cluster vs. star athlete). Instead of semantic vectors that average over
the different meanings of a constituent, disambiguated vectors of the
constituents would be needed in order to see what these more specific
constituent meanings contribute to the meaning of the compound as a whole. This
paper presents a novel approach to this specific problem of word sense
disambiguation: set expansion. We build on the approach developed by Mahabal et
al. (2018) which was originally designed to solve the analogy problem. We
modified their method in such a way that it can address the problem of sense
disambiguation of compound constituents. The results of experiments with a data
set of almost 9000 compounds (LADEC, Gagn\'e et al. 2019) suggest that this
approach is successful, yet the success is sensitive to the frequency with
which the compounds are attested.


title:
Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in
  Reactor Downcomer Geometry

abstract:
  Buoyancy effect on low-flow condition convective heat transfer of
non-conventional coolants, such as liquid metal and molten salts, is a crucial
safety factor to advanced reactors under transient or accidental scenarios. The
distinct heat transfer characteristics of non-unitary Prandtl fluids and the
inherent complexity of the low-flow mixed convection phenomena requires the
development of novel turbulent and heat transfer models that are adaptive to
different spatiotemporal scales involved in the mixed convection heat transfer.
In this work, direct numerical simulation of low-flow mixed convection is
carried out at low-to-unitary Prandtl numbers that are of industrial interest.
Time-averaged statistics, turbulent Prandtl number, as well as time signals are
analyzed to investigate mixed convection phenomenon. From the time-averaged
statistics, buoyant plume altered velocity boundary layer as well as the
intensity of the fluctuation near both walls and channel centerline. Buoyancy
effect also rendered different degree of convective heat transfer enhancement
and impairment depends on Prandtl and Richardson number. Analysis of time
series was conducted on the sodium mixed convection case to emphasize on the
low-Pr mixed convection behavior at transition region. Resulting power spectra
density and wavelet spectrogram suggests possible large convective structure in
transition region. Future work will focus on providing broader data coverage on
Pr-Re-Ri parameter space to facilitate more comprehensive analysis of mixed
convection.


title:
Physics-guided neural networks for feedforward control: From consistent
  identification to feedforward controller design

abstract:
  Model-based feedforward control improves tracking performance of motion
systems, provided that the model describing the inverse dynamics is of
sufficient accuracy. Model sets, such as neural networks (NNs) and
physics-guided neural networks (PGNNs) are typically used as flexible
parametrizations that enable accurate identification of the inverse system
dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics
directly. However, direct identification of the inverse dynamics is sensitive
to noise that is present in the training data, and thereby results in biased
parameter estimates which limit the achievable tracking performance. In order
to push performance further, it is therefore crucial to account for noise when
performing the identification. To address this problem, this paper proposes the
use of a forward system identification using (PG)NNs from noisy data.
Afterwards, two methods are proposed for inverting PGNNs to design a
feedforward controller for high-precision motion control. The developed
methodology is validated on a real-life industrial linear motor, where it
showed significant improvements in tracking performance with respect to the
direct inverse identification.


title: Critical points of modular forms

abstract:
  We count the number of critical points of a modular form in a
$\gamma$-translate of the standard fundamental domain $\mathcal{F}$ (with
$\gamma\in \mathrm{SL}_2(\mathbb{Z})$). Whereas by the valence formula the
(weighted) number of zeros of this modular form in $\gamma\mathcal{F}$ is a
constant only depending on its weight, we give a closed formula for this number
of critical points in terms of those zeros of the modular form lying on the
boundary of $\mathcal{F},$ the value of $\gamma^{-1}(\infty)$ and the weight.
More generally, we indicate what can be said about the number of zeros of a
quasimodular form.


title:
Inverse Design and Experimental Verification of a Bianisotropic
  Metasurface Using Optimization and Machine Learning

abstract:
  Electromagnetic metasurfaces have attracted significant interest recently due
to their low profile and advantageous applications. Practically, many
metasurface designs start with a set of constraints for the radiated far-field,
such as main-beam direction(s) and side lobe levels, and end with a non-uniform
physical structure for the surface. This problem is quite challenging, since
the required tangential field transformations are not completely known when
only constraints are placed on the scattered fields. Hence, the required
surface properties cannot be solved for analytically. Moreover, the translation
of the desired surface properties to the physical unit cells can be
time-consuming and difficult, as it is often a one-to-many mapping in a large
solution space. Here, we divide the inverse design process into two steps: a
macroscopic and microscopic design step. In the former, we use an iterative
optimization process to find the surface properties that radiate a far-field
pattern that complies with specified constraints. This iterative process
exploits non-radiating currents to ensure a passive and lossless design. In the
microscopic step, these optimized surface properties are realized with physical
unit cells using machine learning surrogate models. The effectiveness of this
end-to-end synthesis process is demonstrated through measurement results of a
beam-splitting prototype.

categories: cond-mat.mtrl-sci cs.LG eess.SP physics.app-ph physics.optics


title:
Towards a machine-readable literature: finding relevant papers based on
  an uploaded powder diffraction pattern

abstract:
  We investigate a prototype application for machine-readable literature. The
program is called "pyDataRecognition" and serves as an example of a data-driven
literature search, where the literature search query is an experimental
data-set provided by the user. The user uploads a powder pattern together with
the radiation wavelength. The program compares the user data to a database of
existing powder patterns associated with published papers and produces a rank
ordered according to their similarity score. The program returns the digital
object identifier (doi) and full reference of top ranked papers together with a
stack plot of the user data alongside the top five database entries. The paper
describes the approach and explores successes and challenges.


title: Sequent calculi of finite dimension

abstract:
  In recent work, the authors introduced the notion of n-dimensional Boolean
algebra and the corresponding propositional logic nCL. In this paper, we
introduce a sequent calculus for nCL and we show its soundness and
completeness.


title: AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios

abstract:
  Adaptive text to speech (TTS) can synthesize new voices in zero-shot
scenarios efficiently, by using a well-trained source TTS model without
adapting it on the speech data of new speakers. Considering seen and unseen
speakers have diverse characteristics, zero-shot adaptive TTS requires strong
generalization ability on speaker characteristics, which brings modeling
challenges. In this paper, we develop AdaSpeech 4, a zero-shot adaptive TTS
system for high-quality speech synthesis. We model the speaker characteristics
systematically to improve the generalization on new speakers. Generally, the
modeling of speaker characteristics can be categorized into three steps:
extracting speaker representation, taking this speaker representation as
condition, and synthesizing speech/mel-spectrogram given this speaker
representation. Accordingly, we improve the modeling in three steps: 1) To
extract speaker representation with better generalization, we factorize the
speaker characteristics into basis vectors and extract speaker representation
by weighted combining of these basis vectors through attention. 2) We leverage
conditional layer normalization to integrate the extracted speaker
representation to TTS model. 3) We propose a novel supervision loss based on
the distribution of basis vectors to maintain the corresponding speaker
characteristics in generated mel-spectrograms. Without any fine-tuning,
AdaSpeech 4 achieves better voice quality and similarity than baselines in
multiple datasets.


title: Hilbert squares of degeneracy loci

abstract:
  Let $S$ be the first degeneracy locus of a morphism of vector bundles
corresponding to a general matrix of linear forms in $\mathbb{P}^s$. We prove
that, under certain positivity conditions, its Hilbert square
$\mathrm{Hilb}^2(S)$ is isomorphic to the zero locus of a global section of an
irreducible homogeneous vector bundle on a product of Grassmannians. Our
construction involves a naturally associated Fano variety, and an explicit
description of the isomorphism.


title: Spatially Indirect Exciton Condensation in Superconductor Bilayers

abstract:
  We look at the possibility of spatially indirect exciton condensation in
electrically isolated superconducting bilayers. We find that in a mean-field
approximation bilayers can have superconducting and excitonic order
simultaneously if repulsive interactions between layers are sufficiently
strong. The excitonic order implies interlayer phase coherence, and can be
conveniently studied in a representation of symmetric and antisymmetric bilayer
states. When both orders are present we find several solutions of the
mean-field equations with different values of the the symmetric and
antisymmetric state pair amplitudes. The mixed state necessarily has non-zero
pair amplitudes for electrons in different layers in spite of the repulsive
interlayer interactions, and these are responsible for spatially indirect
Andreev reflection processes in which an incoming electron in one layer can be
reflected as a hole in the opposite layer. We evaluate layer diagonal and
off-diagonal current-voltage relationships that can be used to identify this
state experimentally.


title: Pauli blocking of stimulated emission in a degenerate Fermi gas

abstract:
  The Pauli exclusion principle in quantum mechanics has a profound influence
on the structure of matter and on interactions between fermions. Almost 30
years ago it was predicted that the Pauli exclusion principle could lead to a
suppression of spontaneous emission, and only recently several experiments
confirmed this phenomenon. Here we report that this so-called Pauli blockade
not only affects incoherent processes but also, more generally, coherently
driven systems. It manifests itself as an intriguing sub-Doppler narrowing of a
doubly-forbidden transition profile in an optically trapped Fermi gas of
$^3\mathrm{He}$. By actively pumping atoms out of the excited state, we break
the coherence of the excitation and lift the narrowing effect, confirming the
influence of Pauli blockade on the transition profile. This new insight into
the interplay between quantum statistics and coherent driving is a promising
development for future applications involving fermionic systems.


title:
Approach to equilibrium in translation-invariant quantum systems: some
  structural results

abstract:
  We formulate the problem of approach to equilibrium in algebraic quantum
statistical mechanics and study some of its structural aspects, focusing on the
relation between the zeroth law of thermodynamics (approach to equilibrium) and
the second law (increase of entropy). Our main result is that approach to
equilibrium is necessarily accompanied by a strict increase of the specific
(mean) entropy. In the course of our analysis, we introduce the concept of
quantum weak Gibbs state which is of independent interest.

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


title:
Hochschild homology of mod-$p$ motivic cohomology over algebraically
  closed fields

abstract:
  We perform Hochschild homology calculations in the algebro-geometric setting
of motives. The motivic Hochschild homology coefficient ring contains torsion
classes which arise from the mod-$p$ motivic Steenrod algebra and from
generating functions on the natural numbers with finite non-empty support.
Under the Betti realization, we recover B\"okstedt's calculation of the
topological Hochschild homology of finite prime fields.


title: Marginal Contrastive Correspondence for Guided Image Generation

abstract:
  Exemplar-based image translation establishes dense correspondences between a
conditional input and an exemplar (from two different domains) for leveraging
detailed exemplar styles to achieve realistic image translation. Existing work
builds the cross-domain correspondences implicitly by minimizing feature-wise
distances across the two domains. Without explicit exploitation of
domain-invariant features, this approach may not reduce the domain gap
effectively which often leads to sub-optimal correspondences and image
translation. We design a Marginal Contrastive Learning Network (MCL-Net) that
explores contrastive learning to learn domain-invariant features for realistic
exemplar-based image translation. Specifically, we design an innovative
marginal contrastive loss that guides to establish dense correspondences
explicitly. Nevertheless, building correspondence with domain-invariant
semantics alone may impair the texture patterns and lead to degraded texture
generation. We thus design a Self-Correlation Map (SCM) that incorporates scene
structures as auxiliary information which improves the built correspondences
substantially. Quantitative and qualitative experiments on multifarious image
translation tasks show that the proposed method outperforms the
state-of-the-art consistently.


title:
Differentiating small-scale subhalo distributions in CDM and WDM models
  using persistent homology

abstract:
  The spatial distribution of galaxies at sufficiently small scales will encode
information about the identity of the dark matter. We develop a novel
description of the halo distribution using persistent homology summaries, in
which collections of points are decomposed into clusters, loops and voids. We
apply these methods, together with a set of hypothesis tests, to dark matter
haloes in MW-analog environment regions of the cold dark matter (CDM) and warm
dark matter (WDM) Copernicus Complexio $N$-body cosmological simulations. The
results of the hypothesis tests find statistically significant differences
(p-values $\leq$ 0.001) between the CDM and WDM structures, and the functional
summaries of persistence diagrams detect differences at scales that are
distinct from the comparison spatial point process functional summaries
considered (including the two-point correlation function). The differences
between the models are driven most strongly at filtration scales $\sim100$~kpc,
where CDM generates larger numbers of unconnected halo clusters while WDM
instead generates loops. This study was conducted on dark matter haloes
generally; future work will involve applying the same methods to realistic
galaxy catalogues.


title:
A Gaia view on the star formation in the Monoceros OB1 and R1
  associations

abstract:
  Stellar kinematics provides the key to understanding star formation process.
In this respect, we present a kinematic study of the Monoceros OB1 (Mon OB1)
and R1 (Mon R1) associations using the recent Gaia data and radial velocities
of stars derived from high-resolution spectroscopy and the literature. A total
of 728 members are selected using the criteria based on the intrinsic
properties of young stars, parallaxes, and proper motions. The spatial
distribution and kinematic properties of members show that these associations
have distinct substructures. In Mon OB1, we find one northern group and two
southern groups. Mon R1 is composed of three small stellar groups that are
spatially and kinematically distinct. Some stars are found in a halo around
these two associations. We detect patterns of expansion for most stellar groups
in the associations. In addition, two stellar groups in Mon OB1 show the
signature of rotation, which provides an important constraint on cluster
formation. The star formation history of Mon OB1 is slightly revised. Star
formation first occurred in the southern region and subsequently in the
northern region. Recent star-forming events ignited deeper into the southern
region, while some stars are escaping from Mon OB1, forming a halo. Mon R1
might have formed at the same epoch as the formation of the northern group in
Mon OB1. Given that star formation is taking place on different scales along a
large arc-like structure, Mon OB1 and Mon R1 may be the results of hierarchical
star formation.


title:
Alternative solution to a quantum-mechanical four-particle system in one
  dimension

abstract:
  We propose an alternative solution to a quantum-mechanical four-particle
system in one dimension with two- and three-particle interactions. The solution
of the eigenvalue equation in center-of-mass and Jacobi coordinates is
considerably simpler than a procedure proposed recently based on spherical
coordinates.


title:
Modeling the signatures of interaction in Type II supernovae: UV
  emission, high-velocity features, broad-boxy profiles

abstract:
  Because mass loss is a fundamental phenomenon in massive stars, interaction
with circumstellar material (CSM) should be universal in core-collapse
supernovae (SNe). Leaving aside the extreme CSM density, extent, or mass
typically encountered in Type IIn SNe, we investigate the diverse long-term
radiative signatures of interaction between a Type II SN ejecta and CSM
corresponding to mass loss rates up to 10$^{-3}$ $M_{\odot}$ yr$^{-1}$. Because
these CSM are relatively tenuous and optically-thin to electron-scattering
beyond a few stellar radii, radiation hydrodynamics is not essential and one
may treat the interaction directly as an additional power source in the
non-local thermodynamic equilibrium radiative transfer problem. The CSM
accumulated since shock breakout forms a dense shell in the outer ejecta and
leads to high-velocity absorption features in spectral lines, even for
negligible shock power. Besides Balmer lines, such features may appear in NaID,
HeI lines etc. A stronger interaction strengthens the continuum flux
(preferentially in the UV), quenches the absorption of P-Cygni profiles, boosts
the MgII $\lambda\lambda$ $2795,2802$ doublet, and fosters the production of a
broad boxy H$\alpha$ emission component. The rise in ionization in the outer
ejecta may quench some lines (e.g., the CaII near-infrared triplet). The
interaction power emerges preferentially in the UV, in particular at later
times, shifting the optical color to the blue, but increasing modestly the
optical luminosity. Strong thermalization and clumping seem to be required to
make an interaction superluminous in the optical. The UV range contains
essential signatures that provide critical constraints to infer the mass loss
history and inner workings of core-collapse SN progenitors at death.


title:
Human Evaluation and Correlation with Automatic Metrics in Consultation
  Note Generation

abstract:
  In recent years, machine learning models have rapidly become better at
generating clinical consultation notes; yet, there is little work on how to
properly evaluate the generated consultation notes to understand the impact
they may have on both the clinician using them and the patient's clinical
safety. To address this we present an extensive human evaluation study of
consultation notes where 5 clinicians (i) listen to 57 mock consultations, (ii)
write their own notes, (iii) post-edit a number of automatically generated
notes, and (iv) extract all the errors, both quantitative and qualitative. We
then carry out a correlation study with 18 automatic quality metrics and the
human judgements. We find that a simple, character-based Levenshtein distance
metric performs on par if not better than common model-based metrics like
BertScore. All our findings and annotations are open-sourced.


title:
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech
  Recognition

abstract:
  Aphasia is a common speech and language disorder, typically caused by a brain
injury or a stroke, that affects millions of people worldwide. Detecting and
assessing Aphasia in patients is a difficult, time-consuming process, and
numerous attempts to automate it have been made, the most successful using
machine learning models trained on aphasic speech data. Like in many medical
applications, aphasic speech data is scarce and the problem is exacerbated in
so-called "low resource" languages, which are, for this task, most languages
excluding English. We attempt to leverage available data in English and achieve
zero-shot aphasia detection in low-resource languages such as Greek and French,
by using language-agnostic linguistic features. Current cross-lingual aphasia
detection approaches rely on manually extracted transcripts. We propose an
end-to-end pipeline using pre-trained Automatic Speech Recognition (ASR) models
that share cross-lingual speech representations and are fine-tuned for our
desired low-resource languages. To further boost our ASR model's performance,
we also combine it with a language model. We show that our ASR-based end-to-end
pipeline offers comparable results to previous setups using human-annotated
transcripts.


title:
An image classification approach for hole detection in wireless sensor
  networks

abstract:
  Hole detection is a crucial task for monitoring the status of wireless sensor
networks (WSN) which often consist of low-capability sensors. Holes can form in
WSNs due to the problems during placement of the sensors or power/hardware
failure. In these situations, sensing or transmitting data could be affected
and can interrupt the normal operation of the WSNs. It may also decrease the
lifetime of the network and sensing coverage of the sensors. The problem of
hole detection is especially challenging in WSNs since the exact location of
the sensors is often unknown. In this paper, we propose a novel hole detection
approach called FD-CNN which is based on Force-directed (FD) Algorithm and
Convolutional Neural Network (CNN). In contrast to existing approaches, FD-CNN
is a centralized approach and is able to detect holes from WSNs without relying
on the information related to the location of the sensors. The proposed
approach also alleviates the problem of high computational complexity in
distributed approaches. The proposed approach accepts the network topology of a
WSN as an input and generates the identity of the nodes surrounding each
detected hole in the network as the final output. In the proposed approach, an
FD algorithm is used to generate the layout of the wireless sensor networks
followed by the identification of the holes in the layouts using a trained CNN
model. In order to prepare labeled datasets for training the CNN model, an
unsupervised pre-processing method is also proposed in this paper. After the
holes are detected by the CNN model, two algorithms are proposed to identify
the regions of the holes and corresponding nodes surrounding the regions.
Extensive experiments are conducted to evaluate the proposed approach based on
different datasets. Experimental results show that FD-CNN can achieve 80%
sensitivity and 93% specificity in less than 2 minutes.


title:
Measuring and Characterizing International Collaboration Patterns in
  Indian Scientific Research: Which proximity dimensions matter?

abstract:
  Scientific collaboration at international level has increased manifolds
during last two decades. Collaboration is not only associated with higher
research productivity but has also been found to be positively correlated with
impact. Considering the benefits and advantages of international collaboration
for the national science and technology systems of a country, policymakers in
different countries have designed programs to promote international
collaboration in science. Several proximity dimensions: mainly geographical,
cognitive, institutional, social, and economic, are found to shape and drive
the international collaborations. This paper attempts to measure and
characterize the international collaboration patterns in Indian scientific
research. Research publication data for the last 20 years (2001-2020), obtained
from Web of Science, is analysed for the purpose. Analytical results show that
Indian international collaboration has grown at a rate of 12.27% during this
period, rising from only 20.73% internationally collaborated papers in 2001 to
32.35% internationally collaborated papers in 2020. USA, Germany, England,
South Korea and China are found to be the top 5 major collaborating partners
for India during his period, however, some subject area-wise variations are
also seen. Among the internationally collaborated papers, about 50% papers have
an Indian researcher as lead (first) author, and more than 50% authors of such
internationally collaborated papers are from India. The internationally
collaborated papers from India are also found to have a slight advantage in
terms of citation impact and social media visibility. The cognitive proximity
is found to be the most effective proximity dimension in shaping the
international collaboration patterns of India.


title: Online force-directed algorithms for visualization of dynamic graphs

abstract:
  Force-directed (FD) algorithms can be used to explore relationships in social
networks, visualize money markets, and analyze transaction networks. However,
FD algorithms are mainly designed for visualizing static graphs in which the
topology of the networks remains constant throughout the calculation. In
contrast to static graphs, nodes and edges in dynamic graphs can be added or
removed as time progresses. In these situations, existing FD algorithms do not
scale well, since any changes in the topology will trigger these algorithms to
completely restart the entire computation. To alleviate this problem, we
propose a design and implementation of five online FD algorithms to visualize
dynamic graphs while maintaining their native force models. The online FD
algorithms developed in this paper are able to reuse the force models of
existing FD algorithms without significant modifications. To evaluate the
effectiveness of the proposed approach, online FD algorithms are compared
against static FD algorithms for visualizing dynamic graphs. Experimental
results show that among the five algorithms evaluated, the online FD algorithm
achieves the best number of edge crossings and the standard deviation of edge
lengths for visualizing dynamic graphs.


title:
Vision Transformer with Cross-attention by Temporal Shift for Efficient
  Action Recognition

abstract:
  We propose Multi-head Self/Cross-Attention (MSCA), which introduces a
temporal cross-attention mechanism for action recognition, based on the
structure of the Multi-head Self-Attention (MSA) mechanism of the Vision
Transformer (ViT). Simply applying ViT to each frame of a video frame can
capture frame features, but cannot model temporal features. However, simply
modeling temporal information with CNN or Transfomer is computationally
expensive. TSM that perform feature shifting assume a CNN and cannot take
advantage of the ViT structure. The proposed model captures temporal
information by shifting the Query, Key, and Value in the calculation of MSA of
ViT. This is efficient without additional coinformationmputational effort and
is a suitable structure for extending ViT over temporal. Experiments on
Kineitcs400 show the effectiveness of the proposed method and its superiority
over previous methods.


title:
Optimal Management of a Smart Port with Shore-Connection and Hydrogen
  Supplying by Stochastic Model Predictive Control

abstract:
  The paper proposes an optimal management strategy for a Smart Port equipped
with renewable generation and composed by an electrified quay, operating
Cold-Ironing, and a Hydrogen-based quay, supplying Zero-Emission Ships. One
Battery Energy Storage System and one Hydrogen Energy Storage System are used
to manage renewable energy sources and to supply electric and hydrogen-fueled
ships. A model predictive control based algorithm is designed to define the
best economic strategy to be followed during operations. The control algorithm
takes into account the uncertainties of renewable energy generation using
stochastic optimization. The performance of the approach is tested on a
potential future Smart Port equipped with wind and photovoltaic generation.


title:
Impact of System Correlation Matrix Bordering on the MU-MIMO Ergodic
  Sum-Rate Capacity in the Presence of the Multipath Fading Channel

abstract:
  The research analyzes the problem of capacity analysis of the multi-user
massive multi-input multi-output systems with a banded correlation model. The
presented study impacts the analytical statistical description with closed form
expression of the ergodic sum-rate capacity of the system functioning in
presence of multipath fading channel with complex Nakagami-m statistics of the
complex transmission coefficients. Numerical verification of the derived
expressions was performed and demonstrated excellent correspondence with
simulation. The research demonstrated that the system correlation matrix (in
case when exponential model is considered) can be bordered up to pentadiagonal
structure without introducing any discrepancies in to the sum-rate capacity.


title:
Digital Mentor: towards a conversational bot to identify hypotheses for
  software startups

abstract:
  Software startups develop innovative, software-intensive product and
services. This context leads to uncertainty regarding the software they are
building. Experimentation, a process of testing hypotheses about the product,
helps these companies to reduce uncertainty through different evidence-based
approaches. The first step in experimentation is to identify the hypotheses to
be tested. HyMap is a technique where a facilitator helps a software startup
founder to draw a cognitive map representing her understanding of the context
and, based on that, create hypotheses about the software to be built. In this
paper, we present the Digital Mentor, an working-in-progress conversational bot
to help creating a HyMap without the need of a human facilitator. We report the
proposed solution consisting of a web application with the backend of a natural
language understanding system, the current state of development, the challenges
we faced so far and the next steps we plan to move forward.


title:
Strategyproof and Proportional Chore Division for Piecewise Uniform
  Preferences

abstract:
  Chore division is the problem of fairly dividing some divisible, undesirable
bad, such as a set of chores, among a number of players. Each player has their
own valuation of the chores, and must be satisfied they did not receive more
than their fair share. In this paper, I consider the problem of strategyproof
chore division, in which the algorithm must ensure that each player cannot
benefit from mis-representing their position. I present an algorithm that
performs proportional and strategyproof chore division for any number of
players given piecewise uniform valuation functions.


title: Atomic Filter: a Weak Form of Shift Operator for Graph Signals

abstract:
  The shift operation plays a crucial role in the classical signal processing.
It is the generator of all the filters and the basic operation for
time-frequency analysis, such as windowed Fourier transform and wavelet
transform. With the rapid development of internet technology and big data
science, a large amount of data are expressed as signals defined on graphs. In
order to establish the theory of filtering, windowed Fourier transform and
wavelet transform in the setting of graph signals, we need to extend the shift
operation of classical signals to graph signals.
  It is a fundamental problem since the vertex set of a graph is usually not a
vector space and the addition operation cannot be defined on the vertex set of
the graph. In this paper, based on our understanding on the core role of shift
operation in classical signal processing we propose the concept of atomic
filters, which can be viewed as a weak form of the shift operator for graph
signals. Then, we study the conditions such that an atomic filter is
norm-preserving, periodic, or real-preserving. The property of real-preserving
holds naturally in the classical signal processing, but no the research has
been reported on this topic in the graph signal setting. With these conditions
we propose the concept of normal atomic filters for graph signals, which
degenerates into the classical shift operator under mild conditions if the
graph is circulant. Typical examples of graphs that have or have not normal
atomic filters are given. Finally, as an application, atomic filters are
utilized to construct time-frequency atoms which constitute a frame of the
graph signal space.


title:
Evaluation of Fake News Detection with Knowledge-Enhanced Language
  Models

abstract:
  Recent advances in fake news detection have exploited the success of
large-scale pre-trained language models (PLMs). The predominant
state-of-the-art approaches are based on fine-tuning PLMs on labelled fake news
datasets. However, large-scale PLMs are generally not trained on structured
factual data and hence may not possess priors that are grounded in factually
accurate knowledge. The use of existing knowledge bases (KBs) with rich
human-curated factual information has thus the potential to make fake news
detection more effective and robust. In this paper, we investigate the impact
of knowledge integration into PLMs for fake news detection. We study several
state-of-the-art approaches for knowledge integration, mostly using Wikidata as
KB, on two popular fake news datasets - LIAR, a politics-based dataset, and
COVID-19, a dataset of messages posted on social media relating to the COVID-19
pandemic. Our experiments show that knowledge-enhanced models can significantly
improve fake news detection on LIAR where the KB is relevant and up-to-date.
The mixed results on COVID-19 highlight the reliance on stylistic features and
the importance of domain specific and current KBs.


title:
The Validity Window of Space-Charge-Limited Current Measurements of
  Metal Halide Perovskite Devices

abstract:
  Space-charge-limited current (SCLC) measurements are used to estimate charge
carrier mobilities and electronic trap densities of semiconductors by analysing
the current density-voltage (JV) relationship for unipolar devices predicted by
the Mott-Gurney (MG) law. However, the interpretation of SCLC measurements for
metal-halide perovskites is problematic due to mobile ionic defects which
redistribute to screen electrostatic fields in devices during measurements. To
overcome this, an SCLC measurement protocol was recently suggested that
minimises ionic charge redistribution by probing the current during millisecond
voltage pulses superimposed on a background bias. Here, we use drift-diffusion
simulations with mobile ions to assess the validity of the MG law for analysing
both the standard and new protocol JV measurements. We simulated idealised
perovskite devices with differing mobile ion densities and compared them with
simulations and measurements of devices with typical contact materials. We
found the validity region for the MG law is limited to perovskites with mobile
ion densities lower than the device's equilibrium charge carrier density
(<10^17 cm-3 for 400 nm thick methylammonium lead iodide films) and contacts
with injection/extraction barriers <=0.1 eV. The latter limitation can be
partially overcome by increasing the device thickness, whereas the former
limitation cannot. This restricts the range of perovskite layer compositions
and viable contact materials that can be reliably analysed with the MG law.
Approaches such as estimating trap densities from the apparent voltage onset to
trap-free SCLC regime should also be critically reviewed since they rely on the
same potentially invalid assumptions as the MG law. Our results demonstrate
that extracting meaningful and accurate values for metal halide perovskite
material properties from SCLC maybe challenging, or often not possible.


title:
A novel formulation for the study of the ascending aortic fluid dynamics
  with in vivo data

abstract:
  Numerical simulations to evaluate thoracic aortic hemodynamics include a
computational fluid dynamic (CFD) approach or fluid-structure interaction (FSI)
approach. While CFD neglects the arterial deformation along the cardiac cycle
by applying a rigid wall simplification, on the other side the FSI simulation
requires a lot of assumptions for the material properties definition and high
computational costs. The aim of this study is to investigate the feasibility of
a new strategy, based on Radial Basis Functions (RBF) mesh morphing technique
and transient simulations, able to introduce the patient-specific changes in
aortic geometry during the cardiac cycle. Starting from medical images, aorta
models at different phases of cardiac cycle were reconstructed and a transient
shape deformation was obtained by proper activating incremental RBF solutions
during the simulation process. The results, in terms of main hemodynamic
parameters, were compared with two performed CFD simulations for the aortic
model at minimum and maximum volume. Our implemented strategy copes the actual
arterial variation during cardiac cycle with high accuracy, capturing the
impact of geometrical variations on fluid dynamics, overcoming the complexity
of a standard FSI approach.


title:
Precision Requirements for Monte Carlo Sums within Hierarchical Bayesian
  Inference

abstract:
  Hierarchical Bayesian inference is often conducted with estimates of the
target distribution derived from Monte Carlo sums over samples from separate
analyses of parts of the hierarchy or from mock observations used to estimate
sensitivity to a target population. We investigate requirements on the number
of Monte Carlo samples needed to guarantee the estimator of the target
distribution is precise enough that it does not affect the inference. We
consider probabilistic models of how Monte Carlo samples are generated, showing
that the finite number of samples introduces additional uncertainty as they act
as an imperfect encoding of the components of the hierarchical likelihood.
Additionally, we investigate the behavior of estimators marginalized over
approximate measures of the uncertainty, comparing their performance to the
Monte Carlo point estimate. We find that correlations between the estimators at
nearby points in parameter space are crucial to the precision of the estimate.
Approximate marginalization that neglects these correlations will either
introduce a bias within the inference or be more expensive (require more Monte
Carlo samples) than an inference constructed with point estimates. We therefore
recommend that hierarchical inferences with empirically estimated target
distributions use point estimates.


title: Hochschild homology, and a persistent approach via connectivity digraphs

abstract:
  We introduce a persistent Hochschild homology framework for directed graphs.
Hochschild homology groups of (path algebras of) directed graphs vanish in
degree $i\geq 2$. To extend them to higher degrees, we introduce the notion of
connectivity digraphs and analyse two main examples; the first, arising from
Atkin's $q$-connectivity, and the second, here called $n$-path digraphs,
generalising the classical notion of line graphs. Based on a categorical
setting for persistent homology, we propose a stable pipeline for computing
persistent Hochschild homology groups. This pipeline is also amenable to other
homology theories; for this reason, we complement our work with a survey on
homology theories of digraphs.


title: Boundedness of Bergman projectors on homogeneous Siegel domains

abstract:
  In this paper we study the boundedness of Bergman projectors on weighted
Bergman spaces on homogeneous Siegel domains of Type II. As it appeared to be a
natural approach in the special case of tube domains over irreducible symmetric
cones, we study such boundedness on the scale of mixed-norm weighted Lebesgue
spaces.
  The sharp range for the boundedness of such operators is essentially known
only in the case of tube domains over Lorentz cones.
  In this paper we prove that the boundedness of such Bergman projectors is
equivalent to variuos notions of atomic decomposition, duality, and
characterization of boundary values of the mixed-norm weighted Bergman spaces,
extending results moslty known only in the case of tube domains over
irreducible symmetric cones. Some of our results are new even in the latter
simpler context.
  We also study the simpler, but still quite interesting, case of the
"positive" Bergman projectors, the integral operator in which the Bergman
kernel is replaced by its absolute value. We provide a useful characterization
which was previously known for tube domains.


title:
Differential Liquidity Provision in Uniswap v3 and Implications for
  Contract Design

abstract:
  Decentralized exchanges (DEXs) provide a means for users to trade pairs of
assets on-chain without the need of a trusted third party to effectuate a
trade. Amongst these, constant function market maker (CFMM) DEXs such as
Uniswap handle the most volume of trades between ERC-20 tokens. With the
introduction of Uniswap v3, liquidity providers are given the option to
differentially allocate liquidity to be used for trades that occur within
specific price intervals. In this paper, we formalize the profit and loss that
liquidity providers can earn when providing specific liquidity positions to a
contract. With this in hand, we are able to compute optimal liquidity
allocations for liquidity providers who hold beliefs over how prices evolve
over time. Ultimately, we use this tool to shed light on the design question
regarding how v3 contracts should partition price space for permissible
liquidity allocations. Our results show that a richer space of potential
partitions can simultaneously benefit both liquidity providers and traders.


title:
Deep Neural Convolutive Matrix Factorization for Articulatory
  Representation Decomposition

abstract:
  Most of the research on data-driven speech representation learning has
focused on raw audios in an end-to-end manner, paying little attention to their
internal phonological or gestural structure. This work, investigating the
speech representations derived from articulatory kinematics signals, uses a
neural implementation of convolutive sparse matrix factorization to decompose
the articulatory data into interpretable gestures and gestural scores. By
applying sparse constraints, the gestural scores leverage the discrete
combinatorial properties of phonological gestures. Phoneme recognition
experiments were additionally performed to show that gestural scores indeed
code phonological information successfully. The proposed work thus makes a
bridge between articulatory phonology and deep neural networks to leverage
informative, intelligible, interpretable,and efficient speech representations.


title:
Improved Soft-aided Decoding of Product Codes with Dynamic Reliability
  Scores

abstract:
  Products codes (PCs) are conventionally decoded with efficient iterative
bounded-distance decoding (iBDD) based on hard-decision channel outputs which
entails a performance loss compared to a soft-decision decoder. Recently,
several hybrid algorithms have been proposed aimed to improve the performance
of iBDD decoders via the aid of a certain amount of soft information while
keeping the decoding complexity similarly low as in iBDD. We propose a novel
hybrid low-complexity decoder for PCs based on error-and-erasure (EaE) decoding
and dynamic reliability scores (DRSs). This decoder is based on a novel EaE
component code decoder, which is able to decode beyond the designed distance of
the component code but suffers from an increased miscorrection probability. The
DRSs, reflecting the reliability of a codeword bit, are used to detect and
avoid miscorrections. Simulation results show that this policy can reduce the
miscorrection rate significantly and improves the decoding performance. The
decoder requires only ternary message passing and a slight increase of
computational complexity compared to iBDD, which makes it suitable for
high-speed communication systems. Coding gains of up to 1.2 dB compared to the
conventional iBDD decoder are observed.


title:
Aggregate Processes as Distributed Adaptive Services for the Industrial
  Internet of Things

abstract:
  The Industrial Internet of Things (IIoT) promises to bring many benefits,
including increased productivity, reduced costs, and increased safety to new
generation manufacturing plants. The main ingredients of IIoT are the
connected, communicating devices directly located in the workshop floor (far
edge devices), as well as edge gateways that connect such devices to the
Internet and, in particular, to cloud servers. The field of Edge Computing
advocates that keeping computations as close as possible to the sources of data
can be an effective means of reducing latency, preserving privacy, and improve
the overall efficiency of the system, although building systems where (far)
edge and cloud nodes cooperate is quite challenging. In the present work we
propose the adoption of the Aggregate Programming (AP) paradigm (and, in
particular, the "aggregate process" construct) as a way to simplify building
distributed, intelligent services at the far edge of an IIoT architecture. We
demonstrate the feasibility and efficacy of the approach with simulated
experiments on FCPP (a C++ library for AP), and with some basic experiments on
physical IIoT boards running an ad-hoc porting of FCPP.


title:
A Class of Semiparametric Models with Homogeneous Structure for Panel
  Data Analysis

abstract:
  Stimulated by the analysis of a dataset from China about Covid-19, we propose
a class of semiparametric models for panel data analysis. The proposed models
account for both homogeneity and heterogeneity among the individuals of a panel
data. They strike a nice balance between parsimony and risk of
misspecification. Although stimulated by the analysis of a particular dataset,
the proposed models apply to very broad range of panel data analysis, they are
powerful in exploring nonlinear dynamic patterns of impacts of covariates or
transformed covariates. An estimation procedure is presented, and its
asymptotic properties are established. Intensive simulation studies are also
conducted to demonstrate how well the estimation procedure works and the risk
of ignoring homogeneity or heterogeneity among individuals in panel data
analysis. Finally, we apply the proposed models and estimation procedure to the
Covid-19 data from China, and reveal some interesting dynamic patterns of the
impacts of some important factors.