title:
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point
  Cloud Upsampling

abstract:
  We propose a generative adversarial network for point cloud upsampling, which
can not only make the upsampled points evenly distributed on the underlying
surface but also efficiently generate clean high frequency regions. The
generator of our network includes a dynamic graph hierarchical residual
aggregation unit and a hierarchical residual aggregation unit for point feature
extraction and upsampling, respectively. The former extracts multiscale
point-wise descriptive features, while the latter captures rich feature details
with hierarchical residuals. To generate neat edges, our discriminator uses a
graph filter to extract and retain high frequency points. The generated high
resolution point cloud and corresponding high frequency points help the
discriminator learn the global and high frequency properties of the point
cloud. We also propose an identity distribution loss function to make sure that
the upsampled points remain on the underlying surface of the input low
resolution point cloud. To assess the regularity of the upsampled points in
high frequency regions, we introduce two evaluation metrics. Objective and
subjective results demonstrate that the visual quality of the upsampled point
clouds generated by our method is better than that of the state-of-the-art
methods.


title:
MIAShield: Defending Membership Inference Attacks via Preemptive
  Exclusion of Members

abstract:
  In membership inference attacks (MIAs), an adversary observes the predictions
of a model to determine whether a sample is part of the model's training data.
Existing MIA defenses conceal the presence of a target sample through strong
regularization, knowledge distillation, confidence masking, or differential
privacy.
  We propose MIAShield, a new MIA defense based on preemptive exclusion of
member samples instead of masking the presence of a member. The key insight in
MIAShield is weakening the strong membership signal that stems from the
presence of a target sample by preemptively excluding it at prediction time
without compromising model utility. To that end, we design and evaluate a suite
of preemptive exclusion oracles leveraging model-confidence, exact or
approximate sample signature, and learning-based exclusion of member data
points. To be practical, MIAShield splits a training data into disjoint subsets
and trains each subset to build an ensemble of models. The disjointedness of
subsets ensures that a target sample belongs to only one subset, which isolates
the sample to facilitate the preemptive exclusion goal.
  We evaluate MIAShield on three benchmark image classification datasets. We
show that MIAShield effectively mitigates membership inference (near random
guess) for a wide range of MIAs, achieves far better privacy-utility trade-off
compared with state-of-the-art defenses, and remains resilient against an
adaptive adversary.


title:
Fourth cluster and virial coefficients of a unitary Fermi gas for an
  arbitrary mass ratio

abstract:
  We calculate the fourth cluster coefficients of the homogeneous unitary spin
1/2 Fermi gas as functions of the internal-state mass ratio, over intervals
constrained by the 3- or 4-body Efimov effect. For this we use our 2016
conjecture (validated for equal masses by Hou and Drut in 2020) in a
numerically efficient formulation making the sum over angular momentum converge
faster, which is crucial at large mass ratio. The mean cluster coefficient,
relevant for equal chemical potentials, is not of constant sign and increases
rapidly close to the Efimovian thresholds. We also get the fourth virial
coefficients, which we find to be very poor indicators of interaction-induced
4-body correlations. We obtain analytically for all n the cluster coefficients
of order n + 1 for an infinity-mass impurity fermion, matching the conjecture
for n=3. Finally, in a harmonic potential, we predict a non-monotonic behavior
of the 3 + 1 cluster coefficient with trapping frequency, near mass ratios
where this coefficient vanishes in the homogeneous case.


title:
Machine Learning Methods for Inferring the Number of Passive Emitters
  via Massive MIMO Receive Array

abstract:
  To improve the efficiency and accuracy of direction finding with massive MIMO
receive array, it is necessary to determine the specific number of signal
emitters in advance. In this paper, we present a complete DOA preprocessing
system for inferring the number of passive emitters. Firstly, in order to
improve the accuracy of detecting the number of signals, two high-precision
signal detectors, square root of maximum eigenvalue times minimum eigenvalue
(SR-MME) and geometric mean (GM), are proposed. Compared to other detectors,
SR-MME and GM can achieve a high detection probability while maintaining
extremely low false alarm probability. Secondly, if the existence of emitters
is determined by detectors, we need to further confirm their number, that is a
problem of pattern classification. Therefore, we perform feature extraction on
the the eigenvalue sequence of sample covariance matrix to construct feature
vector and innovatively propose a multi-layer neural network (ML-NN).
Additionally, the support vector machine (SVM), and naive Bayesian classifier
(NBC) are also designed. The simulation results show that the machine
learning-based methods can achieve good results in signal classification,
especially neural networks, which can always maintain the classification
accuracy above 70\% with massive MIMO receive array. Finally, we analyze the
classical signal classification methods, Akaike (AIC) and Minimum description
length (MDL). It is concluded that the two methods are not suitable for
scenarios with massive receive arrays, and they also have much worse
performance than machine learning-based classifiers.


title: Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community

abstract:
  The laboratories and research institutes are the major places for
cutting-edge scientific exploration. Hundreds of millions of research papers
were formed from front-line labs. Behind this glorious achievement were
unsustainable facts. More and more human investment is required in innovative
experimental design and analysis of results. However, the laboratory operating
environment has not been subversively transformed for centuries. This abstract
proposed a smart tracking system, consisting of IoT and Data Visualization
technologies, to track the chemicals in an automatic and timely approach.
Positive feedback has been collected from pilot tests in several labs. The
system benefits various lab users in their daily work and improves their
working efficiency. In the long run, it will play an essential role in
promoting the efficient use of lab resources and achieving the goal of
sustainable labs.


title:
A single-atom level mechano-optical transducer for ultrasensitive force
  sensing

abstract:
  Using light as a probe to detect a mechanical motion is one of the most
successful experimental approaches in physics. The history of mechanical
sensing based on the reflection, refraction and scattering of light dates back
to the 16th century, where in the Cavendish experiment, the angle of rotation
induced by the gravitational force is measured by the deflection of a light
beam reflected from a mirror attached to the suspension. In modern science,
mechano-optical transducers are such devices that could detect, measure and
convert a force or displacement signal to an optical one, and are widely used
for force detection. Especially, ultraweak force sensor with ultrahigh spatial
resolution is highly demanded for detecting force anomaly in surface science,
biomolecule imaging, and atomtronics. Here we show a novel scheme using a
single trapped ion as a mechano-optical transduction. This method utilizes the
force-induced micromotion, converting the micromotion to a time-resolved
fluorescence signal, in which the ion's excess micromotion coupled to the
Doppler shift of the scattered photons. We demonstrate the measurement
sensitivity about 600 $\textrm{zN}/\sqrt{\textrm{Hz}}$ (1 $\textrm{zN}
=10^{-21}$N). By alternating the detection laser beam in all three dimensions,
the amplitude and the direction of a vector force can be precisely determined,
constituting a 3D force sensor. This mechano-optical transducer provides high
sensitivity with spatial resolution in single-atom level, enabling the
applications in material industry and the search for possible exotic
spin-dependent interactions that beyond the standard model.


title: Integer Factorization with Compositional Distributed Representations

abstract:
  In this paper, we present an approach to integer factorization using
distributed representations formed with Vector Symbolic Architectures. The
approach formulates integer factorization in a manner such that it can be
solved using neural networks and potentially implemented on parallel
neuromorphic hardware. We introduce a method for encoding numbers in
distributed vector spaces and explain how the resonator network can solve the
integer factorization problem. We evaluate the approach on factorization of
semiprimes by measuring the factorization accuracy versus the scale of the
problem. We also demonstrate how the proposed approach generalizes beyond the
factorization of semiprimes; in principle, it can be used for factorization of
any composite number. This work demonstrates how a well-known combinatorial
search problem may be formulated and solved within the framework of Vector
Symbolic Architectures, and it opens the door to solving similarly difficult
problems in other domains.


title:
New criterions on nonexistence of periodic orbits of planar dynamical
  systems and their applications

abstract:
  Characterizing existence or not of periodic orbit is a classical problem and
it has both theoretical importance and many real applications. Here, several
new criterions on nonexistence of periodic orbits of the planar dynamical
system $\dot x=y,~\dot y=-g(x)-f(x,y)y$ are obtained in this paper, and by
examples showing that these criterions are applicable, but the known ones are
invalid to them. Based on these criterions, we further characterize the local
topological structures of its equilibrium, which also show that one of the
classical results by A.F. Andreev [Amer. Math. Soc. Transl. 8 (1958), 183--207]
on local topological classification of the degenerate equilibrium is
incomplete. Finally, as another application of these results, we classify the
global phase portraits of a planar differential system, which comes from the
third question in the list of the 33 questions posed by A. Gasull and also from
a mechanical oscillator under suitable restriction to its parameters.


title: Canonical foliations of neural networks: application to robustness

abstract:
  Adversarial attack is an emerging threat to the trustability of machine
learning. Understanding these attacks is becoming a crucial task. We propose a
new vision on neural network robustness using Riemannian geometry and foliation
theory, and create a new adversarial attack by taking into account the
curvature of the data space. This new adversarial attack called the "dog-leg
attack" is a two-step approximation of a geodesic in the data space. The data
space is treated as a (pseudo) Riemannian manifold equipped with the pullback
of the Fisher Information Metric (FIM) of the neural network. In most cases,
this metric is only semi-definite and its kernel becomes a central object to
study. A canonical foliation is derived from this kernel. The curvature of the
foliation's leaves gives the appropriate correction to get a two-step
approximation of the geodesic and hence a new efficient adversarial attack. Our
attack is tested on a toy example, a neural network trained to mimic the
$\texttt{Xor}$ function, and demonstrates better results that the state of the
art attack presented by Zhao et al. (2019).


title: Manipulating electron waves in graphene using carbon nanotube gating

abstract:
  Graphene with its dispersion relation resembling that of photons offers ample
opportunities for applications in electron optics. The spacial variation of
carrier density by external gates can be used to create electron waveguides, in
analogy to optical fiber, with additional confinement of the carriers in
bipolar junctions leading to the formation of few transverse guiding modes. We
show that waveguides created by gating graphene with carbon nanotubes (CNTs)
allow obtaining sharp conductance plateaus, and propose applications in
Aharonov-Bohm and two-path interferometers, and point-like source for injection
of carriers in graphene. Other applications can be extended to Bernal-stacked
or twisted bilayer graphene or two-dimensional electron gas. Thanks to their
versatility, CNT-induced waveguides open various possibilities for electron
manipulation in graphene-based devices.


title:
Translation Invariant Global Estimation of Heading Angle Using Sinogram
  of LiDAR Point Cloud

abstract:
  Global point cloud registration is an essential module for localization, of
which the main difficulty exists in estimating the rotation globally without
initial value. With the aid of gravity alignment, the degree of freedom in
point cloud registration could be reduced to 4DoF, in which only the heading
angle is required for rotation estimation. In this paper, we propose a fast and
accurate global heading angle estimation method for gravity-aligned point
clouds. Our key idea is that we generate a translation invariant representation
based on Radon Transform, allowing us to solve the decoupled heading angle
globally with circular cross-correlation. Besides, for heading angle estimation
between point clouds with different distributions, we implement this heading
angle estimator as a differentiable module to train a feature extraction
network end- to-end. The experimental results validate the effectiveness of the
proposed method in heading angle estimation and show better performance
compared with other methods.


title:
Manapy: MPI-Based framework for solving partial differential equations
  using finite-volume on unstructured-grid

abstract:
  Manapy is a parallel, unstructured, finite-volume based solver for the
solution of partial differential equations (PDE). The framework is written
using Python, it is object-oriented, and is organized in such a way that it is
easy to understand and modify. In this paper, we present the parallel
implementation and scalability of the differential operators used on a general
case of PDE. The performance of massively parallel direct and iterative methods
for solving large sparse systems of linear equations in plasma physics is
evaluated on a latest high performance computing system, and 3D test cases for
plasma physics are presented.


title: Parameterized Image Quality Score Distribution Prediction

abstract:
  Recently, image quality has been generally describedby a mean opinion score
(MOS). However, we observe that thequality scores of an image given by a group
of subjects are verysubjective and diverse. Thus it is not enough to use a MOS
todescribe the image quality. In this paper, we propose to describeimage
quality using a parameterized distribution rather thana MOS, and an objective
method is also proposed to predictthe image quality score distribution (IQSD).
At first, the LIVEdatabase is re-recorded. Specifically, we have invited a
largegroup of subjects to evaluate the quality of all images in theLIVE
database, and each image is evaluated by a large numberof subjects (187 valid
subjects), whose scores can form a reliableIQSD. By analyzing the obtained
subjective quality scores, wefind that the IQSD can be well modeled by an alpha
stable model,and it can reflect much more information than a single MOS, suchas
the skewness of opinion score, the subject diversity and themaximum probability
score for an image. Therefore, we proposeto model the IQSD using the alpha
stable model. Moreover, wepropose a framework and an algorithm to predict the
alphastable model based IQSD, where quality features are extractedfrom each
image based on structural information and statisticalinformation, and support
vector regressors are trained to predictthe alpha stable model parameters.
Experimental results verifythe feasibility of using alpha stable model to
describe the IQSD,and prove the effectiveness of objective alpha stable model
basedIQSD prediction method.


title:
TransDARC: Transformer-based Driver Activity Recognition with Latent
  Space Feature Calibration

abstract:
  Traditional video-based human activity recognition has experienced remarkable
progress linked to the rise of deep learning, but this effect was slower as it
comes to the downstream task of driver behavior understanding. Understanding
the situation inside the vehicle cabin is essential for Advanced Driving
Assistant System (ADAS) as it enables identifying distraction, predicting
driver's intent and leads to more convenient human-vehicle interaction. At the
same time, driver observation systems face substantial obstacles as they need
to capture different granularities of driver states, while the complexity of
such secondary activities grows with the rising automation and increased driver
freedom. Furthermore, a model is rarely deployed under conditions identical to
the ones in the training set, as sensor placements and types vary from vehicle
to vehicle, constituting a substantial obstacle for real-life deployment of
data-driven models. In this work, we present a novel vision-based framework for
recognizing secondary driver behaviours based on visual transformers and an
additional augmented feature distribution calibration module. This module
operates in the latent feature-space enriching and diversifying the training
set at feature-level in order to improve generalization to novel data
appearances, (e.g., sensor changes) and general feature quality. Our framework
consistently leads to better recognition rates, surpassing previous
state-of-the-art results of the public Drive&Act benchmark on all granularity
levels. Our code will be made publicly available at
https://github.com/KPeng9510/TransDARC.


title: Video is All You Need: Attacking PPG-based Biometric Authentication

abstract:
  Unobservable physiological signals enhance biometric authentication systems.
Photoplethysmography (PPG) signals are convenient owning to its ease of
measurement and are usually well protected against remote adversaries in
authentication. Any leaked PPG signals help adversaries compromise the
biometric authentication systems, and the advent of remote PPG (rPPG) enables
adversaries to acquire PPG signals through restoration. While potentially
dangerous, rPPG-based attacks are overlooked because existing methods require
the victim's PPG signals. This paper proposes a novel spoofing attack approach
that uses the waveforms of rPPG signals extracted from video clips to fool the
PPG-based biometric authentication. We develop a new PPG restoration model that
does not require leaked PPG signals for adversarial attacks. Test results on
state-of-art PPG-based biometric authentication show that the signals recovered
through rPPG pose a severe threat to PPG-based biometric authentication.


title:
The impact of AGN outflows on the surface habitability of terrestrial
  planets in the Milky Way

abstract:
  It is well-known that active galactic nuclei (AGN) are accompanied by winds
and outflows, some of which may reach weakly relativistic speeds of about $10$
percent the speed of light. Yet, in spite of their ubiquity, the impact of AGN
outflows in modulating surface habitability of terrestrial planets on galactic
scales, using the Milky Way as the basis for comparison, is poorly investigated
and inadequately understood. In this work, we address this issue by focusing on
two key mechanisms: AGN winds can heat atmospheres and drive atmospheric
escape, as well as stimulate the formation of nitrogen oxides and thence cause
ozone depletion. By developing simple models, we estimate the maximal distance
up to which these deleterious effects are rendered significant for Earth-like
planets in the Milky Way, and thereby demonstrate that this value may extend to
$\lesssim 1$ kpc. In the case of quasars hosting larger supermassive black
holes, such effects could actually influence the AGN host galaxy as a whole.


title:
Plasmonic nanocrystals with complex shapes for photocatalysis and
  growth: Contrasting anisotropic hot-electron generation with the photothermal
  effect

abstract:
  In plasmonics, and particularly in plasmonic photochemistry, the effect of
hot-electron generation is an exciting phenomenon driving new fundamental and
applied research. However, obtaining a microscopic description of the
hot-electron states represents a challenging problem, limiting our capability
to design efficient nanoantennas exploiting these excited carriers. This paper
addresses this limitation and studies the spatial distributions of the
photophysical dynamic parameters controlling the local surface photochemistry
on a plasmonic nanocrystal. We found that the generation of energetic electrons
and holes in small plasmonic nanocrystals with complex shapes is strongly
position-dependent and anisotropic, whereas the phototemperature across the
nanocrystal surface is nearly uniform. Our formalism includes three mechanisms
for the generation of excited carriers: the Drude process, the surface-assisted
generation of hot-electrons in the sp-band, and the excitation of interband
d-holes. Our computations show that the hot-carrier generation originating from
these mechanisms reflects the internal structure of hot spots in nanocrystals
with complex shapes. The injection of energetic carriers and increased surface
phototemperature are driving forces for photocatalytic and photo-growth
processes on the surface of plasmonic nanostructures. Therefore, developing a
consistent microscopic theory of such processes is necessary for designing
efficient nanoantennas for photocatalytic applications.


title:
U-Singer: Multi-Singer Singing Voice Synthesizer that Controls Emotional
  Intensity

abstract:
  We propose U-Singer, the first multi-singer emotional singing voice
synthesizer that expresses various levels of emotional intensity. During
synthesizing singing voices according to the lyrics, pitch, and duration of the
music score, U-Singer reflects singer characteristics and emotional intensity
by adding variances in pitch, energy, and phoneme duration according to singer
ID and emotional intensity. Representing all attributes by conditional residual
embeddings in a single unified embedding space, U-Singer controls mutually
correlated style attributes, minimizing interference. Additionally, we apply
emotion embedding interpolation and extrapolation techniques that lead the
model to learn a linear embedding space and allow the model to express
emotional intensity levels not included in the training data. In experiments,
U-Singer synthesized high-fidelity singing voices reflecting the singer ID and
emotional intensity. The visualization of the unified embedding space exhibits
that U-singer estimates the correct variations in pitch and energy highly
correlated with the singer ID and emotional intensity level. The audio samples
are presented at https://u-singer.github.io.


title: New Sasaki-Einstein 5-manifolds

abstract:
  We prove that closed simply connected $5$-manifolds $2(S^2\times S^3)\# nM_2$
allow Sasaki-Einstein structures, where $M_2$ is the closed simply connected
$5$-manifold with $\mathrm{H}_2(M_2,\mathbb{Z})=\mathbb{Z}/2\mathbb{Z}\oplus
\mathbb{Z}/2\mathbb{Z}$, $nM_2$ is the $n$-fold connected sum of $M_2$, and
$2(S^2\times S^3)$ is the two-fold connected sum of $S^2\times S^3$.


title: Quantum Anomalous Semimetals

abstract:
  The topological states of matter and topological materials have been
attracting extensive interests as one of the frontier topics in condensed
matter physics and materials science since the discovery of quantum Hall effect
in 1980s. So far all the topological phases such as quantum Hall effect,
quantum spin Hall effect and topological insulators and superconductors are
characterized by a nonzero integer or Z and Z2 topological invariant. None is a
half-integer or fractional. Here we propose a novel type of semimetals which
hosts a single cone of Wilson fermions instead of Dirac fermions. The Wilson
fermions possess linear dispersion near the energy crossing point, but breaks
the chiral or parity symmetry such that an unpaired Dirac cone can be realized
on a lattice. They are not prohibited by the Nielsen-Ninomiya theorem and avoid
the fermion doubling problem. We find that the system can be classified by the
relative homotopy group, and the topological invariant is a half-integer. We
term the unexpected and nontrivial quantum phase as "quantum anomalous
semimetal". The topological phase is a synergy of topology of band structure in
solid and quantum anomaly in quantum field theory. The work opens the door
towards exploring novel states of matter with fractional topological charge.


title:
Cross-Layer Optimization: Joint User Scheduling and Beamforming Design
  With QoS Support in Joint Transmission Networks

abstract:
  User scheduling and beamforming design are two crucial yet coupled topics for
wireless communication systems. They are usually optimized separately with
conventional optimization methods. In this paper, a novel cross-layer
optimization problem is considered, namely, the user scheduling and beamforming
are jointly discussed subjecting to the requirement of per-user quality of
service (QoS) and the maximum allowable transmit power for multicell multiuser
joint transmission networks. To achieve the goal, a mixed discrete-continue
variables combinational optimization problem is investigated with aiming at
maximizing the sum rate of the communication system. To circumvent the original
non-convex problem with dynamic solution space, we first transform it into a
0-1 integer and continue variables optimization problem, and then obtain a
tractable form with continuous variables by exploiting the characteristics of
0-1 constraint. Finally, the scheduled users and the optimized beamforming
vectors are simultaneously calculated by an alternating optimization algorithm.
We also theoretically prove that the base stations allocate zero power to the
unscheduled users. Furthermore, two heuristic optimization algorithms are
proposed respectively based on brute-force search and greedy search. Numerical
results validate the effectiveness of our proposed methods, and the
optimization approach gets relatively balanced results compared with the other
two approaches.


title: Sublinearly Morse Boundary of CAT(0) admissible groups

abstract:
  We show that if G is an admissible group acting geometrically on a CAT(0)
space X, then G is a hierarchically hyperbolic space and with mild assumptions
the sublinearly-Morse boundary of the group is a topological model for
associated Poisson boundaries .


title: Continual Learning of Multi-modal Dynamics with External Memory

abstract:
  We study the problem of fitting a model to a dynamical environment when new
modes of behavior emerge sequentially. The learning model is aware when a new
mode appears, but it does not have access to the true modes of individual
training sequences. We devise a novel continual learning method that maintains
a descriptor of the mode of an encountered sequence in a neural episodic
memory. We employ a Dirichlet Process prior on the attention weights of the
memory to foster efficient storage of the mode descriptors. Our method performs
continual learning by transferring knowledge across tasks by retrieving the
descriptors of similar modes of past tasks to the mode of a current sequence
and feeding this descriptor into its transition kernel as control input. We
observe the continual learning performance of our method to compare favorably
to the mainstream parameter transfer approach.


title: ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

abstract:
  Short-term load forecasting (STLF) is a challenging problem due to the
complex nature of the time series expressing multiple seasonality and varying
variance. This paper proposes an extension of a hybrid forecasting model
combining exponential smoothing and dilated recurrent neural network (ES-dRNN)
with a mechanism for dynamic attention. We propose a new gated recurrent cell
-- attentive dilated recurrent cell, which implements an attention mechanism
for dynamic weighting of input vector components. The most relevant components
are assigned greater weights, which are subsequently dynamically fine-tuned.
This attention mechanism helps the model to select input information and, along
with other mechanisms implemented in ES-dRNN, such as adaptive time series
processing, cross-learning, and multiple dilation, leads to a significant
improvement in accuracy when compared to well-established statistical and
state-of-the-art machine learning forecasting models. This was confirmed in the
extensive experimental study concerning STLF for 35 European countries.


title: Neuro-Symbolic Verification of Deep Neural Networks

abstract:
  Formal verification has emerged as a powerful approach to ensure the safety
and reliability of deep neural networks. However, current verification tools
are limited to only a handful of properties that can be expressed as
first-order constraints over the inputs and output of a network. While
adversarial robustness and fairness fall under this category, many real-world
properties (e.g., "an autonomous vehicle has to stop in front of a stop sign")
remain outside the scope of existing verification technology. To mitigate this
severe practical restriction, we introduce a novel framework for verifying
neural networks, named neuro-symbolic verification. The key idea is to use
neural networks as part of the otherwise logical specification, enabling the
verification of a wide variety of complex, real-world properties, including the
one above. Moreover, we demonstrate how neuro-symbolic verification can be
implemented on top of existing verification infrastructure for neural networks,
making our framework easily accessible to researchers and practitioners alike.


title: Non-hermitian Dirac Hamiltonian in the presence of local Fermi velocity

abstract:
  We present a new approach to study a class of non-Hermitian (1+1)-dimensional
Dirac Hamiltonian in the presence of local Fermi velocity. We apply the well
known Nikiforov-Uvarov method to solve such a system. We discuss applications
and explore the solvability of both $\mathcal{PT}$-symmetric and
non-$\mathcal{PT}$ symmetric classes of potentials. In the former case we
obtain the solution of a harmonic oscillator in the presence of a linear vector
potential while in the latter case we solve the shifted harmonic oscillator
problem.


title: Fast Decoding of AG Codes

abstract:
  We present an efficient list decoding algorithm in the style of
Guruswami-Sudan for algebraic geometry codes. Our decoder can decode any such
code using $\tilde{\mathcal O}(s\ell^{\omega}\mu^{\omega-1}(n+g))$ operations
in the underlying finite field, where $n$ is the code length, $g$ is the genus
of the function field used to construct the code, $s$ is the multiplicity
parameter, $\ell$ is the designed list size and $\mu$ is the smallest positive
element in the Weierstrass semigroup at some chosen place; the "soft-O"
notation $\tilde{\mathcal O}(\cdot)$ is similar to the "big-O" notation
${\mathcal O}(\cdot)$, but ignores logarithmic factors. For the interpolation
step, which constitutes the computational bottleneck of our approach, we use
known algorithms for univariate polynomial matrices, while the root-finding
step is solved using existing algorithms for root-finding over univariate power
series.


title:
Testing a New Model of Protostellar Disks Against Observation: The
  Majority of Orion Class 0/I Disks Are Likely Warm, Massive, and
  Gravitationally Unstable

abstract:
  We formulate a parametrized model of protostellar disks and test its ability
to estimate disk properties by fitting dust-continuum observations. The main
physical assumptions of our model are motivated by a recent theoretical study
of protostellar disk formation; these assumptions include that the disk should
be marginally gravitationally unstable, and that the dominant dust heating
mechanism is internal accretion heating instead of external protostellar
irradiation. These assumptions allow our model to reliably estimate the disk
mass even when the observed emission is optically thick and to
self-consistently determine disk (dust) temperature. Using our model to fit
multi-wavelength observations of 156 disks in the VANDAM Orion survey, we find
that the majority (57%) of this sample can be fit well by our model. Moreover,
the observations prefer our fiducial model with marginally unstable Toomre $Q$
compared to models with larger $Q$ values, suggesting that the majority of
disks in our sample are likely gravitationally unstable. Using our model, we
produce new estimates of Orion protostellar disk properties. We find that these
disks are generally warm and massive, with a typical star-to-disk mass ratio
$M_{\rm d}/M_\star = \mathcal O(1)$ in Class 0/I. We also discuss why our
estimates differ from those in previous studies and the implications of our
results on disk evolution and fragmentation.


title: Emergent Hadron Mass in Strong Dynamics

abstract:
  Emergent Hadron Mass (EHM) is a mechanism capable of explaining both the
unnaturally small (pion) and large (proton) masses of hadrons and if not the
origin of confinement, then intimately connected with it. Even though the
modern formulation of EHM has only recently been completed, it is rapidly
becoming a fundamental focus for modern and future experimental efforts aimed
at understanding the strong force within the Standard Model. Using
Dyson-Schwinger equations for the gluon, ghost and quark propagators, I will
introduce the main EHM concepts and illustrate how far this framework can take
us in understanding strong interaction phenomenology.


title:
Neveu's exchange formula for analysis of wireless networks with hotspot
  clusters

abstract:
  Theory of point processes, in particular Palm calculus within the stationary
framework, plays a fundamental role in the analysis of spatial stochastic
models of wireless communication networks. Neveu's exchange formula, which
connects the respective Palm distributions for two jointly stationary point
processes, is known as one of the most important results in the Palm calculus.
However, its use in the analysis of wireless networks seems to be limited so
far and one reason for this may be that the formula in a well-known form is
based upon the Voronoi tessellation. In this paper, we present an alternative
form of Neveu's exchange formula, which does not rely on the Voronoi
tessellation but includes the one as a special case. We then demonstrate that
our new form of the exchange formula is useful for the analysis of wireless
networks with hotspot clusters modeled using cluster point processes.


title: High-order linearly implicit schemes conserving quadratic invariants

abstract:
  In this paper, we propose linearly implicit and arbitrary high-order
conservative numerical schemes for ordinary differential equations with a
quadratic invariant. Many differential equations have invariants, and numerical
schemes for preserving them have been extensively studied. Since linear
invariants can be easily kept after discretisation, quadratic invariants are
essentially the simplest ones. Quadratic invariants are important objects that
appear not only in many physical examples but also in the computationally
efficient conservative schemes for general invariants such as scalar auxiliary
variable approach, which have been studied in recent years. It is known that
quadratic invariants can be kept relatively easily compared to general
invariants, and indeed can be preserved by canonical Runge--Kutta methods.
However, there is no unified method for constructing linearly implicit and high
order conservative schemes. In this paper, we construct such schemes based on
canonical Runge--Kutta methods and prove some properties involving accuracy.


title:
ParaPose: Parameter and Domain Randomization Optimization for Pose
  Estimation using Synthetic Data

abstract:
  Pose estimation is the task of determining the 6D position of an object in a
scene. Pose estimation aid the abilities and flexibility of robotic set-ups.
However, the system must be configured towards the use case to perform
adequately. This configuration is time-consuming and limits the usability of
pose estimation and, thereby, robotic systems.
  Deep learning is a method to overcome this configuration procedure by
learning parameters directly from the dataset. However, obtaining this training
data can also be very time-consuming. The use of synthetic training data avoids
this data collection problem, but a configuration of the training procedure is
necessary to overcome the domain gap problem. Additionally, the pose estimation
parameters also need to be configured. This configuration is jokingly known as
grad student descent as parameters are manually adjusted until satisfactory
results are obtained.
  This paper presents a method for automatic configuration using only synthetic
data. This is accomplished by learning the domain randomization during network
training, and then using the domain randomization to optimize the pose
estimation parameters. The developed approach shows state-of-the-art
performance of 82.0 % recall on the challenging OCCLUSION dataset,
outperforming all previous methods with a large margin. These results prove the
validity of automatic set-up of pose estimation using purely synthetic data.


title: Muography applied to archaelogy

abstract:
  A tomography experiment using muon cosmic rays was conducted on an
archaeological site in Greece, a tumulus. This contribution presents issues
related to the simulation of the experiment and of the site, using tools
commonly used in high-energy physics. The scientific objective is to compare
the results of the simulation with the recorded data to highlight anomalies.


title:
KC-TSS: An Algorithm for Heterogeneous Robot Teams Performing Resilient
  Target Search

abstract:
  This paper proposes KC-TSS: K-Clustered-Traveling Salesman Based Search, a
failure resilient path planning algorithm for heterogeneous robot teams
performing target search in human environments. We separate the sample path
generation problem into Heterogeneous Clustering and multiple Traveling
Salesman Problems. This allows us to provide high-quality candidate paths (i.e.
minimal backtracking, overlap) to an Information-Theoretic utility function for
each agent. First, we generate waypoint candidates from map knowledge and a
target prediction model. All of these candidates are clustered according to the
number of agents and their ability to cover space, or coverage competency. Each
agent solves a Traveling Salesman Problem (TSP) instance over their assigned
cluster and then candidates are fed to a utility function for path selection.
We perform extensive Gazebo simulations and preliminary deployment of real
robots in indoor search and simulated rescue scenarios with static targets. We
compare our proposed method against a state-of-the-art algorithm and show that
ours is able to outperform it in mission time. Our method provides resilience
in the event of single or multi teammate failure by recomputing global team
plans online.


title:
CD-GAN: a robust fusion-based generative adversarial network for
  unsupervised change detection between heterogeneous images

abstract:
  In the context of Earth observation, the detection of changes is performed
from multitemporal images acquired by sensors with possibly different
characteristics and modalities. Even when restricting to the optical modality,
this task has proved to be challenging as soon as the sensors provide images of
different spatial and/or spectral resolutions. This paper proposes a novel
unsupervised change detection method dedicated to such so-called heterogeneous
optical images. This method capitalizes on recent advances which frame the
change detection problem into a robust fusion framework. More precisely, we
show that a deep adversarial network designed and trained beforehand to fuse a
pair of multiband images can be easily complemented by a network with the same
architecture to perform change detection. The resulting overall architecture
itself follows an adversarial strategy where the fusion network and the
additional network are interpreted as essential building blocks of a generator.
A comparison with state-of-the-art change detection methods demonstrate the
versatility and the effectiveness of the proposed approach.


title:
GAP: Differentially Private Graph Neural Networks with Aggregation
  Perturbation

abstract:
  Graph Neural Networks (GNNs) are powerful models designed for graph data that
learn node representation by recursively aggregating information from each
node's local neighborhood. However, despite their state-of-the-art performance
in predictive graph-based applications, recent studies have shown that GNNs can
raise significant privacy concerns when graph data contain sensitive
information. As a result, in this paper, we study the problem of learning GNNs
with Differential Privacy (DP). We propose GAP, a novel differentially private
GNN that safeguards the privacy of nodes and edges using aggregation
perturbation, i.e., adding calibrated stochastic noise to the output of the
GNN's aggregation function, which statistically obfuscates the presence of a
single edge (edge-level privacy) or a single node and all its adjacent edges
(node-level privacy). To circumvent the accumulation of privacy cost at every
forward pass of the model, we tailor the GNN architecture to the specifics of
private learning. In particular, we first precompute private aggregations by
recursively applying neighborhood aggregation and perturbing the output of each
aggregation step. Then, we privately train a deep neural network on the
resulting perturbed aggregations for any node-wise classification task. A major
advantage of GAP over previous approaches is that we guarantee edge-level and
node-level DP not only for training, but also at inference time with no
additional costs beyond the training's privacy budget. We theoretically analyze
the formal privacy guarantees of GAP using R\'enyi DP. Empirical experiments
conducted over three real-world graph datasets demonstrate that GAP achieves a
favorable privacy-accuracy trade-off and significantly outperforms existing
approaches.


title:
Equilibrium points, periodic solutions and the Brouwer fixed point
  theorem for convex and non-convex domains

abstract:
  We show the direct applicability of the Brouwer fixed point theorem for the
existence of equilibrium points and periodic solutions for differential systems
on general domains satisfying geometric conditions at the boundary. We develop
a general approach for arbitrary bound sets and present applications to the
case of convex and star-shaped domains. We also provide an answer to a question
raised in a recent paper of Cid and Mawhin.


title:
Speaker Adaption with Intuitive Prosodic Features for Statistical
  Parametric Speech Synthesis

abstract:
  In this paper, we propose a method of speaker adaption with intuitive
prosodic features for statistical parametric speech synthesis. The intuitive
prosodic features employed in this method include pitch, pitch range, speech
rate and energy considering that they are directly related with the overall
prosodic characteristics of different speakers. The intuitive prosodic features
are extracted at utterance-level or speaker-level, and are further integrated
into the existing speaker-encoding-based and speaker-embedding-based adaptation
frameworks respectively. The acoustic models are sequence-to-sequence ones
based on Tacotron2. Intuitive prosodic features are concatenated with text
encoder outputs and speaker vectors for decoding acoustic features.Experimental
results have demonstrated that our proposed methods can achieve better
objective and subjective performance than the baseline methods without
intuitive prosodic features. Besides, the proposed speaker adaption method with
utterance-level prosodic features has achieved the best similarity of synthetic
speech among all compared methods.


title: Sketched RT3D: How to reconstruct billions of photons per second

abstract:
  Single-photon light detection and ranging (lidar) captures depth and
intensity information of a 3D scene. Reconstructing a scene from observed
photons is a challenging task due to spurious detections associated with
background illumination sources. To tackle this problem, there is a plethora of
3D reconstruction algorithms which exploit spatial regularity of natural scenes
to provide stable reconstructions. However, most existing algorithms have
computational and memory complexity proportional to the number of recorded
photons. This complexity hinders their real-time deployment on modern lidar
arrays which acquire billions of photons per second. Leveraging a recent lidar
sketching framework, we show that it is possible to modify existing
reconstruction algorithms such that they only require a small sketch of the
photon information. In particular, we propose a sketched version of a recent
state-of-the-art algorithm which uses point cloud denoisers to provide
spatially regularized reconstructions. A series of experiments performed on
real lidar datasets demonstrates a significant reduction of execution time and
memory requirements, while achieving the same reconstruction performance than
in the full data case.


title:
Computationally Efficient and Statistically Optimal Robust Low-rank
  Matrix and Tensor Estimation

abstract:
  Low-rank matrix estimation under heavy-tailed noise is challenging, both
computationally and statistically. Convex approaches have been proven
statistically optimal but suffer from high computational costs, especially
since robust loss functions are usually non-smooth. More recently,
computationally fast non-convex approaches via sub-gradient descent are
proposed, which, unfortunately, fail to deliver a statistically consistent
estimator even under sub-Gaussian noise. In this paper, we introduce a novel
Riemannian sub-gradient (RsGrad) algorithm which is not only computationally
efficient with linear convergence but also is statistically optimal, be the
noise Gaussian or heavy-tailed. Convergence theory is established for a general
framework and specific applications to absolute loss, Huber loss, and quantile
loss are investigated. Compared with existing non-convex methods, ours reveals
a surprising phenomenon of dual-phase convergence. In phase one, RsGrad behaves
as in a typical non-smooth optimization that requires gradually decaying
stepsizes. However, phase one only delivers a statistically sub-optimal
estimator which is already observed in the existing literature. Interestingly,
during phase two, RsGrad converges linearly as if minimizing a smooth and
strongly convex objective function and thus a constant stepsize suffices.
Underlying the phase-two convergence is the smoothing effect of random noise to
the non-smooth robust losses in an area close but not too close to the truth.
Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed
noise where a statistically optimal rate is attainable with the same phenomenon
of dual-phase convergence, and a novel shrinkage-based second-order moment
method is guaranteed to deliver a warm initialization. Numerical simulations
confirm our theoretical discovery and showcase the superiority of RsGrad over
prior methods.

categories: math.ST cs.IT math.IT stat.ME stat.ML stat.TH


title: Microscopic study on the origin of the rotational band of nuclei

abstract:
  The origin of the rotational band of nuclei has been investigated by the
angular momentum projection (AMP) on the axial Hartree-Fock solutions, by using
the semi-realistic effective Hamiltonian M3Y-P6. The rotational energy is
decomposed into contributions of the individual terms of the Hamiltonian, and
their ratios to the total rotational energy are calculated. Except for light or
weakly-deformed nuclei, the ratios of the individual terms of the Hamiltonian
are insensitive to nuclides and deformation. The contributions of kinetic
energies are large and close to the rigid-rotor values, although those of
central forces are sizable. For light or weakly-deformed nuclei, the ratios
significantly depend on nuclei and deformation. The contributions of noncentral
forces are not negligible. Regardless of nuclides, the attractive forces
decrease the moment-of-inertia, and the repulsive forces increase it. A general
formula for the rotational energy is derived on the basis of the AMP, which
suggests that higher-order terms of the cumulant expansion play roles in the
rotational energy and the moment-of-inertia for light or weakly-deformed
nuclei.


title: GRASP EARTH: Intuitive Software for Discovering Changes on the Planet

abstract:
  Detecting changes on the Earth, such as urban development, deforestation, or
natural disaster, is one of the research fields that is attracting a great deal
of attention. One promising tool to solve these problems is satellite imagery.
However, satellite images require huge amount of storage, therefore users are
required to set Area of Interests first, which was not suitable for detecting
potential areas for disaster or development. To tackle with this problem, we
develop the novel tool, namely GRASP EARTH, which is the simple change
detection application based on Google Earth Engine. GRASP EARTH allows us to
handle satellite imagery easily and it has used for disaster monitoring and
urban development monitoring.


title:
Distributed Optimization with Coupling Constraints in Multi-Cluster
  Networks Based on Dual Proximal Gradient Method

abstract:
  In this work, we consider solving a distributed optimization problem in a
multi-agent network with multiple clusters. In each cluster, the involved
agents cooperatively optimize a separable composite function with a common
decision variable. Meanwhile, a global cost function of the whole network is
considered associated with an affine coupling constraint across the clusters.
To solve this problem, we propose a cluster-based dual proximal gradient
algorithm by resorting to the dual problem, where the global cost function is
optimized when the agents in each cluster achieve an agreement on the optimal
strategy and the global coupling constraint is satisfied. In addition, the
proposed algorithm allows the agents to only communicate with their immediate
neighbors. The computational complexity of the proposed algorithm with
simple-structured cost functions is discussed and an ergodic convergence with
rate O(1/T) is guaranteed (T is the index of iterations).


title:
Probing strong gravitational lensing by Bardeen black holes in 4D
  Einstein--Gauss--Bonnet gravity

abstract:
  In the strong deflection limits, we investigate gravitational lensing by the
Bardeen black holes in four-dimensional Einstein-Gauss-Bonnet (4D EGB) gravity
and study the effect of coupling constant $\alpha$ and charge $q$.
Interestingly, the spherical photon orbit radius $x_m$, the critical impact
parameter $u_m$, the lensing coefficient $\bar{b}$ are decreasing functions of
$q$ and $\alpha$ whereas $\bar{a}$ shows opposite behaviour with these
parameters. The deflection angle $\alpha_D(\theta)$, angular position of
innermost image $\theta_{\infty}$ decrease, but angular separation $s$
increases with $\alpha$ and $q$. Investigating the astrophysical consequences
of gravitational lensing for supermassive black holes implies that 4D EGB
Bardeen black holes can be distinguished from the Bardeen and Schwarzschild
black hole. The deviations of the angular positions from the Schwarzschild
black hole and Bardeen black holes becomes significant for large $\alpha$ and
$q$ and are not more than $2.45016~\mu$as for Sgr A* and $1.84084~\mu$as for
M87*. Hence, Bardeen black holes in 4D EGB gravity is potentially different
from the Schwarzschild black hole or Bardeen black holes. For Sgr A* and M87*,
the time delay can reach $\sim10.6004$~min and $\sim15161.11$~min and deviate
from Bardeen black holes by $\sim0.1076$~min and $\sim153.93$~min,
respectively. These deviations are inconsequential for Sgr A* being too small,
but for M87* and some other black holes, they are enough for an astronomical
observation to test the 4D EGB gravity.


title: Asymptotic Properties of Quasi-Group Codes

abstract:
  This is a manuscript of a chapter prepared for a book. The good codes possess
large information length and large minimum distance. A class of codes is said
to be asymptotically good if there exists a positive real $\delta$ such that,
for any positive integer $N$ we can find a code in the class with code length
greater than $N$, and with both the rate and the relative minimum distance
greater than $\delta$. The linear codes over any finite field are
asymptotically good. More interestingly, the (asymptotic) GV-bound is a phase
transition point for the linear codes; i.e., asymptotically speaking, the
parameters of most linear codes attain the GV-bound. It is a long-standing open
question: whether or not the cyclic codes over a finite field (which are an
important class of codes) are asymptotically good? However, from a long time
ago the quasi-cyclic codes of index $2$ were proved to be asymptotically good.
This chapter consists of some of our studies on the asymptotic properties of
several classes of quasi-group codes. We'll explain the studies in a consistent
and self-contained style. We begin with the classical results on linear codes.
In many cases we consider the quasi-group codes over finite abelian groups
(including the cyclic case as a subcase of course), and study their asymptotic
properties along two directions: (1) the order of the group (the coindex) is
fixed while the index is going to infinity; (2) the index is small while the
order of the group (the coindex) is going to infinity. Finally we describe the
story on dihedral codes. The dihedral groups are non-abelian but near to cyclic
groups (they have cyclic subgroups of index $2$). The asymptotic goodness of
binary dihedral codes was obtained in the beginning of this century, and
extended to the general dihedral codes recently.


title: Learning Moving-Object Tracking with FMCW LiDAR

abstract:
  In this paper, we propose a learning-based moving-object tracking method
utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave
(FMCW) LiDAR. Compared with most existing commercial LiDAR sensors, our FMCW
LiDAR can provide additional Doppler velocity information to each 3D point of
the point clouds. Benefiting from this, we can generate instance labels as
ground truth in a semi-automatic manner. Given the labels, we propose a
contrastive learning framework, which pulls together the features from the same
instance in embedding space and pushes apart the features from different
instances, to improve the tracking quality. Extensive experiments are conducted
on our recorded driving data, and the results show that our method outperforms
the baseline methods by a large margin.


title:
Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy
  for Image Recognition without Convolutions

abstract:
  With the achievements of Transformer in the field of natural language
processing, the encoder-decoder and the attention mechanism in Transformer have
been applied to computer vision. Recently, in multiple tasks of computer vision
(image classification, object detection, semantic segmentation, etc.),
state-of-the-art convolutional neural networks have introduced some concepts of
Transformer. This proves that Transformer has a good prospect in the field of
image recognition. After Vision Transformer was proposed, more and more works
began to use self-attention to completely replace the convolutional layer. This
work is based on Vision Transformer, combined with the pyramid architecture,
using Split-transform-merge to propose the group encoder and name the network
architecture Aggregated Pyramid Vision Transformer (APVT). We perform image
classification tasks on the CIFAR-10 dataset and object detection tasks on the
COCO 2017 dataset. Compared with other network architectures that use
Transformer as the backbone, APVT has excellent results while reducing the
computational cost. We hope this improved strategy can provide a reference for
future Transformer research in computer vision.


title:
Magneto-Centrifugal Origin for Protostellar Jets Validated through
  Detection of Radial Flow at the Jet Base

abstract:
  Jets can facilitate the mass accretion onto the protostars in star formation.
They are believed to be launched from accretion disks around the protostars by
magneto-centrifugal force, as supported by the detections of rotation and
magnetic field in some of them. Here we report a radial flow of the
textbook-case protostellar jet HH 212 at the base to further support this
jet-launching scenario. This radial flow validates a central prediction of the
magneto-centrifugal theory of jet formation and collimation, namely, the jet is
the densest part of a wide-angle wind that flows radially outward at distances
far from the (small, sub-au) launching region. Additional evidence for the
radially flowing wide-angle component comes from its ability to reproduce the
structure and kinematics of the shells detected around the HH 212 jet. This
component, which can transport material from inner to outer disk, could account
for the chondrules and Ca-Al-rich inclusions detected in the Solar System at
large distances.


title: Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

abstract:
  Extracting class activation maps (CAM) is arguably the most standard step of
generating pseudo masks for weakly-supervised semantic segmentation (WSSS).
Yet, we find that the crux of the unsatisfactory pseudo masks is the binary
cross-entropy loss (BCE) widely used in CAM. Specifically, due to the
sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to
multiple classes co-occurring in the same receptive field. As a result, given a
class, its hot CAM pixels may wrongly invade the area belonging to other
classes, or the non-hot ones may be actually a part of the class. To this end,
we introduce an embarrassingly simple yet surprisingly effective method:
Reactivating the converged CAM with BCE by using softmax cross-entropy loss
(SCE), dubbed \textbf{ReCAM}. Given an image, we use CAM to extract the feature
pixels of each single class, and use them with the class label to learn another
fully-connected layer (after the backbone) with SCE. Once converged, we extract
ReCAM in the same way as in CAM. Thanks to the contrastive nature of SCE, the
pixel response is disentangled into different classes and hence less mask
ambiguity is expected. The evaluation on both PASCAL VOC and MS~COCO shows that
ReCAM not only generates high-quality masks, but also supports plug-and-play in
any CAM variant with little overhead.


title: Follow-Up of Extended Shells around B[e] Stars

abstract:
  B[e] stars are massive B type emission line stars in different evolutionary
stages ranging from pre-main sequence to post-main sequence. Due to their mass
loss and ejection events these objects deposit huge amounts of mass and energy
into their environment and enrich it with chemically processed material,
contributing significantly to the chemical and dynamical evolution of their
host galaxies. However, the large-scale environments of these enigmatic objects
have not attracted much attention. The first and so far only catalog reporting
the detection of extended shells around a sample of B[e] stars was an Ha
imaging survey carried out in the year 2001, and was limited to bright targets
in the northern hemisphere. We have recently started a follow-up of those
targets to detect possible evolution of their nebulae in the plane of the sky
over a baseline of two decades. Furthermore, we extend our survey to southern
targets and fainter northern ones to complement and complete our knowledge on
large-scale ejecta surrounding B[e] stars. Besides imaging in Ha and selected
nebular lines, we utilize long-slit and 3D spectral observations across the
nebulae to derive their physical properties. We discovered pronounced nebula
structures around 15 more objects, resulting in a total of 27 B[e] stars with a
large-scale nebula. Here we present our (preliminary) results for three
selected objects: the two massive supergiants MWC137 and MWC 314, and the
unclassified B[e] star MWC 819.


title: PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

abstract:
  In recent years, knowledge graphs have been widely applied as a uniform way
to organize data and have enhanced many tasks requiring knowledge. In online
shopping platform Taobao, we built a billion-scale e-commerce product knowledge
graph. It organizes data uniformly and provides item knowledge services for
various tasks such as item recommendation. Usually, such knowledge services are
provided through triple data, while this implementation includes (1) tedious
data selection works on product knowledge graph and (2) task model designing
works to infuse those triples knowledge. More importantly, product knowledge
graph is far from complete, resulting error propagation to knowledge enhanced
tasks. To avoid these problems, we propose a Pre-trained Knowledge Graph Model
(PKGM) for the billion-scale product knowledge graph. On the one hand, it could
provide item knowledge services in a uniform way with service vectors for
embedding-based and item-knowledge-related task models without accessing triple
data. On the other hand, it's service is provided based on implicitly completed
product knowledge graph, overcoming the common the incomplete issue. We also
propose two general ways to integrate the service vectors from PKGM into
downstream task models. We test PKGM in five knowledge-related tasks, item
classification, item resolution, item recommendation, scene detection and
sequential recommendation. Experimental results show that PKGM introduces
significant performance gains on these tasks, illustrating the useful of
service vectors from PKGM.


title:
High cooperativity coupling to nuclear spins on a circuit QED
  architecture

abstract:
  Nuclear spins are candidates to encode qubits or qudits due to their
isolation from magnetic noise and potentially long coherence times. However,
their weak coupling to external stimuli makes them hard to integrate into
circuit-QED architectures, the leading technology for solid-state quantum
processors. Here, we study the coupling of $^{173}$Yb(III) nuclear spin states
in an [Yb(trensal)] molecule to superconducting cavities. Experiments have been
performed on magnetically diluted single crystals placed on the inductors of
lumped-element $LC$ superconducting resonators with characteristic frequencies
spanning the range of nuclear and electronic spin transitions. We achieve a
high cooperative coupling to all electronic and most nuclear
[$^{173}$Yb(trensal)] spin transitions. This result is a big leap towards the
implementation of qudit protocols with molecular spins using a hybrid
architecture.


title:
Reflecting Brownian motion in generalized parabolic domains: explosion
  and superdiffusivity

abstract:
  For a multidimensional driftless diffusion in an unbounded, smooth,
sub-linear generalized parabolic domain, with oblique reflection from the
boundary, we give natural conditions under which either explosion occurs, if
the domain narrows sufficiently fast at infinity, or else there is
superdiffusive transience, which we quantify with a strong law of large
numbers. For example, in the case of a planar domain, explosion occurs if and
only if the area of the domain is finite. We develop and apply novel
semimartingale criteria for studying explosions and establishing strong laws,
which are of independent interest.


title:
Multilinear Discriminant Analysis using a new family of tensor-tensor
  products

abstract:
  Multilinear Discriminant Analysis (MDA) is a powerful dimension reduction
method specifically formulated to deal with tensor data. Precisely, the goal of
MDA is to find mode-specific projections that optimally separate tensor data
from different classes. However, to solve this task, standard MDA methods use
alternating optimization heuristics involving the computation of a succession
of tensor-matrix products. Such approaches are most of the time difficult to
solve and not natural, highligthing the difficulty to formulate this problem in
fully tensor form. In this paper, we propose to solve multilinear discriminant
analysis (MDA) by using the concept of transform domain (TD) recently proposed
in \cite{Kilmer2011}. We show here that moving MDA to this specific transform
domain make its resolution easier and more natural. More precisely, each
frontal face of the transformed tensor is processed independently to build a
separate optimization sub-problems easier to solve. Next, the obtained
solutions are converted into projective tensors by inverse transform. By
considering a large number of experiments, we show the effectiveness of our
approach with respect to existing MDA methods.


title:
Direct B\'ezier-Based Trajectory Planner for Improved Local Exploration
  of Unknown Environments

abstract:
  Autonomous exploration is an essential capability for mobile robots, as the
majority of their applications require the ability to efficiently collect
information about their surroundings. In the literature, there are several
approaches, ranging from frontier-based methods to hybrid solutions involving
the ability to plan both local and global exploring paths, but only few of them
focus on improving local exploration by properly tuning the planned trajectory,
often leading to "stop-and-go" like behaviors. In this work we propose a novel
RRT-inspired B\'ezier-based next-best-view trajectory planner able to deal with
the problem of fast local exploration. Gaussian process inference is used to
guarantee fast exploration gain retrieval while still being consistent with the
exploration task. The proposed approach is compared with other available
state-of-the-art algorithms and tested in a real-world scenario. The
implemented code is publicly released as open-source code to encourage further
developments and benchmarking.


title: Passive spiral galaxies deeply captured by Subaru Hyper Suprime-Cam

abstract:
  This paper presents a thousand passive spiral galaxy samples at $z=$ 0.01-0.3
based on a combined analysis of the Third Public Data Release of the Hyper
Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) and the GALEX-SDSS-WISE
Legacy Catalog (GSWLC-2). Among 54871 $gri$ galaxy cutouts taken from the
HSC-SSP PDR3 over 1072 deg$^2$, we conducted a search with deep-learning
morphological classification for candidates of passive spirals below the
star-forming main sequence derived by UV to mid-IR SED fitting in the GSWLC-2.
We then classified the candidates into 1100 passive spirals and 1141 secondary
samples based on visual inspections. Most of the latter cases are considered to
be passive ringed S0 or pseudo-ringed galaxies. The remainder of these
secondary samples has ambiguous morphologies, including two peculiar objects
with diamond-shaped stellar wings. The selected passive spirals have a similar
distribution to the general quiescent galaxies on the
EW$_\mathrm{H\delta}$-D$_n$4000 diagram and concentration indices. Moreover, we
detected an enhanced passive fraction of spiral galaxies in X-ray clusters.
Passive spirals in galaxy clusters are preferentially located in the midterm or
late infall phase on the phase-space diagram, supporting the ram pressure
scenario, which has been widely advocated in previous studies. The source
catalog and $gri$-composite images are available on the HSC-SSP PDR3 website
(https://hsc.mtk.nao.ac.jp/ssp/data-release/). Future updates, including
integration with a citizen science project dedicated to the HSC data, will
achieve more effective and comprehensive classifications.


title:
Modeling and Control of Smart Standalone Microgrids within Cyber
  Physical System Frameworks

abstract:
  The ability of grid-connected microgrids (MG) to operate in islanded mode
makes them an efficient solution for improving power quality and reliability.
This property of MG is very much beneficial for remote and undeveloped areas in
progressing countries. Moreover, ICT technology has led to the development of
Smart Standalone Microgrids (SSMG), which are inundated with a plethora of
sensors. This allows multiple microgrids to be controlled in a coordinated way
to achieve self-sufficiency in power. In such systems, there is much
interdependency between various power, control and communication parameters.
Owing to these developments, the control of physical variables like voltage get
affected by cyber parameters like, communication structure, delay and link
loss. Moreover, due to isolation from main grid and abundance of renewable
power generation units like solar PV, the inertia of these standalone grids is
reduced greatly and calls for advanced control algorithms which use an
abundance of sensors. Hence, the stability of these systems is greatly affected
by sensor failures apart from many physical parameters like load and
environmental conditions. In this thesis, a generic structure of an AC-DC
hybrid microgrid is considered which is subdivided into various AC and DC
counterparts. The AC and DC SSMGs are separately modeled and control solutions
are proposed to improve their stability. The first two contributions propose
adaptive control schemes on the primary level of control in the hybrid
microgrid. Their function is to provide fast and stable parameters regulation
in the DCSSMG when subjected to atmospheric changes along with faults in sensor
readings. The third and fourth contributions cater to development of
coordinated control cyber physical frameworks in the ACSSMG to handle the
presence of simultaneous disturbances from both cyber/physical domains.


title:
Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time
  Series Prediction

abstract:
  As industrial systems become more complex and monitoring sensors for
everything from surveillance to our health become more ubiquitous, multivariate
time series prediction is taking an important place in the smooth-running of
our society. A recurrent neural network with attention to help extend the
prediction windows is the current-state-of-the-art for this task. However, we
argue that their vanishing gradients, short memories, and serial architecture
make RNNs fundamentally unsuited to long-horizon forecasting with complex data.
Temporal convolutional networks (TCNs) do not suffer from gradient problems and
they support parallel calculations, making them a more appropriate choice.
Additionally, they have longer memories than RNNs, albeit with some instability
and efficiency problems. Hence, we propose a framework, called PSTA-TCN, that
combines a parallel spatio-temporal attention mechanism to extract dynamic
internal correlations with stacked TCN backbones to extract features from
different window sizes. The framework makes full use parallel calculations to
dramatically reduce training times, while substantially increasing accuracy
with stable prediction windows up to 13 times longer than the status quo.


title:
Improving Point Cloud Based Place Recognition with Ranking-based Loss
  and Large Batch Training

abstract:
  The paper presents a simple and effective learning-based method for computing
a discriminative 3D point cloud descriptor for place recognition purposes.
Recent state-of-the-art methods have relatively complex architectures such as
multi-scale oyramid of point Transformers combined with a pyramid of feature
aggregation modules. Our method uses a simple and efficient 3D convolutional
feature extraction, based on a sparse voxelized representation, enhanced with
channel attention blocks. We employ recent advances in image retrieval and
propose a modified version of a loss function based on a differentiable average
precision approximation. Such loss function requires training with very large
batches for the best results. This is enabled by using multistaged
backpropagation. Experimental evaluation on the popular benchmarks proves the
effectiveness of our approach, with a consistent improvement over the state of
the art


title: A density peaks clustering algorithm with sparse search and K-d tree

abstract:
  Density peaks clustering has become a nova of clustering algorithm because of
its simplicity and practicality. However, there is one main drawback: it is
time-consuming due to its high computational complexity. Herein, a density
peaks clustering algorithm with sparse search and K-d tree is developed to
solve this problem. Firstly, a sparse distance matrix is calculated by using
K-d tree to replace the original full rank distance matrix, so as to accelerate
the calculation of local density. Secondly, a sparse search strategy is
proposed to accelerate the computation of relative-separation with the
intersection between the set of k nearest neighbors and the set consisting of
the data points with larger local density for any data point. Furthermore, a
second-order difference method for decision values is adopted to determine the
cluster centers adaptively. Finally, experiments are carried out on datasets
with different distribution characteristics, by comparing with other five
typical clustering algorithms. It is proved that the algorithm can effectively
reduce the computational complexity. Especially for larger datasets, the
efficiency is elevated more remarkably. Moreover, the clustering accuracy is
also improved to a certain extent. Therefore, it can be concluded that the
overall performance of the newly proposed algorithm is excellent.


title:
Predicting the temporal dynamics of turbulent channels through deep
  learning

abstract:
  The success of recurrent neural networks (RNNs) has been demonstrated in many
applications related to turbulence, including flow control, optimization,
turbulent features reproduction as well as turbulence prediction and modeling.
With this study we aim to assess the capability of these networks to reproduce
the temporal evolution of a minimal turbulent channel flow. We first obtain a
data-driven model based on a modal decomposition in the Fourier domain (which
we denote as FFT-POD) of the time series sampled from the flow. This particular
case of turbulent flow allows us to accurately simulate the most relevant
coherent structures close to the wall. Long-short-term-memory (LSTM) networks
and a Koopman-based framework (KNF) are trained to predict the temporal
dynamics of the minimal-channel-flow modes. Tests with different configurations
highlight the limits of the KNF method compared to the LSTM, given the
complexity of the flow under study. Long-term prediction for LSTM show
excellent agreement from the statistical point of view, with errors below 2%
for the best models with respect to the reference. Furthermore, the analysis of
the chaotic behaviour through the use of the Lyapunov exponents and of the
dynamic behaviour through Poincar\'e maps emphasizes the ability of the LSTM to
reproduce the temporal dynamics of turbulence. Alternative reduced-order models
(ROMs), based on the identification of different turbulent structures, are
explored and they continue to show a good potential in predicting the temporal
dynamics of the minimal channel.


title: L4KDE: Learning for KinoDynamic Tree Expansion

abstract:
  We present the Learning for KinoDynamic Tree Expansion (L4KDE) method for
kinodynamic planning. Tree-based planning approaches, such as rapidly exploring
random tree (RRT), are the dominant approach to finding globally optimal plans
in continuous state-space motion planning. Central to these approaches is
tree-expansion, the procedure in which new nodes are added into an
ever-expanding tree. We study the kinodynamic variants of tree-based planning,
where we have known system dynamics and kinematic constraints. In the interest
of quickly selecting nodes to connect newly sampled coordinates, existing
methods typically cannot optimise to find nodes which have low cost to
transition to sampled coordinates. Instead they use metrics like Euclidean
distance between coordinates as a heuristic for selecting candidate nodes to
connect to the search tree. We propose L4KDE to address this issue. L4KDE uses
a neural network to predict transition costs between queried states, which can
be efficiently computed in batch, providing much higher quality estimates of
transition cost compared to commonly used heuristics while maintaining
almost-surely asymptotic optimality guarantee. We empirically demonstrate the
significant performance improvement provided by L4KDE on a variety of
challenging system dynamics, with the ability to generalise across different
instances of the same model class, and in conjunction with a suite of modern
tree-based motion planners.


title:
Three-dimensional Printing of Mycelium Hydrogels into Living Complex
  Materials

abstract:
  Biological living materials, such as animal bones and plant stems, are able
to self-heal, regenerate, adapt and make decisions under environmental
pressures. Despite recent successful efforts to imbue synthetic materials with
some of these remarkable functionalities, many emerging properties of complex
adaptive systems found in biology remain unexplored in engineered living
materials. Here, we report on a three-dimensional printing approach that
harnesses the emerging properties of fungal mycelium to create living complex
materials that self-repair, regenerate and adapt to the environment while
fulfilling an engineering function. Hydrogels loaded with the fungus Ganoderma
lucidum are 3D printed into lattice architectures to enable mycelial growth in
a balanced exploration and exploitation pattern that simultaneously promotes
colonization of the gel and bridging of air gaps. To illustrate the potential
of such living complex materials, we 3D print a robotic skin that is
mechanically robust, self-cleaning, and able to autonomously regenerate after
damage.


title: Chained Generalisation Bounds

abstract:
  This work discusses how to derive upper bounds for the expected
generalisation error of supervised learning algorithms by means of the chaining
technique. By developing a general theoretical framework, we establish a
duality between generalisation bounds based on the regularity of the loss
function, and their chained counterparts, which can be obtained by lifting the
regularity assumption from the loss onto its gradient. This allows us to
re-derive the chaining mutual information bound from the literature, and to
obtain novel chained information-theoretic generalisation bounds, based on the
Wasserstein distance and other probability metrics. We show on some toy
examples that the chained generalisation bound can be significantly tighter
than its standard counterpart, particularly when the distribution of the
hypotheses selected by the algorithm is very concentrated.
  Keywords: Generalisation bounds; Chaining; Information-theoretic bounds;
Mutual information; Wasserstein distance; PAC-Bayes.


title: About non-classical gravitational states

abstract:
  Take the gravitational field, a number of macroscopic observers and put them
in a highly-quantum state, made by a coherent superposition of different
classical configurations. How would that look like? I try to study this system
by introducing a spatial distance operator among the observers and looking at
the properties of its expectation values.


title: $K$-Stability of A$\mathbb{T}$-Algebras

abstract:
  We describe a procedure to compute the rational nonstable K-groups of
A$\mathbb{T}$-algebras. As an application, we show that an
A$\mathbb{T}$-algebra is K-stable if and only if it has slow dimension growth.


title:
Boosted Ensemble Learning based on Randomized NNs for Time Series
  Forecasting

abstract:
  Time series forecasting is a challenging problem particularly when a time
series expresses multiple seasonality, nonlinear trend and varying variance. In
this work, to forecast complex time series, we propose ensemble learning which
is based on randomized neural networks, and boosted in three ways. These
comprise ensemble learning based on residuals, corrected targets and opposed
response. The latter two methods are employed to ensure similar forecasting
tasks are solved by all ensemble members, which justifies the use of exactly
the same base models at all stages of ensembling. Unification of the tasks for
all members simplifies ensemble learning and leads to increased forecasting
accuracy. This was confirmed in an experimental study involving forecasting
time series with triple seasonality, in which we compare our three variants of
ensemble boosting. The strong points of the proposed ensembles based on RandNNs
are extremely rapid training and pattern-based time series representation,
which extracts relevant information from time series.


title: Hyperbolic site percolation

abstract:
  Several results are presented for site percolation on quasi-transitive,
planar graphs $G$ with one end, when properly embedded in either the Euclidean
or hyperbolic plane. If $(G_1,G_2)$ is a matching pair derived from some
quasi-transitive mosaic $M$, then $p_u(G_1)+p_c(G_2)=1$, where $p_c$ is the
critical probability for the existence of an infinite cluster, and $p_u$ is the
critical value for the existence of a unique such cluster. This fulfils and
extends to the hyperbolic plane an observation of Sykes and Essam in 1964.
  It follows that $p_u (G)+p_c (G_*)=p_u(G_*)+p_c(G)=1$, where $G_*$ denotes
the matching graph of $G$. In particular, $p_u(G)+p_c(G)\ge 1$ and hence, when
$G$ is amenable we have $p_c(G)=p_u(G) \ge \frac12$. When combined with the
main result of the companion paper by the same authors ("Percolation critical
probabilities of matching lattice-pairs", 2022), we obtain for transitive $G$
that the strict inequality $p_u(G)+p_c(G)> 1$ holds if and only if $G$ is not a
triangulation.
  A key technique is a method for expressing a planar site percolation process
on a matching pair in terms of a dependent bond process on the corresponding
dual pair of graphs. Amongst other things, the results reported here answer
positively two conjectures of Benjamini and Schramm (Conjectures 7 and 8,
Electron. Comm. Probab. 1 (1996) 71--82) in the case of quasi-transitive
graphs.


title:
Splitting of Fermi point of strongly interacting electrons in one
  dimension: A nonlinear effect of spin-charge separation

abstract:
  A system of one-dimensional electrons interacting via a short-range potential
described by Hubbard model is considered in the regime of strong coupling using
the Bethe ansatz approach. We study its momentum distribution function at zero
temperature and find one additional singularity, at the $3k_{\mathrm{F}}$
point. We identify that the second singularity is of the same Luttinger liquid
type as the low-energy one at $k_{\mathrm{F}}$. By calculating the spectral
function simultaneously, we show that the second Luttinger liquid at
$3k_{\mathrm{F}}$ is formed by charge modes only, unlike the known one around
$k_{\mathrm{F}}$ consisting of both spin and charge modes. This result reveals
the ability of the spin-charge separation effect to split the Fermi point of
free electrons into two, demonstrating its robustness beyond the low-energy
limit of Luttinger liquid where it was originally found.


title:
Efficient computation of Cantor's division polynomials of hyperelliptic
  curves over finite fields

abstract:
  Let $p$ be an odd prime number. We propose an algorithm for computing
rational representations of isogenies between Jacobians of hyperelliptic curves
via-adic differential equations with a sharp analysis of the loss of precision.
Consequently, after having possibly lifted the problem in the $p$-adics, we
derive fast algorithms for computing explicitly Cantor's division polynomials
of hyperelliptic curves defined over finite fields.


title: Random walks and the symplectic representation of the braid group

abstract:
  We consider the symplectic representation $\rho_n$ of a braid group $B(n)$ in
$Sp(2l,\mathbb{Z})$, for $l=\Big[\dfrac{n-1}{2}\Big]$. If $P$ is a $4l^2$
polynomial on the coefficients of the matrices in $Sp(2l,\mathbb{Z})$, we show
that the set $\{\beta\in B(n): P(\rho_n(\beta))=0\}$ is transient for non
degenerate random walks on $B(n)$.
  If $n$ is odd, we derive that the $n$-braids $\beta$ verifying
$|\Delta_{\hat{\beta}}(-1)|\leq C$ for some constant $C$ form a transient set:
here $\Delta_{\hat{\beta}}$ denotes the Alexander polynomial of the closure of
$\beta$.
  We also derive that for a random $3$-braid, the quasipositive links
$(\beta\sigma_i\beta^{-1}\sigma_j)^p$ have zero signature for every integer $p$
and $1\leq i,j\leq 2$. \\ As an example of such braids, we investigate the
signature of the Lissajous toric knots with $3$ strands.


title: Boron nitride on SiC(0001)

abstract:
  In the field of van der Waals heterostructures, the twist angle between
stacked two-dimensional (2D) layers has been identified to be of utmost
importance for the properties of the heterostructures. In this context, we
previously reported the growth of a single layer of unconventionally oriented
epitaxial graphene that forms in a surfactant atmosphere [F. C. Bocquet, et
al., Phys. Rev. Lett. 125, 106102 (2020)]. The resulting G-R0$^\circ$ layer is
aligned with the SiC lattice, and hence represents an important milestone
towards high quality twisted bilayer graphene (tBLG), a frequently investigated
model system in this field. Here, we focus on the surface structures obtained
in the same surfactant atmosphere, but at lower preparation temperatures at
which a boron nitride template layer forms on SiC(0001). In a comprehensive
study based on complementary experimental and theoretical techniques, we find
-- in contrast to the literature -- that this template layer is a hexagonal
B$_x$N$_y$ layer, but not high-quality hBN. It is aligned with the SiC lattice
and gradually replaced by low-quality graphene in the 0$^\circ$ orientation of
the B$_x$N$_y$ template layer upon annealing.


title:
Implicit/explicit, BEM/FEM coupled scheme for acoustic waves with the
  wave equation in the second order formulation

abstract:
  Acoustic scattering of waves by bounded inhomogeneities in an unbounded
homogeneous domain is considered. A symmetric coupled system of time-domain
boundary integral equations and the second order formulation of the wave
equation is described. A fully discrete system consists of spatial
discretization by boundary and finite element methods (BEM/FEM), leapfrog
time-stepping in the interior, and convolution quadrature for the boundary
integral equations. Convolution quadrature is based on BDF2, trapezoidal rule,
or a newly introduced truncated trapezoidal rule that has some favourable
properties for both the implementation and quality of approximate solution. We
give a stability and convergence analysis under a CFL conditon of the fully
discrete system. The theoretical results are illustrated by numerical
experiments in two dimensions.


title:
Beyond GAP screening for Lasso by exploiting new dual cutting
  half-spaces with supplementary material

abstract:
  In this paper, we propose a novel safe screening test for Lasso. Our
procedure is based on a safe region with a dome geometry and exploits a
canonical representation of the set of half-spaces (referred to as "dual
cutting half-spaces" in this paper) containing the dual feasible set. The
proposed safe region is shown to be always included in the state-of-the-art
"GAP Sphere" and "GAP Dome" proposed by Fercoq et al. (and strictly so under
very mild conditions) while involving the same computational burden. Numerical
experiments confirm that our new dome enables to devise more powerful screening
tests than GAP regions and lead to significant acceleration to solve Lasso.


title:
The topological RN-AdS black holes cannot be overcharged by the new
  version of gedanken experiment

abstract:
  In this paper, we test the weak cosmic censorship conjecture (WCCC) for n >=
4 dimensional nearly extremal RN-AdS black holes with non-trivial topologies,
namely plane and hyperbola, using the new version of gedanken experiment
proposed by Sorce and Wald. Provided that the non-electromagnetic part of the
stress tensor of matter fields satisfies the null energy condition and the
linear stability condition holds, we find that the black holes cannot be
overcharged under the second-order perturbation approximation, which includes
the self-force and finite-size effects. As a result, we conclude that the
violation of Hubeny type never occurs and the WCCC holds for the topological
RN-AdS black hole.


title: A.C. susceptibility as a probe of low-frequency magnetic dynamics

abstract:
  The experimental technique of a.c. susceptibility can be used as a probe of
magnetic dynamics in a wide variety of systems. Its use is restricted to the
low-frequency regime and thus is sensitive to relatively slow processes. Rather
than measuring the dynamics of single spins, a.c. susceptibility can be used to
probe the dynamics of collective objects, such as domain walls in ferromagnets
or vortex matter in superconductors. In some frustrated systems, such as spin
glasses, the complex interactions lead to substantial spectral weight of
fluctuations in the low-frequency regime, and thus a.c. susceptibility can play
a unique role. We review the theory underlying the technique and magnetic
dynamics more generally and give applications of a.c. susceptibility to a wide
variety of experimental situations.

categories: cond-mat.str-el cond-mat.mtrl-sci cond-mat.stat-mech cond-mat.supr-con


title:
Feasibility of tau g-2 measurements in ultra-peripheral collisions of
  heavy ions

abstract:
  The anomalous magnetic moment of the tau lepton, a_tau = (g_tau - 2)/2, is a
sensitive probe of new physics but is extremely difficult to measure precisely
in contrast to electron and muon moments. The best experimental limits were set
by the DELPHI collaboration more than 15 years ago in studies of the ditau
production in the e+e -> e+e+tau+tau process. Ultra-peripheral collisions
(UPCs) of heavy ions at the LHC may provide a unique opportunity to improve the
a_tau constraints in the studies of Pb+Pb -> Pb+Pb+tau+tau process. We review
recent proposals to study ditau production via semi-leptonic tau decays in
Pb-Pb UPC with the available ATLAS and CMS data and discuss the feasibility to
explore this process down to low transverse momenta of decay leptons with the
ALICE and LHCb experiments.


title:
The Past Mistake is the Future Wisdom: Error-driven Contrastive
  Probability Optimization for Chinese Spell Checking

abstract:
  Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling
errors, which are mainly caused by the phonological or visual similarity.
Recently, pre-trained language models (PLMs) promote the progress of CSC task.
However, there exists a gap between the learned knowledge of PLMs and the goal
of CSC task. PLMs focus on the semantics in text and tend to correct the
erroneous characters to semantically proper or commonly used ones, but these
aren't the ground-truth corrections. To address this issue, we propose an
Error-driven COntrastive Probability Optimization (ECOPO) framework for CSC
task. ECOPO refines the knowledge representations of PLMs, and guides the model
to avoid predicting these common characters through an error-driven way.
Particularly, ECOPO is model-agnostic and it can be combined with existing CSC
methods to achieve better performance. Extensive experiments and detailed
analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.


title:
Efficient Propagation Techniques for Handling Cyclic Symmetries in
  Binary Programs

abstract:
  The presence of symmetries of binary programs typically degrade the
performance of branch-and-bound solvers. In this article, we derive efficient
variable fixing algorithms to discard symmetric solutions from the search space
based on propagation techniques for cyclic groups. Our algorithms come with the
guarantee to find all possible variable fixings that can be derived from
symmetry arguments, i.e., one cannot find more variable fixings than those
found by our algorithms. Since every permutation symmetry group of a binary
program has cyclic subgroups, the derived algorithms can be used to handle
symmetries in any symmetric binary program. In experiments we also provide
numerical evidence that our algorithms handle symmetries more efficiently than
other variable fixing algorithms for cyclic symmetries.


title: Rpkiller: Threat Analysis from an RPKI Relying Party Perspective

abstract:
  The Resource Public Key Infrastructure (RPKI) aims to secure internet routing
by creating an infrastructure where resource holders can make attestations
about their resources. RPKI Certificate Authorities issue these attestations
and publish them at Publication Points. Relying Party software retrieves and
processes the RPKI-related data from all publication points, validates the data
and makes it available to routers so they can make secure routing decisions. In
this work, we create a threat model for Relying Party software, where an
attacker controls a Certificate Authority and Publication Point. We implement a
prototype testbed to analyse how current Relying Party software implementations
react to scenarios originating from that threat model. Our results show that
all current Relying Party software was susceptible to at least one of the
identified threats. In addition to this, we also identified threats stemming
from choices made in the protocol itself. Taken together, these threats
potentially allow an attacker to fully disrupt all RPKI Relying Party software
on a global scale. We performed a Coordinated Vulnerability Disclosure to the
implementers and have made our testbed software available for future studies.


title: Nonseparable states of light: From quantum to classical

abstract:
  Controlling the various degrees-of-freedom (DoFs) of structured light at both
quantum and classical levels is of paramount importance in optics. It is a
conventional paradigm to treat diverse DoFs separately in light shaping. While,
the more general case of nonseparable states of light, in which two or more
DoFs are coupled in a nonseparable way, has become topical recently.
Importantly, classical nonseparable states of light are mathematically analogue
to quantum entangled states. Such similarity has hatched attractive studies in
structured light, e.g. the spin-orbit coupling in vector beams. However,
nonseparable classical states of light are still treated in a fragmented
fashion, while its forms are not limited by vector beams and its potential is
certainly not fully exploited. For instance, exotic space-time coupled pulses
nontrivial light shaping towards ultrafast time scales, and ray-wave geometric
beams provide new dimensions in optical manipulations. Here we provide a bird's
eye view on the rapidly growing but incoherent body of work on general
nonseparable states involving various DoFs of light and introduce a unified
framework for their classification and tailoring, providing a perspective on
new opportunities for both fundamental science and applications.


title: Learning Efficiently Function Approximation for Contextual MDP

abstract:
  We study learning contextual MDPs using a function approximation for both the
rewards and the dynamics. We consider both the case where the dynamics is known
and unknown, and the case that the dynamics dependent or independent of the
context. For all four models we derive polynomial sample and time complexity
(assuming an efficient ERM oracle). Our methodology gives a general reduction
from learning contextual MDP to supervised learning.


title:
A modified convolution quadrature combined with the method of
  fundamental solutions and Galerkin BEM for acoustic scattering

abstract:
  We describe a numerical method for the solution of acoustic exterior
scattering problems based on the time-domain boundary integral representation
of the solution. As the spatial discretization of the resulting time-domain
boundary integral equation we use either the method of fundamental solutions
(MFS) or the Galerkin boundary element method (BEM). In time we apply either a
standard convolution quadrature (CQ) based on an A-stable linear multistep
method or a modified CQ scheme. It is well-known that the standard low-order CQ
schemes for hyperbolic problems suffer from strong dissipation and dispersion
properties. The modified scheme is designed to avoid these properties. We give
a careful description of the modified scheme and its implementation with
differences due to different spatial discretizations highlighted. Numerous
numerical experiments illustrate the effectiveness of the modified scheme and
dramatic improvement with errors up to two orders of magnitude smaller in
comparison with the standard scheme


title:
Whole-body model predictive control with rigid contacts via online
  switching time optimization

abstract:
  This study presents a whole-body model predictive control (MPC) of robotic
systems with rigid contacts, under a given contact sequence using online
switching time optimization (STO). We treat robot dynamics with rigid contacts
as a switched system and formulate an optimal control problem of switched
systems to implement the MPC. We utilize an efficient solution algorithm for
the MPC problem that optimizes the switching times and trajectory
simultaneously. The present efficient algorithm, unlike inefficient existing
methods, enables online optimization as well as switching times. The proposed
MPC with online STO is compared over the conventional MPC with fixed switching
times, through numerical simulations of dynamic jumping motions of a quadruped
robot. In the simulation comparison, the proposed MPC successfully controls the
dynamic jumping motions in twice as many cases as the conventional MPC, which
indicates that the proposed method extends the ability of the whole-body MPC.
We further conduct hardware experiments on the quadrupedal robot Unitree A1 and
prove that the proposed method achieves dynamic motions on the real robot.