title:
Identifying Exoplanets with Machine Learning Methods: A Preliminary
  Study

abstract:
  The discovery of habitable exoplanets has long been a heated topic in
astronomy. Traditional methods for exoplanet identification include the wobble
method, direct imaging, gravitational microlensing, etc., which not only
require a considerable investment of manpower, time, and money, but also are
limited by the performance of astronomical telescopes. In this study, we
proposed the idea of using machine learning methods to identify exoplanets. We
used the Kepler dataset collected by NASA from the Kepler Space Observatory to
conduct supervised learning, which predicts the existence of exoplanet
candidates as a three-categorical classification task, using decision tree,
random forest, na\"ive Bayes, and neural network; we used another NASA dataset
consisted of the confirmed exoplanets data to conduct unsupervised learning,
which divides the confirmed exoplanets into different clusters, using k-means
clustering. As a result, our models achieved accuracies of 99.06%, 92.11%,
88.50%, and 99.79%, respectively, in the supervised learning task and
successfully obtained reasonable clusters in the unsupervised learning task.


title: Twin-width VIII: delineation and win-wins

abstract:
  We introduce the notion of delineation. A graph class $\mathcal C$ is said
delineated if for every hereditary closure $\mathcal D$ of a subclass of
$\mathcal C$, it holds that $\mathcal D$ has bounded twin-width if and only if
$\mathcal D$ is monadically dependent. An effective strengthening of
delineation for a class $\mathcal C$ implies that tractable FO model checking
on $\mathcal C$ is perfectly understood: On hereditary closures $\mathcal D$ of
subclasses of $\mathcal C$, FO model checking is fixed-parameter tractable
(FPT) exactly when $\mathcal D$ has bounded twin-width. Ordered graphs
[BGOdMSTT, STOC '22] and permutation graphs [BKTW, JACM '22] are effectively
delineated, while subcubic graphs are not. On the one hand, we prove that
interval graphs, and even, rooted directed path graphs are delineated. On the
other hand, we show that segment graphs, directed path graphs, and visibility
graphs of simple polygons are not delineated. In an effort to draw the
delineation frontier between interval graphs (that are delineated) and
axis-parallel two-lengthed segment graphs (that are not), we investigate the
twin-width of restricted segment intersection classes. It was known that
(triangle-free) pure axis-parallel unit segment graphs have unbounded
twin-width [BGKTW, SODA '21]. We show that $K_{t,t}$-free segment graphs, and
axis-parallel $H_t$-free unit segment graphs have bounded twin-width, where
$H_t$ is the half-graph or ladder of height $t$. In contrast, axis-parallel
$H_4$-free two-lengthed segment graphs have unbounded twin-width. Our new
results, combined with the known FPT algorithm for FO model checking on graphs
given with $O(1)$-sequences, lead to win-win arguments. For instance, we derive
FPT algorithms for $k$-Ladder on visibility graphs of 1.5D terrains, and
$k$-Independent Set on visibility graphs of simple polygons.


title:
Analysis of Sparse Subspace Clustering: Experiments and Random
  Projection

abstract:
  Clustering can be defined as the process of assembling objects into a number
of groups whose elements are similar to each other in some manner. As a
technique that is used in many domains, such as face clustering, plant
categorization, image segmentation, document classification, clustering is
considered one of the most important unsupervised learning problems. Scientists
have surveyed this problem for years and developed different techniques that
can solve it, such as k-means clustering. We analyze one of these techniques: a
powerful clustering algorithm called Sparse Subspace Clustering. We demonstrate
several experiments using this method and then introduce a new approach that
can reduce the computational time required to perform sparse subspace
clustering.


title: On 2-transitive sets of equiangular lines

abstract:
  All finite sets of equiangular lines spanning finite-dimensional unitary
spaces are determined for which the action on the lines of the set-stabilizer
in the unitary group is
  2-transitive with a regular normal subgroup.


title: Reduced-density-matrix-based ab initio cavity quantum electrodynamics

abstract:
  A reduced-density-matrix (RDM)-based approach to {\em ab initio} cavity
quantum electrodynamics (QED) is developed. The expectation value of the
Pauli-Fierz Hamiltonian is expressed in terms of one- and two-body electronic
and photonic RDMs, and the elements of these RDMs are optimized directly in
polynomial time by semidefinite programming techniques, without knowledge of
the full wave function. QED generalizations of important ensemble
$N$-representability conditions are derived and enforced in this procedure. The
resulting approach is applied to the description of classic ground-state strong
electron correlation problems, augmented by the presence of ultrastrong
light-matter coupling. First, we assess cavity-induced changes to the
singlet-triplet energy gap of the linear oligoacene series; for a heptacene
molecule modeled under experimentally feasible coupling strengths, this gap can
change by as much as 1.9 kcal mol$^{-1}$ (or 15\%). We also explore the
metal-insulator transition in linear hydrogen chains and demonstrate that
strong electron-photon interactions increase the insulating character of these
systems under all coupling strengths considered.


title:
Numerical computation of a preimage domain for an infinite strip with
  rectilinear slits

abstract:
  Let $\Omega$ be the multiply connected domain in the extended complex plane
$\overline{\C}$ obtained by removing $m$ non-overlapping rectilinear segments
from the infinite strip $S=\{z\,:\, \left|\Im z\right|<\pi/2\}$. In this paper,
we present an iterative method for numerical computation of a conformally
equivalent bounded multiply connected domain $G$ in the interior of the unit
disk $\D$ and the exterior of $m$ non-overlapping smooth Jordan curves. We
demonstrate the utility of the proposed method through two applications. First,
we estimate the capacity of condensers of the form $(S,E)$ where $E\subset S$
be a union of disjoint segments. Second, we determine the streamlines
associated with uniform incompressible, inviscid and irrotational flow past
disjoint segments in the strip $S$.


title:
Quantum coherence of an orbital angular momentum multiplexed
  continuous-variable entangled state

abstract:
  Orbital angular momentum (OAM) multiplexed entangled state is an important
quantum resource for high dimensional quantum information processing. In this
paper, we experimentally quantify quantum coherence of OAM multiplexed
continuous-variable (CV) entangled state and characterize its evolution in a
noisy environment. We show that the quantum coherence of the OAM multiplexed CV
entangled state carrying topological charges $l=1$ and $l=2$ are the same as
that of the Gaussian mode with $l=0$ in a noisy channel. Furthermore, we show
that the quantum coherence of OAM multiplexed entangled state is robust to
noise, even though the sudden death of entanglement is observed. Our results
provide reference for applying quantum coherence of OAM multiplexed CV
entangled state in a noisy environment.


title:
CO Emission Delineating the Interface between the Milky Way Nuclear Wind
  Cavity and the Gaseous Disk

abstract:
  Based on the MWISP survey, we study high-z CO emission toward the tangent
points, in which the distances of the molecular clouds (MCs) are well
determined. In the region of l=12-26 deg and |b|<5.1 deg, a total of 321 MCs
with |z|> 110 pc are identified, of which nearly 30 extreme high-z MCs (EHMCs
at |z|> 260 pc) are concentrated in a narrow region of R_GC=2.6-3.1 kpc. The
EHMC concentrations, together with other high-z MCs at R_GC=2.3-2.6 kpc,
constitute molecular crater-wall structures surrounding the edges of the HI
voids that are physically associated with the Fermi bubbles. Intriguingly, some
large high-z MCs, which lie in the crater-walls above and below the Galactic
plane, show cometary structures with the head toward the plane, favouring the
scenario that the entrained molecular gas moves with the multi-phase flows from
the plane to the high-z regions. We suggest that the Milky Way nuclear wind has
a significant impact on the Galactic gaseous disk. The powerful nuclear wind at
~3-6 Myr ago is likely responsible for the observational features, (1) the
enhanced CO gas lying in the edges of the HI voids, (2) the deficiency of
atomic and molecular gas within R_GC<3 kpc, (3) the possible connection between
the EHMC concentrations and the 3-kpc arm, and (4) the elongated high-z MCs
with the tail pointing away from the Galactic plane.


title: The obstacle problem in masonry structures and cable nets

abstract:
  We consider the problem of finding a net that supports prescribed forces
applied at prescribed points, yet avoids certain obstacles, with all the
elements of the net under compression (strut net) or under tension (cable web).
In the case of masonry structures, for instance, this consists in finding a
strut net that supports the forces, is contained within the physical structure,
and avoids regions that may be not accessible due, for instance, to the
presence of holes. We solve such a problem in the two-dimensional case, where
the prescribed forces are applied at the vertices of a convex polygon, and we
treat the cases of both single and multiple obstacles. By approximating the
obstacles by polygonal regions, the task reduces to identifying the feasible
domain in a linear programming problem. For a single obstacle we show how the
region $\Gamma$ available to the obstacle can be enlarged as much as possible
in the sense that there is no other strut net, having a region $\Gamma_1$
available to the obstacle with $\Gamma_1 \subset\Gamma$. The case where some of
the forces are reactive, unprescribed but reacting to the other prescribed
forces, is also treated. It again reduces to identifying the feasible domain in
a linear programming problem. Finally, one may allow a subset of the reactive
forces to each act not at a prescribed point, but rather at any point on a
prescribed line segment. Then the task reduces to identifying the feasible
domain in a quadratic programming problem.


title:
Hamiltonian variational formulation for nonequilibrium thermodynamics of
  simple closed systems

abstract:
  In this paper, we develop a Hamiltonian variational formulation for the
nonequilibrium thermodynamics of simple adiabatically closed systems that is an
extension of Hamilton's phase space principle in mechanics. We introduce the
Hamilton-d'Alembert principle for thermodynamic systems by considering
nonlinear nonholonomic constraints of thermodynamic type. In particular, for
the case in which the given Lagrangian is degenerate, we construct the
Hamiltonian by incorporating the primary constraints via Dirac's theory of
constraints. We illustrate our Hamiltonian variational formulation with some
examples of systems with friction, with internal matter transfer as well as
with chemical reactions.


title:
Intrinsic Frequency Limit of Direct Modulation of
  Resonant-Tunneling-Diode Terahertz Emitters and Effect of External Feedback
  Injection

abstract:
  Output power of resonant-tunneling-diode (RTD) terahertz (THz) emitters can
be modulated by the bias modulation similar to a semiconductor laser. This
property is useful for applications of the THz waves to wireless communications
and radars. In this paper, we theoretically analyze the modulation-frequency
dependence of the output response using an equivalent circuit of the RTD-THz
oscillator. It is shown that there exists an intrinsic cutoff frequency of
modulation in RTD, which is independent of the external circuit that supplies
the modulation signal to the oscillator. This cutoff frequency is determined by
the time constant given by the capacitance of RTD divided by the absolute value
of negative differential conductance minus loss conductance of the oscillator,
and is about 100 GHz for typical RTD-THz oscillators. We also analyze the
effect of external feedback injection on the modulation characteristics. If the
period of the modulation frequency is equal to an integral multiple of the
round-trip time of the feedback, a dip or peak occurs in the modulation
response of the output, depending on whether the THz carrier components in the
output and the feedback are in phase or out of phase. We also discuss the
possibility of increase in cutoff frequency by the feedback with short
round-trip time.


title:
Existence of a conjugate point in the incompressible Euler flow on a
  three-dimensional ellipsoid

abstract:
  The existence of a conjugate point on the volume-preserving diffeomorphism
group of a compact Riemannian manifold M is related to the Lagrangian stability
of a solution of the incompressible Euler equation on M. The Misiolek curvature
is a reasonable criterion for the existence of a conjugate point on the
volume-preserving diffeomorphism group corresponding to a stationary solution
of the incompressible Euler equation. In this article, we introduce a class of
stationary solutions on an arbitrary Riemannian manifold whose behavior is nice
with respect to the Misiolek curvature and give a positivity result of the
Misiolek curvature for solutions belonging to this class. Moreover, we also
show the existence of a conjugate point in the three-dimensional ellipsoid case
as its corollary.


title:
Singular asymptotics for the Clarkson-McLeod solutions of the fourth
  Painlev\'e equation

abstract:
  We consider the Clarkson-McLeod solutions of the fourth Painlev\'e equation.
This family of solutions behave like $\kappa
D_{\alpha-\frac{1}{2}}^2(\sqrt{2}x)$ as $x\rightarrow +\infty$, where $\kappa $
is an arbitrary real constant and $D_{\alpha-\frac{1}{2}}(x)$ is the parabolic
cylinder function. Using the Deift-Zhou nonlinear steepest descent method, we
obtain the singular asymptotics of the solutions as $x\to-\infty$ when $\kappa
\left( \kappa -\kappa ^*\right )>0$ for some real constant $\kappa ^*$. The
connection formulas are also explicitly evaluated. This proves and extends
Clarkson and McLeod's conjecture that when the parameter $\kappa >\kappa ^*>0$,
the Clarkson-McLeod solutions have infinitely many simple poles on the negative
real axis.


title:
SkeleVision: Towards Adversarial Resiliency of Person Tracking with
  Multi-Task Learning

abstract:
  Person tracking using computer vision techniques has wide ranging
applications such as autonomous driving, home security and sports analytics.
However, the growing threat of adversarial attacks raises serious concerns
regarding the security and reliability of such techniques. In this work, we
study the impact of multi-task learning (MTL) on the adversarial robustness of
the widely used SiamRPN tracker, in the context of person tracking.
Specifically, we investigate the effect of jointly learning with semantically
analogous tasks of person tracking and human keypoint detection. We conduct
extensive experiments with more powerful adversarial attacks that can be
physically realizable, demonstrating the practical value of our approach. Our
empirical study with simulated as well as real-world datasets reveals that
training with MTL consistently makes it harder to attack the SiamRPN tracker,
compared to typically training only on the single task of person tracking.


title:
Genetic programming-based learning of carbon interatomic potential for
  materials discovery

abstract:
  Efficient and accurate interatomic potential functions are critical to
computational study of materials while searching for structures with desired
properties. Traditionally, potential functions or energy landscapes are
designed by experts based on theoretical or heuristic knowledge. Here, we
propose a new approach to leverage strongly typed parallel genetic programming
(GP) for potential function discovery. We use a multi-objective evolutionary
algorithm with NSGA-III selection to optimize individual age, fitness, and
complexity through symbolic regression. With a DFT dataset of 863 unique carbon
allotrope configurations drawn from 858 carbon structures, the generated
potentials are able to predict total energies within $\pm 7.70$ eV at low
computational cost while generalizing well across multiple carbon structures.
Our code is open source and available at
\url{http://www.github.com/usccolumbia/mlpotential


title:
Eigenvalue processes of symmetric tridiagonal matrix-valued processes
  associated with Gaussian beta ensemble

abstract:
  We consider the symmetric tridiagonal matrix-valued process associated with
Gaussian beta ensemble (G$\beta$E) by putting independent Brownian motions and
Bessel processes on the diagonal entries and upper (lower)-diagonal ones,
respectively. Then, we derive the stochastic differential equations that the
eigenvalue processes satisfy, and we show that eigenvalues of their (indexed)
principal minor sub-matrices appear in the stochastic differential equations.
By the Cauchy's interlacing argument for eigenvalues, we can characterize the
sufficient condition that the eigenvalue processes never collide with each
other almost surely, by the dimensions of the Bessel processes.


title:
Tungsten vs Selenium as a potential source of kilonova nebular emission
  observed by Spitzer

abstract:
  Infrared emission lines arising from transitions between fine structure
levels of heavy elements are expected to produce kilonova nebular emission. For
the kilonova in GW170817, strong emission at 4.5 ${\rm \mu m}$ at late times
was detected by the Spitzer Space Telescope but no source was detected at 3.6
${\rm \mu m}$. This peculiar spectrum indicates that there exist strong line
emitters around 4.5 ${\rm \mu m}$ and the absence of strong lines around 3.6
${\rm \mu m}$. To model the spectrum we prepare a line list based on the
selection rules in LS coupling from the experimentally calibrated energy levels
in the NIST database. This method enables to generate the synthetic spectra
with accurate line wavelengths. We find that the spectrum is sensitive to the
abundance pattern whether or not the first r-process peak elements are
included. In both cases, the synthetic spectra can match the observed data,
leading to two possible interpretations. If the first peak elements are
abundant a Se III line dominates the flux. If otherwise, W III with Os III, Rh
III, and Ce IV can be the main sources. Observing nebular spectra for the
future kilonovae in a wider wavelength range can provide more conclusive
elemental identification.


title:
Data-Driven Quantum Approximate Optimization Algorithm for
  Cyber-Physical Power Systems

abstract:
  Quantum technology provides a ground-breaking methodology to tackle
challenging computational issues in power systems, especially for Distributed
Energy Resources (DERs) dominant cyber-physical systems that have been widely
developed to promote energy sustainability. The systems' maximum power or data
sections are essential for monitoring, operation, and control, while high
computational effort is required. Quantum Approximate Optimization Algorithm
(QAOA) provides a promising means to search for these sections by leveraging
quantum resources. However, its performance highly relies on the critical
parameters, especially for weighted graphs. We present a data-driven QAOA,
which transfers quasi-optimal parameters between weighted graphs based on the
normalized graph density, and verify the strategy with 39,774 instances.
Without parameter optimization, our data-driven QAOA is comparable with the
Goemans-Williamson algorithm. This work advances QAOA and pilots the practical
application of quantum technique to power systems in noisy intermediate-scale
quantum devices, heralding its next-generation computation in the quantum era.


title:
The accretion flow geometry of MAXI J1820+070 through broadband noise
  research with Insight-HXMT

abstract:
  Here we present a detailed study of the broadband noise in the power density
spectra of the black hole X-ray binary MAXI J1820+070 during the hard state of
its 2018 outburst, using the Hard X-ray Modulation Telescope (Insight-HXMT)
observations. The broadband noise shows two main humps, which might separately
correspond to variability from a variable disk and two Comptonization regions.
We fitted the two humps with multiple Lorentzian functions and studied the
energy-dependent properties of each component up to 100--150 keV and their
evolution with spectral changes. The lowest frequency component is considered
as the sub-harmonic of QPO component and shows different energy dependence
compared with other broadband noise components. We found that although the
fractional rms of all the broadband noise components mainly decrease with
energy, their rms spectra are different in shape. Above $\sim$ 20--30 keV, the
characteristic frequencies of these components increase sharply with energy,
meaning that the high-energy component is more variable on short timescales.
Our results suggest that the hot inner flow in MAXI J1820+070 is likely to be
inhomogeneous. We propose a geometry with a truncated accretion disk, two
Comptonization regions.


title:
Path Development Network with Finite-dimensional Lie Group
  Representation

abstract:
  The path signature, a mathematically principled and universal feature of
sequential data, leads to a performance boost of deep learning-based models in
various sequential data tasks as a complimentary feature. However, it suffers
from the curse of dimensionality when the path dimension is high. To tackle
this problem, we propose a novel, trainable path development layer, which
exploits representations of sequential data with the help of finite-dimensional
matrix Lie groups. We also design the backpropagation algorithm of the
development layer via an optimisation method on manifolds known as
trivialisation. Numerical experiments demonstrate that the path development
consistently and significantly outperforms, in terms of accuracy and
dimensionality, signature features on several empirical datasets. Moreover,
stacking the LSTM with the development layer with a suitable matrix Lie group
is empirically proven to alleviate the gradient issues of LSTMs and the
resulting hybrid model achieves the state-of-the-art performance.


title: Learning to Simplify with Data Hopelessly Out of Alignment

abstract:
  We consider whether it is possible to do text simplification without relying
on a "parallel" corpus, one that is made up of sentence-by-sentence alignments
of complex and ground truth simple sentences. To this end, we introduce a
number of concepts, some new and some not, including what we call Conjoined
Twin Networks, Flip-Flop Auto-Encoders (FFA) and Adversarial Networks (GAN). A
comparison is made between Jensen-Shannon (JS-GAN) and Wasserstein GAN, to see
how they impact performance, with stronger results for the former. An
experiment we conducted with a large dataset derived from Wikipedia found the
solid superiority of Twin Networks equipped with FFA and JS-GAN, over the
current best performing system. Furthermore, we discuss where we stand in a
relation to fully supervised methods in the past literature, and highlight with
examples qualitative differences that exist among simplified sentences
generated by supervision-free systems.


title:
An inequality for the convolutions on unimodular locally compact groups
  and the optimal constant of Young's inequality

abstract:
  Let $\mu$ be the Haar measure of a unimodular locally compact group $G$ and
$m (G)$ as the infimum of the volumes of all open subgroups of $G$. The main
result of this paper is that \begin{align*}
  \int_{G}^{} f \circ \left( \phi_1 * \phi_2 \right) \left( g \right) dg \leq
\int_{\mathbb{R}}^{} f \circ \left( \phi_1^* * \phi_2^* \right) \left( x
\right) dx \end{align*} holds for any measurable functions $\phi_1, \phi_2
\colon G \to \mathbb{R}_{\geq 0}$ with $\mu ( \mathrm{supp} \; \phi_1 ) + \mu (
\mathrm{supp} \; \phi_2 ) \leq m(G)$ and any convex function $f \colon
\mathbb{R}_{\geq 0} \to \mathbb{R}$ with $f(0) = 0$. Here $\phi^*$ is the
rearrangement of $\phi$.
  Let $Y_O(P,G)$ and $Y_R(P,G)$ denote the optimal constants of Young's and the
reverse Young's inequality, respectively, under the assumption of $\mu (
\mathrm{supp} \; \phi_1 ) + \mu ( \mathrm{supp} \; \phi_2 ) \leq m(G)$. Then we
have $Y_O(P,G) \leq Y_O(P,\mathbb{R})$ and $Y_R(P,G) \geq Y_R(P,\mathbb{R})$ as
a corollary. Thus, we obtain that $m (G) = \infty$ if and only if $H (p,G) \leq
H (p, \mathbb{R})$ in the case of $p' := p/(p-1) \in 2 \mathbb{Z}$, where $H
(p,G)$ is the optimal constant of the Hausdorff--Young inequality.


title: Generating recommendations for entity-oriented exploratory search

abstract:
  We introduce the task of recommendation set generation for entity-oriented
exploratory search. Given an input search query which is open-ended or
under-specified, the task is to present the user with an easily-understandable
collection of query recommendations, with the goal of facilitating domain
exploration or clarifying user intent. Traditional query recommendation systems
select recommendations by identifying salient keywords in retrieved documents,
or by querying an existing taxonomy or knowledge base for related concepts. In
this work, we build a text-to-text model capable of generating a collection of
recommendations directly, using the language model as a "soft" knowledge base
capable of proposing new concepts not found in an existing taxonomy or set of
retrieved documents. We train the model to generate recommendation sets which
optimize a cost function designed to encourage comprehensiveness,
interestingness, and non-redundancy. In thorough evaluations performed by crowd
workers, we confirm the generalizability of our approach and the high quality
of the generated recommendations.


title: Recurrence formula for any order evolution equations

abstract:
  Riccati's differential equation is formulated as abstract equation in finite
or infinite dimensional Banach spaces. Since the Riccati's differential
equation with the Cole-Hopf transform shows a relation between the first order
evolution equations and the second order evolution equations, its
generalization suggests the existence of recurrence formula leading to a
sequence of any order evolution equations. In conclusion, by means of the
logarithmic representation of operators, a transform between the first order
evolution equations and the higher order evolution equation is presented.
Several classes of evolution equations with different orders are found, and
some of them are shown as examples.


title: Poincare Map Method for Limit Cycles in a Max-Plus Dynamical System

abstract:
  Dynamical properties of limit cycles in a two-dimensional max-plus dynamical
system are discussed. We apply a Poincare map method to the limit cycles in
order to reveal their stabilities. This method reduces the two dimensional
system to a one-dimensional piecewise linear discrete dynamical system composed
of the Poincare map and its cross section. Basins for one of the limit cycles
are derived by considering the inverse system of the original model. It is
found that the obtained basins show a hierarchic structure. Relationship
between the Poincare map method and the method of piecewise linear mapping
studied in integrable system theory for the limit cycles is discussed.


title:
What to look at and where: Semantic and Spatial Refined Transformer for
  detecting human-object interactions

abstract:
  We propose a novel one-stage Transformer-based semantic and spatial refined
transformer (SSRT) to solve the Human-Object Interaction detection task, which
requires to localize humans and objects, and predicts their interactions.
Differently from previous Transformer-based HOI approaches, which mostly focus
at improving the design of the decoder outputs for the final detection, SSRT
introduces two new modules to help select the most relevant object-action pairs
within an image and refine the queries' representation using rich semantic and
spatial features. These enhancements lead to state-of-the-art results on the
two most popular HOI benchmarks: V-COCO and HICO-DET.


title:
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering
  Techniques

abstract:
  People spend a significant amount of time in indoor spaces (e.g., office
buildings, subway systems, etc.) in their daily lives. Therefore, it is
important to develop efficient indoor spatial query algorithms for supporting
various location-based applications. However, indoor spaces differ from outdoor
spaces because users have to follow the indoor floor plan for their movements.
In addition, positioning in indoor environments is mainly based on sensing
devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot
apply existing spatial query evaluation techniques devised for outdoor
environments for this new challenge. Because Bayesian filtering techniques can
be employed to estimate the state of a system that changes over time using a
sequence of noisy measurements made on the system, in this research, we propose
the Bayesian filtering-based location inference methods as the basis for
evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two
novel models, indoor walking graph model and anchor point indexing model, are
created for tracking object locations in indoor environments. Based on the
inference method and tracking models, we develop innovative indoor range and k
nearest neighbor (kNN) query algorithms. We validate our solution through use
of both synthetic data and real-world data. Our experimental results show that
the proposed algorithms can evaluate indoor spatial queries effectively and
efficiently. We open-source the code, data, and floor plan at
https://github.com/DataScienceLab18/IndoorToolKit.


title:
Least energy positive soultions for $d$-coupled Schr\"{o}dinger systems
  with critical exponent in dimension three

abstract:
  In the present paper, we consider the coupled Schr\"{o}dinger systems with
critical exponent: \begin{equation*} \begin{cases} -\Delta
u_i+\lambda_{i}u_i=\sum\limits_{j=1}^{d} \beta_{ij}|u_j|^{3}|u_i|u_i \quad
~\text{ in } \Omega,\\ u_i \in H_0^1(\Omega) ,\quad i= 1,2,...,d. \end{cases}
\end{equation*} Here, $\Omega\subset \mathbb{R}^{3}$ is a smooth bounded
domain, $d \geq 2$, $\beta_{ii}>0$ for every $i$, and $\beta_{ij}=\beta_{ji}$
for $i \neq j$. We study a Br\'{e}zis-Nirenberg type problem:
$-\lambda_{1}(\Omega)<\lambda_{1},\cdots,\lambda_{d}<-\lambda^*(\Omega)$, where
$\lambda_{1}(\Omega)$ is the first eigenvalue of $-\Delta$ with Dirichlet
boundary conditions and $\lambda^*(\Omega)\in (0, \lambda_1(\Omega))$. We
acquire the existence of least energy positive solutions to this system for
weakly cooperative case ($\beta_{ij}>0$ small) and for purely competitive case
($\beta_{ij}\leq 0$) by variational arguments. The proof is performed by
mathematical induction on the number of equations, and requires more refined
energy estimates for this system. Besides, we present a new nonexistence
result, revealing some different phenomena comparing with the
higher-dimensional case $N\geq 5$. It seems that this is the first paper to
give a rather complete picture for the existence of least energy positive
solutions to critical Schr\"{o}dinger system in dimension three.


title:
Prospects and Strategies for Detecting Nonlinear Gravitational Wave
  Burst with Memory from Potential Merger Event SDSSJ1430$+$2303 by Pulsar
  Timing Arrays

abstract:
  The recently observed chirping signature in the light curves of Seyfert 1
galaxy SDSSJ1430$+$2303 could be explained by a late-inspiralling supermassive
binary black hole (SMBBH) system in the galactic center, which will merge in
the near future (or could have merged already). For the merging SMBBH scenario,
SDSSJ1430$+$2303 can be a source of nonlinear gravitational wave (GW) burst
with memory (BWM), which may provide a promising target for future pulsar
timing array (PTA) observations. In this work, we investigate the prospects for
detecting the BWM signal from SDSSJ1430$+$2303 by the International PTA (IPTA)
and FAST-PTA in the next $5$ years. We firstly propose strategies on searching
for this target signal, including the selection of millisecond pulsars (MSPs)
and the distribution of observation time. Then we simulate PTA observations
based on the proposed strategies and obtain the probability density functions
of the network signal-to-noise ratio and parameter-estimation errors of the BWM
signal, considering the uncertainties of parameters of the SMBBH and both white
and red noises of the selected MSPs. Our result shows that although IPTA can
marginally detect the BWM in $5$ years, FAST-PTA can detect it with
significantly higher confidence. Moreover, the archived IPTA data is important
in estimating the merger time of the SMBBH, when combined with the FAST-PTA
data. This work can serve as a guidance for future PTA observations and
multi-messenger studies on SDSSJ1430$+$2303 and similar systems.


title: Structural randomised selection

abstract:
  An important problem in the analysis of high-dimensional omics data is to
identify subsets of molecular variables that are associated with a phenotype of
interest. This requires addressing the challenges of high dimensionality,
strong multicollinearity and model uncertainty. We propose a new ensemble
learning approach for improving the performance of sparse penalised regression
methods, called STructural RANDomised Selection (STRANDS). The approach, that
builds and improves upon the Random Lasso method, consists of two steps. In
both steps, we reduce dimensionality by repeated subsampling of variables. We
apply a penalised regression method to each subsampled dataset and average the
results. In the first step, subsampling is informed by variable correlation
structure, and in the second step, by variable importance measures from the
first step. STRANDS can be used with any sparse penalised regression approach
as the "base learner". Using synthetic data and real biological datasets, we
demonstrate that STRANDS typically improves upon its base learner, and that
taking account of the correlation structure in the first step can help to
improve the efficiency with which the model space may be explored.


title:
A Review of Data-driven Robotic Process Automation Exploiting Process
  Mining

abstract:
  Purpose: Process mining aims to construct, from event logs, process maps that
can help discover, automate, improve, and monitor organizational processes.
Robotic process automation (RPA) uses software robots to perform some tasks
usually executed by humans. It is usually difficult to determine what processes
and steps to automate, especially with RPA. Process mining is seen as one way
to address such difficulty. This paper aims to assess the applicability of
process mining algorithms in accelerating and improving the implementation of
RPA, along with the challenges encountered throughout project lifecycles.
Methodology: A systematic literature review was conducted to examine the
approaches where process mining techniques were used to understand the as-is
processes that can be automated with software robots. Eight databases were used
to identify papers on this topic. Findings: A total of 19 papers, all published
since 2018, were selected from 158 unique candidate papers and then analyzed.
There is an increase in the number of publications in this domain. Originality:
The literature currently lacks a systematic review that covers the intersection
of process mining and robotic process automation. The literature mainly focuses
on the methods to record the events that occur at the level of user
interactions with the application, and on the preprocessing methods that are
needed to discover routines with the steps that can be automated. Several
challenges are faced with preprocessing such event logs, and many lifecycle
steps of automation project are weakly supported by existing approaches.


title:
Modeling Dynamic User Preference via Dictionary Learning for Sequential
  Recommendation

abstract:
  Capturing the dynamics in user preference is crucial to better predict user
future behaviors because user preferences often drift over time. Many existing
recommendation algorithms -- including both shallow and deep ones -- often
model such dynamics independently, i.e., user static and dynamic preferences
are not modeled under the same latent space, which makes it difficult to fuse
them for recommendation. This paper considers the problem of embedding a user's
sequential behavior into the latent space of user preferences, namely
translating sequence to preference. To this end, we formulate the sequential
recommendation task as a dictionary learning problem, which learns: 1) a shared
dictionary matrix, each row of which represents a partial signal of user
dynamic preferences shared across users; and 2) a posterior distribution
estimator using a deep autoregressive model integrated with Gated Recurrent
Unit (GRU), which can select related rows of the dictionary to represent a
user's dynamic preferences conditioned on his/her past behaviors. Qualitative
studies on the Netflix dataset demonstrate that the proposed method can capture
the user preference drifts over time and quantitative studies on multiple
real-world datasets demonstrate that the proposed method can achieve higher
accuracy compared with state-of-the-art factorization and neural sequential
recommendation methods. The code is available at
https://github.com/cchao0116/S2PNM-TKDE2021.


title:
Achieving Social Optimum in Non-convex Cooperative Aggregative Games: A
  Distributed Stochastic Annealing Approach

abstract:
  This paper designs a distributed stochastic annealing algorithm for
non-convex cooperative aggregative games, whose agents' cost functions not only
depend on agents' own decision variables but also rely on the sum of agents'
decision variables. To seek the the social optimum of cooperative aggregative
games, a distributed stochastic annealing algorithm is proposed, where the
local cost functions are non-convex and the communication topology between
agents is time varying. The weak convergence to the social optimum of the
algorithm is further analyzed. A numerical example is given to illustrate the
effectiveness of the proposed algorithm.


title: Homography Loss for Monocular 3D Object Detection

abstract:
  Monocular 3D object detection is an essential task in autonomous driving.
However, most current methods consider each 3D object in the scene as an
independent training sample, while ignoring their inherent geometric relations,
thus inevitably resulting in a lack of leveraging spatial constraints. In this
paper, we propose a novel method that takes all the objects into consideration
and explores their mutual relationships to help better estimate the 3D boxes.
Moreover, since 2D detection is more reliable currently, we also investigate
how to use the detected 2D boxes as guidance to globally constrain the
optimization of the corresponding predicted 3D boxes. To this end, a
differentiable loss function, termed as Homography Loss, is proposed to achieve
the goal, which exploits both 2D and 3D information, aiming at balancing the
positional relationships between different objects by global constraints, so as
to obtain more accurately predicted 3D boxes. Thanks to the concise design, our
loss function is universal and can be plugged into any mature monocular 3D
detector, while significantly boosting the performance over their baseline.
Experiments demonstrate that our method yields the best performance (Nov. 2021)
compared with the other state-of-the-arts by a large margin on KITTI 3D
datasets.


title: Safe Reinforcement Learning via Shielding for POMDPs

abstract:
  Reinforcement learning (RL) in safety-critical environments requires an agent
to avoid decisions with catastrophic consequences. Various approaches
addressing the safety of RL exist to mitigate this problem. In particular,
so-called shields provide formal safety guarantees on the behavior of RL agents
based on (partial) models of the agents' environment. Yet, the state-of-the-art
generally assumes perfect sensing capabilities of the agents, which is
unrealistic in real-life applications. The standard models to capture scenarios
with limited sensing are partially observable Markov decision processes
(POMDPs). Safe RL for these models remains an open problem so far. We propose
and thoroughly evaluate a tight integration of formally-verified shields for
POMDPs with state-of-the-art deep RL algorithms and create an efficacious
method that safely learns policies under partial observability. We empirically
demonstrate that an RL agent using a shield, beyond being safe, converges to
higher values of expected reward. Moreover, shielded agents need an order of
magnitude fewer training episodes than unshielded agents, especially in
challenging sparse-reward settings.


title:
A generalization of certain associated Bessel functions in connection
  with a group of shifts

abstract:
  Considering the kernel of an integral operator intertwining two realizations
of the group of motions of the pseudo-Euclidian space, we derive two formulas
for series containing Whittaker's functions or Weber's parabolic cylinder
functions. We can consider this kernel as a special function. Some particular
values of parameters involved in this special function are found to coincide
with certain variants of Bessel functions. Using these connections, we also
establish some analogues of orthogonality relations for Macdonald and Hankel
functions.


title: A neural network based heading and position control system of a ship

abstract:
  Heading and position control system of ships has remained a challenging
control problem. It is a nonlinear multiple input multiple output system.
Moreover, the dynamics of the system vary with operating as well as
environmental conditions. Conventionally, simple Proportional Integral
Derivative controller is used which has well known limitations. Other
conventional control techniques have also been investigated but they require an
accurate mathematical model of a ship. Unfortunately, accuracy of mathematical
models is very difficult to achieve. During the past few decades computational
intelligence techniques such as artificial neural networks have been very
successful when an accurate mathematical model is not available. Therefore, in
this paper, an artificial neural network controller is proposed for heading and
position control system. For simulation purposes, a mathematical model with
four effective thrusters have been chosen to test the performance of the
proposed controller. The final closed loop system has been analyzed and tested
through simulation studies. The results are very encouraging.


title: Convert widespread paraelectric perovskite to ferroelectrics

abstract:
  While nature provides a plethora of perovskite materials, only a few exhibits
large ferroelectricity and possibly multiferroicity. The majority of perovskite
materials have the non-polar CaTiO$_3$(CTO)structure, limiting the scope of
their applications. Based on effective Hamiltonian model as well as
first-principles calculations, we propose a general thin-film design method to
stabilize the functional BiFeO$_3$(BFO)-type structure, which is a common
metastable structure in widespread CaTiO$_3$-type perovskite oxides. It is
found that the improper antiferroelectricity in CTO-type perovskite and
ferroelectricity in BFO-type perovskite have distinct dependences on mechanical
and electric boundary conditions, both of which involve oxygen octahedral
rotation and tilt. The above difference can be used to stabilize the highly
polar BFO-type structure in many CTO-type perovskite materials.


title: Weak decays of triply heavy baryons: the $3/2\to1/2$ case

abstract:
  In this work, we investigate the weak decays of spin-3/2 triply heavy baryons
to spin-1/2 doubly/triply heavy baryons. We first obtain the form factors using
the light-front quark model in the diquark picture, and then apply them to some
phenomenological predictions. Our results are expected to be helpful for the
experimental search for triply heavy baryons.


title: The Isoperimetric Problem In Randers Planes

abstract:
  In this paper, the isoperimetric problem in Randers planes,
$(\mathbb{R}^2,F=\alpha +\beta)$, which are slight deformation of the Euclidean
plane $(\mathbb{R}^2,\alpha)$ by suitable one forms $\beta$, have been studied.
We prove that the circles centred at the origin achieves the local maximum area
of the isoperimetric problem with respect to well known volume forms in Finsler
geometry.


title:
Canted Antiferromagnetic phases in the layered candidate Weyl material
  EuMnSb$_2$

abstract:
  EuMnSb$_2$ is a candidate topological material which can be tuned towards a
Weyl semimetal, but there are differing reports for its antiferromagnetic (AFM)
phases. The coupling of bands dominated by pure Sb layers hosting topological
fermions to Mn and Eu magnetic states provides a potential path to tune the
topological properties. We present a detailed analysis of the magnetic
structure on three AFM phases based on single-crystal neutron diffraction,
magnetization, and heat capacity data as well as polycrystalline $^{151}$Eu
M\"ossbauer data. The Mn magnetic sublattice orders into a C-type AFM structure
below $323(1)$~K with the ordered Mn magnetic moment $\mu_{\text{Mn}}$ lying
perpendicular to the layers. AFM ordering of the Eu sublattice occurs below
$23(1)$~K with the ordered Eu magnetic moment $\mu_{\text{Eu}}$ canted away
from the layer normal and $\mu_{\text{Mn}}$ retaining its higher-temperature
order. $\mu_{\text{Eu}}$ is ferromagnetically aligned within each Eu layer but
exhibits a complicated AFM layer stacking. Both of these higher-temperature
phases are described by magnetic space group (MSG)
$Pn^{\prime}m^{\prime}a^{\prime}$ with the chemical and magnetic unit cells
having the same dimensions. Cooling below $=9(1)$~K reveals a third AFM phase
where $\mu_{\text{Mn}}$ remains unchanged but $\mu_{\text{Eu}}$ develops an
additional in-plane canting. This phase has MSG $P11\frac{2_1}{a^{\prime}}$. We
additionally find evidence of short-range magnetic correlations associated with
the Eu between $12~\text{K} \lesssim T \lesssim 30~\text{K}$. Using the
determined magnetic structures, we postulate the signs of nearest-neighbor
intralayer and interlayer exchange constants and the magnetic anisotropy within
a general Heisenberg-model. We then discuss implications of the various AFM
states in EuMnSb$_2$ and its topological properties.


title: Do learned representations respect causal relationships?

abstract:
  Data often has many semantic attributes that are causally associated with
each other. But do attribute-specific learned representations of data also
respect the same causal relations? We answer this question in three steps.
First, we introduce NCINet, an approach for observational causal discovery from
high-dimensional data. It is trained purely on synthetically generated
representations and can be applied to real representations, and is specifically
designed to mitigate the domain gap between the two. Second, we apply NCINet to
identify the causal relations between image representations of different pairs
of attributes with known and unknown causal relations between the labels. For
this purpose, we consider image representations learned for predicting
attributes on the 3D Shapes, CelebA, and the CASIA-WebFace datasets, which we
annotate with multiple multi-class attributes. Third, we analyze the effect on
the underlying causal relation between learned representations induced by
various design choices in representation learning. Our experiments indicate
that (1) NCINet significantly outperforms existing observational causal
discovery approaches for estimating the causal relation between pairs of random
samples, both in the presence and absence of an unobserved confounder, (2)
under controlled scenarios, learned representations can indeed satisfy the
underlying causal relations between their respective labels, and (3) the causal
relations are positively correlated with the predictive capability of the
representations.


title:
Metaphorical User Simulators for Evaluating Task-oriented Dialogue
  Systems

abstract:
  Task-oriented dialogue systems (TDSs) are assessed mainly in an offline
setting or through human evaluation. The evaluation is often limited to
single-turn or very time-intensive. As an alternative, user simulators that
mimic user behavior allow us to consider a broad set of user goals to generate
human-like conversations for simulated evaluation. Employing existing user
simulators to evaluate TDSs is challenging as user simulators are primarily
designed to optimize dialogue policies for TDSs and have limited evaluation
capability. Moreover, the evaluation of user simulators is an open challenge.
In this work, we proposes a metaphorical user simulator for endto-end TDS
evaluation. We also propose a tester-based evaluation framework to generate
variants, i.e., dialogue systems with different capabilities. Our user
simulator constructs a metaphorical user model that assists the simulator in
reasoning by referring to prior knowledge when encountering new items. We
estimate the quality of simulators by checking the simulated interactions
between simulators and variants. Our experiments are conducted using three TDS
datasets. The metaphorical user simulator demonstrates better consistency with
manual evaluation than Agenda-based simulator and Seq2seq model on three
datasets; our tester framework demonstrates efficiency, and our approach
demonstrates better generalization and scalability.


title: A Study of Real-World Data Races in Golang

abstract:
  The concurrent programming literature is rich with tools and techniques for
data race detection. Less, however, has been known about real-world,
industry-scale deployment, experience, and insights about data races. Golang
(Go for short) is a modern programming language that makes concurrency a
first-class citizen. Go offers both message passing and shared memory for
communicating among concurrent threads. Go is gaining popularity in modern
microservice-based systems. Data races in Go stand in the face of its emerging
popularity.
  In this paper, using our industrial codebase as an example, we demonstrate
that Go developers embrace concurrency and show how the abundance of
concurrency alongside language idioms and nuances make Go programs highly
susceptible to data races. Google's Go distribution ships with a built-in
dynamic data race detector based on ThreadSanitizer. However, dynamic race
detectors pose scalability and flakiness challenges; we discuss various
software engineering trade-offs to make this detector work effectively at
scale. We have deployed this detector in Uber's 46 million lines of Go codebase
hosting 2100 distinct microservices, found over 2000 data races, and fixed over
1000 data races, spanning 790 distinct code patches submitted by 210 unique
developers over a six-month period. Based on a detailed investigation of these
data race patterns in Go, we make seven high-level observations relating to the
complex interplay between the Go language paradigm and data races.


title: An analogue of the Riemann Hypothesis via quantum walks

abstract:
  We consider an analogue of the well-known Riemann Hypothesis based on quantum
walks on graphs with the help of the Konno-Sato theorem. Furthermore, we give
some examples for complete, cycle, and star graphs.

categories: quant-ph math-ph math.MP math.NT math.PR


title: The number of locally invariant orderings of a group

abstract:
  We show that if a nontrivial group admits a locally invariant ordering, then
it admits uncountably many locally invariant orderings. For the case of a
left-orderable group, we provide an explicit construction of uncountable
families of locally invariant orderings; for a general group we provide an
existence theorem that applies compactness to yield uncountably many locally
invariant orderings.


title: Thin shell dynamics in Lovelock gravity

abstract:
  We study matching conditions for a spherically symmetric thin shell in
Lovelock gravity which can be read off from the variation of the corresponding
first-order action. In point of fact, the addition of Myers' boundary terms to
the gravitational action eliminates the dependence on the acceleration in this
functional and such that the canonical momentum appears in the surface term in
the variation of the total action. This procedure leads to junction conditions
given by the discontinuity of the canonical momentum defined for an evolution
normal to the boundary. In particular, we correct existing results in the
literature for the thin shell collapse in generic Lovelock theories, which were
mistakenly drawn from an inaccurate analysis of the total derivative terms in
the system.


title:
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ
  Acoustic Feature

abstract:
  The mainstream neural text-to-speech(TTS) pipeline is a cascade system,
including an acoustic model(AM) that predicts acoustic feature from the input
transcript and a vocoder that generates waveform according to the given
acoustic feature. However, the acoustic feature in current TTS systems is
typically mel-spectrogram, which is highly correlated along both time and
frequency axes in a complicated way, leading to a great difficulty for the AM
to predict. Although high-fidelity audio can be generated by recent neural
vocoders from ground-truth(GT) mel-spectrogram, the gap between the GT and the
predicted mel-spectrogram from AM degrades the performance of the entire TTS
system. In this work, we propose VQTTS, consisting of an AM txt2vec and a
vocoder vec2wav, which uses self-supervised vector-quantized(VQ) acoustic
feature rather than mel-spectrogram. We redesign both the AM and the vocoder
accordingly. In particular, txt2vec basically becomes a classification model
instead of a traditional regression model while vec2wav uses an additional
feature encoder before HifiGAN generator for smoothing the discontinuous
quantized feature. Our experiments show that vec2wav achieves better
reconstruction performance than HifiGAN when using self-supervised VQ acoustic
feature. Moreover, our entire TTS system VQTTS achieves state-of-the-art
performance in terms of naturalness among all current publicly available TTS
systems.


title: Variational message passing for online polynomial NARMAX identification

abstract:
  We propose a variational Bayesian inference procedure for online nonlinear
system identification. For each output observation, a set of parameter
posterior distributions is updated, which is then used to form a posterior
predictive distribution for future outputs. We focus on the class of polynomial
NARMAX models, which we cast into probabilistic form and represent in terms of
a Forney-style factor graph. Inference in this graph is efficiently performed
by a variational message passing algorithm. We show empirically that our
variational Bayesian estimator outperforms an online recursive least-squares
estimator, most notably in small sample size settings and low noise regimes,
and performs on par with an iterative least-squares estimator trained offline.


title: Speaker adaptation for Wav2vec2 based dysarthric ASR

abstract:
  Dysarthric speech recognition has posed major challenges due to lack of
training data and heavy mismatch in speaker characteristics. Recent ASR systems
have benefited from readily available pretrained models such as wav2vec2 to
improve the recognition performance. Speaker adaptation using fMLLR and
xvectors have provided major gains for dysarthric speech with very little
adaptation data. However, integration of wav2vec2 with fMLLR features or
xvectors during wav2vec2 finetuning is yet to be explored. In this work, we
propose a simple adaptation network for fine-tuning wav2vec2 using fMLLR
features. The adaptation network is also flexible to handle other speaker
adaptive features such as xvectors. Experimental analysis show steady
improvements using our proposed approach across all impairment severity levels
and attains 57.72\% WER for high severity in UASpeech dataset. We also
performed experiments on German dataset to substantiate the consistency of our
proposed approach across diverse domains.


title:
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement
  Network (E3Net) and Knowledge Distillation

abstract:
  This paper investigates how to improve the runtime speed of personalized
speech enhancement (PSE) networks while maintaining the model quality. Our
approach includes two aspects: architecture and knowledge distillation (KD). We
propose an end-to-end enhancement (E3Net) model architecture, which is
$3\times$ faster than a baseline STFT-based model. Besides, we use KD
techniques to develop compressed student models without significantly degrading
quality. In addition, we investigate using noisy data without reference clean
signals for training the student models, where we combine KD with multi-task
learning (MTL) using automatic speech recognition (ASR) loss. Our results show
that E3Net provides better speech and transcription quality with a lower target
speaker over-suppression (TSOS) rate than the baseline model. Furthermore, we
show that the KD methods can yield student models that are $2-4\times$ faster
than the teacher and provides reasonable quality. Combining KD and MTL improves
the ASR and TSOS metrics without degrading the speech quality.


title: Improved Approximation Algorithm for Graph Burning on Trees

abstract:
  Given a graph $G=(V,E)$, the problem of \gb{} is to find a sequence of nodes
from $V$, called burning sequence, in order to burn the whole graph. This is a
discrete-step process, in each step an unburned vertex is selected as an agent
to spread fire to its neighbors by marking it as a burnt node. A node that is
burnt spreads the fire to its neighbors at the next consecutive step. The goal
is to find the burning sequence of minimum length. The \gb{} problem is NP-Hard
for general graphs and even for binary trees. A few approximation results are
known, including a $3$-approximation algorithm for general graphs and a $2$-
approximation algorithm for trees. In this paper, we propose an approximation
algorithm for trees that produces a burning sequence of length at most $\lfloor
1.75b(T) \rfloor + 1$, where $b(T)$ is length of the optimal burning sequence,
also called the burning number of the tree $T$. In other words, we achieve an
approximation factor of $(\lfloor 1.75b(T) \rfloor + 1)/b(T)$.


title:
Energy Efficient HARQ for Ultrareliability via Novel Outage Probability
  Bound and Geometric Programming

abstract:
  Hybrid automatic repeat 1 request (HARQ) is a key enabler for ultrareliable
communications. This paper optimizes transmit power for the initial
transmission and the subsequent retransmissions of HARQ with either incremental
redundancy or Chase combining, aiming to minimize the expected energy
consumption given the target outage probability and the target latency. The
main challenge is due to the fact that the outage probability is a complicated
function of the power variables which are nested in successive convolutions.
The existing works mostly use a classic upper bound to approximate the outage
probability by assuming unbounded transmit power, then convert the original
problem to a geometric programming (GP) problem. In contrast, we propose a
novel and much tighter upper bound by taking the practical power limit into
consideration. The new bound and the resulting new GP method are further
extended to a broader group of channel models with various fading, multiple
antennas, and multiple receivers. As shown in simulations, the GP method based
on the new bound significantly outperforms the existing strategies that either
fix transmit power or optimize power by the classic bounding technique.


title: A Generalized Family of Exponentiated Composite Distributions

abstract:
  In this paper, we propose a new class of distributions by exponentiating the
random variables associated with the probability density functions of composite
distributions. We also derive some mathematical properties of this new class of
distributions including the moments and the limited moments. Specifically, two
special models in this family are discussed. Two real data sets were chosen to
assess the performance of these two special exponentiated composite models.
When fitting to these two data sets, theses two special exponentiated composite
distributions demonstrate significantly better performance compared to the
original composite distributions.


title: Class Numbers, Cyclic Simple Groups and Arithmetic

abstract:
  Here we initiate a program to study relationships between finite groups and
arithmetic-geometric invariants in a systematic way. To do this we first
introduce a notion of optimal module for a finite group in the setting of
holomorphic mock Jacobi forms. Then we classify optimal modules for the cyclic
groups of prime order, in the special case of weight two and index one, where
class numbers of imaginary quadratic fields play an important role. Finally we
exhibit a connection between the classification we establish and the arithmetic
geometry of imaginary quadratic twists of modular curves of prime level.


title: Non-autonomous stochastic lattice systems with Markovian switching

abstract:
  The aim of this paper is to study the dynamical behavior of non-autonomous
stochastic lattice systems with Markovian switching. We first show existence of
an evolution system of measures of the stochastic system. We then study the
pullback (or forward) asymptotic stability in distribution of the evolution
system of measures. We finally prove that any limit point of a tight sequence
of an evolution system of measures of the stochastic lattice systems must be an
evolution system of measures of the corresponding limiting system as the
intensity of noise converges zero. In particular, when the coefficients are
periodic with respect to time, we show every limit point of a sequence of
periodic measures of the stochastic system must be a periodic measure of the
limiting system as the noise intensity goes to zero.


title:
Revealing the real-world CO2 emission reduction of ridesplitting and its
  determinants based on machine learning

abstract:
  Ridesplitting, which is a form of pooled ridesourcing service, has great
potential to alleviate the negative impacts of ridesourcing on the environment.
However, most existing studies only explored its theoretical environmental
benefits based on optimization models and simulations. To put into practice,
this study aims to reveal the real-world emission reduction of ridesplitting
and its determinants based on the observed data of ridesourcing in Chengdu,
China. Integrating the trip data with the COPERT model, this study calculates
the CO2 emissions of shared rides (ridesplitting) and their substituted single
rides (regular ridesourcing) to estimate the CO2 emission reduction of each
ridesplitting trip. The results show that not all ridesplitting trips reduce
emissions from ridesourcing in the real world. The CO2 emission reduction rate
of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then, the
interpretable machine learning models, gradient boosting machines, are applied
to explore the relationship between the CO2 emission reduction rate of
ridesplitting and its determinants. Based on the SHapley Additive exPlanations
method, the overlap rate and detour rate of shared rides are identified to be
the most important factors that determine the CO2 emission reduction rate of
ridesplitting. Increasing the overlap rate, the number of shared rides, average
speed, and ride distance ratio and decreasing the detour rate, actual trip
distance, ride distance gap can increase the CO2 emission reduction rate of
ridesplitting. In addition, nonlinear effects and interactions of several key
factors are examined through the partial dependence plots. This study provides
a scientific method for the government and ridesourcing companies to better
assess and optimize the environmental benefits of ridesplitting.


title: Distributional Gradient Boosting Machines

abstract:
  We present a unified probabilistic gradient boosting framework for regression
tasks that models and predicts the entire conditional distribution of a
univariate response variable as a function of covariates. Our likelihood-based
approach allows us to either model all conditional moments of a parametric
distribution, or to approximate the conditional cumulative distribution
function via Normalizing Flows. As underlying computational backbones, our
framework is based on XGBoost and LightGBM. Modelling and predicting the entire
conditional distribution greatly enhances existing tree-based gradient boosting
implementations, as it allows to create probabilistic forecasts from which
prediction intervals and quantiles of interest can be derived. Empirical
results show that our framework achieves state-of-the-art forecast accuracy.


title:
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO
  Systems with Limited Feedback

abstract:
  The downlink channel covariance matrix (CCM) acquisition is the key step for
the practical performance of massive multiple-input and multiple-output (MIMO)
systems, including beamforming, channel tracking, and user scheduling. However,
this task is challenging in the popular frequency division duplex massive MIMO
systems with Type I codebook due to the limited channel information feedback.
In this paper, we propose a novel formulation that leverages the structure of
the codebook and feedback values for an accurate estimation of the downlink
CCM. Then, we design a cutting plane algorithm to consecutively shrink the
feasible set containing the downlink CCM, enabled by the careful design of
pilot weighting matrices. Theoretical analysis shows that as the number of
communication rounds increases, the proposed cutting plane algorithm can
recover the ground-truth CCM. Numerical results are presented to demonstrate
the superior performance of the proposed algorithm over the existing benchmark
in CCM reconstruction.


title:
Inhomogeneous and simultaneous Diophantine approximation in beta
  dynamical systems

abstract:
  In this paper, we investigate inhomogeneous and simultaneous Diophantine
approximation in beta dynamical systems. For $\beta>1$ let $T_{\beta}$ be the
$\beta$-transformation on $[0,1]$. We determine the Lebesgue measure and
Hausdorff dimension of the set \[\left\{(x,y)\in [0,1]^2:
|T_{\beta}^nx-f(x,y)|<\varphi(n)\text{ for infinitely many
}n\in\mathbb{N}\right\},\] where $f:[0,1]^2\to [0,1]$ is a Lipschitz function
and $\varphi$ is a positive function on $\mathbb{N}$. Let $\beta_2\geq
\beta_1>1$, $f_1,f_2:[0,1]\to [0,1]$ be two Lipschitz functions,
$\tau_1,\tau_2$ be two positive continuous functions on $[0,1]$ with
$\min_{x\in [0,1]}\tau_1(x)\leq \min_{y\in [0,1]}\tau_2(y)$. We also determine
the Hausdorff dimension of the set \[\left\{(x,y)\in [0,1]^2:
\begin{aligned}&|T_{\beta_1}^nx-f_1(x)|<\beta_1^{-n\tau_1(x)}\\
&|T_{\beta_2}^ny-f_2(y)|<\beta_2^{-n\tau_2(y)}\end{aligned}\text{ for
infinitely many }n\in\mathbb{N}\right\}.\] If in addition
$\tau_1(x),\tau_2(y)<1$ for all $x,y\in [0,1]$ and $\max_{y\in
[0,1]}\tau_{2}(y)< \log_{\beta_{2}}\beta_1$, then the Hausdorff dimension of
the set \[\left\{(x,y)\in [0,1]^2:
\begin{aligned}&|T_{\beta_1}^nx-g_1(x,y)|<\beta_1^{-n\tau_1(x)}\\
&|T_{\beta_2}^ny-g_2(x,y)|<\beta_2^{-n\tau_2(y)}\end{aligned}\text{ for
infinitely many }n\in\mathbb{N}\right\}\] is also determined, where
$g_1,g_2:[0,1]^2\to [0,1]$ are two Lipschitz functions.


title:
The Round Complexity of Local Operations and Classical Communication
  (LOCC) in Random-Party Entanglement Distillation

abstract:
  A powerful operational paradigm for distributed quantum information
processing involves manipulating pre-shared entanglement by local operations
and classical communication (LOCC). The LOCC round complexity of a given task
describes how many rounds of classical communication are needed to complete the
task. Despite some results separating one-round versus two-round protocols,
very little is known about higher round complexities. In this paper, we revisit
the task of one-shot random-party entanglement distillation as a way to
highlight some interesting features of LOCC round complexity. We first show
that for random-party distillation in three qubits, the number of communication
rounds needed in an optimal protocol depends on the entanglement measure used;
for the same fixed state some entanglement measures need only two rounds to
maximize whereas others need an unbounded number of rounds. In doing so, we
construct a family of LOCC instruments that require an unbounded number of
rounds to implement. We then prove explicit tight lower bounds on the LOCC
round number as a function of distillation success probability. Our
calculations show that the original W-state random distillation protocol by
Fortescue and Lo is essentially optimal in terms of round complexity.


title:
Best Approximate Solution for Generalized Nash Games and
  Quasi-optimization Problems

abstract:
  In this article, we consider generalized Nash games where the associated
constraint map is not necessarily self. The classical Nash equilibrium may not
exist for such games and therefore we introduce the notion of best approximate
solution for such games. We investigate the occurrence of best approximate
solutions for such generalized Nash games consisting of infinitely many players
in which each player regulates the strategy variable lying in a topological
vector space. Based on the maximum theorem and a fixed point result for
Kakutani factorizable maps, we derive the existence of best approximate
solutions under the quasi-concavity and weak continuity assumption on players'
objective functions. Furthermore, we demonstrate the occurrence of best
approximate solutions for quasi-optimization problems.


title:
Paoding: Supervised Robustness-preserving Data-free Neural Network
  Pruning

abstract:
  When deploying pre-trained neural network models in real-world applications,
model consumers often encounter resource-constraint platforms such as mobile
and smart devices. They typically use the pruning technique to reduce the size
and complexity of the model, generating a lighter one with less resource
consumption. Nonetheless, most existing pruning methods are proposed with a
premise that the model after being pruned has a chance to be fine-tuned or even
retrained based on the original training data. This may be unrealistic in
practice, as the data controllers are often reluctant to provide their model
consumers with the original data. In this work, we study the neural network
pruning in the \emph{data-free} context, aiming to yield lightweight models
that are not only accurate in prediction but also robust against undesired
inputs in open-world deployments. Considering the absence of the fine-tuning
and retraining that can fix the mis-pruned units, we replace the traditional
aggressive one-shot strategy with a conservative one that treats the pruning as
a progressive process. We propose a pruning method based on stochastic
optimization that uses robustness-related metrics to guide the pruning process.
Our method is implemented as a Python package named \textsc{Paoding} and
evaluated with a series of experiments on diverse neural network models. The
experimental results show that it significantly outperforms existing one-shot
data-free pruning approaches in terms of robustness preservation and accuracy.


title: Various proofs of the Fundamental Theorem of Markov Chains

abstract:
  This paper is a survey of various proofs of the so called {\em fundamental
theorem of Markov chains}: every ergodic Markov chain has a unique positive
stationary distribution and the chain attains this distribution in the limit
independent of the initial distribution the chain started with. As Markov
chains are stochastic processes, it is natural to use probability based
arguments for proofs. At the same time, the dynamics of a Markov chain is
completely captured by its initial distribution, which is a vector, and its
transition probability matrix. Therefore, arguments based on matrix analysis
and linear algebra can also be used. The proofs discussed below use one or the
other of these two types of arguments, except in one case where the argument is
graph theoretic. Appropriate credits to the various proofs are given in the
main text.
  Our first proof is entirely elementary, and yet the proof is also quite
simple. The proof also suggests a mixing time bound, which we prove, but this
bound in many cases will not be the best bound. One approach in proving the
fundamental theorem breaks the proof in two parts: (i) show the existence of a
unique positive stationary distribution for irreducible Markov chains, and (ii)
assuming that an ergodic chain does have a stationary distribution, show that
the chain will converge in the limit to that distribution irrespective of the
initial distribution. For (i), we survey two proofs, one uses probability
arguments, and the other uses graph theoretic arguments. For (ii), first we
give a coupling based proof (coupling is a probability based technique), the
other uses matrix analysis. Finally, we give a proof of the fundamental theorem
using only linear algebra concepts.


title:
Rural Pension System and Farmers' Participation in Residents' Social
  Insurance

abstract:
  As the ageing population and childlessness are increasing in rural China,
social pensions will become the mainstream choice for farmers, and the level of
social pensions must be supported by better social insurance. The paper
compares the history of rural pension insurance system, outlines the current
situation and problems, analyses China Family Panel Studies data and explores
the key factors influencing farmers' participation through an empirical
approach. The paper shows that residents' social pension insurance is facing
problems in the rural areas such as low level of protection and weak management
capacity, which have contributed to the under-insured rate, and finds that
there is a significant impact on farmers' participation in insurance from
personal characteristics factors such as gender, age, health and (family)
financial factors such as savings, personal income, intergenerational mobility
of funds. And use of the Internet can help farmers enroll in pension insurance.
The paper argues for the need to continue to implement the rural revitalisation
strategy, with the government as the lead and the market as the support, in a
concerted effort to improve the protection and popularity of rural pension
insurance.


title:
Large-eddy simulations of wind-driven cross ventilation, Part1:
  validation and sensitivity study

abstract:
  Natural ventilation is gaining popularity in response to an increasing demand
for a sustainable and healthy built environment, but the design of a naturally
ventilated building can be challenging due to the inherent variability in the
operating conditions that determine the natural ventilation flow. Large-eddy
simulations (LES) have significant potential as an analysis method for natural
ventilation flow, since they can provide an accurate prediction of turbulent
flow at any location in the computational domain. However, the simulations can
be computationally expensive, and few validation and sensitivity studies have
been reported. The objectives of this study are to validate LES of wind-driven
cross-ventilation and to quantify the sensitivity of the solution to the grid
resolution and the inflow boundary conditions. We perform LES for an isolated
building with two openings, using three different grid resolutions and two
different inflow conditions with varying turbulence intensities. Predictions of
the ventilation rate are compared to a reference wind-tunnel experiment
available from literature, and we also quantify the age of air and ventilation
efficiency. The results show that a sufficiently fine grid resolution is needed
to provide accurate predictions of the detailed flow pattern and the age of
air, while the inflow condition is found to affect the standard deviation of
the instantaneous ventilation rate. However, for the cross-ventilation case
modeled in this paper, the prediction of the mean ventilation flow rate is very
robust, showing negligible sensitivity to the grid resolution or the inflow
characteristics.


title: All-optical control of excitons in semiconductor quantum wells

abstract:
  Applying the Floquet theory, we developed the method to control excitonic
properties of semiconductor quantum wells by a high-frequency electromagnetic
field. It is demonstrated, particularly, that the field induces the blue shift
of exciton emission from the quantum wells and narrows width of the
corresponding spectral line. As a consequence, the field strongly modifies
optical properties of the quantum wells and, therefore, can be used to tune
characteristics of the optoelectronic devices based on them.


title: A probabilistic scheduling algorithm for networked control systems

abstract:
  This paper deals with the design of scheduling logics for Networked Control
Systems (NCSs) whose communication networks have limited capacity. We assume
that only a subset of the plants can communicate with their controllers at any
time instant. Our contributions are twofold. First, we present a probabilistic
algorithm to design scheduling logics that, under certain conditions on the
plant and controller dynamics and the capacity of the network, ensure
stochastic stability of each plant in an NCS. Second, given the plant dynamics
and the capacity of the network, we design static state-feedback controllers
such that the conditions for stability under our scheduling logics are
satisfied. The main apparatus for our analysis is a Markovian jump linear
system representation of the individual plants in an NCS. Our stability
conditions involve sets of matrix inequalities. We present numerical
experiments to demonstrate our results.


title:
UV Divergences of Scattering Amplitudes in D-dimensional Yang-Mills
  Theories

abstract:
  We consider UV divergences for the on-shell planar gluon-gluon scattering
amplitudes in the gauge theory in arbitrary D-dimensions in the one-loop order.
The amplitudes are evaluated using the standard Feynman diagram technique for
several choices of external gluon polarizations. Using the Passarino - Veltman
reduction, the expansion in terms of the scalar master integrals is constructed
and their calculation is performed within dimensional regularization. The
resulting expressions are confronted with existing results for various cases,
including maximally supersymmetric theories in D=4, 6, 8, and 10 dimensions.
One finds that, contrary to the D=4 case, in D=6 one-loop UV divergences
cancel, while in D=8 and 10 the contributions of gauge, scalar and fermion
loops have the same sign.


title:
SAD: A Large-scale Dataset towards Airport Detection in Synthetic
  Aperture Radar Images

abstract:
  Airports have an important role in both military and civilian domains. The
synthetic aperture radar (SAR) based airport detection has received increasing
attention in recent years. However, due to the high cost of SAR imaging and
annotation process, there is no publicly available SAR dataset for airport
detection. As a result, deep learning methods have not been fully used in
airport detection tasks. To provide a benchmark for airport detection research
in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD).
In order to adequately reflect the demands of real world applications, it
contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with
different scales, orientations and shapes. The experiments of multiple deep
learning approach on this dataset proves its effectiveness. It developing
state-of-the-art airport area detection algorithms or other relevant tasks.


title:
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment
  Analysis

abstract:
  As an extensive research in the field of Natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack of sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Specifically, we design two contrastive strategies, token
level contrastive learning of token embeddings (TL-CTE) and sentiment level
contrastive learning of token embeddings (SL-CTE), to regularize the semantic
space of source and target language to be more uniform. Since our framework can
receive datasets in multiple languages during training, our framework can be
adapted not only for XABSA task, but also for multilingual aspect-based
sentiment analysis (MABSA). To further improve the performance of our model, we
perform knowledge distillation technology leveraging data from unlabeled target
language. In the distillation XABSA task, we further explore the comparative
effectiveness of different data (source dataset, translated dataset, and
code-switched dataset). The results demonstrate that the proposed method has a
certain improvement in the three tasks of XABSA, distillation XABSA and MABSA.
For reproducibility, our code for this paper is available at
https://github.com/GKLMIP/CL-XABSA.


title:
IR-GAN: Image Manipulation with Linguistic Instruction by Increment
  Reasoning

abstract:
  Conditional image generation is an active research topic including text2image
and image translation.
  Recently image manipulation with linguistic instruction brings new challenges
of multimodal conditional generation.
  However, traditional conditional image generation models mainly focus on
generating high-quality and visually realistic images, and lack resolving the
partial consistency between image and instruction.
  To address this issue, we propose an Increment Reasoning Generative
Adversarial Network (IR-GAN), which aims to reason the consistency between
visual increment in images and semantic increment in instructions.
  First, we introduce the word-level and instruction-level instruction encoders
to learn user's intention from history-correlated instructions as semantic
increment.
  Second, we embed the representation of semantic increment into that of source
image for generating target image, where source image plays the role of
referring auxiliary.
  Finally, we propose a reasoning discriminator to measure the consistency
between visual increment and semantic increment, which purifies user's
intention and guarantees the good logic of generated target image.
  Extensive experiments and visualization conducted on two datasets show the
effectiveness of IR-GAN.


title:
Spatially resolved molecular interstellar medium in a $z=6.6$ quasar
  host galaxy

abstract:
  We present high spatial resolution (0.4", 2.2kpc) observations of the
CO(6-5), CO(7-6) and [CI] lines and dust continuum emission from the
interstellar medium in the host galaxy of the quasar J0305$-$3150 at $z=6.6$.
These, together with archival [CII] data at comparable spatial resolution,
enable studies of the spatial distribution and kinematics between the ISM in
different phases. When comparing the radial profiles of CO, [CII] and the dust
continuum, we find that the CO and dust continuum exhibit similar spatial
distributions, both of which are less extended than the [CII], indicating that
the CO and dust continuum are tracing the same gas component, while the [CII]
is tracing a more extended one. In addition, we derive the radial profiles of
the [CII]/CO, [CII]/far-infrared (FIR), CO/FIR, and dust continuum $S_{98.7 \rm
GHz}/S_{258.1 \rm GHz}$ ratios. We find a decreasing $S_{98.7 \rm GHz}/S_{258.1
\rm GHz}$ ratio with radius, possibly indicating a decrease of dust optical
depth with increasing radius. We also detect some of the ISM lines and
continuum emission in the companion galaxies previously discovered in the field
around J0305$-$3150. Through comparing the line-to-line and line-to-FIR ratios,
we find no significant differences between the quasar and its companion
galaxies.


title: R(Det)^2: Randomized Decision Routing for Object Detection

abstract:
  In the paradigm of object detection, the decision head is an important part,
which affects detection performance significantly. Yet how to design a
high-performance decision head remains to be an open issue. In this paper, we
propose a novel approach to combine decision trees and deep neural networks in
an end-to-end learning manner for object detection. First, we disentangle the
decision choices and prediction values by plugging soft decision trees into
neural networks. To facilitate effective learning, we propose randomized
decision routing with node selective and associative losses, which can boost
the feature representative learning and network decision simultaneously.
Second, we develop the decision head for object detection with narrow branches
to generate the routing probabilities and masks, for the purpose of obtaining
divergent decisions from different nodes. We name this approach as the
randomized decision routing for object detection, abbreviated as R(Det)$^2$.
Experiments on MS-COCO dataset demonstrate that R(Det)$^2$ is effective to
improve the detection performance. Equipped with existing detectors, it
achieves $1.4\sim 3.6$\% AP improvement.


title:
Unsupervised Coherent Video Cartoonization with Perceptual Motion
  Consistency

abstract:
  In recent years, creative content generations like style transfer and neural
photo editing have attracted more and more attention. Among these,
cartoonization of real-world scenes has promising applications in entertainment
and industry. Different from image translations focusing on improving the style
effect of generated images, video cartoonization has additional requirements on
the temporal consistency. In this paper, we propose a spatially-adaptive
semantic alignment framework with perceptual motion consistency for coherent
video cartoonization in an unsupervised manner. The semantic alignment module
is designed to restore deformation of semantic structure caused by spatial
information lost in the encoder-decoder architecture. Furthermore, we devise
the spatio-temporal correlative map as a style-independent, global-aware
regularization on the perceptual motion consistency. Deriving from similarity
measurement of high-level features in photo and cartoon frames, it captures
global semantic information beyond raw pixel-value in optical flow. Besides,
the similarity measurement disentangles temporal relationships from
domain-specific style properties, which helps regularize the temporal
consistency without hurting style effects of cartoon images. Qualitative and
quantitative experiments demonstrate our method is able to generate highly
stylistic and temporal consistent cartoon videos.


title:
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity
  Recognition

abstract:
  Cross-lingual Named Entity Recognition (NER) has recently become a research
hotspot because it can alleviate the data-hungry problem for low-resource
languages. However, few researches have focused on the scenario where the
source-language labeled data is also limited in some specific domains. A common
approach for this scenario is to generate more training data through
translation or generation-based data augmentation method. Unfortunately, we
find that simply combining source-language data and the corresponding
translation cannot fully exploit the translated data and the improvements
obtained are somewhat limited. In this paper, we describe our novel
dual-contrastive framework ConCNER for cross-lingual NER under the scenario of
limited source-language labeled data. Specifically, based on the
source-language samples and their translations, we design two contrastive
objectives for cross-language NER at different grammatical levels, namely
Translation Contrastive Learning (TCL) to close sentence representations
between translated sentence pairs and Label Contrastive Learning (LCL) to close
token representations within the same labels. Furthermore, we utilize knowledge
distillation method where the NER model trained above is used as the teacher to
train a student model on unlabeled target-language data to better fit the
target language. We conduct extensive experiments on a wide variety of target
languages, and the results demonstrate that ConCNER tends to outperform
multiple baseline methods. For reproducibility, our code for this paper is
available at https://github.com/GKLMIP/ConCNER.


title:
Improving the Factual Accuracy of Abstractive Clinical Text
  Summarization using Multi-Objective Optimization

abstract:
  While there has been recent progress in abstractive summarization as applied
to different domains including news articles, scientific articles, and blog
posts, the application of these techniques to clinical text summarization has
been limited. This is primarily due to the lack of large-scale training data
and the messy/unstructured nature of clinical notes as opposed to other domains
where massive training data come in structured or semi-structured form.
Further, one of the least explored and critical components of clinical text
summarization is factual accuracy of clinical summaries. This is specifically
crucial in the healthcare domain, cardiology in particular, where an accurate
summary generation that preserves the facts in the source notes is critical to
the well-being of a patient. In this study, we propose a framework for
improving the factual accuracy of abstractive summarization of clinical text
using knowledge-guided multi-objective optimization. We propose to jointly
optimize three cost functions in our proposed architecture during training:
generative loss, entity loss and knowledge loss and evaluate the proposed
architecture on 1) clinical notes of patients with heart failure (HF), which we
collect for this study; and 2) two benchmark datasets, Indiana University Chest
X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We
experiment with three transformer encoder-decoder architectures and demonstrate
that optimizing different loss functions leads to improved performance in terms
of entity-level factual accuracy.


title: The Clifford algebra bundle on loop space

abstract:
  We construct a Clifford algebra bundle formed from the tangent bundle of the
smooth loop space of a Riemannian manifold, which is a bundle of super von
Neumann algebras on the loop space. We show that this bundle is in general
non-trivial, more precisely that its triviality is obstructed by the
transgressions of the second Stiefel-Whitney class and the first (fractional)
Pontrjagin class of the manifold.


title: Gravitational-Wave Signatures of Chiral-Symmetric Technicolor

abstract:
  A chiral-symmetric technicolor model successfully reconciles the tension
between electroweak precision tests and traditional technicolor models.
Focusing on its simplest realization preserving the conventional Higgs
mechanism, we study its primordial gravitational wave signatures originating
from first order phase transitions in the early Universe. We found that
abundant phase transition patterns arise from a physically viable parameter
space. Besides, we have also found gravitational wave signals possibly visible
by future experiments, such as LISA, BBO and u-DECIGO. Our results stress the
importance of gravitational wave detectors in exploring new physics
complementary to ground colliders in the multi-messenger astronomy era.


title:
Introduction to the Artificial Intelligence that can be applied to the
  Network Automation Journey

abstract:
  The computer network world is changing and the NetDevOps approach has brought
the dynamics of applications and systems into the field of communication
infrastructure. Businesses are changing and businesses are faced with
difficulties related to the diversity of hardware and software that make up
those infrastructures. The "Intent-Based Networking - Concepts and Definitions"
document describes the different parts of the ecosystem that could be involved
in NetDevOps. The recognize, generate intent, translate and refine features
need a new way to implement algorithms. This is where artificial intelligence
comes in.


title: Robust Estimation of Conditional Factor Models

abstract:
  This paper develops estimation and inference methods for conditional quantile
factor models. We first introduce a simple sieve estimation, and establish
asymptotic properties of the estimators under large $N$. We then provide a
bootstrap procedure for estimating the distributions of the estimators. We also
provide two consistent estimators for the number of factors. The methods allow
us not only to estimate conditional factor structures of distributions of asset
returns utilizing characteristics, but also to conduct robust inference in
conditional factor models, which enables us to analyze the cross section of
asset returns with heavy tails. We apply the methods to analyze the cross
section of individual US stock returns.


title:
Virtual extrapolation technique for retracing line of response of single
  scattered events in positron emission tomography

abstract:
  Purpose: The scattering phenomenon creates degrading effects in positron
emission tomography (PET) and the corresponding events are rejected
conventionally. We have proposed a mathematical model to retrace the original
line of response of the single-scattered coincident events with the aim to
incorporate such events in PET.
  Methods: We have devised a new Virtual extrapolation technique based on the
concept of the probability density functions. Through which we transformed the
original two-parameter list mode data for the coincident photon pairs to a
one-parameter data set. The procedure of random sampling and sampling
distribution - by utilizing some unique properties like a collective difference
and the length compensation - was employed in the data analysis. We studied the
effect of finite timing and energy resolution of detectors on the performance
of the proposed model.
  Results: We determined the frequency of occurrence of the data values
corresponding to real as well as fictitious scattering points { placed on the
arc of a circle drawn with the constant scattering angle - to observe the
highest counts. As expected, we found the highest counts at the location of
real scattering points. The model was evaluated for a uniform attenuating
phantom medium. The results were found impressive for the case of ideal time
and energy information, less so for the cases of finite resolutions.
  Conclusions: Our work outlines a completely new approach; though, a lot more
sophistication is required for its practical utility. Nonetheless, the
technique seems promising in developing a potential approach to improve the
sensitivity of PET imaging.


title:
End-to-end model for named entity recognition from speech without paired
  training data

abstract:
  Recent works showed that end-to-end neural approaches tend to become very
popular for spoken language understanding (SLU). Through the term end-to-end,
one considers the use of a single model optimized to extract semantic
information directly from the speech signal. A major issue for such models is
the lack of paired audio and textual data with semantic annotation. In this
paper, we propose an approach to build an end-to-end neural model to extract
semantic information in a scenario in which zero paired audio data is
available. Our approach is based on the use of an external model trained to
generate a sequence of vectorial representations from text. These
representations mimic the hidden representations that could be generated inside
an end-to-end automatic speech recognition (ASR) model by processing a speech
signal. An SLU neural module is then trained using these representations as
input and the annotated text as output. Last, the SLU module replaces the top
layers of the ASR model to achieve the construction of the end-to-end model.
Our experiments on named entity recognition, carried out on the QUAERO corpus,
show that this approach is very promising, getting better results than a
comparable cascade approach or than the use of synthetic voices.


title: Diffusion dynamics of competing information on networks

abstract:
  Information diffusion on social networks has been described as a collective
outcome of threshold behaviors in the framework of threshold models. However,
since the existing models do not take into account individuals' optimization
problem, it remains an open question what dynamics emerge in the diffusion
process when individuals face multiple (and possibly incompatible) information.
Here, we develop a microfounded general threshold model that enables us to
analyze the collective dynamics of individual behavior in the propagation of
multiple information. The analysis reveals that the virality of competing
information is fundamentally indeterminate. When individuals maximize
coordination with neighbors, the diffusion process is described as a saddle
path, thereby leading to an unpredictable symmetry breaking. When individuals'
choices are irreversible, there is a continuum of stable equilibria where a
certain degree of social polarization takes place by chance.


title: Microscopic origin of shape coexistence in the N=90, Z=64 region

abstract:
  A microscopic explanation of the nature of shape coexistence in the N=90,
Z=64 region is suggested, based on calculations of single particle energies
through standard covariant density functional theory. It is suggested that
shape coexistence in the N=90 region is caused by the protons, which create
neutron particle-hole (p-h) excitations across the N=112 3-dimensional
isotropic harmonic oscillator (3D-HO) magic number, signaling the start of the
occupation of the 1i13/2 intruder orbital, which triggers stronger
proton-neutron interaction, causing the onset of the deformation and resulting
in the shape/phase transition from spherical to deformed nuclei described by
the X(5) critical point symmetry. A similar effect is seen in the N=60, Z=40
region, in which p-h excitations across the N=70 3D-HO magic number occur,
signaling the start of the occupation of the 1h11/2 intruder orbital.