title: Investigation of CMB constraints for dark matter-helium scattering

abstract:
  We study dark matter-helium scattering in the early Universe and its impact
on constraints from cosmic microwave background (CMB) anisotropy measurements.
We describe possible theoretical frameworks for dark matter-nucleon
interactions via a scalar, pseudoscalar, or vector mediator; such interactions
give rise to hydrogen and helium scattering, with cross sections that have a
power-law dependence on relative velocity. Within these frameworks, we consider
three scenarios: dark matter coupling to only neutrons, to only protons, and to
neutrons and protons with equal strength. For these various cases, we use
\textit{Planck} 2018 temperature, polarization, and lensing anisotropy data to
place constraints on dark matter scattering with hydrogen and/or helium for
dark matter masses between 10 keV and 1 TeV. For any model that permits both
helium and hydrogen scattering with a non-negative power-law velocity
dependence, we find that helium scattering dominates the constraint for dark
matter masses well above the proton mass. Furthermore, we place the first CMB
constraints on dark matter that scatters dominantly/exclusively with helium in
the early Universe.


title:
A Pluto--Charon Sonata IV. Improved Constraints on the Dynamical
  Behavior and Masses of the Small Satellites

abstract:
  We discuss a new set of $\sim$ 500 numerical n-body calculations designed to
constrain the masses and bulk densities of Styx, Nix, Kerberos, and Hydra.
Comparisons of different techniques for deriving the semimajor axis and
eccentricity of the four satellites favor methods relying on the theory of Lee
& Peale (2006), where satellite orbits are derived in the context of the
restricted three body problem (Pluto, Charon, and one massless satellite). In
each simulation, we adopt the nominal satellite masses derived in Kenyon &
Bromley (2019a), multiply the mass of at least one satellite by a numerical
factor $f \ge 1$, and establish whether the system ejects at least one
satellite on a time scale $\le$ 4.5 Gyr. When the total system mass is large
($f \gg 1$), ejections of Kerberos are more common. Systems with lower
satellite masses ($ f \approx$ 1) usually eject Styx. In these calculations,
Styx often `signals' an ejection by moving to higher orbital inclination long
before ejection; Kerberos rarely signals in a useful way. The n-body results
suggest that Styx and Kerberos are more likely to have bulk densities
comparable with water ice, $\rho_{SK} \lesssim$ 2 g cm$^{-3}$, than with rock.
A strong upper limit on the total system mass, $M_{SNKH} \lesssim 9.5 \times
10^{19}$ g, also places robust constraints on the average bulk density of the
four satellites, $\rho_{SNKH} \lesssim$ 1.4 g cm$^{-3}$. These limits support
models where the satellites grow out of icy material ejected during a major
impact on Pluto or Charon.


title: Probing light mediators at the MUonE experiment

abstract:
  The MUonE experiment, that aims to provide a precise measurement of the
hadronic vacuum polarization contribution to the muon $g-2$ via elastic
muon-electron scattering, has also the potential to explore the parameter space
of light new physics. Exploiting the process $\mu^- N \to \mu^- N X$, where $N$
is the target nucleus and X is a new physics light mediator, we demonstrate
that MUonE can be sensitive to new regions of parameter space for sub-GeV dark
photons. In particular, thanks to its muon beam, MUonE will be able to explore
uncharted parameter space regions for the $L_\mu-L_\tau$ model. Finally, we
also find that MUonE can probe the parameter space of axion-like particles for
different assumptions of the couplings to electrons, muons and photons.


title: Gravitational Waves from Domain Walls in Pulsar Timing Array Datasets

abstract:
  We present a model-independent search for the gravitational wave background
from cosmic domain walls (DWs) in the NANOGrav 12.5 years dataset and
International PTA Data Release 2. DWs that annihilate at temperatures $\sim
20-50~\text{MeV}$ with tensions $\sim (40-100~\text{TeV})^3$ provide as good a
fit to both datasets as the astrophysical background from supermassive black
hole mergers. DWs may decay into the Standard Model (SM) or a dark sector. In
the latter case we predict an abundance $\Delta N_{\text{eff}}$ of dark
radiation well within the reach of upcoming CMB surveys. Complementary
signatures at colliders and laboratories can arise if couplings to the SM are
present. As an example, we discuss heavy axion scenarios, where DW annihilation
may interestingly be induced by QCD confinement.


title:
Tunable Stripe Order and Weak Superconductivity in the Moir\'e Hubbard
  Model

abstract:
  The moir\'e Hubbard model describes correlations in certain homobilayer
twisted transition metal dichalcogenides. Using exact diagonalization and
density matrix renormalization group methods, we find magnetic Mott insulating
and metallic phases, which, upon doping exhibit intertwined charge and spin
ordering and, in some regimes, pair binding of holes. The phases are highly
tunable via an interlayer potential difference. Remarkably, the hole binding
energy is found to be highly tunable revealing an experimentally accessible
regime where holes become attractive. In this attractive regime, we study the
superconducting correlation function and point out the possibility of weak
superconductivity.


title:
Study of the equatorial ionosphere using the Giant Metrewave Radio
  Telescope (GMRT) at sub-GHz frequencies

abstract:
  Radio interferometers, which are designed to observe astrophysical objects in
the universe, can also be used to study the Earth's ionosphere. Radio
interferometers like the Giant Metrewave Radio Telescope (GMRT) detect
variations in ionospheric total electron content (TEC) on a much wider spatial
scale at a relatively higher sensitivity than traditional ionospheric probes
like the Global Navigation Satellite System (GNSS). The hybrid configuration of
the GMRT (compact core and extended arms) and its geographical location make
this interferometer an excellent candidate to explore the sensitive regions
between the northern crest of the Equatorial Ionization Anomaly (EIA) and the
magnetic equator. For this work, a bright radio source, 3C68.2, is observed
from post-midnight to post-sunrise ($\sim$\,9 hours) to study the ionospheric
activities at solar-minima. This study presents data reduction and processing
techniques to measure differential TEC ($\delta\rm{TEC}$) between the set of
antennas with an accuracy of $1\times10^{-3}$ TECU. Furthermore, using these
$\delta\rm{TEC}$ measurements, we have demonstrated techniques to compute the
TEC gradient over the full array and micro-scale variation in two-dimensional
TEC gradient surface. These variations are well equipped to probe ionospheric
plasma, especially during the night-time. Our study, for the first time,
reports the capability of the GMRT to detect ionospheric activities. Our result
validates, compared to previous studies with VLA, LOFAR and MWA, the ionosphere
over the GMRT is more active, which is expected due to its location near the
magnetic equator.


title:
Exploring and Validating Exoplanet Atmospheric Retrievals with Solar
  System Analog Observations

abstract:
  Solar System observations that serve as analogs for exoplanet remote sensing
data can provide important opportunities to validate ideas and models related
to exoplanet environments. Critically, and unlike true exoplanet observations,
Solar System analog data benefit from available high-quality ground- or
orbiter-derived "truth" constraints that enable strong validations of exoplanet
data interpretation tools. In this work, we first present a versatile
atmospheric retrieval suite, capable of application to reflected light, thermal
emission, and transmission observations. The tool -- dubbed rfast -- is
designed, in part, to enable exoplanet mission concept feasibility studies.
Following model validation, the retrieval tool is applied to a range of Solar
System analog observations for exoplanet environments. Retrieval studies using
Earth reflected light observations from NASA's EPOXI mission provide a key
proof-of-concept for under-development exo-Earth direct imaging concept
missions. Inverse modeling applied to an infrared spectrum of Earth from the
Mars Global Surveyor Thermal Emission Spectrometer achieves good constraints on
atmospheric gases, including many biosignature gases. Finally, retrieval
analysis applied to a transit spectrum of Titan derived from the Cassini Visual
and Infrared Mapping Spectrometer provides a proof-of-concept for interpreting
more feature-rich transiting exoplanet observations from NASA's James Webb
Space Telescope (JWST). In the future, Solar System analog observations for
exoplanets could be used to verify exoplanet models and parameterizations, and
future exoplanet analog observations of any Solar System worlds from planetary
science missions should be encouraged.


title:
Dark Matter constraints from Planck observations of the Galactic
  polarized synchrotron emission

abstract:
  Dark Matter (DM) annihilation in our Galaxy may produce a linearly polarized
synchrotron signal. We use, for the first time, synchrotron polarization to
constrain the DM annihilation cross section by comparing theoretical
predictions with the latest polarization maps obtained by the Planck satellite
collaboration. We find that synchrotron polarization is typically more
constraining than synchrotron intensity by about one order of magnitude,
independently of uncertainties in the modeling of electron and positron
propagation, or of the Galactic magnetic field. Our bounds compete with Cosmic
Microwave Background limits in the case of leptophilic DM.


title:
The chemical characterisation of halo substructure in the Milky Way
  based on APOGEE

abstract:
  Galactic haloes in a $\Lambda$-Cold Dark Matter ($\Lambda$CDM) universe are
predicted to host today a swarm of debris resulting from cannibalised dwarf
galaxies that have been accreted via the process of hierarchical mass assembly.
The chemo-dynamical information recorded in the Galactic stellar populations
associated with such systems helps elucidate their nature, placing constraints
on the mass assembly history of the Milky Way. Using data from the APOGEE and
\textit{Gaia} surveys, we examine APOGEE targets belonging to the following
substructures in the stellar halo: Heracles, \textit{Gaia}-Enceladus/Sausage
(GES), Sagittarius dSph, the Helmi stream, Sequoia, Thamnos, Aleph, LMS-1,
Arjuna, I'itoi, Nyx, Icarus, and Pontus. We examine the distributions of all
substructures in chemical space, considering the abundances of elements
sampling various nucleosynthetic pathways. Our main findings include: {\it i)}
the chemical properties of GES, Heracles, the Helmi stream, Sequoia, Thamnos,
LMS-1, Arjuna, and I'itoi match qualitatively those of dwarf satellites of the
Milky Way, such as the Sagittarius dSph; {\it ii)} the abundance pattern of the
recently discovered inner Galaxy substructure Heracles differs statistically
from that of populations formed {\it in situ}. Heracles also differs chemically
from all other substructures; {\it iii)} the abundance patterns of Sequoia
(selected in various ways), Arjuna, LMS-1, and I'itoi are indistinguishable
from that of GES, indicating a possible common origin; {\it iv)} the abundance
patterns of the Helmi stream and Thamnos substructures are different from all
other halo substructures; {\it v)} the chemical properties of Nyx and Aleph are
very similar to those of disc stars, implying that these substructures likely
have an \textit{in situ} origin.


title:
A selection of H{\alpha} emitters at z = 2.1-2.5 using the Ks-band
  photometry of ZFOURGE

abstract:
  Large and less-biased samples of star-forming galaxies are essential to
investigate galaxy evolution. H{\alpha} emission line is one of the most
reliable tracers of star-forming galaxies because its strength is directly
related to recent star formation. However, it is observationally expensive to
construct large samples of H{\alpha} emitters by spectroscopic or narrow-band
imaging survey at high-redshifts. In this work, we demonstrate a method to
extract H{\alpha} fluxes of galaxies at z = 2.1-2.5 from Ks broad-band
photometry of ZFOURGE catalog. Combined with 25-39 other filters, we estimate
the emission line fluxes by SED fitting with stellar population models that
incorporate emission-line strengths. 2005 galaxies are selected as H{\alpha}
emitters by our method and their fluxes show good agreement with previous
measurements in the literature. On the other hand, there are more H{\alpha}
luminous galaxies than previously reported. The discrepancy can be explained by
extended H{\alpha} profiles of massive galaxies and a luminosity dependence of
dust attenuation, which are not taken into account in the previous work. We
also find that there are a large number of low-mass galaxies with much higher
specific star formation rate (sSFR) than expected from the extrapolated star
formation main sequence. Such low-mass galaxies exhibit larger ratios between
H{\alpha} and UV fluxes compared to more massive high sSFR galaxies. This
result implies that a "starburst" mode may differ among galaxies: low-mass
galaxies appear to assemble their stellar mass via short-duration bursts while
more massive galaxies tend to experience longer-duration (> 10 Myr) bursts.


title:
Vision-Based American Sign Language Classification Approach via Deep
  Learning

abstract:
  Hearing-impaired is the disability of partial or total hearing loss that
causes a significant problem for communication with other people in society.
American Sign Language (ASL) is one of the sign languages that most commonly
used language used by Hearing impaired communities to communicate with each
other. In this paper, we proposed a simple deep learning model that aims to
classify the American Sign Language letters as a step in a path for removing
communication barriers that are related to disabilities.


title:
ChildCI Framework: Analysis of Motor and Cognitive Development in
  Children-Computer Interaction for Age Detection

abstract:
  This article presents a comprehensive analysis of the different tests
proposed in the recent ChildCI framework, proving its potential for generating
a better understanding of children's neuromotor and cognitive development along
time, as well as their possible application in other research areas such as
e-Health and e-Learning. In particular, we propose a set of over 100 global
features related to motor and cognitive aspects of the children interaction
with mobile devices, some of them collected and adapted from the literature.
Furthermore, we analyse the robustness and discriminative power of the proposed
feature set including experimental results for the task of children age group
detection based on their motor and cognitive behaviors. Two different scenarios
are considered in this study: i) single-test scenario, and ii) multiple-test
scenario. Results over 93% accuracy are achieved using the publicly available
ChildCIdb_v1 database (over 400 children from 18 months to 8 years old),
proving the high correlation of children's age with the way they interact with
mobile devices.


title:
The ultra-high-energy neutrino-nucleon cross section: measurement
  forecasts for an era of cosmic EeV-neutrino discovery

abstract:
  Neutrino interactions with protons and neutrons probe their deep structure
and may reveal new physics. The higher the neutrino energy, the sharper the
probe. So far, the neutrino-nucleon ($\nu N$) cross section is known across
neutrino energies from a few hundred MeV to a few PeV. Soon, ultra-high-energy
(UHE) cosmic neutrinos, with energies above 100 PeV, could take us farther. So
far, they have evaded discovery, but upcoming UHE neutrino telescopes endeavor
to find them. We present the first detailed measurement forecasts of the UHE
$\nu N$ cross section, geared to IceCube-Gen2, one of the leading detectors
under planning. We use state-of-the-art ingredients in every stage of our
forecasts: in the UHE neutrino flux predictions, the neutrino propagation
inside Earth, the emission of neutrino-induced radio signals in the detector,
their propagation and detection, and the treatment of backgrounds. After 10
years, if at least a few tens of UHE neutrino-induced events are detected,
IceCube-Gen2 could measure the $\nu N$ cross section at center-of-mass energies
of $\sqrt{s} \approx 10-100$ TeV for the first time, with a precision
comparable to that of its theory prediction.


title:
Elastic shape analysis of surfaces with second-order Sobolev metrics: a
  comprehensive numerical framework

abstract:
  This paper introduces a set of numerical methods for Riemannian shape
analysis of 3D surfaces within the setting of invariant (elastic) second-order
Sobolev metrics. More specifically, we address the computation of geodesics and
geodesic distances between parametrized or unparametrized immersed surfaces
represented as 3D meshes. Building on this, we develop tools for the
statistical shape analysis of sets of surfaces, including methods for
estimating Karcher means and performing tangent PCA on shape populations, and
for computing parallel transport along paths of surfaces. Our proposed approach
fundamentally relies on a relaxed variational formulation for the geodesic
matching problem via the use of varifold fidelity terms, which enable us to
enforce reparametrization independence when computing geodesics between
unparametrized surfaces, while also yielding versatile algorithms that allow us
to compare surfaces with varying sampling or mesh structures. Importantly, we
demonstrate how our relaxed variational framework can be extended to tackle
partially observed data. The different benefits of our numerical pipeline are
illustrated over various examples, synthetic and real.


title: Heat kernels of non-local Schr\"odinger operators with Kato potentials

abstract:
  We study heat kernels of Schr\"odinger operators whose kinetic terms are
non-local operators built for sufficiently regular symmetric L\'evy measures
with radial decreasing profiles and potentials belong to Kato class. Our
setting is fairly general and novel -- it allows us to treat both heavy- and
light-tailed L\'evy measures in a joint framework. We establish a certain
relative-Kato bound for the corresponding semigroups and potentials. This
enables us to apply a general perturbation technique to construct the heat
kernels and give sharp estimates of them. Assuming that the L\'evy measure and
the potential satisfy a little stronger conditions, we additionally obtain the
regularity of the heat kernels. Finally, we discuss the applications to the
smoothing properties of the corresponding semigroups. Our results cover many
important examples of non-local operators, including fractional and
quasi-relativistic Schr\"odinger operators.


title: Controlling Traffic with Humanoid Social Robot

abstract:
  The advancement of technology such as artificial intelligence, machine
learning and internet of things it became easy to develop more humanoid robots
and automate different processes. An interactive robot must have high social
behavior so that it can be easily accepted by the people using it. In this
study we designed a traffic police robot (TRAPROB) to automate the traffic
control at intersection. The human police officer experiences high stress
because of long duty hours as well as pose the risk of accidents. The digital
electronic signals are automatic but we want to create a system which is more
human like and looks like an officer controlling the traffic at intersection.
We used Thiago++ robot in this study and modified its look to like a police
officer, and then programmed it to imitate and make gestures just like traffic
police officer makes gestures for controlling traffic. We evaluated the looks,
gestures, functionality, and social behavior of the robot. We asked a limited
sample of two participants to identify the TRAPBOT, rate its look, the social
behaviors and gestures in comparison to a real life police officer. we found
that people can identify the robot as traffic police robot. Our analysis also
shows that TRAPBOT has appearance like a traffic robot and can make similar
signal gestures as a traffic police officer.


title:
Nucleation and growth of iron pebbles explains the formation of
  iron-rich planets akin to Mercury

abstract:
  The pathway to forming the iron-rich planet Mercury remains mysterious.
Mercury's core makes up 70% of the planetary mass, which implies a significant
enrichment of iron relative to silicates, while its mantle is strongly depleted
in oxidized iron. The high core mass fraction is traditionally ascribed to
evaporative loss of silicates, e.g. following a giant impact, but the high
abundance of moderately volatile elements in the mantle of Mercury is
inconsistent with reaching temperatures much above 1,000 K during its
formation. Here we explore the nucleation of solid particles from a gas of
solar composition that cools down in the hot inner regions of the
protoplanetary disc. The high surface tension of iron causes iron particles to
nucleate homogeneously (i.e., not on a more refractory substrate) under very
high supersaturation. The low nucleation rates lead to depositional growth of
large iron pebbles on a sparse population of nucleated iron nano-particles.
Silicates in the form of iron-free MgSiO$_3$ nucleate at similar temperatures
but obtain smaller sizes due to the much higher number of nucleated particles.
This results in a chemical separation of large iron particles from silicate
particles with ten times lower Stokes numbers. We propose that such conditions
lead to the formation of iron-rich planetesimals by the streaming instability.
In this view, Mercury formed by accretion of iron-rich planetesimals with a
sub-solar abundance of highly reduced silicate material. Our results imply that
the iron-rich planets known to orbit the Sun and other stars are not required
to have experienced mantle-stripping impacts. Instead their formation could be
a direct consequence of temperature fluctuations in protoplanetary discs and
chemical separation of distinct crystal species through the ensuing nucleation
process.


title: Exploiting complex pattern features for interactive pattern mining

abstract:
  Recent years have seen a shift from a pattern mining process that has users
define constraints before-hand, and sift through the results afterwards, to an
interactive one. This new framework depends on exploiting user feedback to
learn a quality function for patterns. Existing approaches have a weakness in
that they use static pre-defined low-level features, and attempt to learn
independent weights representing their importance to the user. As an
alternative, we propose to work with more complex features that are derived
directly from the pattern ranking imposed by the user. Learned weights are then
aggregated onto lower-level features and help to drive the quality function in
the right direction. We explore the effect of different parameter choices
experimentally and find that using higher-complexity features leads to the
selection of patterns that are better aligned with a hidden quality function
while not adding significantly to the run times of the method.
  Getting good user feedback requires to quickly present diverse patterns,
something that we achieve but pushing an existing diversity constraint into the
sampling component of the interactive mining system LetSip. Resulting patterns
allow in most cases to converge to a good solution more quickly.
  Combining the two improvements, finally, leads to an algorithm showing clear
advantages over the existing state-of-the-art.


title:
Mantle Degassing Lifetimes through Galactic Time and the Maximum Age
  Stagnant-lid Rocky Exoplanets can Support Temperate Climates

abstract:
  The ideal exoplanets to search for life are those within a star's habitable
zone. However, even within the habitable zone planets can still develop
uninhabitable climate states. Sustaining a temperate climate over geologic
($\sim$Gyr) timescales requires a planet contain sufficient internal energy to
power a planetary-scale carbon cycle. A major component of a rocky planet's
energy budget is the heat produced by the decay of radioactive elements,
especially $^{40}$K, $^{232}$Th, $^{235}$U and $^{238}$U. As the planet ages
and these elements decay, this radiogenic energy source dwindles. Here we
estimate the probability distribution of the amount of these heat producing
elements (HPEs) that enter into rocky exoplanets through Galactic history, by
combining the system-to-system variation seen in stellar abundance data with
the results from Galactic chemical evolution models. Using these distributions,
we perform Monte-Carlo thermal evolution models that maximize the mantle
cooling rate. This allows us to create a pessimistic estimate of lifetime a
rocky, stagnant-lid exoplanet can support a global carbon cycle and temperate
climate as a function of its mass and when it in Galactic history. We apply
this framework to a sample of 17 likely rocky exoplanets with measured ages, 7
of which we predict are likely to be actively degassing today despite our
pessimistic assumptions. For the remaining planets, including those orbiting
TRAPPIST-1, we cannot confidently assume they currently contain sufficient
internal heat to support mantle degassing at a rate sufficient to sustain a
global carbon cycle or temperate climate without additional tidal heating or
undergoing plate tectonics.


title:
How does online teamwork change student communication patterns in
  programming courses?

abstract:
  Online teaching has become a new reality due to the COVID-19 pandemic raising
a lot of questions about its learning outcomes. Recent studies have shown that
peer communication positively affects learning outcomes of online teaching.
However, it is not clear how collaborative programming tasks change peer
communication patterns in the learning process. In this study, we compare
communication patterns in MOOCs where peer communication is limited with those
of a blended course in which students are involved in online peer instruction.
We used a mixed-method approach comprising automated text analysis and
community extraction with further qualitative analysis. The results show that
students prefer to seek help in programming from peers and not the teacher.
Team assignment helped to support this habit. Students communicated more
positively and intensively with each other, while only team leaders
communicated with the instructor reducing teacher overload. This shift could
explain how peer communication improves learning outcomes, as has been shown in
previous studies on MOOCs.


title:
Online Emotions During the Storming of the U.S. Capitol: Evidence from
  the Social Media Network Parler

abstract:
  The storming of the U.S. Capitol on January 6, 2021 has led to the killing of
5 people and is widely regarded as an attack on democracy. The storming was
largely coordinated through social media networks such as Twitter and "Parler".
Yet little is known regarding how users interacted on Parler during the
storming of the Capitol. In this work, we examine the emotion dynamics on
Parler during the storming with regard to heterogeneity across time and users.
For this, we segment the user base into different groups (e.g., Trump
supporters and QAnon supporters). We use affective computing to infer the
emotions in content, thereby allowing us to provide a comprehensive assessment
of online emotions. Our evaluation is based on a large-scale dataset from
Parler, comprising of 717,300 posts from 144,003 users. We find that the user
base responded to the storming of the Capitol with an overall negative
sentiment. Akin to this, Trump supporters also expressed a negative sentiment
and high levels of unbelief. In contrast to that, QAnon supporters did not
express a more negative sentiment during the storming. We further provide a
cross-platform analysis and compare the emotion dynamics on Parler and Twitter.
Our findings point at a comparatively less negative response to the incidents
on Parler compared to Twitter accompanied by higher levels of disapproval and
outrage. Our contribution to research is three-fold: (1) We identify online
emotions that were characteristic of the storming; (2) we assess emotion
dynamics across different user groups on Parler; (3) we compare the emotion
dynamics on Parler and Twitter. Thereby, our work offers important implications
for actively managing online emotions to prevent similar incidents in the
future.


title: On subgeometric ergodicity of regime-switching diffusion processes

abstract:
  In this article, we discuss subgeometric ergodicity of a class of
regime-switching diffusion processes. We derive conditions on the drift and
diffusion coefficients, and the switching mechanism which result in
subgeometric ergodicity of the corresponding semigroup with respect to the
total variation distance as well as a class of Wasserstein distances. At the
end, subgeometric ergodicity of certain classes of regime-switching Markov
processes with jumps is also discussed.


title: Clone Detection on Large Scala Codebases

abstract:
  Code clones are identical or similar code segments. The wide existence of
code clones can increase the cost of maintenance and jeopardise the quality of
software. The research community has developed many techniques to detect code
clones, however, there is little evidence of how these techniques may perform
in industrial use cases. In this paper, we aim to uncover the differences when
such techniques are applied in industrial use cases. We conducted large scale
experimental research on the performance of two state-of-the-art code clone
detection techniques, SourcererCC and AutoenCODE, on both open source projects
and an industrial project written in the Scala language. Our results reveal
that both algorithms perform differently on the industrial project, with the
largest drop in precision being 30.7\%, and the largest increase in recall
being 32.4\%. By manually labelling samples of the industrial project by its
developers, we discovered that there are substantially less Type-3 clones in
the aforementioned project than that in the open source projects.


title:
Singular limits of a coupled elasto-plastic damage system as viscosity
  and hardening vanish

abstract:
  The paper studies the asymptotic analysis of a model coupling
elastoplasticity and damage depending on three parameters -- governing
viscosity, plastic hardening, and convergence rate of plastic strain and
displacement to equilibrium -- as they vanish in different orders. The notion
of limit evolution obtained is proven to coincide in any case with a notion
introduced by Crismale and Rossi in 2019; moreover, such solutions are closely
related to those obtained in the vanishing-viscosity limit by Crismale and
Lazzaroni in 2016, for the analogous model where only the viscosity parameter
was present.


title: Monopole Superconductivity in Magnetically Doped Cd$_3$As$_2$

abstract:
  When superconducting pairing occurs between Fermi surfaces with different
Chern numbers, the Cooper pairs experience a nontrivial pair Berry phase and
effective pair monopole charge that enforces nodal monopole harmonic
superconducting order beyond the familiar $s$-, $p$-, and $d$-wave
superconductors. When magnetically doped, the Dirac semimetal Cd$_3$As$_2$
features Fermi pockets enclosing Weyl nodes with chiralities $\chi=\pm1,\pm2$.
This material serves as a candidate platform for realizing monopole
superconductivity with pair monopole charges $q_p=1$ or $2$, depending on the
chemical potential. We demonstrate the distinctions in the superconducting
phase winding due to pairing with different pair monopole charges and due to
pairings in different irreducible representations of the $C_{4h}$ symmetry of
magnetically doped Cd$_3$As$_2$. In all cases, the phase winding patterns are
subject to the topologically invariant total phase winding, which depends only
on the pair monopole charge and guarantees nodes in the superconducting order
for $q_p>0$.


title:
Understanding the Influence of Receptive Field and Network Complexity in
  Neural-Network-Guided TEM Image Analysis

abstract:
  Trained neural networks are promising tools to analyze the ever-increasing
amount of scientific image data, but it is unclear how to best customize these
networks for the unique features in transmission electron micrographs. Here, we
systematically examine how neural network architecture choices affect how
neural networks segment, or pixel-wise separate, crystalline nanoparticles from
amorphous background in transmission electron microscopy (TEM) images. We focus
on decoupling the influence of receptive field, or the area of the input image
that contributes to the output decision, from network complexity, which
dictates the number of trainable parameters. We find that for low-resolution
TEM images which rely on amplitude contrast to distinguish nanoparticles from
background, the receptive field does not significantly influence segmentation
performance. On the other hand, for high-resolution TEM images which rely on a
combination of amplitude and phase contrast changes to identify nanoparticles,
receptive field is a key parameter for increased performance, especially in
images with minimal amplitude contrast. Our results provide insight and
guidance as to how to adapt neural networks for applications with TEM datasets.


title: A Rotating Proposer Mechanism for Team Formation

abstract:
  We present a rotating proposer mechanism for team formation, which implements
a Pareto efficient subgame perfect Nash equilibrium of an extensive-form team
formation game.


title:
pyHIIextractor: A tool to detect and extract physical properties of HII
  regions from Integral Field Spectroscopic data

abstract:
  We present a new code named pyHIIextractor, which detects and extracts the
main features (positions and radii) of clumpy ionized regions, i.e. candidate
HII regions, using H{\alpha} emission line images. Our code is optimized to be
used on the dataproducts provided by the Pipe3D pipeline (or dataproducts with
such a format), applied to high spatial resolution Integral Field Spectroscopy
data (like that provided by the AMUSING++ compilation, using MUSE). The code
provides the properties of both the underlying stellar population and the
emission lines for each detected H ii candidate. Furthermore, the code delivers
a novel estimation of the diffuse ionized gas (DIG) component, independent of
its physical properties, which enables a decontamination of the properties of
the HII regions from the DIG. Using simulated data, mimicking the expected
observations of spiral galaxies, we characterise pyHIIextractor and its ability
to extract the main properties of the H ii regions (and the DIG), including the
line fluxes, ratios and equivalent widths. Finally, we compare our code with
other such tools adopted in the literature, which have been developed or used
for similar purposes: pyhIIexplorer, SourceExtractor, HIIphot, and astrodendro.
We conclude that pyHIIextractor exceeds the performance of previous tools in
aspects such as the number of recovered regions and the distribution of sizes
and fluxes (an improvement that is especially noticeable for the faintest and
smallest regions). pyHIIextractor is therefore an optimals tool to detect
candidate HII regions, offering an accurate estimation of their properties and
a good decontamination of the DIG component.


title:
Assessing Statistical Disclosure Risk for Differentially Private,
  Hierarchical Count Data, with Application to the 2020 U.S. Decennial Census

abstract:
  We propose Bayesian methods to assess the statistical disclosure risk of data
released under zero-concentrated differential privacy, focusing on settings
with a strong hierarchical structure and categorical variables with many
levels. Risk assessment is performed by hypothesizing Bayesian intruders with
various amounts of prior information and examining the distance between their
posteriors and priors. We discuss applications of these risk assessment methods
to differentially private data releases from the 2020 decennial census and
perform simulation studies using public individual-level data from the 1940
decennial census. Among these studies, we examine how the data holder's choice
of privacy parameter affects the disclosure risk and quantify the increase in
risk when a hypothetical intruder incorporates substantial amounts of
hierarchical information.


title:
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a
  Collaborative IoT Intrusion Detection

abstract:
  The continuous strengthening of the security posture of IoT ecosystems is
vital due to the increasing number of interconnected devices and the volume of
sensitive data shared. The utilisation of Machine Learning (ML) capabilities in
the defence against IoT cyber attacks has many potential benefits. However, the
currently proposed frameworks do not consider data privacy, secure
architectures, and/or scalable deployments of IoT ecosystems. In this paper, we
propose a hierarchical blockchain-based federated learning framework to enable
secure and privacy-preserved collaborative IoT intrusion detection. We
highlight and demonstrate the importance of sharing cyber threat intelligence
among inter-organisational IoT networks to improve the model's detection
capabilities. The proposed ML-based intrusion detection framework follows a
hierarchical federated learning architecture to ensure the privacy of the
learning process and organisational data. The transactions (model updates) and
processes will run on a secure immutable ledger, and the conformance of
executed tasks will be verified by the smart contract. We have tested our
solution and demonstrated its feasibility by implementing it and evaluating the
intrusion detection performance using a key IoT data set. The outcome is a
securely designed ML-based intrusion detection system capable of detecting a
wide range of malicious activities while preserving data privacy.


title: Birational Rowmotion and the Octahedron Recurrence

abstract:
  We use the octahedron recurrence to give a simplified statement and proof of
a formula for iterated birational rowmotion on a product of two chains, first
described by Musiker and Roby. Using this, we show that weights of certain
chains in rectangles shift in a predictable way under the action of rowmotion.
We then define generalized Stanley-Thomas words whose cyclic rotation uniquely
determines birational rowmotion on the product of two chains. We also discuss
the relationship between rowmotion and birational RSK and give a birational
analogue of Greene's theorem in this setting.


title: Interpretable AI for policy-making in pandemics

abstract:
  Since the first wave of the COVID-19 pandemic, governments have applied
restrictions in order to slow down its spreading. However, creating such
policies is hard, especially because the government needs to trade-off the
spreading of the pandemic with the economic losses. For this reason, several
works have applied machine learning techniques, often with the help of
special-purpose simulators, to generate policies that were more effective than
the ones obtained by governments. While the performance of such approaches are
promising, they suffer from a fundamental issue: since such approaches are
based on black-box machine learning, their real-world applicability is limited,
because these policies cannot be analyzed, nor tested, and thus they are not
trustable. In this work, we employ a recently developed hybrid approach, which
combines reinforcement learning with evolutionary computation, for the
generation of interpretable policies for containing the pandemic. These
policies, trained on an existing simulator, aim to reduce the spreading of the
pandemic while minimizing the economic losses. Our results show that our
approach is able to find solutions that are extremely simple, yet very
powerful. In fact, our approach has significantly better performance (in
simulated scenarios) than both previous work and government policies.


title:
The Case and Context for Atmospheric Methane as an Exoplanet
  Biosignature

abstract:
  Methane has been proposed as an exoplanet biosignature. Imminent observations
with the James Webb Space Telescope may enable methane detections on
potentially habitable exoplanets, so it is essential to assess in what
planetary contexts methane is a compelling biosignature. Methane's short
photochemical lifetime in terrestrial planet atmospheres implies that abundant
methane requires large replenishment fluxes. While methane can be produced by a
variety of abiotic mechanisms such as outgassing, serpentinizing reactions, and
impacts, we argue that, in contrast to an Earth-like biosphere, known abiotic
processes cannot easily generate atmospheres rich in CH$_4$ and CO$_2$ with
limited CO due to the strong redox disequilibrium between CH$_4$ and CO$_2$.
Methane is thus more likely to be biogenic for planets with 1) a terrestrial
bulk density, high mean-molecular-weight and anoxic atmosphere, and an old host
star; 2) an abundance of CH$_4$ that implies surface fluxes exceeding what
could be supplied by abiotic processes; and 3) atmospheric CO$_2$ with
comparatively little CO.


title: WO-Type Wolf-Rayet Stars: the Last Hurrah of Massive Star Evolution

abstract:
  Are WO-type Wolf Rayet (WR) stars in the final stage of massive star
evolution before core-collapse? Although WC- and WO-type WRs have very similar
spectra, WOs show a much stronger O VI $\lambda \lambda$3811,34 emission-line
feature. This has usually been interpreted to mean that WOs are more oxygen
rich than WCs, and thus further evolved. However, previous studies have failed
to model this line, leaving the relative abundances uncertain, and the
relationship between the two types unresolved. To answer this fundamental
question, we modeled six WCs and two WOs in the LMC using UV, optical, and NIR
spectra with the radiative transfer code CMFGEN in order to determine their
physical properties. We find that WOs are not richer in oxygen; rather, the O
VI feature is insensitive to the abundance. However, the WOs have a
significantly higher carbon and lower helium content than the WCs, and hence
are further evolved. A comparison of our results with single-star Geneva and
binary BPASS evolutionary models show that while many properties match, there
is more carbon and less oxygen in the WOs than either set of evolutionary model
predicts. This discrepancy may be due to the large uncertainty in the
$^{12}$C+$^4$He$\rightarrow^{16}$O nuclear reaction rate; we show that if the
Kunz et al. rate is decreased by a factor of 25-50%, then there would be a good
match with the observations. It would also help explain the LIGO/VIRGO
detection of black holes whose masses are in the theoretical upper mass gap.


title: Evaluating the Adversarial Robustness for Fourier Neural Operators

abstract:
  In recent years, Machine-Learning (ML)-driven approaches have been widely
used in scientific discovery domains. Among them, the Fourier Neural Operator
(FNO) was the first to simulate turbulent flow with zero-shot super-resolution
and superior accuracy, which significantly improves the speed when compared to
traditional partial differential equation (PDE) solvers. To inspect the
trustworthiness, we provide the first study on the adversarial robustness of
scientific discovery models by generating adversarial examples for FNO, based
on norm-bounded data input perturbations. Evaluated on the mean squared error
between the FNO model's output and the PDE solver's output, our results show
that the model's robustness degrades rapidly with increasing perturbation
levels, particularly in non-simplistic cases like the 2D Darcy and the Navier
cases. Our research provides a sensitivity analysis tool and evaluation
principles for assessing the adversarial robustness of ML-based scientific
discovery models.


title: Do People Trust Robots that Learn in the Home?

abstract:
  It is not scalable for assistive robotics to have all functionalities
pre-programmed prior to user introduction. Instead, it is more realistic for
agents to perform supplemental on site learning. This opportunity to learn user
and environment particularities is especially helpful for care robots that
assist with individualized caregiver activities in residential or nursing home
environments. Many assistive robots, ranging in complexity from Roomba to
Pepper, already conduct some of their learning in the home, observable to the
user. We lack an understanding of how witnessing this learning impacts the
user. Thus, we propose to assess end-user attitudes towards the concept of
embodied robots that conduct some learning in the home as compared to robots
that are delivered fully-capable. In this virtual, between-subjects study, we
recruit end users (care-givers and care-takers) from nursing homes, and
investigate user trust in three different domains: navigation, manipulation,
and preparation. Informed by the first study where we identify agent learning
as a key factor in determining trust, we propose a second study to explore how
to modulate that trust. This second, in-person study investigates the
effectiveness of apologies, explanations of robot failure, and transparency of
learning at improving trust in embodied learning robots.


title: Quantum Dots / Spin Qubits

abstract:
  Spin qubits in semiconductor quantum dots represent a prominent family of
solid-state qubits in the effort to build a quantum computer. They are formed
when electrons or holes are confined in a static potential well in a
semiconductor, giving them a quantized energy spectrum. The simplest spin qubit
is a single electron spin located in a quantum dot, but many additional
varieties have been developed, some containing multiple spins in multiple
quantum dots, each of which has different benefits and drawbacks. While these
spins act as simple quantum systems in many ways, they also experience complex
effects due to their semiconductor environment. They can be controlled by both
magnetic and electric fields depending on their configuration and are therefore
dephased by magnetic and electric field noise, with different types of spin
qubits having different control mechanisms and noise susceptibilities. While
initial experiments were primarily performed in gallium arsenide (GaAs) based
materials, silicon qubits have developed substantially and research on qubits
in metal-oxide-semiconductor (Si-MOS), silicon/silicon germanium (Si/SiGe)
heterostructures, and donors in silicon is also being pursued. An increasing
number of spin qubit varieties have attained error rates that are low enough to
be compatible with quantum error correction for single-qubit gates and
two-qubit gates have been performed in several with success rates, or
fidelities, of 90-95%.


title:
Electron Weibel instability induced magnetic fields in optical-field
  ionized plasmas

abstract:
  Generation and amplification of magnetic fields in plasmas is a long-standing
topic that is of great interest to both plasma and space physics. The electron
Weibel instability is a well-known mechanism responsible for self-generating
magnetic fields in plasmas with temperature anisotropy and has been extensively
investigated in both theory and simulations, yet experimental verification of
this instability has been challenging. Recently, we demonstrated a new
experimental platform that enables the controlled initialization of highly
nonthermal and/or anisotropic plasma electron velocity distributions via
optical-field ionization. Using an external electron probe bunch from a linear
accelerator, the onset, saturation and decay of the self-generated magnetic
fields due to electron Weibel instability were measured for the first time to
our knowledge. In this paper, we will first present experimental results on
time-resolved measurements of the Weibel magnetic fields in non-relativistic
plasmas produced by Ti:Sapphire laser pulses (0.8 $\mu m$) and then discuss the
feasibility of extending the study to quasi-relativistic regime by using
intense $\rm CO_2$ (e.g., 9.2 $\mu m$) lasers to produce much hotter plasmas.


title: BioRED: A Comprehensive Biomedical Relation Extraction Dataset

abstract:
  Automated relation extraction (RE) from biomedical literature is critical for
many downstream text mining applications in both research and real-world
settings. However, most existing benchmarking datasets for bio-medical RE only
focus on relations of a single type (e.g., protein-protein interactions) at the
sentence level, greatly limiting the development of RE systems in biomedicine.
In this work, we first review commonly used named entity recognition (NER) and
RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus
with multiple entity types (e.g., gene/protein, disease, chemical) and relation
pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles.
Further, we label each relation as describing either a novel finding or
previously known background knowledge, enabling automated algorithms to
differentiate between novel and background information. We assess the utility
of BioRED by benchmarking several existing state-of-the-art methods, including
BERT-based models, on the NER and RE tasks. Our results show that while
existing approaches can reach high performance on the NER task (F-score of
89.3%), there is much room for improvement for the RE task, especially when
extracting novel relations (F-score of 47.7%). Our experiments also demonstrate
that such a comprehensive dataset can successfully facilitate the development
of more accurate, efficient, and robust RE systems for biomedicine.


title:
Application of Eckart-Hellmann potential to study selected diatomic
  molecules using Nikiforov-Uvarov-Functional Analysis (NUFA) method

abstract:
  The energy levels of the Schr\"odinger equation under the Eckart-Hellmann
potential (EHP) energy function are studied by the Nikiforov-Uvarov-Functional
Analysis (NUFA) method. We obtained the analytic solution of the energy spectra
and the wave function in closed form with the help of Greene-Aldrich
approximation. The numerical bound states energy for various screening
parameters at different quantum states and vibrational energies of EHP for
CuLi, TiH, VH, and TiC diatomic molecules were computed. Four particular cases
of this potential were achieved. To test the accuracy of our results, we
computed the bound states energy eigenvalues of Hellmann potential which are in
excellent agreement with the report of other researchers.


title:
Boundedness of differential transforms for Poisson semigroups generated
  by Bessel operators

abstract:
  In this paper we analyze the convergence of the following type of series
\begin{equation*} T_N f(x)=\sum_{j=N_1}^{N_2} v_j\Big(\mathcal{P}_{a_{j+1}}
f(x)-\mathcal{P}_{a_{j}} f(x)\Big),\quad x\in \mathbb R_+, \end{equation*}
where $\{\mathcal{P}_t \}_{t>0}$ is the Poisson semigroup of the Bessel
operator $\displaystyle \Delta_\lambda:=-{d^2\over dx^2}-{2\lambda\over
x}{d\over dx}$ with $\lambda$ being a positive constant, $N=(N_1, N_2)\in
\mathbb Z^2$ with $N_1<N_2,$ $\{v_j\}_{j\in \mathbb Z}$ is a bounded real
sequences and $\{a_j\}_{j\in \mathbb Z}$ is an increasing real sequence. {Our
analysis will consist in the boundedness, in $L^p(\mathbb{R}_+)$ and in
$BMO(\mathbb{R}_+)$, of the operators $T_N$ and its maximal operator $ T^*f(x)=
sup_N \abs{T_N f(x)}.$} It is also shown that the local size of the maximal
differential transform operators is the same with the order of a singular
integral for functions $f$ having local support.


title: MP-PCA denoising for diffusion MRS data: promises and pitfalls

abstract:
  Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a
lower signal to noise ratio (SNR) compared to conventional MRS owing to the
addition of diffusion attenuation. This technique can therefore strongly
benefit from noise reduction strategies. In the present work, the
Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on
Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4T in the rat
brain. We provide a descriptive study of the effects observed following
different MP-PCA denoising strategies (denoising the entire matrix versus using
a sliding window), in terms of apparent SNR, rank selection, noise correlation
within and across b-values and quantification of metabolite concentrations and
fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent
SNR, a more accurate B0 drift correction between shots, and similar estimates
of metabolite concentrations and diffusivities compared to the raw data. No
spectral residuals on individual shots were observed but correlations in the
noise level across shells were introduced, an effect which was mitigated using
a sliding window, but which should be carefully considered.


title:
Mapping the self-generated magnetic fields due to thermal Weibel
  instability

abstract:
  Weibel-type instability can self-generate and amplify magnetic fields in both
space and laboratory plasmas with temperature anisotropy. The electron Weibel
instability has generally proven more challenging to measure than its ion
counterpart owing to the much smaller inertia of electrons, resulting in a
faster growth rate and smaller characteristic wavelength. Here, we have probed
the evolution of the two-dimensional distribution of the magnetic field
components and the current density due to electron Weibel instability, in $\rm
CO_2$-ionized hydrogen gas (plasma) with picosecond resolution using a
relativistic electron beam. We find that the wavenumber spectra of the magnetic
fields are initially broad but eventually shrink to a narrow spectrum
representing the dominant quasi-single mode. The measured $k$-resolved growth
rates of the instability validate kinetic theory. Concurrently,
self-organization of microscopic plasma currents is observed to amplify the
current modulation magnitude that converts up to $\sim 1\%$ of the plasma
thermal energy into magnetic energy.


title: Magnetic measures of purity for MnBi$_2$Te$_4$

abstract:
  The intrinsically anti-ferromagnetic topological insulator, MnBi$_2$Te$_4$
(MBT), has garnered significant attention recently. The excitement for this
layered van der Waals bonded compound stems from its potential to host numerous
exotic topological quantum states. For instance, quantum anomalous Hall states
are predicted for odd-layer compounds and axion insulator states for the
even-layer compounds. Unfortunately, the realization of these phenomena has
been hindered by experimental challenges such as the existence of negative
charge carriers, i.e., electron doping, which have been linked to anti-site
defects among the Mn and Bi sub-lattices. Based on high level diffusion Monte
Carlo (DMC) and DMC-tuned DFT+U calculations, we provide benchmark quality
results for the bulk Mn magnetization as well as for Mn$_{Bi}$ and Bi$_{Mn}$
defects. We use this information to refine and extend models that estimate the
anti-site defect concentration in actual MBT samples when combined with data
from magnetic susceptibility and intermediate field magnetization measurements.
Our models are validated through favorable comparison with prior experimental
studies that obtained both magnetic and site occupancy data. We then extend our
estimates to a larger set of prior samples to identify a probable zone of low
defect density that has yet to be reached in synthesis. We anticipate our
theoretically based magnetic purity measures may be used as minimization
targets in the cycle of refinement needed to synthesize MBT samples with low
anti-site defect concentrations and more reproducible topological properties.


title:
The evolution of unavoidable bi-chromatic patterns and extremal cases of
  balanceability

abstract:
  We study the color patterns that, for $n$ sufficiently large, are unavoidable
in $2$-colorings of the edges of a complete graph $K_n$ with respect to $\min
\{e(R), e(B)\}$, where $e(R)$ and $e(B)$ are the numbers of red and,
respectively, blue edges. More precisely, we determine how such unavoidable
patterns evolve from the case without restriction in the coloring, namely that
$\min \{e(R), e(B)\} \ge 0$ (given by Ramsey's theorem), to the highest
possible restriction, namely that $|e(R) - e(B)| \le 1$. We also investigate
the effect of forbidding certain sub-structures in each color. In particular,
we show that, in $2$-colorings whose graphs induced by each of the colors are
both free from an induced matching on $r$ edges, the appearance of the
unavoidable patterns is already granted with a much weaker restriction on $\min
\{e(R), e(B)\}$. We finish analyzing the consequences of these results to the
balancing number $bal(n,G)$ of a graph $G$ (i.e. the minimum $k$ such that
every $2$-edge coloring of $K_n$ with $\min \{e(R), e(B)\} > k$ contains a copy
of $G$ with half the edges in each color), and show that, for every
$\varepsilon > 0$, there are graphs $G$ with $bal(n,G) \ge c
n^{2-\varepsilon}$, which is the highest order of magnitude that is possible to
achieve, as well as graphs where $bal(n,G) \le c(G)$, where $c(G)$ is a
constant that depends only $G$. We characterize the latter ones.


title: Conformalized Frequency Estimation from Sketched Data

abstract:
  A flexible conformal inference method is developed to construct confidence
intervals for the frequencies of queried objects in a very large data set,
based on the information contained in a much smaller sketch of those data. The
approach is completely data-adaptive and makes no use of any knowledge of the
population distribution or of the inner workings of the sketching algorithm;
instead, it constructs provably valid frequentist confidence intervals under
the sole assumption of data exchangeability. Although the proposed solution is
much more broadly applicable, this paper explicitly demonstrates its use in
combination with the famous count-min sketch algorithm and a non-linear
variation thereof to facilitate the exposition. The performance is compared to
that of existing frequentist and Bayesian alternatives through several
experiments with synthetic data as well as with real data sets consisting of
SARS-CoV-2 DNA sequences and classic English literature.


title:
Collapse and revival of $n$-photon coherent states and $n$-photon
  squeezed coherent states

abstract:
  We introduce the set of $n$-photon coherent states and the set of $n$-photon
squeezed coherent states and study their collapse and revival and compare their
behavior with the collapse and revival of the corresponding zero-photon
coherent states and zero-photon squeezed coherent states, respectively. The set
of $n$-photon squeezed coherent states ($n=0,1,2,\cdots$) forms a basis of the
Hilbert state of photons and may be of some interest. We notice that the
presence of a few extra photons in the $n$-photon states as compared to the
corresponding zero-photon states makes a large difference in the behavior of
their collapse and revival. This indicates the large effect that a few extra
photons makes in the $n$-photon states as compared to the corresponding
zero-photon states in their collapse and revival.


title:
Matrix Syncer -- A Multi-chain Data Aggregator For Supporting
  Blockchain-based Metaverses

abstract:
  Due to the rising complexity of the metaverse's business logic and the
low-latency nature of the metaverse, developers typically encounter the
challenge of effectively reading, writing, and retrieving historical on-chain
data in order to facilitate their functional implementations at scale. While it
is true that accessing blockchain states is simple, more advanced real-world
operations such as search, aggregation, and conditional filtering are not
available when interacting directly with blockchain networks, particularly when
dealing with requirements for on-chain event reflection. We offer Matrix
Syncer, the ultimate middleware that bridges the data access gap between
blockchains and end-user applications. Matrix Syncer is designed to facilitate
the consolidation of on-chain information into a distributed data warehouse
while also enabling customized on-chain state transformation for a scalable
storage, access, and retrieval. It offers a unified layer for both on- and
off-chain state, as well as a fast and flexible atomic query. Matrix Syncer is
easily incorporated into any infrastructure to aggregate data from various
blockchains concurrently, such as Ethereum and Flow. The system has been
deployed to support several metaverse projects with a total value of more than
$15 million USD.


title:
Dimensionality Reduction in Deep Learning via Kronecker Multi-layer
  Architectures

abstract:
  Deep learning using neural networks is an effective technique for generating
models of complex data. However, training such models can be expensive when
networks have large model capacity resulting from a large number of layers and
nodes. For training in such a computationally prohibitive regime,
dimensionality reduction techniques ease the computational burden, and allow
implementations of more robust networks. We propose a novel type of such
dimensionality reduction via a new deep learning architecture based on fast
matrix multiplication of a Kronecker product decomposition; in particular our
network construction can be viewed as a Kronecker product-induced
sparsification of an "extended" fully connected network. Analysis and practical
examples show that this architecture allows a neural network to be trained and
implemented with a significant reduction in computational time and resources,
while achieving a similar error level compared to a traditional feedforward
neural network.


title: String Diagram Rewriting Modulo Commutative Monoid Structure

abstract:
  We characterise freely generated props with a chosen commutative monoid
structure as certain categories of hypergraphs with interfaces. We use this
result to give a sound and complete interpretation of rewriting modulo
commutative monoid equations in a prop in terms of double-pushout rewriting of
hypergraphs.


title:
Using Kernel-Based Statistical Distance to Study the Dynamics of Charged
  Particle Beams in Particle-Based Simulation Codes

abstract:
  Measures of discrepancy between probability distributions (statistical
distance) are widely used in the fields of artificial intelligence and machine
learning. We describe how certain measures of statistical distance can be
implemented as numerical diagnostics for simulations involving charged-particle
beams. Related measures of statistical dependence are also described. The
resulting diagnostics provide sensitive measures of dynamical processes
important for beams in nonlinear or high-intensity systems, which are otherwise
difficult to characterize. The focus is on kernel-based methods such as Maximum
Mean Discrepancy, which have a well-developed mathematical foundation and
reasonable computational complexity. Several benchmark problems and examples
involving intense beams are discussed. While the focus is on charged-particle
beams, these methods may also be applied to other many-body systems such as
plasmas or gravitational systems.

categories: physics.acc-ph astro-ph.GA physics.comp-ph physics.data-an physics.plasm-ph


title: Quantum computation in a hybrid array of molecules and Rydberg atoms

abstract:
  We show that an array of polar molecules interacting with Rydberg atoms is a
promising hybrid system for scalable quantum computation. Quantum information
is stored in long-lived hyperfine or rotational states of molecules which
interact indirectly through resonant dipole-dipole interactions with Rydberg
atoms. A two-qubit gate based on this interaction has a duration of 1 $\mu$s
and an achievable fidelity of 99.9%. The gate is insensitive to the motional
states of the particles -- the molecules can be in thermal states, the atoms do
not need to be trapped during Rydberg excitation, the gate does not heat the
molecules, and heating of the atoms is irrelevant. Within a large, static
array, the gate can be applied to arbitrary pairs of molecules separated by
tens of micrometres, making the scheme highly scalable. The molecule-atom
interaction can also be used for rapid qubit initialization and efficient,
non-destructive qubit readout, without driving any molecular transitions.
Single qubit gates are driven using microwave pulses alone, exploiting the
strong electric dipole transitions between rotational states. Thus, all
operations required for large scale quantum computation can be done without
moving the molecules or exciting them out of their ground electronic states.


title: Damped Strichartz estimates and the incompressible Euler--Maxwell system

abstract:
  Euler--Maxwell systems describe the dynamics of inviscid plasmas. In this
work, we consider an incompressible two-dimensional version of such systems and
prove the existence and uniqueness of global weak solutions, uniformly with
respect to the speed of light $c\in (c_0,\infty)$, for some threshold value
$c_0>0$ depending only on the initial data. In particular, the condition
$c>c_0$ ensures that the velocity of the plasma nowhere exceeds the speed of
light and allows us to analyze the singular regime $c\to\infty$.
  The functional setting for the fluid velocity lies in the framework of
Yudovich's solutions of the two-dimensional Euler equations, whereas the
analysis of the electromagnetic field hinges upon the refined interactions
between the damping and dispersive phenomena in Maxwell's equations in the
whole space. This analysis is enabled by the new development of a robust
abstract method allowing us to incorporate the damping effect into a variety of
existing estimates. The use of this method is illustrated by the derivation of
damped Strichartz estimates (including endpoint cases) for several dispersive
systems (including the wave and Schr\"odinger equations), as well as damped
maximal regularity estimates for the heat equation. The ensuing damped
Strichartz estimates supersede previously existing results on the same systems.


title: Explicit models of motions to analyze NMR relaxation data in proteins

abstract:
  Nuclear Magnetic Resonance (NMR) is a tool of choice to characterize
molecular motions. In biological macromolecules, pico- to nano-second motions,
in particular, can be probed by nuclear spin relaxation rates which depend on
the time fluctuations of the orientations of spin interaction frames. For the
past 40 years, relaxation rates have been successfully analyzed using the Model
Free (MF) approach which makes no assumption on the nature of motions and
reports on the effective amplitude and time-scale of the motions. However,
obtaining a mechanistic picture of motions from this type of analysis is
difficult at best, unless complemented with molecular dynamics (MD)
simulations. In spite of their limited accuracy, such simulations can be used
to obtain the information necessary to build explicit models of motions
designed to analyze NMR relaxation data. Here, we present how to build such
models, suited in particular to describe motions of methyl-bearing protein
side-chains and compare them with the MF approach. We show on synthetic data
that explicit models of motions are more robust in the presence of rotamer
jumps which dominate the relaxation in methyl groups of protein side-chains. We
expect this work to motivate the use of explicit models of motion to analyze MD
and NMR data.


title:
Grid-connected Soft Switching Partial Resonance Inverter for Distributed
  Generation

abstract:
  This paper presents current control method for a grid-connected partial
resonant soft switching inverter. This inverter does not use an electrolytic
capacitor and is capable of boosting and bucking the voltage. Grid-connected
inverters are used to integrate distributed energy sources to the grid. Current
control is vital in meeting the standards and requirements when connecting to
the grid. The closed-loop current regulation for this type of converters is
analyzed and design guidelines are provided. The control is implemented in the
synchronous frame. In addition active damping techniques using capacitor
voltage and inductor voltage feedback is used to mitigate CL filter resonance
at the output. The mentioned control strategies are implemented on a 400W lab
prototype and the results are presented


title: List covering of regular multigraphs

abstract:
  A graph covering projection, also known as a locally bijective homomorphism,
is a mapping between vertices and edges of two graphs which preserves
incidencies and is a local bijection. This notion stems from topological graph
theory, but has also found applications in combinatorics and theoretical
computer science.
  It has been known that for every fixed simple regular graph $H$ of valency
greater than 2, deciding if an input graph covers $H$ is NP-complete. In recent
years, topological graph theory has developed into heavily relying on multiple
edges, loops, and semi-edges, but only partial results on the complexity of
covering multigraphs with semi-edges are known so far. In this paper we
consider the list version of the problem, called \textsc{List-$H$-Cover}, where
the vertices and edges of the input graph come with lists of admissible
targets. Our main result reads that the \textsc{List-$H$-Cover} problem is
NP-complete for every regular multigraph $H$ of valency greater than 2 which
contains at least one semi-simple vertex (i.e., a vertex which is incident with
no loops, with no multiple edges and with at most one semi-edge). Using this
result we almost show the NP-co/polytime dichotomy for the computational
complexity of \textsc{ List-$H$-Cover} of cubic multigraphs, leaving just five
open cases.


title: Universality of Approximate Message Passing with Semi-Random Matrices

abstract:
  Approximate Message Passing (AMP) is a class of iterative algorithms that
have found applications in many problems in high-dimensional statistics and
machine learning. In its general form, AMP can be formulated as an iterative
procedure driven by a matrix $\mathbf{M}$. Theoretical analyses of AMP
typically assume strong distributional properties on $\mathbf{M}$ such as
$\mathbf{M}$ has i.i.d. sub-Gaussian entries or is drawn from a rotational
invariant ensemble. However, numerical experiments suggest that the behavior of
AMP is universal, as long as the eigenvectors of $\mathbf{M}$ are generic. In
this paper, we take the first step in rigorously understanding this
universality phenomenon. In particular, we investigate a class of memory-free
AMP algorithms (proposed by \c{C}akmak and Opper for mean-field Ising spin
glasses), and show that their asymptotic dynamics is universal on a broad class
of semi-random matrices. In addition to having the standard rotational
invariant ensemble as a special case, the class of semi-random matrices that we
define in this work also includes matrices constructed with very limited
randomness. One such example is a randomly signed version of the Sine model,
introduced by Marinari, Parisi, Potters, and Ritort for spin glasses with fully
deterministic couplings.


title:
Classification of Natural Language Processing Techniques for
  Requirements Engineering

abstract:
  Research in applying natural language processing (NLP) techniques to
requirements engineering (RE) tasks spans more than 40 years, from initial
efforts carried out in the 1980s to more recent attempts with machine learning
(ML) and deep learning (DL) techniques. However, in spite of the progress, our
recent survey shows that there is still a lack of systematic understanding and
organization of commonly used NLP techniques in RE. We believe one hurdle
facing the industry is lack of shared knowledge of NLP techniques and their
usage in RE tasks. In this paper, we present our effort to synthesize and
organize 57 most frequently used NLP techniques in RE. We classify these NLP
techniques in two ways: first, by their NLP tasks in typical pipelines and
second, by their linguist analysis levels. We believe these two ways of
classification are complementary, contributing to a better understanding of the
NLP techniques in RE and such understanding is crucial to the development of
better NLP tools for RE.


title: Generation and detection of axions using guided structures

abstract:
  We propose a new experimental technique to generate and detect axions in the
lab with a good experimental sensitivity over a broad axion mass range. The
scheme relies on using laser-based four-wave mixing, which is mediated by the
hypothetical axion field. Intense pump and Stokes laser beams that are confined
to a waveguide (i.e., for example, an optical fiber) with appropriately chosen
frequencies resonantly drive axion generation. Under such a geometry, we
predict the existence of guided axion waves, which we refer to as "axitons".
These are solutions of the axion Klein-Gordon field equation that are spatially
guided by the profiles of the driving pump and Stokes laser beams. These guided
axitons can then couple to a nearby fiber and mix with another laser, affecting
the propagation of a probe laser beam. A key advantage of the scheme is that
the mass range of the hypothetical axion can be scanned by varying the
frequencies of the pump and the Stokes laser beams. We predict that, using
reasonable parameters, the technique will be able to detect axions in the mass
range $10^{-6} $eV $< m<$ $10^{-2} $eV with a sensitivity at the level of
$10^{-12}$ GeV$^{-1}$ for the axion-photon coupling constant.


title:
Auditory-Based Data Augmentation for End-to-End Automatic Speech
  Recognition

abstract:
  End-to-end models have achieved significant improvement on automatic speech
recognition. One common method to improve performance of these models is
expanding the data-space through data augmentation. Meanwhile, human auditory
inspired front-ends have also demonstrated improvement for automatic speech
recognisers. In this work, a well-verified auditory-based model, which can
simulate various hearing abilities, is investigated for the purpose of data
augmentation for end-to-end speech recognition. By introducing the auditory
model into the data augmentation process, end-to-end systems are encouraged to
ignore variation from the signal that cannot be heard and thereby focus on
robust features for speech recognition. Two mechanisms in the auditory model,
spectral smearing and loudness recruitment, are studied on the LibriSpeech
dataset with a transformer-based end-to-end model. The results show that the
proposed augmentation methods can bring statistically significant improvement
on the performance of the state-of-the-art SpecAugment.


title: On Improving Cross-dataset Generalization of Deepfake Detectors

abstract:
  Facial manipulation by deep fake has caused major security risks and raised
severe societal concerns. As a countermeasure, a number of deep fake detection
methods have been proposed recently. Most of them model deep fake detection as
a binary classification problem using a backbone convolutional neural network
(CNN) architecture pretrained for the task. These CNN-based methods have
demonstrated very high efficacy in deep fake detection with the Area under the
Curve (AUC) as high as 0.99. However, the performance of these methods degrades
significantly when evaluated across datasets. In this paper, we formulate deep
fake detection as a hybrid combination of supervised and reinforcement learning
(RL) to improve its cross-dataset generalization performance. The proposed
method chooses the top-k augmentations for each test sample by an RL agent in
an image-specific manner. The classification scores, obtained using CNN, of all
the augmentations of each test image are averaged together for final real or
fake classification. Through extensive experimental validation, we demonstrate
the superiority of our method over existing published research in cross-dataset
generalization of deep fake detectors, thus obtaining state-of-the-art
performance.


title:
Explaining The Muon $g-2$ Anomaly and New CDF II W-Boson Mass in the
  Framework of (Extra)Ordinary Gauge Mediation

abstract:
  The SUSY contributions $\Delta a_\mu$ to muon $g-2$ anomaly can not even
reach $3\sigma$ in ordinary gauge mediated SUSY breaking (GMSB) scenarios
because of the strong correlations between the colored sparticle masses and the
uncolored EW sparticle masses. An interesting extension to GMSB is the
(Extra)Ordinary Gauge Mediation (EOGM), which can relax the correlations
between squarks and sleptons with non-universal choices for $N_{eff,3}$ and
$N_{eff,2}$. We find that EOGM scenarios with $N_{eff,3}\ll N_{eff,2}$ can
explain the muon $g-2$ anomaly within $3\sigma$ range, however can not explain
the new W-boson mass by CDF II. We also propose to extend EOGM with additional
adjoint $\Sigma_8$ and $\Sigma_3$ messengers at a high scale of order
$1.0\times 10^{14}$ GeV, which can shift the gauge coupling unification scale
to the string scale. Such EOGM extension scenarios with adjoint messengers
could spoil the unwanted gaugino mass ratios and give large SUSY contributions
to $\Delta a_\mu$ for $N_{eff,3}\ll N_{eff,2}$, which can explain the muon
$g-2$ anomaly up to $1\sigma$. Besides, because of the large messenger scale of
order $1.0\times 10^{14}$ GeV, such scenarios will in general lead to large
$|A_t|$ at the EW scale, which can accommodate the 125 GeV Higgs easily and
possibly lead to smaller EWFT as well as BGFT. We discuss the possibility to
explain the new CDF II W-boson mass in the GMSB-type framework. We find that
SUSY contributions can marginally account for the new W-boson mass in the
region with sleptons and wino both being light.


title:
Exploiting Hidden Representations from a DNN-based Speech Recogniser for
  Speech Intelligibility Prediction in Hearing-impaired Listeners

abstract:
  An accurate objective speech intelligibility prediction algorithms is of
great interest for many applications such as speech enhancement for hearing
aids. Most algorithms measures the signal-to-noise ratios or correlations
between the acoustic features of clean reference signals and degraded signals.
However, these hand-picked acoustic features are usually not explicitly
correlated with recognition. Meanwhile, deep neural network (DNN) based
automatic speech recogniser (ASR) is approaching human performance in some
speech recognition tasks. This work leverages the hidden representations from
DNN-based ASR as features for speech intelligibility prediction in
hearing-impaired listeners. The experiments based on a hearing aid
intelligibility database show that the proposed method could make better
prediction than a widely used short-time objective intelligibility (STOI) based
binaural measure.


title:
Unsupervised Uncertainty Measures of Automatic Speech Recognition for
  Non-intrusive Speech Intelligibility Prediction

abstract:
  Non-intrusive intelligibility prediction is important for its application in
realistic scenarios, where a clean reference signal is difficult to access. The
construction of many non-intrusive predictors require either ground truth
intelligibility labels or clean reference signals for supervised learning. In
this work, we leverage an unsupervised uncertainty estimation method for
predicting speech intelligibility, which does not require intelligibility
labels or reference signals to train the predictor. Our experiments demonstrate
that the uncertainty from state-of-the-art end-to-end automatic speech
recognition (ASR) models is highly correlated with speech intelligibility. The
proposed method is evaluated on two databases and the results show that the
unsupervised uncertainty measures of ASR models are more correlated with speech
intelligibility from listening results than the predictions made by widely used
intrusive methods.


title:
Towards Understanding Large-Scale Discourse Structures in Pre-Trained
  and Fine-Tuned Language Models

abstract:
  With a growing number of BERTology work analyzing different components of
pre-trained language models, we extend this line of research through an
in-depth analysis of discourse information in pre-trained and fine-tuned
language models. We move beyond prior work along three dimensions: First, we
describe a novel approach to infer discourse structures from arbitrarily long
documents. Second, we propose a new type of analysis to explore where and how
accurately intrinsic discourse is captured in the BERT and BART models.
Finally, we assess how similar the generated structures are to a variety of
baselines as well as their distribution within and between models.


title:
FikoRE: 5G and Beyond RAN Emulator for Application Level Experimentation
  and Prototyping

abstract:
  Novel and cutting-edge use cases have arisen since the first deployments of
the fifth generation of telecommunication networks (5G). There are plenty of
well-though optimally design 5G simulators and emulators which allow
telecommunication technologies engineers and researchers to thoroughly study
and test the network. However, the 5G ecosystem is not only limited to the
network itself: a fast development of 5G-specific use cases can considerably
accelerate the development of telecommunication technologies. We present
FikoRE, our real-time Radio Access Networks (RAN) emulator carefully designed
for application-level experimentation and prototyping. Its modularity and
straightforward implementation allow multidisciplinary user to rapidly use or
even modify it to test their own applications. In this article, we present
FikoRE's architecture accompanied with relevant validation experiments and
results.


title: Robustly fitting Gaussian graphical models: the R-package robFitConGraph

abstract:
  The paper gives a tutorial-style introduction to the R-package
robFitConGraph, which provides a robust goodness-of-fit test for Gaussian
graphical models.


title: Multi-objective evolution for Generalizable Policy Gradient Algorithms

abstract:
  Performance, generalizability, and stability are three Reinforcement Learning
(RL) challenges relevant to many practical applications in which they present
themselves in combination. Still, state-of-the-art RL algorithms fall short
when addressing multiple RL objectives simultaneously and current human-driven
design practices might not be well-suited for multi-objective RL. In this paper
we present MetaPG, an evolutionary method that discovers new RL algorithms
represented as graphs, following a multi-objective search criteria in which
different RL objectives are encoded in separate fitness scores. Our findings
show that, when using a graph-based implementation of Soft Actor-Critic (SAC)
to initialize the population, our method is able to find new algorithms that
improve upon SAC's performance and generalizability by 3% and 17%,
respectively, and reduce instability up to 65%. In addition, we analyze the
graph structure of the best algorithms in the population and offer an
interpretation of specific elements that help trading performance for
generalizability and vice versa. We validate our findings in three different
continuous control tasks: RWRL Cartpole, RWRL Walker, and Gym Pendulum.


title: Unavoidable patterns in complete simple topological graphs

abstract:
  In this paper, we show that every complete $n$-vertex simple topological
graph contains a topological subgraph on at least $(\log n)^{1/4 - o(1)}$
vertices that is weakly isomorphic to the complete convex geometric graph or
the complete twisted graph. This improves the previously known bound of
$\Omega(\log^{1/8}n)$ due to Pach, Solymosi, and T\'oth. We also show that
every complete $n$-vertex simple topological graph contains a planar path of
length at least $(\log n)^{1 - o(1)}$.


title: In Memoriam Cem Tezer (1955-2020)

abstract:
  Cem Tezer was a fastidious, meticulous, highly idiosyncratic and versatile
scientist. Without him Turkish community of mathematics would be incomplete.
Our sense of gratitude for his work in various areas of mathematics, history of
sciences, literature, music and his encouragement to do mathematics for only
its beauty was hardly unique and even unusual. After he passed away on 27
February 2020, while working actively at Middle East Technical University, the
number of colleagues and former students described the ways in which their
studies and indeed their view towards mathematics had been transformed by
having known him might have surprised only those who had never met him. In this
article not only, his contributions to mathematics will be classified and
summarized but also his unique and distinguished personality as a mathematician
will be emphasized.


title:
Impact of memory and bias in kinetic exchange opinion models on random
  networks

abstract:
  In this work we consider the effects of memory and bias in kinetic exchange
opinion models. We propose a model in which agents remember the sign of their
last interaction with each one of their pairs. This introduces memory effects
in the model, since past interactions can affect future ones. We have also
considered the impact of a parameter $p$ that regulates how often an agent
changes its interaction to match its opinion, thus introducing bias in the
interactions. For high values of $p$ an agent is more likely to start having a
negative interaction with an agent of opposing opinion and a positive
interaction with an agent of the same opinion. The model is defined on the top
of random networks with mean connectivity $\langle k \rangle$. We analyze the
impact of both $p$ and $\langle k \rangle$ on the emergence of ordered and
disordered states in the population. Our results suggest a rich phenomenology
regarding critical phenomena, with the presence of metastable states and a
non-monotonic behavior of the order parameter. We show that the fraction of
neutral agents in the disordered state decreases as the bias $p$ increases.


title:
Solving $X^{2^{3n}+2^{2n}+2^{n}-1}+(X+1)^{2^{3n}+2^{2n}+2^{n}-1}=b$ in
  $GF{2^{4n}}$

abstract:
  This article determines all the solutions in the finite field $GF{2^{4n}}$ of
the equation $x^{2^{3n}+2^{2n}+2^{n}-1}+(x+1)^{2^{3n}+2^{2n}+2^{n}-1}=b$.
Specifically, we explicitly determine the set of $b$'s for which the equation
has $i$ solutions for any positive integer $i$. Such sets, which depend on the
number of solutions $i$, are given explicitly and expressed nicely, employing
the absolute trace function over $GF{2^{n}}$, the norm function over
$GF{2^{4n}}$ relatively to $GF{2^{n}}$ and the set of $2^n+1$st roots of unity
in $GF{2^{4n}}$. The equation considered in this paper comes from an article by
Budaghyan et al. \cite{BCCDK20}. As an immediate consequence of our results, we
prove that the above equation has $2^{2n}$ solutions for one value of $b$,
$2^{2n}-2^n$ solutions for $2^n$ values of $b$ in $GF{2^{4n}}$ and has at most
two solutions for all remaining points $b$, leading to complete proof of the
conjecture raised by Budaghyan et al. We highlight that the recent work of Li
et al., in \cite{Li-et-al-2020} gives the complete differential spectrum of $F$
and also gives an affirmative answer to the conjecture of Budaghyan et al.
However, we emphasize that our approach is interesting and promising by being
different from Li et al. Indeed, on the opposite to their article, our
technique allows determine ultimately the set of $b$'s for which the considered
equation has solutions as well as the solutions of the equation for any $b$ in
$GF{2^{4n}}$.


title:
Learning to modulate random weights can induce task-specific contexts
  for economical meta and continual learning

abstract:
  Neural networks are vulnerable to catastrophic forgetting when data
distributions are non-stationary during continual online learning; learning of
a later task often leads to forgetting of an earlier task. One solution
approach is model-agnostic continual meta-learning, whereby both task-specific
and meta parameters are trained. Here, we depart from this view and introduce a
novel neural-network architecture inspired by neuromodulation in biological
nervous systems. Neuromodulation is the biological mechanism that dynamically
controls and fine-tunes synaptic dynamics to complement the behavioral context
in real-time, which has received limited attention in machine learning. We
introduce a single-hidden-layer network that learns only a relatively small
context vector per task (task-specific parameters) that neuromodulates
unchanging, randomized weights (meta parameters) that transform the input. We
show that when task boundaries are available, this approach can eliminate
catastrophic forgetting entirely while also drastically reducing the number of
learnable parameters relative to other context-vector-based approaches.
Furthermore, by combining this model with a simple meta-learning approach for
inferring task identity, we demonstrate that the model can be generalized into
a framework to perform continual learning without knowledge of task boundaries.
Finally, we showcase the framework in a supervised continual online learning
scenario and discuss the implications of the proposed formalism.


title:
SnP$_2$S$_6$: A Promising Infrared Nonlinear Optical Crystal with Strong
  Non-Resonant Second Harmonic Generation and Phase-matchability

abstract:
  High-power infrared laser systems with broadband tunability are of great
importance due to their wide range of applications in spectroscopy and
free-space communications. These systems require nonlinear optical (NLO)
crystals for wavelength up/down conversion using sum/difference frequency
generation, respectively. NLO crystals need to satisfy many competing criteria,
including large nonlinear optical susceptibility, large laser induced damage
threshold (LIDT), wide transparency range and phase-matchability. Here, we
report bulk single crystals of SnP_2S_6 with a large non-resonant SHG
coefficient of d33= 53 pm/V at 1550nm and a large LIDT of 350 GW/cm^2 for
femtosecond laser pulses. It also exhibits a broad transparency range from 0.54
{\mu}m to 8.5{\mu}m (bandgap of ~2.3 eV) and can be both Type I and Type II
phase-matched. The complete linear and SHG tensors are measured as well as
predicted by first principles calculations, and they are in excellent
agreement. A proximate double-resonance condition in the electronic band
structure for both the fundamental and the SHG light is shown to enhance the
non-resonant SHG response. Therefore, SnP2S6 is an outstanding candidate for
infrared laser applications.


title: Maximally Edge-Connected Realizations and Kundu's $k$-factor Theorem

abstract:
  A simple graph $G$ with edge-connectivity $\lambda(G)$ and minimum degree
$\delta(G)$ is maximally edge connected if $\lambda(G)=\delta(G)$. In 1964,
given a non-increasing degree sequence $\pi=(d_{1},\ldots,d_{n})$, Jack Edmonds
showed that there is a realization $G$ of $\pi$ that is $k$-edge-connected if
and only if $d_{n}\geq k$ with $\sum_{i=1}^{n}d_{i}\geq 2(n-1)$ when $d_{n}=1$.
We strengthen Edmonds's result by showing that given a realization $G_{0}$ of
$\pi$ if $Z_{0}$ is a spanning subgraph of $G_{0}$ with $\delta(Z_{0})\geq 1$
such that $|E(Z_{0})|\geq n-1$ when $\delta(G_{0})=1$, then there is a
maximally edge-connected realization of $\pi$ with $G_{0}-E(Z_{0})$ as a
subgraph. Our theorem tells us that there is a maximally edge-connected
realization of $\pi$ that differs from $G_{0}$ by at most $n-1$ edges. For
$\delta(G_{0})\geq 2$, if $G_{0}$ has a spanning forest with $c$ components,
then our theorem says there is a maximally edge-connected realization that
differs from $G_{0}$ by at most $n-c$ edges. As an application we combine our
work with Kundu's $k$-factor Theorem to find maximally edge-connected
realizations with a $(k_{1},\dots,k_{n})$-factor for $k\leq k_{i}\leq k+1$ and
present a partial result to a conjecture that strengthens the regular case of
Kundu's $k$-factor theorem.


title:
A comment on: Universal control of superexchange in linear triple
  quantum dots with an empty mediator

abstract:
  In a recent preprint arXiv:2203.15521 G. X. Chan, P. Huang, and X. Wang claim
that triple-quantum dot superexchange in a (1, 0, 1) charge configuration
exhibits a change of sign (going from positive to negative) as a function of
middle dot detuning. Furthermore, their claim is that charge sweet-spots exist
for specific values of the inter-dot detuning. Their analysis is based on the
Hubbard model and something to what they refer to as the full
Configuration-Interaction method. All of this findings were already reported by
M. J. Ran\v{c}i\'{c} and G. Burkard in Ref. Phys. Rev. B 96, 201304(R) (2017)
based on the Hubbard model. No reference to this manuscript was made in Ref.
arXiv:2203.15521. I have asked the authors to urgently modify the pre-print and
position their work with respect to the previously conducted study - which they
rejected to do at the current moment, quoting that pp. it is not their style to
modify preprints before they were accepted. This alongside with a very similar
style of some figures lead me to the conclusion that they are deliberately
misleading the scientific community and trying to adopt other peoples work and
ideas as their own.


title:
Preliminary Results on Using Abstract AND-OR Graphs for Generalized
  Solving of Stochastic Shortest Path Problems

abstract:
  Several goal-oriented problems in the real-world can be naturally expressed
as Stochastic Shortest Path Problems (SSPs). However, a key difficulty for
computing solutions for problems in the SSP framework is that the computational
requirements often make finding solutions to even moderately sized problems
intractable. Solutions to many of such problems can often be expressed as
generalized policies that are quite easy to compute from small examples and are
readily applicable to problems with a larger number of objects and/or different
object names. In this paper, we provide a preliminary study on using canonical
abstractions to compute such generalized policies and represent them as AND-OR
graphs that translate to simple non-deterministic, memoryless controllers. Such
policy structures naturally lend themselves to a hierarchical approach for
solving problems and we show that our approach can be embedded in any SSP
solver to compute hierarchically optimal policies. We conducted an empirical
evaluation on some well-known planning benchmarks and difficult robotics
domains and show that our approach is promising, often computing optimal
policies significantly faster than state-of-art SSP solvers.


title:
Long-range order in arrays of composite and monolithic magneto-toroidal
  moments

abstract:
  Magneto-toroidal order, also called ferrotoroidicity, is the most recently
established type of ferroic state. It is based on a spontaneous and uniform
alignment of unit-cell-sized magnetic whirls, called magneto-toroidal moments,
associated with a macroscopic toroidization. Because of its intrinsic
magnetoelectric coupling, this new ferroic state could be useful in the
development of spintronic devices. We exploit two-dimensional periodic arrays
of magnetostatically coupled nanomagnets as model systems for the investigation
of long-range magneto-toroidal order. We present two pathways promoting this
order, namely (i), structures comprising a ring of uniformly magnetized
sub-micrometer-sized bar magnets and (ii), structures in which each magnetic
building block itself hosts a magnetic vortex. For both cases calculations of
the magnetic-dipole interaction and micromagnetic simulations reveal the
conditions for the formation of spontaneous magneto-toroidal order. We confirm
this order and the formation of magneto-toroidal domains in our arrays by
magnetic force microscopy. We identify the presence of two types of domain-wall
states emerging from the competition of two intrinsic microscopic couplings.
Our work not only identifies the microscopic conditions promoting spontaneous
magneto-toroidal order but also highlights the possibility tailor mesoscale
magnetic arrays towards elusive types of ferroic order.


title:
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured
  Session Data

abstract:
  User sessions empower many search and recommendation tasks on a daily basis.
Such session data are semi-structured, which encode heterogeneous relations
between queries and products, and each item is described by the unstructured
text. Despite recent advances in self-supervised learning for text or graphs,
there lack of self-supervised learning models that can effectively capture both
intra-item semantics and inter-item interactions for semi-structured sessions.
To fill this gap, we propose CERES, a graph-based transformer model for
semi-structured session data. CERES learns representations that capture both
inter- and intra-item semantics with (1) a graph-conditioned masked language
pretraining task that jointly learns from item text and item-item relations;
and (2) a graph-conditioned transformer architecture that propagates inter-item
contexts to item-level representations. We pretrained CERES using ~468 million
Amazon sessions and find that CERES outperforms strong pretraining baselines by
up to 9% in three session search and entity linking tasks.


title:
Selective Exciton-Phonon-Phonon Coupling and Anharmonicity with Cavity
  Vibrational Phonons and MoS$_2$ Lattice Phonons in Hybrid Nanobeam Cavities

abstract:
  We report selective coupling between neutral excitons X$^0$, vibrational
phonon modes of a freestanding nanobeam cavity and lattice phonons of a MoS$_2$
monolayer fully encapsulated by hBN. Our experimental findings demonstrate that
the cavity vibrational phonons selectively couple to neutral excitons (X$^0$),
and the coupling to negatively charged trion (X$^-$) being significantly
weaker. We establish this result by studying the lattice temperature induced
broadening of exciton linewidths, where the contribution from the X$^0$-cavity
phonon coupling is clearly observed while the X$^-$-cavity phonon coupling is
not. Furthermore, when the Raman modes of MoS$_2$ lattice phonons A$_{1g}$ and
2LA are tuned into an outgoing resonance with exciton emissions, we observe the
X$^0$-cavity phonon-lattice phonon coupling which inherits the characteristics
rule the of X$^0$-cavity phonon coupling. As a result, X$^0$-induced Raman
scatterings are enhanced, while X$^-$-induced scatterings are suppressed,
revealed by the detuning-dependent Raman intensities and the ratio of
X$^-$/X$^0$ emission intensities. The phonon anharmonicity from the coupling
between cavity vibrational phonons and MoS$_2$ lattice phonons is further
demonstrated by the observed Raman linewidth. Such hybrid couplings between
materials and nanostructures enable the control of phonon-induced processes in
nanophotonic and nanomechanical systems incorporating 2D semiconductors.