title: Structure and pulse profiles of dark matter admixed neutron stars

abstract:
  Neutron stars (NSs) could efficiently capture dark matter (DM) due to their
extreme densities and are considered sensitive probes of the presence and the
properties of DM. The distribution of the DM in DM-admixed NSs (DANSs) is
supposed to be either a dense dark core or an extended dark halo, which is
subject to the DM fraction of DANS ($f_{\chi}$) and the DM properties, such as
the mass ($m_{\chi}$) and the strength of the self-interaction ($y$). In this
paper, we perform an in-depth analysis of the formation criterion for dark
core/dark halo and point out that the relative distribution of these two
components is essentially determined by the ratio of the central enthalpy of
the DM component to that of the baryonic matter component inside DANSs. For the
critical case where the radii of DM and baryonic matter are the same, we
further derive an analytical formula to describe the dependence of $f^{\rm
crit}_{\chi}$ on $m_{\chi}$ and $y$ for given DANS mass. The relative
distribution of the two components in DANSs can lead to different observational
effects on NSs. We here focus on the modification of the pulsar pulse profile
due to the extra light-bending effect in the case of a dark-halo existence and
conduct the first investigation of the dark-halo effects on NS pulse profiles.
We find that the peak flux deviation is strongly dependent on the ratio of the
halo mass to the radius of the DM component. Lastly, we perform Bayesian
parameter estimation on the DM particle properties based on the recent X-ray
observations of PSR J0030+0451 and PSR J0740+6620 by the Neutron Star Interior
Composition Explorer.

categories: astro-ph.HE astro-ph.CO astro-ph.SR hep-ph nucl-th


title: Toward Granular Automatic Unit Test Case Generation

abstract:
  Unit testing verifies the presence of faults in individual software
components. Previous research has been targeting the automatic generation of
unit tests through the adoption of random or search-based algorithms. Despite
their effectiveness, these approaches do not implement any strategy that allows
them to create unit tests in a structured manner: indeed, they aim at creating
tests by optimizing metrics like code coverage without ensuring that the
resulting tests follow good design principles. In order to structure the
automatic test case generation process, we propose a two-step systematic
approach to the generation of unit tests: we first force search-based
algorithms to create tests that cover individual methods of the production
code, hence implementing the so-called intra-method tests; then, we relax the
constraints to enable the creation of intra-class tests that target the
interactions among production code methods.


title:
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient
  Package for Federated Graph Learning

abstract:
  The incredible development of federated learning (FL) has benefited various
tasks in the domains of computer vision and natural language processing, and
the existing frameworks such as TFF and FATE has made the deployment easy in
real-world applications. However, federated graph learning (FGL), even though
graph data are prevalent, has not been well supported due to its unique
characteristics and requirements. The lack of FGL-related framework increases
the efforts for accomplishing reproducible research and deploying in real-world
applications. Motivated by such strong demand, in this paper, we first discuss
the challenges in creating an easy-to-use FGL package and accordingly present
our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified
view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo
and ModelZoo for out-of-the-box FGL capability; (3) an efficient model
auto-tuning component; and (4) off-the-shelf privacy attack and defense
abilities. We validate the effectiveness of FS-G by conducting extensive
experiments, which simultaneously gains many valuable insights about FGL for
the community. Moreover, we employ FS-G to serve the FGL application in
real-world E-commerce scenarios, where the attained improvements indicate great
potential business benefits. We publicly release FS-G, as submodules of
FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's
research and enable broad applications that would otherwise be infeasible due
to the lack of a dedicated package.


title:
Asymptotics for the rotating fluids and primitive systems with large
  ill-prepared initial data in critical spaces

abstract:
  In this article we study the lifespan and asymptotics (in the large rotation
and stratification regime) for the Primitive system for highly ill-prepared
initial data in critical spaces. Compared to our previous works, we simplified
the proof and made it adaptable to the Rotating fluids system with highly
ill-prepared initial data decomposed as a sum of 2D horizontal part and a very
large 3D part. We also provide explicit convergence rates.


title: Loschmidt echo and Momentum Distribution in a Kitaev Spin Chain

abstract:
  We investigate the Loschmidt echo in a one-dimensional spin chain having
Kitaev-type interaction in constant and kicked magnetic fields. The Loschmidt
echo for the initial states having different magnon excitations shows long-time
revivals for smaller chains and has short-time revival peaks for the longer
chains. The system near the critical point shows peculiarly long-time revival
peaks of the Loschmidt echo for relatively larger chains. The presence of a
magnon in the initial state affects the Loschmidt echo revival peaks. The
momentum distribution function exhibits maxima for a few momenta that are
associated with the momentum of the magnon excitation present in the initial
states. The probability maxima decay as O(1/N ) with the system size. For the
Hamiltonian with kicked magnetic fields, the Loschmidt echo depends on the kick
period. For a special kick period, the Loschmidt echo shows no evolution at all
irrespective of the system size.


title:
Constructing constant curvature metrics on Riemann surfaces with
  singularities

abstract:
  By constructing an ODE through a kind of meromorphic 1-forms, we will give an
explicit construction of a kind of conformal metrics of constant curvature on
Riemann surfaces with singularities. As an application, we will classify
constant curvature one metrics on $S^{2}$ with two conical singularities, which
was first proved by Troyanov in \cite{Tr89} by using projective connection.


title: Compact Model Training by Low-Rank Projection with Energy Transfer

abstract:
  Low-rankness plays an important role in traditional machine learning, but is
not so popular in deep learning. Most previous low-rank network compression
methods compress the networks by approximating pre-trained models and
re-training. However, optimal solution in the Euclidean space may be quite
different from the one in the low-rank manifold. A well pre-trained model is
not a good initialization for the model with low-rank constraint. Thus, the
performance of low-rank compressed network degrades significantly. Compared to
other network compression methods such as pruning, low-rank methods attracts
less attention in recent years. In this paper, we devise a new training method,
low-rank projection with energy transfer (LRPET), that trains low-rank
compressed networks from scratch and achieves competitive performance. First,
we propose to alternately perform stochastic gradient descent training and
projection onto the low-rank manifold. This asymptotically approaches the
optimal solution in the low-rank manifold. Compared to re-training on compact
model, this enables fully utilization of model capacity since solution space is
relaxed back to Euclidean space after projection. Second, the matrix energy
(the sum of squares of singular values) reduction caused by projection is
compensated by energy transfer. We uniformly transfer the energy of the pruned
singular values to the remaining ones. We theoretically show that energy
transfer eases the trend of gradient vanishing caused by projection.
Comprehensive experiment on CIFAR-10 and ImageNet have justified that our
method is superior to other low-rank compression methods and also outperforms
recent state-of-the-art pruning methods.


title:
$B^0-\bar B^0$ entanglement for an ideal experiment on the direct CP
  violation $\phi_3/\gamma$ phase

abstract:
  $B^0$--$\bar B^0$ Entanglement offers a conceptual alternative to the single
charged B-decay asymmetry for the measurement of the direct CP violating
$\gamma/\phi_3$ phase. With $f=J/\Psi K_L,J/\Psi K_S$ and
$g=(\pi\pi)^0,(\rho_L\rho_L)^0$ the 16 time-ordered double decay rate
Intensities to $(f, g)$ depend on the relative phase between the the $f$- and
$g$-decay amplitudes given by $\gamma$ at tree-level. Several constraining
consistencies appear. An intrinsic accuracy of the method at the level of $\pm
1^\circ$ could be achievable at Belle-II with an improved determination of the
penguin amplitude to $g$-channels from existing facilities.


title: Pionic atom unveils hidden structure of QCD vacuum

abstract:
  Modern theories of physics tell that the vacuum is not an empty space. Hidden
in the vacuum is a structure of anti-quarks $\bar{q}$ and quarks $q$. The
$\bar{q}$ and $q$ pair has the same quantum number as the vacuum and
condensates in it since the strong interaction of the quantum chromodynamics
(QCD) is too strong to leave it empty. The $\bar{q}q$ condensation breaks the
chiral symmetry of the vacuum. The expectation value $<\bar{q}q>$ is an order
parameter. For higher temperature or higher matter-density, $|<\bar{q}q>|$
decreases reflecting the restoration of the symmetry. In contrast to these
clear-cut arguments, experimental evidence is so far limited. First of all, the
$\bar{q}q$ is nothing but the vacuum itself. It is neither visible nor
perceptible. In this article, we unravel this invisible existence by high
precision measurement of pionic atoms, $\pi^-$-meson-nucleus bound systems.
Using the $\pi^-$ as a probe, we demonstrate that $|<\bar{q}q>|$ is reduced in
the nucleus by a factor of 58 $\pm$ 4% compared with that in the vacuum. This
reduction indicates that the chiral symmetry is partially restored due to the
extremely high density of the nucleus. The present experimental result clearly
exhibits the existence of the hidden structure, the chiral condensate, in the
vacuum.


title:
Tangential contact of free boundaries and the fixed boundary for
  variational solutions to a free transmission Problem

abstract:
  In this article we study functionals of the following type $$ \int_{\Omega}
\Big ( \langle A(x,u)\nabla u, \nabla u\rangle + \Lambda (x,u) \Big )\,dx $$
here $A(x,u)= A_+(x)\chi_{\{u>0\}}+A_-(x) \chi _{\{u\leq 0\}}$ for some
elliptic and bounded matrices $A_{\pm}$ with H\"older continuous entries and
$\Lambda(x,u) = \lambda_+(x) \chi_{\{u>0\}} + \lambda_-(x) \chi_{\{u\le 0\}}$.
We prove that the free boundaries of minimizers of the above functional touches
the fixed boundary $\partial \Omega$ in a tangential fashion, provide the graph
of boundary data touches its zeros smoothly. This assumption is reflected in
the \eqref{DPT} condition.


title: Traveling waves for a quasilinear wave equation

abstract:
  We consider a 2+1 dimensional wave equation appearing in the context of
polarized waves for the nonlinear Maxwell equations. The equation is
quasilinear in the time derivatives and involves two material functions $V$ and
$\Gamma$. We prove the existence of traveling waves which are periodic in the
direction of propagation and localized in the direction orthogonal to the
propagation direction. Depending on the nature of the nonlinearity coeffcient
$\Gamma$ we distinguish between two cases: (a) $\Gamma\in L^\infty$ being
regular and (b) $\Gamma=\gamma\delta_0$ being a multiple of the delta potential
at zero. For both cases we use bifuraction theory to prove the existence of
nontrivial small-amplitude solutions. One can regard our results as a
persistence result which shows that guided modes known for linear wave-guide
geometries survive in the presence of a nonlinear constitutive law. Our main
theorems are derived under a set of conditions on the linear wave operator.
They are subsidised by explicit examples for the coefficients $V$ in front of
the (linear) second time derivative for which our results hold.


title:
Speech Emotion Recognition with Global-Aware Fusion on Multi-scale
  Feature Representation

abstract:
  Speech Emotion Recognition (SER) is a fundamental task to predict the emotion
label from speech data. Recent works mostly focus on using convolutional neural
networks~(CNNs) to learn local attention map on fixed-scale feature
representation by viewing time-varied spectral features as images. However,
rich emotional feature at different scales and important global information are
not able to be well captured due to the limits of existing CNNs for SER. In
this paper, we propose a novel GLobal-Aware Multi-scale (GLAM) neural network
(The code is available at https://github.com/lixiangucas01/GLAM) to learn
multi-scale feature representation with global-aware fusion module to attend
emotional information. Specifically, GLAM iteratively utilizes multiple
convolutional kernels with different scales to learn multiple feature
representation. Then, instead of using attention-based methods, a simple but
effective global-aware fusion module is applied to grab most important
emotional information globally. Experiments on the benchmark corpus IEMOCAP
over four emotions demonstrates the superiority of our proposed model with 2.5%
to 4.5% improvements on four common metrics compared to previous
state-of-the-art approaches.


title: Water regulates the residence time of Benzamidine in Trypsin

abstract:
  We simulate with state-of-the-art enhanced sampling techniques the binding of
Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein
system. We use machine learning methods and in particular Time-lagged
Independent Component Analysis to determine efficient collective coordinates.
These coordinates are used to perform On-the-fly Probability Enhanced Sampling
simulations, which we adapt to calculate also the ligand residence time. Our
results, both static and dynamic, are in good agreement with experiments. We
underline the role of water in the unbinding process and find that the presence
of a water molecule located at the bottom of the binding pocket allows via a
network of hydrogen bonds the ligand to be released into the solution. On a
finer scale, even when unbinding is allowed, another water molecule further
modulates the exit time.


title:
Convolutional recurrent autoencoder network for learning underwater
  ocean acoustics

abstract:
  Underwater ocean acoustics is a complex physical phenomenon involving not
only widely varying physical parameters and dynamical scales but also
uncertainties in the ocean parameters. Thus, it is difficult to construct
generalized physical models which can work in a broad range of situations. In
this regard, we propose a convolutional recurrent autoencoder network (CRAN)
architecture, which is a data-driven deep learning model for acoustic
propagation. Being data-driven it is independent of how the data is obtained
and can be employed for learning various ocean acoustic phenomena. The CRAN
model can learn a reduced-dimensional representation of physical data and can
predict the system evolution efficiently. Two cases of increasing complexity
are considered to demonstrate the generalization ability of the CRAN. The first
case is a one-dimensional wave propagation with spatially-varying discontinuous
initial conditions. The second case corresponds to a far-field transmission
loss distribution in a two-dimensional ocean domain with depth-dependent
sources. For both cases, the CRAN can learn the essential elements of wave
propagation physics such as characteristic patterns while predicting long-time
system evolution with satisfactory accuracy. Such ability of the CRAN to learn
complex ocean acoustics phenomena has the potential of real-time prediction for
marine vessel decision-making and online control.


title:
A Dimension-adaptive Combination Technique for Uncertainty
  Quantification

abstract:
  We present an adaptive algorithm for the computation of quantities of
interest involving the solution of a stochastic elliptic PDE where the
diffusion coefficient is parametrized by means of a Karhunen-Lo\`eve expansion.
The approximation of the equivalent parametric problem requires a restriction
of the countably infinite-dimensional parameter space to a finite-dimensional
parameter set, a spatial discretization and an approximation in the parametric
variables. We consider a sparse grid approach between these approximation
directions in order to reduce the computational effort and propose a
dimension-adaptive combination technique. In addition, a sparse grid quadrature
for the high-dimensional parametric approximation is employed and
simultaneously balanced with the spatial and stochastic approximation. Our
adaptive algorithm constructs a sparse grid approximation based on the
benefit-cost ratio such that the regularity and thus the decay of the
Karhunen-Lo\`eve coefficients is not required beforehand. The decay is detected
and exploited as the algorithm adjusts to the anisotropy in the parametric
variables. We include numerical examples for the Darcy problem with a lognormal
permeability field, which illustrate a good performance of the algorithm: For
sufficiently smooth random fields, we essentially recover the rate of the
spatial discretization as asymptotic convergence rate with respect to the
computational cost.


title:
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative
  3D Object Detection

abstract:
  Autonomous driving faces great safety challenges for a lack of global
perspective and the limitation of long-range perception capabilities. It has
been widely agreed that vehicle-infrastructure cooperation is required to
achieve Level 5 autonomy. However, there is still NO dataset from real
scenarios available for computer vision researchers to work on
vehicle-infrastructure cooperation-related problems. To accelerate computer
vision research and innovation for Vehicle-Infrastructure Cooperative
Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first
large-scale, multi-modality, multi-view dataset from real scenarios for VICAD.
DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames
are captured from real scenes with 3D annotations. The Vehicle-Infrastructure
Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the
problem of collaboratively locating and identifying 3D objects using sensory
inputs from both vehicle and infrastructure. In addition to solving traditional
3D object detection problems, the solution of VIC3D needs to consider the
temporal asynchrony problem between vehicle and infrastructure sensors and the
data transmission cost between them. Furthermore, we propose Time Compensation
Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark
based on DAIR-V2X. Find data, code, and more up-to-date information at
https://thudair.baai.ac.cn/index and https://github.com/AIR-THU/DAIR-V2X.


title: Multi-agent Actor-Critic with Time Dynamical Opponent Model

abstract:
  In multi-agent reinforcement learning, multiple agents learn simultaneously
while interacting with a common environment and each other. Since the agents
adapt their policies during learning, not only the behavior of a single agent
becomes non-stationary, but also the environment as perceived by the agent.
This renders it particularly challenging to perform policy improvement. In this
paper, we propose to exploit the fact that the agents seek to improve their
expected cumulative reward and introduce a novel \textit{Time Dynamical
Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend
to improve over time. We motivate TDOM theoretically by deriving a lower bound
of the log objective of an individual agent and further propose
\textit{Multi-Agent Actor-Critic with Time Dynamical Opponent Model} (TDOM-AC).
We evaluate the proposed TDOM-AC on a differential game and the Multi-agent
Particle Environment. We show empirically that TDOM achieves superior opponent
behavior prediction during test time. The proposed TDOM-AC methodology
outperforms state-of-the-art Actor-Critic methods on the performed experiments
in cooperative and \textbf{especially} in mixed cooperative-competitive
environments. TDOM-AC results in a more stable training and a faster
convergence.


title: Quantum estimation of Kerr nonlinearity in driven-dissipative systems

abstract:
  We mainly investigate the quantum measurement of Kerr nonlinearity in the
driven-dissipative system. Without the dissipation, the measurement precision
of the nonlinearity parameter $\chi$ scales as "super-Heisenberg scaling"
$1/N^2$ with $N$ being the total average number of particles (photons) due to
the nonlinear generator. Here, we find that "super-Heisenberg scaling"
$1/N^{3/2}$ can also be obtained by choosing a proper interrogation time. In
the steady state, the "super-Heisenberg scaling" $1/N^{3/2}$ can only be
achieved when the nonlinearity parameter is close to 0 in the case of the
single-photon loss and the one-photon driving or the two-photon driving. The
"super-Heisenberg scaling" disappears with the increase of the strength of the
nonlinearity. When the system suffers from the two-photon loss in addition to
the single-photon loss, the optimal measurement precision will not appear at
the nonlinearity $\chi=0$ in the case of the one-photon driving.
Counterintuitively, in the case of the two-photon driving we find that it is
not the case that the higher the two-photon loss, the lower the measurement
precision. It means that the measurement precision of $\chi$ can be improved to
some extent by increasing the two-photon loss.


title:
A Study of Complete and Incomplete Reactions of $^{12}$C + $^{169}$Tm
  System at Energy Range $\approx$ 4.16-7.5 Mev/Nucleon

abstract:
  In this paper an attempt was to measure, the excitation functions of
$^{169}$Tm($^{12}$C, 4n)$^{177}$Re, $^{169}$Tm($^{12}$C, 5n)$^{176}$Re,
$^{169}$Tm($^{12}$C, $\alpha$n)$^{176}$Ta, $^{169}$Tm($^{12}$C,
$\alpha$2n)$^{175}$Ta , $^{169}$Tm($^{12}$C, $\alpha$3n)$^{174}$Ta ,
$^{169}$Tm($^{12}$C, $\alpha$4n)$^{173}$Ta, and $^{169}$Tm($^{12}$C,
2$\alpha$2n)$^{171}$Lu reaction channels populated in the interaction of
$^{12}$C projectile with $^{169}$Tm target were considered in order to
investigate the mechanisms of complete and incomplete fusion reactions. The
theoretically predicted excitation functions using PACE4 code were compared
with the previously measured excitation functions. For non alpha emitting
channels cross section values predicted by PACE4 in general were found to be in
good agreement with the experimentally measured values. However, for
alpha-emitting channels the measured cross-section values were found to be
higher than the values predicted by PACE4. The observed disagreement may be
credited to projectile break up in the vicinity of nn interaction. Keywords:
alpha emitted, CF reaction, excitation functions, heavy ion fusion, incomplete
fusion reaction, non alpha emitted


title: Enriching Artificial Intelligence Explanations with Knowledge Fragments

abstract:
  Artificial Intelligence models are increasingly used in manufacturing to
inform decision-making. Responsible decision-making requires accurate forecasts
and an understanding of the models' behavior. Furthermore, the insights into
models' rationale can be enriched with domain knowledge. This research builds
explanations considering feature rankings for a particular forecast, enriching
them with media news entries, datasets' metadata, and entries from the Google
Knowledge Graph. We compare two approaches (embeddings-based and
semantic-based) on a real-world use case regarding demand forecasting.


title:
Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using
  Objective and Subjective Quality Assessment

abstract:
  With the increasing advancements in video compression efficiency achieved by
newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies,
as well as improved bandwidth availability,there has been a proliferation and
acceptance of newer services such as Netflix, Twitch, etc. However, such higher
compression efficiencies are achieved at the cost of higher complexity and
encoding delay, while many applications are delay sensitive. Hence, there is a
requirement for faster, more efficient codecs to achieve higher encoding
efficiency without significant trade-off in terms of both complexity and speed.
We present in this work an evaluation of the latest MPEG-5 Part 2 Low
Complexity Enhancement Video Coding (LCEVC) for live gaming video streaming
applications. The results are presented in terms of bitrate savings using both
subjective and objective quality measures as well as a comparison of the
encoding speeds. Our results indicate that, for the encoding settings used in
this work, LCEVC outperforms both x264 and x265 codecs in terms of bitrate
savings using VMAF by approximately 42\% and 38\%. Using subjective results, it
is found that LCEVC outperforms the respective base codecs, especially for low
bitrates. This effect is more evident for x264 than for x265, i.e., for the
latter the absolute improvement of quality scores is smaller. The objective and
subjective results as well as sample video sequences are made available as part
of an open dataset, LCEVC-LiveGaming at
https://github.com/NabajeetBarman/LCEVC-LiveGaming.


title: Synchronization on star graph with noise

abstract:
  We investigate synchronization in the Kuramoto model with noise on a star
graph. By revising the case of a complete graph, we propose a closed form of
self-consistency equation for the conventional order parameter and generalize
it for a star graph. Using the obtained self-consistency equation, we
demonstrate that there is a crossover between the abrupt synchronization at
small noise and the continuous phase transition for quite large noise. We probe
this crossover numerically and analytically.


title:
Towards Data-Driven Precision Agriculture using Open Data and Open
  Source Software

abstract:
  Information and communications technology (ICT) within the agricultural
sector is characterized by a widespread use of proprietary data formats, a
strong lack of interoperability standards, and a tight connection to specific
hardware implementations resulting from vendor lock-in. This partly explains
why ICT has not yet had its full impact within the domain. By utilizing the
vast amount of publicly available open data, ranging from topographic maps to
multispectral satellite images, the economically and environmentally optimal
farming practices can be advanced beyond state of the art. This paper addresses
the potential of applying publicly available information sources to improve
crop production, with emphasis on yield optimization. This potential is
evaluated based on free public data for the growth season 2016 by examining
winter wheat production for a selected region in Denmark. Data aggregation is
performed by promoting opensource software tools as a foundation for decision
support. That allows the farmer, or another domain expert, to query a certain
crop type, merge this information with other data sets, and perform analysis on
data ranging from sub-field analysis to statistics on national/regional scale.
The registration of field polygons and sowed crop types for fields in Denmark,
alongside with detailed geographic data and free satellite images, enable us to
exploit publicly available data of high quality, which can be applied to
perform further analysis.


title:
A multi-scale approach to microstructure-sensitive thermal fatigue in
  solder joints

abstract:
  This paper presents a multi-scale modelling approach to investigate the
underpinning mechanisms of microstructure-sensitive damage of single crystal
Sn-3Ag-0.5Cu (wt%, SAC305) solder joints of a Ball Grid Array (BGA) board
assembly subject to thermal cycling. The multi-scale scheme couples board-scale
modelling at the continuum macro-scale and individual solder modelling at the
crystal micro-scale. Systematic studies of tin crystal orientation and its role
in fatigue damage have been compared to experimental observations.
Crystallographic orientation is examined with respect to damage development,
providing evidence-based optimal solder microstructural design for in-service
thermomechanical fatigue.


title:
Core formation via filament fragmentation and the impact of ambient
  pressure on it

abstract:
  Prestellar cores are generally spheroidal, some of which appear oblate while
others appear prolate. Very few of them appear circular in projection. Little,
however, is understood about the processes or the physical conditions under
which prolate/oblate cores form. We find that an initially sub-critical
filament experiencing relatively low pressure ($\lesssim 10^{4}$ K cm$^{-3}$)
forms prolate cores (i.e., those with axial ratios in excess of unity) via
gradual accumulation of gas in density crests. Meanwhile, a filament that is
initially transcritical and experiences pressure similar to that in the Solar
neighbourhood (between $\mathrm{few}\ \times 10^{4}$ K cm$^{-3}$ -
$\mathrm{few}\ \times 10^{5}$ K cm$^{-3}$) forms oblate cores (i.e., those with
axial ratios less than unity) via \emph{Jeans like} fragmentation. At higher
pressure, however, fragments within the filament do not tend to survive as they
rebound soon after formation. We also argue that quasi-oscillatory features of
velocity gradient observed along the filament axis, and in the direction
orthogonal to the axis, are integral to the filament evolution process and
arise due to the growth of corrugations on its surface. The axial component of
the velocity gradient, in particular, traces the gas-flow along the filament
length. We therefore posit that it could be used to constrain the
filament-formation mechanism. The magnitude of the respective components of
velocity gradients increases with increasing external pressure.


title:
SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient
  object detection

abstract:
  Convolutional neural networks (CNNs) are good at extracting contexture
features within certain receptive fields, while transformers can model the
global long-range dependency features. By absorbing the advantage of
transformer and the merit of CNN, Swin Transformer shows strong feature
representation ability. Based on it, we propose a cross-modality fusion model
SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin
Transformer to extract the hierarchical features, boosted by attention
mechanism to bridge the gap between two modalities, and guided by edge
information to sharp the contour of salient object. To be specific, two-stream
Swin Transformer encoder first extracts multi-modality features, and then
spatial alignment and channel re-calibration module is presented to optimize
intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided
decoder achieves inter-level cross-modality fusion under the guidance of edge
features. The proposed model outperforms the state-of-the-art models on RGB-D
and RGB-T datasets, showing that it provides more insight into the
cross-modality complementarity task.


title:
Spinsim: a GPU optimized python package for simulating spin-half and
  spin-one quantum systems

abstract:
  The Spinsim python package simulates spin-half and spin-one quantum
mechanical systems following a time dependent Shroedinger equation. It makes
use of numba.cuda, which is an LLVM (Low Level Virtual Machine) compiler for
Nvidia Cuda compatible systems using GPU parallelization. Along with other
optimizations, this allows for speed improvements from 3 to 4 orders of
magnitude while staying just as accurate, compared to industry standard
packages. It is available for installation on PyPI, and the source code is
available on github. The initial use-case for the Spinsim will be to simulate
quantum sensing-based ultracold atom experiments for the Monash University
School of Physics \& Astronomy spinor Bose-Einstein condensate (spinor BEC)
lab, but we anticipate it will be useful in simulating any range of spin-half
or spin-one quantum systems with time dependent Hamiltonians that cannot be
solved analytically. These appear in the fields of nuclear magnetic resonance
(NMR), nuclear quadrupole resonance (NQR) and magnetic resonance imaging (MRI)
experiments and quantum sensing, and with the spin-one systems of nitrogen
vacancy centres (NVCs), ultracold atoms, and BECs.


title: Hold-out estimates of prediction models for Markov processes

abstract:
  We consider the selection of prediction models for Markovian time series. For
this purpose, we study the theoretical properties of the hold-out method. In
the econometrics literature, the hold-out method is called out-of-sample and is
the main method to select a suitable time series model. This method consists of
estimating models on a learning set and picking up the model with minimal
empirical error on a validation set of future observations. Hold-out estimates
are well studied in the independent case, but, as far as we know, this is not
the case when the validation set is not independent of the learning set. In
this paper, assuming uniform ergodicity of the Markov chain, we state
generalization bounds and oracle inequalities for such method; in particular,
we show that the out-of-sample selection method is adaptative to noise
condition.


title:
Disentangling electrical switching of antiferromagnetic NiO using high
  magnetic fields

abstract:
  Recent demonstrations of the electrical switching of antiferromagnets (AFs)
have given an enormous impulse to the field of AF spintronics. Many of these
observations are plagued by non-magnetic effects that are very difficult to
distinguish from the actual magnetic ones. Here, we study the electrical
switching of thin (5 nm) NiO films in Pt/NiO devices using magnetic fields up
to 15 T to quantitatively disentangle these magnetic and non-magnetic effects.
We demonstrate that these fields suppress the magnetic components of the
electrical switching of NiO, but leave the non-magnetic components intact.
Using a monodomainization model the contributions are separated, showing how
they behave as a function of the current density. These results show that
combining electrical methods and strong magnetic fields can be an invaluable
tool for AF spintronics, allowing for implementing and studying electrical
switching of AFs in more complex systems.


title: Symplectic conditions on Grassmannian, flag, and Schubert varieties

abstract:
  In this paper, the authors give a description of the set-theoretical defining
equations of symplectic (type C) Grassmannian/flag/Schubert varieties in
ordinary (type A) varieties, as linear polynomials in Pl$\ddot{u}$cker
coordinates, then further prove that such equations generate the defining ideal
of type C varieties in type A ones. Locally the number of equations required to
obtain a type C Schubert variety from type A Schubert variety is computed,
which also reveals further geometric properties of type C Schubert varieties.
Finally, a comparative study on the smoothness and tangent spaces of type A and
type C varieties, especially those in symplectic Grassmannians corresponding to
neither minuscule nor cominuscule parabolic subgroups, is launched.


title:
Phenotypic heterogeneity in a model of tumor growth: existence of
  solutions and incompressible limit

abstract:
  We consider a cross-diffusion model of tumor growth structured by phenotypic
trait. We prove the existence of weak solutions and the incompressible limit as
the pressure becomes stiff extending methods recently introduced in the context
of two-species cross-diffusion systems. Moreover, we recover additional
regularity estimates. We show that an L2-version of the celebrated
Aronson-B{\'e}nilan estimate extends to structured models. As a consequence, we
recover a sharp L1-bound on the Laplacian of the pressure. In particular, we
are able to remove a technical constraint on the reaction terms assumed by
Gwiazda et al. for the two-species model, by proving a new L4-bound on the
pressure gradient.


title:
Automatic detection of glaucoma via fundus imaging and artificial
  intelligence: A review

abstract:
  Glaucoma is a leading cause of irreversible vision impairment globally and
cases are continuously rising worldwide. Early detection is crucial, allowing
timely intervention which can prevent further visual field loss. To detect
glaucoma, examination of the optic nerve head via fundus imaging can be
performed, at the centre of which is the assessment of the optic cup and disc
boundaries. Fundus imaging is non-invasive and low-cost; however, the image
examination relies on subjective, time-consuming, and costly expert
assessments. A timely question to ask is can artificial intelligence mimic
glaucoma assessments made by experts. Namely, can artificial intelligence
automatically find the boundaries of the optic cup and disc (providing a
so-called segmented fundus image) and then use the segmented image to identify
glaucoma with high accuracy. We conducted a comprehensive review on artificial
intelligence-enabled glaucoma detection frameworks that produce and use
segmented fundus images. We found 28 papers and identified two main approaches:
1) logical rule-based frameworks, based on a set of simplistic decision rules;
and 2) machine learning/statistical modelling based frameworks. We summarise
the state-of-art of the two approaches and highlight the key hurdles to
overcome for artificial intelligence-enabled glaucoma detection frameworks to
be translated into clinical practice.


title: A Central Limit Theorem for Integer Partitions into Small Powers

abstract:
  The study of the well-known partition function $p(n)$ counting the number of
solutions to $n = a_{1} + \dots + a_{\ell}$ with integers $1 \leq a_{1} \leq
\dots \leq a_{\ell}$ has a long history in combinatorics. In this paper, we
study a variant, namely partitions of integers into \begin{equation*}
  n=\lfloor a_1^\alpha\rfloor + \cdots + \lfloor a_\ell^\alpha\rfloor
\end{equation*} with $1\leq a_1 < \cdots < a_\ell$ and some fixed $0 < \alpha <
1$. In particular, we prove a central limit theorem for the number of summands
in such partitions, using the saddle point method.


title:
Light propagation in a plasma on an axially symmetric and stationary
  spacetime: Separability of the Hamilton-Jacobi equation and shadow

abstract:
  The properties of light rays around compact objects surrounded by a plasma
are affected by both strong gravitational fields described by a
general-relativistic spacetime and by a dispersive and refractive medium,
characterized by the density distribution of the plasma. We study these effects
employing the relativistic Hamiltonian formalism under the assumption of
stationarity and axisymmetry. The necessary and sufficient conditions on the
metric and on the plasma frequency are formulated, such that the rays can be
analytically determined from a fully separated Hamilton-Jacobi equation. We
demonstrate how these results allow to analytically calculate the photon region
and the shadow, if they exist. Several specific examples are discussed in
detail: the "hairy" Kerr black holes, the Hartle-Thorne spacetime metrics, the
Melvin universe, and the Teo rotating traversable wormhole. In all of these
cases a plasma medium is present as well.


title:
Generalization of Gurjar's Hyperplane section theorem to arbitrary
  analytic varieties and A$\mathbb{m}$AC classes

abstract:
  The aim of this paper is to generalize the hyperplane section theorem of
Gurjar to arbitrary (local) analytic varieties even if the intersection with of
hyperplanes is not necessarily isolated.
  In case of formal varieties, we generalize the statement to work for
different classes of functions than just hyperplanes. We call these classes
(which are subsets of formal power series ring) to be algebraic
$\mathbb{m}$-adicaly closed (A$\mathbb{m}$AC).


title: Cosmology in Brans-Dick dRGT massive gravity

abstract:
  We introduce the Brans-Dick de Rham-Gabadadze-Tolley massive gravity theory
which is the new extension of nonlinear massive gravity. We demonstrate a
detailed study of the cosmological properties of this theory of gravity, and we
show the transformation of the Jordan frame to the Einstein frame. We obtain
the cosmological background equations and show the analyses of
self-accelerating solutions for explaining the accelerated expansion of the
Universe. In the following, we analyze the background perturbations, which
consist of tensor, vector, and scalar perturbations within the framework of the
Brans-Dick dRGT massive gravity in the Friedman-Lema{\i}tre-Robertson-Walker
cosmology.


title:
On the Equity of Nuclear Norm Maximization in Unsupervised Domain
  Adaptation

abstract:
  Nuclear norm maximization has shown the power to enhance the transferability
of unsupervised domain adaptation model (UDA) in an empirical scheme. In this
paper, we identify a new property termed equity, which indicates the balance
degree of predicted classes, to demystify the efficacy of nuclear norm
maximization for UDA theoretically. With this in mind, we offer a new
discriminability-and-equity maximization paradigm built on squares loss, such
that predictions are equalized explicitly. To verify its feasibility and
flexibility, two new losses termed Class Weighted Squares Maximization (CWSM)
and Normalized Squares Maximization (NSM), are proposed to maximize both
predictive discriminability and equity, from the class level and the sample
level, respectively. Importantly, we theoretically relate these two novel
losses (i.e., CWSM and NSM) to the equity maximization under mild conditions,
and empirically suggest the importance of the predictive equity in UDA.
Moreover, it is very efficient to realize the equity constraints in both
losses. Experiments of cross-domain image classification on three popular
benchmark datasets show that both CWSM and NSM contribute to outperforming the
corresponding counterparts.


title:
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the
  Knapsack Problem with Stochastic Profits

abstract:
  Evolutionary algorithms have been widely used for a range of stochastic
optimization problems in order to address complex real-world optimization
problems. We consider the knapsack problem where the profits involve
uncertainties. Such a stochastic setting reflects important real-world
scenarios where the profit that can be realized is uncertain. We introduce
different ways of dealing with stochastic profits based on tail inequalities
such as Chebyshev's inequality and Hoeffding bounds that allow to limit the
impact of uncertainties. We examine simple evolutionary algorithms and the use
of heavy tail mutation and a problem-specific crossover operator for optimizing
uncertain profits. Our experimental investigations on different benchmarks
instances show the results of different approaches based on tail inequalities
as well as improvements achievable through heavy tail mutation and the problem
specific crossover operator.


title:
Modified interlayer stacking and insulator to correlated-metal
  transition driven by uniaxial strain in 1$T$-TaS$_{2}$

abstract:
  Interlayer coupling is strongly implicated in the complex electronic
properties of 1$T$-TaS$_2$ , but the interplay between this and electronic
correlations remains unresolved. Here, we employ angle-resolved photoemission
spectroscopy (ARPES) to reveal the effect of uniaxial strain engineering on the
electronic structure and interlayer coupling in 1$T$-TaS$_2$ . The normally
insulating ground state is transformed into a correlated-metal phase under
strain, as evidenced by the emergence of a narrow band at the Fermi level.
Temperature dependent ARPES measurements reveal that the metallic behaviour
only develops below the commensurate charge density wave (CCDW) transition,
where interlayer dimerization produces a band-insulator in unstrained samples.
Electronic structure calculations demonstrate that the correlated metallic
behaviour is stabilized by a previously predicted but unobserved bulk stacking
structure with a modified interlayer coupling of the Ta d$_{z^{2}}$ electrons.
Our combined approach lays bare the role of correlations and interlayer
coupling in 1$T$-TaS$_2$ , providing critical input for understanding
superconductivity under pressure and the metastable hidden phase induced using
non-equilibrium protocols in this platform material for correlated physics.


title: HyperDet3D: Learning a Scene-conditioned 3D Object Detector

abstract:
  A bathtub in a library, a sink in an office, a bed in a laundry room -- the
counter-intuition suggests that scene provides important prior knowledge for 3D
object detection, which instructs to eliminate the ambiguous detection of
similar objects. In this paper, we propose HyperDet3D to explore
scene-conditioned prior knowledge for 3D object detection. Existing methods
strive for better representation of local elements and their relations without
scene-conditioned knowledge, which may cause ambiguity merely based on the
understanding of individual points and object candidates. Instead, HyperDet3D
simultaneously learns scene-agnostic embeddings and scene-specific knowledge
through scene-conditioned hypernetworks. More specifically, our HyperDet3D not
only explores the sharable abstracts from various 3D scenes, but also adapts
the detector to the given scene at test time. We propose a discriminative
Multi-head Scene-specific Attention (MSA) module to dynamically control the
layer parameters of the detector conditioned on the fusion of scene-conditioned
knowledge. Our HyperDet3D achieves state-of-the-art results on the 3D object
detection benchmark of the ScanNet and SUN RGB-D datasets. Moreover, through
cross-dataset evaluation, we show the acquired scene-conditioned prior
knowledge still takes effect when facing 3D scenes with domain gap.


title:
Automated and manual testing as part of the research software
  development process of RCE

abstract:
  Research software is often developed by individual researchers or small teams
in parallel to their research work. The more people and research projects rely
on the software in question, the more important it is that software updates
implement new features correctly and do not introduce regressions. Thus,
developers of research software must balance their limited resources between
implementing new features and thoroughly testing any code changes.
  We present the processes we use for developing the distributed integration
framework RCE at DLR. These processes aim to strike a balance between
automation and manual testing, reducing the testing overhead while addressing
issues as early as possible. We furthermore briefly describe how these testing
processes integrate with the surrounding processes for development and
releasing.


title: Testing General Relativity with Cosmological Large Scale Structure

abstract:
  In this paper I investigate the possibility to test Einstein's equations with
observations of cosmological large scale structure. I first show that we have
not tested the equations in observations concerning only the homogeneous and
isotropic Universe. I then show with several examples how we can do better when
considering the fluctuations of both, the energy momentum tensor and the
metric. This is illustrated with galaxy number counts, intensity mapping and
cosmic shear, three examples that are by no means exhaustive.


title:
Strategic model reduction by analysing model sloppiness: a case study in
  coral calcification

abstract:
  It can be difficult to identify ways to reduce the complexity of large models
whilst maintaining predictive power, particularly where there are hidden
parameter interdependencies. Here, we demonstrate that the analysis of model
sloppiness can be a new invaluable tool for strategically simplifying complex
models. Such an analysis identifies parameter combinations which strongly
and/or weakly inform model behaviours, yet the approach has not previously been
used to inform model reduction. Using a case study on a coral calcification
model calibrated to experimental data, we show how the analysis of model
sloppiness can strategically inform model simplifications which maintain
predictive power. Additionally, when comparing various approaches to analysing
sloppiness, we find that Bayesian methods can be advantageous when unambiguous
identification of the best-fit model parameters is a challenge for standard
optimisation procedures.


title:
Observations of a Flare-ignited broad Quasi-periodic Fast-propagating
  wave train

abstract:
  Large-scale Extreme-ultraviolet (EUV) waves are frequently observed as an
accompanying phenomenon of flares and coronal mass ejections (CMEs). Previous
studies mainly focus on EUV waves with single wavefronts that are generally
thought to be driven by the lateral expansion of CMEs. Using high
spatio-temporal resolution multi-angle imaging observations taken by the Solar
Dynamic Observatory and the Solar Terrestrial Relations Observatory, we present
the observation of a broad quasi-periodic fast propagating (QFP) wave train
composed of multiple wavefronts along the solar surface during the rising phase
of a GOES M3.5 flare on 2011 February 24. The wave train transmitted through a
lunate coronal hole (CH) with a speed of 840 +/-67 km/s, and the wavefronts
showed an intriguing refraction effect when they passed through the boundaries
of the CH. Due to the lunate shape of the CH, the transmitted wavefronts from
the north and south arms of the CH started to approach each other and finally
collided, leading to the significant intensity enhancement at the collision
site. This enhancement might hint the occurrence of interference between the
two transmitted wave trains. The estimated magnetosonic Mach number of the wave
train is about 1.13, which indicates that the observed wave train was a weak
shock. Period analysis reveals that the period of wave train was $\sim$90
seconds, in good agreement with that of the accompanying flare. Based on our
analysis results, we conclude that the broad QFP wave train was a
large-amplitude fast-mode magnetosonic wave or a weak shock driven by some
non-linear energy release processes in the accompanying flare.


title: Towards Open-Set Object Detection and Discovery

abstract:
  With the human pursuit of knowledge, open-set object detection (OSOD) has
been designed to identify unknown objects in a dynamic world. However, an issue
with the current setting is that all the predicted unknown objects share the
same category as "unknown", which require incremental learning via a
human-in-the-loop approach to label novel classes. In order to address this
problem, we present a new task, namely Open-Set Object Detection and Discovery
(OSODD). This new task aims to extend the ability of open-set object detectors
to further discover the categories of unknown objects based on their visual
appearance without human effort. We propose a two-stage method that first uses
an open-set object detector to predict both known and unknown objects. Then, we
study the representation of predicted objects in an unsupervised manner and
discover new categories from the set of unknown objects. With this method, a
detector is able to detect objects belonging to known classes and define novel
categories for objects of unknown classes with minimal supervision. We show the
performance of our model on the MS-COCO dataset under a thorough evaluation
protocol. We hope that our work will promote further research towards a more
robust real-world detection system.


title:
Regression or Classification? Reflection on BP prediction from PPG data
  using Deep Neural Networks in the scope of practical applications

abstract:
  Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart
rate analysis or blood oxygen level monitoring. In the recent past, research
focused extensively on non-invasive PPG-based approaches to blood pressure (BP)
estimation. These approaches can be subdivided into regression and
classification methods. The latter assign PPG signals to predefined BP
intervals that represent clinically relevant ranges. The former predict
systolic (SBP) and diastolic (DBP) BP as continuous variables and are of
particular interest to the research community. However, the reported accuracies
of BP regression methods vary widely among publications with some authors even
questioning the feasibility of PPG-based BP regression altogether. In our work,
we compare BP regression and classification approaches. We argue that BP
classification might provide diagnostic value that is equivalent to regression
in many clinically relevant scenarios while being similar or even superior in
terms of performance. We compare several established neural architectures using
publicly available PPG data for SBP regression and classification with and
without personalization using subject-specific data. We found that
classification and regression models perform similar before personalization.
However, after personalization, the accuracy of classification based methods
outperformed regression approaches. We conclude that BP classification might be
preferable over BP regression in certain scenarios where a coarser segmentation
of the BP range is sufficient.


title:
OGLE-2019-BLG-1470LABc: Another Microlensing Giant Planet in a Binary
  System

abstract:
  We report the discovery and analysis of a triple-lens single-source (3L1S)
microlensing event, OGLE-2019-BLG-1470. This event was first classified as a
normal binary-lens single-source (2L1S) event, but a careful 2L1S modelling
showed that it needs an additional lens or source to fit the observed data. It
is found that the 3L1S model provides the best fit. The binary-lens
binary-source (2L2S) model is excluded by $\Delta\chi^2 = 47$ and is also
strong disfavored by its unreasonably small lens-source relative proper motion
$\mu_{rel} \sim 0.8~{mas,yr^{-1}}$. We find that four 3L1S solutions can fit
the observed data, and all of them contain a planet in a binary system. A
Bayesian analysis based on a Galactic model indicates that the planet is
super-Jovian and the binary stars are likely two M or K dwarfs. The projected
primary-planet separation is about 3 AU. By investigating the properties of all
microlensing planets in binary systems, we find another "missing planetary
caustics" problem in the KMTNet planetary simple. A systematic KMTNet planetary
anomaly search for planets in binary systems is needed.


title: Conditioning diffusion processes with killing rates

abstract:
  When the unconditioned process is a diffusion submitted to a space-dependent
killing rate $k(\vec x)$, various conditioning constraints can be imposed for a
finite time horizon $T$. We first analyze the conditioned process when one
imposes both the surviving distribution at time $T$ and the
killing-distribution for the intermediate times $t \in [0,T]$. When the
conditioning constraints are less-detailed than these full distributions, we
construct the appropriate conditioned processes via the optimization of the
dynamical large deviations at Level 2.5 in the presence of the conditioning
constraints that one wishes to impose. Finally, we describe various conditioned
processes for the infinite horizon $T \to +\infty$. This general construction
is then applied to two illustrative examples in order to generate stochastic
trajectories satisfying various types of conditioning constraints : the first
example concerns the pure diffusion in dimension $d$ with the quadratic killing
rate $k(\vec x)= \gamma \vec x^2$, while the second example is the Brownian
motion with uniform drift submitted to the delta killing rate $k(x)=k
\delta(x)$ localized at the origin $x=0$.


title:
Detection of Long Range Dependence in the Time Domain for
  (In)Finite-Variance Time Series

abstract:
  Empirical detection of long range dependence (LRD) of a time series often
consists of deciding whether an estimate of the memory parameter $d$
corresponds to LRD. Surprisingly, the literature offers numerous spectral
domain estimators for $d$ but there are only a few estimators in the time
domain. Moreover, the latter estimators are criticized for relying on visual
inspection to determine an observation window $[n_1, n_2]$ for a linear
regression to run on. The theoretically motivated choice of $n_1$ and $n_2$ has
been missing so far. In this paper, we take the well-known, yet shunned
variance plot estimator and provide rigorous asymptotic conditions on $[n_1,
n_2]$ to ensure the estimator's consistency under LRD. Thus, our paper fills a
theoretical gap and provides practitioners with a simple to implement estimator
that was previously not theoretically justified. Another novelty of our paper
is the LRD detection for infinite-variance time series with the variance plot
estimator. A simulation study indicates that the variance plot estimator can
detect LRD better than the popular spectral domain GPH estimator.


title: Low Latency Time Domain Multichannel Speech and Music Source Separation

abstract:
  The Goal is to obtain a simple multichannel source separation with very low
latency. Applications can be teleconferencing, hearing aids, augmented reality,
or selective active noise cancellation. These real time applications need a
very low latency, usually less than about 6 ms, and low complexity, because
they usually run on small portable devices. For that we don't need the best
separation, but "useful" separation, and not just on speech, but also music and
noise. Usual frequency domain approaches have higher latency and complexity.
Hence we introduce a novel probabilistic optimization method which we call
"Random Directions", which can overcome local minima, applied to a simple time
domain unmixing structure, and which is scalable for low complexity. Then it is
compared to frequency domain approaches on separating speech and music sources,
and using 3D microphone setups.


title:
Stylized Knowledge-Grounded Dialogue Generation via Disentangled
  Template Rewriting

abstract:
  Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in
producing rational and factual responses. However, to establish long-term
relationships with users, the KDG model needs the capability to generate
responses in a desired style or attribute. Thus, we study a new problem:
Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two
challenges: (1) How to train a SKDG model where no <context, knowledge,
stylized response> triples are available. (2) How to cohere with context and
preserve the knowledge when generating a stylized response. In this paper, we
propose a novel disentangled template rewriting (DTR) method which generates
responses via combing disentangled style templates (from monolingual stylized
corpus) and content templates (from KDG corpus). The entire framework is
end-to-end differentiable and learned without supervision. Extensive
experiments on two benchmarks indicate that DTR achieves a significant
improvement on all evaluation metrics compared with previous state-of-the-art
stylized dialogue generation methods. Besides, DTR achieves comparable
performance with the state-of-the-art KDG methods in standard KDG evaluation
setting.


title:
Portfolio Optimization Using a Consistent Vector-Based MSE Estimation
  Approach

abstract:
  This paper is concerned with optimizing the global minimum-variance
portfolio's (GMVP) weights in high-dimensional settings where both observation
and population dimensions grow at a bounded ratio. Optimizing the GMVP weights
is highly influenced by the data covariance matrix estimation. In a
high-dimensional setting, it is well known that the sample covariance matrix is
not a proper estimator of the true covariance matrix since it is not invertible
when we have fewer observations than the data dimension. Even with more
observations, the sample covariance matrix may not be well-conditioned. This
paper determines the GMVP weights based on a regularized covariance matrix
estimator to overcome the aforementioned difficulties. Unlike other methods,
the proper selection of the regularization parameter is achieved by minimizing
the mean-squared error of an estimate of the noise vector that accounts for the
uncertainty in the data mean estimation. Using random-matrix-theory tools, we
derive a consistent estimator of the achievable mean-squared error that allows
us to find the optimal regularization parameter using a simple line search.
Simulation results demonstrate the effectiveness of the proposed method when
the data dimension is larger than the number of data samples or of the same
order.


title:
Several series expansions for real powers and several closed-form
  formulas for partial Bell polynomials with relation to the sinc and sinhc
  functions in terms of central factorial numbers and Stirling numbers of the
  second kind

abstract:
  In the paper, with the help of the Fa\'a di Bruno formula, in terms of
central factorial numbers of the second kind, and with the terminology of the
Stirling numbers of the second kind, the author derives several series
expansions for any positive integer powers of the sinc and sinhc functions,
discovers several closed-form formulas for partial Bell polynomials with
relation to derivatives of the sinc function, establishes several series
expansions for any real powers of the sinc and sinhc functions, and presents
several identities for central factorial numbers of the second kind and for the
Stirling numbers of the second kind.


title:
The Quasar Feedback Survey: Revealing the Interplay of Jets, Winds \&
  Emission Line Gas in Type 2 Quasars with Radio Polarization

abstract:
  We present results from a combined radio polarization and emission line study
of five type 2 quasars at $z<0.2$ with the Karl G. Jansky Very Large Array
(VLA) B-array at 5 GHz and Hubble Space Telescope (HST) [O~{\sc{iii}}]
observations. These five sources are known to exhibit close association between
radio structures and ionized gas morphology and kinematics. Four sources
(J0945+1737, J1000+1242, J1356+1026 and J1430+1339) show polarization in the
current data. J1010+1413 is the unpolarized source in our sample. We detect
$0.5-1\%$ fractional polarization in the radio cores and a high fractional
polarization ($10-30\%$) in the lobes of these sources. The morphological,
spectral and polarization properties suggest a jet origin for radio emission in
J0945+1737, J1000+1242, J1010+1413 and J1430+1339 whereas the current data
cannot fully discern the origin of radio emission (jet or wind) in J1356+1026.
An anti-correlation between various polarized knots in the radio and
[O~{\sc{iii}}] emission is observed in our sources, similar to that observed in
some radio-loud AGN in the literature. This suggests that the radio emission is
likely to be depolarized by the emission-line gas. By modeling the
depolarization effects, we estimate the size of the emission-line gas clouds to
be $\sim(2.8\pm1.7)\times10^{-5}$ parsec and the amount of thermal material
mixed with the synchrotron plasma to be $\sim(9.2\pm0.8)\times10^{5}$~M$_\odot$
in the lobe of J0945+1737 (which exhibits the most prominent polarization
signature in its lobe). The current work demonstrates that the interplay of
jets/winds and emission-line gas is most likely responsible for the nature of
radio outflows in radio-quiet AGN.


title: Capillary rising in a tube with corners

abstract:
  We study the dynamics of a fluid rising in a capillary tube with corners. In
the cornered tube, unlike the circular tube, fluid rises with two parts, the
bulk part where the entire cross-section is occupied by the fluid, and the
finger part where the cross-section is only partially filled. Using Onsager
principle, we derive coupled time-evolution equations for the two parts. We
show that (a) at the early stage of rising, the dynamics is dominated by the
bulk part and the fluid height $h_0(t)$ shows the same behavior as that in the
circular tube, and (b) at the late stage, the bulk part stops rising, but the
finger part keeps rising following the scaling law of $h_1(t) \sim t^{1/3}$. We
also show that due to the coupling between the two parts, the equilibrium bulk
height is smaller than the Jurin's height which ignores the effect of the
finger part.


title: Normalized Power Prior Bayesian Analysis

abstract:
  The elicitation of power priors, based on the availability of historical
data, is realized by raising the likelihood function of the historical data to
a fractional power {\delta}, which quantifies the degree of discounting of the
historical information in making inference with the current data. When {\delta}
is not pre-specified and is treated as random, it can be estimated from the
data using Bayesian updating paradigm. However, in the original form of the
joint power prior Bayesian approach, certain positive constants before the
likelihood of the historical data could be multiplied when different settings
of sufficient statistics are employed. This would change the power priors with
different constants, and hence the likelihood principle is violated. In this
article, we investigate a normalized power prior approach which obeys the
likelihood principle and is a modified form of the joint power prior. The
optimality properties of the normalized power prior in the sense of minimizing
the weighted Kullback-Leibler divergence is investigated. By examining the
posteriors of several commonly used distributions, we show that the discrepancy
between the historical and the current data can be well quantified by the power
parameter under the normalized power prior setting. Efficient algorithms to
compute the scale factor is also proposed. In addition, we illustrate the use
of the normalized power prior Bayesian analysis with three data examples, and
provide an implementation with an R package NPP.


title: The PADME beam line Monte Carlo simulation

abstract:
  The PADME experiment at the DA$\Phi$NE Beam-Test Facility (BTF) of the INFN
Laboratory of Frascati is designed to search for invisible decays of dark
sector particles produced in electron-positron annihilation events with a
positron beam and a thin fixed target, by measuring the missing mass of
single-photon final states. The presence of backgrounds originating from beam
halo particles can significantly reduce the sensitivity of the experiment. To
thoroughly understand the origin of the beam background contribution, a
detailed Geant4-based Monte Carlo simulation has been developed, containing a
full description of the detector together with the beam line and its optical
elements. This simulation allows the full interactions of each particle to be
described, both during beam line transport and during detection, a possibility
which represents an innovative way to obtain reliable background predictions


title: ASR in German: A Detailed Error Analysis

abstract:
  The amount of freely available systems for automatic speech recognition (ASR)
based on neural networks is growing steadily, with equally increasingly
reliable predictions. However, the evaluation of trained models is typically
exclusively based on statistical metrics such as WER or CER, which do not
provide any insight into the nature or impact of the errors produced when
predicting transcripts from speech input. This work presents a selection of ASR
model architectures that are pretrained on the German language and evaluates
them on a benchmark of diverse test datasets. It identifies cross-architectural
prediction errors, classifies those into categories and traces the sources of
errors per category back into training data as well as other sources. Finally,
it discusses solutions in order to create qualitatively better training
datasets and more robust ASR systems.


title:
When Should We Prefer Offline Reinforcement Learning Over Behavioral
  Cloning?

abstract:
  Offline reinforcement learning (RL) algorithms can acquire effective policies
by utilizing previously collected experience, without any online interaction.
It is widely understood that offline RL is able to extract good policies even
from highly suboptimal data, a scenario where imitation learning finds
suboptimal solutions that do not improve over the demonstrator that generated
the dataset. However, another common use case for practitioners is to learn
from data that resembles demonstrations. In this case, one can choose to apply
offline RL, but can also use behavioral cloning (BC) algorithms, which mimic a
subset of the dataset via supervised learning. Therefore, it seems natural to
ask: when can an offline RL method outperform BC with an equal amount of expert
data, even when BC is a natural choice? To answer this question, we
characterize the properties of environments that allow offline RL methods to
perform better than BC methods, even when only provided with expert data.
Additionally, we show that policies trained on sufficiently noisy suboptimal
data can attain better performance than even BC algorithms with expert data,
especially on long-horizon problems. We validate our theoretical results via
extensive experiments on both diagnostic and high-dimensional domains including
robotic manipulation, maze navigation, and Atari games, with a variety of data
distributions. We observe that, under specific but common conditions such as
sparse rewards or noisy data sources, modern offline RL methods can
significantly outperform BC.


title: Large Quantum Fluctuations in Planar Josephson Junctions

abstract:
  We investigate the stochastic phase dynamics of planar Josephson junctions
(JJs) and superconducting quantum interference devices (SQUIDs) defined in
epitaxial InAs/Al heterostructures. The interplay of large quantum fluctuations
and phase diffusion results in an intriguing regime where the mean switching
current of a JJ is a small fraction of the critical current and macroscopic
quantum tunneling is the most relevant switching mechanism up to the critical
temperature. Phase locking between two JJs further modifies the phase dynamics,
resulting in a different switching current between that of a JJ measured in
isolation and that of the same JJ measured in an asymmetric SQUID loop. The
relative contribution of quantum tunneling and phase diffusion is further tuned
by a magnetic flux in the SQUID loop.


title:
Stabilization and application of asymmetric N\'eel skyrmions in hybrid
  nanostructures

abstract:
  Increasing amounts of information force the continuous improvement of
information storage and processing technologies, further device
miniaturization, and their efficiency increase. Magnetic skyrmions, topological
quasiparticles, and the smallest stable magnetic textures possess intriguing
properties and potential for data storage applications. Hybrid nanostructures
with elements of different magnetization orientations can offer additional
advantages for developing skyrmion-based spintronic and magnonic devices. We
show that an N\'eel-type skyrmion confined within a nanodot placed on top of a
ferromagnetic stripe produces a unique and compelling platform for exploring
mutual coupling between magnetization textures. The skyrmion induces an imprint
upon the stripe, which, in turn, asymmetrically squeezes the skyrmion in the
dot, increasing their size and the range of skyrmion stability for small values
of DMI, as well as introducing skyrmion bi-stability. At the end, we present a
proof-of-concept technique for unconstrained transport of a skyrmion along a
racetrack based on proposed hybrid systems. Our results demonstrate a hybrid
structure that is promising for applications in magnonics and spintronics.


title:
Phase transition and entropy force in Reissner-Nordstr\"om-de Sitter
  spacetime

abstract:
  In this paper, thermodynamic properties of the Reissner-Nordstr\"om-de Sitter
(RN-dS) black hole have been studied on the basis of the correlation between
the black hole and cosmological horizons. It is found that the RN-dS black hole
experiences a phase transition, when its state parameters satisfy the certain
conditions. From the analysis of the interaction between two horizons in RN-dS
spacetime, we get the numerical solution of the interaction between two
horizons. It makes us to realize the force between the black hole and
cosmological horizons, which can be regarded as a candidate to explain our
accelerating expansion universe. That provides a new window to explore the
physical mechanism of the cosmic accelerating expansion.


title: Eigen-Adjusted Functional Principal Component Analysis

abstract:
  Functional Principal Component Analysis (FPCA) has become a widely-used
dimension reduction tool for functional data analysis. When additional
covariates are available, existing FPCA models integrate them either in the
mean function or in both the mean function and the covariance function.
However, methods of the first kind are not suitable for data that display
second-order variation, while those of the second kind are time-consuming and
make it difficult to perform subsequent statistical analyses on the
dimension-reduced representations. To tackle these issues, we introduce an
eigen-adjusted FPCA model that integrates covariates in the covariance function
only through its eigenvalues. In particular, different structures on the
covariate-specific eigenvalues -- corresponding to different practical problems
-- are discussed to illustrate the model's flexibility as well as utility. To
handle functional observations under different sampling schemes, we employ
local linear smoothers to estimate the mean function and the pooled covariance
function, and a weighted least square approach to estimate the
covariate-specific eigenvalues. The convergence rates of the proposed
estimators are further investigated under the different sampling schemes. In
addition to simulation studies, the proposed model is applied to functional
Magnetic Resonance Imaging scans, collected within the Human Connectome
Project, for functional connectivity investigation.


title: Beautiful secrets: using aesthetic images to authenticate users

abstract:
  We propose and evaluate an authentication scheme that improves usability and
user experience issues in the authentication process due to its reliance on
people's aesthetic tastes and preferences. The scheme uses aesthetic images to
verify the identity of computer users. It relies on three major premises
regarding visual aesthetics: (i) that an individual has different preferences
for different aesthetic stimuli; (ii) that these preferences are relatively
consistent; and (iii) that aesthetic tastes are subjective and, therefore,
there are considerable individual differences in aesthetic preferences.
Following a review of the scientific basis for these premises, we describe the
concept of the aesthetic evaluation-based authentication (AEbA) method and
illustrate an implementation of it. We address AEbA's advantages and
disadvantages relative to other related methods and conclude that it is
adequate for low-to-medium security domains. It cannot serve as a compulsory
method because we suspect that a certain portion of the user population lacks
the degree of aesthetic sensitivity required to use the system effectively. On
the plus side, the method offers a positive experience. It alleviates the
burden of memorizing passwords to a minimum, and relative to other
usability-oriented schemes provides better security in terms of
shoulder-surfing, phishing, and password space. Finally, we report on a pilot
evaluation of the concept and its feasibility that supports the method's main
tenets, provides insights about implementation challenges and suggestions for
improvements.


title: Continual Predictive Learning from Videos

abstract:
  Predictive learning ideally builds the world model of physical processes in
one or more given environments. Typical setups assume that we can collect data
from all environments at all times. In practice, however, different prediction
tasks may arrive sequentially so that the environments may change persistently
throughout the training procedure. Can we develop predictive learning
algorithms that can deal with more realistic, non-stationary physical
environments? In this paper, we study a new continual learning problem in the
context of video prediction, and observe that most existing methods suffer from
severe catastrophic forgetting in this setup. To tackle this problem, we
propose the continual predictive learning (CPL) approach, which learns a
mixture world model via predictive experience replay and performs test-time
adaptation with non-parametric task inference. We construct two new benchmarks
based on RoboNet and KTH, in which different tasks correspond to different
physical robotic environments or human actions. Our approach is shown to
effectively mitigate forgetting and remarkably outperform the na\"ive
combinations of previous art in video prediction and continual learning.


title:
Limit theorems and localization of three state quantum walks on a line
  defined by generalized Grover coins

abstract:
  In this article, we undertake a detailed study of the limiting behavior of a
three-state discrete-time quantum walk on one dimensional lattice with
generalized Grover coins. Two limit theorems are proved and consequently we
show that the quantum walk exhibits localization at its initial position, for a
wide range of coin parameters. Finally, we discuss the effect of the coin
parameters on the peak velocities of probability distributions of the
underlying quantum walks.


title: X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks

abstract:
  In this paper, we study the challenging instance-wise vision-language tasks,
where the free-form language is required to align with the objects instead of
the whole image. To address these tasks, we propose X-DETR, whose architecture
has three major components: an object detector, a language encoder, and
vision-language alignment. The vision and language streams are independent
until the end and they are aligned using an efficient dot-product operation.
The whole network is trained end-to-end, such that the detector is optimized
for the vision-language tasks instead of an off-the-shelf component. To
overcome the limited size of paired object-language annotations, we leverage
other weak types of supervision to expand the knowledge coverage. This simple
yet effective architecture of X-DETR shows good accuracy and fast speeds for
multiple instance-wise vision-language tasks, e.g., 16.4 AP on LVIS detection
of 1.2K categories at ~20 frames per second without using any LVIS annotation
during training.


title:
Proximal Policy Optimization Learning based Control of Congested Freeway
  Traffic

abstract:
  This study proposes a delay-compensated feedback controller based on proximal
policy optimization (PPO) reinforcement learning to stabilize traffic flow in
the congested regime by manipulating the time-gap of adaptive cruise
control-equipped (ACC-equipped) vehicles.The traffic dynamics on a freeway
segment are governed by an Aw-Rascle-Zhang (ARZ) model, consisting of $2\times
2$ nonlinear first-order partial differential equations (PDEs).Inspired by the
backstepping delay compensator [18] but different from whose complex segmented
control scheme, the PPO control is composed of three feedbacks, namely the
current traffic flow velocity, the current traffic flow density and previous
one step control input. The control gains for the three feedbacks are learned
from the interaction between the PPO and the numerical simulator of the traffic
system without knowing the system dynamics. Numerical simulation experiments
are designed to compare the Lyapunov control, the backstepping control and the
PPO control. The results show that for a delay-free system, the PPO control has
faster convergence rate and less control effort than the Lyapunov control. For
a traffic system with input delay, the performance of the PPO controller is
comparable to that of the Backstepping controller, even for the situation that
the delay value does not match. However, the PPO is robust to parameter
perturbations, while the Backstepping controller cannot stabilize a system
where one of the parameters is disturbed by Gaussian noise.


title: Linearly ordered colourings of hypergraphs

abstract:
  A linearly ordered (LO) $k$-colouring of an $r$-uniform hypergraph assigns an
integer from $\{1, \ldots, k \}$ to every vertex so that, in every edge, the
(multi)set of colours has a unique maximum. Equivalently, for $r=3$, if two
vertices in an edge are assigned the same colour, then the third vertex is
assigned a larger colour (as opposed to a different colour, as in classic
non-monochromatic colouring). Barto, Battistelli, and Berg [STACS'21] studied
LO colourings on $3$-uniform hypergraphs in the context of promise constraint
satisfaction problems (PCSPs). We show two results.
  First, given a $3$-uniform hypergraph that admits an LO $2$-colouring, one
can find in polynomial time an LO $k$-colouring with $k=O(\sqrt{n\log\log
n}/\log n)$, where $n$ is the number of vertices of the input hypergraph. This
is established by building on ideas from algorithms designed for approximate
graph colourings.
  Second, given an $r$-uniform hypergraph that admits an LO $2$-colouring, we
establish NP-hardness of finding an LO $k$-colouring for every constant
uniformity $r\geq k+2$. In fact, we determine relationships between
polymorphism minions for all uniformities $r\geq 3$, which reveals a key
difference between $r<k+2$ and $r\geq k+2$ and which may be of independent
interest. Using the algebraic approach to PCSPs, we actually show a more
general result establishing NP-hardness of finding an LO $\ell$-colouring for
LO $k$-colourable $r$-uniform hypergraphs for $2 \leq k \leq \ell$ and $r \geq
\ell - k + 4$.


title: The topology of independence complexes of square grids

abstract:
  The independence complex of a graph G is a simplicial complex whose simplices
are the independent sets in G. In the last couple of decades, the independence
complexes of square grids (with various boundary conditions) have gained much
attention because of their connections with the hard square model from
statistical physics. In this article, we prove that if G is an $m\times n$ grid
with open or cylindrical boundary condition then its independence complex is
homotopy equivalent to a wedge of spheres. A part of this result settles a
conjecture of Iriye.


title: An intrinsic characterization of moment functionals in the compact case

abstract:
  We consider the class of all linear functionals $L$ on a unital commutative
real algebra $A$ that can be represented as an integral w.r.t. to a Radon
measure with compact support in the character space of $A$. Exploiting a recent
generalization of the classical Nussbaum theorem, we establish a new
characterization of this class of moment functionals solely in terms of a
growth condition intrinsic to the given linear functional. To the best of our
knowledge, our result is the first to exactly identify the compact support of
the representing Radon measure. We also describe the compact support in terms
of the largest Archimedean quadratic module on which $L$ is non-negative and in
terms of the smallest submultiplicative seminorm w.r.t. which $L$ is
continuous. Moreover, we derive a formula for computing the measure of each
singleton in the compact support, which in turn gives a necessary and
sufficient condition for the support to be a finite set. Finally, some aspects
related to our growth condition for topological algebras are also investigated.


title: Multivariable evolution in parton showers with initial state partons

abstract:
  One can use more than one scale variable to define the family of surfaces in
the space of parton splitting parameters that define the evolution of a parton
shower. In an earlier paper, we developed this idea for electron-positron
annihilation. Here, we use multiple scale variables for a parton shower with
initial state partons. Then we need a more sophisticated analysis because the
evolution of parton distribution functions must be coordinated with the parton
shower evolution. We make the needed connections more precise than in our
earlier work, even for the case of just one scale variable. Then we develop an
example with three scale variables, which leads to advantages compared to the
usual shower formulation with only one scale variable. We provide results for
Drell-Yan muon pair production.


title: Malware Analysis with Symbolic Execution and Graph Kernel

abstract:
  Malware analysis techniques are divided into static and dynamic analysis.
Both techniques can be bypassed by circumvention techniques such as
obfuscation. In a series of works, the authors have promoted the use of
symbolic executions combined with machine learning to avoid such traps. Most of
those works rely on natural graph-based representations that can then be
plugged into graph-based learning algorithms such as Gspan. There are two main
problems with this approach. The first one is in the cost of computing the
graph. Indeed, working with graphs requires one to compute and representing the
entire state-space of the file under analysis. As such computation is too
cumbersome, the techniques often rely on developing strategies to compute a
representative subgraph of the behaviors. Unfortunately, efficient
graph-building strategies remain weakly explored. The second problem is in the
classification itself. Graph-based machine learning algorithms rely on
comparing the biggest common structures. This sidelines small but specific
parts of the malware signature. In addition, it does not allow us to work with
efficient algorithms such as support vector machine. We propose a new efficient
open source toolchain for machine learning-based classification. We also
explore how graph-kernel techniques can be used in the process. We focus on the
1-dimensional Weisfeiler-Lehman kernel, which can capture local similarities
between graphs. Our experimental results show that our approach outperforms
existing ones by an impressive factor.


title:
Asymptotics for Christoffel functions associated to continuum
  Schr\"odinger operators

abstract:
  We prove asymptotics of the Christoffel function, $\lambda_L(\xi)$, of a
continuum Schr\"odinger operator for points in the interior of the essential
spectrum under some mild conditions on the spectral measure. It is shown that
$L\lambda_L(\xi)$ has a limit and that this limit is given by the
Radon--Nikodym derivative of the spectral measure with respect to the Martin
measure. Combining this with a recently developed local criterion for
universality limits at scale $\lambda_L(\xi)$, we compute universality limits
for continuum Schr\"odinger operators at scale $L$ and obtain clock spacing of
the eigenvalues of the finite range truncations.


title:
Idiomify -- Building a Collocation-supplemented Reverse Dictionary of
  English Idioms with Word2Vec for non-native learners

abstract:
  The aim of idiomify is to build a collocation-supplemented reverse dictionary
of idioms for the non-native learners of English. We aim to do so because the
reverse dictionary could help the non-natives explore idioms on demand, and the
collocations could also guide them on using idioms more adequately. The
cornerstone of the project is a reliable way of mining idioms from corpora,
which is however a challenge because idioms extensively vary in forms. We
tackle this by automatically deriving matching rules from their base forms. We
use Point-wise Mutual Inclusion (PMI), Term Frequency - Inverse Document
Frequency (TF-IDF) to model collocations, since both of them are popular metric
for pairwise significance. We also try Term Frequency (TF) as the baseline
model. As for implementing the reverse-dictionary, three approaches could be
taken: inverted index, graphs and distributional semantics. We choose to take
the last approach and implement the reverse dictionary with Word2Vec, because
it is the most flexible approach of all and Word2Vec is a simple yet strong
baseline. Evaluating the methods has revealed rooms for improvement. We learn
that we can better identify idioms with the help of slop, wildcard and
reordering techniques. We also learn that we can get the best of both PMI and
TF-IDF if we use machine learning to find the sweet spot. Lastly, We learn that
Idiomify could be further improved with a mixture of inverted index and
distributional semantics approach. The limits aside, the proposed methods are
feasible, and their benefits to the non-natives are apparent, which therefore
can be used to aid the non-natives in acquiring English idioms.


title:
Dimerization of dehydrogenated polycyclic aromatic hydrocarbons on
  graphene

abstract:
  Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet
poorly understood, step in the on-surface synthesis of graphene (nanoribbon),
soot formation, and growth of carbonaceous dust grains in the interstellar
medium (ISM). The on-surface synthesis of graphene and the growth of
carbonaceous dust grains in the ISM require the chemical dimerization in which
chemical bonds are formed between PAH monomers. An accurate and cheap method of
exploring structure rearrangements is needed to reveal the mechanism of
chemical dimerization on surfaces. This work has investigated the chemical
dimerization of two dehydrogenated PAHs (coronene and pentacene) on graphene
via an evolutionary algorithm augmented by machine learning surrogate
potentials and a set of customized structure operators. Different dimer
structures on surfaces have been successfully located by our structure search
methods. Their binding energies are within the experimental errors of
temperature programmed desorption measurements. The mechanism of coronene dimer
formation on graphene is further studied and discussed.


title:
Effects of magnetic field orientations in dense cores on gas kinematics
  in protostellar envelopes

abstract:
  Theoretically, misalignment between the magnetic field and rotational axis in
a dense core is considered to be dynamically important in the star formation
process, however, extent of this influence remains observationally unclear. For
a sample of 32 Class 0 and I protostars in the Perseus Molecular Cloud, we
analyzed gas motions using C$^{18}$O data from the SMA MASSES survey and the
magnetic field structures using 850 $\mu$m polarimetric data from the JCMT
BISTRO-1 survey and archive. We do not find any significant correlation between
the velocity gradients in the C$^{18}$O emission in the protostellar envelopes
at a 1,000 au scale and the misalignment between the outflows and magnetic
field orientations in the dense cores at a 4,000 au scale, and there is also no
correlation between the velocity gradients and the angular dispersions of the
magnetic fields. However, a significant dependence on the misalignment angles
emerges after we normalize the rotational motion by the infalling motion, where
the ratios increase from $\lesssim1$ to $\gtrsim1$ with increasing misalignment
angles. This suggests that the misalignment could prompt angular momentum
transportation to the envelope scale but is not a dominant factor in
determining the envelope rotation, and other parameters, like mass accretion in
protostellar sources, also play an important role. These results remain valid
after taking into account projection effects. The comparison between our
estimated angular momentum in the protostellar envelopes and the sizes of the
known protostellar disks suggests that significant angular momentum is likely
lost between radii of $\sim$1,000-100 au in protostellar envelopes.


title: Hybrid Quantum-Classical Heuristic for the Bin Packing Problem

abstract:
  Optimization problems is one of the most challenging applications of quantum
computers, as well as one of the most relevants. As a consequence, it has
attracted huge efforts to obtain a speedup over classical algorithms using
quantum resources. Up to now, many problems of different nature have been
addressed through the perspective of this revolutionary computation paradigm,
but there are still many open questions. In this work, a hybrid
classical-quantum approach is presented for dealing with the one-dimensional
Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one
designed for being executed in different computational ecosystems. First, a
quantum subroutine seeks a set of feasible bin configurations of the problem at
hand. Secondly, a classical computation subroutine builds complete solutions to
the problem from the subsets given by the quantum subroutine. Being a hybrid
solver, we have called our method H-BPP. To test our algorithm, we have built
18 different 1dBPP instances as a benchmarking set, in which we analyse the
fitness, the number of solutions and the performance of the QC subroutine.
Based on these figures of merit we verify that H-BPP is a valid technique to
address the 1dBPP.


title: Graded Prime Ideals Over Graded Near Ring

abstract:
  In this paper, we consider graded near-rings over a monoid $G$ as a
generalizations of graded rings over groups. We introduce certain innovative
graded prime ideals and study some of its basic properties over graded
near-rings.


title: Neural Network Pruning by Cooperative Coevolution

abstract:
  Neural network pruning is a popular model compression method which can
significantly reduce the computing cost with negligible loss of accuracy.
Recently, filters are often pruned directly by designing proper criteria or
using auxiliary modules to measure their importance, which, however, requires
expertise and trial-and-error. Due to the advantage of automation, pruning by
evolutionary algorithms (EAs) has attracted much attention, but the performance
is limited for deep neural networks as the search space can be quite large. In
this paper, we propose a new filter pruning algorithm CCEP by cooperative
coevolution, which prunes the filters in each layer by EAs separately. That is,
CCEP reduces the pruning space by a divide-and-conquer strategy. The
experiments show that CCEP can achieve a competitive performance with the
state-of-the-art pruning methods, e.g., prune ResNet56 for $63.42\%$ FLOPs on
CIFAR10 with $-0.24\%$ accuracy drop, and ResNet50 for $44.56\%$ FLOPs on
ImageNet with $0.07\%$ accuracy drop.


title: Black holes: on the universality of the Kerr hypothesis

abstract:
  To what extent are all astrophysical, dark, compact objects both black holes
(BHs) and described by the Kerr geometry? We embark on the exercise of defying
the universality of this remarkable idea, often called the "Kerr hypothesis".
After establishing its rationale and timeliness, we define a minimal set of
reasonability criteria for alternative models of dark compact objects. Then, as
proof of principle, we discuss concrete, dynamically robust non-Kerr BHs and
horizonless imitators, that 1) pass the basic theoretical, and in particular
dynamical, tests, 2) match (some of the) state of the art astrophysical
observables and 3) only emerge at some (macroscopic) scales. These examples
illustrate how the universality (at all macroscopic scales) of the Kerr
hypothesis can be challenged.


title: Dynamical hadron formation in long-range interacting quantum spin chains

abstract:
  The study of confinement in quantum spin chains has seen a large surge of
interest in recent years. It is not only important for understanding a range of
effective one-dimensional condensed matter realizations, but also shares some
of the non-perturbative physics with quantum chromodynamics (QCD) which makes
it a prime target for current quantum simulation efforts. In analogy with QCD,
the confinement-induced two-particle boundstates that appear in these models
are dubbed mesons. Here, we study scattering events due to meson collisions in
a quantum spin chain with long-range interactions such that two mesons have an
extended interaction. We show how novel hadronic boundstates, e.g. with four
constituent particles akin to tetraquarks, may form dynamically in fusion
events. In a natural collision their signal is weak as elastic meson scattering
dominates. However, we propose two controllable protocols which allow for a
clear observation of dynamical hadron formation. We discuss how this physics
can be simulated in trapped ion or Rydberg atom set-ups.


title: Single-pass, nonlinear frequency conversion of full Poincar\'e beams

abstract:
  Full Poincar\'e (FP) beams, a special class of fully correlated beams
generated through the coaxial superposition of a Laguerre-Gauss and fundamental
Gaussian modes of orthogonal polarizations, contain all possible polarization
states on the surface of the Poincar\'e sphere in a single beam. While the
presence of all unconventional polarization states makes the FP beams useful
for various applications, the dependence of the refractive index on the
polarization restricts the efficient generation of FP beams across the
electromagnetic spectrum through nonlinear frequency processes. To avoid such
difficulty, we use two contiguous BIBO crystals with orthogonal optic axes and
generate an ultrafast FP beam at 405 nm with average power as high as 18.3 mW
at a single-pass conversion efficiency of 2.19%. Using Stokes parameters and
Stokes phases, we observed the doubling of C-points and L-lines singularities
and the orbital angular momentum in the SHG process. We have also devised a new
technique to estimate the polarization coverage of the pump and SHG FP beams
and observed the variation in polarization coverage with the intensity
weightage of the constituting beams. Interestingly, the SHG beam has the
highest polarization coverage for the FP beam of equal intensity weightage of
the superposed beams. We validated our experimental results in close agreement
with the theoretical results.


title: Confirming Mathematical Conjectures by Analogy

abstract:
  Analogy has received attention as a form of inductive reasoning in the
empirical sciences. However, its role in pure mathematics has received less
consideration. This paper provides an account of how an analogy with a more
familiar mathematical domain can contribute to the confirmation of a
mathematical conjecture. By reference to case-studies, we propose a distinction
between an incremental and a non-incremental form of confirmation by
mathematical analogy. We offer an account of the former within the popular
framework of Bayesian confirmation theory. As for the non-incremental notion,
we defend its role in rationally informing the prior credences of
mathematicians in those circumstances in which no new mathematical evidence is
introduced. The resulting 'hybrid' framework captures many important aspects of
the use of analogical inference in the realm of pure mathematics.