title:
Automatic Creation of High-Bandwidth Memory Architectures from
  Domain-Specific Languages: The Case of Computational Fluid Dynamics

abstract:
  Numerical simulations are increasingly used for solving complex problems.
Most of these algorithms are massively parallel and can benefit from the
spatial parallelism offered by reconfigurable logic. Modern FPGA devices can
benefit from high-bandwidth memory technologies, but most of these applications
are memory-bound and require designers to craft advanced communication and
memory architectures for efficient data movement and on-chip storage. This
development process requires hardware design skills that are uncommon in
domain-specific experts. In this paper, we propose an automated tool flow from
a Domain-Specific Language (DSL) to generate massively-parallel accelerators on
FPGA to address these challenges. We use the case of Computational Fluid
Dynamics (CFD) as a paradigmatic example. Our flow starts from the high-level
specification of tensor operations and combines an MLIR-based compiler with an
in-house hardware generation flow to automatically design systems. These
systems integrate several parallel accelerators that operate on independent
data and a specialized memory architecture that moves data efficiently, aiming
at fully exploiting the available CPU-FPGA bandwidth for data transfers. We
simulated applications with millions of elements, achieving up to 100 GFLOPS
with one compute unit and custom precision when targeting a Xilinx Alveo U280.
Our FPGA implementation is almost 25x more energy-efficient than Intel
implementations. We also discuss how to address practical limitations when
scaling up the parallelism with multiple computing units on the same FPGA
board.


title:
The 21-cm signal from the Cosmic Dawn: metallicity dependence of high
  mass X-ray binaries

abstract:
  X-rays from High-Mass X-ray Binaries (HMXBs) are likely the main source of
heating of the intergalactic medium (IGM) during Cosmic Dawn (CD), before the
completion of reionization. This Epoch of Heating (EoH; $z\sim 10-15$) should
soon be detected via the redshifted 21-cm line from neutral hydrogen, allowing
us to indirectly study the properties of HMXBs in the unseen, first galaxies.
Low-redshift observations, as well as theoretical models, imply that the
integrated X-ray luminosity to star formation rate of HMXBs ($L_{\rm X}/{\rm
SFR}$) should increase in metal-poor environments, typical of early galaxies.
Here we study the impact of the metallicity ($Z$) dependence of $L_{\rm X}/{\rm
SFR}$ during the EoH. For our fiducial models, galaxies with star formation
rates of order $10^{-3} - 10^{-1}$ $M_\odot$ yr$^{-1}$ and metallicities of
order $10^{-3} - 10^{-2}$ $Z_\odot$ are the dominant contributors to the X-ray
background (XRB) during this period. Different $L_{\rm X}/{\rm SFR}$-$Z$
relations result in factors of $\sim$ 3 differences in these ranges, as well as
in the mean IGM temperature and the large-scale 21-cm power, at a given
redshift. We compute mock 21-cm observations adopting as a baseline a 1000h
integration with the upcoming Square Kilometer Array (SKA), for two different
$L_{\rm X}/{\rm SFR}$-$Z$ relations. We perform inference on these mock
observations using the common simplification of a constant $L_{\rm X}/{\rm
SFR}$, finding that constant $L_{\rm X}/{\rm SFR}$ models can recover the IGM
evolution of the more complicated $L_{\rm X}/{\rm SFR}$-$Z$ simulations only
during the EoH. At $z<10$, where the typical galaxies are more polluted,
constant $L_{\rm X}/{\rm SFR}$ models over-predict the XRB and its relative
contribution to the early stages of the reionization.


title: Multi-modal learning for predicting the genotype of glioma

abstract:
  The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker
for the diagnosis and prognosis of glioma. It is promising to better predict
glioma genotype by integrating focal tumor image and geometric features with
brain network features derived from MRI. Convolutions neural networks show
reasonable performance in predicting IDH mutation, which, however, cannot learn
from non-Euclidean data, e.g., geometric and network data. In this study, we
propose a multi-modal learning framework using three separate encoders to
extract features of focal tumor image, tumor geometrics and global brain
networks. To mitigate the limited availability of diffusion MRI, we develop a
self-supervised approach to generate brain networks from anatomical
multi-sequence MRI. Moreover, to extract tumor-related features from the brain
network, we design a hierarchical attention module for the brain network
encoder. Further, we design a bi-level multi-modal contrastive loss to align
the multi-modal features and tackle the domain gap at the focal tumor and
global brain. Finally, we propose a weighted population graph to integrate the
multi-modal features for genotype prediction. Experimental results on the
testing set show that the proposed model outperforms the baseline deep learning
models. The ablation experiments validate the performance of different
components of the framework. The visualized interpretation corresponds to
clinical knowledge with further validation. In conclusion, the proposed
learning framework provides a novel approach for predicting the genotype of
glioma.


title: Boost Test-Time Performance with Closed-Loop Inference

abstract:
  Conventional deep models predict a test sample with a single forward
propagation, which, however, may not be sufficient for predicting
hard-classified samples. On the contrary, we human beings may need to carefully
check the sample many times before making a final decision. During the recheck
process, one may refine/adjust the prediction by referring to related samples.
Motivated by this, we propose to predict those hard-classified test samples in
a looped manner to boost the model performance. However, this idea may pose a
critical challenge: how to construct looped inference, so that the original
erroneous predictions on these hard test samples can be corrected with little
additional effort. To address this, we propose a general Closed-Loop Inference
(CLI) method. Specifically, we first devise a filtering criterion to identify
those hard-classified test samples that need additional inference loops. For
each hard sample, we construct an additional auxiliary learning task based on
its original top-$K$ predictions to calibrate the model, and then use the
calibrated model to obtain the final prediction. Promising results on ImageNet
(in-distribution test samples) and ImageNet-C (out-of-distribution test
samples) demonstrate the effectiveness of CLI in improving the performance of
any pre-trained model.


title: Paraphrasing Techniques for Maritime QA system

abstract:
  There has been an increasing interest in incorporating Artificial
Intelligence (AI) into Defence and military systems to complement and augment
human intelligence and capabilities. However, much work still needs to be done
toward achieving an effective human-machine partnership. This work is aimed at
enhancing human-machine communications by developing a capability for
automatically translating human natural language into a machine-understandable
language (e.g., SQL queries). Techniques toward achieving this goal typically
involve building a semantic parser trained on a very large amount of
high-quality manually-annotated data. However, in many real-world Defence
scenarios, it is not feasible to obtain such a large amount of training data.
To the best of our knowledge, there are few works trying to explore the
possibility of training a semantic parser with limited manually-paraphrased
data, in other words, zero-shot. In this paper, we investigate how to exploit
paraphrasing methods for the automated generation of large-scale training
datasets (in the form of paraphrased utterances and their corresponding logical
forms in SQL format) and present our experimental results using real-world data
in the maritime domain.


title:
Bose gases in the Gross-Pitaevskii limit: a survey of some rigorous
  results

abstract:
  We review some mathematical work on the Bose gas in the Gross-Pitaevskii
regime. We start with the classical results by Lieb, Seiringer and Yngvason on
the ground state energy and by Lieb and Seiringer on the existence of
Bose-Einstein condensation. Afterwards, we discuss some more recent progress,
based on a rigorous version of Bogoliubov theory.


title: RGB-Depth Fusion GAN for Indoor Depth Completion

abstract:
  The raw depth image captured by the indoor depth sensor usually has an
extensive range of missing depth values due to inherent limitations such as the
inability to perceive transparent objects and limited distance range. The
incomplete depth map burdens many downstream vision tasks, and a rising number
of depth completion methods have been proposed to alleviate this issue. While
most existing methods can generate accurate dense depth maps from sparse and
uniformly sampled depth maps, they are not suitable for complementing the large
contiguous regions of missing depth values, which is common and critical. In
this paper, we design a novel two-branch end-to-end fusion network, which takes
a pair of RGB and incomplete depth images as input to predict a dense and
completed depth map. The first branch employs an encoder-decoder structure to
regress the local dense depth values from the raw depth map, with the help of
local guidance information extracted from the RGB image. In the other branch,
we propose an RGB-depth fusion GAN to transfer the RGB image to the
fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN
to propagate the features across the two branches, and we append a confidence
fusion head to fuse the two outputs of the branches for the final depth map.
Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our
proposed method clearly improves the depth completion performance, especially
in a more realistic setting of indoor environments with the help of the pseudo
depth map.


title: Monotone metric tensors in Quantum Information Geometry

abstract:
  We review some geometrical aspects pertaining to the world of monotone
quantum metrics in finite dimensions. Particular emphasis is given to an
unfolded perspective for quantum states that is built out of the spectral
theorem and is naturally suited to investigate the comparison with the
classical case of probability distributions.


title:
Multi-class Label Noise Learning via Loss Decomposition and Centroid
  Estimation

abstract:
  In real-world scenarios, many large-scale datasets often contain inaccurate
labels, i.e., noisy labels, which may confuse model training and lead to
performance degradation. To overcome this issue, Label Noise Learning (LNL) has
recently attracted much attention, and various methods have been proposed to
design an unbiased risk estimator to the noise-free dataset to combat such
label noise. Among them, a trend of works based on Loss Decomposition and
Centroid Estimation (LDCE) has shown very promising performance. However,
existing LNL methods based on LDCE are only designed for binary classification,
and they are not directly extendable to multi-class situations. In this paper,
we propose a novel multi-class robust learning method for LDCE, which is termed
"MC-LDCE". Specifically, we decompose the commonly adopted loss (e.g., mean
squared loss) function into a label-dependent part and a label-independent
part, in which only the former is influenced by label noise. Further, by
defining a new form of data centroid, we transform the recovery problem of a
label-dependent part to a centroid estimation problem. Finally, by critically
examining the mathematical expectation of clean data centroid given the
observed noisy set, the centroid can be estimated which helps to build an
unbiased risk estimator for multi-class learning. The proposed MC-LDCE method
is general and applicable to different types (i.e., linear and nonlinear) of
classification models. The experimental results on five public datasets
demonstrate the superiority of the proposed MC-LDCE against other
representative LNL methods in tackling multi-class label noise problem.


title:
Detection of periodic flares in 6.7 GHz methanol masers G45.804-0.356
  and G49.043-1.079

abstract:
  Context: Periodicity in 6.7 GHz methanol maser sources is a rare phenomenon
that was discovered during long-term monitoring programmes. Understanding the
underlying processes that lead to periodic variability might provide insights
into the physical processes in high-mass star-forming regions. Aims: We aim to
identify and describe new periodic methanol masers. Methods: The observations
were obtained with the Torun 32m antenna. Time series analysis was conducted
using well-proven statistical methods. Additionally, NEOWISE data were used to
search for a correlation between infrared and maser fluxes. Results: We found
two new periodic sources, G45.804-0.356 and G49.043-1.079, with periods of
416.9 and 469.3 days, respectively. For G49.043-1.079, infrared variability is
simultaneous with methanol flares. Conclusions: A most likely cause of the
periodicity in G49.043-1.079 is modulated accretion. For G45.804-0.356, the
periodicity cannot be explained with the available data, and further research
is needed.


title:
Propagation of regularity for transport equations. A Littlewood-Paley
  approach

abstract:
  It is known that linear advection equations with Sobolev velocity fields have
very poor regularity properties: Solutions propagate only derivatives of
logarithmic order, which can be measured in terms of suitable Gagliardo
seminorms. We propose a new approach to the study of regularity that is based
on Littlewood-Paley theory, thus measuring regularity in terms of Besov norms.
We recover the results that are available in the literature and extend these
optimally to the diffusive setting. As a consequence, we derive sharp bounds on
rates of convergence in the zero-diffusivity limit.


title: The modular class of a singular foliation

abstract:
  The modular class of a regular foliation is a cohomological obstruction to
the existence of a volume form transverse to the leaves which is invariant
under the flow of the vector fields of the foliation. By drawing on the
relationship between Lie algebroids and regular foliations, this paper extends
the notion of modular class to the realm of singular foliations. The
singularities are dealt with by replacing the singular foliations by any of
their universal Lie $\infty$-algebroids, and by picking up the modular class of
the latter. The geometric meaning of the modular class of a singular foliation
$\mathcal{F}$ is not as transparent as for regular foliations: it is an
obstruction to the existence of a universal Lie $\infty$-algebroid of
$\mathcal{F}$ whose Berezinian line bundle is a trivial $\mathcal{F}$-module.
This paper illustrates the relevance of using universal Lie $\infty$-algebroids
to extend mathematical notions from regular foliations to singular ones, thus
paving the road to defining other characteristic classes in this way.


title:
Cosmological Parameter Estimation Using Current and Future Observations
  of Strong Gravitational Lensing

abstract:
  Remarkable development of cosmology is benefited from the increasingly
improved measurements of cosmic distances including absolute distances and
relative distances. In recent years, however, the emerged cosmological tensions
motivate us to explore the independent and precise late-universe probes. The
two observational effects of strong gravitational lensing (SGL), the velocity
dispersions of lens galaxies and the time delays between multiple images, can
provide measurements of relative and absolute distances respectively, and their
combination is possible to break the degeneracies between cosmological
parameters and enable tight constraints on cosmological parameters. In this
paper, we combine the observed 130 SGL systems with velocity-dispersion
measurements and 7 SGL systems with time-delay measurements to constrain
dark-energy cosmological models. It is found that the combination of the two
effects does not significantly break the degeneracies between cosmological
parameters as expected. However, with the simulations of 8000 SGL systems with
well-measured velocity dispersions and 55 SGL systems with well-measured time
delays based on the forthcoming LSST survey, we find that the combination of
two effects can significantly break the parameter degeneracies, and make the
constraint precision of cosmological parameters meet the standard of precision
cosmology. We conclude that the observations of SGL will become a useful
late-universe probe for precisely measuring cosmological parameters.


title: The Isotopic Links from Planet Forming Regions to the Solar System

abstract:
  Isotopic ratios provide a powerful tool for understanding the origins of
materials, including the volatile and refractory matter within solar system
bodies. Recent high sensitivity observations of molecular isotopologues, in
particular with ALMA, have brought us new information on isotopic ratios of
hydrogen, carbon, nitrogen and oxygen in star and planet forming regions as
well as the solar system objects. Solar system exploration missions, such as
Rosetta and Cassini, have given us further new insights. Meanwhile, the recent
development of sophisticated models for isotope chemistry including detailed
gas-phase and grain surface reaction network has made it possible to discuss
how isotope fractionation in star and planet forming regions is imprinted into
the icy mantles of dust grains, preserving a record of the initial isotopic
state of solar system materials. This chapter reviews recent progress in
observations of molecular isotopologues in extra-solar planet forming regions,
prestellar/protostellar cores and protoplanetary disks, as well as objects in
our solar system -- comets, meteorites, and planetary/satellite atmospheres --
and discusses their connection by means of isotope chemical models.


title: Coresets for Weight-Constrained Anisotropic Assignment and Clustering

abstract:
  The present paper constructs coresets for weight-constrained anisotropic
assignment and clustering. In contrast to the well-studied unconstrained
least-squares clustering problem, approximating the centroids of the clusters
no longer suffices in the weight-constrained anisotropic case, as even the
assignment of the points to best sites is involved. This assignment step is
often the limiting factor in materials science, a problem that partially
motivates our work. We build on a paper by Har-Peled and Kushal, who
constructed coresets of size
$\mathcal{O}\bigl(\frac{k^3}{\epsilon^{d+1}}\bigr)$ for unconstrained
least-squares clustering. We generalize and improve on their results in various
ways, leading to even smaller coresets with a size of only
$\mathcal{O}\bigl(\frac{k^2}{\epsilon^{d+1}}\bigr)$ for weight-constrained
anisotropic clustering. Moreover, we answer an open question on coreset designs
in the negative, by showing that the total sensitivity can become as large as
the cardinality of the original data set in the constrained case. Consequently,
many techniques based on importance sampling do not apply to weight-constrained
clustering.


title:
Inverse Scale Space Iterations for Non-Convex Variational Problems: The
  Continuous and Discrete Case

abstract:
  Non-linear filtering approaches allow to obtain decompositions of images with
respect to a non-classical notion of scale, induced by the choice of a convex,
absolutely one-homogeneous regularizer. The associated inverse scale space flow
can be obtained using the classical Bregman iteration with quadratic data term.
We apply the Bregman iteration to lifted, i.e. higher-dimensional and convex,
functionals in order to extend the scope of these approaches to functionals
with arbitrary data term. We provide conditions for the subgradients of the
regularizer -- in the continuous and discrete setting -- under which this
lifted iteration reduces to the standard Bregman iteration. We show
experimental results for the convex and non-convex case.


title:
Unsupervised Heterophilous Network Embedding via r-Ego Network
  Discrimination

abstract:
  Recently, supervised network embedding (NE) has emerged as a predominant
technique for representing complex systems that take the form of networks, and
various downstream node- and network-level tasks have benefited from its
remarkable developments. However, unsupervised NE still remains challenging due
to the uncertainty in defining a learning objective. In addition, it is still
an unexplored research question whether existing NE methods adapt well to
heterophilous networks. This paper introduces the first empirical study on the
influence of homophily ratio on the performance of existing unsupervised NE
methods and reveals their limitations. Inspired by our empirical findings, we
design unsupervised NE task as an r-ego network discrimination problem and
further develop a SELf-supErvised Network Embedding (Selene) framework for
learning useful node representations for both homophilous and heterophilous
networks. Specifically, we propose a dual-channel feature embedding mechanism
to fuse node attributes and network structure information and leverage a
sampling and anonymisation strategy to break the implicit homophily assumption
of existing embedding mechanisms. Lastly, we introduce a negative-sample-free
SSL objective function to optimise the framework. We conduct extensive
experiments and a series of ablation studies on 12 real-world datasets and 20
synthetic networks. Results demonstrate Selene's superior performance and
confirm the effectiveness of each component.


title: Statistical classification for Raman spectra of tumoral genomic DNA

abstract:
  We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous
droplets of genomic DNA deposited onto silver-coated silicon nanowires and we
show that it is possible to efficiently discriminate between spectra of tumoral
and healthy cells. To assess the robustness of the proposed technique, we
develop two different statistical approaches, one based on the Principal
Component Analysis of spectral data and one based on the computation of the
$\ell^2$ distance between spectra. Both methods prove to be highly efficient
and we test their accuracy via the so-called Cohen's $\kappa$ statistics. We
show that the synergistic combination of the SERS spectroscopy and the
statistical analysis methods leads to efficient and fast cancer diagnostic
applications allowing a rapid and unexpansive discrimination between healthy
and tumoral genomic DNA alternative to the more complex and expensive DNA
sequencing.


title:
The Hawking-Page-like Phase Transition from FRW Spacetime to McVittie
  Black Hole

abstract:
  In this paper, we investigate the thermodynamics especially the
Hawking-Page-like phase transition of the McVittie space-time. We formulate the
first law of thermodynamics for the McVittie black hole, and find that the work
density $W$ of the perfect fluid plays the role of the thermodynamic pressure,
i.e. $P$:=$-W$. We also construct the thermodynamic equation of state for the
McVittie black hole. Most importantly, by analysing the Gibbs free energy, we
find that the Hawking-Page-like phase transition from FRW spacetime to McVittie
black hole is possible in the case $P>0$.


title:
Well-posedness for a diffusion-reaction compartmental model simulating
  the spread of COVID-19

abstract:
  This paper is concerned with the well-posedness of a diffusion-reaction
system for a Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model.
This model is written in terms of four nonlinear partial differential equations
with nonlinear diffusions, depending on the total amount of the SEIR
populations. The model aims at describing the spatio-temporal spread of the
COVID-19 pandemic and is a variation of the one recently introduced, discussed
and tested in [A. Viguerie et al, Diffusion-reaction compartmental models
formulated in a continuum mechanics framework: application to COVID-19,
mathematical analysis, and numerical study, Comput. Mech. 66 (2020) 1131-1152].
Here, we deal with the mathematical analysis of the resulting Cauchy--Neumann
problem: the existence of solutions is proved in a rather general setting and a
suitable time discretization procedure is employed. It is worth mentioning that
the uniform boundedness of the discrete solution is shown by carefully
exploiting the structure of the system. Uniform estimates and passage to the
limit with respect to the time step allow to complete the existence proof.
Then, two uniqueness theorems are offered, one in the case of a constant
diffusion coefficient and the other for more regular data, in combination with
a regularity result for the solutions.


title:
Multipolar social systems: Measuring polarization beyond dichotomous
  contexts

abstract:
  Social polarization is a growing concern worldwide, as it strains social
relations, erodes trust in institutions, and thus threatens democratic
societies. Academic efforts to understand this phenomenon have traditionally
approached it from a one-dimensional perspective, focusing on bipolar or
dichotomous systems. However, political conflicts often involve not only two,
but multiple potentially dissenting factions. The most representative examples
are multi-party democracies, where the multilateral tensions among different
parties often lead to gridlock and uncertainty. Despite the prevalence of these
multipolar systems, there is still a lack of suitable analytical tools to study
their intricate polarization patterns. In this work, we develop an analytical
framework consisting of an inherently multipolar model for unbiased ideological
spaces, a method to infer multidimensional opinions from interaction networks,
and novel multidimensional polarization metrics that quantify several aspects
of ideological polarization and bring new insights into the analysis of
high-dimensional opinion distributions. Crucially, our multidimensional
framework does not assume the underlying ideological structure, such as
conservative vs progressive, liberal vs authoritarian, etc. Instead, it reveals
the natural space that best describes the social landscape, which does not
necessarily correspond to traditional categories. We illustrate the application
of this framework in quadripolar and pentapolar real-world democratic
processes, finding non-trivial ideological structures with clear connections to
the underlying social context. Our methodology offers a comprehensive
perspective of multilateral social tensions, as it incorporates complementary
aspects of polarization: network segregation, opinion extremeness, and issue
alignment.


title:
Follow the Leader: Technical and Inspirational Leadership in Open Source
  Software

abstract:
  We conduct the first comprehensive study of the behavioral factors which
predict leader emergence within open source software (OSS) virtual teams. We
leverage the full history of developers' interactions with their teammates and
projects at github.com between January 2010 and April 2017 (representing about
133 million interactions) to establish that - contrary to a common narrative
describing open source as a pure "technical meritocracy" - developers'
communication abilities and community building skills are significant
predictors of whether they emerge as team leaders. Inspirational communication
therefore appears as central to the process of leader emergence in virtual
teams, even in a setting like OSS, where technical contributions have often
been conceptualized as the sole pathway to gaining community recognition. Those
results should be of interest to researchers and practitioners theorizing about
OSS in particular and, more generally, leadership in geographically dispersed
virtual teams, as well as to online community managers.


title:
Third post-Newtonian gravitational radiation from two-body scattering
  II. Hereditary Energy radiation

abstract:
  We compute the hereditary part of the third post-Newtonian accurate
gravitational energy radiation from hyperbolic scatterings (and parabolic
scatterings) of non-spinning compact objects. We employ large angular momentum
($j$) expansion, and compute it to the relative $1/j^{11}$ order (so the first
12 terms). For parabolic scattering case, the exact solution is computed. At
the end, the completely collected expression of the energy radiation upto the
third post-Newtonian and from $1/j^{3}$ to $1/j^{15}$ order, is presented
including the instantaneous contribution.


title: Partially adaptive filtering using randomized projections

abstract:
  This short note addresses the design of a partially adaptive filter to
retrieve a signal of interest in the presence of strong low-rank interference
and thermal noise. We consider a generalized sidelobe canceler implementation
where the dimension-reducing transformation is build resorting to ideas
borrowed from randomized matrix approximations. More precisely, the main
subspace of the auxiliary data $Z$ is approximated by $Z\Omega$ where $\Omega$
is a random matrix or a matrix that picks at random columns of $Z$. These
transformations do not require eigenvalue decomposition, yet they provide
performance similar to those of a principal component filter.


title:
The general labelled partitioning process in action: recombination,
  selection, mutation, and more

abstract:
  We recently provided a recursive construction of the solution of the
selection-recombination equation under the assumption of single-crossover and
with selection acting on a single site. This recursion is intimately connected
to various dual processes that describe the ancestry of a random sample,
backward in time. We now aim to unify and generalise this approach by
introducing a new labelled partitioning process with general Markovian labels.
As a concrete example, we consider the selection-mutation-recombination
equation.


title:
The De Rham, complex Hodge and $p$-adic Hodge realization functor on the
  derived category of relative motives over a field of characteristic zero

abstract:
  We introduce the categories of geometric mixed Hodge modules on algebraic
varieties over a subfield $k\subset\mathbb C$, and for a prime number $p$, the
categories of geometric $p$-adic mixed Hodge modules on algebraic varieties
over a subfield $k\subset K\subset\mathbb C_p$ of a p-adic field $K$. We then
give an Hodge realization functor on the de derived category of relative
motives over $k\subset\mathbb C$, and for a prime number $p$ a $p$-adic Hodge
realization functor on the derived category of relative motives over $k\subset
K\subset\mathbb C_p$.


title:
Validation of a wave heated 3D MHD coronal-wind model using Polarized
  Brightness and EUV observations

abstract:
  The physical properties responsible for the formation and evolution of the
corona and heliosphere are still not completely understood. 3D MHD global
modeling is a powerful tool to investigate all the possible candidate
processes. To fully understand the role of each of them, we need a validation
process where the output from the simulations is quantitatively compared to the
observational data. In this work, we present the results from our validation
process applied to the wave turbulence driven 3D MHD corona-wind model
WindPredict-AW. At this stage of the model development, we focus the work to
the coronal regime in quiescent condition. We analyze three simulations
results, which differ by the boundary values. We use the 3D distributions of
density and temperature, output from the simulations at the time of around the
First Parker Solar Probe perihelion (during minimum of the solar activity), to
synthesize both extreme ultraviolet (EUV) and white light polarized (WL pB)
images to reproduce the observed solar corona. For these tests, we selected AIA
193 A, 211 A and 171 A EUV emissions, MLSO K-Cor and LASCO C2 pB images
obtained the 6 and 7 November 2018. We then make quantitative comparisons of
the disk and off limb corona. We show that our model is able to produce
synthetic images comparable to those of the observed corona.


title: Emergent Cosmology in Models of Nonlinear Electrodynamics

abstract:
  Nonlinear electrodynamics, which acts as a source of gravity Einstein field
equations, leads to emergent cosmology, an alternative solution which can avoid
Big Bang singularity. In this paper, we explore the emerging universe in models
of non-linear electrodynamics (described by dimensional parameter $\beta$) by
using the equation of state parameter $\omega$ and see how the parameter
$\beta$ helps the universe to cause a transition from a quasi-static Minkowski
phase to the inflationary phase of expansion through the point of emergence and
subsequently to the phase of normal thermal expansion. We predict the spectral
index parameter $n_s = 0.97467$ (scalar spectral index), $r =0.10133$ (tensor
to scalar ratio) and $n_T = -0.01267$ (tensor spectral index) of the
inflationary perturbation in emergent cosmology of nonlinear electrodynamics
corresponding to $\beta$ = 0.1 and $B_0=10^{-10}$G.


title:
Observation of Ultrafast Interfacial Exciton Formation and Recombination
  in Graphene/MoS2 Heterostructure

abstract:
  In this study,we combined time-resolved terahertz spectroscopy along with
transient absorption spectroscopy to revisit the interlayer non-equilibrium
carrier dynamics in largely lateral size Gr/MoS2 heterostructure fabricated
with chemical vapor deposition method. Our experimental results reveal that,
with photon-energy below the A-exciton of MoS2 monolayer, hot electrons
transfer from graphene to MoS2 takes place in time scale of less than 0.5 ps,
resulting in ultrafast formation of interfacial exciton in the heterostructure,
subsequently, recombination relaxation of the interfacial exciton occurs in
time scale of ~18 ps. A new model considering carrier heating and photogating
effect in graphene is proposed to estimate the amount of carrier transfer in
the heterostructure, which shows a good agreement with experimental result.
Moreover, when the photon-energy is on-resonance with the A-exciton of MoS2,
photogenerated holes in MoS2 are transferred to graphene layer within 0.5 ps,
leading to the formation of interfacial exciton, the subsequent
photoconductivity (PC) relaxation of graphene and bleaching recovery of
A-exciton in MoS2 take place around ~10 ps time scale, ascribing to the
interfacial exciton recombination. The faster recombination time of interfacial
exciton with on-resonance excitation could come from the reduced interface
barrier caused by bandgap renormalization effect. Our study provides deep
insight into the understanding of interfacial charge transfer as well as the
relaxation dynamics in graphene-based heterostructures, which are promising for
the applications of graphene-based optoelectronic devices.


title: Iterative Refinement of Schur decompositions

abstract:
  The Schur decomposition of a square matrix $A$ is an important intermediate
step of state-of-the-art numerical algorithms for addressing eigenvalue
problems, matrix functions, and matrix equations. This work is concerned with
the following task: Compute a (more) accurate Schur decomposition of $A$ from a
given approximate Schur decomposition. This task arises, for example, in the
context of parameter-dependent eigenvalue problems and mixed precision
computations. We have developed a Newton-like algorithm that requires the
solution of a triangular matrix equation and an approximate orthogonalization
step in every iteration. We prove local quadratic convergence for matrices with
mutually distinct eigenvalues and observe fast convergence in practice. In a
mixed low-high precision environment, our algorithm essentially reduces to only
four high-precision matrix-matrix multiplications per iteration. When refining
double to quadruple precision, it often needs only 3-4 iterations, which
reduces the time of computing a quadruple precision Schur decomposition by up
to a factor of 10-20.


title:
The bending of the star-forming main sequence traces the cold- to
  hot-accretion transition mass over 0<z<4

abstract:
  We analyse measurements of the evolving stellar mass (M0) at which the
bending of the star-forming main sequence (MS) occurs over 0<z<4. We find
M0~10^{10}Msun over 0<z<1, then M0 rises up to ~10^{11}Msun at z=2, and then
stays flat or slowly increases towards higher redshifts. When converting M0
values into hosting dark matter halo masses, we show that this behaviour is
remarkably consistent with the evolving cold- to hot-accretion transition mass,
as predicted by theory and defined by the redshift-independent Mshock at z<1.4
and by the rising Mstream at z>1.4 (for which we propose a revision in
agreement with latest simulations). We hence argue that the MS bending is
primarily due to the lessening of cold-accretion causing a reduction in
available cold gas in galaxies and supports predictions of gas feeding theory.
In particular, the rapidly rising M0 with redshift at z>1 is confirming
evidence for the cold-streams scenario. In this picture, a progressive fueling
reduction rather than its sudden suppression in halos more massive than
Mshock/Mstream produces a nearly constant star-formation rate in galaxies with
stellar masses larger than M0, and not their quenching, for which other
physical processes are thus required. Compared to the knee M* in the stellar
mass function of galaxies, M0 is significantly lower at z<1.5, and higher at
z>2, suggesting that the imprint of gas deprivation on the distribution of
galaxy masses happened at early times (z>1.5-2). The typical mass at which
galaxies inside the MS become bulge-dominated evolves differently from M0,
consistent with the idea that bulge-formation is a distinct process from the
phasing-out of cold-accretion.


title: Transit Light-curves for Exomoons: Analytical Formalisms

abstract:
  The photometric transit method has been the most effective method to detect
and characterize exoplanets as several ground-based as well as space-based
survey missions have discovered thousands of exoplanets using this method. With
the advent of the upcoming next generation large telescopes, the detection of
exomoons in a few of these exoplanetary systems is very plausible. In this
paper, we present a comprehensive analytical formalism in order to model the
transit light curves for such moon hosting exoplanets. In order to achieve
analytical formalism, we have considered circular orbit of the exomoon around
the host planet, which is indeed the case for tidally locked moons. The
formalisms uses the radius and orbital properties of both the host planet and
its moon as model parameters. The co-alignement or non-coalignment of the
orbits of the planet and the moon is parametrized by two angles of separation
and thus can be used to model all the possible orbital alignments for a
star-planet-moon system. This formalism also provides unique and direct
solutions to every possible star-planet-moon three circular body alignments.
Using the formula derived, a few representative light curves are also
presented.


title:
Efficient Remote Photoplethysmography with Temporal Derivative Modules
  and Time-Shift Invariant Loss

abstract:
  We present a lightweight neural model for remote heart rate estimation
focused on the efficient spatio-temporal learning of facial
photoplethysmography (PPG) based on i) modelling of PPG dynamics by
combinations of multiple convolutional derivatives, and ii) increased
flexibility of the model to learn possible offsets between the facial video PPG
and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module
(TDM) constructed by the incremental aggregation of multiple convolutional
derivatives, emulating a Taylor series expansion up to the desired order.
Robustness to ground truth offsets is handled by the introduction of TALOS
(Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based
models. We verify the effectiveness of our model by reporting accuracy and
efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to
existing models, our approach shows competitive heart rate estimation accuracy
with a much lower number of parameters and lower computational cost.


title: Efficient Algorithms for Extreme Bandits

abstract:
  In this paper, we contribute to the Extreme Bandit problem, a variant of
Multi-Armed Bandits in which the learner seeks to collect the largest possible
reward. We first study the concentration of the maximum of i.i.d random
variables under mild assumptions on the tail of the rewards distributions. This
analysis motivates the introduction of Quantile of Maxima (QoMax). The
properties of QoMax are sufficient to build an Explore-Then-Commit (ETC)
strategy, QoMax-ETC, achieving strong asymptotic guarantees despite its
simplicity. We then propose and analyze a more adaptive, anytime algorithm,
QoMax-SDA, which combines QoMax with a subsampling method recently introduced
by Baudry et al. (2021). Both algorithms are more efficient than existing
approaches in two aspects (1) they lead to better empirical performance (2)
they enjoy a significant reduction of the memory and time complexities.


title: Long-lived storage of orbital angular momentum quantum states

abstract:
  Quantum memories are indispensible for establishing a long-distance quantum
network. High-dimensional quantum memories enable a higher channel capacity
compared to a quantum memory working in a two-dimensional space, and have a
lower requirement for storage lifetime in the field of quantum coomunication.
The photonic transverse spatial modes such as Laguerra-Gaussian modes orbital
angular momentum (OAM) are ideal candidates for encoding high-dimensional
information, because it can form an infinite-dimensional Hilbert space.
Although the faithful storage of an OAM qubit or qutrit has been realized in
pioneering works, the longest storage lifetime for the former is only in the
order of a few microseconds, and hundreds of nano-seconds for the latter. Here
we implement a quantum memory for OAM qubits and qutrits using a cold atomic
ensemble, the experimental results clearly show that our memory can still beat
the classical limit after a storage time of $400\mu s$,which is two orders of
magnitude higher than the previous work. The retrieval efficiency at this time
equals to $44\%$ of the value when the storage time is set to be $10\mu s$. Our
work is very promising for establishing a high dimensional quantum network.


title:
Zoom Out and Observe: News Environment Perception for Fake News
  Detection

abstract:
  Fake news detection is crucial for preventing the dissemination of
misinformation on social media. To differentiate fake news from real ones,
existing methods observe the language patterns of the news post and "zoom in"
to verify its content with knowledge sources or check its readers' replies.
However, these methods neglect the information in the external news environment
where a fake news post is created and disseminated. The news environment
represents recent mainstream media opinion and public attention, which is an
important inspiration of fake news fabrication because fake news is often
designed to ride the wave of popular events and catch public attention with
unexpected novel content for greater exposure and spread. To capture the
environmental signals of news posts, we "zoom out" to observe the news
environment and propose the News Environment Perception Framework (NEP). For
each post, we construct its macro and micro news environment from recent
mainstream news. Then we design a popularity-oriented and a novelty-oriented
module to perceive useful signals and further assist final prediction.
Experiments on our newly built datasets show that the NEP can efficiently
improve the performance of basic fake news detectors.


title:
ELIC: Efficient Learned Image Compression with Unevenly Grouped
  Space-Channel Contextual Adaptive Coding

abstract:
  Recently, learned image compression techniques have achieved remarkable
performance, even surpassing the best manually designed lossy image coders.
They are promising to be large-scale adopted. For the sake of practicality, a
thorough investigation of the architecture design of learned image compression,
regarding both compression performance and running speed, is essential. In this
paper, we first propose uneven channel-conditional adaptive coding, motivated
by the observation of energy compaction in learned image compression. Combining
the proposed uneven grouping model with existing context models, we obtain a
spatial-channel contextual adaptive model to improve the coding performance
without damage to running speed. Then we study the structure of the main
transform and propose an efficient model, ELIC, to achieve state-of-the-art
speed and compression ability. With superior performance, the proposed model
also supports extremely fast preview decoding and progressive decoding, which
makes the coming application of learning-based image compression more
promising.


title:
Revisiting Domain Generalized Stereo Matching Networks from a Feature
  Consistency Perspective

abstract:
  Despite recent stereo matching networks achieving impressive performance
given sufficient training data, they suffer from domain shifts and generalize
poorly to unseen domains. We argue that maintaining feature consistency between
matching pixels is a vital factor for promoting the generalization capability
of stereo matching networks, which has not been adequately considered. Here we
address this issue by proposing a simple pixel-wise contrastive learning across
the viewpoints. The stereo contrastive feature loss function explicitly
constrains the consistency between learned features of matching pixel pairs
which are observations of the same 3D points. A stereo selective whitening loss
is further introduced to better preserve the stereo feature consistency across
domains, which decorrelates stereo features from stereo viewpoint-specific
style information. Counter-intuitively, the generalization of feature
consistency between two viewpoints in the same scene translates to the
generalization of stereo matching performance to unseen domains. Our method is
generic in nature as it can be easily embedded into existing stereo networks
and does not require access to the samples in the target domain. When trained
on synthetic data and generalized to four real-world testing sets, our method
achieves superior performance over several state-of-the-art networks.


title: Proposal for Quantum Ciphertext-Policy Attribute-Based Encryption

abstract:
  A Quantum Ciphertext-Policy Attribute-Based Encryption scheme (QCP-ABE) has
been presented. In classical domain, most of the popular ABE schemes are based
on the hardness of the Bilinear Diffie-Hellman Exponent problem, which has been
proven to be vulnerable against Shor's algorithm. Recently, some quantum safe
ABE schemes have been proposed exploiting the Lattice problem. However, no
efficient Quantum Attribute-Based Encryption scheme has been reported till
date. In this backdrop, in the present initiative, we propose a quantum CP-ABE
scheme exploiting Quantum Key Distribution (QKD) and Quantum Error Correcting
code. A Semi Quantum version of the scheme has also been considered. Finally,
we introduced dynamic access structure in our proposed protocols.


title:
On conjugation invariant norms, asymptotic cones, metric ultraproducts
  and contractibility

abstract:
  In the present paper we prove lemmata on strong contractibility in asymptotic
cones and metric ultraproducts which we apply to both the case of finitely
generated word norms and the case of conjugation invariant norms. We recover
classically known contractibility results on free products and prove the
contractibility of the asymptotic cone of the infinite symmetric group
$\Sym_\infty$ equipped with a conjugation invariant norm. Furthermore, we give
examples of contractible metric ultraproducts arising from subgroups of general
linear groups. Additionally, we discuss algebraic properties of groups arising
as asymptotic cones for conjugation invariant norms. For example, we show that
the asymptotic cone of the infinite symmetric group is itself an algebraically
simple group relating strongly to the universally sophic groups defined by Elek
and Szabo.


title:
Testing black holes in non-linear electrodynamics from the observed
  quasi-periodic oscillations

abstract:
  Quasi-periodic oscillations (QPOs), in particular, the ones with high
frequencies, often observed in the power spectrum of black holes, are useful in
understanding the nature of strong gravity since they are associated with the
motion of matter in the vicinity of the black hole horizon. Interestingly,
these high frequency QPOs (HFQPOs) are observed in commensurable pairs, the
most common ratio being 3:2. Several theoretical models are proposed in the
literature which explain the HFQPOs in terms of the orbital and epicyclic
frequencies of matter rotating around the central object. Since these
frequencies are sensitive to the background spacetime, the observed HFQPOs can
potentially extract useful information regarding the nature of the same. In
this work, we investigate the role of regular black holes with a Minkowski
core, which arise in gravity coupled to non-linear electrodynamics, in
explaining the HFQPOs. Regular black holes are particularly interesting as they
provide a possible resolution to the singularity problem in general relativity.
We compare the model dependent QPO frequencies with the available observations
of the quasi-periodic oscillations from black hole sources and perform a \c{hi}
2 analysis. Our study reveals that most QPO models favor small but non-trivial
values of the non-linear electrodynamics charge parameter. In particular, black
holes with large values of non-linear electrodynamics charge parameter are
generically disfavored by present observations related to QPOs.


title: Looptree, Fennec, and Snake of ICRT

abstract:
  We introduce a new theory of plane $\mathbb R$-tree, to define plane ICRT
(inhomogeneous continuum random tree), and its looptree, fennec (a Gaussian
free field on the looptree), and snake. We prove that a.s. the looptree is
compact, and that a.s. the fennec and snake are continuous. We compute the
looptree's fractal dimensions, and the fennec and snake's H\"older exponent.
Alongside, we define a Gaussian free field on the ICRT, and prove a condition
for its continuity. In a companion paper , we prove that the looptrees,
fennecs, and snakes of trees with fixed degree sequence converge toward the
looptrees, fennecs and snakes of ICRT.


title:
OWC-enabled Spine and Leaf Architecture Towards Energy Efficient Data
  Center Networks

abstract:
  Due to the emergence of new paradigms and services such as 5G/6G, IoT, and
more, current deployed wired Data Center Networks (DCNs) are not meeting the
required performance metrics due to their limited reconfigurability,
scalability, and throughput. To that end, wireless DCNs using technologies such
as Optical Wireless Communication (OWC) have become viable and costeffective
solutions as they offer higher capacity, better energy efficiency, and better
scalability. This paper proposes an OWC-based spine and leaf DCNs where the
leaf switches are enabled with OWC transceivers, and the spine switches are
replaced by Access Points (APs) in the ceiling connected to a backbone network.
The APs are interconnected through a Passive Optical Network (PON) that also
connects the architecture with upper network layers. An Infrared (IR) OWC
system that employs Wavelength Division Multiplexing (WDM) is proposed to
enhance the DCN downlink communication. The simulation (i.e., channel modeling)
results show that our proposed data center links achieve good data rates in the
data center up to 15 Gbps. For the PON, Arrayed Waveguide Grating Routers
(AWGRs) that enable WDM are proposed to connect the APs. We evaluate the
performance of the considered architecture in term of its power efficiency
compared to traditional spine and leaf data centers. The results show that the
OWC-enabled DCN reduces the power consumption by 42% compared to traditional
the spine and leaf architecture.


title:
Fully-probabilistic Terrain Modelling with Stochastic Variational
  Gaussian Process Maps

abstract:
  Gaussian processes (GPs) are becoming a standard tool to build terrain
representations thanks to their capacity to model map uncertainty. This
effectively yields a reliability measure of the areas of the map, which can be
directly utilized by Bayes filtering algorithms in robot localization problems.
A key insight is that this uncertainty can incorporate the noise intrinsic to
the terrain surveying process through the GPs ability to train on uncertain
inputs (UIs). However, existing techniques to build GP maps with UIs in a
tractable manner are restricted in the form and degree of the input noise. In
this letter, we propose a flexible and efficient framework to build large-scale
GP maps with UIs based on Stochastic Variational GPs and Monte Carlo sampling
of the UIs distributions. We validate our mapping approach on a large
bathymetric survey collected with an AUV and analyze its performance against
the use of deterministic inputs (DI). Finally, we show how using UI SVGP maps
yields more accurate particle filter localization results than DI SVGP on a
real AUV mission over an entirely predicted area.


title: New energy method in the study of the instability near Couette flow

abstract:
  In this paper, we provide a new energy method to study the growth mechanism
of unstable shear flows. As applications, we prove two open questions about the
instability of shear flows. First, we obtain the optimal stability threshold of
the Couette flow for Navier-Stokes equations with small viscosity $\nu>0$, when
the perturbations are in critical spaces. More precisely, we prove the
instability for some perturbation of size $\nu^{\frac{1}{2}-\delta_0}$ with any
small $\delta_0>0$, which implies that $\nu^{\frac{1}{2}}$ is the sharp
stability threshold. Second, we study the instability of the linearized Euler
equations around shear flows that are near Couette flow. We prove the existence
of a growing mode for the corresponding Rayleigh operator and give a precise
location of the eigenvalues. We think our method has a lot of possible other
applications.


title:
COSMOS2020: The cosmic evolution of the stellar-to-halo mass relation
  for central and satellite galaxies up to z~5

abstract:
  We use the COSMOS2020 catalogue to measure the stellar-to-halo mass relation
(SHMR) divided by central and satellite galaxies from $z=0.2$ to $z = 5.5$.
Starting from accurate photometric redshifts we measure the near-infrared
selected two-point angular correlation and stellar mass functions in ten
redshift bins and fit them with an HOD-based model. At each redshift, we
measure the ratio of stellar mass to halo mass, $M_*/M_h$, which shows the
characteristic strong dependence of halo mass with a peak at $M_h^{\rm peak}
\sim 2$. Our results are in accordance with the scenario in which the peak of
star-formation efficiency moves towards more massive halos at higher redshifts.
We also measure the fraction of satellites as a function of stellar mass and
redshift. For all stellar mass thresholds the satellite fraction decreases at
higher redshifts. At a given redshift there is a higher fraction of low-mass
satellites. The satellite contribution to the total stellar mass budget in
halos becomes more important than centrals at halo masses of about $M_h >
10^{13} \, M_{\odot}$ and always stays below by peak, indicating that quenching
mechanisms are present in massive halos that keep the star-formation efficiency
low. Finally, we compare our results with three hydrodynamical simulations
Horizon-AGN, Illustris-TNG-100 and EAGLE. We find that the most significant
discrepancy is at the high mass end, where the simulations generally show that
satellites have a higher contribution to the total stellar mass budget than the
observations. This, together with the finding that the fraction of satellites
is higher in the simulations, indicates that the feedback mechanisms acting in
group-and cluster-scale halos appear to be less efficient in quenching the mass
assembly of satellites, and/or that quenching occurs much later in the
simulations.


title:
Identification of Friction Models for MPC-based Control of a PowerCube
  Serial Robot

abstract:
  For model-based control, an accurate and in its complexity suitable
representation of the real system is a decisive prerequisite for high and
robust control quality. In a structured step-by-step procedure, a model
predictive control (MPC) scheme for a Schunk PowerCube robot is derived.
Neweul-M$^2$ provides the necessary nonlinear model in symbolical and numerical
form. To handle the heavy online computational burden involved with the derived
nonlinear model, a linear time-varying MPC scheme is developed based on
linearizing the nonlinear system concerning the desired trajectory and the a
priori known corresponding feed-forward controller. To improve the
identification of the nonlinear friction models of the joints, a nonlinear
regression method and the Sparse Identification of Nonlinear Dynamics (SINDy)
are compared with each other concerning robustness, online adaptivity, and
necessary preprocessing of the input data. Everything is implemented on a slim,
low-cost control system with a standard laptop PC.


title:
Unified Multivariate Gaussian Mixture for Efficient Neural Image
  Compression

abstract:
  Modeling latent variables with priors and hyperpriors is an essential problem
in variational image compression. Formally, trade-off between rate and
distortion is handled well if priors and hyperpriors precisely describe latent
variables. Current practices only adopt univariate priors and process each
variable individually. However, we find inter-correlations and
intra-correlations exist when observing latent variables in a vectorized
perspective. These findings reveal visual redundancies to improve
rate-distortion performance and parallel processing ability to speed up
compression. This encourages us to propose a novel vectorized prior.
Specifically, a multivariate Gaussian mixture is proposed with means and
covariances to be estimated. Then, a novel probabilistic vector quantization is
utilized to effectively approximate means, and remaining covariances are
further induced to a unified mixture and solved by cascaded estimation without
context models involved. Furthermore, codebooks involved in quantization are
extended to multi-codebooks for complexity reduction, which formulates an
efficient compression procedure. Extensive experiments on benchmark datasets
against state-of-the-art indicate our model has better rate-distortion
performance and an impressive $3.18\times$ compression speed up, giving us the
ability to perform real-time, high-quality variational image compression in
practice. Our source code is publicly available at
\url{https://github.com/xiaosu-zhu/McQuic}.


title: First-passage times to anisotropic partially reactive targets

abstract:
  We investigate restricted diffusion in a bounded domain towards a small
partially reactive target in three- and higher-dimensional spaces. We propose a
simple explicit approximation for the principal eigenvalue of the Laplace
operator with mixed Robin-Neumann boundary conditions. This approximation
involves the harmonic capacity and the surface area of the target, the volume
of the confining domain, the diffusion coefficient and the reactivity. The
accuracy of the approximation is checked by using a finite-elements method. The
proposed approximation determines also the mean first-reaction time, the
long-time decay of the survival probability, and the overall reaction rate on
that target. We identify the relevant length scale of the target, which
determines its trapping capacity, and investigate its relation to the target
shape. In particular, we study the effect of target anisotropy on the principal
eigenvalue by computing the harmonic capacity of prolate and oblate spheroids
in various space dimensions. Some implications of these results in chemical
physics and biophysics are briefly discussed.

categories: cond-mat.stat-mech math-ph math.MP physics.chem-ph


title:
The Case for Technosignatures: Why They May Be Abundant, Long-lived,
  Highly Detectable, and Unambiguous

abstract:
  The intuition suggested by the Drake equation implies that technology should
be less prevalent than biology in the galaxy. However, it has been appreciated
for decades in the SETI community that technosignatures could be more abundant,
longer-lived, more detectable, and less ambiguous than biosignatures. We
collect the arguments for and against technosignatures' ubiquity and discuss
the implications of some properties of technological life that fundamentally
differ from nontechnological life in the context of modern astrobiology: It can
spread among the stars to many sites, it can be more easily detected at large
distances, and it can produce signs that are unambiguously technological. As an
illustration in terms of the Drake equation, we consider two Drake-like
equations, for technosignatures (calculating N(tech)) and biosignatures
(calculating N(bio)). We argue that Earth and humanity may be poor guides to
the longevity term L and that its maximum value could be very large, in that
technology can outlive its creators and even its host star. We conclude that
while the Drake equation implies that N(bio)>>N(tech), it is also plausible
that N(tech)>>N(bio). As a consequence, as we seek possible indicators of
extraterrestrial life, for instance, via characterization of the atmospheres of
habitable exoplanets, we should search for both biosignatures and
technosignatures. This exercise also illustrates ways in which biosignature and
technosignature searches can complement and supplement each other and how
methods of technosignature search, including old ideas from SETI, can inform
the search for biosignatures and life generally.

categories: astro-ph.EP astro-ph.IM cs.CR physics.pop-ph


title:
Document-Level Relation Extraction with Adaptive Focal Loss and
  Knowledge Distillation

abstract:
  Document-level Relation Extraction (DocRE) is a more challenging task
compared to its sentence-level counterpart. It aims to extract relations from
multiple sentences at once. In this paper, we propose a semi-supervised
framework for DocRE with three novel components. Firstly, we use an axial
attention module for learning the interdependency among entity-pairs, which
improves the performance on two-hop relations. Secondly, we propose an adaptive
focal loss to tackle the class imbalance problem of DocRE. Lastly, we use
knowledge distillation to overcome the differences between human annotated data
and distantly supervised data. We conducted experiments on two DocRE datasets.
Our model consistently outperforms strong baselines and its performance exceeds
the previous SOTA by 1.36 F1 and 1.46 Ign_F1 score on the DocRED leaderboard.
Our code and data will be released at https://github.com/tonytan48/KD-DocRE.


title:
Extended Lagrangian approach for the numerical study of multidimensional
  dispersive waves: applications to the Serre-Green-Naghdi equations

abstract:
  In this paper we study two multidimensional nonlinear dispersive systems: the
Serre-Green-Naghdi (SGN) equations describing dispersive shallow water flows,
and Iordanskii-Kogarko-Wijngaarden (IKW) equations describing fluids containing
small compressible gas bubbles. These models are Euler-Lagrange equations for a
given Lagrangian and share common mathematical structure, namely the dependence
of the pressure on material derivatives of macroscopic variables. We develop a
generic dispersive model such that SGN and IKW systems become its special cases
if only one specifies the appropriate Lagrangian, and then use the extended
Lagragian approach proposed in Favrie and Gavrilyuk (2017) to build its
hyperbolic approximation. The new approximate model is unconditionally
hyperbolic for both SGN and IKW cases, and accurately describes dispersive
phenomena, which allows to impose discontinuous initial data and study
dispersive shock waves. We consider the 2-D hyperbolic version of SGN system as
an example for numerical simulations and apply a second order implicit-explicit
scheme in order to numerically integrate the system. The obtained 1-D and 2-D
results are in close agreement with available exact solutions and numerical
tests.
  \textbf{Keywords: } dispersive shallow water equations, bubbly fluids,
Euler-Lagrange equations, hyperbolic conservation laws, multidimensional waves,
implicit-explicit numerical methods


title:
Integrity Fingerprinting of DNN with Double Black-box Design and
  Verification

abstract:
  Cloud-enabled Machine Learning as a Service (MLaaS) has shown enormous
promise to transform how deep learning models are developed and deployed.
Nonetheless, there is a potential risk associated with the use of such services
since a malicious party can modify them to achieve an adverse result.
Therefore, it is imperative for model owners, service providers, and end-users
to verify whether the deployed model has not been tampered with or not. Such
verification requires public verifiability (i.e., fingerprinting patterns are
available to all parties, including adversaries) and black-box access to the
deployed model via APIs. Existing watermarking and fingerprinting approaches,
however, require white-box knowledge (such as gradient) to design the
fingerprinting and only support private verifiability, i.e., verification by an
honest party.
  In this paper, we describe a practical watermarking technique that enables
black-box knowledge in fingerprint design and black-box queries during
verification. The service ensures the integrity of cloud-based services through
public verification (i.e. fingerprinting patterns are available to all parties,
including adversaries). If an adversary manipulates a model, this will result
in a shift in the decision boundary. Thus, the underlying principle of
double-black watermarking is that a model's decision boundary could serve as an
inherent fingerprint for watermarking. Our approach captures the decision
boundary by generating a limited number of encysted sample fingerprints, which
are a set of naturally transformed and augmented inputs enclosed around the
model's decision boundary in order to capture the inherent fingerprints of the
model. We evaluated our watermarking approach against a variety of model
integrity attacks and model compression attacks.


title: Formal Modeling and Initial Analysis of the 4SECURail Case Study

abstract:
  We present the case study developed in the context of the 4SECURail project
and the approach used for its formal modeling and analysis. Starting from a
simple SysML/UML behavioral model of the system requirements, three formal
models have been developed using three different frameworks, namely UMC, ProB,
and CADP/LNT. The paper shows how the different ways to represent and analyze
the system from the three different points of view allow us to take advantage
of the resulting diversity.


title: On the energy efficiency of Laser-based Optical Wireless Networks

abstract:
  Optical wireless Communication (OWC) is a strong candidate in the next
generation (6G) of cellular networks. In this paper, a laser-based optical
wireless network is deployed in an indoor environment using Vertical Cavity
Surface Emitting Lasers (VCSELS) as transmitters serving multiple users.
Specifically, a commercially available low-cost VCSEL operating at 850nm
wavelength is used. Considering the confined coverage area of each VCSEL, an
array of VCSELs is designed to transmit data to multiple users through narrow
beams taking into account eye safety regulations. To manage multi-user
interference (MUI), Zero Forcing (ZF) is implemented to maximize the
multiplexing gain of the network. The energy efficiency of the network is
studied under different laser beam waists to find the effective laser beam size
that results in throughput enhancement. The results show that the energy
efficiency increases with the laser beam waist. Moreover, using micro lenses
placed in front of the VCSELs leads to significant increase in the energy
efficiency.


title: Self-Imitation Learning from Demonstrations

abstract:
  Despite the numerous breakthroughs achieved with Reinforcement Learning (RL),
solving environments with sparse rewards remains a challenging task that
requires sophisticated exploration. Learning from Demonstrations (LfD) remedies
this issue by guiding the agent's exploration towards states experienced by an
expert. Naturally, the benefits of this approach hinge on the quality of
demonstrations, which are rarely optimal in realistic scenarios. Modern LfD
algorithms require meticulous tuning of hyperparameters that control the
influence of demonstrations and, as we show in the paper, struggle with
learning from suboptimal demonstrations. To address these issues, we extend
Self-Imitation Learning (SIL), a recent RL algorithm that exploits the agent's
past good experience, to the LfD setup by initializing its replay buffer with
demonstrations. We denote our algorithm as SIL from Demonstrations (SILfD). We
empirically show that SILfD can learn from demonstrations that are noisy or far
from optimal and can automatically adjust the influence of demonstrations
throughout the training without additional hyperparameters or handcrafted
schedules. We also find SILfD superior to the existing state-of-the-art LfD
algorithms in sparse environments, especially when demonstrations are highly
suboptimal.


title: Bayesian inference in Epidemics: linear noise analysis

abstract:
  This paper offers a qualitative insight into the convergence of Bayesian
parameter inference in a setup which mimics the modeling of the spread of a
disease with associated disease measurements. Specifically, we are interested
in the Bayesian model's convergence with increasing amounts of data under
measurement limitations. Depending on how weakly informative the disease
measurements are, we offer a kind of `best case' as well as a `worst case'
analysis where, in the former case, we assume that the prevalence is directly
accessible, while in the latter that only a binary signal corresponding to a
prevalence detection threshold is available. Both cases are studied under an
assumed so-called linear noise approximation as to the true dynamics. Numerical
experiments test the sharpness of our results when confronted with more
realistic situations for which analytical results are unavailable.


title: X-point and divertor filament dynamics from Gas Puff Imaging on TCV

abstract:
  A new Gas Puff Imaging (GPI) diagnostic has been installed on the TCV
tokamak, providing two-dimensional insights into Scrape-Off-Layer (SOL)
turbulence dynamics above, at and below the magnetic X-point. A detailed study
in L-mode, attached, lower single-null discharges shows that statistical
properties have little poloidal variations, while vast differences are present
in the 2D behaviour of intermittent filaments. Strongly elongated filaments,
just above the X-point and in the divertor far-SOL, show a good consistency in
shape and dynamics with field-line tracing from filaments at the outboard
midplane, highlighting their connection. In the near-SOL of the outer divertor
leg, shortlived, high frequency and more circular (diameter $\sim$15 sound
Larmour radii) filaments are observed. These divertor-localised filaments
appear born radially at the position of maximum density and display a radially
outward motion with velocity $\approx$400\,m/s that is comparable to radial
velocities of upstream-connected filaments. Conversely, in these discharges
($B\times\nabla B$ pointing away from the divertor), these divertor filaments'
poloidal velocities differ strongly from those of upstream-connected filaments.
The importance of divertor-localised filaments upon radial transport and
profile broadening is explored using filament statistics and in-situ kinetic
profile measurements along the divertor leg. This provides evidence that these
filaments contribute significantly to electron density profile broadening in
the divertor.


title: Energy density as a probe of band representations in photonic crystals

abstract:
  Topological Quantum Chemistry (TQC) has recently emerged as a instrumental
tool to characterize the topological nature of both fermionic and bosonic band
structures. TQC is based on the study of band representations and the
localization of maximally localized Wannier functions. In this article, we
study various two-dimensional photonic crystal structures analyzing their
topological character through a combined study of TQC, their Wilson-loop
spectra and the electromagnetic energy density. Our study demonstrates that the
analysis of the spatial localization of the energy density complements the
study of the topological properties in terms of the spectrum of the Wilson-loop
operator and TQC.


title:
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific
  Emotion and Appraisal Annotations

abstract:
  Emotion classification is often formulated as the task to categorize texts
into a predefined set of emotion classes. So far, this task has been the
recognition of the emotion of writers and readers, as well as that of entities
mentioned in the text. We argue that a classification setup for emotion
analysis should be performed in an integrated manner, including the different
semantic roles that participate in an emotion episode. Based on appraisal
theories in psychology, which treat emotions as reactions to events, we compile
an English corpus of written event descriptions. The descriptions depict
emotion-eliciting circumstances, and they contain mentions of people who
responded emotionally. We annotate all experiencers, including the original
author, with the emotions they likely felt. In addition, we link them to the
event they found salient (which can be different for different experiencers in
a text) by annotating event properties, or appraisals (e.g., the perceived
event undesirability, the uncertainty of its outcome). Our analysis reveals
patterns in the co-occurrence of people's emotions in interaction. Hence, this
richly-annotated resource provides useful data to study emotions and event
evaluations from the perspective of different roles, and it enables the
development of experiencer-specific emotion and appraisal classification
systems.


title:
Quad2Plane: An Intermediate Training Procedure for Online Exploration in
  Aerial Robotics via Receding Horizon Control

abstract:
  Data driven robotics relies upon accurate real-world representations to learn
useful policies. Despite our best-efforts, zero-shot sim-to-real transfer is
still an unsolved problem, and we often need to allow our agents to explore
online to learn useful policies for a given task. For many applications of
field robotics online exploration is prohibitively expensive and dangerous,
this is especially true in fixed-wing aerial robotics. To address these
challenges we offer an intermediary solution for learning in field robotics. We
investigate the use of dissimilar platform vehicle for learning and offer a
procedure to mimic the behavior of one vehicle with another. We specifically
consider the problem of training fixed-wing aircraft, an expensive and
dangerous vehicle type, using a multi-rotor host platform. Using a Model
Predictive Control approach, we design a controller capable of mimicking
another vehicles behavior in both simulation and the real-world.


title: Higher order evolution inequalities with nonlinear convolution terms

abstract:
  We are concerned with the study of existence and nonexistence of weak
solutions to $$ \begin{cases} &\displaystyle \frac{\partial^k u}{\partial
t^k}+(-\Delta)^m u\geq (K\ast |u|^p)|u|^q \quad\mbox{ in } \mathbb R^N \times
\mathbb R_+,\\[0.1in] &\displaystyle \frac{\partial^i u}{\partial t^i}(x,0) =
u_i(x) \,\, \text{ in } \mathbb R^N,\, 0\leq i\leq k-1,\\ \end{cases} $$ where
$N,k,m\geq 1$ are positive integers, $p,q>0$ and $u_i\in L^1_{\rm
loc}(\mathbb{R}^N)$ for $0\leq i\leq k-1$. We assume that $K$ is a radial
positive and continuous function which decreases in a neighbourhood of
infinity. In the above problem, $K\ast |u|^p$ denotes the standard convolution
operation between $K(|x|)$ and $|u|^p$. We obtain necessary conditions on
$N,m,k,p$ and $q$ such that the above problem has solutions. Our analysis
emphasizes the role played by the sign of $\displaystyle \frac{\partial^{k-1}
u}{\partial t^{k-1}}$.


title: Depth Completion using Geometry-Aware Embedding

abstract:
  Exploiting internal spatial geometric constraints of sparse LiDARs is
beneficial to depth completion, however, has been not explored well. This paper
proposes an efficient method to learn geometry-aware embedding, which encodes
the local and global geometric structure information from 3D points, e.g.,
scene layout, object's sizes and shapes, to guide dense depth estimation.
Specifically, we utilize the dynamic graph representation to model generalized
geometric relationship from irregular point clouds in a flexible and efficient
manner. Further, we joint this embedding and corresponded RGB appearance
information to infer missing depths of the scene with well structure-preserved
details. The key to our method is to integrate implicit 3D geometric
representation into a 2D learning architecture, which leads to a better
trade-off between the performance and efficiency. Extensive experiments
demonstrate that the proposed method outperforms previous works and could
reconstruct fine depths with crisp boundaries in regions that are over-smoothed
by them. The ablation study gives more insights into our method that could
achieve significant gains with a simple design, while having better
generalization capability and stability. The code is available at
https://github.com/Wenchao-Du/GAENet.


title: An effective decomposition theorem for Schubert varieties

abstract:
  Given a Schubert variety $\mathcal{S}$ contained in a Grassmannian
$\mathbb{G}_{k}(\mathbb{C}^{l})$, we show how to obtain further information on
the direct summands of the derived pushforward $R \pi_{*}
\mathbb{Q}_{\tilde{\mathcal{S}}}$ given by the application of the decomposition
theorem to a suitable resolution of singularities $\pi: \tilde{\mathcal{S}}
\rightarrow \mathcal{S}$. As a by-product, Poincar\'e polynomial expressions
are obtained along with an algorithm which computes the unknown terms in such
expressions and which shows that the actual number of direct summands happens
to be less than the number of supports of the decomposition.


title:
Optimality conditions for nonsmooth nonconvex-nonconcave min-max
  problems and generative adversarial networks

abstract:
  This paper considers a class of nonsmooth nonconvex-nonconcave min-max
problems in machine learning and games. We first provide sufficient conditions
for the existence of global minimax points and local minimax points. Next, we
establish the first-order and second-order optimality conditions for local
minimax points by using directional derivatives. These conditions reduce to
smooth min-max problems with Fr{\'e}chet derivatives. We apply our theoretical
results to generative adversarial networks (GANs) in which two neural networks
contest with each other in a game. Examples are used to illustrate applications
of the new theory for training GANs.


title:
A Euclidean Fourier-analytic approach to vertical projections in the
  Heisenberg group

abstract:
  An improved a.e. lower bound is given for Hausdorff dimension under vertical
projections in the first Heisenberg group, with respect to the
Carnot-Carath\'eodory metric. This improves the known lower bound for sets $A$
with $1< \dim A < 7/2$, and answers a question of F\"assler and Hovila. The
approach uses the Euclidean Fourier transform.


title: DBSOP: An Efficient Heuristic for Speedy MCMC Sampling on Polytopes

abstract:
  Markov Chain Monte Carlo (MCMC) techniques have long been studied in
computational geometry subjects whereabouts the problems to be studied are
complex geometric objects which by their nature require optimized techniques to
be deployed or to gain useful insights by them. MCMC approaches are directly
answering to geometric problems we are attempting to answer, and how these
problems could be deployed from theory to practice. Polytope which is a limited
volume in n-dimensional space specified by a collection of linear inequality
constraints require specific approximation. Therefore, sampling across density
based polytopes can not be performed without the use of such methods in which
the amount of repetition required is defined as a property of error margin. In
this work we propose a simple accurate sampling approach based on the
triangulation (tessellation) of a polytope. Moreover, we propose an efficient
algorithm named Density Based Sampling on Polytopes (DBSOP) for speedy MCMC
sampling where the time required to perform sampling is significantly lower
compared to existing approaches in low dimensions with complexity
$\mathcal{O}^{*}\left(n^{3}\right)$. Ultimately, we highlight possible future
aspects and how the proposed scheme can be further improved with the
integration of reservoir-sampling based methods resulting in more speedy and
efficient solution.


title:
Angular Momentum Inheritance from the Schwinger Effect in
  (Chromo)electromagnetic Fields

abstract:
  The angular momentum of fermion pairs generated by the Schwinger effect is
studied in (chromo)electromagnetic fields, mimicking the early stages of a
heavy-ion collision. It is demonstrated that the angular momentum density of
produced pairs is proportional to that of the background fields. This is argued
both heuristically in a virtual breaking condensate model by evaluating Wong's
equations, and out-of-equilibrium to one-loop using the in-in formalism.


title: A robotics leg inspired from an insect leg

abstract:
  Legged robots can operate in complex terrains that are unreachable for most
wheeled robots. While insect legs contact the ground with the tarsal segments
and pretarsus, most insect-inspired robots come with a simple tarsus such as a
hemispherical foot tip. Insects, like the M. Torquata beetle, use the claws as
their attachment devices on rough surfaces. Their sharp claws can smoothly
attach and detach on plant surfaces by actuating one muscle. Thus, legged
robots can mimic tarsal structures to improve their locomotion on inclined and
rough surfaces. We conducted two types of experiments to test the hypothesis
that the tarsal flexibility and rigidity play a role in the beetle's smooth
walking. By cutting the membrane between the tarsomeres, we disabled the
tarsus' rigid ability, so the claws cannot securely hook onto the walking
surface. Conversely, after eliminating the tarsal flexibility, the beetle
struggled to draw the claws out of the substrate. The results confirm the
significance of the tarsus' properties on beetle walking. Then, we designed a
cable-driven 3D printed tarsus structure to validate the function of the tarsus
via a robotic leg. The tarsal configuration allows the prototype to hook onto
and detach smoothly from the walking surface.


title:
Universal fluctuations and ergodicity of generalized diffusivity on
  critical percolation clusters

abstract:
  Despite a long history and a clear overall understanding of properties of
random walks on an incipient infinite cluster in percolation, some important
information on it seems to be missing in the literature. In the present work,
we revisit the problem by performing massive numerical simulations for
(sub)diffusion of particles on such clusters. Thus, we discuss the shape of the
probability density function (PDF) of particles' displacements, and the way it
converges to its long-time limiting scaling form. Moreover, we discuss the
properties of the mean squared displacement (MSD) of a particle diffusing on
the infinite cluster at criticality. This one is known not to be
self-averaging. We show that the fluctuations of the MSD in different
realizations of the cluster are universal, and discuss the properties of the
distribution of these fluctuations. These strong fluctuations coexist with the
ergodicity of subdiffusive behavior in the time domain. The dependence of the
relative strength of fluctuations in time-averaged MSD on the total trajectory
length (total simulation time) is characteristic for diffusion in a percolation
system and can be used as an additional test to distinguish this process with
disorder-induced memory from processes with otherwise similar behavior, like
fractional Brownian motion with the same value of the Hurst exponent.


title:
Towards integrating hardware Data Plane acceleration in Network
  Functions Virtualization

abstract:
  This paper proposes a framework for integrating data plane (DP) acceleration
within the Network Functions Virtualization (NFV) architecture. Data plane
programming (DPP) proves to be beneficial for NFV environments, as it provides
full packet forwarding flexibility through the use of self-designed algorithms.
Additionally, DPP provides high-performance networking, as the DP can be
configured to execute specific functions on dedicated hardware. We present an
integration of the DP acceleration within the ETSI NFV architecture that
leverages custom DP functions implemented in hardware switches using P4
language. Besides, OpenStack and Kubernetes are used as Virtualized
Infrastructure Managers (VIMs) and Open Source MANO (OSM) as the Management and
Orchestration (MANO) element.


title:
Design and fabrication of ridge waveguide-based nanobeam cavities for
  on-chip single-photon sources

abstract:
  We report on the design of nanohole/nanobeam cavities in ridge waveguides for
on-chip, quantum-dot-based single-photon generation. Our design overcomes
limitations of a low-refractive-index-contrast material platform in terms of
emitter-mode coupling efficiency and yields an outcoupling efficiency of 0.73
to the output ridge waveguide. Importantly, this high coupling efficiency is
combined with broadband operation of 9 nm full-width half-maximum. We provide
an explicit design procedure for identifying the optimum geometrical parameters
according to the developed design. Besides, we fabricate and optically
characterize a proof-of-concept waveguide structure. The results of the
microphotoluminescence measurements provide evidence for cavity-enhanced
spontaneous emission from the quantum dot, thus supporting the potential of our
design for on-chip single-photon sources applications.


title:
Who Should Review Your Proposal? Interdisciplinary Topic Path Detection
  for Research Proposals

abstract:
  The peer merit review of research proposals has been the major mechanism to
decide grant awards. Nowadays, research proposals have become increasingly
interdisciplinary. It has been a longstanding challenge to assign proposals to
appropriate reviewers. One of the critical steps in reviewer assignment is to
generate accurate interdisciplinary topic labels for proposals. Existing
systems mainly collect topic labels manually reported by discipline
investigators. However, such human-reported labels can be non-accurate and
incomplete. What role can AI play in developing a fair and precise proposal
review system? In this evidential study, we collaborate with the National
Science Foundation of China to address the task of automated interdisciplinary
topic path detection. For this purpose, we develop a deep Hierarchical
Interdisciplinary Research Proposal Classification Network (HIRPCN). We first
propose a hierarchical transformer to extract the textual semantic information
of proposals. We then design an interdisciplinary graph and leverage GNNs to
learn representations of each discipline in order to extract interdisciplinary
knowledge. After extracting the semantic and interdisciplinary knowledge, we
design a level-wise prediction component to fuse the two types of knowledge
representations and detect interdisciplinary topic paths for each proposal. We
conduct extensive experiments and expert evaluations on three real-world
datasets to demonstrate the effectiveness of our proposed model.


title: TinyMLOps: Operational Challenges for Widespread Edge AI Adoption

abstract:
  Deploying machine learning applications on edge devices can bring clear
benefits such as improved reliability, latency and privacy but it also
introduces its own set of challenges. Most works focus on the limited
computational resources of edge platforms but this is not the only bottleneck
standing in the way of widespread adoption. In this paper we list several other
challenges that a TinyML practitioner might need to consider when
operationalizing an application on edge devices. We focus on tasks such as
monitoring and managing the application, common functionality for a MLOps
platform, and show how they are complicated by the distributed nature of edge
deployment. We also discuss issues that are unique to edge applications such as
protecting a model's intellectual property and verifying its integrity.


title:
Antiferromagnetic cavity magnon polaritons in collinear and canted
  phases of hematite

abstract:
  Cavity spintronics explores light matter interactions at the interface
between spintronic and quantum phenomena. Until now, studies have focused on
the hybridization between ferromagnets and cavity photons.In this article, we
realize antiferromagnetic cavity-magnon polaritons. The collective spin motion
in single hematite crystals ({\alpha}-Fe2O3) hybridizes with 18 - 45 GHz
microwave cavity photons with required specific symmetries. We show
theoretically and experimentally that the photon-magnon coupling in the
collinear phase is mediated by the dynamical Neel vector and the weak magnetic
moment in the canted phase by measuring across the Morin transition. The
coupling strength g is shown to scale with the anisotropy field in the
collinear phase and with the Dzyaloshinskii-Moriya field in the canted phase.
We achieve a strong coupling regime both in canted (C > 25 at 300 K) and
noncolinear phases (C > 4 at 150 K) and thus coherent information exchange with
antiferromagnets These results evidence a generic strategy to achieve
cavity-magnon polaritons in antiferromagnets for different symmetries, opening
the field of cavity spintronics to antiferromagnetic materials.


title:
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised
  Monocular Depth Estimation

abstract:
  Self-supervised monocular depth estimation, aiming to learn scene depths from
single images in a self-supervised manner, has received much attention
recently. In spite of recent efforts in this field, how to learn accurate scene
depths and alleviate the negative influence of occlusions for self-supervised
depth estimation, still remains an open problem. Addressing this problem, we
firstly empirically analyze the effects of both the continuous and discrete
depth constraints which are widely used in the training process of many
existing works. Then inspired by the above empirical analysis, we propose a
novel network to learn an Occlusion-aware Coarse-to-Fine Depth map for
self-supervised monocular depth estimation, called OCFD-Net. Given an arbitrary
training set of stereo image pairs, the proposed OCFD-Net does not only employ
a discrete depth constraint for learning a coarse-level depth map, but also
employ a continuous depth constraint for learning a scene depth residual,
resulting in a fine-level depth map. In addition, an occlusion-aware module is
designed under the proposed OCFD-Net, which is able to improve the capability
of the learnt fine-level depth map for handling occlusions. Extensive
experimental results on the public KITTI and Make3D datasets demonstrate that
the proposed method outperforms 20 existing state-of-the-art methods in most
cases.


title:
3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge
  Modality Attention

abstract:
  Online 3D multi-object tracking (MOT) has witnessed significant research
interest in recent years, largely driven by demand from the autonomous systems
community. However, 3D offline MOT is relatively less explored. Labeling 3D
trajectory scene data at a large scale while not relying on high-cost human
experts is still an open research question. In this work, we propose Batch3DMOT
that follows the tracking-by-detection paradigm and represents real-world
scenes as directed, acyclic, and category-disjoint tracking graphs that are
attributed using various modalities such as camera, LiDAR, and radar. We
present a multi-modal graph neural network that uses a cross-edge attention
mechanism mitigating modality intermittence, which translates into sparsity in
the graph domain. Additionally, we present attention-weighted convolutions over
frame-wise k-NN neighborhoods as suitable means to allow information exchange
across disconnected graph components. We evaluate our approach using various
sensor modalities and model configurations on the challenging nuScenes and
KITTI datasets. Extensive experiments demonstrate that our proposed approach
yields an overall improvement of 2.8% in the AMOTA score on nuScenes thereby
setting a new benchmark for 3D tracking methods and successfully enhances false
positive filtering.


title: Dynamical quantum phase transitions in a noisy lattice gauge theory

abstract:
  Lattice gauge theories (LGTs) form an intriguing class of theories highly
relevant to both high-energy particle physics and low-energy condensed matter
physics with the rapid development of engineered quantum devices providing new
tools to study e.g. dynamics of such theories. The massive Schwinger model is
known to exhibit intricate properties of more complicated theories and has
recently been shown to undergo dynamical quantum phase transitions out of
equilibrium. With current technology, noise is inevitable and potentially fatal
for a successful quantum simulation. This paper studies the dynamics subject to
noise of a $(1+1)$D U$(1)$ quantum link model following a quench of the sign of
the mass term. We find that not only is the system capable of handling noise at
rates realistic in NISQ-era devices, promising the possiblity to study the
target dynamics with current technology, but the effect of noise can be
understood in terms of simple models. Specifically the gauge-breaking nature of
bit-flip channels results in exponential dampening of state amplitudes, and
thus observables, which does not affect the structures of interest. This is
especially important as it demonstrates that the gauge theory can be
successfully studied with devices that only exhibit approximate gauge
invariance.


title: Increasing extractable work in small qubit landscapes

abstract:
  An interesting class of physical systems, including those associated with
life, demonstrates the ability to hold thermalization at bay and perpetuate
states of high free energy compared to a local environment. In this work we
study simple quantum systems with no external sources or sinks for energy,
heat, work, or entropy that allow for high free-energy subsystems to form and
persist. We initialize qubit systems in mixed, uncorrelated states and evolve
them subject to a conservation law. We find that four qubits, one of which sets
the reference temperature, make up the minimal system for which these
restricted dynamics and initial conditions allow an increase in extractable
work for a subsystem. We then consider landscapes of eight qubits where
unitary, energy and excitation-number conserving evolution happens among
neighbors randomly selected at each step. We study the frequency of time-steps
with positive change in extractable work, and the total increase in extractable
work. We show how the level of diversity in the initial state, the connectivity
of the qubits, and the choice of unitary within the allowed family affect the
frequency of instances where individual qubits experience an increase in
extractable work.


title:
General and Domain Adaptive Chinese Spelling Check with Error Consistent
  Pretraining

abstract:
  The lack of label data is one of the significant bottlenecks for Chinese
Spelling Check (CSC). Existing researches use the method of automatic
generation by exploiting unlabeled data to expand the supervised corpus.
However, there is a big gap between the real input scenario and automatic
generated corpus. Thus, we develop a competitive general speller ECSpell which
adopts the Error Consistent masking strategy to create data for pretraining.
This error consistency masking strategy is used to specify the error types of
automatically generated sentences which is consistent with real scene. The
experimental result indicates our model outperforms previous state-of-the-art
models on the general benchmark. Moreover, spellers often work within a
particular domain in real life. Due to lots of uncommon domain terms,
experiments on our built domain specific datasets show that general models
perform terribly. Inspired by the common practice of input methods, we propose
to add an alterable user dictionary to handle the zero-shot domain adaption
problem. Specifically, we attach a User Dictionary guided inference module (UD)
to a general token classification based speller. Our experiments demonstrate
that ECSpell$^{UD}$, namely ECSpell combined with UD, surpasses all the other
baselines largely, even approaching the performance on the general benchmark.


title:
An integrated Auto Encoder-Block Switching defense approach to prevent
  adversarial attacks

abstract:
  According to recent studies, the vulnerability of state-of-the-art Neural
Networks to adversarial input samples has increased drastically. A neural
network is an intermediate path or technique by which a computer learns to
perform tasks using Machine learning algorithms. Machine Learning and
Artificial Intelligence model has become a fundamental aspect of life, such as
self-driving cars [1], smart home devices, so any vulnerability is a
significant concern. The smallest input deviations can fool these extremely
literal systems and deceive their users as well as administrator into
precarious situations. This article proposes a defense algorithm that utilizes
the combination of an auto-encoder [3] and block-switching architecture.
Auto-coder is intended to remove any perturbations found in input images
whereas the block switching method is used to make it more robust against
White-box attacks. The attack is planned using FGSM [9] model, and the
subsequent counter-attack by the proposed architecture will take place thereby
demonstrating the feasibility and security delivered by the algorithm.