title:
Deep Learning-Based Inverse Design for Engineering Systems:
  Multidisciplinary Design Optimization of Automotive Brakes

abstract:
  The braking performance of the brake system is a target performance that must
be considered for vehicle development. Apparent piston travel (APT) and drag
torque are the most representative factors for evaluating braking performance.
In particular, as the two performance factors have a conflicting relationship
with each other, a multidisciplinary design optimization (MDO) approach is
required for brake design. However, the computational cost of MDO increases as
the number of disciplines increases. Recent studies on inverse design that use
deep learning (DL) have established the possibility of instantly generating an
optimal design that can satisfy the target performance without implementing an
iterative optimization process. This study proposes a DL-based
multidisciplinary inverse design (MID) that simultaneously satisfies multiple
targets, such as the APT and drag torque of the brake system. Results show that
the proposed inverse design can find the optimal design more efficiently
compared with the conventional optimization methods, such as backpropagation
and sequential quadratic programming. The MID achieved a similar performance to
the single-disciplinary inverse design in terms of accuracy and computational
cost. A novel design was derived on the basis of results, and the same
performance was satisfied as that of the existing design.


title:
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain
  Adaptation

abstract:
  Unsupervised domain adaptation (UDA) aims to learn transferable knowledge
from a labeled source domain and adapts a trained model to an unlabeled target
domain. To bridge the gap between source and target domains, one prevailing
strategy is to minimize the distribution discrepancy by aligning their semantic
features extracted by deep models. The existing alignment-based methods mainly
focus on reducing domain divergence in the same model layer. However, the same
level of semantic information could distribute across model layers due to the
domain shifts. To further boost model adaptation performance, we propose a
novel method called Attention-based Cross-layer Domain Alignment (ACDA), which
captures the semantic relationship between the source and target domains across
model layers and calibrates each level of semantic information automatically
through a dynamic attention mechanism. An elaborate attention mechanism is
designed to reweight each cross-layer pair based on their semantic similarity
for precise domain alignment, effectively matching each level of semantic
information during model adaptation. Extensive experiments on multiple
benchmark datasets consistently show that the proposed method ACDA yields
state-of-the-art performance.


title: Universal Thermodynamic Uncertainty Relation in Non-Equilibrium Dynamics

abstract:
  We derive a universal thermodynamic uncertainty relation (TUR) that applies
to an arbitrary observable in a general Markovian system. The generality of our
result allows us to make two findings: (1) for an arbitrary out-of-equilibrium
system, the imbalance between the entropy production and the \textit{degree of
non-stationarity} is required to bound the strength of a thermodynamic current;
(2) by removing the antisymmetric constraint on observables, the TUR in physics
and a fundamental inequality in theoretical finance can be unified in a single
framework.


title:
Sampling in Dirichlet Process Mixture Models for Clustering Streaming
  Data

abstract:
  Practical tools for clustering streaming data must be fast enough to handle
the arrival rate of the observations. Typically, they also must adapt on the
fly to possible lack of stationarity; i.e., the data statistics may be
time-dependent due to various forms of drifts, changes in the number of
clusters, etc. The Dirichlet Process Mixture Model (DPMM), whose Bayesian
nonparametric nature allows it to adapt its complexity to the data, seems a
natural choice for the streaming-data case. In its classical formulation,
however, the DPMM cannot capture common types of drifts in the data statistics.
Moreover, and regardless of that limitation, existing methods for online DPMM
inference are too slow to handle rapid data streams. In this work we propose
adapting both the DPMM and a known DPMM sampling-based non-streaming inference
method for streaming-data clustering. We demonstrate the utility of the
proposed method on several challenging settings, where it obtains
state-of-the-art results while being on par with other methods in terms of
speed.


title:
An Efficient End-to-End 3D Model Reconstruction based on Neural
  Architecture Search

abstract:
  Using neural networks to represent 3D objects has become popular. However,
many previous works employ neural networks with fixed architecture and size to
represent different 3D objects, which lead to excessive network parameters for
simple objects and limited reconstruction accuracy for complex objects. For
each 3D model, it is desirable to have an end-to-end neural network with as few
parameters as possible to achieve high-fidelity reconstruction. In this paper,
we propose an efficient model reconstruction method utilizing neural
architecture search (NAS) and binary classification. Taking the number of
layers, the number of nodes in each layer, and the activation function of each
layer as the search space, a specific network architecture can be obtained
based on reinforcement learning technology. Furthermore, to get rid of the
traditional surface reconstruction algorithms (e.g., marching cube) used after
network inference, we complete the end-to-end network by classifying binary
voxels. Compared to other signed distance field (SDF) prediction or binary
classification networks, our method achieves significantly higher
reconstruction accuracy using fewer network parameters.


title:
Gaussian theory for estimating fluctuating perturbations with back
  action evasive oscillator variables

abstract:
  We apply a Gaussian state formalism to track fluctuating perturbations that
act on the position and momentum quadrature variables of a harmonic oscillator.
Following a seminal proposal by Tsang and Caves [Phys. Rev. Lett. 105, 123601
(2010)], Einstein-Podolsky-Rosen correlations with the quadrature variables of
an ancillary harmonic oscillator are leveraged to significantly improve the
estimates as relevant sensor variables can be arbitrarily squeezed while
evading adverse effects from the conjugate, anti-squeezed variables. Our
real-time analysis of the continuous monitoring of the system employs a hybrid
quantum-classical description of the quantum probe and the unknown classical
perturbations, and it provides a general formalism to establish the
achievements of the sensing scheme and how they depend on different parameters.


title:
Exciton-coherence generation through diabatic and adiabatic dynamics of
  Floquet state

abstract:
  Floquet engineering of electronic systems is a promising way of controlling
quantum material properties on an ultrafast time scale. So far, the energy
structure of Floquet states in solids has been observed through time and
angle-resolved photoelectron spectroscopy or pump-probe measurement techniques.
However, the dynamical aspects of the photon-dressed states under ultrashort
pulse driving have not been explored yet. Their dynamics become highly
sensitive to the envelope of the driving field when the light-matter
interaction enters non-perturbative regime, and thus, understanding of them is
crucial for ultrafast manipulation of quantum state. Here, we observed coherent
exciton emissions under intense and non-resonant mid-infrared fields in
monolayer WSe2 at room temperature, which is unexpected in perturbative
nonlinear optics. Together with numerical calculations, our measurements
revealed that the coherent exciton emission reflects the diabatic and adiabatic
dynamics of Floquet states. Our results provide a new approach to probing the
dynamics of the Floquet state and lead to control of quantum materials through
pulse shaping of the driving field.


title:
Massive Unsourced Random Access over Rician Fading Channels: Design,
  Analysis, and Optimization

abstract:
  In this paper, we investigate an unsourced random access scheme for massive
machine-type communications (mMTC) in the sixth-generation (6G) wireless
networks with sporadic data traffic. Firstly, we establish a general framework
for massive unsourced random access based on a two-layer signal coding, i.e.,
an outer code and an inner code. In particular, considering Rician fading in
the scenario of mMTC, we design a novel codeword activity detection algorithm
for the inner code of unsourced random access based on the distribution of
received signals by exploiting the maximum likelihood (ML) method. Then, we
analyze the performance of the proposed codeword activity detection algorithm
exploiting Fisher Information Matrix, which facilitates the derivative of the
approximated distribution of the estimation error of the codeword activity
vector when the number of base station (BS) antennas is sufficiently large.
Furthermore, for the outer code, we propose an optimization algorithm to
allocate the lengths of message bits and parity check bits, so as to strike a
balance between the error probability and the complexity required for outer
decoding. Finally, extensive simulation results validate the effectiveness of
the proposed detection algorithm and the optimized length allocation scheme
compared with an existing detection algorithm and a fixed length allocation
scheme.


title: Scarring effects of the COVID-19 pandemic on the Italian labour market

abstract:
  The COVID-19 pandemic raised the share of inactive individuals in 2020 in
Italy, mostly at the expense of permanent and fix-term employment. We document
sizable asymmetric effects across categories of individuals, defined on the
basis of gender, age and geographical area. In particular, the pandemic
disproportionately affected females and, among those, more severely the ones
living in large households in the North and Center of Italy. These findings
find a rationale both in the presence of young children, which imposes strong
constraints to the female labour force participation, and in the worse labour
market opportunities in the South, which lead to a strong self-selection of
women in the labour market. Despite the short period of observation after the
burst of COVID-19 pandemic (four quarters of 2020), the identified effects
appear large and persistent, raising awareness about the likely long-lasting
scarring effects of the COVID-19 pandemic on the labour market choices and
opportunities of Italian women.


title:
Application of Event-Triggered Sliding Mode Control of 2-DOF Humanoid's
  Lower-Limb Powered by Series Elastic Actuator

abstract:
  This paper proposes an event-triggered sliding mode control (SMC) scheme
combined with a backstepping algorithm for control of 2-DOF humanoid's
lower-limb powered by Series elastic actuator (SEA). First, the modelling
process for the lower-limb system is implemented by using the Euler-Lagrange
theory. With the obtained dynamical equations of lower-limb, the model of the
SEA is achieved in both mechanical and electrical perspectives. Then, the
event-triggered SMC approach is utilized to ensure the system's stability and
eliminate the effect of bounded external disturbance. Next, some assumptions
and designed thresholds for the tracking error are given, together with the
proof for the convergence of the inter-event time. The backstepping algorithm
is applied in the end to determine the needed input control voltage signal.
Finally, the results of this research are demonstrated through some simulations
in order to prove the effi-ciency and appropriation of this method.


title: Orbit determination of the moons of the Pluto-Charon system

abstract:
  Four small moons (Styx, Nix, Kerberos and Hydra) are at present known to
orbit around the barycenter of Pluto and Charon, which are themselves
considered a binary dwarf-planet due to their relatively high mass ratio. The
central, non-axisymmetric potential induces moon orbits inconvenient to be
described by Keplerian osculating elements. Here, we report that observed
orbital variations, may not be the result of orbital eccentricities or
observational uncertainties, but may be due to forced oscillations caused by
the central binary. We show, using numerical integration and analytical
considerations, that the differences reported on their orbital elements, may
well arise from this intrinsic behavior rather than limitations on our
instruments.


title:
Revisiting the action of a subgroup of the modular group on imaginary
  quadratic number fields

abstract:
  Consider the modular group $\mbox{PSL}(2,\mathbb{Z})=\langle x, \, y \,|\,
x^2=y^3=1\rangle$ generated by the transformations $x: z\mapsto -1/z$ and
$y:z\mapsto (z-1)/z$. Let $H$ be the proper subgroup $\langle y,\,v\,|\,
y^3=v^3=1\rangle$ of $\mbox{PSL}(2,\mathbb{Z})$, where $v=xyx$. The reference
(M. Ashiq and Q. Mushtaq, {\em Actions of a subgroup of the modular group on an
imaginary quadratic field}, Quasigropus and Related Systems {\bf 14} (2006),
133--146) proposed results concerning the action of $H$ on the subset
$\{\frac{a+\sqrt{-n}}{c}\,|\, a,b=\frac{a^2+n}{c}, c \in \mathbb{Z}, c\neq 0\}$
of the imaginary quadratic number field $\mathbb{Q}(\sqrt{-n})$ for a positive
square-free integer $n$. In the current article, the author points out and
corrects errors appearing in the aforementioned reference. Most importantly,
the corrected estimate for the number of orbits arising from this action is
given.


title: Bayesian Robust Tensor Ring Model for Incomplete Multiway Data

abstract:
  Low-rank tensor completion aims to recover missing entries from the observed
data. However, the observed data may be disturbed by noise and outliers.
Therefore, robust tensor completion (RTC) is proposed to solve this problem.
The recently proposed tensor ring (TR) structure is applied to RTC due to its
superior abilities in dealing with high-dimensional data with predesigned TR
rank. To avoid manual rank selection and achieve a balance between low-rank
component and sparse component, in this paper, we propose a Bayesian robust
tensor ring (BRTR) decomposition method for RTC problem. Furthermore, we
develop a variational Bayesian (VB) algorithm to infer the probability
distribution of posteriors. During the learning process, the frontal slices of
previous tensor and horizontal slices of latter tensor shared with the same TR
rank with zero components are pruned, resulting in automatic rank
determination. Compared with existing methods, BRTR can automatically learn TR
rank without manual fine-tuning of parameters. Extensive experiments indicate
that BRTR has better recovery performance and ability to remove noise than
other state-of-the-art methods.


title:
Influence of spherical anisotropy on optical mass sensing in a
  molecular-plasmonic optomechanical system

abstract:
  We use an all-optical pump-probe method to develop a mass sensing mechanism
in a molecular plasmonic system at room temperature. The system consists of a
double-clamped graphene nanoribbon that parametrically interacts with two types
of isotropic and anisotropic spherical plasmonic cavities in the presence of a
strong pump field and a weak probe pulse. Based on the mode-selective
quantization scheme and analogy with the canonical model of the cavity
optomechanics, we formulate the Hamiltonian of the system in terms of the
electromagnetic Greens tensor. In this manner, we derive an explicit form of
size-dependent optomechanical coupling function and plasmonic damping rate,
which include the modal, geometrical, and material features of the plasmonic
structure. Engineering material features of the plasmonic nanostructure, we
find that the intensity of the probe field transmission spectrum for radially
anisotropic spherical nanocavity enhances significantly compared to the silver
sphere nanocavity due to the mode volume reduction. This scheme can provide to
achieve the minimum measurable mass at room temperature.


title:
High Modulation Efficiency and Large Bandwidth Thin-Film Lithium Niobate
  Modulator for Visible Light

abstract:
  We experimentally demonstrate a visible light thin-film lithium niobate
modulator at 532 nm. The waveguides feature a propagation loss of 2.2 dB/mm
while a grating for fiber interface has a coupling loss of 5 dB. Our
demonstrated modulator represents a low voltage-length product of 1.1 V*cm and
a large bandwidth beyond 30 GHz.


title:
A self-similar morphology detected in a composite produced by
  densification of co-crumpled metallic thin foils

abstract:
  A new kind of composite were manufactured by densification of co-crumpled
aluminium and tantalum thin foils using close die compression. It was shown by
optical micrography that its microstructure is highly interlocked. The
morphology was analysed quantitatively in terms of the following three
parameters: the area of the foil interface per unit volume, the interface
tortuosity, and a characteristic of the local orientation of the foil surface.
Based on these parameters, co-crumpled material studied has been compared with
conventional laminates. A fractal nature of its self-similar structure was
revealed.


title:
Efficient Stochastic Simulation of Network Topology Effects on the Peak
  Number of Infections in Epidemic Outbreaks

abstract:
  This paper investigates the effect of the structure of the contact network on
the dynamics of the epidemic outbreak. In particular, we focus on the peak
number of critically infected nodes (PCIN), determining the maximum workload of
intensive healthcare units which should be kept low. As a model and simulation
method, we develop a continuous-time Markov chain (CTMC) model and an efficient
simulation-based on Gillespie's Stochastic Simulation Algorithm (SSA). This
methods combine a realistic approximation of the stochastic process not relying
on the assumptions of mean-field models and large asymptotically large
population sizes as in differential equation models, and at the same time an
efficient way to simulate networks of moderate size. The approach is analysed
for different scenarios, based on data from the COVID-19 outbreak and
demographic data from Ukraine. From these results we extract network topology
features that need to be considered to effectively decrease the peak number of
infections. The CTMC simulation is implemented in python and integrated in a
dashboard that can be used for interactive exploration and it is made openly
available.


title:
Computer-assisted proofs of Hopf bubbles and degenerate Hopf
  bifurcations

abstract:
  We present a computer-assisted approach to prove the existence of Hopf
bubbles and degenerate Hopf bifurcations in ordinary and delay differential
equations. We apply the method to rigorously investigate these nonlocal
bifurcation structures in the FitzHugh- Nagumo equation, the extended Lorenz-84
model and a time-delay SI model.


title:
Focusing nonlocal nonlinear Schr\"odinger equation with asymmetric
  boundary conditions: large-time behavior

abstract:
  We consider the focusing integrable nonlocal nonlinear Schr\"odinger equation
\[\mathrm{i}q_{t}(x,t)+q_{xx}(x,t)+2q^{2}(x,t)\bar{q}(-x,t)=0\] with asymmetric
nonzero boundary conditions: $q(x,t)\to\pm A\mathrm{e}^{-2\mathrm{i}A^2t}$ as
$x\to\pm\infty$, where $A>0$ is an arbitrary constant. The goal of this work is
to study the asymptotics of the solution of the initial value problem for this
equation as $t\to+\infty$. For a class of initial values we show that there
exist three qualitatively different asymptotic zones in the $(x,t)$ plane.
Namely, there are regions where the parameters are modulated (being dependent
on the ratio $x/t$) and a central region, where the parameters are unmodulated.
This asymptotic picture is reminiscent of that for the defocusing classical
nonlinear Schr\"odinger equation, but with some important differences. In
particular, the absolute value of the solution in all three regions depends on
details of the initial data.


title:
Thinking Outside the Ball: Optimal Learning with Gradient Descent for
  Generalized Linear Stochastic Convex Optimization

abstract:
  We consider linear prediction with a convex Lipschitz loss, or more
generally, stochastic convex optimization problems of generalized linear form,
i.e.~where each instantaneous loss is a scalar convex function of a linear
function. We show that in this setting, early stopped Gradient Descent (GD),
without any explicit regularization or projection, ensures excess error at most
$\epsilon$ (compared to the best possible with unit Euclidean norm) with an
optimal, up to logarithmic factors, sample complexity of
$\tilde{O}(1/\epsilon^2)$ and only $\tilde{O}(1/\epsilon^2)$ iterations. This
contrasts with general stochastic convex optimization, where
$\Omega(1/\epsilon^4)$ iterations are needed Amir et al. [2021b]. The lower
iteration complexity is ensured by leveraging uniform convergence rather than
stability. But instead of uniform convergence in a norm ball, which we show can
guarantee suboptimal learning using $\Theta(1/\epsilon^4)$ samples, we rely on
uniform convergence in a distribution-dependent ball.


title:
Global martingale and pathwise solutions and infinite regularity of
  invariant measures for a stochastic modified Swift-Hohenberg equation

abstract:
  We consider a 2D stochastic modified Swift-Hohenberg equations with
multiplicative noise and periodic boundary. First, we establish the existence
of local and global martingale and pathwise solutions in the regular Sobolev
space $H^{2m}$ for each $m\geqslant1$. Associated with the unique global
pathwise solution, we obtain a Markovian transition semigroup. Then, we show
the existence of invariant measures and ergodic invariant measures for this
Markovian semigroup on $H^{2m}$. At last, we improve the regularity of the
obtained invariant measures to $H^{2(m+1)}$. With appropriate conditions on the
diffusion coefficient, we can deduce the infinite regularity of the invariant
measures, which was conjectured by Glatt-Holtz \textit{et al.} in their
situation.


title: DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following

abstract:
  Language-guided Embodied AI benchmarks requiring an agent to navigate an
environment and manipulate objects typically allow one-way communication: the
human user gives a natural language command to the agent, and the agent can
only follow the command passively. We present DialFRED, a dialogue-enabled
embodied instruction following benchmark based on the ALFRED benchmark.
DialFRED allows an agent to actively ask questions to the human user; the
additional information in the user's response is used by the agent to better
complete its task. We release a human-annotated dataset with 53K task-relevant
questions and answers and an oracle to answer questions. To solve DialFRED, we
propose a questioner-performer framework wherein the questioner is pre-trained
with the human-annotated data and fine-tuned with reinforcement learning. We
make DialFRED publicly available and encourage researchers to propose and
evaluate their solutions to building dialog-enabled embodied agents.


title:
Topology-Preserving Segmentation Network: A Deep Learning Segmentation
  Framework for Connected Component

abstract:
  Medical image segmentation, which aims to automatically extract anatomical or
pathological structures, plays a key role in computer-aided diagnosis and
disease analysis. Despite the problem has been widely studied, existing methods
are prone to topological errors. In medical imaging, the topology of the
structure, such as the kidney or lung, is usually known. Preserving the
topology of the structure in the segmentation process is of utmost importance
for accurate image analysis. In this work, a novel learning-based segmentation
model is proposed. A {\it topology-preserving segmentation network (TPSN)} is
trained to give an accurate segmentation result of an input image that
preserves the prescribed topology. TPSN is a deformation-based model that
yields a deformation map through a UNet, which takes the medical image and a
template mask as inputs. The main idea is to deform a template mask describing
the prescribed topology by a diffeomorphism to segment the object in the image.
The topology of the shape in the template mask is well preserved under the
diffeomorphic map. The diffeomorphic property of the map is controlled by
introducing a regularization term related to the Jacobian in the loss function.
As such, a topology-preserving segmentation result can be guaranteed.
Furthermore, a multi-scale TPSN is developed in this paper that incorporates
multi-level information of images to produce more precise segmentation results.
To evaluate our method, we applied the 2D TPSN on Ham10000 and 3D TPSN on
KiTS21. Experimental results illustrate our method outperforms the baseline
UNet segmentation model with/without connected-component analysis (CCA) by both
the dice score and IoU score. Besides, results show that our method can produce
reliable results even in challenging cases, where pixel-wise segmentation
models by UNet and CCA fail to obtain accurate results.


title:
Time-resolved Imaging of Stochastic Cascade Reactions over a
  Submillisecond to Second Time Range at the Angstrom Level

abstract:
  Most chemical reactions are cascade reactions in which a series of transient
intermediates appear and disappear stochastically over an extended period. The
mechanisms of such reactions are challenging to study, even in ultrafast
pump-probe experiments. The dimerization of a van der Waals dimer of
[60]fullerene producing a short carbon nanotube is a typical cascade reaction
and is probably the most frequently studied in carbon materials chemistry. As
many as 23 intermediates were predicted by theory, but only the first stable
one has been verified experimentally. With the aid of fast electron microscopy,
we obtained cinematographic recordings of individual molecules at a maximum
frame rate of 1600 frames per second. Using Chambolle total variation algorithm
processing and automated cross-correlation image matching analysis, we report
on the identification of several metastable intermediates by their shape and
size. Although the reaction events occurred stochastically, varying the
lifetime of each intermediate accordingly, the average lifetime for each
intermediate structure could be obtained from statistical analysis of many
cinematographic images for the cascade reaction. Among the shortest-living
intermediates, we detected one that lasted for less than 3 ms in three
independent cascade reactions. We anticipate that the rapid technological
development of microscopy and image processing will soon initiate an era of
cinematographic studies of chemical reactions.


title: Galactic molecular clouds as sources of secondary positrons

abstract:
  Secondary positrons produced inside Galactic Molecular Clouds (GMCs) can
significantly contribute to the observed positron spectrum on Earth.
Multi-wavelength data of GMCs are particularly useful in building this model. A
very recent survey implemented the optical/IR dust extinction measurements to
trace 567 GMCs within 4 kpc of Earth, residing in the Galactic plane. We use
the updated catalog of GMCs reported in recent papers, distributed in the
Galactic plane, to find the secondary positrons produced in them in
interactions of cosmic rays with molecular hydrogen. Moreover, by analyzing the
\textit{Fermi}-LAT data, new GMCs have been discovered near the Galactic plane.
We also include some of these GMCs closest to the Earth, where cosmic ray
interactions produce secondaries. It has been speculated earlier that cosmic
rays may be reaccelerated in some GMCs. We select 7 GMCs out of 567 GMCs
recently reported, within 4 kpc of Earth, where reacceleration due to
magnetized turbulence is assumed. We include a hardened component of secondary
positrons produced from the interaction of reaccelerated CRs in those 7 GMCs.
We use publicly available code \texttt{DRAGON} for our simulation setup to
study CR propagation in the Galaxy and show that the observed positron spectrum
can be well explained in the energy range of 1 to 1000 GeV by our
self-consistent model.


title:
Aging transition under discrete time-dependent coupling: Restoring
  rhythmicity from aging

abstract:
  We explore the aging transition in a network of globally coupled
Stuart-Landau oscillators under a discrete time-dependent coupling. In this
coupling, the connections among the oscillators are turned ON and OFF in a
systematic manner, having either a symmetric or an asymmetric time interval. We
discover that depending upon the time period and duty cycle of the ON-OFF
intervals, the aging region shrinks drastically in the parameter space,
therefore promoting restoration of oscillatory dynamics from the aging. In the
case of symmetric discrete coupling (where the ON-OFF intervals are equal), the
aging zone decreases significantly with the resumption of dynamism with an
increasing time period of the ON-OFF intervals. On the other hand, in the case
of asymmetric coupling (where the ON-OFF intervals are not equal), we find that
the ratio of the ON and OFF intervals controls the aging dynamics: the aging
state is revoked more effectively if the interval of the OFF state is greater
than the ON state. Finally, we study the transition in aging using a discrete
pulse coupling: we note that the pulse interval plays a crucial role in
determining the aging region. For all the cases of discrete time-dependent
couplings, the aging regions are shrinking and the rhythmicity gets enhanced in
a controlled manner. Our findings suggest that this type of coupling can act as
a noninvasive way to restore the oscillatory dynamics from an aging state in a
network of coupled oscillators.


title: A logic-based algorithmic meta-theorem for mim-width

abstract:
  We introduce a logic called distance neighborhood logic with acyclicity and
connectivity constraints ($\mathsf{A\&C~DN}$ for short) which extends
existential $\mathsf{MSO_1}$ with predicates for querying neighborhoods of
vertex sets and for verifying connectivity and acyclicity of vertex sets in
various powers of a graph. Building upon [Bergougnoux and Kant\'e, ESA 2019;
SIDMA 2021], we show that the model checking problem for every fixed
$\mathsf{A\&C~DN}$ formula is solvable in $n^{O(w)}$ time when the input graph
is given together with a branch decomposition of mim-width $w$. Nearly all
problems that are known to be solvable in polynomial time given a branch
decomposition of constant mim-width can be expressed in this framework. We add
several natural problems to this list, including problems asking for diverse
sets of solutions. Our model checking algorithm is efficient whenever the given
branch decomposition of the input graph has small index in terms of the
$d$-neighborhood equivalence [Bui-Xuan, Telle, and Vatshelle, TCS 2013]. We
therefore unify and extend known algorithms for tree-width, clique-width and
rank-width. Our algorithm has a single-exponential dependence on these three
width measures and asymptotically matches run times of the fastest known
algorithms for several problems. This results in algorithms with tight run
times under the Exponential Time Hypothesis ($\mathsf{ETH}$) for tree-width and
clique-width; the above mentioned run time for mim-width is nearly tight under
the $\mathsf{ETH}$ for several problems as well. Our results are also tight in
terms of the expressive power of the logic: we show that already slight
extensions of our logic make the model checking problem para-$\mathsf{NP}$-hard
when parameterized by mim-width plus formula length.


title:
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical
  Cyclone Track Forecast

abstract:
  Tropical cyclone (TC) is an extreme tropical weather system and its
trajectory can be described by a variety of spatio-temporal data. Effective
mining of these data is the key to accurate TCs track forecasting. However,
existing methods face the problem that the model complexity is too high or it
is difficult to efficiently extract features from multi-modal data. In this
paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) --
a novel multi-horizon tropical cyclone track forecasting model which fuses the
multi-modal features efficiently. DBF-Net contains a TC features branch that
extracts temporal features from 1D inherent features of TCs and a pressure
field branch that extracts spatio-temporal features from reanalysis 2D pressure
field. Through the encoder-decoder-based architecture and efficient feature
fusion, DBF-Net can fully mine the information of the two types of data, and
achieve good TCs track prediction results. Extensive experiments on historical
TCs track data in the Northwest Pacific show that our DBF-Net achieves
significant improvement compared with existing statistical and deep learning
TCs track forecast methods.


title: Towards Robust Off-policy Learning for Runtime Uncertainty

abstract:
  Off-policy learning plays a pivotal role in optimizing and evaluating
policies prior to the online deployment. However, during the real-time serving,
we observe varieties of interventions and constraints that cause inconsistency
between the online and offline settings, which we summarize and term as runtime
uncertainty. Such uncertainty cannot be learned from the logged data due to its
abnormality and rareness nature. To assert a certain level of robustness, we
perturb the off-policy estimators along an adversarial direction in view of the
runtime uncertainty. It allows the resulting estimators to be robust not only
to observed but also unexpected runtime uncertainties. Leveraging this idea, we
bring runtime-uncertainty robustness to three major off-policy learning
methods: the inverse propensity score method, reward-model method, and doubly
robust method. We theoretically justify the robustness of our methods to
runtime uncertainty, and demonstrate their effectiveness using both the
simulation and the real-world online experiments.


title:
A one-size-fits-all artificial pancreas for people with type 1 diabetes
  based on physiological insight and feedback control

abstract:
  We propose a model-free artificial pancreas (AP) for people with type 1
diabetes. The algorithmic parameters are tuned to a virtual population of
1,000,000 individuals, and the AP repeatedly estimates the basal and bolus
insulin requirements necessary for maintaining normal blood glucose levels.
Therefore, the AP can be used without healthcare personnel or engineers
customizing the algorithm to each user. The estimates are based on bodyweight,
measurements from a continuous glucose monitor (CGM), and estimates of the meal
carbohydrate contents. In a virtual clinical trial with all 1,000,000
individuals (i.e., a Monte Carlo closed-loop simulation), the AP achieves a
mean time in range of more than 87% and almost 89% of the participants satisfy
several glycemic targets.


title:
Explorations of pseudo-Dirac dark matter having keV splittings and
  interacting via transition electric and magnetic dipole moments

abstract:
  We study a minimal model of pseudo-Dirac dark matter, interacting through
transition electric and magnetic dipole moments. Motivated by the fact that
xenon experiments can detect electrons down to $\sim\,$keV recoil energies, we
consider $\mathcal{O}$(keV) splittings between the mass eigenstates. We study
the production of this dark matter candidate via the freeze-in mechanism. We
discuss the direct detection signatures of the model arising from the
down-scattering of the heavier state, that are produced in Solar upscattering,
finding observable signatures at the current and near-future xenon based direct
detection experiments. We also study complementary constraints on the model
from fixed target experiments, lepton colliders, supernovae cooling and
cosmology. We show that next generation xenon experiments can either discover
this well motivated and minimal dark matter candidate, or constrain how
strongly inelastic dark matter can interact via the dipole moment operators.


title: Enumeration of chordal planar graphs and maps

abstract:
  We determine the number of labelled chordal planar graphs with $n$ vertices,
which is asymptotically $c_1\cdot n^{-5/2} \gamma^n n!$ for a constant $c_1>0$
and $\gamma \approx 11.89235$. We also determine the number of rooted simple
chordal planar maps with $n$ edges, which is asymptotically $c_2 n^{-3/2}
\delta^n$, where $\delta = 1/\sigma \approx 6.40375$, and $\sigma$ is an
algebraic number of degree 12. The proofs are based on combinatorial
decompositions and singularity analysis. Chordal planar graphs (or maps) are a
natural example of a subcritical class of graphs in which the class of
3-connected graphs is relatively rich. The 3-connected members are precisely
chordal triangulations, those obtained starting from $K_4$ by repeatedly adding
vertices adjacent to an existing triangular face.


title: Data Overlap: A Prerequisite For Disentanglement

abstract:
  Learning disentangled representations with variational autoencoders (VAEs) is
often attributed to the regularisation component of the loss. In this work, we
highlight the interaction between data and the reconstruction term of the loss
as the main contributor to disentanglement in VAEs. We note that standardised
benchmark datasets are constructed in a way that is conducive to learning what
appear to be disentangled representations. We design an intuitive adversarial
dataset that exploits this mechanism to break existing state-of-the-art
disentanglement frameworks. Finally, we provide solutions in the form of a
modified reconstruction loss suggesting that VAEs are accidental distance
learners.


title:
Restricted modules for gap-$p$ Virasoro algebra and twisted modules for
  certain vertex algebras

abstract:
  This paper studies restricted modules of gap-$p$ Virasoro algebra $\L$ and
their intrinsic connection to twisted modules of certain vertex algebras. We
first establish an equivalence between the category of restricted $\L$-modules
of level $\el$ and the category of twisted modules of vertex algebra
$V_{\mathcal{N}_{p}}(\el,0)$, where $\mathcal{N}_{p}$ is a new Lie algebra,
$\el:=(\ell_{0},0,\cdots,0)\in\C^{\halfp+1}$,
  $\ell_{0}\in\C$ is the action of the Virasoro center. Then we focus on the
construction and classification of simple restricted $\L$-modules of level
$\el$. More explicitly, we give a uniform construction of simple restricted
$\L$-modules as induced modules. We present several equivalent
characterizations of simple restricted $\L$-modules, as locally nilpotent
(equivalently, locally finite) modules with respect to certain positive part of
$\L$. Moreover, simple restricted $\L$-modules of level $\el$ are classified.
They are either highest weight modules or simple induced modules. At the end,
we exhibit several concrete examples of simple restricted $\L$-modules of level
$\el$ (including Whittaker modules).


title:
Entanglement spectrum and quantum phase diagram of the long-range XXZ
  chain

abstract:
  Entanglement is a central feature of many-body quantum system and plays a
unique role in quantum phase transitions. In many cases, the entanglement
spectrum, which represents the spectrum of the density matrix of a bipartite
system, contains valuable information beyond the sole entanglement entropy.
Here we investigate the entanglement spectrum of the long-range XXZ model. We
show that within the critical phase, it exhibits a remarkable self-similarity.
The breakdown of self-similarity and the transition away from a Luttinger
liquid is consistent with renormalization group theory. Combining the two, we
are able to determine the quantum phase diagram of the model and precisely
locate the corresponding phase transitions. Our results are confirmed by
numerically-exact calculations using tensor network techniques. Moreover, we
show that the self-similar rescaling extends to the geometrical entanglement as
well as the Luttinger parameter in the critical phase. Our results pave the way
to further studies of entanglement properties in long-range quantum models.


title:
Drowsiness detection using combined neuroimaging: Overview and
  Challenges

abstract:
  Brain-computer interfaces (BCIs) collect, analyze, and convert brain activity
into instructions and send it to the detection system. BCI is becoming popular
in under-brain activities in certain conditions such as attention-based tasks.
Researchers have recently used combined neuroimaging techniques such as
EEG+fNIRS and EEG+fMRI to solve many real-world problems. Drowsiness detection
or sleep inertia is one of the central research areas for the combined
neuroimaging techniques. This paper aims to investigate the recent application
of combined neuroimaging-based BCI on drowsiness detection or sleep inertia. To
this end, this is the only overview paper of the combined neuroimaging-based
drowsiness detection system.


title: Induced dynamics of non-autonomous dynamical systems

abstract:
  Let $f_{0,\infty}=\{f_n\}_{n=0}^{\infty}$ be a sequence of continuous
self-maps on a compact metric space $X$. The non-autonomous dynamical system
$(X,f_{0,\infty})$ induces the set-valued system $(\mathcal{K}(X),
\bar{f}_{0,\infty})$ and the fuzzified system
$(\mathcal{F}(X),\tilde{f}_{0,\infty})$. We prove that under some natural
conditions, positive topological entropy of $(X,f_{0,\infty})$ implies infinite
entropy of $(\mathcal{K}(X),\bar{f}_{0,\infty})$ and
$(\mathcal{F}(X),\tilde{f}_{0,\infty})$, respectively; and zero entropy of
$(S^1,f_{0,\infty})$ implies zero entropy of some invariant subsystems of
$(\mathcal{K}(S^1),\bar{f}_{0,\infty})$ and
$(\mathcal{F}(S^1),\tilde{f}_{0,\infty})$, respectively. We confirm that
$(\mathcal{K}(I), \bar{f})$ and $(\mathcal{F}(I), \tilde{f})$ have infinite
entropy for any transitive interval map $f$. In contrast, we construct a
transitive non-autonomous system $(I, f_{0,\infty})$ such that both
$(\mathcal{K}(I), \bar{f}_{0,\infty})$ and $(\mathcal{F}(I),
\tilde{f}_{0,\infty})$ have zero entropy. We obtain that
$(\mathcal{K}(X),\bar{f}_{0,\infty})$ is chain weakly mixing of all orders if
and only if $(\mathcal{F}^1(X),\tilde{f}_{0,\infty})$ is so, and chain mixing
(resp. $h$-shadowing and multi-$\mathscr{F}$-sensitivity) among
$(X,f_{0,\infty})$, $(\mathcal{K}(X),\bar{f}_{0,\infty})$ and
$(\mathcal{F}^1(X),\tilde{f}_{0,\infty})$ are equivalent, where
$(\mathcal{F}^1(X),\tilde{f}_{0,\infty})$ is the induced normal fuzzification.


title:
Vortex shedding frequency of a moving obstacle in a Bose-Einstein
  condensate

abstract:
  We experimentally investigate the periodic vortex shedding dynamics in a
highly oblate Bose-Einstein condensate using a moving penetrable Gaussian
obstacle. The shedding frequency $f_v$ is measured as a function of the
obstacle velocity $v$ and characterized by a linear relationship of
$f_v=a(v-v_c)$ with $v_c$ being the critical velocity. The proportionality
constant $a$ is linearly decreased with a decrease in the obstacle strength,
whereas $v_c$ approaches the speed of sound. When the obstacle size increases,
both $a$ and $v_c$ are decreased. The critical vortex shedding is further
investigated for an oscillating obstacle and found to be consistent with the
measured $f_v$. When the obstacle's maximum velocity exceeds $v_c$ but its
oscillation amplitude is not large enough to create a vortex dipole, we observe
that vortices are generated in the low-density boundary region of the trapped
condensate, which is attributed to the phonon emission from the oscillating
obstacle. Finally, we discuss a possible asymptotic association of $a$ with the
Strouhal number in the context of universal shedding dynamics of a superfluid.


title:
A Robust Multimodal Remote Sensing Image Registration Method and System
  Using Steerable Filters with First- and Second-order Gradients

abstract:
  Co-registration of multimodal remote sensing images is still an ongoing
challenge because of nonlinear radiometric differences (NRD) and significant
geometric distortions (e.g., scale and rotation changes) between these images.
In this paper, a robust matching method based on the Steerable filters is
proposed consisting of two critical steps. First, to address severe NRD, a
novel structural descriptor named the Steerable Filters of first- and
second-Order Channels (SFOC) is constructed, which combines the first- and
second-order gradient information by using the steerable filters with a
multi-scale strategy to depict more discriminative structure features of
images. Then, a fast similarity measure is established called Fast Normalized
Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform
technique and the integral image to improve the matching efficiency.
Furthermore, to achieve reliable registration performance, a coarse-to-fine
multimodal registration system is designed consisting of two pivotal modules.
The local coarse registration is first conducted by involving both detection of
interest points (IPs) and local geometric correction, which effectively
utilizes the prior georeferencing information of RS images to address global
geometric distortions. In the fine registration stage, the proposed SFOC is
used to resist significant NRD, and to detect control points between multimodal
images by a template matching scheme. The performance of the proposed matching
method has been evaluated with many different kinds of multimodal RS images.
The results show its superior matching performance compared with the
state-of-the-art methods. Moreover, the designed registration system also
outperforms the popular commercial software in both registration accuracy and
computational efficiency. Our system is available at
https://github.com/yeyuanxin110.


title:
Experimental study of turbulent thermal diffusion of particles in
  inhomogeneous and anisotropic turbulence

abstract:
  We study experimentally turbulent thermal diffusion of small particles in
inhomogeneous and anisotropic stably stratified turbulence produced by one
oscillating grid in the air flow. The velocity fields have been measured using
a Particle Image Velocimetry (PIV). We have determined various turbulence
characteristics: the mean and turbulent velocities, two-point correlation
functions of the velocity field and an integral scale of turbulence from the
measured velocity fields. The temperature field have been measured with a
temperature probe equipped with 12 E thermocouples. Spatial distributions of
micron size particles have been determined by a PIV system using the effect of
the Mie light scattering by particles in the flow. The experiments have
demonstrated that particles are accumulated at the minimum of mean fluid
temperature due to phenomenon of turbulent thermal diffusion. Using measured
spatial distributions of particles and temperature fields, we have determined
the effective turbulent thermal diffusion coefficient of particles in
inhomogeneous temperature stratified turbulence. This experimental study has
clearly detected phenomenon of turbulent thermal diffusion in inhomogeneous
turbulence.


title: Influence of entropy changes on reactor period

abstract:
  The period of a nuclear reactor is considered as the average value of the
first passage time. This approach makes it possible to find the effect of
entropy changes on the period of the reactor during this period. Differences in
the reactor periods with and without entropy changes are found, as well as
possible features of the behavior of the reactor period when entropy changes
are taken into account. The relation of entropy changes to the period of the
reactor provides opportunities for controlling the behavior of the reactor.


title:
Gamow shell model description of the radiative capture reaction
  $^8$Li$(n,\gamma)$$^9$Li

abstract:
  The $^8$Li$(n,\gamma)$$^9$Li reaction plays a critical role in several
reaction chains leading to the nucleosynthesis of $A>12$ nuclei. Due to
unstable nature of $^8$Li and the unavailability of neutron targets, direct
measurements of this reaction are exceedingly difficult. Only upper limits of
this cross section, provided by the indirect experiments, have been obtained so
far. In this work, we use the Gamow shell model (GSM) in the coupled-channel
representation (GSM-CC) to study the properties of $^9$Li and the radiative
capture reaction $^8$Li$(n,\gamma)$$^9$Li. In GSM-CC calculations, a
translationally invariant Hamiltonian is used with a finite-range two-body
interaction tuned to reproduce the low-energy spectra of $^{8-9}$Li. In the
calculation of $^8$Li$(n,\gamma)$$^9$Li cross section, all relevant E1, M1, and
E2 transitions from the initial continuum states to the final bound states
${3/2}_1^-$, ${1/2}_1^-$ and the resonance ${5/2}_1^-$ of $^9$Li are included.
The GSM-CC approach reproduces the experimental low-energy spectrum, neutron
emission threshold, and spectroscopic factors in $^9$Li. The calculated
reaction rate is consistent with the experimental upper limit of the reaction
rate obtained in the indirect measurements at stellar energies. The GSM-CC
calculations suggest that the $^8$Li$(n,\gamma)$$^9$Li reaction does not reduce
significantly heavy-element production via the main chain
$^7$Li($n,\gamma$)$^8$Li($\alpha,n$)$^{11}$B($n,\gamma$)$^{12}$B($\beta^+$)$^{12}$C.
Major contribution to the calculated cross section is given by the direct E1
transition to the ground state of $^8$Li. The contribution of excited states to
the reaction rate does not exceed $\sim$20% of the total reaction rate.


title:
Split HE: Fast Secure Inference Combining Split Learning and Homomorphic
  Encryption

abstract:
  This work presents a novel protocol for fast secure inference of neural
networks applied to computer vision applications. It focuses on improving the
overall performance of the online execution by deploying a subset of the model
weights in plaintext on the client's machine, in the fashion of SplitNNs. We
evaluate our protocol on benchmark neural networks trained on the CIFAR-10
dataset using SEAL via TenSEAL and discuss runtime and security performances.
Empirical security evaluation using Membership Inference and Model Extraction
attacks showed that the protocol was more resilient under the same attacks than
a similar approach also based on SplitNN. When compared to related work, we
demonstrate improvements of 2.5x-10x for the inference time and 14x-290x in
communication costs.


title:
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly
  Supervised Relation Extraction

abstract:
  Distant supervision assumes that any sentence containing the same entity
pairs reflects identical relationships. Previous works of distantly supervised
relation extraction (DSRE) task generally focus on sentence-level or bag-level
de-noising techniques independently, neglecting the explicit interaction with
cross levels. In this paper, we propose a hierarchical contrastive learning
Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy
sentences, which integrate the global structural information and local
fine-grained interaction. Specifically, we propose a three-level hierarchical
learning framework to interact with cross levels, generating the de-noising
context-aware representations via adapting the existing multi-head
self-attention, named Multi-Granularity Recontextualization. Meanwhile, pseudo
positive samples are also provided in the specific level for contrastive
learning via a dynamic gradient-based data augmentation strategy, named Dynamic
Gradient Adversarial Perturbation. Experiments demonstrate that HiCLRE
significantly outperforms strong baselines in various mainstream DSRE datasets.


title:
Robust Self-Supervised LiDAR Odometry via Representative Structure
  Discovery and 3D Inherent Error Modeling

abstract:
  The correct ego-motion estimation basically relies on the understanding of
correspondences between adjacent LiDAR scans. However, given the complex
scenarios and the low-resolution LiDAR, finding reliable structures for
identifying correspondences can be challenging. In this paper, we delve into
structure reliability for accurate self-supervised ego-motion estimation and
aim to alleviate the influence of unreliable structures in training, inference
and mapping phases. We improve the self-supervised LiDAR odometry substantially
from three aspects: 1) A two-stage odometry estimation network is developed,
where we obtain the ego-motion by estimating a set of sub-region
transformations and averaging them with a motion voting mechanism, to encourage
the network focusing on representative structures. 2) The inherent alignment
errors, which cannot be eliminated via ego-motion optimization, are
down-weighted in losses based on the 3D point covariance estimations. 3) The
discovered representative structures and learned point covariances are
incorporated in the mapping module to improve the robustness of map
construction. Our two-frame odometry outperforms the previous state of the arts
by 16%/12% in terms of translational/rotational errors on the KITTI dataset and
performs consistently well on the Apollo-Southbay datasets. We can even rival
the fully supervised counterparts with our mapping module and more unlabeled
training data.


title: Quantum secure non-malleable-codes in the split-state model

abstract:
  Non-malleable-codes introduced by Dziembowski, Pietrzak and Wichs [DPW18]
encode a classical message $S$ in a manner such that tampering the codeword
results in the decoder either outputting the original message $S$ or a message
that is unrelated/independent of $S$. Providing such non-malleable security for
various tampering function families has received significant attention in
recent years. We consider the well-studied (2-part) split-state model, in which
the message $S$ is encoded into two parts $X$ and $Y$, and the adversary is
allowed to arbitrarily tamper with each $X$ and $Y$ individually. We consider
the security of non-malleable-codes in the split-state model when the adversary
is allowed to make use of arbitrary entanglement to tamper the parts $X$ and
$Y$. We construct explicit quantum secure non-malleable-codes in the
split-state model. Our construction of quantum secure non-malleable-codes is
based on the recent construction of quantum secure $2$-source
non-malleable-extractors by Boddu, Jain and Kapshikar [BJK21].


title: Pulse shape analysis in GERDA Phase II

abstract:
  The GERmanium Detector Array (GERDA) collaboration searched for neutrinoless
double-$\beta$ decay in $^{76}$Ge using isotopically enriched high purity
germanium detectors at the Laboratori Nazionali del Gran Sasso of INFN. After
Phase I (2011-2013), the experiment benefited from several upgrades, including
an additional active veto based on LAr instrumentation and a significant
increase of mass by point-contact germanium detectors that improved the
half-life sensitivity of Phase II (2015-2019) by an order of magnitude. At the
core of the background mitigation strategy, the analysis of the time profile of
individual pulses provides a powerful topological discrimination of signal-like
and background-like events. Data from regular $^{228}$Th calibrations and
physics data were both considered in the evaluation of the pulse shape
discrimination performance. In this work, we describe the various methods
applied to the data collected in GERDA Phase II corresponding to an exposure of
103.7 kg$\cdot$yr. These methods suppress the background by a factor of about 5
in the region of interest around Q$_{\beta\beta}$ = 2039 keV, while preserving
(81$\pm$3)% of the signal. In addition, an exhaustive list of parameters is
provided which were used in the final data analysis.


title: A reconstruction of quantum theory for nonspinning particles

abstract:
  This work is based on the idea that the classical counterpart of quantum
theory (QT) is not mechanics but probabilistic mechanics. We therefore choose
the theory of statistical ensembles in phase space as starting point for a
reconstruction of QT. These ensembles are described by a probability density
$\rho (q, p, t)$ and an action variable $S (q, p, t)$. Since the state
variables of QT only depend on $q$ and $t$, our first step is to carry out a
projection $p \Rightarrow M (q, t)$ from phase space to configuration space. We
next show that instead of the momentum components $M_{k}$ one must introduce
suitable potentials as dynamic variables. The quasi-quantal theory resulting
from the projection is only locally valid. To correct this failure, we have to
perform as a second step a linearization or randomization, which ultimately
leads to QT. In this work we represent $M$ as an irrotational field, where all
components $M_{k}$ may be derived from a single function $S (q, t)$. We obtain
the usual Schr\"odinger equation for a nonspinning particle. However, space is
three-dimensional and $M$ must be described by $3$ independent functions. In
the fourth work of this series, a complete representation of $M$ will be used,
which explains the origin of spin. We discuss several fundamental questions
that do not depend on the form of $M$ and compare our theory with other recent
reconstructions of QT.


title:
A posteriori error analysis for variable-coefficient multiterm
  time-fractional subdiffusion equations

abstract:
  An initial-boundary value problem of subdiffusion type is considered; the
temporal component of the differential operator has the form
$\sum_{i=1}^{\ell}q_i(t)\, D _t ^{\alpha_i} u(x,t)$, where the $q_i$ are
continuous functions, each $D _t ^{\alpha_i}$ is a Caputo derivative, and the
$\alpha_i$ lie in $(0,1]$. Maximum/comparison principles for this problem are
proved under weak hypotheses. A new positivity result for the multinomial
Mittag-Leffler function is derived. A posteriori error bounds are obtained in
$L_2(\Omega)$ and $L_\infty(\Omega)$, where the spatial domain $\Omega$ lies in
$\bR^d$ with $d\in\{1,2,3\}$. An adaptive algorithm based on this theory is
tested extensively and shown to yield accurate numerical solutions on the
meshes generated by the algorithm.


title:
Heavy Majorana neutrino pair production from $Z^\prime$ at hadron and
  lepton colliders

abstract:
  A gauged U$(1)$ extension of the Standard Model (SM) is a simple and anomaly
free framework where three generations of Majorana type right-handed neutrinos
(RHNs) are introduced to generate light neutrino mass and flavor mixings
through the seesaw mechanism. We investigate such models at different hadron
and lepton colliders via $Z^\prime$ induced Majorana type RHNs pair production.
We derive bounds on U$(1)$ gauge coupling $(g^\prime)$ comparing the model
cross sections with experimentally observed data for different $Z^\prime$ mass
$(M_{Z^\prime})$ and RHNs mass $(M_N)$. Using these limits we estimate the
allowed RHN pair production cross section which can be manifested by lepton
number violating signatures in association with fat-jets at the hadron
colliders depending on the mass of the RHNs. Hence we study dilepton and
trilepton modes with fat-jet/s of the signal for different benchmark values of
$M_{Z^\prime}$ and $M_N$. Using fat-jet signatures and studying the signal and
corresponding SM backgrounds, we estimate bounds on $M_N-M_{Z^\prime}$ plane at
different center of mass energies which could be probed at different hadron
colliders. In the context of the lepton colliders we consider electron positron
initial states where Majorana type RHNs can be produced from $Z^\prime$
manifesting same sign dilepton plus jets signature and trilepton plus jets in
association with missing energy. Studying the signal and corresponding SM
backgrounds we estimate the bounds on the $M_N-M_{Z^\prime}$ plane for
different center of mass energies. In the context of U$(1)$ extension of the SM
there is an SM singlet BSM scalar which couples with the RHNs. We can probe the
Majorana nature of RHNs via this BSM scalar production at electron positron
colliders.


title: Stochastic quantisation of Yang-Mills

abstract:
  We review two works arXiv:2006.04987 and arXiv:2201.03487 which study the
stochastic quantisation equations of Yang-Mills on two and three dimensional
Euclidean space with finite volume. The main result of these works is that one
can renormalise the 2D and 3D stochastic Yang-Mills heat flow so that the
dynamic becomes gauge covariant in law. Furthermore, there is a state space of
distributional $1$-forms $\mathcal{S}$ to which gauge equivalence $\sim$
extends and such that the renormalised stochastic Yang-Mills heat flow projects
to a Markov process on the quotient space of gauge orbits $\mathcal{S}/{\sim}$.
In this review, we give unified statements of the main results of these works,
highlight differences in the methods, and point out a number of open problems.


title: On automorphisms of semistable G-bundles with decorations

abstract:
  We prove a rigidity result for automorphisms of points of certain stacks
admitting adequate moduli spaces. This includes several variations of the
moduli of $G$-bundles on a smooth projective curve for a reductive algebraic
group $G$. For example, it applies to the stack of semistable $G$-bundles,
stacks of semistable Hitchin pairs, and stacks of semistable parabolic
$G$-bundles. Similar arguments apply to Gieseker semistable $G$-bundles in
higher dimensions.


title: Benign Underfitting of Stochastic Gradient Descent

abstract:
  We study to what extent may stochastic gradient descent (SGD) be understood
as a "conventional" learning rule that achieves generalization performance by
obtaining a good fit to training data. We consider the fundamental stochastic
convex optimization framework, where (one pass, without-replacement) SGD is
classically known to minimize the population risk at rate $O(1/\sqrt n)$, and
prove that, surprisingly, there exist problem instances where the SGD solution
exhibits both empirical risk and generalization gap of $\Omega(1)$.
Consequently, it turns out that SGD is not algorithmically stable in any sense,
and its generalization ability cannot be explained by uniform convergence or
any other currently known generalization bound technique for that matter (other
than that of its classical analysis). We then continue to analyze the closely
related with-replacement SGD, for which we show that an analogous phenomenon
does not occur and prove that its population risk does in fact converge at the
optimal rate. Finally, we interpret our main results in the context of
without-replacement SGD for finite-sum convex optimization problems, and derive
upper and lower bounds for the multi-epoch regime that significantly improve
upon previously known results.


title:
Reversible hydrogen storage capacity of Sc and Y functionalized
  [1,1]paracyclophane: Insights from density functional study

abstract:
  This work reports the hydrogen storage, and delivery capacities of Sc and Y
functionalized [1,1]paracyclophane using dispersion corrected density
functional theory calculations. The Sc and Y atoms are bind strongly with
benzene rings of [1,1]paracyclophane. Each Sc and Y atom functionalized over
[1,1]paracyclophane adsorb up to 6 H2 molecules via Kubas interaction achieving
maximum gravimetric density up to 8.22 wt% and 6.33 wt%, respectively. The
calculated average hydrogen adsorption energy (0.36 eV) is lower than the
chemisorption but higher than the physisorption process. The kinetic
stabilities are verified through the HOMO-LUMO gap and different global
reactive descriptors. ADMP molecular dynamics simulations reveal the
reversibility of adsorbed H2 molecules at sufficiently above the room
temperature and the solidity of host material at 500 K. Average Van't Hoff
desorption temperature for Sc and Y decorated system was calculated to be 439 K
and 412 K respectively at 1 atm of pressure. The estimated thermodynamically
usable hydrogen capacities are found to be 5.92 wt% and 5.87 wt% of hydrogen
which fulfil the hydrogen energy criteria (by 2025) of US-DOE. Hence, we
believe that Sc and Y functionalized [1,1]paracyclophane can be considered as a
thermodynamically viable, and potential reversible hydrogen storage materials
at ambient environment.


title:
Variational Autoencoder with Disentanglement Priors for Low-Resource
  Task-Specific Natural Language Generation

abstract:
  In this paper, we propose a variational autoencoder with disentanglement
priors, VAE-DPRIOR, for conditional natural language generation with none or a
handful of task-specific labeled examples. In order to improve compositional
generalization, our model performs disentangled representation learning by
introducing a prior for the latent content space and another prior for the
latent label space. We show both empirically and theoretically that the
conditional priors can already disentangle representations even without
specific regularizations as in the prior work. We can also sample diverse
content representations from the content space without accessing data of the
seen tasks, and fuse them with the representations of novel tasks for
generating diverse texts in the low-resource settings. Our extensive
experiments demonstrate the superior performance of our model over competitive
baselines in terms of i) data augmentation in continuous zero/few-shot
learning, and ii) text style transfer in both zero/few-shot settings.


title: A reconstruction of quantum theory for spinning particles

abstract:
  As part of a probabilistic reconstruction of quantum theory (QT), we show
that spin is not a purely quantum mechanical phenomenon, as has long been
assumed. Rather, this phenomenon occurs before the transition to QT takes
place, namely in the area of the quasi-classical (here better quasi-quantum)
theory. This borderland between classical physics and QT can be reached within
the framework of our reconstruction by the replacement $p \rightarrow M (q,
t)$, where $p$ is the momentum variable of the particle and $M(q, t)$ is the
momentum field in configuration space. The occurrence of spin, and its special
value $1/2$ , is a consequence of the fact that $M(q,t)$ must have exactly
three independent components $M_{k}(q,t)$ for a single particle because of the
three-dimensionality of space. In the Schr\"odinger equation for a "particle
with spin zero", the momentum field is usually represented as a gradient of a
single function $S$. This implies dependencies between the components
$M_{k}(q,t)$ for which no explanation exists. In reality, $M(q,t)$ needs to be
represented by three functions, two of which are rotational degrees of freedom.
The latter are responsible for the existence of spin. All massive structureless
particles in nature must therefore be spin-one-half particles, simply because
they have to be described by $4$ real fields, one of which has the physical
meaning of a probability density, while the other three are required to
represent the momentum field in three-dimensional space. We derive the
Pauli-Schr\"odinger equation, the correct value $g=2$ of the gyromagnetic
ratio, the classical limit of the Pauli-Schr\"odinger equation, and clarify
some other open questions in the borderland between classical physics and QT.


title:
Designing weights for quartet-based methods when data is heterogeneous
  across lineages

abstract:
  Homogeneity across lineages is a common assumption in phylogenetics according
to which nucleotide substitution rates remain constant in time and do not
depend on lineages. This is a simplifying hypothesis which is often adopted to
make the process of sequence evolution more tractable. However, its validity
has been explored and put into question in several papers. On the other hand,
dealing successfully with the general case (heterogeneity across lineages) is
one of the key features of phylogenetic reconstruction methods based on
algebraic tools.
  The goal of this paper is twofold. First, we present a new weighting system
for quartets (ASAQ) based on algebraic and semi-algebraic tools, thus specially
indicated to deal with data evolving under heterogeneus rates. This method
combines the weights two previous methods by means of a test based on the
positivity of the branch length estimated with the paralinear distance. ASAQ is
statistically consistent when applied to GM data, considers rate and base
composition heterogeneity among lineages and does not assume stationarity nor
time-reversibility. Second, we test and compare the performance of several
quartet-based methods for phylogenetic tree reconstruction (namely, Quartet
Puzzling, Weight Optimization and Wilson's method) in combination with ASAQ
weights and other weights based on algebraic and semi-algebraic methods or on
the paralinear distance. These tests are applied to both simulated and real
data and support Weight Optimization with ASAQ weights as a reliable and
successful reconstruction method.


title: Hole doping in a negative charge transfer insulator

abstract:
  $RE$NiO$_3$ is a negative charge transfer energy system and exhibits a
temperature-driven metal-insulator transition (MIT), which is also accompanied
by a bond disproportionation (BD) transition. In order to explore how hole
doping affects the BD transition, we have investigated the electronic structure
of single-crystalline thin films of Nd$_{1-x}$Ca$_x$NiO$_3$ by synchrotron
based experiments and {\it ab-initio} calculations. For a small value of $x$,
we find that the doped holes are localized on one or more Ni sites around the
dopant Ca$^{2+}$ ions, while the BD state for the rest of the lattice remains
intact. The effective charge transfer energy ($\Delta$) increases with Ca
concentration and the formation of BD phase is not favored above a critical
$x$, suppressing the insulating phase. Our present study firmly demonstrates
that the appearance of BD mode is essential for the MIT and settles a
long-standing debate about the role of structural distortions for the MIT of
the $RE$NiO$_3$ series.


title: Age Optimal Sampling Under Unknown Delay Statistics

abstract:
  This paper revisits the problem of sampling and transmitting status updates
through a channel with random delay under a sampling frequency constraint
\cite{sun_17_tit}. We use the Age of Information (AoI) to characterize the
status information freshness at the receiver. The goal is to design a sampling
policy that can minimize the average AoI when the statistics of delay is
unknown. We reformulate the problem as the optimization of a renewal-reward
process, and propose an online sampling strategy based on the Robbins-Monro
algorithm. We prove that the proposed algorithm satisfies the sampling
frequency constraint. Moreover, when the transmission delay is bounded and its
distribution is absolutely continuous, the average AoI obtained by the proposed
algorithm converges to the minimum AoI when the number of samples $K$ goes to
infinity with probability 1. We show that the optimality gap decays with rate
$\mathcal{O}\left(\ln K/K\right)$, and the proposed algorithm is minimax rate
optimal. Simulation results validate the performance of our proposed algorithm.


title:
A nonhydrostatic atmospheric dynamical core on cubed sphere using hybrid
  multi-moment finite-volume/finite difference methods: formulations and
  preliminary tests

abstract:
  A nonhydrostatic dynamical core has been developed by using the multi-moment
finite volume method that ensures the rigorous numerical conservation. To
represent the spherical geometry free of polar problems, the cubed-sphere grid
is adopted. A fourth-order multi-moment discretization formulation is applied
to solve the governing equations cast in the local curvilinear coordinates on
each patch of cubed sphere through a gnomonic projection. In vertical
direction, the height-based terrain-following grid is used to deal with the
topography and a conservative finite difference scheme is adopted for the
spatial discretization. The dynamical core adopts the nonhydrostatic governing
equations. To get around the CFL stability restriction imposed by sound wave
propagation and relatively small grid spacing in the vertical direction, the
dimensional-splitting time integration algorithm using the HEVI
(horizontally-explicit and vertically-implicit) strategy is implemented by
applying the IMEX (implicit-explicit) Runge-Kutta method. The proposed model
was checked by the widely-used benchmark tests in this study. The numerical
results show that the multi-moment model has superior solution quality and
great practical potential as a numerical platform for development of the
atmospheric general circulation models.


abstract:
  This work proposes a comprehensively progressive Bayesian neural network for
robust continual learning of a sequence of tasks. A Bayesian neural network is
progressively pruned and grown such that there are sufficient network resources
to represent a sequence of tasks, while the network does not explode. It starts
with the contention that similar tasks should have the same number of total
network resources, to ensure fair representation of all tasks in a continual
learning scenario. Thus, as the data for new task streams in, sufficient
neurons are added to the network such that the total number of neurons in each
layer of the network, including the shared representations with previous tasks
and individual task related representation, are equal for all tasks. The
weights that are redundant at the end of training each task are also pruned
through re-initialization, in order to be efficiently utilized in the
subsequent task. Thus, the network grows progressively, but ensures effective
utilization of network resources. We refer to our proposed method as 'Robust
Continual Learning through a Comprehensively Progressive Bayesian Neural
Network (RCL-CPB)' and evaluate the proposed approach on the MNIST data set,
under three different continual learning scenarios. Further to this, we
evaluate the performance of RCL-CPB on a homogeneous sequence of tasks using
split CIFAR100 (20 tasks of 5 classes each), and a heterogeneous sequence of
tasks using MNIST, SVHN and CIFAR10 data sets. The demonstrations and the
performance results show that the proposed strategies for progressive BNN
enable robust continual learning.


title: Submodule codes as spherical codes in buildings

abstract:
  In this paper, we give a generalization of subspace codes by means of codes
of modules over finite commutative chain rings. We define the new class of
Sperner codes and use results from extremal combinatorics to prove the
optimality of such codes in different cases. Moreover, we explain the
connection with Bruhat-Tits buildings and show how our codes are the buildings'
analogue of spherical codes in the Euclidean sense.


title:
Hydrogen Storage Performance Enhancement and Bandgap Opening of
  M-Decorated ($\mathrm{M}=\mathrm{Li}$, Na and K)
  III\textsubscript{4}-V\textsubscript{4} Monolayer by Fluorine
  Functionalization

abstract:
  The effects of fluorine functionalization on the hydrogen storage capability
of alkaline decorated III\textsubscript{4}-V\textsubscript{4} monolayers is
studied. This structure can store up to two hydrogen molecules per alkaline
atom. Here, we demonstrate that functionalizing alkaline decorated monolayer
with a high electronegative element of fluorine can significantly enhance both
binding energy and the maximum number of the stored hydrogen molecules. In this
regard, the hydrogen molecule storage capability is notably improved from two
to four and when absolute binding is considered. In addition,
F-functionalization of M-decorated III\textsubscript{4}-V\textsubscript{4} can
demonstrate bandgap opening effect, introduce semiconducting characteristics
and forming a new two-dimensional semiconductor structure. The bandgap for
Li-Al$_4$P$_4$-F is 1.23 eV which is very close to the solar peak. The resulted
bandgap in the M-III\textsubscript{4}-V\textsubscript{4}-F structure is even
significantly larger than that of pristine
III\textsubscript{4}-V\textsubscript{4} monolayer. The binding of the hydrogen
molecule to the alkaline atom is also improved from 0.114 to 0.272 eV by the
fluorine functionalization.


title:
Weakly Supervised Learning for cell recognition in immunohistochemical
  cytoplasm staining images

abstract:
  Cell classification and counting in immunohistochemical cytoplasm staining
images play a pivotal role in cancer diagnosis. Weakly supervised learning is a
potential method to deal with labor-intensive labeling. However, the inconstant
cell morphology and subtle differences between classes also bring challenges.
To this end, we present a novel cell recognition framework based on multi-task
learning, which utilizes two additional auxiliary tasks to guide robust
representation learning of the main task. To deal with misclassification, the
tissue prior learning branch is introduced to capture the spatial
representation of tumor cells without additional tissue annotation. Moreover,
dynamic masks and consistency learning are adopted to learn the invariance of
cell scale and shape. We have evaluated our framework on immunohistochemical
cytoplasm staining images, and the results demonstrate that our method
outperforms recent cell recognition approaches. Besides, we have also done some
ablation studies to show significant improvements after adding the auxiliary
branches.


title: New exocomets of $\beta$ Pic

abstract:
  Aims. The aim of our work is to analyze the light curves of $\beta$ Pic
recently observed by TESS in sectors 32, 33, and 34, searching for the
signatures of exocomet transits. Methods. We process the $\beta$ Pic light
curves from the MAST database, applying the frequency analysis to remove
harmonic signals due to the star's pulsations and use a simple 1-D model to fit
the profiles of the found events. Results. We recover events previously found
by other authors in sectors 5 and 6 and find five new distinct aperiodic
dipping events with asymmetric shapes resembling the expected profiles due to
the passage of a comet-like body across the star disk. These dips are rather
shallow, with the flux drop at a level of 0.03\% and a duration of less than 1
day. No periodic transits were found in the sectors investigated. Conclusions.
The depth and duration of the identified dips are similar to the recently
discovered transits in the $\beta$ Pic light curves from sector 5 of the TESS
observations as well as to those found in the light curves of KIC 354116 and
KIC 1108472 from the Kepler database. It indicates that aperiodic shallow dips
are not likely an exceptional phenomenon, at least for the $\beta$ Pic system.


title:
The Central 1000 au of a Pre-stellar Core Revealed with ALMA. II. Almost
  Complete Freeze-out

abstract:
  Pre-stellar cores represent the initial conditions in the process of star and
planet formation. Their low temperatures ($<$10 K) allow the formation of thick
icy dust mantles, which will be partially preserved in the future
protoplanetary disks, ultimately affecting the chemical composition of
planetary systems. Previous observations have shown that carbon- and
oxygen-bearing species, in particular CO, are heavily depleted in pre-stellar
cores due to the efficient molecular freeze-out onto the surface of cold dust
grains. However, N-bearing species such as NH$_3$ and, in particular, its
deuterated isotopologues, appear to maintain high abundances where CO molecules
are mainly in solid phase. Thanks to ALMA, we present here the first clear
observational evidence of NH$_2$D freeze-out toward the L1544 pre-stellar core,
suggestive of the presence of a"complete-depletion zone" within a $\simeq$1800
au radius, in agreement with astrochemical pre-stellar core model predictions.
Our state-of-the-art chemical model coupled with a non-LTE radiative transfer
code demonstrates that NH$_2$D becomes mainly incorporated in icy mantles in
the central 2000 au and starts freezing-out already at $\simeq$7000 au.
Radiative transfer effects within the pre-stellar core cause the
NH$_2$D(1$_{11}$-1$_{01}$) emission to appear centrally concentrated, with a
flattened distribution within the central $\simeq$3000 au, unlike the 1.3 mm
dust continuum emission which shows a clear peak within the central
$\simeq$1800 au. This prevented NH$_2$D freeze-out to be detected in previous
observations, where the central 1000 au cannot be spatially resolved.


title:
Dynamical signatures of the one-dimensional deconfined quantum critical
  point

abstract:
  We study the critical scaling and dynamical signatures of fractionalized
excitations at two different deconfined quantum critical points (DQCPs) in an
$S = 1/2$ spin chain by using the time evolution of infinite matrix product
states. The scaling of the correlation functions and the dispersion of the
conserved current correlations explicitly show the emergence of enhanced
continuous symmetries at these DQCPs. The dynamical structure factors in
several different channels reveal the development of deconfined fractionalized
excitations at the DQCPs. Furthermore, we find an effective spin-charge
separation at the DQCP between the ferromagnetic (FM) and valence bond solid
(VBS) phases, and identify two continua associated to different types of
fractionalized excitations at the DQCP between the $X$-direction and
$Z$-direction FM phases. Our findings not only provide direct evidence for the
DQCP in one dimension but also shed light on exploring the DQCP in higher
dimensions.


title: Possible TeV $\gamma$-ray binary origin of HESS J1828-099

abstract:
  The High Energy Stereoscopic System (H.E.S.S.) observatory has carried a deep
survey of the Galactic plane, in the course of which the existence of a
significant number of ($\sim$ 78) TeV $\gamma$-ray sources was confirmed, many
of which remain unidentified. HESS J1828-099 is a point-like (Gaussian stand.
dev. $<$ 0.07$^{\circ}$) unidentified source among the 17 confirmed point-like
sources in the H.E.S.S. Galactic Plane Survey (HGPS) catalog. This source is
also unique because it does not seem to have any apparent association with any
object detected at other wavelengths. We investigate the nature and association
of HESS J1828-099 with multi-wavelength observational data. A high mass X-Ray
binary (HMXB) - comprising of pulsar XTE J1829-098 and a companion Be star -
has been observed earlier in the X-ray and infrared bands, 14$'$ away from HESS
J1828-099. With 12 years of $\textit{Fermi}$-LAT $\gamma$-ray data, we explore
the possibility of 4FGL J1830.2-1005 being the GeV counterpart of HESS
J1828-099. Within the RXTE confidence region, a steep spectrum
($\alpha_{radio}$ = - 0.746 $\pm$ 0.284), plausible counterpart is detected in
data from existing radio frequency surveys. In this letter, we probe for the
first time using multi-wavelength data, whether HESS J1828-099, 4FGL
J1830.2-1005 and the HMXB system have a common origin. Our study indicates that
HESS J1828-099 might be a TeV high mass $\gamma$-ray binary source.


title:
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple
  Feature Aggregation

abstract:
  LiDAR sensor is essential to the perception system in autonomous vehicles and
intelligent robots. To fulfill the real-time requirements in real-world
applications, it is necessary to efficiently segment the LiDAR scans. Most of
previous approaches directly project 3D point cloud onto the 2D spherical range
image so that they can make use of the efficient 2D convolutional operations
for image segmentation. Although having achieved the encouraging results, the
neighborhood information is not well-preserved in the spherical projection.
Moreover, the temporal information is not taken into consideration in the
single scan segmentation task. To tackle these problems, we propose a novel
approach to semantic segmentation for LiDAR sequences named Meta-RangeSeg,
where a novel range residual image representation is introduced to capture the
spatial-temporal information. Specifically, Meta-Kernel is employed to extract
the meta features, which reduces the inconsistency between the 2D range image
coordinates input and Cartesian coordinates output. An efficient U-Net backbone
is used to obtain the multi-scale features. Furthermore, Feature Aggregation
Module (FAM) aggregates the meta features and multi-scale features, which tends
to strengthen the role of range channel. We have conducted extensive
experiments for performance evaluation on SemanticKITTI, which is the de-facto
dataset for LiDAR semantic segmentation. The promising results show that our
proposed Meta-RangeSeg method is more efficient and effective than the existing
approaches. Our full implementation is publicly available at
https://github.com/songw-zju/Meta-RangeSeg .


title: Theory introduction to baryon spectroscopy

abstract:
  In these introductory notes I give a brief overview on some theoretical
aspects of light baryon spectroscopy. The first part contains a discussion of
the symmetries of the baryon spectrum, the construction of flavor wave
functions and some basic features of quark models. The second part examines the
need for relativistic quantum field theory and focusses on spectrum
calculations with functional methods.


title: Towards early dark energy and $n_s$=1 with Planck, ACT and SPT

abstract:
  We investigate the constraints on early dark energy (EDE) by combining the
most recent CMB observations available, ACT DR4, SPT-3G, and Planck2018
($\ell_\text{TT,max}=1000$) data. This combined CMB dataset favors non-zero EDE
fractions and large Hubble constants. The inclusion of BAO+Pantheon data has
little effect on the results, leads to $H_0=71.6( 72.9)_{-1.5}^{+2.0}$ and
$73.17(72.74)^{+0.55}_{-0.77}$ km/s/Mpc for axion-like EDE and AdS-EDE,
respectively. The axion-like EDE can fit the data significantly better ($\Delta
\chi^2 \lesssim -10$) than $\Lambda$CDM, which is mainly driven by the ACT
data. It is found again that if the current $H_0$ measured locally is correct,
complete resolution of the Hubble tension seems to be pointing towards a scale
invariant Harrison-Zeldovich spectrum of primordial scalar perturbation, i.e.
$n_s=1$ for $H_0\sim 73$ km/s/Mpc.


title:
The study of possible molecular states of $D_s^{(*)}D_s^{(*)}$ and
  $B_s^{(*)}B_s^{(*)}$

abstract:
  Recently the LHCb collaboration reported a new exotic state $T^+_{cc}$ which
is conjectured to be a molecular state of $D^0D^{*+}$ (or $D^{*0}D^{+}$)
theoretically. Belle Collaboration also searched for tetraquark state
$X_{ccss}$ in $D_sD_s (D^*_sD^*_s )$ final states but no significant signals
were observed, which did not rule out the possibility of $X_{ccss}$ to be a
molecular state of $D_sD_s (D^*_sD^*_s )$. Inspired by these experimental
results on double charmed exotic state, in this paper we study whether the
molecular bound states of $D_s^{(*)}D_s^{(*)}$ and $B_s^{(*)}B_s^{(*)}$ can
exist with the Bethe-Salpeter (B-S) equation approach. We employ heavy meson
chiral perturbation theory with one boson-exchange approximation to calculate
the interaction kernels in the B-S equations. Our numerical results suggest
that two $B_s^{*}$ mesons perhaps form a $0^+$ molecular state. Future
experimental search for $X_{ccss}$ states in other decay channels may shed
light on the structure of double charmed exotic state.


title:
Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing
  System in the Dense Environment

abstract:
  This paper proposes a perception-shared and swarm trajectory global optimal
(STGO) algorithm fused UAVs formation motion planning framework aided by an
active sensing system. First, the point cloud received by each UAV is fit by
the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from
the received GMM contributes to a global map, which is used as the foundation
for consensus. Second, to improve flight safety, an active sensing system is
designed to plan the observation angle of each UAV considering the unknown
field, overlap of the field of view (FOV), velocity direction and smoothness of
yaw rotation, and this planning problem is solved by the distributed particle
swarm optimization (DPSO) algorithm. Last, for the formation motion planning,
to ensure obstacle avoidance, the formation structure is allowed for affine
transformation and is treated as the soft constraint on the control points of
the B-spline. Besides, the STGO is introduced to avoid local minima. The
combination of GMM communication and STGO guarantees a safe and strict
consensus between UAVs. Tests on different formations in the simulation show
that our algorithm can contribute to a strict consensus and has a success rate
of at least 80% for obstacle avoidance in a dense environment. Besides, the
active sensing system can increase the success rate of obstacle avoidance from
50% to 100% in some scenarios.


title:
A selection procedure for extracting the unique Feller weak solution of
  degenerate diffusions

abstract:
  In this work, we show that for the martingale problem for a class of
degenerate diffusions with bounded continuous drift and diffusion coefficients,
the small noise limit of non-degenerate approximations leads to a unique Feller
limit. The proof uses the theory of viscosity solutions applied to the
associated backward Kolmogorov equations. Under appropriate conditions on drift
and diffusion coefficients, we will establish a comparison principle and a
one-one correspondence between Feller solutions to the martingale problem and
continuous viscosity solutions of the associated Kolmogorov equation. This work
can be considered as an extension to the work of V. S. Borkar and K. S. Kumar
(2010).


title:
Versatile optical frequency combs based on multi-seeded femtosecond
  optical parametric generation

abstract:
  This study proposes and demonstrates a versatile method for near and
mid-infrared optical frequency comb generation using multi-seeded femtosecond
optical parametric generation. The method allows one to divide the repetition
rate by an arbitrarily large integer factor, freely tune the offset frequency,
and adjust the common phase offset of the comb modes. Since all possible
degrees of freedom are adjustable, the proposed method manifests itself as
versatile optical frequency synthesis.


title: From sub-aging to hyper-aging in structural glasses

abstract:
  We demonstrate non-equilibrium scaling laws for the aging dynamics in glass
formers that emerge from combining a recent application of Onsager's theory of
irreversible processes with the equilibrium scaling laws of glassy dynamics.
Different scaling regimes are predicted for the evolution of the system's
structural relaxation time $\tau$ with age (waiting time $t_w$), depending on
the depth of the quench from the liquid into the glass: \emph{simple aging}
($\tau\sim t_w$) applies for quenches close to the critical point of
mode-coupling theory (MCT) and implies \emph{sub-aging} ($\tau\approx\
t_w^\delta$ with $\delta<1$) as a broad cross-over for quenches to
nearly-arrested equilibrium states; \emph{hyper-aging} (or \emph{super-aging},
$\tau\sim t_w^{\delta'}$ with $\delta'>1$) emerges for quenches deep into the
glass. The latter is cut off by non-mean-field fluctuations that we account for
within a recent extension of MCT, the stochastic $\beta$-relaxation theory
(SBR). We exemplify the scaling laws by a schematic model that allows to
quantitatively fit recent simulation results for density-quenched
hard-sphere-like particles.


title:
The time asymptotic expansion for the compressible Euler equations with
  time-dependent damping

abstract:
  In this paper, we study the compressible Euler equations with time-dependent
damping $-\frac{1}{(1+t)^{\lambda}}\rho u$. We propose a time asymptotic
expansion around the self-similar solution of the generalized porous media
equation (GPME) and rigorously justify this expansion as $\lambda \in
(\frac17,1)$. In other word, instead of the self-similar solution of GPME, the
expansion is the best asymptotic profile of the solution to the compressible
Euler equations with time-dependent damping.


title: Quantum-Memory-Enhanced Preparation of Nonlocal Graph States

abstract:
  Graph states are an important class of multipartite entangled states.
Previous experimental generation of graph states and in particular the
Greenberger-Horne-Zeilinger (GHZ) states in linear optics quantum information
schemes is subjected to an exponential decay in efficiency versus the system
size, which limits its large-scale applications in quantum networks. Here we
demonstrate an efficient scheme to prepare graph states with only a polynomial
overhead using long-lived atomic quantum memories. We generate atom-photon
entangled states in two atomic ensembles asynchronously, retrieve the stored
atomic excitations only when both sides succeed, and further project them into
a four-photon GHZ state. We measure the fidelity of this GHZ state and further
demonstrate its applications in the violation of Bell-type inequalities and in
quantum cryptography. Our work demonstrates the prospect of efficient
generation of multipartite entangled states in large-scale distributed systems
with applications in quantum information processing and metrology.


title:
New efficient algorithms for computing Gr\"obner bases of saturation
  ideals (F4SAT) and colon ideals (Sparse-FGLM-colon)

abstract:
  This paper is concerned with linear algebra based methods for solving exactly
polynomial systems through so-called Gr\"obner bases, which allow one to
compute modulo the polynomial ideal generated by the input equations. This is a
topical issue in non-linear algebra and more broadly in computational
mathematics because of its numerous applications in engineering and computing
sciences. Such applications often require geometric computing features such as
representing the closure of the set difference of two solution sets to given
polynomial systems. Algebraically, this boils down to computing Gr\"obner bases
of colon and/or saturation polynomial ideals. In this paper, we describe and
analyze new Gr\"obner bases algorithms for this task and present
implementations which are more efficient by several orders of magnitude than
the state-of-the-art software.


title: PanoFlow: Learning Optical Flow for Panoramic Images

abstract:
  Optical flow estimation is a basic task in self-driving and robotics systems,
which enables to temporally interpret the traffic scene. Autonomous vehicles
clearly benefit from the ultra-wide Field of View (FoV) offered by 360-degree
panoramic sensors. However, due to the unique imaging process of panoramic
images, models designed for pinhole images do not directly generalize
satisfactorily to 360-degree panoramic images. In this paper, we put forward a
novel network framework--PanoFlow, to learn optical flow for panoramic images.
To overcome the distortions introduced by equirectangular projection in
panoramic transformation, we design a Flow Distortion Augmentation (FDA)
method. We further propose a Cyclic Flow Estimation (CFE) method by leveraging
the cyclicity of spherical images to infer 360-degree optical flow and
converting large displacement to relatively small displacement. PanoFlow is
applicable to any existing flow estimation method and benefit from the progress
of narrow-FoV flow estimation. In addition, we create and release a synthetic
panoramic dataset Flow360 based on CARLA to facilitate training and
quantitative analysis. PanoFlow achieves state-of-the-art performance. Our
proposed approach reduces the End-Point-Error (EPE) on the established Flow360
dataset by 26%. On the public OmniFlowNet dataset, PanoFlow achieves an EPE of
3.34 pixels, a 53.1% error reduction from the best published result (7.12
pixels). We also validate our method via an outdoor collection vehicle,
indicating strong potential and robustness for real-world navigation
applications. Code and dataset are publicly available at
https://github.com/MasterHow/PanoFlow.


title: Evoking realistic affective touch experiences in virtual reality

abstract:
  This study aims to better understand the emotional and physiological
correlates of being caressed in VR depending on the type of multisensory
feedback provided and the animate or inanimate nature of the virtual
representation that touches an embodied virtual body. We evaluated how
pleasure, arousal, embodiment, and the illusion of being touched in VR were
influenced by the inclusion of only visual feedback compared to visuotactile
stimulation conditions, where participants, in addition to seeing an avatar or
feather caressing their virtual bodies, also perceived congruent mid-air
ultrasonic tactile stimulation or real interpersonal touch. We found that
visuotactile feedback, either based on ultrasound or real interpersonal touch,
boosts the illusion of being affectively touched and embodied in a virtual body
compared to conditions only based on visual feedback. However, real
interpersonal touch led to the strongest behavioral and emotional responses
compared to the other conditions. Moreover, arousal and the desire to withdraw
the caressed hand was highest when being touched by a female avatar compared to
a virtual feather. Female participants reported a stronger illusion of being
caressed in VR compared to males. Overall, this study advances knowledge of the
emotional and physiological impact of affective touch in VR.


title:
The Normalized Laplacian Spectrum Analysis of Fractal Mobius Octagonal
  Networks and its Applications

abstract:
  The study and calculation of spectrum of networks can be used to describe
networks structure and quantify analysis of networks performance. The fractal
M\"{o}bius octagonal networks, denoted by $Q_n$, is derived from the inverse
identification of the opposite lateral edges of fractal linear octagonal
networks. In this paper, the normalized Laplacian spectrum of $Q_n$ is
determined by two matrices $\mathcal {L}_A$ and $\mathcal {L}_S$. As an
important application of our results, some topological indices (multiplicative
degree-Kirchhoff index, the number of spanning trees) formulas of $Q_n$ are
obtained.


title:
Effect of spin-orbit interaction on circular current: Pure spin current
  phenomena within a ring conductor

abstract:
  A net circulating current may appear within a quantum ring under finite bias.
We study the characteristic features of the circular current in the presence of
Rashba spin-orbit interaction (RSOI). Both charge and spin currents appear
within the ring. Whereas when the ring is symmetrically connected to the
external leads, we can get a pure charge current at non-zero Fermi-energy. On
the other hand, for asymmetric ring-to-leads configuration, at zero
Fermi-energy, the spin current vanishes but a pure charge current flows within
the ring. Tuning RSOI, we demonstrate a way to control the pure spin current
externally. This new perspective of the generation of the pure spin circular
current can open a new basis for the highly efficient, low energy cost
spintronic devices.