title:
Measurements of droplets from singing, laughing, reciting poetry, and
  playing wind instruments

abstract:
  We present initial results from measurements of exhaled droplets by two
singers during singing, speaking, laughing, and recitation of poetry. We also
conducted measurements with a flute, a clarinet, a tuba, and only the tuba's
mouthpiece. To be able to image and track droplets, we have developed and built
a portable measurement set-up. We have detected droplets with diameters of
approximately 50 micrometre and above. We found that for a single subject, the
largest amount of droplets are produced during singing, but that laughing can
produce a comparable number of droplets. Speaking and recitation of poetry do
not produce as many droplets. We repeated exercises at varying distances and
found that the number of detected droplets decreased rapidly with increasing
distance. Most droplets we detected follow a ballistic trajectory, and hit the
ground after a distance of approximately 1 m. In case of the musical
instruments, we did not detect droplets exiting a tuba and only few single
droplets from a flute and a clarinet. A clarinet usually points downwards, such
that droplets follow a downward trajectory and hit the ground after a short
distance. The situation was different when the tubist used only the mouthpiece
(that is, detached from the tuba) during warm-up while partially blocking the
opening with a finger: in this case, many droplets were ejected from the
mouthpiece. These followed ballistic trajectories at different speeds, with
some droplets exiting the measurement volume. Two important limitations of this
study are that (i) the sample size is very small, such that a statistical
analysis is beyond our scope; and (ii) that we cannot draw conclusions about a
possible risk of infection with COVID-19 when performing any of the said
activities.


title:
Finding Clusters of Similar-minded People on Twitter Regarding the
  Covid-19 Pandemic

abstract:
  Two clustering methods to determine users with similar opinions on the
Covid-19 pandemic and the related public debate in Germany will be presented in
this paper. We believe, they can help gaining an overview over similar-minded
groups and could support the prevention of fake-news distribution. The first
method uses a new approach to create a network based on retweet relationships
between users and the most retweeted users, the so-called influencers. The
second method extracts hashtags from users posts to create a "user feature
vector" which is then clustered, using a consensus matrix based on previous
work, to identify groups using the same language. With both approaches it was
possible to identify clusters that seem to fit groups of different public
opinions in Germany. However, we also found that clusters from one approach can
not be associated with clusters from the other due to filtering steps in the
two methods.


title: Robot Hacking Manual (RHM)

abstract:
  Robots are often shipped insecure and in some cases fully unprotected. The
rationale behind is fourfold: first, defensive security mechanisms for robots
are still on their early stages, not covering the complete threat landscape.
Second, the inherent complexity of robotic systems makes their protection
costly, both technically and economically. Third, robot vendors do not
generally take responsibility in a timely manner, extending the zero-days
exposure window (time until mitigation of a zero-day) to several years on
average. Fourth, contrary to the common-sense expectations in 21st century and
similar to Ford in the 1920s with cars, most robot manufacturers oppose or
difficult robot repairs.
  The Robot Hacking Manual (RHM) is an introductory series about cybersecurity
for robots, with an attempt to provide comprehensive case studies and
step-by-step tutorials with the intent to raise awareness in the field and
highlight the importance of taking a security-first approach. The material
available here is also a personal learning attempt and it's disconnected from
any particular organization. Content is provided as is and by no means it's
encouraged or promoted the unauthorized tampering of robotic systems or related
technologies.


title:
Relativistic second-order dissipative spin hydrodynamics from the method
  of moments

abstract:
  We derive relativistic second-order dissipative fluid-dynamical equations of
motion for massive spin-1/2 particles from kinetic theory using the method of
moments. Besides the usual conservation laws for charge, energy, and momentum,
such a theory of relativistic dissipative spin hydrodynamics features an
equation of motion for the rank-3 spin tensor, which follows from the
conservation of total angular momentum. Extending the conventional method of
moments for spin-0 particles, we expand the spin-dependent distribution
function near local equilibrium in terms of moments of the momentum and spin
variables. We work to next-to-leading order in the Planck constant $\hbar$. As
shown in previous work, at this order in $\hbar$ the Boltzmann equation for
spin-1/2 particles features a nonlocal collision term. From the Boltzmann
equation, we then obtain an infinite set of equations of motion for the
irreducible moments of the deviation of the single-particle distribution
function from local equilibrium. In order to close this system of moment
equations, a truncation procedure is needed. We employ the "14+24-moment
approximation", where "14" corresponds to the components of the charge current
and the energy-momentum tensor and "24" to the components of the spin tensor,
which completes the derivation of the equations of motion of second-order
dissipative spin hydrodynamics. For applications to heavy-ion phenomenology, we
also determine dissipative corrections to the Pauli-Lubanski vector.


title:
A practical framework for multi-domain speech recognition and an
  instance sampling method to neural language modeling

abstract:
  Automatic speech recognition (ASR) systems used on smart phones or vehicles
are usually required to process speech queries from very different domains. In
such situations, a vanilla ASR system usually fails to perform well on every
domain. This paper proposes a multi-domain ASR framework for Tencent Map, a
navigation app used on smart phones and in-vehicle infotainment systems. The
proposed framework consists of three core parts: a basic ASR module to generate
n-best lists of a speech query, a text classification module to determine which
domain the speech query belongs to, and a reranking module to rescore n-best
lists using domain-specific language models. In addition, an instance sampling
based method to training neural network language models (NNLMs) is proposed to
address the data imbalance problem in multi-domain ASR. In experiments, the
proposed framework was evaluated on navigation domain and music domain, since
navigating and playing music are two main features of Tencent Map. Compared to
a general ASR system, the proposed framework achieves a relative 13% $\sim$ 22%
character error rate reduction on several test sets collected from Tencent Map
and our in-car voice assistant.


title:
Explainable Machine Learning for Predicting Homicide Clearance in the
  United States

abstract:
  Purpose: To explore the potential of Explainable Machine Learning in the
prediction and detection of drivers of cleared homicides at the national- and
state-levels in the United States.
  Methods: First, nine algorithmic approaches are compared to assess the best
performance in predicting cleared homicides country-wise, using data from the
Murder Accountability Project. The most accurate algorithm among all (XGBoost)
is then used for predicting clearance outcomes state-wise. Second, SHAP, a
framework for Explainable Artificial Intelligence, is employed to capture the
most important features in explaining clearance patterns both at the national
and state levels.
  Results: At the national level, XGBoost demonstrates to achieve the best
performance overall. Substantial predictive variability is detected state-wise.
In terms of explainability, SHAP highlights the relevance of several features
in consistently predicting investigation outcomes. These include homicide
circumstances, weapons, victims' sex and race, as well as number of involved
offenders and victims.
  Conclusions: Explainable Machine Learning demonstrates to be a helpful
framework for predicting homicide clearance. SHAP outcomes suggest a more
organic integration of the two theoretical perspectives emerged in the
literature. Furthermore, jurisdictional heterogeneity highlights the importance
of developing ad hoc state-level strategies to improve police performance in
clearing homicides.


title: Autoregressive based Drift Detection Method

abstract:
  In the classic machine learning framework, models are trained on historical
data and used to predict future values. It is assumed that the data
distribution does not change over time (stationarity). However, in real-world
scenarios, the data generation process changes over time and the model has to
adapt to the new incoming data. This phenomenon is known as concept drift and
leads to a decrease in the predictive model's performance. In this study, we
propose a new concept drift detection method based on autoregressive models
called ADDM. This method can be integrated into any machine learning algorithm
from deep neural networks to simple linear regression model. Our results show
that this new concept drift detection method outperforms the state-of-the-art
drift detection methods, both on synthetic data sets and real-world data sets.
Our approach is theoretically guaranteed as well as empirical and effective for
the detection of various concept drifts. In addition to the drift detector, we
proposed a new method of concept drift adaptation based on the severity of the
drift.


title: Non-Smooth Integrability Theory

abstract:
  We study a calculation method for utility function from a candidate of a
demand function that is not differentiable but locally Lipschitz. Using this
method, we obtain two new necessary and sufficient condition for a candidate of
a demand function to be a demand function. First is the symmetry and negative
semi-definiteness of the Slutsky matrix, and second is the global existence of
a unique concave solution for some partial differential equation. Moreover, we
present under several assumptions, the upper semi-continuous weak order that
corresponds to the demand function is unique, and this weak order is
represented by our calculated utility function. We also provide applications of
these results to econometric theory. First, we show that under several
requirements, if a sequence of demand functions converges to some function with
respect to a metric corresponds to the topology of compact convergence, then
the limit function is also a demand function. Second, the space of demand
functions that have uniform Lipschitz constant on any compact set is complete
under the above metric. Third, under some additional requirement, the mapping
from a demand function into a utility function we calculate becomes continuous.
This implies that a consistent estimation method for the demand function
immediately defines a consistent estimation method for the utility function by
using our calculation method.


title:
Multiscale Convolutional Transformer with Center Mask Pretraining for
  Hyperspectral Image Classification

abstract:
  Hyperspectral images (HSI) not only have a broad macroscopic field of view
but also contain rich spectral information, and the types of surface objects
can be identified through spectral information, which is one of the main
applications in hyperspectral image related research.In recent years, more and
more deep learning methods have been proposed, among which convolutional neural
networks (CNN) are the most influential. However, CNN-based methods are
difficult to capture long-range dependencies, and also require a large amount
of labeled data for model training.Besides, most of the self-supervised
training methods in the field of HSI classification are based on the
reconstruction of input samples, and it is difficult to achieve effective use
of unlabeled samples. To address the shortcomings of CNN networks, we propose a
noval multi-scale convolutional embedding module for HSI to realize effective
extraction of spatial-spectral information, which can be better combined with
Transformer network.In order to make more efficient use of unlabeled data, we
propose a new self-supervised pretask. Similar to Mask autoencoder, but our
pre-training method only masks the corresponding token of the central pixel in
the encoder, and inputs the remaining token into the decoder to reconstruct the
spectral information of the central pixel.Such a pretask can better model the
relationship between the central feature and the domain feature, and obtain
more stable training results.


title:
Modelling of saturated external MHD instabilities in tokamaks: a
  comparison of 3D free boundary equilibria and nonlinear stability
  calculations

abstract:
  3D free boundary equilibrium computations have recently been used to model
external kinks and edge harmonic oscillations (EHOs), comparing with linear MHD
stability codes, and nonlinear analytic theory [Kleiner et al, PPCF 61 084005
(2019)]. In this study, results of the VMEC equilibrium code are compared
further with nonlinear reduced MHD simulations, using the JOREK code, to
investigate the extent to which the modelling approaches agree. For the
simulated external kink, where the instability is dominated by a single
toroidal harmonic, good agreement is found. The JOREK simulation is run with a
low resistivity inside the plasma, and large resistivity outside, approximating
the ideal MHD assumptions in VMEC. Assuming a more realistic resistivity, and
including flows, the saturated state in JOREK and VMEC remain consistent.
Modelling EHOs where multiple toroidal harmonics are linearly unstable, the
saturated perturbation observed can differ in the dominant toroidal harmonic.
On the ideal timescale, a n=2 EHO is observed in JOREK, while the saturated
perturbation predicted by VMEC is a n=1 mode. Extending simulations into
timescales where resistive effects can play a role, similar $n=1$ perturbations
can be found. The coupling of different linearly unstable toroidal harmonics in
the JOREK simulation broadens the magnetic energy spectrum and ergodises the
plasma edge region, resulting in a more localised pressure perturbation. These
effects are not observed in VMEC, because closed magnetic flux surfaces are
enforced. Despite these differences, the results of the two approaches are
considered to be in reasonable agreement, even in this more advanced case.


title: Double arcsine transform not appropriate for meta-analysis

abstract:
  The variance-stabilizing Freeman-Tukey double arcsine transform was
originally proposed for inference on single proportions. Subsequently, its use
has been suggested in the context of meta-analysis of proportions. Not only has
some erratic behaviour been observed before, but this transform quite generally
does not appear suitable for meta-analysis purposes.


title: Tailored vertex ordering for faster triangle listing in large graphs

abstract:
  Listing triangles is a fundamental graph problem with many applications, and
large graphs require fast algorithms. Vertex ordering allows to orient the
edges from lower to higher vertex indices, and state-of-the-art triangle
listing algorithms use this to accelerate their execution and to bound their
time complexity. Yet, only two basic orderings have been tested. In this paper,
we show that studying the precise cost of algorithms instead of their bounded
complexity leads to faster solutions. We introduce cost functions that link
ordering properties with the running time of a given algorithm. We prove that
their minimization is NP-hard and propose heuristics to obtain new orderings
with different trade-offs between cost reduction and ordering time. Using
datasets with up to two billion edges, we show that our heuristics accelerate
the listing of triangles by an average of 30% when the ordering is already
given as an input, and 15% when the ordering time is included.


title: Software Training in High Energy Physics

abstract:
  Among the upgrades in current high energy physics (HEP) experiments and the
new facilities coming online, solving software challenges has become integral
for the success of the collaborations, The demand for human resources
highly-skilled in both HEP and software domains is increasing. With a highly
distributed environment in human resources, the sustainability of the HEP
ecosystem requires a continuous effort in the equipment of physicists with the
required abilities in software development. In this paper, the collective
software training program in HEP and its activities led by the HEP Software
Foundation (HSF) and the Institute for Research and Innovation in Software in
HEP (IRIS-HEP) are presented. Experiment-agnostic, open, and accessible modules
for training have been developed, focusing on common software material with
ranges from core software skills needed by everyone to advanced training
required to produce high-quality sustainable software. A basic software
curriculum was built, and an introductory software training event has been
prepared to serve HEP entrants. This program serves individuals with
transferable skills that are becoming increasingly important to careers in the
realm of software and computing, whether inside or outside HEP.


title: NURSE: eNd-UseR IoT malware detection tool for Smart homEs

abstract:
  Traditional techniques to detect malware infections were not meant to be used
by the end-user and current malware removal tools and security software cannot
handle the heterogeneity of IoT devices. In this paper, we design, develop and
evaluate a tool, called NURSE, to fill this information gap, i.e., enabling
end-users to detect IoT-malware infections in their home networks. NURSE
follows a modular approach to analyze IoT traffic as captured by means of an
ARP spoofing technique which does not require any network modification or
specific hardware. Thus, NURSE provides zero-configuration IoT traffic analysis
within everybody's reach. After testing NURSE in 83 different IoT network
scenarios with a wide variety of IoT device types, results show that NURSE
identifies malware-infected IoT devices with high accuracy (86.7%) using device
network behavior and contacted destinations.


title:
Abundance of LIGO/Virgo Black Holes from Microlensing Observations of
  Quasars with Reverberation Mapping Size Estimates

abstract:
  Assuming a population of Black Holes (BHs) with masses in the range inferred
by LIGO/Virgo from BH mergers, we use quasar microlensing observations to
estimate their abundances. We consider a mixed population of stars and BHs and
the presence of a smooth dark matter component. We adopt reverberation mapping
estimates of the quasar size. According to a Bayesian analysis of the measured
microlensing magnifications, a population of BHs with masses $\sim$
30$M_{\odot}$ constitutes less than 0.4 % of the total matter at 68 %
confidence level (less than 0.9 % at 90 % confidence). We have explored the
whole mass range of LIGO/Virgo BHs finding that this upper limit ranges from
0.5 % to 0.4 % at 68 % C.L. (from 1.1 % to 0.9 % at 90 % C.L.) when the BHs
mass change from 10 to 60$M_{\odot}$. We estimate a 16 % contribution from the
stars, in agreement with previous studies based on a single mass population
that do not consider explicitly the presence of BHs. These results are
consistent with the estimates of BH abundances from the statistics of
LIGO/Virgo mergers and rule out that PBHs (or any type of compact objects), in
this mass range constitute a significant fraction of the dark matter.


title: Special Issue on Advances in Chiral Quark Models

abstract:
  The number of exotic candidates in both light- and heavy-quark hadron sectors
has increased dramatically since the discovery by the Belle Collaboration of
the so-called $X(3872)$ in 2003. It is clear that the simple quark model
picture needs an extension and thus the last twenty years have witnessed an
explosion of related theoretical and experimental activity. The ultimate goal
of theory is to describe the properties of exotic states from the first
principles of Quantum Chromodynamics (QCD), which is the non-Abelian Quantum
Field Theory that describes the strong interaction. However, since this task is
quite challenging, a more modest goal to start with is the development of
QCD-motivated phenomenological models that specify the colored constituents,
how they are clustered, and the forces between them. This Special Issue invited
contributions reporting recent advances of phenomenological quark models in the
study of hadron's spectrocopy, structure, and interactions, paying special
attention to the exotic candidates but without losing sight of the conventional
states. In response to the call for papers, and after a comprehensive peer
review process, 8 articles qualified for acceptance in the final edition of the
Special Issue. The authors are from geographically distributed countries such
as Spain, South Africa, Ghana, China, Brazil, Argentina. This reflects the
impact of the proposed topic and the effective organization of the guest
editorial team of this Special Issue.


title:
Predictive data-driven model based on generative adversarial network for
  premixed turbulence-combustion regimes

abstract:
  Premixed flames exhibit different asymptotic regimes of interaction between
heat release and turbulence depending on their respective length scales. At
high Karlovitz number, the dilatation caused by heat release does not have any
relevant effect on turbulent kinetic energy with respect to non-reacting flow,
while at low Karlovitz number, the mean shear is a sink of turbulent kinetic
energy, and counter-gradient transport is observed. This latter phenomenon is
not well captured by closure models commonly used in Large Eddy Simulations
that are based on gradient diffusion. The massive amount of data available from
Direct Numerical Simulation (DNS) opens the possibility to develop data-driven
models able to represent physical mechanisms and non-linear features present in
both these regimes. In this work, the databases are formed by DNSs of two
planar hydrogen/air flames at different Karlovitz numbers corresponding to the
two asymptotic regimes. In this context, the Generative Adversarial Network
(GAN) gives the possibility to successfully recognize and reconstruct both
gradient and counter-gradient phenomena if trained with databases where both
regimes are included. Two GAN models were first trained each for a specific
Karlovitz number and tested using the same dataset in order to verify the
capability of the models to learn the features of a single asymptotic regime
and assess its accuracy. In both cases, the GAN models were able to reconstruct
the Reynolds stress subfilter scales accurately. Later, the GAN was trained
with a mixture of both datasets to create a model containing physical knowledge
of both combustion regimes. This model was able to reconstruct the subfilter
scales for both cases capturing the interaction between heat release and
turbulence closely to the DNS as shown from the turbulent kinetic budget and
barycentric maps.


title:
Intelligent resonance tracking of a microwave plasmonic resonator for
  compact wireless sensors

abstract:
  Plasmonic sensing has been in the spotlight for decades, the concept and
applications of which have been generalized to spoof surface plasmons (SSPs) in
the microwave band. Here, we report a compact and wireless sensor within a
printed circuit board size of 18 mm * 12 mm, tracking the resonance frequency
shift of a microwave plasmonic resonator via a software-defined scheme. The
microwave plasmonic resonator yields a deep-subwavelength size, enhanced
sensitivity, and a good electromagnetic compatibility performance. The
software-defined resonance tracking scheme minimalizes the hardware circuit and
the consumed spectrum resources, and makes the detection intelligently adaptive
to the target resonance, with a signal-to-noise ratio of 69 dB and a data rate
of 2272 measuring points per second. The sensor has been validated via acetone
vapor concentration sensing, while its applications can be widely extended by
replacing the transducer materials. This approach provides compact, sensitive,
accurate and intelligent solutions for resonant sensors in the Internet of
things (IoT).


title:
How many Observations are Enough? Knowledge Distillation for Trajectory
  Forecasting

abstract:
  Accurate prediction of future human positions is an essential task for modern
video-surveillance systems. Current state-of-the-art models usually rely on a
"history" of past tracked locations (e.g., 3 to 5 seconds) to predict a
plausible sequence of future locations (e.g., up to the next 5 seconds). We
feel that this common schema neglects critical traits of realistic
applications: as the collection of input trajectories involves machine
perception (i.e., detection and tracking), incorrect detection and
fragmentation errors may accumulate in crowded scenes, leading to tracking
drifts. On this account, the model would be fed with corrupted and noisy input
data, thus fatally affecting its prediction performance.
  In this regard, we focus on delivering accurate predictions when only few
input observations are used, thus potentially lowering the risks associated
with automatic perception. To this end, we conceive a novel distillation
strategy that allows a knowledge transfer from a teacher network to a student
one, the latter fed with fewer observations (just two ones). We show that a
properly defined teacher supervision allows a student network to perform
comparably to state-of-the-art approaches that demand more observations.
Besides, extensive experiments on common trajectory forecasting datasets
highlight that our student network better generalizes to unseen scenarios.


title: Predicting interstellar radiation fields from chemical evolution models

abstract:
  We present a new method to obtain the interstellar radiation field (ISRF) of
regions that contain stars, dust and gas. Starting from stellar spectra, we
first run the radiative transfer code Skirt to compute the mean intensity field
due to the stellar component alone. Then, we import that result to the
photoionisation code Cloudy to compute the emissivity and opacity of the given
mixture of gas and dust as a function of wavelength. Finally, we ask Skirt
again to compute the mean intensity field, adding the total contribution of
stars, gas and dust. This process is repeated iteratively, calling both codes
sequentially in order to obtain increasingly accurate estimates. We have
designed a first test, reminiscent of an HII region, that consists of a B star,
approximated as a black-body, surrounded by a spherical shell of gas and dust
with uniform density. We find that the results of our three-dimensional
radiative transfer method are in excellent agreement with a spherically
symmetric Cloudy simulation. As a realistic scientific application, we
calculate the interstellar radiation field of a Milky Way-like galaxy based on
two different chemical evolution models. Both of them give results broadly
consistent with previous ones reported in the literature for the interstellar
radiation field of our Galaxy, albeit they systematically underestimate the
mid-infrared emission, with significant differences in this range, as well as
in the ultraviolet, stemming from the input stellar and ISM properties. These
results show the feasibility of our method to incorporate radiative transfer to
chemical evolution models, increasing their predictive power and using this
interstellar radiation field to further constrain their parameters. Python
source code to implement our method is publicly available at
https://github.com/MarioRomeroC/Mixclask.


title:
Novel Distributed Algorithms Design for Nonsmooth Resource Allocation on
  Weight-Balanced Digraphs

abstract:
  In this paper, the distributed resource allocation problem on strongly
connected and weight-balanced digraphs is investigated, where the decisions of
each agent are restricted to satisfy the coupled network resource constraints
and heterogeneous general convex sets. Moreover, the local cost function can be
non-smooth. In order to achieve the exact optimum of the nonsmooth resource
allocation problem, a novel continuous-time distributed algorithm based on the
gradient descent scheme and differentiated projection operators is proposed.
With the help of the set-valued LaSalle invariance principle and nonsmooth
analysis, it is demonstrated that the algorithm converges asymptotically to the
global optimal allocation. Moreover, for the situation where local constraints
are not involved and the cost functions are differentiable with Lipschitz
gradients, the convergence of the algorithm to the exact optimal solution is
exponentially fast. Finally, the effectiveness of the proposed algorithms is
illustrated by simulation examples.


title:
Energy plus maximum bound preserving Runge-Kutta methods for the
  Allen-Cahn equation

abstract:
  It is difficult to design high order numerical schemes which could preserve
both the maximum bound property (MBP) and energy dissipation law for certain
phase field equations. Strong stability preserving (SSP) Runge-Kutta methods
have been developed for numerical solution of hyperbolic partial differential
equations in the past few decades, where strong stability means the
non-increasing of the maximum bound of the underlying solutions. However,
existing framework of SSP RK methods can not handle nonlinear stabilities like
energy dissipation law. The aim of this work is to extend this SSP theory to
deal with the nonlinear phase field equation of the Allen-Cahn type which
typically satisfies both maximum bound preserving (MBP) and energy dissipation
law. More precisely, for Runge-Kutta time discretizations, we first derive a
general necessary and sufficient condition under which MBP is satisfied; and we
further provide a necessary condition under which the MBP scheme satisfies
energy dissipation.


title: The nonlinear stability of n+1 dimensional FLRW spacetimes

abstract:
  We prove nonlinear Lyapunov stability of a family of `$n+1$'-dimensional
cosmological models of general relativity locally isometric to the Friedman
Lema\^itre Robertson Walker (FLRW) spacetimes including a positive cosmological
constant. In particular, we show that the perturbed solutions to the
Einstein-Euler field equations around a class of spatially compact FLRW metrics
(for which the spatial slices are compact negative Einstein spaces in general
and hyperbolic for the physically relevant $n=3$ case) arising from regular
Cauchy data remain uniformly bounded and decay to a family of metrics with
constant negative spatial scalar curvature. To accomplish this result, we
employ an energy method for the coupled Einstein-Euler field equations in
constant mean extrinsic curvature spatial harmonic gauge (CMCSH). In order to
handle Euler's equations, we construct energy from a current that is similar to
the one derived by Christodoulou \cite{christodoulou} (and which coincides with
Christodoulou's current on the Minkowski space) and show that this energy
controls the desired norm of the fluid degrees of freedom. The use of a fluid
energy current together with the CMCSH gauge condition casts the Einstein-Euler
field equations into a coupled elliptic-hyperbolic system. Utilizing the
estimates derived from the elliptic equations, we first show that the
gravity-fluid energy functional remains uniformly bounded in the expanding
direction. Using this uniform boundedness property, we later obtain sharp decay
estimates if a positive cosmological constant $\Lambda$ is included, which
suggests that the accelerated expansion of the physical universe that is
induced by the positive cosmological constant is sufficient to control the
non-linearities in the case of small data. A few physical consequences of this
stability result are discussed.


title: Enhance Topics Analysis based on Keywords Properties

abstract:
  Topic Modelling is one of the most prevalent text analysis technique used to
explore and retrieve collection of documents. The evaluation of the topic model
algorithms is still a very challenging tasks due to the absence of
gold-standard list of topics to compare against for every corpus. In this work,
we present a specificity score based on keywords properties that is able to
select the most informative topics. This approach helps the user to focus on
the most informative topics. In the experiments, we show that we are able to
compress the state-of-the-art topic modelling results of different factors with
an information loss that is much lower than the solution based on the recent
coherence score presented in literature.


title:
Simple analytic formula relating the mass and spin of accreting compact
  objects to their rapid X-ray variability

abstract:
  Following the previous research on epicyclic oscillations of accretion disks
around black holes (BHs) and neutron stars (NSs), a new model of high-frequency
quasi-periodic oscillations (QPOs) has been proposed (CT model), which deals
with oscillations of fluid in marginally overflowing accretion tori (i.e., tori
terminated by cusps). According to preliminary investigations, the model
provides better fits of the NS QPO data compared to the relativistic precession
(RP) model. It also implies a significantly higher upper limit on the Galactic
microquasars BH spin. A short analytic formula has been noticed to well
reproduce the model's predictions on the QPO frequencies in Schwarzschild
spacetimes. Here we derive an extended version of this formula that applies to
rotating compact objects. We start with the consideration of Kerr spacetimes
and derive a formula that is not restricted to a particular specific angular
momentum distribution of the inner accretion flow, such as Keplerian or
constant. Finally, we consider Hartle-Thorne spacetimes and include corrections
implied by the NS oblateness. For a particular choice of a single parameter,
our relation provides frequencies predicted by the CT model. For another value,
it provides frequencies predicted by the RP model. We conclude that the formula
is well applicable for rotating oblateness NSs and both models. We briefly
illustrate application of our simple formula on several NS sources and confirm
the expectation that the CT model is compatible with realistic values of the NS
mass and provides better fits of data than the RP model.


title: No Efficient Disjunction or Conjunction of Switch-Lists

abstract:
  It is shown that disjunction of two switch-lists can blow up the
representation size exponentially. Since switch-lists can be negated without
any increase in size, this shows that conjunction of switch-lists also leads to
an exponential blow-up in general.


title:
Non-equilibrium hole dynamics in antiferromagnets: damped strings and
  polarons

abstract:
  We develop a nonperturbative theory for the hole dynamics in
antiferromagnetic spin lattices, as described by the t-J model. This is
achieved by generalizing the self-consistent Born approximation to
non-equilibrium systems, making it possible to calculate the full
time-dependent many-body wave function. Our approach reveals three distinct
dynamical regimes, ultimately leading to the formation of magnetic polarons.
The initial hole motion is found to be ballistic but slower than a free quantum
walk due to zero-point spin fluctuations. Afterwards, the coherent formation of
string excitations gives rise to characteristic oscillations in the hole
density. Eventually, the damping of these strings leaves behind magnetic
polarons undergoing ballistic motion with a greatly reduced velocity. The
developed theory provides a rigorous framework for understanding the
non-equilibrium physics of defects in quantum magnets and quantitatively
explains recent observations from cold-atom quantum simulations in the strong
coupling regime.


title:
Approximate Acoustic Boundary Conditions in the Time-Domain using Volume
  Penalization

abstract:
  Immersed boundary methods allow describing complex objects on simple
Cartesian grids in time-domain simulations. The penalization technique employed
here is a physically motivated Brinkman method that models objects as porous
material by including a friction term and an effective volume. We investigate
how the approach can mimic different acoustic boundary conditions. It is
validated concerning different acoustic setups, including rigid walls and
various absorber configurations.


title: Dimensionality Reduction and Prioritized Exploration for Policy Search

abstract:
  Black-box policy optimization is a class of reinforcement learning algorithms
that explores and updates the policies at the parameter level. This class of
algorithms is widely applied in robotics with movement primitives or
non-differentiable policies. Furthermore, these approaches are particularly
relevant where exploration at the action level could cause actuator damage or
other safety issues. However, Black-box optimization does not scale well with
the increasing dimensionality of the policy, leading to high demand for
samples, which are expensive to obtain in real-world systems. In many practical
applications, policy parameters do not contribute equally to the return.
Identifying the most relevant parameters allows to narrow down the exploration
and speed up the learning. Furthermore, updating only the effective parameters
requires fewer samples, improving the scalability of the method. We present a
novel method to prioritize the exploration of effective parameters and cope
with full covariance matrix updates. Our algorithm learns faster than recent
approaches and requires fewer samples to achieve state-of-the-art results. To
select the effective parameters, we consider both the Pearson correlation
coefficient and the Mutual Information. We showcase the capabilities of our
approach on the Relative Entropy Policy Search algorithm in several simulated
environments, including robotics simulations. Code is available at
https://git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps}{git.ias.informatik.tu-darmstadt.de/ias\_code/aistats2022/dr-creps.


title: Explosive instability of dust settling in a protoplanetary disc

abstract:
  It is shown that gas-dust perturbations in a disc with dust settling to the
disc midplane exhibit the non-linear three-wave resonant interactions between
streaming dust wave (SDW) and two inertial waves (IW). In the particular case
considered in this paper, SDW at the wavenumber $k^\bullet = 2\kappa / (g_z
t_s)$, where $\kappa$, $g_z$ and $t_s$ are, respectively, epicyclic frequency,
vertical gravitational acceleration and particle's stopping time, interacts
with two IW at the lower wavenumbers $k^\prime$ and $k^{\prime\prime}$ such
that $k^\prime < k_{\rm DSI} < k^{\prime\prime} < k^\bullet$, where $k_{\rm
DSI} = \kappa / (g_z t_s)$ is the wavenumber of the linear resonance between
SDW and IW associated with the previously discovered linear dust settling
instability. The problem is solved analytically in the limit of the small dust
fraction. As soon as the dynamical dust back reaction on gas is taken into
account, ${\bf k}^\bullet$, ${\bf k}^\prime$ and ${\bf k}^{\prime\prime}$
become slightly non-collinear and the emerging interaction of waves leads to
simultaneous explosive growth of their amplitudes. This growth is explained by
the conservative exchange with energy between the waves. The amplitudes of all
three waves grow because the negative energy SDW transfers its energy to the
positive energy IW. The product of the dimensionless amplitude of initially
dominant wave and the time of explosion can be less than Keplerian time in a
disc. It is shown that, generally, the three-wave resonance of an explosive
type exists in a wide range of wavenumbers $0 < k^\bullet \leq 2\kappa / (g_z
t_s)$. An explosive instability of gas-dust mixture may facilitate the dust
clumping and the subsequent formation of planetesimals in young protoplanetary
discs.


title:
Crystal growth engineering and origin of the weak ferromagnetism in
  antiferromagnetic matrix of orthochromates from $t$-$e$ orbital hybridization

abstract:
  We report a combined experimental and theoretical study on intriguing
magnetic properties of quasiferroelectric orthochromates. Large single crystals
of the family of RECrO$_3$ (RE = Y, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu)
compounds were successfully grown. Neutron Laue study indicates a good quality
of the obtained single crystals. Applied magnetic-field and temperature
dependent magnetization measurements reveal their intrinsic magnetic
properties, especially the antiferromagnetic (AFM) transition temperatures.
Density functional theory studies of the electronic structures were carried out
using the Perdew-Burke-Ernzerhof functional plus Hubbard $U$ method.
Crystallographic information and magnetism were theoretically optimized
systematically. When RE$^{3+}$ cations vary from Y$^{3+}$ and Eu$^{3+}$ to
Lu$^{3+}$ ions, the calculated $t$-$e$ orbital hybridization degree and
N\'{e}el temperature behave similarly to the experimentally-determined AFM
transition temperature with variation in cationic radius. We found that the
$t$-$e$ hybridization is anisotropic, causing a magnetic anisotropy of
Cr$^{3+}$ sublattices. This was evaluated with the nearest-neighbour
$J_1$-$J_2$ model. Our research provides a picture of the electronic structures
during the $t$-$e$ hybridization process while changing RE ions and sheds light
on the nature of the weak ferromagnetism coexisting with predominated
antiferromagnetism. The available large RECrO$_3$ single crystals build a
platform for further studies of orthochromates.

categories: cond-mat.str-el cond-mat.mtrl-sci physics.app-ph physics.comp-ph


title: Geometric Optimisation on Manifolds with Applications to Deep Learning

abstract:
  We design and implement a Python library to help the non-expert using all
these powerful tools in a way that is efficient, extensible, and simple to
incorporate into the workflow of the data scientist, practitioner, and applied
researcher. The algorithms implemented in this library have been designed with
usability and GPU efficiency in mind, and they can be added to any PyTorch
model with just one extra line of code.
  We showcase the effectiveness of these tools on an application of
optimisation on manifolds in the setting of time series analysis. In this
setting, orthogonal and unitary optimisation is used to constraint and
regularise recurrent models and avoid vanishing and exploding gradient
problems. The algorithms designed for GeoTorch allow us to achieve state of the
art results in the standard tests for this family of models.
  We use tools from comparison geometry to give bounds on quantities that are
of interest in optimisation problems. In particular, we build on the work of
(Kaul 1976) to give explicit bounds on the norm of the second derivative of the
Riemannian exponential.


title: The Philos Trust Algorithm: Preventing Exploitation of Distributed Trust

abstract:
  The Philos Marketplace blockchain system is a proposed hierarchical
blockchain architecture which allows a large number of individual blockchains
to operate in parallel. These parallel chains achieve consensus among one
another on a limited set of core operations, while allowing each on-chain
application to manage its own data independently of others.
  This architecture addresses the scalability issues of traditional linear
blockchains, but requires novel consensus mechanisms. A central feature of the
Philos consensus mechanism is its trust algorithm, which assigns each network
node a numerical trust value (or score) indicating the quality of recent past
performance.
  This trust value is then used to determine a node's voting weight at the
higher levels of consensus. In this paper, we formally define the Philos trust
algorithm, and provide several illustrations of its operation, both
theoretically and empirically. We also ask whether a misbehaving node can
strategically exploit the algorithm for its personal gain, and show that this
type of exploitation can be universally prevented simply by enforcing a mild
limit on the number of participants in each of the parallel chains.


title:
Magnons in antiferromagnetic bcc-Cr and Cr$_2$O$_3$ from time-dependent
  density functional theory

abstract:
  We apply time-dependent density functional theory to calculate the transverse
magnetic susceptibility of bcc-Cr and Cr$_2$O$_3$, which constitute
prototypical examples of antiferromagnets with itinerant and localized magnetic
moments respectively. The exchange-correlation kernel is rescaled in order to
enforce the Goldstone condition and the magnon dispersion relations are
extracted based on a symmetry analysis relying on the generalized Onsager
relation. Doing so, our calculations yield the characteristic linear magnon
dispersion of antiferromagnets in the long wavelength limit. In the case of
Cr$_2$O$_3$, we find that the adiabatic local density approximation yields a
good qualitative agreement with the measured dispersion, but overestimates the
magnon velocity and bandwidth by a factor of two. Including a Hubbard
correction improves the magnon velocity, but at the expense of the overall
qualitative agreement with the experimental magnon dispersion. For bcc-Cr we
find a sharp acoustic magnon mode at low energies with a velocity in agreement
with previously reported values. At higher energies, the acoustic magnon mode
becomes subject to strong Landau damping and rapidly vanishes once it enters
the Stoner continuum. In addition to the acoustic magnon mode, we also observe
an additional collective mode along the $\Gamma\rightarrow\mathrm{R}$ direction
with an energy of $\sim$ 1 eV, which is located inside the Stoner continuum,
but appears to elude the effect of Landau damping.


title:
Transmission line approach to transport of heat in chiral and
  drift-diffusion systems with dissipation

abstract:
  Measurements of the energy relaxation in the integer quantum hall edge at
filling factor $\nu=2$ suggest the breakdown of heat current quantization [H.
le Sueur et al., Phys. Rev. Lett. 105, 056803]. It was shown, in a hydrodynamic
model, that dissipative neutral modes contributing apparently less than a
quantum of heat can be an explanation for the missing heat flux [A Goremykina
et al., arXiv preprint arXiv:1908.01213]. This hydrodynamic model relies on the
introduction of an artificial high-energy cut-off and lacks a way of a priori
obtaining the correct definition of the heat flux. In this work we overcome
these limitations and present a formalism, effectively modeling dissipation in
the quantum hall edge, proving the quantization of heat flux for all modes. We
mapped the QHE to a transmission line by analogy and used the Langevin
equations and scattering theory to extract the heat current in the presence of
dissipation and (chirality breaking) diffusion.


title:
Interacting $ud$ and $uds$ quark matter at finite densities and quark
  stars

abstract:
  The stability and equation of state of quark matter are studied within both
two-flavor and (2+1)-flavor Nambu-Jona-Lasinio (NJL) models including the
vector interactions. With a free parameter $\alpha$, the Lagrangian is
constructed by two parts, the original NJL Lagrangian and the Fierz
transformation of it, as $L=(1-\alpha) L_{\rm{NJL}}+\alpha L_{\rm{Fierz}}$. We
find that there is a possibility for both $ud$ nonstrange and $uds$ strange
matter being absolute stable, depending on the interplay of the confinement
with quark vector interaction and the exchange interaction channels. The
calculated quark star properties can reconcile with the recently measured
masses and radii of PSR J0030+0451 and PSR J0740+6620, as well as the tidal
deformability of GW170817. Furthermore, the more strongly-interacting quark
matter in the nonstrange stars allows a stiffer equation of state and
consequently a higher maximum mass ($\sim2.7\, M_{\odot}$) than the strange
ones ($\sim2.1\, M_{\odot}$). The sound velocities in strange and nonstrange
quark star matter are briefly discussed compared to those of neutron star
matter.


title:
Evaluation of YOLO Models with Sliced Inference for Small Object
  Detection

abstract:
  Small object detection has major applications in the fields of UAVs,
surveillance, farming and many others. In this work we investigate the
performance of state of the art Yolo based object detection models for the task
of small object detection as they are one of the most popular and easy to use
object detection models. We evaluated YOLOv5 and YOLOX models in this study. We
also investigate the effects of slicing aided inference and fine-tuning the
model for slicing aided inference. We used the VisDrone2019Det dataset for
training and evaluating our models. This dataset is challenging in the sense
that most objects are relatively small compared to the image sizes. This work
aims to benchmark the YOLOv5 and YOLOX models for small object detection. We
have seen that sliced inference increases the AP50 score in all experiments,
this effect was greater for the YOLOv5 models compared to the YOLOX models. The
effects of sliced fine-tuning and sliced inference combined produced
substantial improvement for all models. The highest AP50 score was achieved by
the YOLOv5- Large model on the VisDrone2019Det test-dev subset with the score
being 48.8.


title: Usage Control Specification, Enforcement, and Robustness: A Survey

abstract:
  The management of data and digital assets poses various challenges, including
the need to adhere to legal requirements with respect to personal data
protection and copyright. Usage control technologies could be used by software
platform providers to manage data and digital assets responsibly and to provide
more control to data and digital asset owners. In order to better understand
the potential of various usage control proposals, we collate and categorize
usage control requirements, compare the predominant usage control frameworks
based on said requirements, and identify existing challenges and opportunities
that could be used to guide future research directions.


title: Extensions of valuations to rational function fields over completions

abstract:
  Given a valued field $(K,v)$ and its completion $(\widehat{K},v)$, we study
the set of all possible extensions of $v$ to $\widehat{K}(X)$. We show that any
such extension is closely connected with the underlying subextension
$(K(X)|K,v)$. The connections between these extensions are studied via minimal
pairs, key polynomials, pseudo-Cauchy sequences and implicit constant fields.
As a consequence, we obtain strong ramification theoretic properties of
$(\widehat{K},v)$. We also give necessary and sufficient conditions for
$(K(X),v)$ to be dense in $(\widehat{K}(X),v)$.


title:
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for
  Weakly-supervised 6D Object Pose Estimation

abstract:
  Pose estimation of 3D objects in monocular images is a fundamental and
long-standing problem in computer vision. Existing deep learning approaches for
6D pose estimation typically rely on the assumption of availability of 3D
object models and 6D pose annotations. However, precise annotation of 6D poses
in real data is intricate, time-consuming and not scalable, while synthetic
data scales well but lacks realism. To avoid these problems, we present a
weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs
only 2D object segmentation and known relative camera poses during training.
Following the first-reconstruct-then-regress idea, we first reconstruct the
objects from multiple views in the form of an implicit neural representation.
Then, we train a pose regression network to predict pixel-wise 2D-3D
correspondences between images and the reconstructed model. At inference, the
approach only needs a single image as input. A NeRF-enabled PnP+RANSAC
algorithm is used to estimate stable and accurate pose from the predicted
correspondences. Experiments on LineMod and LineMod-Occlusion show that the
proposed method has state-of-the-art accuracy in comparison to the best 6D pose
estimation methods in spite of being trained only with weak labels. Besides, we
extend the Homebrewed DB dataset with more real training images to support the
weakly supervised task and achieve compelling results on this dataset. The
extended dataset and code will be released soon.


title: Limited Associativity Caching in the Data Plane

abstract:
  In-network caching promises to improve the performance of networked and edge
applications as it shortens the paths data need to travel. This is by storing
so-called hot items in the network switches on-route between clients who access
the data and the storage servers who maintain it. Since the data flows through
those switches in any case, it is natural to cache hot items there.
  Most software-managed caches treat the cache as a fully associative region.
Alas, a fully associative design seems to be at odds with programmable
switches' goal of handling packets in a short bounded amount of time, as well
as their restricted programming model. In this work, we present PKache, a
generic limited associativity cache implementation in the programmable
switches' domain-specific P4 language, and demonstrate its utility by realizing
multiple popular cache management schemes.


title:
Comment on "Nonlinear charge-voltage relationship in constant phase
  element" [AEU-Int. J. Electron. Commun. 117, 153104 (2020)]

abstract:
  In this comment, we show a dimensional inconsistency that plagues one of the
main founding equations, Eq. (5), of the manuscript, [Fouda et al., AEU-Int. J.
Electron. Commun. 117, 153104 (2020)]. Also, a resolution of the inconsistency
as well as a generalized yet a better version of the equation are suggested.


title:
High sensitivity air-coupled MHz frequency ultrasound detection using an
  on-chip microtoroid cavity

abstract:
  Cavity optomechanical systems provide an ideal platform for ultrasound
sensing, due to its dual-resonance enhanced sensitivity. Here we realize high
sensitivity air-coupled ultrasound sensing in the megahertz (MHz) frequency
range, using a microtoroid cavity. Benefitting from both the high optical Q
factor (~10^7) and mechanical Q factor (~700), we achieve sensitivity of 46
{\mu}Pa/Hz^1/2 -10 mPa/Hz^1/2 in a frequency range of 0.25-3.2 MHz.
Thermal-noise-limited sensitivity is realized around the mechanical resonance
at 2.56 MHz, in a frequency range of 0.6 MHz. We also observe the second- and
third-order mechanical sidebands, when driving the microcavity with an
ultrasonic wave at the mechanical resonance and quantitatively study the
intensities of each mechanical sideband as a function of the mechanical
displacement. Measuring the combination of signal to noise ratios at all the
sidebands has the potential to extend the dynamic range of displacement
sensing.


title: One-Shot Learning from a Demonstration with Hierarchical Latent Language

abstract:
  Humans have the capability, aided by the expressive compositionality of their
language, to learn quickly by demonstration. They are able to describe unseen
task-performing procedures and generalize their execution to other contexts. In
this work, we introduce DescribeWorld, an environment designed to test this
sort of generalization skill in grounded agents, where tasks are linguistically
and procedurally composed of elementary concepts. The agent observes a single
task demonstration in a Minecraft-like grid world, and is then asked to carry
out the same task in a new map. To enable such a level of generalization, we
propose a neural agent infused with hierarchical latent language--both at the
level of task inference and subtask planning. Our agent first generates a
textual description of the demonstrated unseen task, then leverages this
description to replicate it. Through multiple evaluation scenarios and a suite
of generalization tests, we find that agents that perform text-based inference
are better equipped for the challenge under a random split of tasks.


title: Formation of singularities for a family of 1D quasilinear wave equations

abstract:
  We consider the blow-up of solutions to the following parameterized nonlinear
wave equation: $ u_{tt} = c(u)^{2} u_{xx} + \lambda c(u)c'(u)( u_x)^2$ with the
real parameter $\lambda$. In previous works, it was reported that there exist
finite time blow-up solutions with $\lambda =1$ and $2$. However, the
construction of a blow-up solution depends on the symmetric structure of the
equation (e.g., the energy conservation law). In the present paper, we extend
the blow-up result with $\lambda =1$ to the case with $\lambda \in (0,1]$ by
using a new $L^{2/\lambda}$ estimate. Moreover, some properties for the blow-up
solution including the H\"older continuity are also discussed.


title:
Time-variant Nonlinear Participation Factors Considering Resonances in
  Power Systems

abstract:
  The participation factor (PF), as an important modal property for
small-signal stability, evaluates the linkage between a state variable and a
mode. Applying the normal form theory, a nonlinear PF can be defined to
evaluate the participation of a state variable into modal dynamics following a
large disturbance, that gives considerations to resonances and nonlinearities
up to a desired order. However, existing nonlinear PFs are inconsistent with
the conventional linear PF when nonlinear dynamics following a large
disturbance attenuate and linear modal dynamics become dominating. This paper
proposes a time-variant nonlinear PF by introducing a time decaying factor and
the definition of a nonlinear mode. The new PFs consider modes of resonances
and their values naturally transition to a linear PF when the system state
becomes close to its equilibrium. The case study on a two-area four-generator
system shows that the new PF can correctly rank generators by their
participations in natural and resonance modes of nonlinear oscillation subject
to a large disturbance.


title:
Impact of thermal SZ effect on cross-correlations between Planck CMB
  lensing and SDSS galaxy density fields

abstract:
  Residual foreground contamination by thermal Sunyaev-Zeldovich (tSZ) effect
from galaxy clusters in cosmic microwave background (CMB) maps propagates into
the reconstructed CMB lensing field, and thus biases the intrinsic
cross-correlation between CMB lensing and large-scale structure (LSS). Through
stacking analysis, we show that residual tSZ contamination causes an increment
of lensing convergence in the central part of the clusters and a decrement of
lensing convergence in the cluster outskirts. We quantify the impact of
residual tSZ contamination on cross-correlations between the Planck 2018 CMB
lensing convergence maps and the SDSS-IV galaxy density data through
cross-power spectrum computation. In contrast with the Planck 2018
tSZ-deprojected SMICA lensing map, our analysis using the tSZ-contaminated
SMICA lensing map measures a $\sim2.5\%$ negative bias at multipoles
$\ell\lesssim 500$ and transits to a $\sim9\%$ positive bias at
$\ell\gtrsim1500$, which validates earlier theoretical predictions of the
overall shape of such tSZ-induced spurious cross-correlation. The tSZ-induced
lensing convergence field in Planck CMB data is detected with more than
$1\sigma$ significance at $\ell\lesssim 500$ and more than $14\sigma$
significance at $\ell\gtrsim1500$, yielding an overall $14.8\sigma$ detection.
We also show that masking galaxy clusters in CMB data is not sufficient to
eliminate the spurious lensing signal, still detecting a non-negligible bias
with $5.5\sigma$ significance on cross-correlations with galaxy density fields.
Our results emphasize how essential it is to deproject the tSZ effect from CMB
maps at the component separation stage and adopt tSZ-free CMB lensing maps for
cross-correlations with LSS data.


title: Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

abstract:
  This technical note describes the recent updates of Graphormer, including
architecture design modifications, and the adaption to 3D molecular dynamics
simulation. With these simple modifications, Graphormer could attain better
results on large-scale molecular modeling datasets than the vanilla one, and
the performance gain could be consistently obtained on 2D and 3D molecular
graph modeling tasks. In addition, we show that with a global receptive field
and an adaptive aggregation strategy, Graphormer is more powerful than classic
message-passing-based GNNs. Empirically, Graphormer could achieve much less MAE
than the originally reported results on the PCQM4M quantum chemistry dataset
used in KDD Cup 2021. In the meanwhile, it greatly outperforms the competitors
in the recent Open Catalyst Challenge, which is a competition track on NeurIPS
2021 workshop, and aims to model the catalyst-adsorbate reaction system with
advanced AI models. All codes could be found at
https://github.com/Microsoft/Graphormer.


title:
Ground State for the Klein-Gordon field in anti-de Sitter spacetime with
  dynamical Wentzell boundary conditions

abstract:
  We consider a real Klein-Gordon field in the Poincar\'e patch of
$(d+1)$-dimensional anti-de Sitter spacetime, PAdS$_{d+1}$, and impose
dynamical boundary condition on the asymptotic boundary of PAdS$_{d+1}$ that
depend explicitly on the second time derivative of the field at the boundary.
These boundary conditions are of generalized Wentzell type. We construct the
Wightman two-point function for the ground state of the Klein-Gordon theory
whenever the parameters of the theory (the field mass, curvature coupling and
boundary condition parameters) render such ground state admissible. In the
cases in which the mass of the Klein-Gordon field and the curvature coupling
term yield an effectively massless theory, we can define a boundary field whose
dynamics are ruled by the dynamical boundary condition and construct, in
addition to the Wightman function for the Klein-Gordon field,
boundary-to-boundary, boundary-to-bulk and bulk-to-boundary propagators.


title:
A high-precision self-supervised monocular visual odometry in foggy
  weather based on robust cycled generative adversarial networks and multi-task
  learning aided depth estimation

abstract:
  This paper proposes a high-precision self-supervised monocular VO, which is
specifically designed for navigation in foggy weather. A cycled generative
adversarial network is designed to obtain high-quality self-supervised loss via
forcing the forward and backward half-cycle to output consistent estimation.
Moreover, gradient-based loss and perceptual loss are introduced to eliminate
the interference of complex photometric change on self-supervised loss in foggy
weather. To solve the ill-posed problem of depth estimation, a self-supervised
multi-task learning aided depth estimation module is designed based on the
strong correlation between the depth estimation and transmission map
calculation of hazy images in foggy weather. The experimental results on the
synthetic foggy KITTI dataset show that the proposed self-supervised monocular
VO performs better in depth and pose estimation than other state-of-the-art
monocular VO in the literature, indicating the designed method is more suitable
for foggy weather.


title:
Social Engineering Attacks and Defenses in the Physical World vs.
  Cyberspace: A Contrast Study

abstract:
  Social engineering attacks are phenomena that are equally applicable to both
the physical world and cyberspace. These attacks in the physical world have
been studied for a much longer time than their counterpart in cyberspace. This
motivates us to investigate how social engineering attacks in the physical
world and cyberspace relate to each other, including their common
characteristics and unique features. For this purpose, we propose a methodology
to unify social engineering attacks and defenses in the physical world and
cyberspace into a single framework, including: (i) a systematic model based on
psychological principles for describing these attacks; (ii) a systematization
of these attacks; and (iii) a systematization of defenses against them. Our
study leads to several insights, which shed light on future research directions
towards adequately defending against social engineering attacks in cyberspace.


title:
Text-DIAE: Degradation Invariant Autoencoders for Text Recognition and
  Document Enhancement

abstract:
  In this work, we propose Text-Degradation Invariant Auto Encoder (Text-DIAE)
aimed to solve two tasks, text recognition (handwritten or scene-text) and
document image enhancement. We define three pretext tasks as learning
objectives to be optimized during pre-training without the usage of labelled
data. Each of the pre-text objectives is specifically tailored for the final
downstream tasks. We conduct several ablation experiments that show the
importance of each degradation for a specific domain. Exhaustive
experimentation shows that our method does not have limitations of previous
state-of-the-art based on contrastive losses while at the same time requiring
essentially fewer data samples to converge. Finally, we demonstrate that our
method surpasses the state-of-the-art significantly in existing supervised and
self-supervised settings in handwritten and scene text recognition and document
image enhancement. Our code and trained models will be made publicly available
at~\url{ http://Upon_Acceptance}.


title:
Machine Learning based Optimal Feedback Control for Microgrid
  Stabilization

abstract:
  Microgrids have more operational flexibilities as well as uncertainties than
conventional power grids, especially when renewable energy resources are
utilized. An energy storage based feedback controller can compensate undesired
dynamics of a microgrid to improve its stability. However, the optimal feedback
control of a microgrid subject to a large disturbance needs to solve a
Hamilton-Jacobi-Bellman problem. This paper proposes a machine learning-based
optimal feedback control scheme. Its training dataset is generated from a
linear-quadratic regulator and a brute-force method respectively addressing
small and large disturbances. Then, a three-layer neural network is constructed
from the data for the purpose of optimal feedback control. A case study is
carried out for a microgrid model based on a modified Kundur two-area system to
test the real-time performance of the proposed control scheme.


title: Deletion-Restriction for Logarithmic Forms on Multiarrangements

abstract:
  We consider the behaviour of logarithmic differential forms on arrangements
and multiarrangements of hyperplanes under the operations of deletion and
restriction, extending early work of G\"unter Ziegler. The restriction of
logarithmic forms to a hyperplane may or may not be surjective, and we measure
the failure of surjectivity in terms of commutative algebra of logarithmic
forms and derivations. We find that the dual notion of restriction of
logarithmic vector fields behaves similarly but inequivalently. A main result
is that, if an arrangement is free, then any arrangement obtained by adding a
hyperplane has the "dual strongly plus-one generated" property. As an
application, we give another proof of a main result of a paper by the first
author, which gives a combinatorial characterization of when adding a
hyperplane to a free arrangement remains free.


title: BV quantization of dynamical fuzzy spectral triples

abstract:
  This paper provides a systematic study of gauge symmetries in the dynamical
fuzzy spectral triple models for quantum gravity that have been proposed by
Barrett and collaborators. We develop both the classical and the perturbative
quantum BV formalism for these models, which in particular leads to an explicit
homological construction of the perturbative quantum correlation functions. We
show that the relevance of ghost and antifield contributions to such
correlation functions depends strongly on the background Dirac operator $D_0$
around which one perturbs, and in particular on the amount of gauge symmetry
that it breaks. This will be illustrated by studying quantum perturbations
around 1.) the gauge-invariant zero Dirac operator $D_0=0$ in a general
$(p,q)$-model, and 2.) a simple example of a non-trivial $D_0$ in the quartic
$(0,1)$-model.


title:
Decay of multiple dark matter particles to dark radiation in different
  epochs does not alleviate the Hubble tension

abstract:
  Decaying cold dark matter (CDM) has been considered as a mechanism to tackle
the tensions in the Hubble expansion rate and the clustering of matter.
However, polarization measurements of the cosmic microwave background (CMB)
severely constrain the fraction of dark matter decaying before recombination,
and lensing of the CMB anisotropies by large-scale structure set strong
constraints on dark matter decaying after recombination. Together, these
constraints make an explanation of the Hubble tension in terms of decaying dark
matter unlikely. In response to this situation, we investigate whether a dark
matter ensemble with CDM particles decaying into free streaming dark radiation
in different epochs can alleviate the problem. We find that it does not.


title:
Practical Considerations of DER Coordination with Distributed Optimal
  Power Flow

abstract:
  The coordination of prosumer-owned, behind-the-meter distributed energy
resources (DER) can be achieved using a multiperiod, distributed optimal power
flow (DOPF), which satisfies network constraints and preserves the privacy of
prosumers. To solve the problem in a distributed fashion, it is decomposed and
solved using the alternating direction method of multipliers (ADMM), which may
require many iterations between prosumers and the central entity (i.e., an
aggregator). Furthermore, the computational burden is shared among the agents
with different processing capacities. Therefore, computational constraints and
communication requirements may make the DOPF infeasible or impractical. In this
paper, part of the DOPF (some of the prosumer subproblems) is executed on a
Raspberry Pi-based hardware prototype, which emulates a low processing power,
edge computing device. Four important aspects are analyzed using test cases of
different complexities. The first is the computation cost of executing the
subproblems in the edge computing device. The second is the algorithm operation
on congested electrical networks, which impacts the convergence speed of DOPF
solutions. Third, the precision of the computed solution, including the
trade-off between solution quality and the number of iterations, is examined.
Fourth, the communication requirements for implementation across different
communication networks are investigated. The above metrics are analyzed in four
scenarios involving 26-bus and 51-bus networks.


title:
Participation Factor-Based Adaptive Model Reduction for Fast Power
  System Simulation

abstract:
  This paper describes an adaptive method to reduce a nonlinear power system
model for fast and accurate transient stability simulation. It presents an
approach to analyze and rank participation factors of each system state
variable into dominant system modes excited by a disturbance so as to determine
which regions or generators can be reduced without impacting the accuracy of
simulation for a study area. In this approach, the generator models located in
an external area with large participation factors are nonlinearly reduced and
the rest of the generators will be linearized. The simulation results confirm
that the assessment of the level of interaction between generators and system
modes by participation factors is effective in enhancing the accuracy and speed
of power system models. The proposed method is applied to the Northeastern
Power Coordinating Council region system with 48-machine, 140-bus power system
model and the results are compared with two cases including fully linearized
model reduction and model reduction using the rotor angle deviation criteria.


title:
Neural Network Training on In-memory-computing Hardware with Radix-4
  Gradients

abstract:
  Deep learning training involves a large number of operations, which are
dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has
motivated hardware accelerators to enhance compute efficiency, but where data
movement and accessing are proving to be key bottlenecks. In-Memory Computing
(IMC) is an approach with the potential to overcome this, whereby computations
are performed in-place within dense 2-D memory. However, IMC fundamentally
trades efficiency and throughput gains for dynamic-range limitations, raising
distinct challenges for training, where compute precision requirements are seen
to be substantially higher than for inferencing. This paper explores training
on IMC hardware by leveraging two recent developments: (1) a training algorithm
enabling aggressive quantization through a radix-4 number representation; (2)
IMC leveraging compute based on precision capacitors, whereby analog noise
effects can be made well below quantization effects. Energy modeling calibrated
to a measured silicon prototype implemented in 16nm CMOS shows that energy
savings of over 400x can be achieved with full quantizer adaptability, where
all training MVMs can be mapped to IMC, and 3x can be achieved for two-level
quantizer adaptability, where two of the three training MVMs can be mapped to
IMC.


title:
A high-precision underwater object detection based on joint
  self-supervised deblurring and improved spatial transformer network

abstract:
  Deep learning-based underwater object detection (UOD) remains a major
challenge due to the degraded visibility and difficulty to obtain sufficient
underwater object images captured from various perspectives for training. To
address these issues, this paper presents a high-precision UOD based on joint
self-supervised deblurring and improved spatial transformer network. A
self-supervised deblurring subnetwork is introduced into the designed
multi-task learning aided object detection architecture to force the shared
feature extraction module to output clean features for detection subnetwork.
Aiming at alleviating the limitation of insufficient photos from different
perspectives, an improved spatial transformer network is designed based on
perspective transformation, adaptively enriching image features within the
network. The experimental results show that the proposed UOD approach achieved
47.9 mAP in URPC2017 and 70.3 mAP in URPC2018, outperforming many
state-of-the-art UOD methods and indicating the designed method is more
suitable for UOD.


title: Deformations of singular Fano and Calabi-Yau varieties

abstract:
  The goal of this paper is to generalize results concerning the deformation
theory of Calabi-Yau and Fano threefolds with isolated hypersurface
singularites, due to the first author, Namikawa and Steenbrink. In particular,
under the assumption of terminal singularities, Namikawa proved smoothability
in the Fano case and also for generalized Calabi-Yau threefolds assuming that a
certain topological first order condition is satisfied. In the case of
dimension $3$, we extend their results by, among other things, replacing
terminal with canonical. In higher dimensions, we identify a class of
singularities to which our method applies. A surprising aspect of our study is
the role played by the higher Du Bois and higher rational singularities,
recently introduced by Musta\c{t}\u{a}, Popa, Saito and their coauthors. While
the results in this paper only require the notion of $1$-rational and $1$-Du
Bois singularities, we also consider the case of $k$-rational and $k$-Du Bois
isolated hypersurface singularities for $k\geq 1$, which is of independent
interest. In particular, we propose a general definition for $k$-rational
singularities and show that it agrees with the previous definition in the case
of isolated hypersurface singularities. Among other deformation theoretic
results in higher dimensions, we obtain smoothing results for generalized Fano
varieties whose singularities are not $1$-rational, and for generalized
Calabi-Yau varieties whose singularities are not $1$-rational but are $1$-Du
Bois under a topological condition on the links which is similar to the first
order obstruction in dimension $3$.


title: de Sitter-Invariant Approach to Cosmology

abstract:
  One of the crucial problems of modern physics is the inconsistency of general
relativity with quantum mechanics, a problem inherited from the more
fundamental inconsistency of special relativity with quantum mechanics. A
possible solution to this puzzle is to replace the Poincar\'e-invariant
Einstein special relativity with a de Sitter-invariant special relativity.
Since the latter admits a finite invariant length parameter while preserving
the Lorentz symmetry, it turns out to be consistent with quantum mechanics.
Under such replacement, general relativity changes to the de Sitter-modified
general relativity. Although conceived to be consistent with quantum mechanics,
this theory turns up substantially different from ordinary general relativity,
providing an entirely new approach to cosmology. In this paper, the de
Sitter-modified Friedmann equations are obtained, and a brief discussion of
their consequences for cosmology is presented.


title: Efficient Sub-structured Knowledge Distillation

abstract:
  Structured prediction models aim at solving a type of problem where the
output is a complex structure, rather than a single variable. Performing
knowledge distillation for such models is not trivial due to their
exponentially large output space. In this work, we propose an approach that is
much simpler in its formulation and far more efficient for training than
existing approaches. Specifically, we transfer the knowledge from a teacher
model to its student model by locally matching their predictions on all
sub-structures, instead of the whole output space. In this manner, we avoid
adopting some time-consuming techniques like dynamic programming (DP) for
decoding output structures, which permits parallel computation and makes the
training process even faster in practice. Besides, it encourages the student
model to better mimic the internal behavior of the teacher model. Experiments
on two structured prediction tasks demonstrate that our approach outperforms
previous methods and halves the time cost for one training epoch.


title:
Surface Brightness Profile of Lyman-$\alpha$ Halos out to 320 kpc in
  HETDEX

abstract:
  We present the median-stacked Lyman-$\alpha$ surface brightness profile of
968 spectroscopically selected Lyman-$\alpha$ emitting galaxies (LAEs) at
redshifts $1.9<z<3.5$ in the early data of the Hobby-Eberly Telescope Dark
Energy Experiment (HETDEX). The selected LAEs are high-confidence
Lyman-$\alpha$ detections with large signal-to-noise ratios observed with good
seeing conditions (point-spread-function full-width-at-half-maximum $<1.4"$),
excluding active galactic nuclei (AGN). The Lyman-$\alpha$ luminosities of the
LAEs are $10^{42.4}-10^{43}\, \mathrm{erg}\, \mathrm{s}^{-1}$. We detect faint
emission in the median-stacked radial profiles at the level of $(3.6\pm
1.3)\times
10^{-20}\,\mathrm{erg}\,\mathrm{s}^{-1}\,\mathrm{cm}^{-2}\,\mathrm{arcsec}^{-2}$
from the surrounding Lyman-$\alpha$ halos out to $r\simeq 160$ kpc (physical).
The shape of the median-stacked radial profile is consistent at
$r<80\,\mathrm{kpc}$ with that of much fainter LAEs at $3<z<4$ observed with
the Multi Unit Spectroscopic Explorer (MUSE), indicating that the
median-stacked Lyman-$\alpha$ profiles have similar shapes at redshifts $2<z<4$
and across a factor of $10$ in Lyman-$\alpha$ luminosity. While we agree with
the results from the MUSE sample at $r<80\,\mathrm{kpc}$, we extend the profile
over a factor of two in radius. At $r>80\,\mathrm{kpc}$, our profile is flatter
than the MUSE model. The measured profile agrees at most radii with that of
galaxies in the Byrohl et al. (2021) cosmological radiative transfer simulation
at $z=3$. This suggests that the surface brightness of a Lyman-$\alpha$ halo at
$r\lesssim 100$ kpc is dominated by resonant scattering of Lyman-$\alpha$
photons from star-forming regions in the central galaxy, whereas at $r > 100$
kpc it is dominated by photons from galaxies in surrounding dark matter halos.


title: Three-space from quantum mechanics

abstract:
  The spin geometry theorem of Penrose is extended from $SU(2)$ to $E(3)$
(Euclidean) invariant elementary quantum mechanical systems. Using the natural
decomposition of the total angular momentum into its spin and orbital parts,
the \emph{distance} between the centre-of-mass lines of the elementary
subsystems of a classical composite system can be recovered from their
\emph{relative orbital angular momenta} by $E(3)$-invariant classical
observables. Motivated by this observation, an expression for the `empirical
distance' between the elementary subsystems of a \emph{composite quantum
mechanical system}, given in terms of $E(3)$-invariant quantum observables, is
suggested. It is shown that, in the classical limit, this expression reproduces
the \emph{a priori} Euclidean distance between the subsystems, though at the
quantum level it has a discrete character. `Empirical' angles and 3-volume
elements are also considered.


title: Global fits of SUSY at future Higgs factories

abstract:
  In this work, we study the impact of the electroweak and Higgs precision
measurements at future electron-positron colliders on several typical
supersymmetric models, including the Constrained Minimal Supersymmetric
Standard Model (CMSSM), Non-Universal Higgs Mass generalisations (NUHM1,
NUHM2), and the 7-dimensional Minimal Supersymmetric Standard Model (MSSM7).
Using the publicly data of SUSY global fits from the GAMBIT community, we
post-process the data with additional likelihoods to explore the discovery
potential at the Higgs factories, such as the Circular Electron Positron
Collider (CEPC), the Future Circular Collider (FCC) and the International
Linear Collider (ILC). We show that the currently allowed parameter space of
these models will be further tested by the future precision measurements. In
particular, the different dark matter annihilation mechanisms may be
distinguished by precisely measuring the central values of Higgs observables.


title: Bayesian Multi-Arm De-Intensification Designs

abstract:
  In recent years new cancer treatments improved survival in multiple
histologies. Some of these therapeutics, and in particular treatment
combinations, are often associated with severe treatment-related adverse events
(AEs). It is therefore important to identify alternative de-intensified
therapies, for example dose-reduced therapies, with reduced AEs and similar
efficacy. We introduce a sequential design for multi-arm de-intensification
studies. The design evaluates multiple de-intensified therapies at different
dose levels, one at the time, based on modeling of toxicity and efficacy
endpoints. We study the utility of the design in oropharynx cancer
de-intensification studies. We use a Bayesian nonparametric model for efficacy
and toxicity outcomes to define decision rules at interim and final analysis.
Interim decisions include early termination of the study due to inferior
survival of experimental arms compared to the standard of care (SOC), and
transitions from one de-intensified treatment arm to another with a further
reduced dose when there is sufficient evidence of non-inferior survival. We
evaluate the operating characteristics of the design using simulations and data
from recent de-intensification studies in human papillomavirus (HPV)-associated
oropharynx cancer.


title: Cosmic Birefringence from Planck Public Release 4

abstract:
  We search for the signature of parity-violating physics in the Cosmic
Microwave Background using Planck polarization data from the Public Release 4
(PR4 or $\mathtt{NPIPE}$). For nearly full-sky data, we initially find a
birefringence angle $\beta=0.30^\circ\pm0.11^\circ$ ($68\%$~C.L.). We also find
that the values of $\beta$ decrease as we enlarge the Galactic mask, which can
be interpreted as the effect of polarized foreground emission. We use two
independent approaches to model this effect and mitigate its impact on $\beta$.
Although results are promising, and the good agreement between both models is
encouraging, we do not assign cosmological significance to the measured value
of $\beta$ until we improve our knowledge of the foreground polarization.
Acknowledging that the miscalibration of polarization angles is not the only
instrumental systematic that can create spurious TB and EB correlations, we
also perform a detailed study of $\mathtt{NPIPE}$ end-to-end simulations to
prove that our measurements of $\beta$ are not significantly affected by any of
the known systematics.


title: Automatic Language Identification for Celtic Texts

abstract:
  Language identification is an important Natural Language Processing task. It
has been thoroughly researched in the literature. However, some issues are
still open. This work addresses the identification of the related low-resource
languages on the example of the Celtic language family.
  This work's main goals were: (1) to collect the dataset of three Celtic
languages; (2) to prepare a method to identify the languages from the Celtic
family, i.e. to train a successful classification model; (3) to evaluate the
influence of different feature extraction methods, and explore the
applicability of the unsupervised models as a feature extraction technique; (4)
to experiment with the unsupervised feature extraction on a reduced annotated
set.
  We collected a new dataset including Irish, Scottish, Welsh and English
records. We tested supervised models such as SVM and neural networks with
traditional statistical features alongside the output of clustering,
autoencoder, and topic modelling methods. The analysis showed that the
unsupervised features could serve as a valuable extension to the n-gram feature
vectors. It led to an improvement in performance for more entangled classes.
The best model achieved a 98\% F1 score and 97\% MCC. The dense neural network
consistently outperformed the SVM model.
  The low-resource languages are also challenging due to the scarcity of
available annotated training data. This work evaluated the performance of the
classifiers using the unsupervised feature extraction on the reduced labelled
dataset to handle this issue. The results uncovered that the unsupervised
feature vectors are more robust to the labelled set reduction. Therefore, they
proved to help achieve comparable classification performance with much less
labelled data.


title: On proving consistency of equational theories in Bounded Arithmetic

abstract:
  We consider pure equational theories that allow substitution but disallow
induction, which we denote as PETS, based on recursive definition of their
function symbols. We show that the Bounded Arithmetic theory $S^1_2$ proves the
consistency of PETS. Our approach employs models for PETS based on approximate
values resembling notions from domain theory in Bounded Arithmetic, which may
be of independent interest.


title:
Alignment of elongated swimmers in a laminar and turbulent Kolmogorov
  flow

abstract:
  Many aquatic microorganisms are able to swim. In natural environments they
typically do so in the presence of flows. In recent years it has been shown
that the interplay of swimming and flows can give rise to interesting and
biologically relevant phenomena, such as accumulation of microorganisms in
specific flow regions and local alignment with the flow properties. Here, we
consider a mechanical model for elongated microswimmers in a Kolmogorov flow, a
prototypic shear flow, both in steady and in turbulent conditions. By means of
direct numerical simulations, supported by analytical calculation in a
simplified stochastic setting, we find that the alignment of the swimming
direction with the local velocity is a general phenomenon. We also explore how
the accumulation of microorganisms, typically observed in steady flows, is
modified by the presence of unsteady fluctuations.


title: Computing unsatisfiable cores for LTLf specifications

abstract:
  Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a
de-facto standard to produce specifications in many application domains (e.g.,
planning, business process management, run-time monitoring, reactive
synthesis). Several studies approached the respective satisfiability problem.
In this paper, we investigate the problem of extracting the unsatisfiable core
in LTLf specifications. We provide four algorithms for extracting an
unsatisfiable core leveraging the adaptation of state-of-the-art approaches to
LTLf satisfiability checking. We implement the different approaches within the
respective tools and carry out an experimental evaluation on a set of reference
benchmarks, restricting to the unsatisfiable ones. The results show the
feasibility, effectiveness, and complementarities of the different algorithms
and tools.


title: The Hubble tension in the non-flat Super-$\Lambda {\rm CDM}$ model

abstract:
  We investigate the Hubble tension in the non-flat Super-$\Lambda {\rm CDM}$
model. The non-flat Super-$\Lambda {\rm CDM}$ model extends the Super-$\Lambda
{\rm CDM}$ model by including the spatial curvature as a free parameter. The
Super-$\Lambda {\rm CDM}$ model extends the standard $\Lambda {\rm CDM}$ model
of cosmology through additional parameters accounting for the possible effect
of a trispectrum in the primordial fluctuations. In the cosmic microwave
background data, this effect can be parameterized using parameters that change
the observed angular power spectrum from the theoretical power spectrum due to
a trispectrum that couples long and short wavelength modes. In this work, we
perform Markov Chain Monte Carlo (MCMC) data analysis on the recent Planck 2018
temperature and polarization fluctuations data and the local Hubble constant
measurements from supernovae data assuming a non-flat Super-$\Lambda {\rm CDM}$
model. We find that there is a preference for non-zero values of the spatial
curvature parameter $\Omega_k$ and the Super-$\Lambda {\rm CDM}$ parameter
$A_0$ at a level of $\Delta \chi^2$ improvement of approximately 23.


title:
Max Weight Independent Set in graphs with no long claws: An analog of
  the Gy\'arf\'as' path argument

abstract:
  We revisit recent developments for the Maximum Weight Independent Set problem
in graphs excluding a subdivided claw $S_{t,t,t}$ as an induced subgraph
[Chudnovsky, Pilipczuk, Pilipczuk, Thomass\'{e}, SODA 2020] and provide a
subexponential-time algorithm with improved running time
$2^{\mathcal{O}(\sqrt{n}\log n)}$ and a quasipolynomial-time approximation
scheme with improved running time $2^{\mathcal{O}(\varepsilon^{-1} \log^{5}
n)}$.
  The Gy\'arf\'as' path argument, a powerful tool that is the main building
block for many algorithms in $P_t$-free graphs, ensures that given an
$n$-vertex $P_t$-free graph, in polynomial time we can find a set $P$ of at
most $t-1$ vertices, such that every connected component of $G-N[P]$ has at
most $n/2$ vertices. Our main technical contribution is an analog of this
result for $S_{t,t,t}$-free graphs: given an $n$-vertex $S_{t,t,t}$-free graph,
in polynomial time we can find a set $P$ of $\mathcal{O}(t \log n)$ vertices
and an extended strip decomposition (an appropriate analog of the decomposition
into connected components) of $G-N[P]$ such that every particle (an appropriate
analog of a connected component to recurse on) of the said extended strip
decomposition has at most $n/2$ vertices.


title:
'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on
  YouTube

abstract:
  Over the last few years, YouTube Kids has emerged as one of the highly
competitive alternatives to television for children's entertainment.
Consequently, YouTube Kids' content should receive an additional level of
scrutiny to ensure children's safety. While research on detecting offensive or
inappropriate content for kids is gaining momentum, little or no current work
exists that investigates to what extent AI applications can (accidentally)
introduce content that is inappropriate for kids.
  In this paper, we present a novel (and troubling) finding that well-known
automatic speech recognition (ASR) systems may produce text content highly
inappropriate for kids while transcribing YouTube Kids' videos. We dub this
phenomenon as \emph{inappropriate content hallucination}. Our analyses suggest
that such hallucinations are far from occasional, and the ASR systems often
produce them with high confidence. We release a first-of-its-kind data set of
audios for which the existing state-of-the-art ASR systems hallucinate
inappropriate content for kids. In addition, we demonstrate that some of these
errors can be fixed using language models.


title:
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with
  Transformers

abstract:
  Pixel-wise semantic segmentation of RGB images can be advanced by exploiting
informative features from supplementary modalities. In this work, we propose
CMX, a vision-transformer-based cross-modal fusion framework for RGB-X semantic
segmentation. To generalize to different sensing modalities encompassing
various supplements and uncertainties, we consider that comprehensive
cross-modal interactions should be provided. CMX is built with two streams to
extract features from RGB images and the complementary modality (X-modality).
In each feature extraction stage, we design a Cross-Modal Feature Rectification
Module (CM-FRM) to calibrate the feature of the current modality by combining
the feature from the other modality, in spatial- and channel-wise dimensions.
With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to mix
them for the final semantic prediction. FFM is constructed with a
cross-attention mechanism, which enables exchange of long-range contexts,
enhancing both modalities' features at a global level. Extensive experiments
show that CMX generalizes to diverse multi-modal combinations, achieving
state-of-the-art performances on five RGB-Depth benchmarks, as well as
RGB-Thermal and RGB-Polarization datasets. Besides, to investigate the
generalizability to dense-sparse data fusion, we establish an RGB-Event
semantic segmentation benchmark based on the EventScape dataset, on which CMX
sets the new state-of-the-art. Code is available at
https://github.com/huaaaliu/RGBX_Semantic_Segmentation.


title:
Evidence of ac-Stark-shifted resonances in intense two-color circularly
  polarized laser fields

abstract:
  We report on the appearance of a structure at low energies in the
photo-electron momentum distribution of the hydrogen atom exposed to two-color
counter-rotating bi-circular laser fields. These structures, which arise due to
AC-Stark shifted resonances, break the three-fold symmetry, typical for the
$\omega-2\omega$ bi-circular fields. We discuss the physical origin of this
structure in terms of partial-wave interference between direct ionization
channels and a resonant pathway, that passes through the AC-Stark shifted state
and show how the underlying Rydberg state population depend on the field
strength and pulse duration.


title:
Pathological set of initial data for scaling-supercritical nonlinear
  Schr\"odinger equations

abstract:
  The purpose of this work is to evidence a pathological set of initial data
for which the regularized solutions by convolution experience a norm-inflation
mechanism, in arbitrarily short time. The result is in the spirit of the
construction from Sun and Tzvetkov, where the pathological set contains
superposition of profiles that concentrate at different points. Thanks to
finite propagation speed of the wave equation, and given a certain time, at
most one profile exhibits significant growth. However, for Schr\"odinger-type
equations, we cannot preclude the profiles from interacting between each other.
Instead, we propose a method that exploits the regularizing effect of the
approximate identity which, at a given scale, rules out the norm inflation of
the profiles that are concentrated at smaller scales.


title: Direct laser cooling of calcium monohydride molecules

abstract:
  We demonstrate optical cycling and sub-Doppler laser cooling of a cryogenic
buffer-gas beam of calcium monohydride (CaH) molecules. We measure vibrational
branching ratios for laser cooling transitions for both excited electronic
states A and B. We measure further that repeated photon scattering via the
$A\leftarrow X$ transition is achievable at a rate of $\sim$ $1.6\times10^6$
photons/s and demonstrate the interaction-time limited scattering of $\sim$
$200$ photons by repumping the largest vibrational decay channel. We also
demonstrate the ability to sub-Doppler cool a molecular beam of CaH through the
magnetically assisted Sisyphus effect. Using a standing wave of light, we lower
the molecular beam's transverse temperature from $12.2(1.2)$ mK to $5.7(1.1)$
mK. We compare these results to sub-Doppler forces modeled using optical Bloch
equations and Monte Carlo simulations of the molecular beam trajectories. This
work establishes a clear pathway for creating a magneto-optical trap (MOT) of
CaH molecules. Such a MOT could serve as a starting point for production of
ultracold hydrogen gas via dissociation of a trapped CaH cloud.