title: Exploring Dual-task Correlation for Pose Guided Person Image Generation

abstract:
  Pose Guided Person Image Generation (PGPIG) is the task of transforming a
person image from the source pose to a given target pose. Most of the existing
methods only focus on the ill-posed source-to-target task and fail to capture
reasonable texture mapping. To address this problem, we propose a novel
Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task
(i.e., source-to-source task) and exploits the dual-task correlation to promote
the performance of PGPIG. The DPTN is of a Siamese structure, containing a
source-to-source self-reconstruction branch, and a transformation branch for
source-to-target generation. By sharing partial weights between them, the
knowledge learned by the source-to-source task can effectively assist the
source-to-target learning. Furthermore, we bridge the two branches with a
proposed Pose Transformer Module (PTM) to adaptively explore the correlation
between features from dual tasks. Such correlation can establish the
fine-grained mapping of all the pixels between the sources and the targets, and
promote the source texture transmission to enhance the details of the generated
target images. Extensive experiments show that our DPTN outperforms
state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only
contains 9.79 million parameters, which is significantly smaller than other
approaches. Our code is available at:
https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network.


title:
Optimal control of a nonsmooth PDE arising in the modeling of
  shear-thickening fluids

abstract:
  This paper focuses on the analysis of an optimal control problem governed by
a nonsmooth quasilinear partial differential equation that models a stationary
discontinuous shear-thickening fluid. We start by studying the directional
differentiability of the non-smooth term within the state equation as a prior
step to demonstrate the directional differentiability of the solution operator.
Thereafter, we establish a primal first order necessary optimality condition
(Bouligand (B) stationarity), which is derived from the directional
differentiability of the solution operator. By using a local regularization of
the nonsmooth term and carrying out an asymptotic analysis thereafter, we
rigorously derive a weak stationarity system for local minima. By combining the
B- and weak stationarity conditions, and using the regularity of the Lagrange
multiplier, we are able to obtain a strong stationarity system that includes
pointwise inequalities for the scalar product between the symmetrized gradient
of the state and the Lagrange multiplier.


title:
Graph Neural Network Enhanced Language Models for Efficient Multilingual
  Text Classification

abstract:
  Online social media works as a source of various valuable and actionable
information during disasters. These information might be available in multiple
languages due to the nature of user generated content. An effective system to
automatically identify and categorize these actionable information should be
capable to handle multiple languages and under limited supervision. However,
existing works mostly focus on English language only with the assumption that
sufficient labeled data is available. To overcome these challenges, we propose
a multilingual disaster related text classification system which is capable to
work under \{mono, cross and multi\} lingual scenarios and under limited
supervision. Our end-to-end trainable framework combines the versatility of
graph neural networks, by applying over the corpus, with the power of
transformer based large language models, over examples, with the help of
cross-attention between the two. We evaluate our framework over total nine
English, Non-English and monolingual datasets in \{mono, cross and multi\}
lingual classification scenarios. Our framework outperforms state-of-the-art
models in disaster domain and multilingual BERT baseline in terms of Weighted
F$_1$ score. We also show the generalizability of the proposed model under
limited supervision.


title:
Delorme's intertwining conditions for sections of homogeneous vector
  bundles on two and three dimensional hyperbolic spaces

abstract:
  The description of the Paley-Wiener space for compactly supported smooth
functions $C_c^\infty(G)$ on a semi-simple Lie group $G$, involves certain
intertwining conditions, that are difficult to handle. In the present paper, we
make them completely explicit for $G = \mathbf{SL}(2,\mathbf{R})^d$ ($d \in
\mathbf{N}$) and $G = \mathbf{SL}(2,\mathbf{C})$. Our results are based on a
defining criterion for the Paley-Wiener space, valid for general groups of real
rank one, that we derive from Delorme's proof of the Paley-Wiener theorem. In a
forthcoming paper, we will show how these results can be used to study
solvability of invariant differential operators between sections of homogeneous
vector bundles over the corresponding symmetric spaces.


title: Haake-Lewenstein-Wilkens approach to spin-glasses revisited

abstract:
  We revisit the Haake-Lewenstein-Wilkens (HLW) approach to Edwards-Anderson
(EA) model of Ising spin glass [Phys. Rev. Lett. 55, 2606 (1985)]. This
approach consists in evaluation and analysis of the probability distribution of
configurations of two replicas of the system, averaged over quenched disorder.
This probability distribution generates squares of thermal copies of spin
variables from the two copies of the systems, averaged over disorder, that is
the terms that enter the standard definition of the original EA order
parameter, qEA. We use saddle point/steepest descent method to calculate the
average of the Gaussian disorder in higher dimensions. This approximate result
suggest that qEA >0 at 0 <T <Tc in 3D and 4D. The case of 2D seems to be a
little more subtle, since in the present approach energy increase for a domain
wall competes with boundary/edge effects more strongly in 2D; still our
approach predicts spin glass order at sufficiently low temperature. We
speculate, how these predictions confirm/contradict widely spread opinions
that: i) There exist only one (up to the spin flip) ground state in EA model in
2D, 3D and 4D; ii) There is (no) spin glass transition in 3D and 4D (2D). This
paper is dedicated to the memories of Fritz Haake and Marek Cieplak.


title:
Intense, wideband optical waveform generation by self-balanced
  amplification of fiber electro-optical sideband modulation

abstract:
  We demonstrate a simple method to obtain accurate optical waveforms with a
GHz-level programmable modulation bandwidth and Watt-level output power for
wideband optical control of free atoms and molecules. Arbitrary amplitude and
phase modulations are transferred from microwave to light with a low-power
fiber electro-optical modulator. The sub-milliWatt optical sideband is
co-amplified with the optical carrier in a power-balanced fashion through a
tapered semiconductor amplifier (TSA). By automatically keeping TSA near
saturation in a quasi-continuous manner, typical noise channels associated with
pulsed high-gain amplifications are efficiently suppressed. As an example
application, we demonstrate interleaved cooling and trapping of two rubidium
isotopes with coherent nanosecond pulses.


title: PanFormer: a Transformer Based Model for Pan-sharpening

abstract:
  Pan-sharpening aims at producing a high-resolution (HR) multi-spectral (MS)
image from a low-resolution (LR) multi-spectral (MS) image and its
corresponding panchromatic (PAN) image acquired by a same satellite. Inspired
by a new fashion in recent deep learning community, we propose a novel
Transformer based model for pan-sharpening. We explore the potential of
Transformer in image feature extraction and fusion. Following the successful
development of vision transformers, we design a two-stream network with the
self-attention to extract the modality-specific features from the PAN and MS
modalities and apply a cross-attention module to merge the spectral and spatial
features. The pan-sharpened image is produced from the enhanced fused features.
Extensive experiments on GaoFen-2 and WorldView-3 images demonstrate that our
Transformer based model achieves impressive results and outperforms many
existing CNN based methods, which shows the great potential of introducing
Transformer to the pan-sharpening task. Codes are available at
https://github.com/zhysora/PanFormer.


title: Intertwining of Complementary Thue-Morse Factors

abstract:
  We consider the positions of occurrences of a factor $x$ and its binary
complement $\overline{x}$ in the Thue-Morse word ${\bf t} = {\tt 01101001}
\cdots$, and show that these occurrences are "intertwined" in essentially two
different ways. Our proof method consists of stating the needed properties as a
first-order logic formula $\varphi$, and then using a theorem-prover to prove
$\varphi$.


title:
Lipschitz and H\"older stable determination of nonlinear terms for
  elliptic equations

abstract:
  We consider the inverse problem of determining some class of nonlinear terms
appearing in an elliptic equation from boundary measurements. More precisely,
we study the stability issue for this class of inverse problems. Under suitable
assumptions, we prove a Lipschitz and a H\"older stability estimate associated
with the determination of quasilinear and semilinear terms appearing in this
class of elliptic equations from measurements restricted to an arbitrary parts
of the boundary of the domain. Besides their mathematical interest, our
stability estimates can be useful for improving numerical reconstruction of
this class of nonlinear terms. Our approach combines the linearization
technique with applications of suitable class of singular solutions.


title:
Probing two Higgs oscillations in a one-dimensional Fermi superfluid
  with Raman-type spin-orbit coupling

abstract:
  We theoretically investigate the Higgs oscillation in a one-dimensional
Raman-type spin-orbit-coupled Fermi superfluid with the time-dependent
Bogoliubov-de Gennes equations. By linearly ramping or abruptly changing the
effective Zeeman field in both the Bardeen-Cooper-Schrieffer state and the
topological superfluid state, we find the amplitude of the order parameter
exhibits an oscillating behaviour over time with two different frequencies
(i.e., two Higgs oscillations) in contrast to the single one in a conventional
Fermi superfluid. The observed period of oscillations has a great agreement
with the one calculated using the previous prediction [Volkov and Kogan, J.
Exp. Theor. Phys. 38, 1018 (1974)], where the oscillating periods are now
determined by the minimums of two quasi-particle spectrum in this system. We
further verify the existence of two Higgs oscillations using a periodic ramp
strategy with theoretically calculated driving frequency. Our predictions would
be useful for further theoretical and experimental studies of these Higgs
oscillations in spin-orbit-coupled systems.


title: Toward inertial sensing with a monochromatic $ 2^3 S $ positronium beam

abstract:
  In this work, we discuss the possibility of inertial sensing with positronium
in the $2^3 S$ metastable state for the measurement of optical dipole,
relativistic and gravitational forces on a purely leptonic matter-antimatter
system. Starting from the characteristics of an available $2^3 S$ beam, we
estimate the time necessary to measure accelerations ranging from $\sim10^5$
$m/s^2$ to 9.1 $m/s^2$ with two different inertial sensitive devices: a
classical moir\'e deflectometer and a Mach-Zehnder interferometer. The
sensitivity of the Mach-Zehnder interferometer has been estimated to be several
tens of times better than that of the moir\'e deflectometer, for the same
measurement time.\\ Different strategies to strengthen the $2^3 S$ beam flux
and to improve the sensitivity of the devices are proposed and analyzed. Among
them, the most promising are reducing the divergence of the positronium beam
through 2D laser Doppler cooling and coherent positronium Raman excitation from
the ground state to the $2^3 S$ level. If implemented, these improvements
promise to result in the time required to measure an acceleration of 9.1
$m/s^2$ of few weeks and 100 $m/s^2$ of a few hours. Different detection
schemes for resolving the fringe pattern shift generated on $2^3 S$ positronium
crossing the deflectometer/interferometer are also discussed.


title:
Superfluidity of a Raman spin-orbit-coupled Bose gas at finite
  temperature

abstract:
  We investigate the superfluidity of a three-dimensional weakly interacting
Bose gas with a one-dimensional Raman-type spin-orbit coupling at both zero and
finite temperatures. Using the imaginary-time Green's function within the
Bogoliubov approximation, we explicitly derive analytic expressions of the
current-current response functions in the plane-wave and zero-momentum phases,
from which we extract the superfluid density in the limits of long wavelength
and zero frequency. At zero temperature, we check that the resultant superfluid
density agrees exactly with our previous analytic prediction obtained from a
phase-twist approach. Both results also satisfy a generalized Josephson
relation in the presence of spin-orbit coupling. At finite temperature, we find
a significant non-monotonic temperature dependence of superfluid density near
the transition from the plane-wave phase to the zero-momentum phase. We show
that this non-trivial behavior might be understood from the sound velocity,
which has a similar temperature dependence. The non-monotonic temperature
dependence is also shared by Landau critical velocity, above which the
spin-orbit-coupled Bose gas loses its superfluidity. Our results would be
useful for further theoretical and experimental studies of superfluidity in
exotic spin-orbit coupled quantum gases.


title:
Exploring the radio spectral energy distribution of the ultraluminous
  radio-quiet quasar SDSS J0100+2802 at redshift 6.3

abstract:
  We report deep Karl G. Jansky Very Large Array (VLA) observations of the
optically ultraluminous and radio-quiet quasar SDSS J010013.02 + 280225.8
(hereafter J0100+2802) at redshift $z=$6.3. We detected the radio continuum
emission at 1.5 GHz, 6 GHz, and 10 GHz. This leads to a radio power-law
spectral index of $\alpha = -0.52\pm0.18$ ($S \propto \nu^{\alpha}$). The radio
source is unresolved in all VLA bands with an upper limit to the size of
$0.2^{\prime \prime}$ (i.e., $\sim$ 1.1 kpc) at 10 GHz. We find variability in
the flux density (increase by $\sim 33\%$) and the spectral index (steepened)
between observations in 2016 and 2017. We also find that the VLA 1.5 GHz flux
density observed in the same year is 1.5 times that detected with the Very Long
Baseline Array (VLBA) in 2016 at the same frequency. This difference suggests
that half of the radio emission from J0100+2802 comes from a compact core
within 40 pc, and the rest comes from the surrounding few kpc area which is
diffuse and resolved out in the VLBA observations. The diffuse emission is four
times brighter than that would be expected if driven by star formation. We
conclude that the central active galactic nucleus is the dominant power engine
of the radio emission in J0100+2802.


title:
GeoDiff: a Geometric Diffusion Model for Molecular Conformation
  Generation

abstract:
  Predicting molecular conformations from molecular graphs is a fundamental
problem in cheminformatics and drug discovery. Recently, significant progress
has been achieved with machine learning approaches, especially with deep
generative models. Inspired by the diffusion process in classical
non-equilibrium thermodynamics where heated particles will diffuse from
original states to a noise distribution, in this paper, we propose a novel
generative model named GeoDiff for molecular conformation prediction. GeoDiff
treats each atom as a particle and learns to directly reverse the diffusion
process (i.e., transforming from a noise distribution to stable conformations)
as a Markov chain. Modeling such a generation process is however very
challenging as the likelihood of conformations should be roto-translational
invariant. We theoretically show that Markov chains evolving with equivariant
Markov kernels can induce an invariant distribution by design, and further
propose building blocks for the Markov kernels to preserve the desirable
equivariance property. The whole framework can be efficiently trained in an
end-to-end fashion by optimizing a weighted variational lower bound to the
(conditional) likelihood. Experiments on multiple benchmarks show that GeoDiff
is superior or comparable to existing state-of-the-art approaches, especially
on large molecules.


title: Adiabatic accretion onto black holes in Einstein-Maxwell-scalar theory

abstract:
  We study the adiabatic accretion process of ordinary baryonic gas onto
spherically symmetric black holes in Einstein-Maxwell-scalar theory, with two
parameters $\alpha$ and $\beta$ in the coupling term. Especially, we
demonstrate the range of the transonic points in terms of the charge-to-mass
ratio squared and the dimensionless coordinate radius, in two important classes
of black holes as examples. Further, we find that the two coupling parameters
give modifications to the mass accretion rate at different orders of the sound
speed at infinity. We also present their different effects on the temperature
ratios of the accreted gas.


title:
Weakly Supervised Temporal Action Localization via Representative
  Snippet Knowledge Propagation

abstract:
  Weakly supervised temporal action localization aims to localize temporal
boundaries of actions and simultaneously identify their categories with only
video-level category labels. Many existing methods seek to generate pseudo
labels for bridging the discrepancy between classification and localization,
but usually only make use of limited contextual information for pseudo label
generation. To alleviate this problem, we propose a representative snippet
summarization and propagation framework. Our method seeks to mine the
representative snippets in each video for propagating information between video
snippets to generate better pseudo labels. For each video, its own
representative snippets and the representative snippets from a memory bank are
propagated to update the input features in an intra- and inter-video manner.
The pseudo labels are generated from the temporal class activation maps of the
updated features to rectify the predictions of the main branch. Our method
obtains superior performance in comparison to the existing methods on two
benchmarks, THUMOS14 and ActivityNet1.3, achieving gains as high as 1.2% in
terms of average mAP on THUMOS14.


title: Generic symmetry defect set of an algebraic curve

abstract:
  Let $X \subset \mathbb{C}^{2n}$ be an $n$-dimensional algebraic variety. We
define the algebraic version of the generic symmetry defect set (Wigner
caustic) of $X$. Moreover, we compute its singularities for $X_d$ being a
generic curve of degree $d$ in $\mathbb{C}^2$.


title: Enabling Automated Machine Learning for Model-Driven AI Engineering

abstract:
  Developing smart software services requires both Software Engineering and
Artificial Intelligence (AI) skills. AI practitioners, such as data scientists
often focus on the AI side, for example, creating and training Machine Learning
(ML) models given a specific use case and data. They are typically not
concerned with the entire software development life-cycle, architectural
decisions for the system and performance issues beyond the predictive ML models
(e.g., regarding the security, privacy, throughput, scalability, availability,
as well as ethical, legal and regulatory compliance). In this manuscript, we
propose a novel approach to enable Model-Driven Software Engineering and
Model-Driven AI Engineering. In particular, we support Automated ML, thus
assisting software engineers without deep AI knowledge in developing
AI-intensive systems by choosing the most appropriate ML model, algorithm and
techniques with suitable hyper-parameters for the task at hand. To validate our
work, we carry out a case study in the smart energy domain.


title:
Evaluation of Interpretability Methods and Perturbation Artifacts in
  Deep Neural Networks

abstract:
  The challenge of interpreting predictions from deep neural networks has
prompted the development of numerous interpretability methods. Many of
interpretability methods attempt to quantify the importance of input features
with respect to the class probabilities, and are called importance estimators
or saliency maps. A popular approach to evaluate such interpretability methods
is to perturb input features deemed important for predictions and observe the
decrease in accuracy. However, perturbation-based evaluation methods may
confound the sources of accuracy degradation. We conduct computational
experiments that allow to empirically estimate the $\textit{fidelity}$ of
interpretability methods and the contribution of perturbation artifacts. All
considered importance estimators clearly outperform a random baseline, which
contradicts the findings of ROAR [arXiv:1806.10758]. We further compare our
results to the crop-and-resize evaluation framework [arXiv:1705.07857], which
are largely in agreement. Our study suggests that we can estimate the impact of
artifacts and thus empirically evaluate interpretability methods without
retraining.


title:
Blow-up versus global radial solution for free boundary system with
  nonlinear inhomogeneous gradient and source terms

abstract:
  This paper concerns the characterization of blowup and global radial
solutions of a two-free boundaries system read by \begin{align}\label{bs_pr}
\tag{1.1} \left\{\begin{array}{rl} u_t(t,r)= \Delta u(t,r) -
\lambda(t,x)|\nabla u(t,r)|^{\alpha} + a(t,x)v^{p}(t,r),& t>0,\ 0<r<h(t),\\
v_t(t,r) = \Delta v(t,r) - \lambda(t,x) |\nabla v(t,r)|^{\alpha}+
a(t,x)u^{p}(t,r), & t>0,\ 0 < r < g(t), \end{array}\right. \end{align} where $r
= |x|,\ x \in \R^N$, $p, \alpha >1 $ are given constants and $\lambda(t,x),
a(t,x)$ satisfy suitable prescribed growth conditions. First, we show the
well-posedness of the local solution to (\ref{bs_pr}). Second, we succeed to
classify the blowup and global phenomena by establishing some relations between
$\alpha$, $p$ and growth rate of the coefficients, in which proving a
comparison principle based on the Stampacchia truncation method plays the
central role. In particular, if $1<\alpha < p$ and $ (u(0,r);v(0,r)) = A
(\phi(r); \varphi(r))$ is an initial data of (\ref{bs_pr}), we find two certain
positive thresholds $A^*_G$ and $A^*_B$ such that the global fast solution
exists for $0 < A < A_G^{*}$ and the global slow solution exists for a suitable
value of $A$ such that $A^*_G \leq A < A^*_{B}$ while blow-up solutions hold
for $A \geq A^{*}_{B}$. On the other hand, if $\alpha \geq p$ incorporating
with a suitable comparison on $\beta, p$ and $\alpha$, then there exist global
solutions with nonnegative initial data of exponential decay. Our approach is
being far different from the celebrated works \cite{MF,PS}, where the authors
can only handled the equations with constant coefficients. To our knowledge,
this is the first work revealing the influence of the inhomogeneous
coefficients to the blow-up and global phenomena to the cooperative system with
nonlinear gradient and different free boundaries.


title:
From topological amplitude to rescattering dynamics in doubly charmed
  baryon decays

abstract:
  The doubly charmed baryon $\Xi^{++}_{cc}$ was observed by LHCb cooperation in
2017. The branching fractions of two-body doubly charmed baryon decays were
predicted in the framework of rescattering mechanism, and some $SU(3)_F$
relations were investigated in the topological amplitudes. In this work, we
study the correlation between topological diagram at quark level and
rescattering triangle diagram at hadron level in the doubly charmed baryon
decay. The completeness of our framework is confirmed from the fact that all
the twelve possible structures of meson-baryon scattering appear once each in
the the intermediate form between topological diagram and triangle diagram,
topological-scattering diagram. It is found the triangle diagrams derived from
the topological diagrams are consistent with the ones derived directly from the
chiral Lagrangian. The relative magnitudes of rescattering contributions in the
$C$, $C^\prime$, $E$, $E^\prime$, $P$ and $P^\prime$ diagrams extracted from
$SU(3)_F$ symmetry are consistent with the numerical analysis in literature.
Taking $\Xi^{++}_{cc}\to \Xi^+_c\pi^+$, $\Xi^{+}_{cc}\to \Xi^0_c\pi^+$ and
$\Xi^{+}_{cc}\to \Xi^+_c\pi^0$ modes as examples, we show the isospin relation
is satisfied in terms of triangle diagrams.


title: The odderon discovery by the D0 and TOTEM collaborations

abstract:
  We describe the discovery of the colorless $C$-odd gluonic compound, the
odderon, by the D0 and TOTEM Collaborations by comparing elastic differential
cross sections measured in $pp$ and $p \bar{p}$ interactions at high energies.


title: Doctor Recommendation in Online Health Forums via Expertise Learning

abstract:
  Huge volumes of patient queries are daily generated on online health forums,
rendering manual doctor allocation a labor-intensive task. To better help
patients, this paper studies a novel task of doctor recommendation to enable
automatic pairing of a patient to a doctor with relevant expertise. While most
prior work in recommendation focuses on modeling target users from their past
behavior, we can only rely on the limited words in a query to infer a patient's
needs for privacy reasons. For doctor modeling, we study the joint effects of
their profiles and previous dialogues with other patients and explore their
interactions via self-learning. The learned doctor embeddings are further
employed to estimate their capabilities of handling a patient query with a
multi-head attention mechanism. For experiments, a large-scale dataset is
collected from Chunyu Yisheng, a Chinese online health forum, where our model
exhibits the state-of-the-art results, outperforming baselines only consider
profiles and past dialogues to characterize a doctor.


title: Predicting continuum breakdown with deep neural networks

abstract:
  The multi-scale nature of gaseous flows poses tremendous difficulties for
theoretical and numerical analysis. The Boltzmann equation, while possessing a
wider applicability than hydrodynamic equations, requires significantly more
computational resources due to the increased degrees of freedom in the model.
The success of a hybrid fluid-kinetic flow solver for the study of multi-scale
flows relies on accurate prediction of flow regimes. In this paper, we draw on
binary classification in machine learning and propose the first neural network
classifier to detect near-equilibrium and non-equilibrium flow regimes based on
local flow conditions. Compared with classical semi-empirical criteria of
continuum breakdown, the current method provides a data-driven alternative
where the parameterized implicit function is trained by solutions of the
Boltzmann equation. The ground-truth labels are derived rigorously from the
deviation of particle distribution functions and the approximations based on
the Chapman-Enskog ansatz. Therefore, no tunable parameter is needed in the
criterion. Following the entropy closure of the Boltzmann moment system, a data
generation strategy is developed to produce training and test sets. Numerical
analysis shows its superiority over simulation-based samplings. A hybrid
Boltzmann-Navier-Stokes flow solver is built correspondingly with adaptive
partition of local flow regimes. Numerical experiments including
one-dimensional Riemann problem, shear flow layer and hypersonic flow around
circular cylinder are presented to validate the current scheme for simulating
cross-scale and non-equilibrium flow physics. The quantitative comparison with
a semi-empirical criterion and benchmark results demonstrates the capability of
the current neural classifier to accurately predict continuum breakdown.


title:
Two-Dimensional Electron Gas with High Mobility Forming at BaO/SrTiO3
  Interface

abstract:
  Two-dimensional electron gas (2DEG) formed at the interface between two
insulating oxides offers an opportunity for fundamental research and device
applications. Binary alkaline earth metal oxides possess compatible lattice
constants with both silicon and perovskite oxides, exhibiting an enormous
potential to bridging those two materials classes for multifunctionalities.
Here we report the formation of 2DEG at the interface between the rock-salt BaO
and SrTiO3. The highest electron mobility reaches 69000 cm^2 V.S^-1 at 2 K,
leading to the typical Shubniko de Haas (SdH) oscillations under the high
magnetic fields. The presence of SdH oscillations at different field-angles
reveals a quasi-two-dimensional character of the Fermi surface. The
first-principles calculations suggest that the effective charge transfer from
the BaO to Ti 3dxy orbital at the interfaces is responsible to the observed
high carrier mobility. Our results demonstrate that the BaO/STO heterointerface
is a platform for exploring the correlated quantum phases, opening a door to
the low-power and mesoscopic electronic devices.


title: Direct imaging of polymer filaments pulled from rebounding drops

abstract:
  Polymer filaments form the foundation of biology from cell scaffolding to
DNA. Their study and fabrication play an important role in a wide range of
processes from tissue engineering to molecular machines. We present a simple
method to deposit stretched polymer fibers between micro-pillars. This occurs
when a polymeric drop impacts on and rebounds from an inclined superhydrophobic
substrate. It wets the top of the pillars and pulls out liquid filaments which
are stretched and can attach to adjacent pillars leaving minuscule threads,
with the solvent evaporating to leave the exposed polymers. We use high-speed
video at the microscale to characterize the most robust filament-forming
configurations, by varying the impact velocity, substrate structure and
inclination angle, as well as the PEO-polymer concentration. Impacts onto plant
leaves or randomized nano-structured surface leads to the formation of a
branched structure, through filament mergers at the free surface of the drop.
SEM shows the deposition of filament bundles which are thinner than those
formed by evaporation or rolling drops. Raman spectroscopy identifies mode B
stretched DNA filaments from aqueous-solution droplets.


title:
A major asymmetric ice trap in a planet-forming disk: III. First
  detection of dimethyl ether

abstract:
  The complex organic molecules (COMs) detected in star-forming regions are the
precursors of the prebiotic molecules that can lead to the emergence of life.
By studying COMs in more evolved protoplanetary disks we can gain a better
understanding of how they are incorporated into planets. This paper presents
ALMA band 7 observations of the dust and ice trap in the protoplanetary disk
around Oph IRS 48. We report the first detection of dimethyl ether (CH3OCH3) in
a planet-forming disk and a tentative detection of methyl formate (CH3OCHO). We
determined column densities for the detected molecules and upper limits on
non-detected species using the CASSIS spectral analysis tool. The inferred
column densities of CH3OCH3 and CH3OCHO with respect to methanol (CH3OH) are of
order unity, indicating unusually high abundances of these species compared to
other environments. Alternatively, the 12CH3OH emission is optically thick and
beam diluted, implying a higher CH3OH column density and a smaller emitting
area than originally thought. The presence of these complex molecules can be
explained by thermal ice sublimation, where the dust cavity edge is heated by
irradiation and the full volatile ice content is observable in the gas phase.
This work confirms the presence of oxygen-bearing molecules more complex than
CH3OH in protoplanetary disks for the first time. It also shows that it is
indeed possible to trace the full interstellar journey of COMs across the
different evolutionary stages of star, disk, and planet formation.


title: Generalization of Baire spaces using games

abstract:
  Generalizations of Baire spaces are constructed with the help of topological
games.


title: Lifting statistical structures

abstract:
  We consider some natural (functorial) lifts of geometric objects associated
with statistical manifolds (metric tensor, dual connections, skewness tensor,
etc.) to higher tangent bundles. It turns out that the lifted objects form
again a statistical manifold structure, this time on the higher tangent
bundles, with the only difference that the metric tensor is pseudo-Riemannian.
What is more, natural lifts of potentials (called also divergence or contrast
functions) turn out to be again potentials, this time for the lifted
statistical structures. We propose an analogous procedure for lifting
statistical structures on Lie algebroids and lifting contrast functions which
are defined on Lie groupoids. In particular, we study in detail Lie groupoid
structures of higher tangent bundles of Lie groupoids. Our geometric
constructions of lifts are illustrated by explicit examples, including some
important statistical models and potential functions on Lie groupoids.


title:
Homological- and analytical-preserving serendipity framework for
  polytopal complexes, with application to the DDR method

abstract:
  In this work we investigate from a broad perspective the reduction of degrees
of freedom through serendipity techniques for polytopal methods compatible with
Hilbert complexes. We first establish an abstract framework that, given two
complexes connected by graded maps, identifies a set of properties enabling the
transfer of the homological and analytical properties from one complex to the
other. This abstract framework is designed having in mind discrete complexes,
with one of them being a reduced version of the other, such as occurring when
applying serendipty techniques numerical methods. We then use this framework as
an overarching blueprint to design a serendipity DDR complex. Thanks to the
combined use of higher-order reconstructions and serendipity, this complex
compares favorably in terms of DOF count to all the other polytopal methods
previously introduced and also to finite elements on certain element
geometries. The gain resulting from such a reduction in the number of DOFs is
numerically evaluated on a model problem inspired by magnetostatics.


title:
Detection of Parasitic Eggs from Microscopy Images and the emergence of
  a new dataset

abstract:
  Automatic detection of parasitic eggs in microscopy images has the potential
to increase the efficiency of human experts whilst also providing an objective
assessment. The time saved by such a process would both help ensure a prompt
treatment to patients, and off-load excessive work from experts' shoulders.
Advances in deep learning inspired us to exploit successful architectures for
detection, adapting them to tackle a different domain. We propose a framework
that exploits two such state-of-the-art models. Specifically, we demonstrate
results produced by both a Generative Adversarial Network (GAN) and
Faster-RCNN, for image enhancement and object detection respectively, on
microscopy images of varying quality. The use of these techniques yields
encouraging results, though further improvements are still needed for certain
egg types whose detection still proves challenging. As a result, a new dataset
has been created and made publicly available, providing an even wider range of
classes and variability.


title:
Single microphone speaker extraction using unified time-frequency
  Siamese-Unet

abstract:
  In this paper we present a unified time-frequency method for speaker
extraction in clean and noisy conditions. Given a mixed signal, along with a
reference signal, the common approaches for extracting the desired speaker are
either applied in the time-domain or in the frequency-domain. In our approach,
we propose a Siamese-Unet architecture that uses both representations. The
Siamese encoders are applied in the frequency-domain to infer the embedding of
the noisy and reference spectra, respectively. The concatenated representations
are then fed into the decoder to estimate the real and imaginary components of
the desired speaker, which are then inverse-transformed to the time-domain. The
model is trained with the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
loss to exploit the time-domain information. The time-domain loss is also
regularized with frequency-domain loss to preserve the speech patterns.
Experimental results demonstrate that the unified approach is not only very
easy to train, but also provides superior results as compared with
state-of-the-art (SOTA) Blind Source Separation (BSS) methods, as well as
commonly used speaker extraction approach.


title: C-P Map: A Novel Evaluation Toolkit for Speaker Verification

abstract:
  Evaluation trials are used to probe performance of automatic speaker
verification (ASV) systems. In spite of the clear importance and impact,
evaluation trials have not been seriously treated in research and engineering
practice. This paper firstly presents a theoretical analysis on evaluation
trials and highlights potential bias with the most popular cross-pairing
approach used in trials design. To interpret and settle this problem, we define
the concept of trial config and C-P map derived from it. The C-P map measures
the performance of an ASV system on various trial configs in a 2-dimensional
map. On the map, each location represents a particular trial config and its
corresponding color represents the system performance. Experiments conducted on
representative ASV systems show that the proposed C-P map offers a powerful
evaluation toolkit for ASV performance analysis and comparison. The source code
for C-P map has been release at https://gitlab.com/csltstu/sunine.


title:
On the weak convergence rate in the discretization of rough volatility
  models

abstract:
  We study the weak convergence rate in the discretization of rough volatility
models. After showing a lower bound $2H$ under a general model, where $H$ is
the Hurst index of the volatility process, we give a sharper bound $H + 1/2$
under a linear model.


title: CNN self-attention voice activity detector

abstract:
  In this work we present a novel single-channel Voice Activity Detector (VAD)
approach. We utilize a Convolutional Neural Network (CNN) which exploits the
spatial information of the noisy input spectrum to extract frame-wise embedding
sequence, followed by a Self Attention (SA) Encoder with a goal of finding
contextual information from the embedding sequence. Different from previous
works which were employed on each frame (with context frames) separately, our
method is capable of processing the entire signal at once, and thus enabling
long receptive field. We show that the fusion of CNN and SA architectures
outperforms methods based solely on CNN and SA. Extensive experimental-study
shows that our model outperforms previous models on real-life benchmarks, and
provides State Of The Art (SOTA) results with relatively small and lightweight
model.


title:
Electron flow in Monolayer Molybdenum Disulfide Quantum Dot with
  Magnetic Flux

abstract:
  We study the Dirac electron scattering problem on a potential barrier in a
circular quantum dot of Monolayer Molybdenum Disulfide quantum dot MoS2
subjected to magnetic flux. By solving Dirac's equation, we formulate
analytical expressions for the eigenstates, scattering coefficients, scattering
efficiency, and the reflected current's radial component. We show that the
scattering coefficients, the scattering efficiency, and the radial component of
the reflected current depend explicitly on the magnetic flux. The magnetic flux
may cause a slight shift in the oscillation position of the scattering
coefficients. For scattering efficiency, magnetic flux may also lead to a
slight shift in their oscillation position and an increase in amplitude when
the magnetic flux decreases.


title: On Steering Multi-Annotations per Sample for Multi-Task Learning

abstract:
  The study of multi-task learning has drawn great attention from the
community. Despite the remarkable progress, the challenge of optimally learning
different tasks simultaneously remains to be explored. Previous works attempt
to modify the gradients from different tasks. Yet these methods give a
subjective assumption of the relationship between tasks, and the modified
gradient may be less accurate. In this paper, we introduce Stochastic Task
Allocation~(STA), a mechanism that addresses this issue by a task allocation
approach, in which each sample is randomly allocated a subset of tasks. For
further progress, we propose Interleaved Stochastic Task Allocation~(ISTA) to
iteratively allocate all tasks to each example during several consecutive
iterations. We evaluate STA and ISTA on various datasets and applications:
NYUv2, Cityscapes, and COCO for scene understanding and instance segmentation.
Our experiments show both STA and ISTA outperform current state-of-the-art
methods. The code will be available.


title:
Linien: A versatile, user-friendly, open-source FPGA-based tool for
  frequency stabilization and spectroscopy parameter optimization

abstract:
  We present a user-friendly and versatile tool for laser frequency
stabilization. Its main focus is spectroscopy locking, but the software is
suitable for lock-in techniques in general as well as bare PID operation.
Besides allowing for sinusoidal modulation (up to 50 MHz), triangular ramp
scanning, IQ demodulation (1 $f$ to 5 $f$), IIR and PID filtering, Linien
features two different algorithms for automatic lock point selection; one of
them performs time-critical tasks completely on FPGA. Linien is capable of
autonomously optimizing spectroscopy parameters by means of machine learning
and can measure the error signal's power spectral density. The software is
built in a modular way, providing both a graphical user interface as well as a
Python scripting interface. It is based on the RedPitaya STEMLab platform but
may be ported to different systems.


title:
Geometric singular perturbation analysis of the multiple-timescale
  Hodgkin-Huxley equations

abstract:
  We consider a non-dimensionalised version of the four-dimensional
Hodgkin-Huxley equations [J. Rubin and M. Wechselberger, Giant squid-hidden
canard: the 3D geometry of the Hodgkin-Huxley model, Biological Cybernetics, 97
(2007), pp. 5-32], and we present a novel and global three-dimensional
reduction that is based on geometric singular perturbation theory (GSPT). We
investigate the dynamics of the resulting reduced system in two parameter
regimes in which the flow evolves on three distinct timescales. Specifically,
we demonstrate that the system exhibits bifurcations of oscillatory dynamics
and complex mixed-mode oscillations (MMOs), in accordance with the geometric
mechanisms introduced in [P. Kaklamanos, N. Popovi\'c, and K. U. Kristiansen,
Bifurcations of mixed-mode oscillations in three-timescale systems: An extended
prototypical example, Chaos: An Interdisciplinary Journal of Nonlinear Science,
32 (2022), p. 013108], and we classify the various firing patterns in
dependence of the external applied current. While such patterns have been
documented in [S. Doi, S. Nabetani, and S. Kumagai, Complex nonlinear dynamics
of the Hodgkin-Huxley equations induced by time scale changes, Biological
Cybernetics, 85 (2001), pp. 51-64] for the multi-timescale Hodgkin-Huxley
equations, we elucidate the geometry that underlies the transitions between
them, which had not been previously emphasised.


title: Random walks on crystal lattices and multiple zeta functions

abstract:
  Crystal lattices are known to be one of the generalizations of classical
periodic lattices which can be embedded into some Euclidean spaces properly. As
to make a wide range of multidimensional discrete distributions on Euclidean
spaces more treatable, multidimensional Euler products and multidimensional
Shintani zeta functions on crystal lattices are introduced. They are completely
different from existing Ihara zeta functions on graphs in that our zeta
functions are defined on crystal lattices directly. Via a concept of periodic
realizations of crystal lattices, we make it possible to provide many kinds of
multidimensional discrete distributions explicitly. In particular, random walks
on crystal lattices whose range is infinite and such random walks whose range
is finite are constructed by multidimensional Euler products and
multidimensional Shintani zeta functions, respectively. We give some
comprehensible examples as well.


title:
Generalised analytical results on $n$-ejection-collision orbits in the
  RTBP. Analysis of bifurcations

abstract:
  In the planar circular restricted three-body problem and for any value of the
mass parameter $\mu \in (0,1)$ and $n\ge 1$, we prove the existence of four
families of $n$-ejection-collision ($n$-EC) orbits, that is, orbits where the
particle ejects from a primary, reaches $n$ maxima in the distance with respect
to it and finally collides with the primary. Such EC orbits have a value of the
Jacobi constant of the form $C=3\mu +Ln^{2/3}(1-\mu)^{2/3}$, where $L>0$ is big
enough but independent of $\mu$ and $n$. In order to prove this optimal result,
we consider Levi-Civita's transformation to regularize the collision with one
primary and a perturbative approach using an ad hoc small parameter once a
suitable scale in the configuration plane and time has previously been applied.
This result improves a previous work where the existence of the $n$-EC orbits
was stated when the mass parameter $\mu>0$ was small enough. In this paper, any
possible value of $\mu\in (0,1)$ and $n\ge 1$ is considered. Moreover, for
decreasing values of $C$, there appear some bifurcations which are first
numerically investigated and afterwards explicit expressions for the
approximation of the bifurcation values of $C$ are discussed. Finally, a
detailed analysis of the existence of $n$-EC orbits when $\mu \to 1$ is also
described. In a natural way Hill's problem shows up. For this problem, we prove
an analytical result on the existence of four families of $n$-EC orbits and
numerically we describe them as well as the appearing bifurcations.


title:
Conditional Bilingual Mutual Information Based Adaptive Training for
  Neural Machine Translation

abstract:
  Token-level adaptive training approaches can alleviate the token imbalance
problem and thus improve neural machine translation, through re-weighting the
losses of different target tokens based on specific statistical metrics (e.g.,
token frequency or mutual information). Given that standard translation models
make predictions on the condition of previous target contexts, we argue that
the above statistical metrics ignore target context information and may assign
inappropriate weights to target tokens. While one possible solution is to
directly take target contexts into these statistical metrics, the
target-context-aware statistical computing is extremely expensive, and the
corresponding storage overhead is unrealistic. To solve the above issues, we
propose a target-context-aware metric, named conditional bilingual mutual
information (CBMI), which makes it feasible to supplement target context
information for statistical metrics. Particularly, our CBMI can be formalized
as the log quotient of the translation model probability and language model
probability by decomposing the conditional joint distribution. Thus CBMI can be
efficiently calculated during model training without any pre-specific
statistical calculations and large storage overhead. Furthermore, we propose an
effective adaptive training approach based on both the token- and
sentence-level CBMI. Experimental results on WMT14 English-German and WMT19
Chinese-English tasks show our approach can significantly outperform the
Transformer baseline and other related methods.


title: Zero-Divisor Graphs and Zero-Divisor Functors

abstract:
  Inspired by a very recent work of A. {\DH}uri\'c, S. Jev{\dj}eni\'c and N.
Stopar, we introduce a new definition of zero-divisor graphs attached to rings,
that includes all of the classical definitions already known in the literature.
We provide an interpretation of such graphs as images of a functor, that we
call zero-divisor functor and which is associated with a family of special
equivalence relations fixed beforehand. We thus recover and generalize many
known results for zero-divisor graphs and provide a framework which might be
useful for further investigations on this topic.


title: Precise Point Spread Function Estimation

abstract:
  Point spread function (PSF) plays a crucial role in many fields, such as
shape from focus/defocus, depth estimation, and imaging process in fluorescence
microscopy. However, the mathematical model of the defocus process is still
unclear because several variables in the point spread function are hard to
measure accurately, such as the f-number of cameras, the physical size of a
pixel, the focus depth, etc. In this work, we develop a precise mathematical
model of the camera's point spread function to describe the defocus process. We
first derive the mathematical algorithm for the PSF and extract two parameters
A and e. A is the composite of camera's f-number, pixel-size, output scale, and
scaling factor of the circle of confusion; e is the deviation of the focus
depth. We design a novel metric based on the defocus histogram to evaluate the
difference between the simulated focused image and the actual focused image to
obtain optimal A and e. We also construct a hardware system consisting of a
focusing system and a structured light system to acquire the all-in-focus
image, the focused image with corresponding focus depth, and the depth map in
the same view. The three types of images, as a dataset, are used to obtain the
precise PSF. Our experiments on standard planes and actual objects show that
the proposed algorithm can accurately describe the defocus process. The
accuracy of our algorithm is further proved by evaluating the difference among
the actual focused images, the focused image generated by our algorithm, the
focused image generated by others. The results show that the loss of our
algorithm is 40% less than others on average. The dataset, code, and model are
available on GitHub: https://github.com/cubhe/
precise-point-spread-function-estimation.


title:
On the importance of stationarity, strong baselines and benchmarks in
  transport prediction problems

abstract:
  Over the last years, the transportation community has witnessed a tremendous
amount of research contributions on new deep learning approaches for
spatio-temporal forecasting. These contributions tend to emphasize the modeling
of spatial correlations, while neglecting the fairly stable and recurrent
nature of human mobility patterns. In this short paper, we show that a naive
baseline method based on the average weekly pattern and linear regression can
achieve comparable results to many state-of-the-art deep learning approaches
for spatio-temporal forecasting in transportation, or even outperform them on
several datasets, thus contrasting the importance of stationarity and recurrent
patterns in the data with the importance of spatial correlations. Furthermore,
we establish 9 different reference benchmarks that can be used to compare new
approaches for spatio-temporal forecasting, and provide a discussion on best
practices and the direction that the field is taking.


title: Twitter Dataset for 2022 Russo-Ukrainian Crisis

abstract:
  Online Social Networks (OSNs) play a significant role in information sharing
during a crisis. The data collected during such a crisis can reflect the large
scale public opinions and sentiment. In addition, OSN data can also be used to
study different campaigns that are employed by various entities to engineer
public opinions. Such information sharing campaigns can range from spreading
factual information to propaganda and misinformation. We provide a Twitter
dataset of the 2022 Russo-Ukrainian conflict. In the first release, we share
over 1.6 million tweets shared during the 1st week of the crisis.


title:
What does it mean to represent? Mental representations as falsifiable
  memory patterns

abstract:
  Representation is a key notion in neuroscience and artificial intelligence
(AI). However, a longstanding philosophical debate highlights that specifying
what counts as representation is trickier than it seems. With this brief
opinion paper we would like to bring the philosophical problem of
representation into attention and provide an implementable solution. We note
that causal and teleological approaches often assumed by neuroscientists and
engineers fail to provide a satisfactory account of representation. We sketch
an alternative according to which representations correspond to inferred latent
structures in the world, identified on the basis of conditional patterns of
activation. These structures are assumed to have certain properties
objectively, which allows for planning, prediction, and detection of unexpected
events. We illustrate our proposal with the simulation of a simple neural
network model. We believe this stronger notion of representation could inform
future research in neuroscience and AI.


title:
A globally convergent regularized Newton method for $\ell_q$-norm
  composite optimization problems

abstract:
  This paper is concerned with $\ell_q\,(0\!<\!q\!<\!1)$-norm regularized
minimization problems with a twice continuously differentiable loss function.
For this class of nonconvex and nonsmooth composite problems, many algorithms
have been proposed to solve them and most of which are of the first-order type.
In this work, we propose a hybrid of proximal gradient method and subspace
regularized Newton method, named HpgSRN. Unlike existing global convergent
results for (regularized) Newton methods, the whole iterate sequence produced
by HpgSRN is proved to have a finite length and converge to an $L$-type
stationary point under a mild curve-ratio condition and the
Kurdyka-{\L}ojasiewicz property of the cost function, which does linearly if
further a Kurdyka-{\L}ojasiewicz property of exponent $1/2$ holds. Moreover, a
superlinear convergence rate for the iterate sequence is also achieved under an
additional local error bound condition. Our convergence results do not require
the isolatedness and strict local minimum properties of the $L$-stationary
point. Numerical comparisons with ZeroFPR, a hybrid of proximal gradient method
and quasi-Newton method for the forward-backward envelope of the cost function,
proposed in [A. Themelis, L. Stella, and P. Patrinos, SIAM J. Optim. 28(2018),
pp. 2274-2303] for the $\ell_q$-norm regularized linear and logistic
regressions on real data indicate that HpgSRN not only requires much less
computing time but also yields comparable even better sparsities and objective
values.


title:
Towards a Responsible AI Development Lifecycle: Lessons From Information
  Security

abstract:
  Legislation and public sentiment throughout the world have promoted fairness
metrics, explainability, and interpretability as prescriptions for the
responsible development of ethical artificial intelligence systems. Despite the
importance of these three pillars in the foundation of the field, they can be
challenging to operationalize and attempts to solve the problems in production
environments often feel Sisyphean. This difficulty stems from a number of
factors: fairness metrics are computationally difficult to incorporate into
training and rarely alleviate all of the harms perpetrated by these systems.
Interpretability and explainability can be gamed to appear fair, may
inadvertently reduce the privacy of personal information contained in training
data, and increase user confidence in predictions -- even when the explanations
are wrong. In this work, we propose a framework for responsibly developing
artificial intelligence systems by incorporating lessons from the field of
information security and the secure development lifecycle to overcome
challenges associated with protecting users in adversarial settings. In
particular, we propose leveraging the concepts of threat modeling, design
review, penetration testing, and incident response in the context of developing
AI systems as ways to resolve shortcomings in the aforementioned methods.


title:
A Perspective on Robotic Telepresence and Teleoperation using Cognition:
  Are we there yet?

abstract:
  Telepresence and teleoperation robotics have attracted a great amount of
attention in the last 10 years. With the Artificial Intelligence (AI)
revolution already being started, we can see a wide range of robotic
applications being realized. Intelligent robotic systems are being deployed
both in industrial and domestic environments. Telepresence is the idea of being
present in a remote location virtually or via robotic avatars. Similarly, the
idea of operating a robot from a remote location for various tasks is called
teleoperation. These technologies find significant application in health care,
education, surveillance, disaster recovery, and corporate/government sectors.
But question still remains about their maturity, security and safety levels. We
also need to think about enhancing the user experience and trust in such
technologies going into the next generation of computing.


title:
The homotopy theory of Rota-Baxter associative algebras of arbitrary
  weights

abstract:
  This paper investigates Rota-Baxter associative algebras of of arbitrary
weights, that is, associative algebras endowed with Rota-Baxter operators of
arbitrary weights from an operadic viewpoint. Denote by $\RB$ the operad of
Rota-Baxter associative algebras. A homotopy cooperad is explicitly
constructed, which can be seen as the Koszul dual of $\RB$ as it is proven that
the cobar construction of this homotopy cooperad is exactly the minimal model
of $\RB$. This enables us to give the notion of homotopy Rota-Baxter
associative algebras. The deformation complex of a Rota-Baxter associative
algebra and the underlying $L_\infty$-algebra structure over it are exhibited
as well.


title: Invariant Spinors on Homogeneous Spheres

abstract:
  Using the characterization of the spin representation in terms of exterior
forms, we give a complete classification of invariant spinors on the nine
homogeneous realizations of the sphere $S^n$. In each of the cases we determine
the dimension of the space of such spinors, give their explicit description,
and study the underlying related geometric structures depending on the metric.
We recover some known results in the Sasaki and 3-Sasaki cases and find several
new examples: in particular we give the first known examples of generalized
Killing spinors with four distinct eigenvalues.


title: Optimal FFT-accelerated Finite Element Solver for Homogenization

abstract:
  We propose a matrix-free finite element (FE) homogenization scheme that is
considerably more efficient than generic FE implementations. The efficiency of
our scheme follows from a preconditioned well-scaled reformulation allowing for
the use of the conjugate gradient or similar iterative solvers. The
geometrically-optimal preconditioner -- a discretized Green's function of a
periodic homogeneous reference problem -- has a block-diagonal structure in the
Fourier space which permits its efficient inversion using the fast Fourier
transform (FFT) techniques for generic regular meshes. This implies that the
scheme scales as $\mathcal{O}(n \log(n))$ like FFT, rendering it equivalent to
spectral solvers in terms of computational efficiency. However, in contrast to
classical spectral solvers, the proposed scheme works with FE shape functions
with local supports and is free of the Fourier ringing phenomenon. We showcase
that the scheme achieves the number of iterations that are almost independent
of spatial discretisation and scales mildly with the phase contrast.
Additionally, we discuss the equivalence between our displacement-based scheme
and the recently proposed strain-based homogenization technique with
finite-element projection.


title:
Coupling between the accreting corona and the relativistic jet in the
  micro quasar GRS 1915+105

abstract:
  GRS 1915+105 was the first stellar-mass black-hole in our Galaxy to display a
superluminal radio jet, similar to those observed in active galactic nuclei
with a supermassive black hole at the centre. It has been proposed that the
radio emission in GRS 1915+105 is fed by instabilities in the accretion disc by
which the inner parts of the accretion flow is ejected in the jet. Here we show
that there is a significant correlation between: (i) the radio flux, coming
from the jet, and the flux of the iron emission line, coming from the disc and,
(ii) the temperature of the corona that produces the high-energy part of the
X-ray spectrum via inverse Compton scattering and the amplitude of a
high-frequency variability component coming from the innermost part of the
accretion flow. At the same time, the radio flux and the flux of the iron line
are strongly anti-correlated with the temperature of the X-ray corona and the
amplitude of the high-frequency variability component. These correlations
persist over ~10 years, despite the highly variable X-ray and radio properties
of the source in that period. Our findings provide, for the first time,
incontrovertible evidence that the energy that powers this black-hole system
can be directed either to the X-ray corona or the jet. When this energy is used
to power the corona, raising its temperature, there is less energy left to fuel
the jet and the radio flux drops, and vice versa. These facts, plus the
modelling of the variability in this source show conclusively that in GRS
1915+105 the X-ray corona morphs into the jet.


title: World-wide Evidence for Gender Difference in Sociality

abstract:
  One of the most contested questions about human behaviour is whether there
are inherent sex or gender differences in the formation and maintenance of
social bonds. On one hand, female and male brains are structurally almost
identical, and while there are sex differences in the endocrine system, these
are small, while much of gendered identity and behaviour is learned. On the
other hand, sex differences in some aspects of social behaviour have deep
evolutionary roots, and are widely present in non-human animals. This
observational study recorded the frequency of same-aged, adult human groups
appearing in public spaces through 2636 hours, recording group formation by
1.2mn people via 170 research assistants in 46 countries across the world. The
results show (a) a significant sex-gender difference in same-sex-same-age
frequency, in that ~50% more female-female than male-male pairs are observed in
public spaces globally, and (b) that despite regional variation, the patterns
holds up in every global region. This is the first study of sex-gender
difference in dyadic social behaviour across the world on this scale, and the
first global study that uses direct rather than internet-based observations.


title: Properties of doubly heavy baryons in QCD

abstract:
  The study of the properties of doubly heavy baryons represents a promising
area in particle physics. It can provide us with information about
Cabibbo-Kobayashi-Maskawa (CKM) matrix elements and the low energy dynamics of
QCD. They have a very rich phenomenology. The investigation of weak,
electromagnetic, and strong decays has vital importance for understanding the
dynamics of doubly heavy baryons. The main ingredients of such studies are the
spectroscopic parameters, the strong coupling constants, and the transition
form factors. For calculations of these quantities, non-perturbative methods
are needed. One of these methods is the QCD sum rules. In the present work, we
review our studies on the properties of the doubly heavy baryons within the sum
rules method, focusing mainly on the mass and strong coupling constants of the
doubly heavy baryons. In addition, the radiative decays of doubly heavy baryons
are estimated using the vector dominance model. We also make few remarks on the
semileptonic decays of the doubly heavy baryons.


title:
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for
  Temporal Sentence Grounding

abstract:
  Temporal sentence grounding aims to localize a target segment in an untrimmed
video semantically according to a given sentence query. Most previous works
focus on learning frame-level features of each whole frame in the entire video,
and directly match them with the textual information. Such frame-level feature
extraction leads to the obstacles of these methods in distinguishing ambiguous
video frames with complicated contents and subtle appearance differences, thus
limiting their performance. In order to differentiate fine-grained appearance
similarities among consecutive frames, some state-of-the-art methods
additionally employ a detection model like Faster R-CNN to obtain detailed
object-level features in each frame for filtering out the redundant background
contents. However, these methods suffer from missing motion analysis since the
object detection module in Faster R-CNN lacks temporal modeling. To alleviate
the above limitations, in this paper, we propose a novel Motion- and
Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates
optical-flow-guided motion-aware, detection-based appearance-aware, and
3D-aware object-level features to better reason the spatial-temporal object
relations for accurately modelling the activity among consecutive frames.
Specifically, we first develop three individual branches for motion,
appearance, and 3D encoding separately to learn fine-grained motion-guided,
appearance-guided, and 3D-aware object features, respectively. Then, both
motion and appearance information from corresponding branches are associated to
enhance the 3D-aware features for the final precise grounding. Extensive
experiments on three challenging datasets (ActivityNet Caption, Charades-STA
and TACoS) demonstrate that the proposed MA3SRN model achieves a new
state-of-the-art.


title: Variational Auto-Encoder based Mandarin Speech Cloning

abstract:
  Speech cloning technology is becoming more sophisticated thanks to the
advances in machine learning. Researchers have successfully implemented
natural-sounding English speech synthesis and good English speech cloning by
some effective models. However, because of prosodic phrasing and large
character set of Mandarin, Chinese utilization of these models is not yet
complete. By creating a new dataset and replacing Tacotron synthesizer with
VAENAR-TTS, we improved the existing speech cloning technique CV2TTS to almost
real-time speech cloning while guaranteeing synthesis quality. In the process,
we customized the subjective tests of synthesis quality assessment by attaching
various scenarios, so that subjects focus on the differences between voice and
our improvements maybe were more advantageous to practical applications. The
results of the A/B test, real-time factor (RTF) and 2.74 mean opinion score
(MOS) in terms of naturalness and similarity, reflect the real-time
high-quality Mandarin speech cloning we achieved.


title: Improved Quantum Query Upper Bounds Based on Classical Decision Trees

abstract:
  Given a classical query algorithm as a decision tree, when does there exist a
quantum query algorithm with a speed-up over the classical one? We provide a
general construction based on the structure of the underlying decision tree,
and prove that this can give us an up-to-quadratic quantum speed-up. In
particular, we obtain a bounded-error quantum query algorithm of cost
$O(\sqrt{s})$ to compute a Boolean function (more generally, a relation) that
can be computed by a classical (even randomized) decision tree of size $s$.
  Lin and Lin [ToC'16] and Beigi and Taghavi [Quantum'20] showed results of a
similar flavor, and gave upper bounds in terms of a quantity which we call the
"guessing complexity" of a decision tree. We identify that the guessing
complexity of a decision tree equals its rank, a notion introduced by
Ehrenfeucht and Haussler [Inf. Comp.'89] in the context of learning theory.
This answers a question posed by Lin and Lin, who asked whether the guessing
complexity of a decision tree is related to any complexity-theoretic measure.
We also show a polynomial separation between rank and randomized rank for the
complete binary AND-OR tree.
  Beigi and Taghavi constructed span programs and dual adversary solutions for
Boolean functions given classical decision trees computing them and an
assignment of non-negative weights to its edges. We explore the effect of
changing these weights on the resulting span program complexity and objective
value of the dual adversary bound, and capture the best possible weighting
scheme by an optimization program. We exhibit a solution to this program and
argue its optimality from first principles. We also exhibit decision trees for
which our bounds are asymptotically stronger than those of Lin and Lin, and
Beigi and Taghavi. This answers a question of Beigi and Taghavi, who asked
whether different weighting schemes could yield better upper bounds.


title: Elongated Skyrmion as Spin Torque Nano-Oscillator and Magnonic Waveguide

abstract:
  Spin Torque Nano-Oscillator has been extensively studied both theoretically
and experimentally in recent decades due to its potential applications in
future microwave communication technology and neuromorphic computing. In this
work, we present a skyrmion-based spin torque nano-oscillator driven by a
spatially uniform direct current, where the skyrmion is confined by two pinning
sites. Different from other skyrmion-based oscillators that arise from the
circular motion or the breathing mode of a skyrmion, the steady-state
oscillatory motions are produced by the periodic deformation of an elongated
skyrmion. Through micromagnetic simulations, we find that the oscillation
frequency depends on the driving current, the damping constant as well as the
characteristics of pinning sites. This nonlinear response to direct current
turns out to be universal and can also appear in the case of antiskyrmions,
skyrmioniums and domain walls. Furthermore, the elongated skyrmion possesses a
rectangle-like domain wall, which could also serve as a magnonic waveguide.
Utilizing the propagation of spin waves in this waveguide, we propose a device
design of logic gate and demonstrate its performance.


title:
Holomorphic Shadowing for H\'enon Maps Revisited: an Implicit Function
  Theorem Perspective

abstract:
  In studying the complex H\'enon maps, Mummert (in "Holomorphic shadowing for
H\'enon maps" Nonlinearity 21 pp. 2887-2898, 2008) defined an operator the
fixed points of which give rise to bounded orbits. This enabled him to obtain
an estimate of the solenoid locus. Instead of the contraction mapping theorem,
in this paper we present an implicit function theorem version of his result by
providing an alternative proof of a theorem of Hubbard and Oberste-Vorth (in
Real and Complex Dynamical Systems, pp.89-132, 1995).


title: Nowhere-zero 3-flows in Cayley graphs on supersolvable groups

abstract:
  Tutte's 3-flow conjecture asserts that every $4$-edge-connected graph admits
a nowhere-zero $3$-flow. We prove that this conjecture is true for every Cayley
graph of valency at least four on any supersolvable group with a noncyclic
Sylow $2$-subgroup and every Cayley graph of valency at least four on any group
whose derived subgroup is of square-free order.


title:
Limits of eventual families of sets with application to algorithms for
  the common fixed point problem

abstract:
  We present an abstract framework for asymptotic analysis of convergence based
on the notions of eventual families of sets that we define. A family of subsets
of a given set is called here an "eventual family" if it is upper hereditary
with respect to inclusion. We define accumulation points of eventual families
in a Hausdorff Topological space and define the "image family" of an eventual
family. Focusing on eventual families in the set of the integers enables us to
talk about sequences of points. We expand our work to the notion of a
"multiset" which is a modification of the concept of a set that allows for
multiple instances of its elements and enable the development of
"multifamilies" which are either "increasing" or "decreasing". The abstract
structure created here is motivated by, and feeds back to, our look at the
convergence analysis of an iterative process for asymptotically finding a
common fixed point of a family of operators.


title:
Mixed-norm of orthogonal projections and analytic interpolation on
  dimensions of measures

abstract:
  Suppose $\mu, \nu$ are compactly supported Radon measures on $\mathbb{R}^d$
and $V\in G(d,n)$ is an $n$-dimensional subspace. In this paper we
systematically study the mixed-norm
$$\int\|\pi^y\mu\|_{L^p(G(d,n))}^q\,d\nu(y),\ \forall\,p,q\in[1,\infty),$$
where $\pi_V:\mathbb{R}^d\rightarrow V$ denotes the orthogonal projection and
$$\pi^y\mu(V)=\int_{y+V^\perp}\mu\,d\mathcal{H}^{d-n}=\pi_V\mu(\pi_Vy),\
\text{if $\mu$ has continuous density}.$$ When $n=d-1$ and $p=q$, our result
significantly improves a previous result of Orponen.
  In the proof we consider integer exponents first, then interpolate
analytically, not only on $p,q$, but also on dimensions of measures. We also
introduce a new quantity called $s$-amplitude, to present our results and
illustrate our ideas. This mechanism provides new perspectives on operators
with measures, thus has its own interest. We also give an alternative proof of
a recent result of D\k{a}browski, Orponen, Villa on
$\|\pi_V\mu\|_{L^p(\mathcal{H}^n\times G(d,n))}$.
  The following consequences are also interesting.
  $\bullet$ We discover jump discontinuities in the range of $p$ at the
critical line segment
  $$\{(s_\mu, s_\nu)\in(0,d)^2: s_\mu+s_\nu=2n,\, 0<s_\nu<n\},$$
  $\ \ $ where $s_\mu, s_\nu$ are Frostman exponents of $\mu, \nu$ respectiely.
This is unexpected and surprising.
  $\bullet$ Given $1\leq m\leq d-1$ and $E, F\subset\mathbb{R}^d$, we obtain
dimensional threshold on whether there exists $y\in F$ such that
  $$\gamma_{d,m}\{V\in G(d,m): V=\operatorname{Span}\{x_1-y,\dots,x_m-y\}:
x_1,\dots,x_m\in E\}>0.$$
  $\ \ $ This generalizes the visibility problem ($m=1$). In particular, when
$m>\frac{d}{2}$ and $\dim_{\mathcal{H}} E$ is large enough, the exceptional set
has Hausdorff dimension $0$.


title: $2d$ Sigma Models and Geometry

abstract:
  Supersymmetric nonlinear sigma models have target spaces that carry
interesting geometry. The geometry is richer the more supersymmetries the model
has. The study of models with two dimensional world sheets is particularly
rewarding since they allow for torsionful geometries. In this review I describe
and exemplify the relation of $2d$ supersymmetry to Riemannian, complex,
bihermitian, $(p,q)$ Hermitean, K\"ahler, hyperk\"ahler, generalised geometry
and more


title: Sub-Riemannian geometry applied to incompressible, inviscid fluids

abstract:
  One field of fluid dynamics concerns the search for variational principles.
So far, the Hamiltonian view and Riemannian geometry has been applied to find
geodesics for hydrodynamic systems. Compared to Riemannian geometry
sub-Riemannian geometry can be applied to search for geodesics of constrained
systems such as vortex flows, where the vortex motion is restricted by
rotations that are expressed by vortex-related conservation laws. The Nambu
formulation of incompressible, inviscid fluid dynamics provides a nilpotent Lie
algebra for two- and three-dimensional fluids, called Vortex-Heisenberg
algebra, which makes it natural to apply sub-Riemannian geometry. As a first
approach we consider discretized models. The resulting vortex geodesics for
two-dimensional incompressible, inviscid discrete point vortices is a known
special point vortex constellation, the equilibrium. We also outline a concept
for the derivation of 3D vortex geodesics.


title: A generalisation of the toric resolution of curves

abstract:
  Let $k$ be a perfect field and let $C_0:f=0$ be a smooth curve in the torus
$\mathbb{G}_{m,k}^2$. Let $\mathbb{T}_\Delta$ be the toric variety associated
to the Newton polygon of $f$. Extending the toric resolution of $C_0$ on
$\mathbb{T}_\Delta$, we construct an explicit model over $k$ of the smooth
completion of $C_0$. Such a model exists for any smooth projective curve and
can be described via a combinatorial algorithm using an iterative construction
of Newton polygons.


title:
Orientation-dependent propulsion of cone-shaped nano- and microparticles
  by a traveling ultrasound wave

abstract:
  Previous studies on ultrasound-propelled nano- and microparticles have
considered only systems where the particle orientation is perpendicular to the
direction of propagation of the ultrasound. However, in future applications of
these particles, they will typically be able to attain also other orientations.
Therefore, using direct acoustofluidic simulations, we here study how the
propulsion of cone-shaped nano- and microparticles, which are known to have a
particularly efficient acoustic propulsion and are therefore promising
candidates for future applications, depends on their orientation relative to
the propagation direction of a traveling ultrasound wave. Our results reveal
that the propulsion of the particles depends strongly on their orientation
relative to the direction of wave propagation and that the particles tend to
orient perpendicularly to the wave direction. We also present the
orientation-averaged translational and angular velocities of the particles,
which correspond to the particles' effective propulsion for an isotropic
exposure to ultrasound. Our results allow assessing how free
ultrasound-propelled colloidal particles move in three spatial dimensions and
thus constitute an important step towards the realization of the envisaged
future applications of such particles.

categories: cond-mat.soft cond-mat.mes-hall physics.comp-ph physics.flu-dyn physics.med-ph


title:
Robustness of Exponential Stability of a Class of Switched Linear
  Systems with State Delays

abstract:
  This paper investigates the robustness of exponential stability of a class of
switched systems described by linear functional differential equations under
arbitrary switching. We will measure the stability robustness of such a system,
subject to parameter affine perturbations of its constituent subsystems
matrices, by introducing the notion of structured stability radius. The lower
bounds and the upper bounds for this radius are established under the
assumption that certain associated positive linear systems upper bounding the
given subsystems have a common linear copositive Lyapunov function. In the case
of switched positive linear systems with discrete multiple delays or
distributed delay the obtained results yield some tractably computable bounds
for the stability radius. Examples are given to illustrate the proposed method.


title: Age of Incorrect Information under Delay

abstract:
  This paper investigates the problem of minimizing the Age of Incorrect
Information (AoII) when the communication channel has a random delay. We
consider a slotted-time system where a transmitter observes a dynamic source
and decides when to send updates to a remote receiver through a channel with
random delay. The receiver maintains estimates of the state of the dynamic
source based on the received updates. In this paper, we adopt AoII as the
performance metric and investigate the problem of optimizing the transmitter's
action in each time slot to minimize AoII. We first characterize the considered
problem using Markov Decision Process (MDP). Then, leveraging the policy
improvement theorem and under an easy-to-verify condition, we prove that the
optimal decision for the transmitter is to initiate a transmission whenever the
channel is idle and AoII is not zero. The results apply to generic delay
distribution. Lastly, we verify the condition numerically and provide the
numerical results that highlight the performance of the optimal policy.


title: On triangle-free list assignments

abstract:
  We show that Bernshteyn's proof of the breakthrough result of Molloy that
triangle-free graphs are choosable from lists of size
$(1+o(1))\Delta/\log\Delta$ can be adapted to yield a stronger result. In
particular one may prove that such list sizes are sufficient to colour any
graph of maximum degree $\Delta$ provided that vertices sharing a common colour
in their lists do not induce a triangle in $G$, which encompasses all cases
covered by Molloy's theorem. This was thus far known to be true for lists of
size $(1000+o(1))\Delta/\log\Delta$, as implies a more general result due to
Amini and Reed. We also prove that lists of length $2(r-2)\Delta
\log_2\log_2\Delta/\log_2\Delta$ are sufficient if one replaces the triangle by
any $K_r$ with $r\geq 4$, pushing also slightly the multiplicative factor of
$200r$ from Bernshteyn's result down to $2(r-2)$. All bounds presented are also
valid within the more general setting of correspondence colourings.


title: Revisiting the pion Regge trajectories

abstract:
  We propose a model-independent ansatz
$M={\beta_x}\left(x+c_0\right)^{\nu}+c_1$ ($x=l,\,n_r$) and then use it to fit
the orbital and radial pion Regge trajectories without the preset values. It is
shown that nonzero $c_1$ is reasonable and acceptable. Nonzero $c_1$ gives an
explanation for the nonlinearity of the pion Regge trajectories in the usually
employed $(M^2,\,x)$ plane. As $m_R$ or $c_1$ is chosen appropriately, both the
orbital and radial pion Regge trajectories are linear in the $((M-m_R)^2,\,x)$
plane whether the $\pi^0$ is included or not on the Regge trajectories. The
fitted pion Regge trajectories suggest $0.45\le\nu\le0.5$, which indicates the
confining potential $r^a$ with $9/11{\le}a\le1$. Moreover, it is illustrated in
appendix B that $m_R$ can be nonzero for the light mesons and the fitted Regge
trajectories are also linear when they are plotted in the $((M-m_R)^2,\,x)$
plane. We present discussions in the appendix A on the structure of the Regge
trajectories plotted in the $(M,\,x)$ plane and on the structure of the Regge
trajectories in the $((M-m_R)^2,\,x)$ plane based on the potential models and
the string models.


title: A Survey of Implicit Discourse Relation Recognition

abstract:
  A discourse containing one or more sentences describes daily issues and
events for people to communicate their thoughts and opinions. As sentences are
normally consist of multiple text segments, correct understanding of the theme
of a discourse should take into consideration of the relations in between text
segments. Although sometimes a connective exists in raw texts for conveying
relations, it is more often the cases that no connective exists in between two
text segments but some implicit relation does exist in between them. The task
of implicit discourse relation recognition (IDRR) is to detect implicit
relation and classify its sense between two text segments without a connective.
Indeed, the IDRR task is important to diverse downstream natural language
processing tasks, such as text summarization, machine translation and so on.
This article provides a comprehensive and up-to-date survey for the IDRR task.
We first summarize the task definition and data sources widely used in the
field. We categorize the main solution approaches for the IDRR task from the
viewpoint of its development history. In each solution category, we present and
analyze the most representative methods, including their origins, ideas,
strengths and weaknesses. We also present performance comparisons for those
solutions experimented on a public corpus with standard data processing
procedures. Finally, we discuss future research directions for discourse
relation analysis.


title: Transient Chaos in Strongly Monotone Dynamical Systems

abstract:
  For strongly monotone dynamical systems, it is shown that the principal
Lyapunov exponent $\lambda_{1}(x)\leq0$ holds on a prevalent set in the
measure-theoretic sense. The nonexistence of observable chaos is thus obtained
as a by-product. Together with the existing works, we point out that transient
chaos is ubiquitous in strongly monotone dynamical systems, which is a form of
unobservable chaos introduced by Young [Commun. Pure Appl. Math. 66(2013)].


title:
Reducing the Dynamic State Index to its main information using Principal
  Component Analysis

abstract:
  The Dynamic State Index is a scalar quantity designed to identify atmospheric
developments such as fronts, hurricanes or specific weather pattern. The DSI is
defined as Jacobian-determinant of three constitutive quantities that
characterize three-dimensional fluid flows: the Bernoulli stream function, the
potential vorticity (PV) and the potential temperature. Here, we tackle the
questions (i) if the mathematical formulation of the DSI can be reduced, while
keeping the main information, and (ii) does the reduction of the DSI depend on
the spatial scale? Applying principle component analysis we find that three of
six DSI terms that sum up to the Jacobi-determinant are sufficient for future
DSI calculations.


title:
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for
  Knowledge-based Visual Question Answering

abstract:
  Knowledge-based visual question answering (VQA) is a vision-language task
that requires an agent to correctly answer image-related questions using
knowledge that is not presented in the given image. It is not only a more
challenging task than regular VQA but also a vital step towards building a
general VQA system. Most existing knowledge-based VQA systems process knowledge
and image information similarly and ignore the fact that the knowledge base
(KB) contains complete information about a triplet, while the extracted image
information might be incomplete as the relations between two objects are
missing or wrongly detected. In this paper, we propose a novel model named
dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which
performs explicit and implicit reasoning over a key-value knowledge memory
module and a spatial-aware image graph, respectively. Specifically, the memory
module learns a dynamic knowledge representation and generates a
knowledge-aware question representation at each reasoning step. Then, this
representation is used to guide a graph attention operator over the
spatial-aware image graph. Our model achieves new state-of-the-art accuracy on
the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the
effectiveness of each component of the proposed model.


title: Modeling Coreference Relations in Visual Dialog

abstract:
  Visual dialog is a vision-language task where an agent needs to answer a
series of questions grounded in an image based on the understanding of the
dialog history and the image. The occurrences of coreference relations in the
dialog makes it a more challenging task than visual question-answering. Most
previous works have focused on learning better multi-modal representations or
on exploring different ways of fusing visual and language features, while the
coreferences in the dialog are mainly ignored. In this paper, based on
linguistic knowledge and discourse features of human dialog we propose two soft
constraints that can improve the model's ability of resolving coreferences in
dialog in an unsupervised way. Experimental results on the VisDial v1.0 dataset
shows that our model, which integrates two novel and linguistically inspired
soft constraints in a deep transformer neural architecture, obtains new
state-of-the-art performance in terms of recall at 1 and other evaluation
metrics compared to current existing models and this without pretraining on
other vision-language datasets. Our qualitative results also demonstrate the
effectiveness of the method that we propose.


title: Stochastic threshold in cell size control

abstract:
  Classic models of cell size control consider cells divide while reaching a
threshold, e.g. size, age, or size extension. The molecular basis of the
threshold involves multiple layers of regulation as well as gene noises. In
this work, we study cell cycle as first-passage problem with stochastic
threshold and discover such stochasticity affects the inter-division
statistics, which bewilders the criteria to distinguish the types of size
control models. The analytic results show the autocorrelation in the threshold
can drive a sizer model to the adder-like and even timer-like inter-division
statistics, which is supported by simulations. Following the picture that the
autocorrelation in the threshold can propagate to the inter-division
statistics, we further show that the adder model can be driven to the
timer-like one by positive autocorrelated threshold, and even to the sizer-like
one when the threshold is negatively autocorrelated. This work highlights the
importance to examine gene noise in size control.


title:
Election in Fully Anonymous Shared Memory Systems: Tight Space Bounds
  and Algorithms

abstract:
  This article addresses election in fully anonymous systems made up of $n$
asynchronous processes that communicate through atomic read-write registers or
atomic read-modify-write registers. Given an integer $d\in\{1,\dots, n-1\}$,
two elections problems are considered: $d$-election (at least one and at most
$d$ processes are elected) and exact $d$-election (exactly $d$ processes are
elected). Full anonymity means that both the processes and the shared registers
are anonymous. Memory anonymity means that the processes may disagree on the
names of the shared registers. That is, the same register name $A$ can denote
different registers for different processes, and the register name $A$ used by
a process and the register name $B$ used by another process can address the
same shared register.


title: Security of a High Dimensional Two-Way Quantum Key Distribution Protocol

abstract:
  Two-way quantum key distribution protocols utilize bi-directional quantum
communication to establish a shared secret key. Due to the increased attack
surface, security analyses remain challenging. Here we investigate a
high-dimensional variant of the Ping Pong protocol and perform an information
theoretic security analysis in the finite-key setting. Our methods may be
broadly applicable to other QKD protocols, especially those relying on two-way
channels. Along the way, we show some fascinating benefits to high-dimensional
quantum states applied to two-way quantum communication.


title:
Collocation-based harmonic balance framework for highly accurate
  periodic solution of nonlinear dynamical system

abstract:
  Periodic dynamical systems ubiquitously exist in science and engineering. The
harmonic balance (HB) method and its variants have been the most widely-used
approaches for such systems, but are either confined to low-order
approximations or impaired by aliasing and improper-sampling problems. Here we
propose a collocation-based harmonic balance framework to successfully unify
and reconstruct the HB-like methods. Under this framework a new conditional
identity, which exactly bridges the gap between frequency-domain and
time-domain harmonic analyses, is discovered by introducing a novel aliasing
matrix. Upon enforcing the aliasing matrix to vanish, we propose a powerful
reconstruction harmonic balance (RHB) method that obtains extremely high-order
(>100) non-aliasing solutions, previously deemed out-of-reach, for a range of
complex nonlinear systems including the cavitation bubble equation and the
three-body problem. We show that the present method is 2-3 orders of magnitude
more accurate and simultaneously much faster than the state-of-the-art method.
Hence, it has immediate applications in multi-disciplinary problems where
highly accurate periodic solutions are sought.


title:
Extended Load Flexibility of Industrial P2H Plants: A Process
  Constraint-Aware Scheduling Approach

abstract:
  The operational flexibility of industrial power-to-hydrogen (P2H) plants
enables admittance of volatile renewable power and provides auxiliary
regulatory services for the power grid. Aiming to extend the flexibility of the
P2H plant further, this work presents a scheduling method by considering
detailed process constraints of the alkaline electrolyzers. Unlike existing
works that assume constant load range, the presented scheduling framework fully
exploits the dynamic processes of the electrolyzer, including temperature and
hydrogen-to-oxygen (HTO) crossover, to improve operational flexibility. Varying
energy conversion efficiency under different load levels and temperature is
also considered. The scheduling model is solved by proper mathematical
transformation as a mixed-integer linear programming (MILP), which determines
the on-off-standby states and power levels of different electrolyzers in the
P2H plant for daily operation. With experiment-verified constraints, a case
study show that compared to the existing scheduling approach, the improved
flexibility leads to a 1.627% profit increase when the P2H plant is directly
coupled to the photovoltaic power.


title: Locally checkable problems in bounded clique-width graphs

abstract:
  We continue the study initiated by Bonomo-Braberman and Gonzalez in 2020 on
$r$-locally checkable problems. We propose a dynamic programming algorithm that
takes as input a graph with an associated clique-width expression and solves a
$1$-locally checkable problem under certain restrictions. We show that it runs
in polynomial time in graphs of bounded clique-width, when the number of colors
of the locally checkable problem is fixed. Furthermore, we present a first
extension of our framework to global properties by taking into account the
sizes of the color classes, and consequently enlarge the set of problems
solvable in polynomial time with our approach in graphs of bounded
clique-width. As examples, we apply this setting to the $k$-Roman domination
problem, as well as the $k$-community problem and some of its variants, thus
providing the first linear and polynomial-time algorithms, respectively, in
graphs of bounded clique-width for these problems.


title: A Better Computational Framework for $\text{L}_2\text{E}$ Regression

abstract:
  Building on previous research of Chi and Chi (2022), the current paper
revisits estimation in robust structured regression under the
$\text{L}_2\text{E}$ criterion. We adopt the majorization-minimization (MM)
principle to design a new algorithm for updating the vector of regression
coefficients. Our sharp majorization achieves faster convergence than the
previous alternating projected gradient descent algorithm (Chi and Chi, 2022).
In addition, we reparameterize the model by substituting precision for scale
and estimate precision via a modified Newton's method. This simplifies and
accelerates overall estimation. Finally, we introduce distance to set penalties
to allow constrained estimation under nonconvex constraint sets. This tactic
also improves performance in coefficient estimation and structure recovery. We
demonstrate the merits of the refined framework through a rich set of
simulation examples.


title: Interactive Disambiguation for Behavior Tree Execution

abstract:
  In recent years, robots are used in an increasing variety of tasks,
especially by small- and medium- sized enterprises. These tasks are usually
fast-changing, they have a collaborative scenario and happen in unpredictable
environments with possible ambiguities. It is important to have methods capable
of generating robot programs easily, that are made as general as possible by
handling uncertainties. We present a system that integrates a method to learn
Behavior Trees (BTs) from demonstration for pick and place tasks, with a
framework that uses verbal interaction to ask follow-up clarification questions
to resolve ambiguities. During the execution of a task, the system asks for
user input when there is need to disambiguate an object in the scene, when the
targets of the task are objects of a same type that are present in multiple
instances. The integrated system is demonstrated on different scenarios of a
pick and place task, with increasing level of ambiguities. The code used for
this paper is made publicly available.


title:
Changes in the nature of the spectral continuum and the stability of the
  cyclotron line in the X-ray pulsar GRO J2058+42

abstract:
  The results of the broadband spectral and timing study of the transient X-ray
pulsar GRO J2058+42 in a wide energy range at a low luminosity $L_{x} \simeq
2.5\times 10^{36}$ erg s$^{-1}$ are reported. The data revealed that the pulse
profile and pulse fraction of the source are significantly changed in
comparison with previous NuSTAR observations, when the source was ten times
brighter. The cyclotron absorption line at $\sim10$ keV in the narrow phase
interval is consistent with the high state observations. Spectral analysis
showed that at high luminosities $L_{x}\simeq (2.7-3.2)\times 10^{37}$ erg
s$^{-1}$ the spectrum has a shape typical of accreting pulsars, while when the
luminosity drops by about an order of magnitude, to $2.5\times 10^{36}$ erg
s$^{-1}$ a two-component model is necessary to its describing. This behavior
fits into a model in which the low-energy part of the spectrum is formed in a
hot spot, and the high-energy part is formed as a result of resonant Compton
scattering by incident matter in an accretion channel above the surface of a
neutron star.


title:
The growth mechanism of boundary layers for the 2D Navier-Stokes
  equations

abstract:
  We give a detailed description of formation of the boundary layers in the
inviscid limit problem. To be more specific, we prove that the magnitude of the
vorticity near the boundary is growing to the size of $1/\sqrt{\nu}$ and the
width of the layer is spreading out to be proportional the $\sqrt{\nu}$ in a
finite time period. In fact, the growth time scaling is almost ${\nu}$


title:
Smoothing with the Best Rectangle Window is Optimal for All Tapered
  Rectangle Windows

abstract:
  We investigate the optimal selection of weight windows for the problem of
weighted least squares. We show that weight windows should be symmetric around
its center, which is also its peak. We consider the class of tapered rectangle
window weights, which are nonincreasing away from the center. We show that the
best rectangle window is optimal for such window definitions. We also extend
our results to the least absolutes and more general case of arbitrary loss
functions to find similar results.