title: Towards an Antivirus for Quantum Computers

abstract:
  Researchers are today exploring models for cloud-based usage of quantum
computers where multi-tenancy can be used to share quantum computer hardware
among multiple users. Multi-tenancy has a promise of allowing better
utilization of the quantum computer hardware, but also opens up the quantum
computer to new types of security attacks. As this and other recent research
shows, it is possible to perform a fault injection attack using crosstalk on
quantum computers when a victim and attacker circuits are instantiated as
co-tenants on the same quantum computer. To ensure such attacks do not happen,
this paper proposes that new techniques should be developed to help catch
malicious circuits before they are loaded onto quantum computer hardware.
Following ideas from classical computers, a compile-time technique can be
designed to scan quantum computer programs for malicious or suspicious code
patterns before they are compiled into quantum circuits that run on a quantum
computer. This paper presents ongoing work which demonstrates how crosstalk can
affect Grover's algorithm, and then presents suggestions of how quantum
programs could be analyzed to catch circuits that generate large amounts of
crosstalk with malicious intent.


title:
Vision-based Distributed Multi-UAV Collision Avoidance via Deep
  Reinforcement Learning for Navigation

abstract:
  Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems
is considered a challenging task. It needs to ensure collision-free path
planning in real-time, especially when the multi-UAV systems can become very
crowded on certain occasions. In this paper, we presented a vision-based
decentralized collision-avoidance policy for multi-UAV systems, which takes
depth images and inertial measurements as sensory inputs and outputs UAV's
steering commands. The policy is trained together with the latent
representation of depth images using a policy gradient-based reinforcement
learning algorithm and autoencoder in the multi-UAV threedimensional
workspaces. Each UAV follows the same trained policy and acts independently to
reach the goal without colliding or communicating with other UAVs. We validate
our policy in various simulated scenarios. The experimental results show that
our learned policy can guarantee fully autonomous collision-free navigation for
multi-UAV in the three-dimensional workspaces with good robustness and
scalability.


title:
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter
  Pruning

abstract:
  Conventional NAS-based pruning algorithms aim to find the sub-network with
the best validation performance. However, validation performance does not
successfully represent test performance, i.e., potential performance. Also,
although fine-tuning the pruned network to restore the performance drop is an
inevitable process, few studies have handled this issue. This paper proposes a
novel sub-network search and fine-tuning method that is named Ensemble
Knowledge Guidance (EKG). First, we experimentally prove that the fluctuation
of the loss landscape is an effective metric to evaluate the potential
performance. In order to search a sub-network with the smoothest loss landscape
at a low cost, we propose a pseudo-supernet built by an ensemble sub-network
knowledge distillation. Next, we propose a novel fine-tuning that re-uses the
information of the search phase. We store the interim sub-networks, that is,
the by-products of the search phase, and transfer their knowledge into the
pruned network. Note that EKG is easy to be plugged-in and computationally
efficient. For example, in the case of ResNet-50, about 45% of FLOPS is removed
without any performance drop in only 315 GPU hours. The implemented code is
available at https://github.com/sseung0703/EKG.


title:
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing
  Models

abstract:
  Semantic parsing is a key NLP task that maps natural language to structured
meaning representations. As in many other NLP tasks, SOTA performance in
semantic parsing is now attained by fine-tuning a large pretrained language
model (PLM). While effective, this approach is inefficient in the presence of
multiple downstream tasks, as a new set of values for all parameters of the PLM
needs to be stored for each task separately. Recent work has explored methods
for adapting PLMs to downstream tasks while keeping most (or all) of their
parameters frozen. We examine two such promising techniques, prefix tuning and
bias-term tuning, specifically on semantic parsing. We compare them against
each other on two different semantic parsing datasets, and we also compare them
against full and partial fine-tuning, both in few-shot and conventional data
settings. While prefix tuning is shown to do poorly for semantic parsing tasks
off the shelf, we modify it by adding special token embeddings, which results
in very strong performance without compromising parameter savings.


title: Estimating the circumference of a graph in terms of its leaf number

abstract:
  Let $\mathcal{T}$ be the set of spanning trees of $G$ and let $L(T)$ be the
number of leaves in a tree $T$. The leaf number $L(G)$ of $G$ is defined as
$L(G)=\max\{L(T)|T\in \mathcal{T}\}$. Let $G$ be a connected graph of order $n$
and minimum degree $\delta$ such that $L(G)\leq 2\delta-1$. We show that the
circumference of $G$ is at least $n-1$, and that if $G$ is regular then $G$ is
hamiltonian.


title:
A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation
  Protocol for Segment-level Video Copy Detection

abstract:
  In this paper, we introduce VCSL (Video Copy Segment Localization), a new
comprehensive segment-level annotated video copy dataset. Compared with
existing copy detection datasets restricted by either video-level annotation or
small-scale, VCSL not only has two orders of magnitude more segment-level
labelled data, with 160k realistic video copy pairs containing more than 280k
localized copied segment pairs, but also covers a variety of video categories
and a wide range of video duration. All the copied segments inside each
collected video pair are manually extracted and accompanied by precisely
annotated starting and ending timestamps. Alongside the dataset, we also
propose a novel evaluation protocol that better measures the prediction
accuracy of copy overlapping segments between a video pair and shows improved
adaptability in different scenarios. By benchmarking several baseline and
state-of-the-art segment-level video copy detection methods with the proposed
dataset and evaluation metric, we provide a comprehensive analysis that
uncovers the strengths and weaknesses of current approaches, hoping to open up
promising directions for future works. The VCSL dataset, metric and benchmark
codes are all publicly available at https://github.com/alipay/VCSL.


title:
Audio-visual speech separation based on joint feature representation
  with cross-modal attention

abstract:
  Multi-modal based speech separation has exhibited a specific advantage on
isolating the target character in multi-talker noisy environments.
Unfortunately, most of current separation strategies prefer a straightforward
fusion based on feature learning of each single modality, which is far from
sufficient consideration of inter-relationships between modalites. Inspired by
learning joint feature representations from audio and visual streams with
attention mechanism, in this study, a novel cross-modal fusion strategy is
proposed to benefit the whole framework with semantic correlations between
different modalities. To further improve audio-visual speech separation, the
dense optical flow of lip motion is incorporated to strengthen the robustness
of visual representation. The evaluation of the proposed work is performed on
two public audio-visual speech separation benchmark datasets. The overall
improvement of the performance has demonstrated that the additional motion
network effectively enhances the visual representation of the combined lip
images and audio signal, as well as outperforming the baseline in terms of all
metrics with the proposed cross-modal fusion.


abstract:
  Network embedding is an effective technique to learn the low-dimensional
representations of nodes in networks. Real-world networks are usually with
multiplex or having multi-view representations from different relations.
Recently, there has been increasing interest in network embedding on multiplex
data. However, most existing multiplex approaches assume that the data is
complete in all views. But in real applications, it is often the case that each
view suffers from the missing of some data and therefore results in partial
multiplex data. In this paper, we present a novel Deep Partial Multiplex
Network Embedding approach to deal with incomplete data. In particular, the
network embeddings are learned by simultaneously minimizing the deep
reconstruction loss with the autoencoder neural network, enforcing the data
consistency across views via common latent subspace learning, and preserving
the data topological structure within the same network through graph Laplacian.
We further prove the orthogonal invariant property of the learned embeddings
and connect our approach with the binary embedding techniques. Experiments on
four multiplex benchmarks demonstrate the superior performance of the proposed
approach over several state-of-the-art methods on node classification, link
prediction and clustering tasks.


title:
Quantifying the Predictability of ENSO Complexity Using a Statistically
  Accurate Multiscale Stochastic Model and Information Theory

abstract:
  An information-theoretic framework is developed to assess the predictability
of ENSO complexity, which is a central problem in contemporary meteorology with
large societal impacts. The information theory advances a unique way to
quantify the forecast uncertainty and allows to distinguish the predictability
limit of different ENSO events. One key step in applying the framework to
compute the information gain representing the predictability is to build a
statistically accurate dynamical model. To this end, a recently developed
multiscale stochastic model, which succeeds in capturing both the large-scale
dynamics and many crucial statistical properties of the observed ENSO
complexity, is incorporated into the information-theoretic framework. It is
shown that different ENSO events possess very distinct predictability limits.
In addition to the ensemble mean, the ensemble spread also has remarkable
contributions to the predictability. While the information theory indicates
that predicting the onset of the eastern Pacific El Ni\~nos is challenging, it
reveals a universal tendency to convert strong predictability to skillful
forecast for predicting many central Pacific El Ni\~nos about two years in
advance. In addition, strong predictability is found for the La Ni\~na events,
corresponding to the effective discharge process. In the climate change
scenario with the strengthening of the background Walker circulation, the
predictability of sea surface temperature in central Pacific has a significant
response with a notable increase in summer and fall. Finally, the Gaussian
approximation is shown to be accurate in computing the information gain, which
facilitates the use of more sophisticated models to study the ENSO
predictability.


title: Koopman operator for time-dependent reliability analysis

abstract:
  Time-dependent structural reliability analysis of nonlinear dynamical systems
is non-trivial; subsequently, scope of most of the structural reliability
analysis methods is limited to time-independent reliability analysis only. In
this work, we propose a Koopman operator based approach for time-dependent
reliability analysis of nonlinear dynamical systems. Since the Koopman
representations can transform any nonlinear dynamical system into a linear
dynamical system, the time evolution of dynamical systems can be obtained by
Koopman operators seamlessly regardless of the nonlinear or chaotic behavior.
Despite the fact that the Koopman theory has been in vogue a long time back,
identifying intrinsic coordinates is a challenging task; to address this, we
propose an end-to-end deep learning architecture that learns the Koopman
observables and then use it for time marching the dynamical response. Unlike
purely data-driven approaches, the proposed approach is robust even in the
presence of uncertainties; this renders the proposed approach suitable for
time-dependent reliability analysis. We propose two architectures; one suitable
for time-dependent reliability analysis when the system is subjected to random
initial condition and the other suitable when the underlying system have
uncertainties in system parameters. The proposed approach is robust and
generalizes to unseen environment (out-of-distribution prediction). Efficacy of
the proposed approached is illustrated using three numerical examples. Results
obtained indicate supremacy of the proposed approach as compared to purely
data-driven auto-regressive neural network and long-short term memory network.


title: The disk wind in GRS 1915+105 as seen by Insight-HXMT

abstract:
  We analyze three observations of GRS 1915+105 in 2017 by Insight-HXMT when
the source was in a spectrally soft state. We find strong absorption lines from
highly ionized iron, which are due to absorption by disk wind outflowing at a
velocity of $\sim$ 1000 km s$^{-1}$ along our line of sight. Two of the three
observations show large amplitude oscillation in their light curves and the
variation pattern corresponds to state $\kappa$ of GRS 1915+105. From
time-averaged and flux-resolved analysis, we find that the variation of the
ionization state of the disk wind follows the X-ray continuum on timescales
from hundreds seconds to months. The radial location of the disk wind is
consistent with thermal driving. The mass-loss rate due to the outflowing wind
is comparable to the mass accretion rate in the inner disk, which demonstrates
the important role of the disk wind in the disk accretion system.


title:
MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive
  Graph Neural Networks

abstract:
  Memory-related vulnerabilities constitute severe threats to the security of
modern software. Despite the success of deep learning-based approaches to
generic vulnerability detection, they are still limited by the underutilization
of flow information when applied for detecting memory-related vulnerabilities,
leading to high false positives.
  In this paper,we propose MVD, a statement-level Memory-related Vulnerability
Detection approach based on flow-sensitive graph neural networks (FS-GNN).
FS-GNN is employed to jointly embed both unstructured information (i.e., source
code) and structured information (i.e., control- and data-flow) to capture
implicit memory-related vulnerability patterns. We evaluate MVD on the dataset
which contains 4,353 real-world memory-related vulnerabilities, and compare our
approach with three state-of-the-art deep learning-based approaches as well as
five popular static analysisbased memory detectors. The experiment results show
that MVD achieves better detection accuracy, outperforming both state-of-theart
DL-based and static analysis-based approaches. Furthermore, MVD makes a great
trade-off between accuracy and efficiency.


title: On integer values of sum and product of three positive rational numbers

abstract:
  In 1997 we proved that if $n$ is of the form $$ 4k, \quad 8k-1\quad {\rm or}
\quad 2^{2m+1}(2k-1)+3, $$ where $k,m\in \mathbb N,$ then there are no positive
rational numbers $x,y,z$ satisfying $$ xyz = 1, \quad x+y+z = n. $$ Recently,
N. X. Tho proved the following statement: let $a\in\mathbb N$ be odd and let
either $n\equiv 0\pmod 4$ or $n\equiv 7\pmod 8$. Then the system of equations
$$ xyz = a, \quad x+y+z = an. $$ has no solutions in positive rational numbers
$x,y,z.$
  A representative example of our result is the following statement: assume
that $a,n\in\mathbb N$ are such that at least one of the following conditions
hold:
  $\bullet$ $n\equiv 0\pmod 4$
  $\bullet$ $n\equiv 7\pmod 8 $
  $\bullet$ $a\equiv 0\pmod 4$
  $\bullet$ $a\equiv 0\pmod 2$ and $n\equiv 3\pmod 4$
  $\bullet$ $a^2n^3=2^{2m+1}(2k-1)+27$ for some $k,m\in \mathbb N.$
  Then the system of equations $$ xyz = a, \quad x+y+z = an. $$ has no
solutions in positive rational numbers $x,y,z.$


title: A Survey on Metaverse: Fundamentals, Security, and Privacy

abstract:
  Metaverse, as an evolving paradigm of the next-generation Internet, aims to
build a fully immersive, hyper spatiotemporal, and self-sustaining virtual
shared space for humans to play, work, and socialize. Driven by recent advances
in emerging technologies such as extended reality, artificial intelligence, and
blockchain, metaverse is stepping from the science fiction to an upcoming
reality. However, severe privacy invasions and security breaches (inherited
from underlying technologies or emerged in the new digital ecology) of
metaverse can impede its wide deployment. At the same time, a series of
fundamental challenges (e.g., scalability and interoperability) can arise in
metaverse security provisioning owing to the intrinsic characteristics of
metaverse, such as immersive realism, hyper spatiotemporality, sustainability,
and heterogeneity. In this paper, we present a comprehensive survey of the
fundamentals, security, and privacy of metaverse. Specifically, we first
investigate a novel distributed metaverse architecture and its key
characteristics with ternary-world interactions. Then, we discuss the security
and privacy threats, present the critical challenges of metaverse systems, and
review the state-of-the-art countermeasures. Finally, we draw open research
directions for building future metaverse systems.


title:
The generalized Marchenko method in the inverse scattering problem for a
  first-order linear system

abstract:
  The Marchenko method is developed in the inverse scattering problem for a
linear system of first-order differential equations containing potentials
proportional to the spectral parameter. The corresponding Marchenko system of
integral equations is derived in such a way that the method can be applied to
some other linear systems for which a Marchenko method is not yet available. It
is shown how the potentials and the scattering solutions to the linear system
are constructed from the solution to the Marchenko system. The bound-state
information for the linear system with any number of bound states and any
multiplicities is described in terms of a pair of constant matrix triplets.
When the potentials in the linear system are reflectionless, some explicit
solution formulas are presented in closed form for the potentials and for the
scattering solutions to the linear system. The theory is illustrated with some
explicit examples.


title:
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic
  Segmentation with Transformers

abstract:
  Weakly-supervised semantic segmentation (WSSS) with image-level labels is an
important and challenging task. Due to the high training efficiency, end-to-end
solutions for WSSS have received increasing attention from the community.
However, current methods are mainly based on convolutional neural networks and
fail to explore the global information properly, thus usually resulting in
incomplete object regions. In this paper, to address the aforementioned
problem, we introduce Transformers, which naturally integrate global
information, to generate more integral initial pseudo labels for end-to-end
WSSS. Motivated by the inherent consistency between the self-attention in
Transformers and the semantic affinity, we propose an Affinity from Attention
(AFA) module to learn semantic affinity from the multi-head self-attention
(MHSA) in Transformers. The learned affinity is then leveraged to refine the
initial pseudo labels for segmentation. In addition, to efficiently derive
reliable affinity labels for supervising AFA and ensure the local consistency
of pseudo labels, we devise a Pixel-Adaptive Refinement module that
incorporates low-level image appearance information to refine the pseudo
labels. We perform extensive experiments and our method achieves 66.0% and
38.9% mIoU on the PASCAL VOC 2012 and MS COCO 2014 datasets, respectively,
significantly outperforming recent end-to-end methods and several multi-stage
competitors. Code is available at https://github.com/rulixiang/afa.


title: On unit weighted zero-sum constants of $\mathbb Z_n$

abstract:
  The $A$-weighted Gao constant $E_A(n)$ is defined to be the smallest natural
number $k$, such that any sequence of $k$ elements in $\mathbb Z_n$ has a
subsequence of length $n$, whose $A$-weighted sum is zero. When $A=U(n)$ (the
set of all units in $\mathbb Z_n$), the value of $E_A(n)$ has been determined
by Simon Griffiths and by Florian Luca. We give another proof of this result
and also determine the values of two related constants $C_A(n)$ and $D_A(n)$,
by using the corresponding constants $E(\mathbb Z_2^a),C(\mathbb Z_2^a)$ and
$D(\mathbb Z_2^a)$ for the group $\mathbb Z_2^a$. We also characterize all
sequences of length $E_A(n)-1$ in $\mathbb Z_n$, which do not have any
$A$-weighted zero-sum subsequence of length $n$, when $n$ is a power of 2 and
$A=U(n)$.


title:
Thermal Hysteresis Behavior of Skyrmion Lattices in the van der Waals
  Ferromagnet Fe3GeTe2

abstract:
  Understanding the physics of phase transitions in two-dimensional (2D)
systems underpins the research in diverse fields including statistical
mechanics, quantum systems, nanomagnetism, and soft condensed matter. However,
many fundamental aspects of 2D phase transitions are still not well understood,
including the effects of interparticle potential, polydispersity, and particle
shape. Magnetic skyrmions, which are non-trivial chiral spin structures, can be
considered as quasi-particles that form two-dimensional lattices. Here we show,
by real-space imaging using in situ cryo-Lorentz transmission electron
microscopy coupled with machine learning, the ordering behavior of N\'eel
skyrmion lattices in van der Waals Fe3GeTe2. We demonstrate a distinct change
in the skyrmion size distribution during field-cooling, which leads to a loss
of lattice order and an evolution of the skyrmion liquid phase. Remarkably, the
lattice order is restored during field heating and demonstrates a thermal
hysteresis. Our quantitative analysis explains this behavior based on the
energy landscape of skyrmions and demonstrates the potential to control the
lattice order in 2D phase transitions.


title: Security of Underwater and Air-Water Wireless Communication

abstract:
  We present a first detailed survey that focuses on the security challenges
faced by the underwater and air-water (A-W) wireless communication networks
(WCNs), as well as the countermeasures proposed to date. Specifically, we
provide a detailed literature review of the various kinds of active and passive
attacks which hamper the confidentiality, integrity, authentication and
availability of both underwater and A-W WCNs. For clarity of exposition, this
survey paper is mainly divided into two parts. The first part of the paper is
essentially a primer on underwater and A-W WCNs whereby we outline the benefits
and drawbacks of the three promising underwater and A-W candidate technologies:
radio frequency (RF), acoustic, and optical, along with channel modelling. To
this end, we also describe the indirect (relay-aided) and direct mechanisms for
the A-W WCNs along with channel modelling. This sets the stage for the second
part (and main contribution) of the paper whereby we provide a thorough
comparative discussion of a vast set of works that have reported the security
breaches (as well as viable countermeasures) for many diverse configurations of
the underwater and A-W WCNs. Finally, we highlight some research gaps in the
open literature and identify some open problems for the future work.


title:
Cross Language Image Matching for Weakly Supervised Semantic
  Segmentation

abstract:
  It has been widely known that CAM (Class Activation Map) usually only
activates discriminative object regions and falsely includes lots of
object-related backgrounds. As only a fixed set of image-level object labels
are available to the WSSS (weakly supervised semantic segmentation) model, it
could be very difficult to suppress those diverse background regions consisting
of open set objects. In this paper, we propose a novel Cross Language Image
Matching (CLIMS) framework, based on the recently introduced Contrastive
Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our
framework is to introduce natural language supervision to activate more
complete object regions and suppress closely-related open background regions.
In particular, we design object, background region and text label matching
losses to guide the model to excite more reasonable object regions for CAM of
each category. In addition, we design a co-occurring background suppression
loss to prevent the model from activating closely-related background regions,
with a predefined set of class-related background text descriptions. These
designs enable the proposed CLIMS to generate a more complete and compact
activation map for the target objects. Extensive experiments on PASCAL VOC2012
dataset show that our CLIMS significantly outperforms the previous
state-of-the-art methods.


title: A Similarity-based Framework for Classification Task

abstract:
  Similarity-based method gives rise to a new class of methods for multi-label
learning and also achieves promising performance. In this paper, we generalize
this method, resulting in a new framework for classification task.
Specifically, we unite similarity-based learning and generalized linear models
to achieve the best of both worlds. This allows us to capture interdependencies
between classes and prevent from impairing performance of noisy classes. Each
learned parameter of the model can reveal the contribution of one class to
another, providing interpretability to some extent. Experiment results show the
effectiveness of the proposed approach on multi-class and multi-label datasets


title:
Extending the Fisher Information Matrix in Gravitational-wave Data
  Analysis

abstract:
  The Fisher information matrix (FM) plays an important role in forecasts and
inferences in many areas of physics. While giving fast parameter estimation
with the Gaussian likelihood approximation in the parameter space, the FM can
only give the ellipsoidal posterior contours of parameters and lose the
higher-order information beyond Gaussianity. We extend the FM in
gravitational-wave (GW) data analysis using the Derivative Approximation for
LIkelihoods (DALI), a method to expand the likelihood while keeping it positive
definite and normalizable at every order, for more accurate forecasts and
inferences. When applied to the two real GW events, GW150914 and GW170817, DALI
can reduce the difference between FM approximation and the real posterior by 5
times in the best case. The calculation time of DALI and FM is at the same
order of magnitude, while obtaining the real full posterior will take several
orders of magnitude longer. Besides more accurate approximations, higher-order
correction from DALI provides a fast assessment on the FM analysis and gives
suggestions for complex sampling techniques which are computationally
intensive. We recommend using the DALI method as an extension to the FM method
in GW data analysis to pursue better accuracy while still keeping the speed.


title: Octonionic Planes and Real Forms of G2, F4 and E6

abstract:
  In this work we present a useful way to introduce the octonionic projective
and hyperbolic plane through the use of Veronese vectors. Then we focus on
their relation with the exceptional Jordan algebra and show that the Veronese
vectors are the rank-one elements of the algebra. We then study groups of
motions over the octonionic plane recovering all real forms of G2, F4and E6
groups and finally give a classification of all octonionic and split-octonionic
planes as symmetric spaces.


title: Broadband Spectro-temporal Study on Blazar TXS 1700+685

abstract:
  We attempt to present a multiwavelength variability and correlation study as
well as detailed multi-waveband spectral characteristics of the May 2021
$\gamma$-ray flare of the blazar source TXS 1700+685. The multi-wavelength
observation from \textit{Fermi}-LAT, \textit{Swift}-XRT/UVOT as well as radio
archival data are used for our spectro-temporal investigation. We estimate the
variability time-scale of the source from the flux doubling time in different
flaring regions detected in \textit{Fermi}-LAT observation and the shortest
variability time is used to put a constraint on the minimum Doppler factor and
on the size of the emission region. We have detected a statistically
significant quasi-periodic oscillation feature (QPO) at $\sim$ 17 days. The
broad-band emission is satisfactorily represented during its flaring state with
a leptonic synchrotron and inverse Compton component. From the broadband
spectral modeling, we observe the external Comptonization of the seed photons
originating in the broad line region to be dominant compared to the dusty
torus. This is further supported by the fact that the emission region is also
found to be residing within the BLR. The equipartition value implies the energy
density of the magnetic field in the jet comoving frame is weak, and that is
also reflected in the magnetic field and low power corresponding to the
magnetic field component of the jet. In order to produce the high energy hump,
we need the injection of a large population of high energy electrons and/or the
presence of strong magnetic field; and we observe the later component to be
sub-dominant in our case. The flat rising and steep falling profile in the
$\gamma$-ray SED as well as the break or spectral curvature at $\sim$ 1 GeV are
in commensuration with the flat-spectrum radio quasar (FSRQ) nature of the
source.


title:
Null Cosmic Strings: Scattering by Black Holes, Optics and Spacetime
  Content

abstract:
  Equations of motion of null cosmic strings near black holes, or other massive
sources, are solved exactly in the weak field approximation. The stress-energy
tensor of a null string in a curved spacetime is introduced and used to show
how scattering by black holes transforms linear and angular momenta of the
string. The corresponding recoil effect of a black hole and change of its
angular momentum caused by a null cosmic string are calculated. For a null
string, its energy $\mu$ per unit length evolves along the null direction of
the string trajectory. The evolution of $\mu$ is connected with a string
optical scalar $Z$. Optical properties of null strings are that their energy is
concentrated on caustics, where $Z$ has poles. String parameters $\mu$ and $Z$
capture important features of the spacetime where strings move. Explicit
dependence of $\mu$ and $Z$ on the strain and Bondi news tensors of
gravitational wave background, mass and angular momentum aspects are
established, near the future null infinity, up to the 4-th order in expansion
in an inverse null parameter in asymptotically flat spacetimes.


title:
Feasibility and method of multi-step Hermitization of crypto-Hermitian
  quantum Hamiltonians

abstract:
  In the popular ${\cal PT}-$symmetry-based formulation of quantum mechanics of
closed systems one can build unitary models using non-Hermitian Hamiltonians
(i.e., $H \neq H^\dagger$) which are Hermitizable (so that one can write,
simultaneously, $H = H^\ddagger$). The essence of the trick is that the
reference Hilbert space $\cal R$ (in which we use the conventional inner
product $\langle \psi_a|\psi_b\rangle$ and write $H \neq H^\dagger$) is
declared unphysical. The necessary Hermiticity of the Hamiltonian $H =
H^\ddagger$ can be then achieved by the mere metric-mediated amendment $\langle
\psi_a|\Theta|\psi_b\rangle$ to the inner product. This converts $\cal R$ into
a correct physical Hilbert space $\cal H$. The feasibility of the construction
is based on a factorization postulate $\Theta={\cal PC}$ where, usually, ${\cal
P}$ is parity and ${\cal C}$ is charge. In our paper we propose a more general
factorization recipe in which one constructs $\Theta=Z_NZ_{N-1}\ldots Z_1$, at
any $N$, in terms of suitable auxiliary pre-metric operators $Z_k$.


title: An Alternate proof for a case of a Malmsten integral

abstract:
  In this paper, a direct proof is presented for a case of a Malmsten integral.
The method used in solving the integral is a direct one that the author has not
come accross in any old or recent publication. Integration by parts, Laplace
transform, an integral representation for the hyperbolic secant function, and
the digamma representation for an alternating series are employed to derive the
result.


title:
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with
  Heterogeneous Pipelines

abstract:
  The use of FPGAs for efficient graph processing has attracted significant
interest. Recent memory subsystem upgrades including the introduction of HBM in
FPGAs promise to further alleviate memory bottlenecks. However, modern
multi-channel HBM requires much more processing pipelines to fully utilize its
bandwidth potential. Existing designs do not scale well, resulting in
underutilization of the HBM facilities even when all other resources are fully
consumed. In this paper, we re-examined the graph processing workloads and
found much diversity in processing. We also found that the diverse workloads
can be easily classified into two types, namely dense and sparse partitions.
This motivates us to propose a resource-efficient heterogeneous pipeline
architecture. Our heterogeneous architecture comprises of two types of
pipelines: Little pipelines to process dense partitions with good locality and
Big pipelines to process sparse partitions with the extremely poor locality.
Unlike traditional monolithic pipeline designs, the heterogeneous pipelines are
tailored for more specific memory access patterns, and hence are more
lightweight, allowing the architecture to scale up to more effectively with
limited resources. In addition, we propose a model-guided task scheduling
method that schedules partitions to the right pipeline types, generates the
most efficient pipeline combination and balances workloads. Furthermore, we
develop an automated open-source framework, called ReGraph, which automates the
entire development process. ReGraph outperforms state-of-the-art FPGA
accelerators by up to 5.9 times in terms of performance and 12times in terms of
resource efficiency.


title:
Tightly Coupled Optimization-based GPS-Visual-Inertial Odometry with
  Online Calibration and Initialization

abstract:
  In this paper, we present a tightly coupled optimization-based
GPS-Visual-Inertial odometry system to solve the trajectory drift of the
visual-inertial odometry especially over long-term runs. Visual reprojection
residuals, IMU residuals, and GPS measurement residuals are jointly minimized
within a local bundle adjustment, in which we apply GPS measurements and IMU
preintegration used for the IMU residuals to formulate a novel GPS residual. To
improve the efficiency and robustness of the system, we propose a fast
reference frames initialization method and an online calibration method for
GPS-IMU extrinsic and time offset. In addition, we further test the performance
and convergence of our online calibration method. Experimental results on EuRoC
datasets show that our method consistently outperforms other tightly coupled
and loosely coupled approaches. Meanwhile, this system has been validated on
KAIST datasets, which proves that our system can work well in the case of
visual or GPS failure.


title:
NeuralDPS: Neural Deterministic Plus Stochastic Model with Multiband
  Excitation for Noise-Controllable Waveform Generation

abstract:
  The traditional vocoders have the advantages of high synthesis efficiency,
strong interpretability, and speech editability, while the neural vocoders have
the advantage of high synthesis quality. To combine the advantages of two
vocoders, inspired by the traditional deterministic plus stochastic model, this
paper proposes a novel neural vocoder named NeuralDPS which can retain high
speech quality and acquire high synthesis efficiency and noise controllability.
Firstly, this framework contains four modules: a deterministic source module, a
stochastic source module, a neural V/UV decision module and a neural filter
module. The input required by the vocoder is just the spectral parameter, which
avoids the error caused by estimating additional parameters, such as F0.
Secondly, to solve the problem that different frequency bands may have
different proportions of deterministic components and stochastic components, a
multiband excitation strategy is used to generate a more accurate excitation
signal and reduce the neural filter's burden. Thirdly, a method to control
noise components of speech is proposed. In this way, the signal-to-noise ratio
(SNR) of speech can be adjusted easily. Objective and subjective experimental
results show that our proposed NeuralDPS vocoder can obtain similar performance
with the WaveNet and it generates waveforms at least 280 times faster than the
WaveNet vocoder. It is also 28% faster than WaveGAN's synthesis efficiency on a
single CPU core. We have also verified through experiments that this method can
effectively control the noise components in the predicted speech and adjust the
SNR of speech. Examples of generated speech can be found at
https://hairuo55.github.io/NeuralDPS.


title: Just Rank: Rethinking Evaluation with Word and Sentence Similarities

abstract:
  Word and sentence embeddings are useful feature representations in natural
language processing. However, intrinsic evaluation for embeddings lags far
behind, and there has been no significant update since the past decade. Word
and sentence similarity tasks have become the de facto evaluation method. It
leads models to overfit to such evaluations, negatively impacting embedding
models' development. This paper first points out the problems using semantic
similarity as the gold standard for word and sentence embedding evaluations.
Further, we propose a new intrinsic evaluation method called EvalRank, which
shows a much stronger correlation with downstream tasks. Extensive experiments
are conducted based on 60+ models and popular datasets to certify our
judgments. Finally, the practical evaluation toolkit is released for future
benchmarking purposes.


title: Language vs Speaker Change: A Comparative Study

abstract:
  Spoken language change detection (LCD) refers to detecting language switching
points in a multilingual speech signal. Speaker change detection (SCD) refers
to locating the speaker change points in a multispeaker speech signal. The
objective of this work is to understand the challenges in LCD task by comparing
it with SCD task. Human subjective study for change detection is performed for
LCD and SCD. This study demonstrates that LCD requires larger duration
spectro-temporal information around the change point compared to SCD. Based on
this, the work explores automatic distance based and model based LCD
approaches. The model based ones include Gaussian mixture model and universal
background model (GMM-UBM), attention, and Generative adversarial network (GAN)
based approaches. Both the human and automatic LCD tasks infer that the
performance of the LCD task improves by incorporating more and more
spectro-temporal duration.


title: Electronic structure of higher-order Ruddlesden-Popper nickelates

abstract:
  We analyze the electronic structure of the recently synthesized higher-order
nickelate Ruddlesden-Popper phases La$_{n+1}$Ni$_n$O$_{3n+1}$ ($n=4-6$) using
first-principles calculations. For all materials, our results show large
holelike Fermi surfaces with $d_{x^2-y^2}$ character that closely resemble
those of optimally hole-doped cuprates. For higher values of $n$, extra
non-cuprate-like bands of $d_{z^2}$ orbital character appear. These aspects
highlight that this Ruddlesden-Popper series can provide a means to modify the
electronic ground states of nickelates by tuning their dimensionality. With
their similarities and differences to the cuprates, this new family of
materials can potentially shed light on the physics of copper-based oxides.


title:
Cross section measurements of the processes $e^+e^- \rightarrow
  \omega\pi^{0}$ and $\omega\eta$ at center-of-mass energies between 3.773 and
  4.701 GeV

abstract:
  The Born cross sections of the processes $e^+e^- \rightarrow \omega\pi^{0}$
and $e^+e^- \rightarrow \omega\eta$ are measured at center-of-mass energies
between 3.773 and 4.701 GeV using a total integrated luminosity of 22.7
fb$^{-1}$ collected with the BESIII detector operating at the BEPCII collider.
A simple $s^{-n}$ dependence for the continuum process can describe the
measured Born cross sections. No significant contributions from the
$\psi(4160)$, $Y(4230)$, $Y(4360)$, $\psi(4415)$, $Y(4660)$ resonances are
found.


title:
The Proof is in the Pudding: Using Automated Theorem Proving to Generate
  Cooking Recipes

abstract:
  This paper presents FASTFOOD, a rule-based Natural Language Generation
Program for cooking recipes. Recipes are generated by using an Automated
Theorem Proving procedure to select the ingredients and instructions, with
ingredients corresponding to axioms and instructions to implications. FASTFOOD
also contains a temporal optimization module which can rearrange the recipe to
make it more time-efficient for the user, e.g. the recipe specifies to chop the
vegetables while the rice is boiling. The system is described in detail, using
a framework which divides Natural Language Generation into 4 phases: content
production, content selection, content organisation and content realisation. A
comparison is then made with similar existing systems and techniques.


title:
EsDNN: Deep Neural Network based Multivariate Workload Prediction
  Approach in Cloud Environment

abstract:
  Cloud computing has been regarded as a successful paradigm for IT industry by
providing benefits for both service providers and customers. In spite of the
advantages, cloud computing also suffers from distinct challenges, and one of
them is the inefficient resource provisioning for dynamic workloads. Accurate
workload predictions for cloud computing can support efficient resource
provisioning and avoid resource wastage. However, due to the high-dimensional
and high-variable features of cloud workloads, it is difficult to predict the
workloads effectively and accurately. The current dominant work for cloud
workload prediction is based on regression approaches or recurrent neural
networks, which fail to capture the long-term variance of workloads. To address
the challenges and overcome the limitations of existing works, we proposed an
efficient supervised learning-based Deep Neural Network (esDNN}) approach for
cloud workload prediction. Firstly, we utilize a sliding window to convert the
multivariate data into supervised learning time series that allow deep learning
for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve
accurate prediction. To show the effectiveness of esDNN, we also conduct
comprehensive experiments based on realistic traces derived from Alibaba and
Google cloud data centers. The experimental results demonstrate that esDNN can
accurately and efficiently predict cloud workloads. Compared with the
state-of-the-art baselines, esDNN can reduce the mean square errors
significantly, e.g. 15% than the approach using GRU only. We also apply esDNN
for machines auto-scaling, which illustrates that esDNN can reduce the number
of active hosts efficiently, thus the costs of service providers can be
optimized.


title:
Thermodynamic geometric analysis of BTZ black hole in f(R,{\phi})
  Gravity

abstract:
  In this work, we obtain the thermodynamic geometry of the extended black hole
solution under f(R) gravity. This is the foundation of our work. We also obtain
the exact form of the f(R) model for some solutions. In the thermodynamic
analysis, we calculate the thermodynamic quantities such as temperature and
entropy of these solutions and compare them with the corresponding BTZ black
holes. After that, we study the Ruppeiner geometry, such as the f(R)-gravity
(extended black hole solution) of the BTZ black hole, and analyze the
thermodynamic comparison of the Ruppeiner geometry with the van der Waals
equation for its phase transition process.


title: Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones

abstract:
  The Perspective-n-Point problem aims to estimate the relative pose between a
calibrated monocular camera and a known 3D model, by aligning pairs of 2D
captured image points to their corresponding 3D points in the model. We suggest
an algorithm that runs on weak IoT devices in real-time but still provides
provable theoretical guarantees for both running time and correctness. Existing
solvers provide only one of these requirements. Our main motivation was to turn
the popular DJI's Tello Drone (<90gr, <\$100) into an autonomous drone that
navigates in an indoor environment with no external human/laptop/sensor, by
simply attaching a Raspberry PI Zero (<9gr, <\$25) to it. This tiny
micro-processor takes as input a real-time video from a tiny RGB camera, and
runs our PnP solver on-board. Extensive experimental results, open source code,
and a demonstration video are included.


title:
Capturing the geometry, function, and evolution of enzymes with 3D
  templates

abstract:
  Structural templates are 3D signatures representing protein functional sites,
such as ligand binding cavities, metal coordination motifs or catalytic sites.
Here we explore methods to generate template libraries and algorithms to query
structures for conserved 3D motifs. Applications of templates are discussed, as
well as some exemplar cases for examining evolutionary links in enzymes. We
also introduce the concept of using more than one template per structure to
represent flexible sites, as an approach to better understand catalysis through
snapshots captured in enzyme structures. Functional annotation from structure
is an important topic that has recently resurfaced due to the new more accurate
methods of protein structure prediction. Therefore, we anticipate that
template-based functional site detection will be a powerful tool in the task of
characterising a vast number of new protein models.


title:
Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object
  Detection

abstract:
  The recently proposed camouflaged object detection (COD) attempts to segment
objects that are visually blended into their surroundings, which is extremely
complex and difficult in real-world scenarios. Apart from high intrinsic
similarity between the camouflaged objects and their background, the objects
are usually diverse in scale, fuzzy in appearance, and even severely occluded.
To deal with these problems, we propose a mixed-scale triplet network,
\textbf{ZoomNet}, which mimics the behavior of humans when observing vague
images, i.e., zooming in and out. Specifically, our ZoomNet employs the zoom
strategy to learn the discriminative mixed-scale semantics by the designed
scale integration unit and hierarchical mixed-scale unit, which fully explores
imperceptible clues between the candidate objects and background surroundings.
Moreover, considering the uncertainty and ambiguity derived from
indistinguishable textures, we construct a simple yet effective regularization
constraint, uncertainty-aware loss, to promote the model to accurately produce
predictions with higher confidence in candidate regions. Without bells and
whistles, our proposed highly task-friendly model consistently surpasses the
existing 23 state-of-the-art methods on four public datasets. Besides, the
superior performance over the recent cutting-edge models on the SOD task also
verifies the effectiveness and generality of our model. The code will be
available at \url{https://github.com/lartpang/ZoomNet}.


title:
Federated and Generalized Person Re-identification through Domain and
  Feature Hallucinating

abstract:
  In this paper, we study the problem of federated domain generalization
(FedDG) for person re-identification (re-ID), which aims to learn a generalized
model with multiple decentralized labeled source domains. An empirical method
(FedAvg) trains local models individually and averages them to obtain the
global model for further local fine-tuning or deploying in unseen target
domains. One drawback of FedAvg is neglecting the data distributions of other
clients during local training, making the local model overfit local data and
producing a poorly-generalized global model. To solve this problem, we propose
a novel method, called "Domain and Feature Hallucinating (DFH)", to produce
diverse features for learning generalized local and global models.
Specifically, after each model aggregation process, we share the Domain-level
Feature Statistics (DFS) among different clients without violating data
privacy. During local training, the DFS are used to synthesize novel domain
statistics with the proposed domain hallucinating, which is achieved by
re-weighting DFS with random weights. Then, we propose feature hallucinating to
diversify local features by scaling and shifting them to the distribution of
the obtained novel domain. The synthesized novel features retain the original
pair-wise similarities, enabling us to utilize them to optimize the model in a
supervised manner. Extensive experiments verify that the proposed DFH can
effectively improve the generalization ability of the global model. Our method
achieves the state-of-the-art performance for FedDG on four large-scale re-ID
benchmarks.


title:
IDmUNet: A new image decomposition induced network for sparse feature
  segmentation

abstract:
  UNet and its variants are among the most popular methods for medical image
segmentation. Despite their successes in task generality, most of them consider
little mathematical modeling behind specific applications. In this paper, we
focus on the sparse feature segmentation task and make a task-oriented network
design, in which the target objects are sparsely distributed and the background
is hard to be mathematically modeled. We start from an image decomposition
model with sparsity regularization, and propose a deep unfolding network,
namely IDNet, based on an iterative solver, scaled alternating direction method
of multipliers (scaled-ADMM). The IDNet splits raw inputs into double feature
layers. Then a new task-oriented segmentation network is constructed, dubbed as
IDmUNet, based on the proposed IDNets and a mini-UNet. Because of the sparsity
prior and deep unfolding method in the structure design, this IDmUNet combines
the advantages of mathematical modeling and data-driven approaches. Firstly,
our approach has mathematical interpretability and can achieve favorable
performance with far fewer learnable parameters. Secondly, our IDmUNet is
robust in a simple end-to-end training with explainable behaviors. In the
experiments of retinal vessel segmentation (RVS), IDmUNet produces the
state-of-the-art results with only 0.07m parameters, whereas SA-UNet, one of
the latest variants of UNet, contains 0.54m and the original UNet 31.04m.
Moreover, the training procedure of our network converges faster without
overfitting phenomenon. This decomposition-based network construction strategy
can be generalized to other problems with mathematically clear targets and
complicated unclear backgrounds.


title:
Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their
  Protocols, Resource Allocation, and Performance

abstract:
  Reconfigurable intelligent surfaces (RISs) assist in paving the way for the
evolution of conventional vehicular networks to autonomous driving. Having said
that, the 3rd Generation Partnership Project (3GPP) faces numerous open
challenges concerning the RIS-aided vehicle-to-everything (V2X) solutions of
the near future. To tackle these challenges and to stimulate future research,
this article focuses on the prospective transmission design of RIS-aided V2X
communications. In particular, two V2X sidelink modes are enhanced by
exploiting RISs and their variants, followed by a customized transmission frame
structure that partitions the transmission efforts into different phases. Next,
effective channel tracking and resource allocation techniques are developed for
attaining a high beamforming gain at low overhead and complexity. Finally,
promising research topics are highlighted and future 3GPP standardization items
are proposed for RISaided V2X systems.


title: Glide-Reflection Symmetric Topological Phononic Crystal Waveguide

abstract:
  The topological backscattering immunity of waves guided along a domain wall
is associated with symmetry protection in valley-Hall and quantum-Hall phononic
crystal waveguides. This desirable property results from a topological
transition at a Dirac point of the 2D crystal, leading to the opening of an
initially closed band gap but to a limited available bandwidth compared to wide
band gap topologically trivial crystals. Introducing a glide-symmetric
dislocation of a square lattice crystal of circular inclusions, we show that a
pair of wide-bandwidth, single-mode, and symmetry-protected guided waves are
created, without resorting to bulk boundary correspondence. A demonstration
experiment is performed with acoustic waves in water, at ultrasonic
frequencies. The concept applies further to different types of waves, including
elastic waves in solids, but also optical and electromagnetic waves.


title:
Better Approximation Guarantees for the NSGA-II by Using the Current
  Crowding Distance

abstract:
  A recent runtime analysis (Zheng, Liu, Doerr (2022)) has shown that a variant
of the NSGA-II algorithm can efficiently compute the full Pareto front of the
OneMinMax problem when the population size is by a constant factor larger than
the Pareto front, but that this is not possible when the population size is
only equal to the Pareto front size. In this work, we analyze how well the
NSGA-II approximates the Pareto front when it cannot compute the whole front.
We observe experimentally and by mathematical means that already when the
population size is half the Pareto front size, relatively large gaps in the
Pareto front remain. The reason for this phenomenon is that the NSGA-II in the
selection stage computes the crowding distance once and then repeatedly removes
individuals with smallest crowding distance without updating the crowding
distance after each removal. We propose an efficient way to implement the
NSGA-II using the momentary crowding distance. In our experiments, this
algorithm approximates the Pareto front much better than the previous version.
We also prove that the gaps in the Pareto front are at most a constant factor
larger than the theoretical minimum.


title:
TOI-1696: a nearby M4 dwarf with a $3R_\oplus$ planet in the Neptunian
  desert

abstract:
  We present the discovery and validation of a temperate sub-Neptune around the
nearby mid-M dwarf TIC 470381900 (TOI-1696), with a radius of $3.09 \pm 0.11
\,R_\oplus$ and an orbital period of $2.5 \,\rm{days}$, using a combination of
TESS and follow-up observations using ground-based telescopes. Joint analysis
of multi-band photometry from TESS, MuSCAT, MuSCAT3, Sinistro, and KeplerCam
confirmed the transit signal to be achromatic as well as refined the orbital
ephemeris. High-resolution imaging with Gemini/'Alopeke and high-resolution
spectroscopy with the Subaru/IRD confirmed that there are no stellar companions
or background sources to the star. The spectroscopic observations with IRD and
IRTF/SpeX were used to determine the stellar parameters, and found the host
star is an M4 dwarf with an effective temperature of $T_{eff} = 3185 \pm
76\,\rm{K}$ and a metallicity of [Fe/H] $=0.336 \pm 0.060 \,\rm{dex}$. The
radial velocities measured from IRD set a $2$-$\sigma$ upper limit on the
planetary mass to be $48.8 \,M_\oplus$. The large radius ratio ($R_p/R_\star
\sim 0.1$) and the relatively bright NIR magnitude ($J=12.2 \,\rm{mag}$) make
this planet an attractive target for further followup observations. TOI-1696b
is one of the planets belonging to the Neptunian desert with the highest
transmission spectroscopy metric discovered to date, making it an interesting
candidate for atmospheric characterizations with JWST.


title: Generating Posets with Interfaces

abstract:
  We generate and count isomorphism classes of gluing-parallel posets with
interfaces (iposets) on up to eight points, and on up to ten points with
interfaces removed. In order to do so, we introduce a new class of iposets with
full interfaces and show that considering these is sufficient. We also describe
the software (written in Julia) that we have used for our exploration and
define a new incomplete isomorphism invariant which may be computed in
polynomial time yet identifies only very few pairs of non-isomorphic iposets.


title: Boosting the Learning for Ranking Patterns

abstract:
  Discovering relevant patterns for a particular user remains a challenging
tasks in data mining. Several approaches have been proposed to learn
user-specific pattern ranking functions. These approaches generalize well, but
at the expense of the running time. On the other hand, several measures are
often used to evaluate the interestingness of patterns, with the hope to reveal
a ranking that is as close as possible to the user-specific ranking. In this
paper, we formulate the problem of learning pattern ranking functions as a
multicriteria decision making problem. Our approach aggregates different
interestingness measures into a single weighted linear ranking function, using
an interactive learning procedure that operates in either passive or active
modes. A fast learning step is used for eliciting the weights of all the
measures by mean of pairwise comparisons.
  This approach is based on Analytic Hierarchy Process (AHP), and a set of
user-ranked patterns to build a preference matrix, which compares the
importance of measures according to the user-specific interestingness. A
sensitivity based heuristic is proposed for the active learning mode, in order
to insure high quality results with few user ranking queries. Experiments
conducted on well-known datasets show that our approach significantly reduces
the running time and returns precise pattern ranking, while being robust to
user-error compared with state-of-the-art approaches.


title: Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts

abstract:
  According to the published papers and books since the turn of the century,
Pareto optimization is the dominating assessment method for multi-objective
nonlinear optimization problems treated by population-based optimizers like
Evolutionary Algorithms. However, is it always the method of choice for
real-world applications, where either more than four objectives have to be
considered, or the same type of task is repeated again and again with only
minor modifications, in an automated optimization or planning process? This
paper presents a classification of application scenarios and compares the
Pareto approach with an extended version of the weighted sum, called cascaded
weighted sum, for the different scenarios. Its range of application within the
field of multi-objective optimization is discussed as well as its strengths and
weaknesses.


title:
Photo-ionization Initiated Differential Ultrafast Charge Migration:
  Impact of Molecular Symmetries and Tautomeric Forms

abstract:
  Photo-ionization induced ultrafast electron dynamics is considered as a
precursor to the slower nuclear dynamics associated with molecular
dissociation. Here, using ab initio multielectron wave-packet propagation
method, we study the overall many-electron dynamics, triggered by the
ionization of outer-valence orbitals of different tautomers of a prototype
molecule with more than one symmetry element. From the time evolution of the
initially created averaged hole density of each system, we identify distinctly
different charge dynamics response in the tautomers. We observe that keto form
shows charge migration direction away from the nitrogen bonded with tautomeric
hydrogen, while in enol - away from oxygen bonded to tautomeric hydrogen.
Additionally, the dynamics following ionization of molecular orbitals of
different symmetry reveal that a' orbitals show fast and highly delocalized
charge in comparison to a" symmetry. These observations indicate why different
tautomers respond differently to an XUV ionization, and might explain the
subsequent different fragmentation pathways. An experimental schematics
allowing detection and reconstruction of such charge dynamics is also proposed.
Although the present study uses a simple, prototypical bio-relevant molecule,
it reveals the explicit role of molecular symmetry and tautomerism in the
ionization-triggered charge migration that controls many ultrafast physical,
chemical, and biological processes, making tautomeric forms a promising tool of
molecular design for desired charge migration.


title:
A Study of Preference and Comfort for Users Immersed in a Telepresence
  Robot

abstract:
  In this paper, we show that unwinding the rotations of a user immersed in a
telepresence robot is preferred and may increase the feeling of presence or
"being there". By immersive telepresence, we mean a scenario where a user
wearing a head-mounted display embodies a mobile robot equipped with a
360{\deg} camera in another location, such that the user can move the robot and
communicate with people around it. By unwinding the rotations, the user never
perceives rotational motion through the head-mounted display while staying
stationary, avoiding sensory mismatch which causes a major part of VR sickness.
We performed a user study (N=32) on a Dolly mobile robot platform, mimicking an
earlier similar study done in simulation. Unlike the simulated study, in this
study there is no significant difference in the VR sickness suffered by the
participants, or the condition they find more comfortable (unwinding or
automatic rotations). However, participants still prefer the unwinding
condition, and they judge it to render a stronger feeling of presence, a major
piece in natural communication. We show that participants aboard a real
telepresence robot perceive distances similarly suitable as in simulation,
presenting further evidence on the applicability of VR as a research platform
for robotics and human-robot interaction.


title: ECMG: Exemplar-based Commit Message Generation

abstract:
  Commit messages concisely describe the content of code diffs (i.e., code
changes) and the intent behind them. Recently, many approaches have been
proposed to generate commit messages automatically. The information
retrieval-based methods reuse the commit messages of similar code diffs, while
the neural-based methods learn the semantic connection between code diffs and
commit messages. However, the reused commit messages might not accurately
describe the content/intent of code diffs and neural-based methods tend to
generate high-frequent and repetitive tokens in the corpus. In this paper, we
combine the advantages of the two technical routes and propose a novel
exemplar-based neural commit message generation model, which treats the similar
commit message as an exemplar and leverages it to guide the neural network
model to generate an accurate commit message. We perform extensive experiments
and the results confirm the effectiveness of our model.


title: Some new identities for Schur polynomials

abstract:
  For the Schur polynomials bounded and unbounded generalizations of the Cauchy
identities are found.


title: The magnetic topology of the inverse Evershed flow

abstract:
  The inverse Evershed flow (IEF) is a mass motion towards sunspots at
chromospheric heights. We combined high-resolution observations of NOAA 12418
from the Dunn Solar Telescope and vector magnetic field measurements from the
Helioseismic and Magnetic Imager (HMI) to determine the driver of the IEF. We
derived chromospheric line-of-sight (LOS) velocities from spectra of H$\alpha$
and Ca II IR. The HMI data were used in a non-force-free magnetic field
extrapolation to track closed field lines near the sunspot in the active
region. We determined their length and height, located their inner and outer
foot points, and derived flow velocities along them. The magnetic field lines
related to the IEF reach on average a height of 3 Mm over a length of 13 Mm.
The inner (outer) foot points are located at 1.2 (1.9) sunspot radii. The
average field strength difference $\Delta B$ between inner and outer foot
points is +400 G. The temperature difference $\Delta T$ is anti-correlated with
$\Delta B$ with an average value of -100 K. The pressure difference $\Delta p$
is dominated by $\Delta B$ and is primarily positive with a driving force
towards the inner foot points of 1.7 kPa on average. The velocities predicted
from $\Delta p$ reproduce the LOS velocities of 2-10 km s$^{-1}$ with a
square-root dependence. We find that the IEF is driven along magnetic field
lines connecting network elements with the outer penumbra by a gas pressure
difference that results from a difference in field strength as predicted by the
classical siphon flow scenario.


title:
HI-DWA: Human-Influenced Dynamic Window Approach for Shared Control of a
  Telepresence Robot

abstract:
  This paper considers the problem of enabling the user to modify the path of a
telepresence robot. The robot is capable of autonomously navigating to the goal
indicated by the user, but the user might still want to modify the path without
changing the goal, for example, to go further away from other people, or to go
closer to landmarks she wants to see on the way. We propose Human-Influenced
Dynamic Window Approach (HI-DWA), a shared control method aimed for
telepresence robots based on Dynamic Window Approach (DWA) that allows the user
to influence the control input given to the robot. To verify the proposed
method, we performed a user study (N=32) in Virtual Reality (VR) to compare
HI-DWA with switching between autonomous navigation and manual control for
controlling a simulated telepresence robot moving in a virtual environment.
Results showed that users reached their goal faster using HI-DWA controller and
found it easier to use. Preference between the two methods was split equally.
Qualitative analysis revealed that a major reason for the participants that
preferred switching between two modes was the feeling of control. We also
analyzed the affect of different input methods, joystick and gesture, on the
preference and perceived workload.


title:
Reconfigurable Intelligent Surface-Aided Joint Radar and Covert
  Communications: Fundamentals, Optimization, and Challenges

abstract:
  Future wireless communication systems will evolve toward multi-functional
integrated systems to improve spectrum utilization and reduce equipment sizes.
A joint radar and communication (JRC) system, which can support simultaneous
information transmission and target detection, has been regarded as a promising
solution for emerging applications such as autonomous vehicles. In JRC, data
security and privacy protection are critical issues. Thus, we first apply
covert communication into JRC and propose a joint radar and covert
communication (JRCC) system to achieve high spectrum utilization and secure
data transmission simultaneously. In the JRCC system, an existence of sensitive
data transmission is hidden from a maliciously observant warden. However, the
performance of JRCC is restricted by severe signal propagation environment and
hardware devices. Fortunately, reconfigurable intelligent surfaces (RISs) can
change the signal propagation smartly to improve the networks performance with
low cost. We first overview fundamental concepts of JRCC and RIS and then
propose the RIS-aided JRCC system design. Furthermore, both covert
communication and radar performance metrics are investigated and a game
theory-based covert rate optimization scheme is designed to achieve secure
communication. Finally, we present several promising applications and future
directions of RIS-aided JRCC systems.


title: Counterexamples to a conjecture of M. Pellegrini and P. Shumyatsky

abstract:
  In this article, we provide counterexamples to a conjecture of M. Pellegrini
and P. Shumyatsky which states that each coset of the centralizer of an
involution in a finite non-abelian simple group $G$ contains an odd order
element, unless $G=\text{PSL}(n,2)$ for $n\geq 4$. More precisely, we show that
the conjecture does not hold for the alternating group $A_{2^n}$ for all $n\geq
4$.


title: ISO/TS 15066: How Different Interpretations Affect Risk Assessment

abstract:
  The current technical specification ISO/TS15066:2016(E) for safe human-robot
interaction contains logically conflicting definitions for the contact between
human and robot. This may result in different interpretations for the contact
classification and thus no unique outcome can be expected, which may even cause
a risk to the human. In previous work, we showed a first set of implications.
This paper addresses the possible interpretations of a collision scenario as a
result of the varying interpretations for a risk assessment. With an experiment
including four commercially available robot systems we demonstrate the
procedure of the risk assessment following the different interpretations of the
TS. The results indicate possible incorrect use of the technical specification,
which we believe needs to be resolved in future revisions. For this, we suggest
tools in form of a decision tree and constrained collision force maps, which
enable a simple, unambiguous risk assessment for HRI.


title:
High friction limits of Euler-Navier-Stokes-Korteweg equations for
  multicomponent models

abstract:
  In this paper we analyze the high friction regime for the Navier Stokes
Korteweg equations for multicomponent systems. According to the shape of the
mixing and friction terms, we shall perform two limits: the high friction limit
toward an equilibrium system for the limit densities and the barycentric
velocity, and, after an appropriate time scaling, the diffusive relaxation
toward parabolic, gradient flow equations for the limit densities. The rigorous
justification of these limits is done by means of relative entropy techniques
in the framework of weak, finite energy solutions of the relaxation models,
rewritten in the enlarged formulation in terms of the drift velocity, toward
smooth solutions of the corresponding equilibrium dynamics. Finally, since our
estimates are uniform for small viscosity, the results are also valid for the
Euler Korteweg multicomponent models, and the corresponding estimates can be
obtained by sending the viscosity to zero.


title:
High-resolution Coastline Extraction in SAR Images via MISP-GGD
  Superpixel Segmentation

abstract:
  High accuracy coastline/shoreline extraction from SAR imagery is a crucial
step in a number of maritime and coastal monitoring applications. We present a
method based on image segmentation using the Generalised Gamma Mixture Model
superpixel algorithm (MISP-GGD). MISP-GGD produces superpixels adhering with
great accuracy to object edges in the image, such as the coastline.
Unsupervised clustering of the generated superpixels according to textural and
radiometric features allows for generation of a land/water mask from which a
highly accurate coastline can be extracted. We present results of our proposed
method on a number of SAR images of varying characteristics.


title:
Wasserstein Distance-based Spectral Clustering with Application to
  Transaction Data

abstract:
  With the rapid development of online payment platforms, it is now possible to
record massive transaction data. The economic behaviors are embedded in the
transaction data for merchants using these platforms. Therefore, clustering on
transaction data significantly contributes to analyzing merchants' behavior
patterns. This may help the platforms provide differentiated services or
implement risk management strategies. However, traditional methods exploit
transactions by generating low-dimensional features, leading to inevitable
information loss. To fully use the transaction data, we utilize the empirical
cumulative distribution of transaction amount to characterize merchants. Then,
we adopt Wasserstein distance to measure the dissimilarity between any two
merchants and propose the Wasserstein-distance-based spectral clustering (WSC)
approach. Considering the sample imbalance in real datasets, we incorporate
indices based on local outlier factors to improve the clustering performance.
Furthermore, to ensure feasibility if the proposed method on large-scale
datasets with limited computational resources, we propose a subsampling version
of WSC. The associated theoretical properties are investigated to verify the
efficiency of the proposed approach. The simulations and empirical study
demonstrate that the proposed method outperforms feature-based methods in
finding behavior patterns of merchants.


title:
Wide-band view of High Frequency QPOs of GRS 1915+105 in 'softer'
  variability classes observed with AstroSat

abstract:
  We present a comprehensive temporal and spectral analysis of the 'softer'
variability classes ($i.e.$, $\theta$, $\beta$, $\delta$, $\rho$, $\kappa$,
$\omega$ and $\gamma$) of the source GRS 1915+105 observed by AstroSat during
$2016-2021$ campaign. Wide-band ($3-60$ keV) timing studies reveal the
detection of High Frequency Quasi-periodic Oscillations (HFQPOs) with frequency
of $68.14-72.32$ Hz, significance of $2.75-11 \sigma$, and rms amplitude of
$1.48-2.66\%$ in $\delta$, $\kappa$, $\omega$ and $\gamma$ variability classes.
Energy dependent power spectra impart that HFQPOs are detected only in $6-25$
keV energy band and rms amplitude is found to increase ($1-8\%$) with energy.
The dynamical power spectra of $\kappa$ and $\omega$ classes demonstrate that
HFQPOs seem to be correlated with high count rates. We observe that wide-band
($0.7-50$ keV) energy spectra can be described by the thermal Comptonization
component (nthComp) with photon index ($\Gamma_{\rm nth}$) of $1.83-2.89$ along
with an additional steep ($\Gamma_{\rm PL}\sim3$) powerlaw component. The
electron temperature ($kT_e$) of $1.82 -3.66$ keV and optical depth ($\tau$) of
$2-14$ indicate the presence of a cool and optically thick corona. In addition,
nthComp components ($1.97 \lesssim \Gamma_{\rm nth} \lesssim 2.44$, $1.06
\times 10^{-8} \lesssim F_{\rm nth} ~({\rm erg} {\rm ~cm}^{-2} {\rm ~s}^{-1})
\lesssim 4.46\times 10^{-8}$) are found to dominate in presence of HFQPOs.
Overall, these findings infer that HFQPOs are possibly resulted due to the
modulation of the 'Comptonizing corona'. Further, we find that the bolometric
luminosity ($0.3-100$ keV) of the source lies within the sub-Eddington
($3-34\%$ $L_{\rm Edd}$) regime. Finally, we discuss and compare the obtained
results in the context of existing models on HFQPOs.


title: Meta Mirror Descent: Optimiser Learning for Fast Convergence

abstract:
  Optimisers are an essential component for training machine learning models,
and their design influences learning speed and generalisation. Several studies
have attempted to learn more effective gradient-descent optimisers via solving
a bi-level optimisation problem where generalisation error is minimised with
respect to optimiser parameters. However, most existing optimiser learning
methods are intuitively motivated, without clear theoretical support. We take a
different perspective starting from mirror descent rather than gradient
descent, and meta-learning the corresponding Bregman divergence. Within this
paradigm, we formalise a novel meta-learning objective of minimising the regret
bound of learning. The resulting framework, termed Meta Mirror Descent
(MetaMD), learns to accelerate optimisation speed. Unlike many meta-learned
optimisers, it also supports convergence and generalisation guarantees and
uniquely does so without requiring validation data. We evaluate our framework
on a variety of tasks and architectures in terms of convergence rate and
generalisation error and demonstrate strong performance.


title:
Compact objects in quadratic Palatini gravity generated by a free scalar
  field

abstract:
  We study the correspondence that connects the space of solutions of General
Relativity (GR) with that of Ricci-based Gravity theories (RBGs) of the
$f(R,Q)$ type in the metric-affine formulation, where
$Q=R_{(\mu\nu)}R^{(\mu\nu)}$. We focus on the case of scalar matter and show
that when one considers a free massless scalar in the GR frame, important
simplifications arise that allow to establish the correspondence for arbitrary
$f(R,Q)$ Lagrangian. We particularize the analysis to a quadratic $f(R,Q)$
theory and use the spherically symmetric, static solution of
Jannis-Newman-Winicour as seed to generate new compact objects in our target
theory. We find that two different types of solutions emerge, one representing
naked singularities and another corresponding to asymmetric wormholes with
bounded curvature scalars everywhere. The latter solutions, nonetheless, are
geodesically incomplete.


title:
Evidence of a complex structure within the 2013 August 19 coronal mass
  ejection. Radial and longitudinal evolution in the inner heliosphere

abstract:
  Context: Late on 2013 August 19, a coronal mass ejection (CME) erupted from
an active region located near the far-side central meridian from Earth's
perspective. The event and its accompanying shock were remotely observed by the
STEREO-A, STEREO-B and SOHO spacecraft. The interplanetary counterpart (ICME)
was intercepted by MESSENGER near 0.3 au, and by both STEREO-A and STEREO-B,
near 1 au, which were separated by 78{\deg} in heliolongitude. The main
objective of this study is to follow the radial and longitudinal evolution of
the ICME throughout the inner heliosphere, and to examine possible scenarios
for the different magnetic flux-rope configuration observed on the solar disk,
and measured in situ at the locations of MESSENGER and STEREO-A, separated by
15{\deg} in heliolongitude, and at STEREO-B, which detected the ICME flank.
Results: We find that the magnetic flux-rope structure detected at STEREO-B
belongs to the same ICME detected at MESSENGER and STEREO-A. The opposite
helicity deduced at STEREO-B, might be due to the spacecraft intercepting one
of the legs of the structure far from the flux-rope axis, while STEREO-A and
MESSENGER are crossing through the core of the magnetic flux rope. The
different flux-rope orientations measured at MESSENGER and STEREO-A arise
probably because the two spacecraft measure a curved, highly distorted and
rather complex magnetic flux-rope topology. The ICME may have suffered
additional distortion in its evolution in the inner heliosphere, such as the
west flank is propagating faster than the east flank when arriving 1 au.
Conclusions: This work illustrates how the ambient conditions can significantly
affect the expansion and propagation of the CME/ICME, introducing additional
irregularities to the already asymmetric eruption, and how these complex
structures cannot be directly reconstructed with the current models available.


title: Towards Efficient and Scalable Sharpness-Aware Minimization

abstract:
  Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of
the loss landscape and generalization, has demonstrated significant performance
boosts on training large-scale models such as vision transformers. However, the
update rule of SAM requires two sequential (non-parallelizable) gradient
computations at each step, which can double the computational overhead. In this
paper, we propose a novel algorithm LookSAM - that only periodically calculates
the inner gradient ascent, to significantly reduce the additional training cost
of SAM. The empirical results illustrate that LookSAM achieves similar accuracy
gains to SAM while being tremendously faster - it enjoys comparable
computational complexity with first-order optimizers such as SGD or Adam. To
further evaluate the performance and scalability of LookSAM, we incorporate a
layer-wise modification and perform experiments in the large-batch training
scenario, which is more prone to converge to sharp local minima. We are the
first to successfully scale up the batch size when training Vision Transformers
(ViTs). With a 64k batch size, we are able to train ViTs from scratch in
minutes while maintaining competitive performance.


title: Efficient Reachability Ratio Computation for 2-hop Labeling Scheme

abstract:
  As one of the fundamental graph operations, reachability queries processing
has been extensively studied during the past decades. Many approaches followed
the line of designing 2-hop labels to make acceleration. Considering that the
index size cannot be bounded when using all nodes to construct 2-hop labels,
researchers proposed to use a part of important nodes to construct 2-hop labels
(partial 2-hop labels) to cover as much reachability information as possible.
Then, we may achieve better query performance with limited index size and index
construction time. However, partial 2-hop labels do not always perform well on
different graphs.
  In this paper, we focus on the problem of how to efficiently compute
reachability ratio, such that to help users determine whether partial 2-hop
labels should be used to answer reachability queries for the given graph.
Intuitively, reachability ratio denotes the ratio of the number of reachable
queries that can be answered by partial 2-hop labels over the total number of
reachable queries involved in the given graph. We discuss the difficulties of
reachability ratio computation, and propose an incremental-partition algorithm
for reachability ratio computation. We show by rich experimental results that
our algorithm can efficiently get the result of reachability ratio, and show
how the overall query performance is affected by different partial 2-hop
labels. Based on the experimental results, we give out our findings on whether
partial 2-hop labels should be used to the given graph for reachability queries
processing.


title:
Stability of mixed FEMs for non-selfadjoint indefinite second-order
  linear elliptic PDEs

abstract:
  For a well-posed non-selfadjoint indefinite second-order linear elliptic PDE
with general coefficients $\mathbf A, \mathbf b,\gamma$ in $L^\infty$ and
symmetric and uniformly positive definite coefficient matrix $\mathbf A$, this
paper proves that mixed finite element problems are uniquely solvable and the
discrete solutions are uniformly bounded, whenever the underlying shape-regular
triangulation is sufficiently fine. This applies to the Raviart-Thomas (RT) and
Brezzi-Douglas-Marini (BDM) finite element families of any order and in any
space dimension and leads to the best-approximation estimate in $H(div)\times
L^2$ as well as in in $L^2\times L^2$ up to oscillations. This generalises
earlier contributions for piecewise Lipschitz continuous coefficients to
$L^\infty$ coefficients. The compactness argument of Schatz and Wang for the
displacement-oriented problem does not apply immediately to the mixed
formulation in $H(div)\times L^2$. But it allows the uniform approximation of
some $L^2$ contributions and can be combined with a recent $L^2$
best-approximation result from the medius analysis. This technique circumvents
any regularity assumption and the application of a Fortin interpolation
operator.


title:
Type Ibn supernova SN~2010al: Powerful mass loss half year prior to the
  explosion

abstract:
  Type Ibn supernova SN~2010al is explored to infer parameters of supernova and
a circumstellar (CS) shell. The CS interaction model combined with the spectral
model of 4600\AA\ blend suggests the explosion of a WR star with the energy of
$(1-1.5)\times10^{51}$ erg inside a dense confined CS shell with the mass of
$\sim0.1$\msun\ and kinetic energy of $\sim 10^{48}$ erg. The confined CS shell
has been formed during the last 0.4 yr prior to the core collapse.


title: Accurate Estimate of the Joint Density of States via Flat Scan Sampling

abstract:
  A Monte Carlo method to estimate the Joint Density of States g(E,M) of the
Ising and Ising-like models is presented. The method is applied to the
well-known 2D Ising model, and is shown to be accurate, efficient, and
embarrassingly parallel. The method presented offers major improvements over
existing approaches. Furthermore, we obtain g(E,M) estimates for the spin S
Ising model, with the spin number S = {1/2, 1, 3/2, 2}, thus showing that the
algorithm can handle larger and more complex (E, M) phase spaces.


title:
DroidRL: Reinforcement Learning Driven Feature Selection for Android
  Malware Detection

abstract:
  Due to the completely open-source nature of Android, the exploitable
vulnerability of malware attacks is increasing. Machine learning has led to a
great evolution in Android malware detection in recent years, which is
typically applied in the classification phase. Applying neural networks to
feature selection phase is another topic worth investigating, while the
correlation between features could be ignored in some traditional ranking-based
feature selection algorithms. And it is time-consuming for exploring all
possible valid feature subsets when processing a large number of Android
features using a wrapper-based approach. Attempting to tackle the problem of
computational expense and the vulnerability of the syntax integrity of
multi-source data, this paper proposed the DroidRL framework. The main idea is
to apply Double DQN(DDQN) algorithm to select a subset of features that can be
effectively used for malware classification. To select a better subset of
features over a larger range, the exploration-exploitation policy is applied in
the model training phase. The RNN is used as the decision network of DDQN to
give the framework the ability to sequentially select features. Word embedding
is applied for feature representation to enhance the framework's ability to
find the semantic relevance of features. On one hand, The framework's feature
selection exhibits high performance without any human intervention. On the
other hand, the proposed framework can easily be ported to other feature
selection tasks with some minor changes. The experiment results compared with
different classifiers, decision networks, and related work showed a significant
effect using the Random Forest classifier, reaching 95.6% accuracy with only 24
features selected.


title: Bayesian Learning Approach to Model Predictive Control

abstract:
  This study presents a Bayesian learning perspective towards model predictive
control algorithms. High-level frameworks have been developed separately in the
earlier studies on Bayesian learning and sampling-based model predictive
control. On one hand, the Bayesian learning rule provides a general framework
capable of generating various machine learning algorithms as special instances.
On the other hand, the dynamic mirror descent model predictive control
framework is capable of diversifying sample-rollout-based control algorithms.
However, connections between the two frameworks have still not been fully
appreciated in the context of stochastic optimal control. This study combines
the Bayesian learning rule point of view into the model predictive control
setting by taking inspirations from the view of understanding model predictive
controller as an online learner. The selection of posterior class and natural
gradient approximation for the variational formulation governs diversification
of model predictive control algorithms in the Bayesian learning approach to
model predictive control. This alternative viewpoint complements the dynamic
mirror descent framework through streamlining the explanation of design
choices.


title: Consistent Representation Learning for Continual Relation Extraction

abstract:
  Continual relation extraction (CRE) aims to continuously train a model on
data with new relations while avoiding forgetting old ones. Some previous work
has proved that storing a few typical samples of old relations and replaying
them when learning new relations can effectively avoid forgetting. However,
these memory-based methods tend to overfit the memory samples and perform
poorly on imbalanced datasets. To solve these challenges, a consistent
representation learning method is proposed, which maintains the stability of
the relation embedding by adopting contrastive learning and knowledge
distillation when replaying memory. Specifically, supervised contrastive
learning based on a memory bank is first used to train each new task so that
the model can effectively learn the relation representation. Then, contrastive
replay is conducted of the samples in memory and makes the model retain the
knowledge of historical relations through memory knowledge distillation to
prevent the catastrophic forgetting of the old task. The proposed method can
better learn consistent representations to alleviate forgetting effectively.
Extensive experiments on FewRel and TACRED datasets show that our method
significantly outperforms state-of-the-art baselines and yield strong
robustness on the imbalanced dataset.


title: Asteroseismology of the double-radial mode $\delta$ Scuti star BP Pegasi

abstract:
  Using the ASAS data, we determine the pulsational frequencies of the
high-amplitude $\delta$ Sct star BP Pegasi. The analysis revealed only the two
known, independent frequencies. On the basis of multicolour Str\"omgren
photometry, we independently find that both frequencies can only be associated
with radial modes which, according to the frequency ratio, are fundamental and
first overtone modes. The models fitting the two frequencies depend strongly on
the opacity data. For low values of the mixing length parameter $\alpha_{\rm
MLT}\approx 0.5$, only the OPAL seismic models in the post-main sequence phase
of evolution are caught within the observed error box. Seismic OP and OPLIB
models can only reach the error box if we increase $\alpha_{\rm MLT}$ to at
least 2.0. Then, including the non-adiabatic parameter $f$ into our seismic
modelling, we constrain various parameters, employing Monte Carlo-based
Bayesian analysis.


title: A Novel Dual Dense Connection Network for Video Super-resolution

abstract:
  Video super-resolution (VSR) refers to the reconstruction of high-resolution
(HR) video from the corresponding low-resolution (LR) video. Recently, VSR has
received increasing attention. In this paper, we propose a novel dual dense
connection network that can generate high-quality super-resolution (SR)
results. The input frames are creatively divided into reference frame,
pre-temporal group and post-temporal group, representing information in
different time periods. This grouping method provides accurate information of
different time periods without causing time information disorder. Meanwhile, we
produce a new loss function, which is beneficial to enhance the convergence
ability of the model. Experiments show that our model is superior to other
advanced models in Vid4 datasets and SPMCS-11 datasets.


title:
Worst-Case Analysis of LPT Scheduling on Small Number of Non-Identical
  Processors

abstract:
  The approximation ratio of the longest processing time (LPT) scheduling
algorithm has been studied in several papers. While the tight approximation
ratio is known for the case when all processors are identical, the ratio is not
yet known when the processors have different speeds. In this work, we give a
tight approximation ratio for the case when the number of processors is 3,4,
and 5. We show that the ratio for those cases are no more than the lower bound
provided by Gonzalez, Ibarra, and Sahni (SIAM J. Computing 1977). They are
approximately 1.38 for three processors, 1.43 for four processors, and 1.46 for
five processors.


title:
Analysis of a fully discrete approximation to a moving-boundary problem
  describing rubber exposed to diffusants

abstract:
  We present a fully discrete scheme for the numerical approximation of a
moving-boundary problem describing diffusants penetration into rubber. Our
scheme utilizes the Galerkin finite element method for the space discretization
combined with the backward Euler method for the time discretization. Besides
dealing with the existence and uniqueness of solution to the fully discrete
problem, we derive a \textit{a priori} error estimate for the mass
concentration of the diffusants, and respectively, for the position of the
moving boundary. Numerical illustrations verify the obtained theoretical order
of convergence in physical parameter regimes.


title: Enumeration of Maximal Cycles Generated by Orthogonal Cellular Automata

abstract:
  Cellular Automata (CA) are an interesting computational model for designing
Pseudorandom Number Generators (PRNG), due to the complex dynamical behavior
they can exhibit depending on the underlying local rule. Most of the CA-based
PRNGs proposed in the literature, however, suffer from poor diffusion since a
change in a single cell can propagate only within its neighborhood during a
single time step. This might pose a problem especially when such PRNGs are used
for cryptographic purposes. In this paper, we consider an alternative approach
to generate pseudorandom sequences through \emph{orthogonal CA} (OCA), which
guarantees a better amount of diffusion. After defining the related PRNG, we
perform an empirical investigation of the maximal cycles in OCA pairs up to
diameter $d=8$. Next, we focus on OCA induced by linear rules, giving a
characterization of their cycle structure based on the rational canonical form
of the associated Sylvester matrix. Finally, we devise an algorithm to
enumerate all linear OCA pairs characterized by a single maximal cycle, and
apply it up to diameter $d=16$ and $d=13$ for OCA respectively over the binary
and ternary alphabets.


title: Unstable Nambu-Goldstone modes

abstract:
  Nambu-Goldstone (NG) modes for 0-form and higher-form symmetries can become
unstable in the presence of background fields. The examples include the
instability of a photon with a time-dependent axion background or with a
chirality imbalance, known as the chiral plasma instability, and the
instability of a dynamical axion with a background electric field. We show that
all these phenomena can be universally described by a symmetry algebra for
0-form and higher-form symmetries. We prove a counting rule for the number of
unstable NG modes in terms of correlation functions of broken symmetry
generators. Based on our unified description, we further give a simple new
example where one of the NG modes associated with the spontaneous 0-form
symmetry breaking $U(1) \times U(1) \to \{1\}$ becomes unstable.

categories: hep-th astro-ph.CO cond-mat.mes-hall hep-ph


title:
An End-to-End Approach for Seam Carving Detection using Deep Neural
  Networks

abstract:
  Seam carving is a computational method capable of resizing images for both
reduction and expansion based on its content, instead of the image geometry.
Although the technique is mostly employed to deal with redundant information,
i.e., regions composed of pixels with similar intensity, it can also be used
for tampering images by inserting or removing relevant objects. Therefore,
detecting such a process is of extreme importance regarding the image security
domain. However, recognizing seam-carved images does not represent a
straightforward task even for human eyes, and robust computation tools capable
of identifying such alterations are very desirable. In this paper, we propose
an end-to-end approach to cope with the problem of automatic seam carving
detection that can obtain state-of-the-art results. Experiments conducted over
public and private datasets with several tampering configurations evidence the
suitability of the proposed model.


title:
Discrete Gompertz and Generalized Logistic Models for early monitoring
  of the COVID-19 pandemic in Cuba

abstract:
  For the last few years there has been a resurgence in the use of
phenomenological growth models for predicting the early dynamics of infectious
diseases. These models assume that time is a continuous variable whereas in the
present contribution, the discrete versions of Gompertz and Generalized
Logistic models are used for early monitoring and short-term forecasting of the
spread of an epidemic in a region. The time-continuous models are represented
mathematically by first-order differential equations while their discrete
versions are represented by first-order difference equations that involve
parameters that should be estimated prior to forecasting. The methodology for
estimating such parameters is described in detail. Real data of COVID-19
infection in Cuba is used to illustrate this methodology. The proposed
methodology was implemented for the first thirty-five days, being able to
predict with very good precision the data reported for the following twenty
days. The codes implemented to study the Gompertz model in differences are
included in an appendix with each step of the methodology identified.