title:
Strategy Synthesis for Zero-sum Neuro-symbolic Concurrent Stochastic
  Games

abstract:
  Neuro-symbolic approaches to artificial intelligence, which combine neural
networks with classical symbolic techniques, are growing in prominence,
necessitating formal approaches to reason about their correctness. We propose a
novel modelling formalism called neuro-symbolic concurrent stochastic games
(NS-CSGs), which comprise a set of probabilistic finite-state agents
interacting in a shared continuous-state environment, observed through
perception mechanisms implemented as neural networks. Since the environment
state space is continuous, we focus on the class of NS-CSGs with Borel state
spaces and Borel measurability restrictions on the components of the model.
  We consider the problem of zero-sum discounted cumulative reward, proving
that NS-CSGs are determined and therefore have a value which corresponds to a
unique fixed point. From an algorithmic perspective, existing methods to
compute values and optimal strategies for CSGs focus on finite state spaces. We
present, for the first time, value iteration and policy iteration algorithms to
solve a class of uncountable state space CSGs, and prove their convergence. Our
approach works by formulating piecewise linear or constant representations of
the value functions and strategies of NS-CSGs. We validate the approach with a
prototype implementation applied to a dynamic vehicle parking example.


title:
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly
  Detection

abstract:
  Recent anomaly detection algorithms have shown powerful performance by
adopting frame predicting autoencoders. However, these methods face two
challenging circumstances. First, they are likely to be trained to be
excessively powerful, generating even abnormal frames well, which leads to
failure in detecting anomalies. Second, they are distracted by the large number
of objects captured in both foreground and background. To solve these problems,
we propose a novel superpixel-based video data transformation technique named
Random Superpixel Erasing on Moving Objects (RandomSEMO) and Moving Object Loss
(MOLoss), built on top of a simple lightweight autoencoder. RandomSEMO is
applied to the moving object regions by randomly erasing their superpixels. It
enforces the network to pay attention to the foreground objects and learn the
normal features more effectively, rather than simply predicting the future
frame. Moreover, MOLoss urges the model to focus on learning normal objects
captured within RandomSEMO by amplifying the loss on the pixels near the moving
objects. The experimental results show that our model outperforms
state-of-the-arts on three benchmarks.


title:
Fine-Grained Population Mobility Data-Based Community-Level COVID-19
  Prediction Model

abstract:
  Predicting the number of infections in the anti-epidemic process is extremely
beneficial to the government in developing anti-epidemic strategies, especially
in fine-grained geographic units. Previous works focus on low spatial
resolution prediction, e.g., county-level, and preprocess data to the same
geographic level, which loses some useful information. In this paper, we
propose a fine-grained population mobility data-based model (FGC-COVID)
utilizing data of two geographic levels for community-level COVID-19
prediction. We use the population mobility data between Census Block Groups
(CBGs), which is a finer-grained geographic level than community, to build the
graph and capture the dependencies between CBGs using graph neural networks
(GNNs). To mine as finer-grained patterns as possible for prediction, a spatial
weighted aggregation module is introduced to aggregate the embeddings of CBGs
to community level based on their geographic affiliation and spatial
autocorrelation. Extensive experiments on 300 days LA city COVID-19 data
indicate our model outperforms existing forecasting models on community-level
COVID-19 prediction.


title: Flowformer: Linearizing Transformers with Conservation Flows

abstract:
  Transformers based on the attention mechanism have achieved impressive
success in various areas. However, the attention mechanism has a quadratic
complexity, significantly impeding Transformers from dealing with numerous
tokens and scaling up to bigger models. Previous methods mainly utilize the
similarity decomposition and the associativity of matrix multiplication to
devise linear-time attention mechanisms. They avoid degeneration of attention
to a trivial distribution by reintroducing inductive biases such as the
locality, thereby at the expense of model generality and expressiveness. In
this paper, we linearize Transformers free from specific inductive biases based
on the flow network theory. We cast attention as the information flow
aggregated from the sources (values) to the sinks (results) through the learned
flow capacities (attentions). Within this framework, we apply the property of
flow conservation with attention and propose the Flow-Attention mechanism of
linear complexity. By respectively conserving the incoming flow of sinks for
source competition and the outgoing flow of sources for sink allocation,
Flow-Attention inherently generates informative attentions without using
specific inductive biases. Empowered by the Flow-Attention, Flowformer yields
strong performance in linear time for wide areas, including long sequence, time
series, vision, natural language, and reinforcement learning.


title: New Approximation Algorithms for Fair $k$-median Problem

abstract:
  The fair $k$-median problem is one of the important clustering problems. The
current best approximation ratio is 4.675 for this problem with 1-fair
violation, which was proposed by Bercea et al. [APPROX-RANDOM'2019]. As far as
we know, there is no available approximation algorithm for the problem without
any fair violation. In this paper, we consider the fair $k$-median problem in
bounded doubling metrics and general metrics. We provide the first QPTAS for
fair $k$-median problem in doubling metrics. Based on the split-tree
decomposition of doubling metrics, we present a dynamic programming process to
find the candidate centers, and apply min-cost max-flow method to deal with the
assignment of clients. Especially, for overcoming the difficulties caused by
the fair constraints, we construct an auxiliary graph and use minimum weighted
perfect matching to get part of the cost. For the fair $k$-median problem in
general metrics, we present an approximation algorithm with ratio $O(\log k)$,
which is based on the embedding of given space into tree metrics, and the
dynamic programming method. Our two approximation algorithms for the fair
$k$-median problem are the first results for the corresponding problems without
any fair violation, respectively.


title: LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation

abstract:
  Purpose: Bronchoscopic intervention is a widely-used clinical technique for
pulmonary diseases, which requires an accurate and topological complete airway
map for its localization and guidance. The airway map could be extracted from
chest computed tomography (CT) scans automatically by airway segmentation
methods. Due to the complex tree-like structure of the airway, preserving its
topology completeness while maintaining the segmentation accuracy is a
challenging task.
  Methods: In this paper, a long-term slice propagation (LTSP) method is
proposed for accurate airway segmentation from pathological CT scans. We also
design a two-stage end-to-end segmentation framework utilizing the LTSP method
in the decoding process. Stage 1 is used to generate a coarse feature map by an
encoder-decoder architecture. Stage 2 is to adopt the proposed LTSP method for
exploiting the continuity information and enhancing the weak airway features in
the coarse feature map. The final segmentation result is predicted from the
refined feature map.
  Results: Extensive experiments were conducted to evaluate the performance of
the proposed method on 70 clinical CT scans. The results demonstrate the
considerable improvements of the proposed method compared to some
state-of-the-art methods as most breakages are eliminated and more tiny bronchi
are detected. The ablation studies further confirm the effectiveness of the
constituents of the proposed method.
  Conclusion: Slice continuity information is beneficial to accurate airway
segmentation. Furthermore, by propagating the long-term slice feature, the
airway topology connectivity is preserved with overall segmentation accuracy
maintained.


title:
Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and
  Inclusion of Agents and Switching Topologies

abstract:
  Some of the issues associated with the practical applications of consensus of
multi-agent systems (MAS) include switching topologies, attrition and inclusion
of agents from an existing network, and model uncertainties of agents. In this
paper, a single distributed dynamic state-feedback protocol referred to as the
Robust Attrition-Inclusion Distributed Dynamic (RAIDD) consensus protocol, is
synthesized for achieving the consensus of MAS with attrition and inclusion of
linear time-invariant higher-order uncertain homogeneous agents and switching
topologies. A state consensus problem termed as the Robust Attrition-Inclusion
(RAI) consensus problem is formulated to find this RAIDD consensus protocol. To
solve this RAI consensus problem, first, the sufficient condition for the
existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based
simultaneous stabilization approach. Next, the RAIDD consensus protocol is
attained using the Glover-McFarlane robust stabilization method if the
sufficient condition is satisfied. The performance of this RAIDD protocol is
validated by numerical simulations.


title: Locating Charging Stations: Connected, Capacitated and Prize- Collecting

abstract:
  In this paper, we study locating charging station problem as facility
location problem and its variants ($k$-Median, $k$-Facility location and
$k$-center). We study the connectivity and the capacity constraints in these
problem.
  Capacity and connectivity constraints have been studied in the literature
separately for all these problems. We give first constant factor approximations
when both the constraints are present. Extending/modifying the techniques used
for connected variants of the problem to include capacities or for capacitated
variants of problem to include connectivity is a tedious and challenging task.
In this paper, we combine the two constraints by reducing the problem to
underlying well studied problems, solving them as black box and combine the
obtained solutions. We also, combine the two constraints in the
prize-collection set up.
  In the prize-collecting set up, the problems are not even studied when one of
the constraint is present. We present constant factor approximation for them as
well.


title:
LighTN: Light-weight Transformer Network for Performance-overhead
  Tradeoff in Point Cloud Downsampling

abstract:
  Compared with traditional task-irrelevant downsampling methods, task-oriented
neural networks have shown improved performance in point cloud downsampling
range. Recently, Transformer family of networks has shown a more powerful
learning capacity in visual tasks. However, Transformer-based architectures
potentially consume too many resources which are usually worthless for low
overhead task networks in downsampling range. This paper proposes a novel
light-weight Transformer network (LighTN) for task-oriented point cloud
downsampling, as an end-to-end and plug-and-play solution. In LighTN, a
single-head self-correlation module is presented to extract refined global
contextual features, where three projection matrices are simultaneously
eliminated to save resource overhead, and the output of symmetric matrix
satisfies the permutation invariant. Then, we design a novel downsampling loss
function to guide LighTN focuses on critical point cloud regions with more
uniform distribution and prominent points coverage. Furthermore, We introduce a
feed-forward network scaling mechanism to enhance the learnable capacity of
LighTN according to the expand-reduce strategy. The result of extensive
experiments on classification and registration tasks demonstrates LighTN can
achieve state-of-the-art performance with limited resource overhead.


title: A Simplified Variant of G\"odel's Ontological Argument

abstract:
  A simplified variant of G\"odel's ontological argument is presented. The
simplified argument is valid already in basic modal logics K or KT, it does not
suffer from modal collapse, and it avoids the rather complex predicates of
essence (Ess.) and necessary existence (NE) as used by G\"odel. The variant
presented has been obtained as a side result of a series of theory
simplification experiments conducted in interaction with a modern proof
assistant system. The starting point for these experiments was the computer
encoding of G\"odel's argument, and then automated reasoning techniques were
systematically applied to arrive at the simplified variant presented. The
presented work thus exemplifies a fruitful human-computer interaction in
computational metaphysics. Whether the presented result increases or decreases
the attractiveness and persuasiveness of the ontological argument is a question
I would like to pass on to philosophy and theology.


title:
On approximation of solutions to the heat equation from Lebesgue class
  $L^2$ by more regular solutions

abstract:
  Let $s \in {\mathbb N}$, $T_1,T_2 \in {\mathbb R}$, $T_1<T_2$, and $\Omega,
\omega $ be bounded domains in ${\mathbb R}^n$, $n \geq 1$, such that $\omega
\subset \Omega$ and the complement $\Omega \setminus \omega$ has no (non-empty)
compact components in $\Omega$. We prove that this is the necessary and
sufficient condition for the space $H^{2s,s} _{\mathcal H} (\Omega \times
(T_1,T_2))$ of solutions to the heat operator ${\mathcal H} $ in a cylinder
domain $\Omega \times (T_1,T_2)$ from the anisotropic Sobolev space $H^{2s,s}
(\Omega \times (T_1,T_2))$ to be dense in the space $L^{2} _{\mathcal H}(\omega
\times (T_1,T_2))$, consisting of solutions in the domain $\omega \times
(T_1,T_2)$ from the Lebesgue class $L^{2} (\omega \times (T_1,T_2))$. As an
important corollary we obtain the theorem on the existence of a basis with the
double orthogonality property for the pair of the Hilbert spaces $H^{2s,s}
_{\mathcal H} (\Omega \times (T_1,T_2))$ and $L^{2} _{\mathcal H}(\omega \times
(T_1,T_2))$ .


title:
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing
  Regions

abstract:
  Deep image inpainting research mainly focuses on constructing various neural
network architectures or imposing novel optimization objectives. However, on
the one hand, building a state-of-the-art deep inpainting model is an extremely
complex task, and on the other hand, the resulting performance gains are
sometimes very limited. We believe that besides the frameworks of inpainting
models, lightweight traditional image processing techniques, which are often
overlooked, can actually be helpful to these deep models. In this paper, we
enhance the deep image inpainting models with the help of classical image
complexity metrics. A knowledge-assisted index composed of missingness
complexity and forward loss is presented to guide the batch selection in the
training procedure. This index helps find samples that are more conducive to
optimization in each iteration and ultimately boost the overall inpainting
performance. The proposed approach is simple and can be plugged into many deep
inpainting models by changing only a few lines of code. We experimentally
demonstrate the improvements for several recently developed image inpainting
models on various datasets.


title: T-Controllability of Evolution Systems having Non-instantaneous Impulses

abstract:
  In this manuscript, we have considered the system governed by a
noninstantaneous impulsive dynamical system of integer order with classical and
nonlocal conditions and derived sufficient conditions for the trajectory
controllability of the system on the Banach space. The conditions were obtained
through the concept of semigroup properties of operator and Gronwall's
inequality. Finally, illustrations with classical and nonlocal conditions were
also added to validate the derived conditions.


title: BViT: Broad Attention based Vision Transformer

abstract:
  Recent works have demonstrated that transformer can achieve promising
performance in computer vision, by exploiting the relationship among image
patches with self-attention. While they only consider the attention in a single
feature layer, but ignore the complementarity of attention in different levels.
In this paper, we propose the broad attention to improve the performance by
incorporating the attention relationship of different layers for vision
transformer, which is called BViT. The broad attention is implemented by broad
connection and parameter-free attention. Broad connection of each transformer
layer promotes the transmission and integration of information for BViT.
Without introducing additional trainable parameters, parameter-free attention
jointly focuses on the already available attention information in different
layers for extracting useful information and building their relationship.
Experiments on image classification tasks demonstrate that BViT delivers
state-of-the-art accuracy of 74.8\%/81.6\% top-1 accuracy on ImageNet with
5M/22M parameters. Moreover, we transfer BViT to downstream object recognition
benchmarks to achieve 98.9\% and 89.9\% on CIFAR10 and CIFAR100 respectively
that exceed ViT with fewer parameters. For the generalization test, the broad
attention in Swin Transformer and T2T-ViT also bring an improvement of more
than 1\%. To sum up, broad attention is promising to promote the performance of
attention based models. Code and pre-trained models are available at
https://github.com/DRL-CASIA/Broad_ViT.


title: $q$-Supercongruences on triple and quadruple sums

abstract:
  Inspired by the recent work of El Bachraoui, we present some new
$q$-supercongruences on triple and quadruple sums of basic hypergeometric
series. In particular, we give a $q$-supercongruence modulo the fifth power of
a cyclotomic polynomial, which is a $q$-analogue of the quadruple sum of Van
Hamme's supercongruence (G.2).


title:
Dynamical regimes of polar vortices on terrestrial planets with a
  seasonal cycle

abstract:
  Polar vortices are common planetary flows that encircle the pole in the
middle or high latitudes, and are observed on most of the solar systems'
planetary atmospheres. The polar vortices on Earth, Mars, and Titan are
dynamically related to the mean meridional circulation and exhibits a
significant seasonal cycle. However, the polar vortex's characteristics vary
between the three planets. To understand the mechanisms that influence the
polar vortex's dynamics and dependence on planetary parameters, we use an
idealized general circulation model with a seasonal cycle in which we varied
the obliquity, rotation rate, and orbital period. We find that there are
distinct regimes for the polar vortex seasonal cycle across the parameter
space. Some regimes have similarities to the observed polar vortices, including
a weakening of the polar vortex during midwinter at slow rotation rates,
similar to Titan's polar vortex. However, other regimes found within the
parameter space have no counterpart in the solar system. In addition, we show
that for a significant fraction of the parameter space, the vortex's potential
vorticity latitudinal structure is annular, similar to the observed structure
of the polar vortex on Mars and Titan. We also find a suppression of storm
activity during midwinter that resembles the suppression observed on Mars and
Earth, which occurs in simulations where the jet speed is greater than ~60
ms$^{-1}$. This wide variety of polar vortex dynamical regimes that shares
similarities to observed polar vortices suggests that among exoplanets, there
can be a wide variability of polar vortices.


title: Model-based Testing of Scratch Programs

abstract:
  Learners are often introduced to programming via dedicated languages such as
Scratch, where block-based commands are assembled visually in order to control
the interactions of graphical sprites. Automated testing of such programs is an
important prerequisite for supporting debugging, providing hints, or assessing
learning outcomes. However, writing tests for Scratch programs can be
challenging: The game-like and randomised nature of typical Scratch programs
makes it difficult to identify specific timed input sequences used to control
the programs. Furthermore, precise test assertions to check the resulting
program states are incompatible with the fundamental principle of creative
freedom in programming in Scratch, where correct program behaviour may be
implemented with deviations in the graphical appearance or timing of the
program. The event-driven and actor-oriented nature of Scratch programs,
however, makes them a natural fit for describing program behaviour using finite
state machines. In this paper, we introduce a model-based testing approach by
extending Whisker, an automated testing framework for Scratch programs. The
model-based extension describes expected program behaviour in terms of state
machines, which makes it feasible to check the abstract behaviour of a program
independent of exact timing and pixel-precise graphical details, and to
automatically derive test inputs testing even challenging programs. A video
demonstrating model-based testing with Whisker is available at the following
URL: https://youtu.be/edgCNbGSGEY


title:
Critical-layer instability of shallow water magnetohydrodynamic shear
  flows

abstract:
  In this paper, the instability of shallow water shear flow with a sheared
parallel magnetic field is studied. Waves propagating in such magnetic shear
flows encounter critical levels where the phase velocity relative to the basic
flow $c-U(y)$ matches the Alfv\'en wave velocities $\pm B(y)/\sqrt{\mu\rho}$,
based on the local magnetic field $B(y)$, the magnetic permeability $\mu$ and
the mass density of the fluid $\rho$. It is shown that when the two critical
levels are close to each other, the critical layer can generate an instability.
The instability problem is solved, combining asymptotic solutions at large
wavenumbers and numerical solutions, and the mechanism of instability explained
using the conservation of momentum. For the shallow water MHD system, the paper
gives the general form of the local differential equation governing such
coalescing critical layers for any generic field and flow profiles, and
determines precisely how the magnetic field modifies the purely hydrodynamic
stability criterion based on the potential vorticity gradient in the critical
layer. The curvature of the magnetic field profile, or equivalently the
electric current gradient, $J' = - B''/\mu$ in the critical layer is found to
play a complementary role in the instability.


title: Continuous Occupancy Mapping in Dynamic Environments Using Particles

abstract:
  Dynamic occupancy maps were proposed in recent years to model the obstacles
in dynamic environments. Among these maps, the particle-based map offers a
solid theoretical basis and the ability to model complex-shaped obstacles.
Current particle-based maps describe the occupancy status in discrete grid form
and suffer from the grid size problem, namely: large grid size is unfavorable
for path planning while small grid size lowers efficiency and causes gaps and
inconsistencies. To tackle this problem, this paper generalizes the
particle-based map into continuous space and builds an efficient 3D local map.
A dual-structure subspace division paradigm, composed of a voxel subspace
division and a novel pyramid-like subspace division, is proposed to propagate
particles and update the map efficiently with the consideration of occlusions.
The occupancy status of an arbitrary point can then be estimated with the
cardinality expectation. To reduce the noise in modeling static and dynamic
obstacles simultaneously, an initial velocity estimation approach and a mixture
model are utilized. Experimental results show that our map can effectively and
efficiently model both dynamic obstacles and static obstacles. Compared to the
state-of-the-art grid-form particle-based map, our map enables continuous
occupancy estimation and substantially improves the performance in different
resolutions. We also deployed the map on a quadrotor to demonstrate the bright
prospect of using this map in obstacle avoidance tasks of small-scale robotics
systems.


title: Automated Test Generation for Scratch Programs

abstract:
  The importance of programming education has lead to dedicated educational
programming environments, where users visually arrange block-based programming
constructs that typically control graphical, interactive game-like programs.
The Scratch programming environment is particularly popular, with more than 70
million registered users at the time of this writing. While the block-based
nature of Scratch helps learners by preventing syntactical mistakes, there
nevertheless remains a need to provide feedback and support in order to
implement desired functionality. To support individual learning and classroom
settings, this feedback and support should ideally be provided in an automated
fashion, which requires tests to enable dynamic program analysis. The Whisker
framework enables automated testing of Scratch programs, but creating these
automated tests for Scratch programs is challenging. In this paper, we
therefore investigate how to automatically generate Whisker tests. This raises
important challenges: First, game-like programs are typically randomised,
leading to flaky tests. Second, Scratch programs usually consist of animations
and interactions with long delays, inhibiting the application of classical test
generation approaches. Evaluation on common programming exercises, a random
sample of 1000 Scratch user programs, and the 1000 most popular Scratch
programs demonstrates that our approach enables Whisker to reliably accelerate
test executions, and even though many Scratch programs are small and easy to
cover, there are many unique challenges for which advanced search-based test
generation using many-objective algorithms is needed in order to achieve high
coverage.


title: Non-Hermitian topological exciton-polariton corner modes

abstract:
  We theoretically study two-dimensional exciton-polariton lattices and predict
that non-Hermitian topological corner modes can be formed under non-resonant
pumping. As a generalization of the non-Hermitian skin effect, all eigenstates
are localized at the two corners in our model. This is also a higher
dimensional topology compared to other proposals in exciton-polariton systems
and we find that it allows propagating signals in the bulk of the system to
travel around defects, which is not possible in one-dimensional topological
lattices or two-dimensional lattices with Hermitian edge states. Furthermore,
as all polariton states are localized away from an excitation spot, the system
offers an opportunity for more accurate measurement of the polariton-polariton
interaction strength as the pump-induced exciton-reservoir is spatially
separated from all polariton states.


title: Natural Image Stitching Using Depth Maps

abstract:
  Natural image stitching (NIS) aims to create one natural-looking mosaic from
two overlapping images that capture a same 3D scene from different viewing
positions. Challenges inevitably arise when the scene is non-planar and the
camera baseline is wide, since parallax becomes not negligible in such cases.
In this paper, we propose a novel NIS method using depth maps, which generates
natural-looking mosaics against parallax in both overlapping and
non-overlapping regions. Firstly, we estimate a pixel-to-pixel transformation
based on feature matches and their depth values. Then, we draw a triangulation
of the target image and estimate multiple local homographies, one per triangle,
based on the locations of their vertices and the rectified depth values.
Finally, the warping image is composited by the backward mapping of piece-wise
homographies. Experimental results demonstrate that the proposed method not
only provides accurate alignment in the overlapping regions, but also virtual
naturalness in the non-overlapping region.


title:
Scattering of Ly{\alpha} Photons through the Reionizing Intergalactic
  Medium: I. Spectral Energy Distribution

abstract:
  During reionization, a fraction of galactic Ly$\alpha$ emission is scattered
in the intergalactic medium (IGM) and appears as a diffuse light extending
megaparsecs from the source. We investigate how to probe the properties of the
early galaxies and their surrounding IGM using this scattered light. We create
a Monte Carlo algorithm to track individual photons and reproduce several test
cases from previous literature. Then, we run our code on the simulated IGM of
the CoDaII simulation. We find that the scattered light can leave an observable
imprint on the emergent spectrum if collected over $\sim{\rm arcmin}^2$.
Scattering can redden the emission by increasing the path lengths of photons,
but it can also make the photons bluer by upscattering them according to the
peculiar motion of the scatterer. The photons emitted on the far blue side of
the resonance appear more extended in both frequency and space compared to
those emitted near the resonance. This provides a discriminating feature for
the blueward emission, which cannot be constrained from the unscattered light
directly coming from the source. The ionization state of the IGM also affects
the scattered light spectrum. When the source is in a small HII region, the
emission goes through more scatterings in the surrounding HI region regardless
of the initial frequency and ends up more redshifted and spatially extended.
This can result in a weakening of the scattered light toward high-$z$ during
reionization. Our results provide a framework for interpreting the scattered
light to be measured by high-$z$ integral-field-unit surveys.


title: I'm Hearing (Different) Voices: Anonymous Voices to Protect User Privacy

abstract:
  In this paper, we present AltVoice -- a system designed to help user's
protect their privacy when using remotely accessed voice services. The system
allows a user to conceal their true voice identity information with no
cooperation from the remote voice service: AltVoice re-synthesizes user's
spoken audio to sound as if it has been spoken by a different, private
identity. The system converts audio to its textual representation at its
midpoint, and thus removes any linkage between the user's voice and the
generated private voices. We implement AltVoice and we propose six different
methods to generate private voice identities, each is based on a user-known
secret. We identify the system's trade-offs, and we investigate them for each
of the proposed identity generation methods. Specifically, we investigate
generated voices' diversity, word error rate, perceived speech quality and the
success of attackers under privacy compromise and authentication compromise
attack scenarios. Our results show that AltVoice-generated voices are not
easily linked to original users, enabling users to protect themselves from
voice data leakages and allowing for the revocability of (generated) voice
data; akin to using passwords. However the results also show further work is
needed on ensuring that the produced audio is natural, and that identities of
private voices are distinct from one another. We discuss the future steps into
improving AltVoice and the new implications that its existence has for the
creations of remotely accessed voice services.


title: Optimization of the first CUPID detector module

abstract:
  CUPID will be a next generation experiment searching for the neutrinoless
double $\beta$ decay, whose discovery would establish the Majorana nature of
the neutrino. Based on the experience achieved with the CUORE experiment,
presently taking data at LNGS, CUPID aims to reach a background free
environment by means of scintillating Li$_{2}$$^{100}$MoO$_4$ crystals coupled
to light detectors. Indeed, the simultaneous heat and light detection allows us
to reject the dominant background of $\alpha$ particles, as proven by the
CUPID-0 and CUPID-Mo demonstrators. In this work we present the results of the
first test of the CUPID baseline module. In particular, we propose a new
optimized detector structure and light sensors design to enhance the
engineering and the light collection, respectively. We characterized the heat
detectors, achieving an energy resolution of (5.9 $\pm$ 0.2) keV FWHM at the
$Q$-value of $^{100}$Mo (about 3034 keV). We studied the light collection of
the baseline CUPID design with respect to an alternative configuration which
features gravity-assisted light detectors' mounting. In both cases we obtained
an improvement in the light collection with respect to past measures and we
validated the particle identification capability of the detector, which ensures
an $\alpha$ particle rejection higher than 99.9%, fully satisfying the
requirements for CUPID.


title: On the complexity of All $\varepsilon$-Best Arms Identification

abstract:
  We consider the question introduced by \cite{Mason2020} of identifying all
the $\varepsilon$-optimal arms in a finite stochastic multi-armed bandit with
Gaussian rewards. We give two lower bounds on the sample complexity of any
algorithm solving the problem with a confidence at least $1-\delta$. The first,
unimprovable in the asymptotic regime, motivates the design of a Track-and-Stop
strategy whose average sample complexity is asymptotically optimal when the
risk $\delta$ goes to zero. Notably, we provide an efficient numerical method
to solve the convex max-min program that appears in the lower bound. Our method
is based on a complete characterization of the alternative bandit instances
that the optimal sampling strategy needs to rule out, thus making our bound
tighter than the one provided by \cite{Mason2020}. The second lower bound deals
with the regime of high and moderate values of the risk $\delta$, and
characterizes the behavior of any algorithm in the initial phase. It emphasizes
the linear dependency of the sample complexity in the number of arms. Finally,
we report on numerical simulations demonstrating our algorithm's advantage over
state-of-the-art methods, even for moderate risks.


title: Graph-adaptive Rectified Linear Unit for Graph Neural Networks

abstract:
  Graph Neural Networks (GNNs) have achieved remarkable success by extending
traditional convolution to learning on non-Euclidean data. The key to the GNNs
is adopting the neural message-passing paradigm with two stages: aggregation
and update. The current design of GNNs considers the topology information in
the aggregation stage. However, in the updating stage, all nodes share the same
updating function. The identical updating function treats each node embedding
as i.i.d. random variables and thus ignores the implicit relationships between
neighborhoods, which limits the capacity of the GNNs. The updating function is
usually implemented with a linear transformation followed by a non-linear
activation function. To make the updating function topology-aware, we inject
the topological information into the non-linear activation function and propose
Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric
activation function incorporating the neighborhood information in a novel and
efficient way. The parameters of GReLU are obtained from a hyperfunction based
on both node features and the corresponding adjacent matrix. To reduce the risk
of overfitting and the computational cost, we decompose the hyperfunction as
two independent components for nodes and features respectively. We conduct
comprehensive experiments to show that our plug-and-play GReLU method is
efficient and effective given different GNN backbones and various downstream
tasks.


title:
Distributed Periodic Event-triggered Control of Nonlinear Multi-Agent
  Systems

abstract:
  We present a general emulation-based framework to address the distributed
control of multi-agent systems over packet-based networks. We consider the
setup where information is only transmitted at (non-uniform) sampling times and
where packets are received with unknown delays. We design local dynamic event
triggering mechanisms to generate the transmissions. The triggering mechanisms
can run on non-synchronized digital platforms, i.e., we ensure that the
conditions must only be verified at asynchronous sampling times, which differ
for each platform. Different stability and performance characteristics can be
considered as we follow a general dissipativity-based approach. Moreover,
Zeno-free properties are guaranteed by design. The results are illustrated on a
consensus problem.


title: Zero-Reference Image Restoration for Under-Display Camera of UAV

abstract:
  The exposed cameras of UAV can shake, shift, or even malfunction under the
influence of harsh weather, while the add-on devices (Dupont lines) are very
vulnerable to damage.
  We can place a low-cost T-OLED overlay around the camera to protect it, but
this would also introduce image degradation issues.
  In particular, the temperature variations in the atmosphere can create mist
that adsorbs to the T-OLED, which can cause secondary disasters (i.e., more
severe image degradation) during the UAV's filming process.
  To solve the image degradation problem caused by overlaying T-OLEDs, in this
paper we propose a new method to enhance the visual experience by enhancing the
texture and color of images.
  Specifically, our method trains a lightweight network to estimate a low-rank
affine grid on the input image, and then utilizes the grid to enhance the input
image at block granularity.
  The advantages of our method are that no reference image is required and the
loss function is developed from visual experience.
  In addition, our model can perform high-quality recovery of images of
arbitrary resolution in real time.
  In the end, the limitations of our model and the collected datasets
(including the daytime and nighttime scenes) are discussed.


title:
Significant Low-dimensional Spectral-temporal Features for Seizure
  Detection

abstract:
  Seizure onset detection in electroencephalography (EEG) signals is a
challenging task due to the non-stereotyped seizure activities as well as their
stochastic and non-stationary characteristics in nature. Joint
spectral-temporal features are believed to contain sufficient and powerful
feature information for absence seizure detection. However, the resulting
high-dimensional features involve redundant information and require heavy
computational load. Here, we discover significant low-dimensional
spectral-temporal features in terms of mean-standard deviation of wavelet
transform coefficient (MS-WTC), based on which a novel absence seizure
detection framework is developed. The EEG signals are transformed into the
spectral-temporal domain, with their low-dimensional features fed into a
convolutional neural network. Superior detection performance is achieved on the
widely-used benchmark dataset as well as a clinical dataset from the Chinese
301 Hospital. For the former, seven classification tasks were evaluated with
the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a
mean accuracy of 94.7%, overwhelming other methods with low-dimensional
temporal and spectral features. Experimental results on two seizure datasets
demonstrate reliability, efficiency and stability of our proposed MS-WTC
method, validating the significance of the extracted low-dimensional
spectral-temporal features.


title: On the Relationship between Shy and Warded Datalog+/-

abstract:
  Datalog^E is the extension of Datalog with existential quantification. While
its high expressive power, underpinned by a simple syntax and the support for
full recursion, renders it particularly suitable for modern applications on
knowledge graphs, query answering (QA) over such language is known to be
undecidable in general. For this reason, different fragments have emerged,
introducing syntactic limitations to Datalog^E that strike a balance between
its expressive power and the computational complexity of QA, to achieve
decidability. In this short paper, we focus on two promising tractable
candidates, namely Shy and Warded Datalog+/-. Reacting to an explicit interest
from the community, we shed light on the relationship between these fragments.
Moreover, we carry out an experimental analysis of the systems implementing Shy
and Warded, respectively DLV^E and Vadalog.


title: Consecutive Primes in Piatetski-Shapiro Sequences

abstract:
  The Piatetski-Shapiro sequences are of the form $\mathcal{N}^{(c)} :=
(\lfloor n^c \rfloor)_{n=1}^\infty$ with $c > 1, c \notin \mathbb{N}$. In this
paper, we study the distribution of pairs $(p, p^{\#})$ of consecutive primes
such that $p \in \mathcal{N}^{(c_1)}$ and $p \in \mathcal{N}^{(c_2)}$ for $c_1,
c_2 > 1$ and give a conjecture with the prime counting functions of the pairs
$(p, p^{\#})$. We give a heuristic argument to support this prediction which
relies on a strong form of the Hardy-Littlewood conjectures.


title: Sur le biais d'une loi de probabilit\'e relative aux entiers friables

abstract:
  The standard probability law on the set $S(x,y)$ of $y$-friable integers not
exceeding $x$ assigns to each friable integer $n$ a probability proportional to
$1/n^\alpha$ where $\alpha=\alpha(x,y)$ is the saddle-point of the inverse
Laplace integral for $\Psi(x,y):=|S(x,y)|$. This law presents a structural bias
inasmuch it weights integers $>x$. We propose a quantitative measure of this
bias and exhibit a related Gaussian distribution.


title:
A macro modelling continuum approach with embedded discontinuities for
  the assessment of masonry arch bridges under earthquake loading

abstract:
  The paper presents an effective macro-modelling approach, utilising an
anisotropic material model with embedded discontinuities, for masonry arches
and bridges under cyclic loading, including dynamic actions induced by
earthquakes. Realistic numerical simulations of masonry arch bridges under
static and dynamic loading require accurate models representing the
anionotropic nature of masonry and material nonlinearity due to opening and
closure of tensile cracks and shear sliding along mortar joints. The proposed
3D modelling approach allows for masonry bond via simple calibration, and
enables the representation of tensile cracking, crushing and shear damage in
the brickwork. A two-scale representation is adopted, where 3D continuum
elements at the structural scale are linked to embedded nonlinear interfaces
representing the meso-structure of the material. The potential and accuracy of
the proposed approach are shown in numerical examples and comparisons against
physical experiments on masonry arches and bridges under static and dynamic
loading.


title:
Qualitative properties of solutions to a non-local free boundary problem
  modeling cell polarization

abstract:
  We consider a parabolic non-local free boundary problem that has been derived
as a limit of a bulk-surface reaction diffusion system of equations which
models cell polarization. The authors have justified the well-posedness of this
problem and have further proved uniqueness of solutions and global stability of
steady states. In this paper we investigate qualitative properties of the free
boundary. We present necessary and sufficient conditions for the initial data
that imply continuity of the support at $t=0$. If one of these assumptions
fail, then jumps of the support take place. In addition we provide a complete
characterization of the jumps for a large class of initial data.


title:
Thermodynamic and Optical Behaviors of Quintessential Hayward-AdS Black
  Holes

abstract:
  Motivated by Dark Energy (DE) activities, we study certain physical behaviors
of the quintessential Hayward-AdS black holes in four dimensions. We generalize
some physical properties of the ordinary Hayward AdS black holes without the
dark sector. We elaborate a study in terms of the new quantities $c$ and
$\omega_q$ parametrizing the dark sector moduli space. We investigate the
effect of such parameters on certain thermodynamic and optical aspects. To show
the quintessential thermodynamic behaviors, we first reconsider the critical
properties of ordinary solutions. We find that the equation of state predicts a
universal ratio given by $\chi_0=\frac{P_cv_c}{T_c}=\frac{27-3\sqrt{6}}{50}$,
which is different than the universal one appearing for Van der Waals fluids.
Considering the quintessential solutions and taking certain values of the DE
state parameter $\omega_q$, we observe that the new ratio depends on the DE
scalar field intensity $c$. In certain regions of the moduli space, we show
that this ratio can be factorized using two terms describing the absence and
the presence of the dark sector. Then, we analyze also the DE effect on the
heat engines. For the optical aspect, we study the influence of DE on the
shadows using one-dimensional real curves. Finally, we discuss the associated
energy emission rate, using the dark sector.


title: On a question of Moshe Roitman and Euler class of stably free module

abstract:
  Let $A$ be a ring of dimension $d$ containing an infinite field $k$,
$T_1,\ldots,T_r$ be variables over $A$ and $P$ be a projective
$A[T_1,\ldots,T_r]$-module of rank $n$. Assume one of the following conditions
hold.
  (1) $2n\geq d+3$ and $P$ is extended from $A$.
  (2) $2n\geq d+2$, $A$ is an affine $\overline {\mathbb F}_p$-algebra and $P$
is extended from $A$.
  (3) $2n\geq d+3$ and singular locus of $Spec(A)$ is a closed set $V(\mathcal
J)$ with ht $\mathcal J\geq d-n+2$.
  Assume $Um(P_f)\neq \varnothing$ for some monic polynomial $f(T_r)\in
A[T_1,\ldots,T_r]$. Then $Um(P)\neq \varnothing$.


title: Generalized Unrelated Machine Scheduling Problem

abstract:
  We study the generalized load-balancing (GLB) problem, where we are given $n$
jobs, each of which needs to be assigned to one of $m$ unrelated machines with
processing times $\{p_{ij}\}$. Under a job assignment $\sigma$, the load of
each machine $i$ is $\psi_i(\mathbf{p}_{i}[\sigma])$ where
$\psi_i:\mathbb{R}^n\rightarrow\mathbb{R}_{\geq0}$ is a symmetric monotone norm
and $\mathbf{p}_{i}[\sigma]$ is the $n$-dimensional vector $\{p_{ij}\cdot
\mathbf{1}[\sigma(j)=i]\}_{j\in [n]}$. Our goal is to minimize the generalized
makespan $\phi(\mathsf{load}(\sigma))$, where
$\phi:\mathbb{R}^m\rightarrow\mathbb{R}_{\geq0}$ is another symmetric monotone
norm and $\mathsf{load}(\sigma)$ is the $m$-dimensional machine load vector.
This problem significantly generalizes many classic optimization problems,
e.g., makespan minimization, set cover, minimum-norm load-balancing, etc.
  We obtain a polynomial time randomized algorithm that achieves an
approximation factor of $O(\log n)$, matching the lower bound of set cover up
to constant factor. We achieve this by rounding a novel configuration LP
relaxation with exponential number of variables. To approximately solve the
configuration LP, we design an approximate separation oracle for its dual
program. In particular, the separation oracle can be reduced to the norm
minimization with a linear constraint (NormLin) problem and we devise a
polynomial time approximation scheme (PTAS) for it, which may be of independent
interest.


title:
Numerical Implementation of the Improved Sugama Collision Operator Using
  a Moment Approach

abstract:
  The numerical implementation of the linearized gyrokinetic (GK) and
drift-kinetic (DK) improved Sugama (IS) collision operators, recently
introduced by Sugama et al. [Phys. Plasmas 26, 102108 (2019)], is reported. The
IS collision operator extends the validity of the widely-used original Sugama
(OS) operator [Sugama et. al., Phys. Plasmas 16, 112503 (2009)] to the
Pfirsch-Schl\"uter collisionality regime. Using a Hermite-Laguerre
velocity-space decomposition of the perturbed gyrocenter distribution function
that we refer to as the gyro-moment approach, the IS collision operator is
written in a form of algebraic coefficients that depend on the mass and
temperature ratios of the colliding species and perpendicular wavenumber. A
comparison between the IS, OS and Coulomb collision operators is performed,
showing that the IS collision operator is able to approximate the Coulomb
collision operator in the case of trapped electron mode (TEM) in H-mode
pedestal conditions better than the OS operator. Additionally, we show that the
IS operator produces a zonal flow (ZF) residual level at intermediate values
between the Coulomb and OS collision operators.


title:
Solutions of the Euler equations and stationary structures in an
  inviscid fluid

abstract:
  The Euler equations describing two-dimensional steady flows of an inviscid
fluid are studied. These equations are reduced to one equation for the stream
function and then, using the Hirota function, solutions of three nonlinear
elliptic equations are found. %: Sine-Gordon, Sinh-Gordon and Tzitz\'{e}ica.
The solutions found are interpreted as sources in a rotating fluid, jets,
chains of sources and sinks, vortex structures. We propose a new simple method
for constructing solutions in the form of rational expressions of elliptic
functions. It is shown that the flux of fluid across a closed curve is
quantized in the case of the elliptic Sin-Gordon equation.


title:
Three-dimensional simulation of a core-collapse supernova for a binary
  star progenitor of SN 1987A

abstract:
  We present results from a self-consistent, non-rotating core-collapse
supernova (CCSN) simulation in three spatial dimensions using a binary
evolution progenitor model of SN 1987A by Urushibata et al. (2018). This 18.3
solar-mass progenitor model is evolved from a slow-merger of 14 and 8
solar-mass stars, and it satisfies most of the observational constraints such
as red-to-blue evolution, lifetime, total mass and position in the
Hertzsprung-Russell diagram at collapse, and chemical anomalies. We obtain the
neutrino-driven shock revival for this progenitor at 350 ms after bounce,
leading to the formation of a newly-born neutron star with normal gravitational
mass of 1.35 solar mass and spin period of 0.1 s. We also discuss the
detectability of gravitational wave and neutrino signals for a Galactic event.
At our final simulation time (660 ms postbounce), the diagnostic explosion
energy, though still growing, is smaller (0.15 foe) compared to the observed
value (1.5 foe). Long-term simulation including several missing physical
ingredients in our 3D models (such as rotation, magnetic fields, or more
elaborate neutrino opacity) should be done to bridge the gap between the
theoretical predictions and the observed values.


title:
Generalized hyper-Ramsey-Bord\'e matter-wave interferometry: quantum
  engineering of robust atomic sensors with composite pulses

abstract:
  A new class of atomic interferences using ultra-narrow optical transitions
are pushing quantum engineering control to a very high level of precision for a
next generation of sensors and quantum gate operations. In such context, we
propose a new quantum engineering approach to Ramsey-Bord\'e interferometry
introducing multiple composite laser pulses with tailored pulse duration, Rabi
field amplitude, frequency detuning and laser phase-step. We explore quantum
metrology with hyper-Ramsey and hyper-Hahn-Ramsey clocks below the $10^-18$
level of fractional accuracy by a fine tuning control of light excitation
parameters leading to spinor interferences protected against light-shift
coupled to laser-probe field variation. We review cooperative composite pulse
protocols to generate robust Ramsey-Bord\'e, Mach-Zehnder and double-loop
atomic sensors shielded against measurement distortion related to
Doppler-shifts and light-shifts coupled to pulse area errors. Fault-tolerant
auto-balanced hyper-interferometers are introduced eliminating several
technical laser pulse defects that can occur during the entire probing
interrogation protocol. Quantum sensors with composite pulses and ultra-cold
atomic sources should offer a new level of high accuracy in detection of
acceleration and rotation inducing phase-shifts, a strong improvement in tests
of fundamental physics with hyper-clocks while paving the way to a new
conception of atomic interferometers tracking space-time gravitational waves
with a very high sensitivity.


title:
Obtaining weights for Gr\"obner basis computation in parameter
  identifiability problems

abstract:
  We consider a specific class of polynomial systems that arise in parameter
identifiability problems of models of ordinary differential equations (ODE) and
discover a method for speeding up the Gr\"obner basis computation by using a
weighted ordering. Our method explores the structure of the ODE model to
generate weight assignments for each variable. We provide empirical results
that show improvement across different symbolic computing frameworks


title: $E$-separated semigroups

abstract:
  A semigroup is called $E$-$separated$ if for any distinct idempotents $x,y\in
X$ there exists a homomorphism $h:X\to Y$ to a semilattice $Y$ such that
$h(x)\ne h(y)$. Developing results of Putcha and Weissglass, we characterize
$E$-separated semigroups via certain commutativity properties of idempotents of
$X$. Also we characterize $E$-separated semigroups in the class of
$\pi$-regular $E$-semigroups.


title:
Motion Sickness Modeling with Visual Vertical Estimation and Its
  Application to Autonomous Personal Mobility Vehicles

abstract:
  Passengers (drivers) of level 3-5 autonomous personal mobility vehicles
(APMV) and cars can perform non-driving tasks, such as reading books and
smartphones, while driving. It has been pointed out that such activities may
increase motion sickness. Many studies have been conducted to build
countermeasures, of which various computational motion sickness models have
been developed. Many of these are based on subjective vertical conflict (SVC)
theory, which describes vertical changes in direction sensed by human sensory
organs vs. those expected by the central nervous system. Such models are
expected to be applied to autonomous driving scenarios. However, no current
computational model can integrate visual vertical information with vestibular
sensations.
  We proposed a 6 DoF SVC-VV model which add a visually perceived vertical
block into a conventional six-degrees-of-freedom SVC model to predict VV
directions from image data simulating the visual input of a human. Hence, a
simple image-based VV estimation method is proposed.
  As the validation of the proposed model, this paper focuses on describing the
fact that the motion sickness increases as a passenger reads a book while using
an AMPV, assuming that visual vertical (VV) plays an important role. In the
static experiment, it is demonstrated that the estimated VV by the proposed
method accurately described the gravitational acceleration direction with a low
mean absolute deviation. In addition, the results of the driving experiment
using an APMV demonstrated that the proposed 6 DoF SVC-VV model could describe
that the increased motion sickness experienced when the VV and gravitational
acceleration directions were different.


title: Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction

abstract:
  Lighting prediction from a single image is becoming increasingly important in
many vision and augmented reality (AR) applications in which shading and shadow
consistency between virtual and real objects should be guaranteed. However,
this is a notoriously ill-posed problem, especially for indoor scenarios,
because of the complexity of indoor luminaires and the limited information
involved in 2D images. In this paper, we propose a graph learning-based
framework for indoor lighting estimation. At its core is a new lighting model
(dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph
Convolutional Network (GCN) that infers the new lighting representation from a
single LDR image of limited field-of-view. Our lighting model builds 128 evenly
distributed SGs over the indoor panorama, where each SG encoding the lighting
and the depth around that node. The proposed GCN then learns the mapping from
the input image to DSGLight. Compared with existing lighting models, our
DSGLight encodes both direct lighting and indirect environmental lighting more
faithfully and compactly. It also makes network training and inference more
stable. The estimated depth distribution enables temporally stable shading and
shadows under spatially-varying lighting. Through thorough experiments, we show
that our method obviously outperforms existing methods both qualitatively and
quantitatively.


title:
A new estimation of astrometric exoplanet detection limits in the
  habitable zone around nearby stars

abstract:
  Astrometry is less sensitive to stellar activity than the radial velocity
technique when attempting to detect Earth mass planets in the habitable zone of
solar-type stars. This is due to a smaller number of physical processes
affecting the signal, and a larger ratio of the amplitude of the planetary
signal to the stellar signal. A few high-precision astrometric missions have
therefore been proposed over the past two decades. We aim to re-estimate the
detection limits in astrometry for the nearby stars which are the main targets
proposed for the THEIA astrometric mission, the most elaborate mission to
search for planets, and to characterise its performance. This analysis is
performed for the 55 F-G-K stars in the THEIA sample. We used realistic
simulations of stellar activity and selected those that correspond best to each
star in terms of spectral type and average activity level. Then, we performed
blind tests to estimate the performance. We find worse detection limits
compared to those previously obtained for that sample based on a careful
analysis of the false positive rate, with values in the Earth-mass regime for
most stars of the sample. The difference is attributed to the fact that we
analysed full time series, adapted to each star in the sample, rather than
using the expected solar jitter only. Although these detection limits have a
relatively low signal-to-noise ratio, the fitted parameters have small
uncertainties. We confirm the low impact of stellar activity on exoplanet
detectability for solar-type stars, although it plays a significant role for
the closest stars such as alpha Cen A and B. However, for the few stars in the
sample with a habitable zone corresponding to long periods, namely subgiants,
the THEIA observational strategy is not well adapted and should prevent the
detection of planets in the habitable zone, unless a longer mission can be
proposed.


title:
The Grothendieck algebras of certain smash product semisimple Hopf
  algebras

abstract:
  Let $H$ be a semisimple Hopf algebra over an algebraically closed field
$\mathbbm{k}$ of characteristic $p>\dim_{\mathbbm{k}}(H)^{1/2}$ and $p\nmid
2\dim_{\mathbbm{k}}(H)$. In this paper, we consider the smash product
semisimple Hopf algebra $H\#\mathbbm{k}G$, where $G$ is a cyclic group of order
$n:=2\dim_{\mathbbm{k}}(H)$. Using irreducible representations of $H$ and those
of $\mathbbm{k}G$, we determine all non-isomorphic irreducible representations
of $H\#\mathbbm{k}G$. There is a close relationship between the Grothendieck
algebra $(G_0(H\#\mathbbm{k}G)\otimes_{\mathbb{Z}}\mathbbm{k},*)$ of
$H\#\mathbbm{k}G$ and the Grothendieck algebra
$(G_0(H)\otimes_{\mathbb{Z}}\mathbbm{k},*)$ of $H$. To establish this
connection, we endow with a new multiplication operator $\star$ on
$G_0(H)\otimes_{\mathbb{Z}}\mathbbm{k}$ and show that the Grothendieck algebra
$(G_0(H\#\mathbbm{k}G)\otimes_{\mathbb{Z}}\mathbbm{k},\ast)$ is isomorphic to
the direct sum of
$(G_0(H)\otimes_{\mathbb{Z}}\mathbbm{k},*)^{\oplus\frac{n}{2}}$ and
$(G_0(H)\otimes_{\mathbb{Z}}\mathbbm{k},\star)^{\oplus\frac{n}{2}}$.


title:
On the Exactness of an Energy-efficient Train Control model based on
  Convex Optimization

abstract:
  In this paper, we demonstrate the exactness proof for the energy-efficient
train control (EETC) model based on convex optimization. The proof of exactness
shows that the convex optimization model will share the same optimization
results with the initial model on which the convex relaxations are conducted.
We first show how the relaxation on the initial non-convex model is conducted
and provide analysis to show that the relaxations are convex constraints and
the relaxed model is thus a convex model. Subsequently, we prove that the
relaxed convex model will always achieve its optimal solution on the initial
equality constraints and the optimal solution achieved by convex optimization
will be the same as the one obtained by the initial non-convex model and the
relaxations applied are exact. A numerical verification has been conducted
based on a typical urban rail system with a steep gradient. The results of this
paper shed lights on further applications of convex optimization on
energy-efficient train control and relevant areas related to operation and
control of low-carbon transportation systems.


title: Ill_posedness for a two_component Novikov system in Besov space

abstract:
  In this paper, we consider the Cauchy problem for a two-component Novikov
system on the line. By specially constructed initial data $(\rho_0, u_0)$ in
$B_{p, \infty}^{s-1}(\mathbb{R})\times B_{p, \infty}^s(\mathbb{R})$ with
$s>\max\{2+\frac{1}{p}, \frac{5}{2}\}$ and $1\leq p \leq \infty$, we show that
any energy bounded solution starting from $(\rho_0, u_0)$ does not converge
back to $(\rho_0, u_0)$ in the metric of $B_{p, \infty}^{s-1}(\mathbb{R})\times
B_{p, \infty}^s(\mathbb{R})$ as time goes to zero, thus results in
discontinuity of the data-to-solution map and ill-posedness.


title: Stability Problems in Symbolic Integration

abstract:
  This paper aims to initialize a dynamical aspect of symbolic integration by
studying stability problems in differential fields. We present some basic
properties of stable elementary functions and D-finite power series that enable
us to characterize three special families of stable elementary functions
involving rational functions, logarithmic functions, and exponential functions.
Some problems for future studies are proposed towards deeper dynamical studies
in differential and difference algebra.


title:
An Analysis of Variations in the Effectiveness of Query Performance
  Prediction

abstract:
  A query performance predictor estimates the retrieval effectiveness of an IR
system for a given query. An important characteristic of QPP evaluation is
that, since the ground truth retrieval effectiveness for QPP evaluation can be
measured with different metrics, the ground truth itself is not absolute, which
is in contrast to other retrieval tasks, such as that of ad-hoc retrieval.
Motivated by this argument, the objective of this paper is to investigate how
such variances in the ground truth for QPP evaluation can affect the outcomes
of QPP experiments. We consider this not only in terms of the absolute values
of the evaluation metrics being reported (e.g. Pearson's $r$, Kendall's
$\tau$), but also with respect to the changes in the ranks of different QPP
systems when ordered by the QPP metric scores. Our experiments reveal that the
observed QPP outcomes can vary considerably, both in terms of the absolute
evaluation metric values and also in terms of the relative system ranks.
Through our analysis, we report the optimal combinations of QPP evaluation
metric and experimental settings that are likely to lead to smaller variations
in the observed results.


title:
Learning Asymmetric Embedding for Attributed Networks via Convolutional
  Neural Network

abstract:
  Recently network embedding has gained increasing attention due to its
advantages in facilitating network computation tasks such as link prediction,
node classification and node clustering. The objective of network embedding is
to represent network nodes in a low-dimensional vector space while retaining as
much information as possible from the original network including structural,
relational, and semantic information. However, asymmetric nature of directed
networks poses many challenges as how to best preserve edge directions in the
embedding process. Here, we propose a novel deep asymmetric attributed network
embedding model based on convolutional graph neural network, called AAGCN. The
main idea is to maximally preserve the asymmetric proximity and asymmetric
similarity of directed attributed networks. AAGCN introduces two neighbourhood
feature aggregation schemes to separately aggregate the features of a node with
the features of its in- and out- neighbours. Then, it learns two embedding
vectors for each node, one source embedding vector and one target embedding
vector. The final representations are the results of concatenating source and
target embedding vectors. We test the performance of AAGCN on three real-world
networks for network reconstruction, link prediction, node classification and
visualization tasks. The experimental results show the superiority of AAGCN
against state-of-the-art embedding methods.


title: Pseudo-finiteness of arbitrary graphs of bounded shrub-depth

abstract:
  We consider classes of arbitrary (finite or infinite) graphs of bounded
shrub-depth, specifically the classes $\mathrm{TM}_r(d)$ of arbitrary graphs
that have tree models of height $d$ and $r$ labels. We show that the graphs of
$\mathrm{TM}_r(d)$ are $\mathrm{MSO}$-pseudo-finite relative to the class
$\mathrm{TM}^{\text{f}}_r(d)$ of finite graphs of $\mathrm{TM}_r(d)$; that is,
that every $\mathrm{MSO}$ sentence true in a graph of $\mathrm{TM}_r(d)$ is
also true in a graph of $\mathrm{TM}^{\text{f}}_r(d)$. We also show that
$\mathrm{TM}_r(d)$ is closed under ultraproducts and ultraroots. These results
have two consequences. The first is that the index of the
$\mathrm{MSO}[m]$-equivalence relation on graphs of $\mathrm{TM}_r(d)$ is
bounded by a $(d+1)$-fold exponential in $m$. The second is that
$\mathrm{TM}_r(d)$ is exactly the class of all graphs that are
$\mathrm{MSO}$-pseudo-finite relative to $\mathrm{TM}^{\text{f}}_r(d)$.


title:
T-fluctuations and dynamics of the resistive transition in thin
  superconducting films

abstract:
  We investigate the temporal and spatial scales of resistance fluctuations
(R-fluctuations) at the superconducting resistive transition in thin epitaxial
TiN films, accessed through voltage fluctuations measurements in current-biased
samples. The measured Lorentzian spectrum of the R-fluctuations identifies
their correlation time, which is shown to coincide with the thermal relaxation
time determined by a combination of the electron-phonon relaxation and the
diffusion in reservoirs. Our data is quantitatively consistent with the model
of spontaneous temperature fluctuations, the T-fluctuations, with a variance
given by the textbook thermodynamic expression. We discuss the experimental
indication that the T-fluctuations contribute to the broadening of the
transition, in spite of vanishing in the limit of infinite sample volume. Our
observations highlight important caveats in the accepted physical picture of
the resistive transition.


title:
Resistance fluctuation spectroscopy of the superconducting transition in
  epitaxial TiN films

abstract:
  We investigate the impact of various fluctuation mechanisms on the
time-averaged resistance and noise performance in superconducting devices based
on epitaxial titanium nitride (TiN) films. We show that a very large noise at
the resistive transition originates from spontaneous resistance (R)
fluctuations. These fluctuations are characterized by a Lorentzian spectrum and
a correlation time equal to that of electron energy relaxation time. The
observed R-fluctuations in turn originate from temperature (T) fluctuations due
to spontaneous energy exchange between the electron system and the thermal
bath. We present a model that describes the broadening of the resistive
transition by T-fluctuations, and agrees well with data for thicker TiN
samples. In thinner films, we propose an additional effect of surface magnetic
disorder that can explain the deviations from the model. Our results point to
new mechanisms that can significantly affect resistive properties of
superconductors.


title: On maximum parallel classes in packings

abstract:
  The integer $\beta (\rho, v, k)$ is defined to be the maximum number of
blocks in any $(v, k)$-packing in which the maximum partial parallel class (or
PPC) has size $\rho$. This problem was introduced and studied by Stinson for
the case $k=3$. Here, we mainly consider the case $k = 4$ and we obtain some
upper bounds and lower bounds on $\beta (\rho, v, 4)$. We also provide some
explicit constructions of $(v,4)$-packings having a maximum PPC of a given size
$\rho$. For small values of $\rho$, the number of blocks of the constructed
packings are very close to the upper bounds on $\beta (\rho, v, 4)$. Some of
our methods are extended to the cases $k > 4$.


title:
Adversarial Fine-tuning for Backdoor Defense: Connecting Backdoor
  Attacks to Adversarial Attacks

abstract:
  Deep neural networks (DNNs) are known to be vulnerable to both backdoor
attacks as well as adversarial attacks. In the literature, these two types of
attacks are commonly treated as distinct problems and solved separately, since
they belong to training-time and inference-time attacks respectively. However,
in this paper we find an intriguing connection between them: for a model
planted with backdoors, we observe that its adversarial examples have similar
behaviors as its triggered samples, i.e., both activate the same subset of DNN
neurons. It indicates that planting a backdoor into a model will significantly
affect the model's adversarial examples. Based on this observations, we design
a new Adversarial Fine-Tuning (AFT) algorithm to defend against backdoor
attacks. We empirically show that, against 5 state-of-the-art backdoor attacks,
our AFT can effectively erase the backdoor triggers without obvious performance
degradation on clean samples and significantly outperforms existing defense
methods.


title: Continuous-variable entanglement dynamics in Lorentzian environment

abstract:
  We address the non-Markovian entanglement dynamics for bimodal continuous
variable quantum systems interacting with two independent structured
reservoirs. We derive an analytical expression for the entanglement of
formation without performing the Markov and the secular approximations. We
observe a variety of qualitative features such as entanglement sudden death,
dynamical generation, and protection for two types of Lorentzian spectral
densities, assuming the two modes initially excited in a twin-beam state. Our
quantitative analysis shows that these cases with different reservoir spectrum,
the environmental temperature and the initial amount of entanglement differ
significantly in these qualitative features.


title:
Time-dependent analytic solutions for water waves above sea of varying
  depths

abstract:
  We investigate a hydrodynamic equation system which - with some approximation
- is capable to describe the tsunami propagation in the open ocean with the
time-dependent self-similar Ansatz. We found analytic solutions how the wave
height and velocity behave in time and space for constant and linear seabed
functions. First we study waves on open water, where the seabed can be
considered relatively constant, sufficiently far from the shore. In the second
part of the study we also consider a seabed which is oblique. Finally, we apply
the most common traveling wave Ansatz and present almost trivial solutions as
well.


title:
Towards Decentralised Cloud Storage with IPFS: Opportunities,
  Challenges, and Future Directions

abstract:
  The InterPlanetary File System (IPFS) is a novel decentralised storage
architecture, which attempts to provide decentralised cloud storage by building
on founding principles of P2P networking and content addressing. IPFS is used
by more than 230k peers per week and serves tens of millions of requests per
day, which makes it an interesting large-scale operational network to study.
While it is used as a building block in several projects and studies, its inner
workings, properties, and implications have only been marginally explored in
research. Thus, we provide an overview of the IPFS design and its core
features, along with the opportunities that it opens as well as the challenges
that it faces because of its properties. Overall, IPFS presents an interesting
set of characteristics and offers lessons which can help building decentralised
systems of the future.


title: Reverse Back Propagation to Make Full Use of Derivative

abstract:
  The development of the back-propagation algorithm represents a landmark in
neural networks. We provide an approach that conducts the back-propagation
again to reverse the traditional back-propagation process to optimize the input
loss at the input end of a neural network for better effects without extra
costs during the inference time. Then we further analyzed its principles and
advantages and disadvantages, reformulated the weight initialization strategy
for our method. And experiments on MNIST, CIFAR10, and CIFAR100 convinced our
approaches could adapt to a larger range of learning rate and learn better than
vanilla back-propagation.


title: Off-Policy Evaluation for Large Action Spaces via Embeddings

abstract:
  Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in
real-world systems, since it enables offline evaluation of new policies using
only historic log data. Unfortunately, when the number of actions is large,
existing OPE estimators -- most of which are based on inverse propensity score
weighting -- degrade severely and can suffer from extreme bias and variance.
This foils the use of OPE in many applications from recommender systems to
language models. To overcome this issue, we propose a new OPE estimator that
leverages marginalized importance weights when action embeddings provide
structure in the action space. We characterize the bias, variance, and mean
squared error of the proposed estimator and analyze the conditions under which
the action embedding provides statistical benefits over conventional
estimators. In addition to the theoretical analysis, we find that the empirical
performance improvement can be substantial, enabling reliable OPE even when
existing estimators collapse due to a large number of actions.


title: Lattice path enumeration for semi-magic squares by Latin rectangles

abstract:
  Similar to how standard Young tableaux represent paths in the Young lattice,
Latin rectangles may be use to enumerate paths in the poset of semi-magic
squares with entries zero or one. The symmetries associated to determinant
preserve this poset, and we completely describe the orbits, covering data, and
maximal chains for squares of size 4, 5, and 6. The last item gives the number
of Latin squares in these cases. To calculate efficiently for size 6, we in
turn identify orbits with certain equivalence classes of hypergraphs.


title:
A Group-Equivariant Autoencoder for Identifying Spontaneously Broken
  Symmetries in the Ising Model

abstract:
  We introduce the group-equivariant autoencoder (GE-autoencoder) -- a novel
deep neural network method that locates phase boundaries in the Ising
universality class by determining which symmetries of the Hamiltonian are
broken at each temperature. The encoder network of the GE-autoencoder models
the order parameter observable associated with the phase transition. The
parameters of the GE-autoencoder are constrained such that the encoder is
invariant to the subgroup of symmetries that never break; this results in a
dramatic reduction in the number of free parameters such that the
GE-autoencoder size is independent of the system size. The loss function of the
GE-autoencoder includes regularization terms that enforce equivariance to the
remaining quotient group of symmetries. We test the GE-autoencoder method on
the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that
the GE-autoencoder (1) accurately determines which symmetries are broken at
each temperature, and (2) estimates the critical temperature with greater
accuracy and time-efficiency than a symmetry-agnostic autoencoder, once
finite-size scaling analysis is taken into account.

categories: cond-mat.stat-mech cond-mat.dis-nn physics.comp-ph stat.ML


title:
Adaptive Control with Guaranteed Transient Behavior and Zero
  Steady-State Error for Systems with Time-Varying Parameters

abstract:
  It is nontrivial to achieve global zero-error regulation for uncertain
nonlinear systems. The underlying problem becomes even more challenging if
mismatched uncertainties and unknown time-varying control gain are involved,
yet certain performance specifications are also pursued. In this work, we
present an adaptive control method, which, without the persistent excitation
(PE) condition, is able to ensure global zero-error regulation with guaranteed
output performance for parametric strict-feedback systems involving fast
time-varying parameters in the feedback path and input path. The development of
our control scheme benefits from generalized t-dependent and x-dependent
functions, a novel coordinate transformation and "congelation of variables"
method. Both theoretical analysis and numerical simulation verify the
effectiveness and benefits of the proposed method.


title: John-Nirenberg inequalities for parabolic BMO

abstract:
  We discuss a parabolic version of the space of functions of bounded mean
oscillation related to a doubly nonlinear parabolic partial differential
equation. Parabolic John-Nirenberg inequalities, which give exponential decay
estimates for the oscillation of a function, are shown in the natural geometry
of the partial differential equation. Chaining arguments are applied to change
the time lag in the parabolic John-Nirenberg inequality. We also show that the
quasihyperbolic boundary condition is a necessary and sufficient condition for
a global parabolic John-Nirenberg inequality. Moreover, we consider
John-Nirenberg inequalities with medians instead of integral averages and show
that this approach gives the same class of functions as the original
definition.


title:
Fragmentation dynamics of diatomic molecules under proton impact:
  Kinetic energy release spectra of CO^{q+} and NO^{q+} (q = 2, 3) molecular
  ions

abstract:
  We report on the fragmentation dynamics of triply charged, diatomic,
molecular ions of NO and CO. Dissociative fragmentation after multiple
ionization of NO and CO is studied under the impact of 200 keV proton beam
using recoil-ion momentum spectrometer. Kinetic Energy Release distributions
(KERDs) for various fragmentation channels were obtained. We have also
calculated the potential energy curves (PECs) for ground and several excited
states of NO^{3+} and CO^{3+} molecular ion. The obtained KERDs are discussed
in the background of the calculated PECs as well as the simple Coulomb
excitation model. Coulomb breakup of the unstable precursor molecular ion shows
a clear preference for the N^{2+} + O^+ (and C^{2+} + O^+) fragmentation
channel.


title:
Hybrid continuum-discrete macro modelling of multi ring masonry arch
  bridges

abstract:
  This paper presents a hybrid continuum discrete macro modelling strategy with
a multiscale calibration procedure for realistic simulations of brick masonry
bridges. The response of these structures is affected by the intrinsic
nonlinearity of the masonry material, which in turn depends upon the mechanical
properties of units and mortar joints and the bond characteristics. Finite
element approaches based upon homogenised representations are widely employed
to assess the nonlinear behaviour up to collapse, as they are generally
associated with a limited computational demand. However, such models require an
accurate calibration of model material parameters to properly allow for masonry
bond. According to the proposed approach, the macroscale material parameters
are determined by an advanced multi objective strategy with genetic algorithms
from the results of mesoscale virtual tests through the minimisation of
appropriate functionals of the scale transition error. The developed continuum
discrete finite element macroscale description and the calibration procedure
are applied to simulate the nonlinear behaviour up to collapse of multi ring
arch bridge specimens focusing on the 2D planar response. The results obtained
are compared to those achieved using detailed mesoscale models confirming the
effectiveness and accuracy of the proposed approach for realistic nonlinear
simulations of masonry arch bridges.


title:
Approximating transmission and reflection spectra near isolated
  nondegenerate resonances

abstract:
  A linear scattering problem for which incoming and outgoing waves are
restricted to a finite number of radiation channels can be precisely described
by a frequency-dependent scattering matrix. The entries of the scattering
matrix, as functions of the frequency, give rise to the transmission and
reflection spectra. To find the scattering matrix rigorously, it is necessary
to solve numerically the partial differential equations governing the relevant
waves. In this paper, we consider resonant structures with an isolated
nondegenerate resonant mode of complex frequency $\omega_\star$, and show that
for real frequencies near $\omega_0 = \mbox{Re}(\omega_\star)$, the
transmission and reflection spectra can be approximated using only the
scattering matrix at $\omega_0$ and information about the resonant mode. We
also present a revised temporal coupled-mode theory that produces the same
approximate formulas for the transmission and reflection spectra. Numerical
examples for diffraction of plane waves by periodic structures are presented to
validate our theory.


title: Single-cell Bayesian deconvolution

abstract:
  Flow cytometry enables monitoring protein abundance and activity at the
single-cell level in a high-throughput manner, through the use of fluorescent
labeling. Given the significant levels of autofluorescence emitted by cells at
the spectral ranges used by this technique, removing the corresponding
background signal is necessary for a correct assessment of cellular
biochemistry. Existing methods of autofluorescence removal usually require
dedicated monitoring resources, such as additional fluorescence channels or
laser sources, which are costly and not universally accessible. Here, we have
developed a computational method that enables autofluorescence subtraction
without requiring dedicated measurement resources. The method uses a
non-parametric Bayesian approach to deconvolve the target signal distribution
from independent measurements of labeled and unlabeled cells readily available
in a typical experiment. The distributions are approximated by mixtures of
gamma functions, and the target distribution is obtained by sampling the
posterior distribution using Markov chain Monte Carlo and nested sampling
approaches. We tested the method systematically using synthetic data, and
validated it using experimental data from mouse embryonic stem cells.

categories: q-bio.QM nlin.AO physics.bio-ph physics.data-an q-bio.CB


title:
Highly Scalable Beaver Triple Generator from Additive-only Homomorphic
  Encryption

abstract:
  In a convolution neural network, a composition of linear scalar product,
non-linear activation function and maximum pooling computations are intensively
invoked. As such, to design and implement privacy-preserving, high efficiency
machine learning mechanisms, one highly demands a practical crypto tool for
secure arithmetic computations. SPDZ, an interesting framework of secure
multi-party computations is a promising technique deployed for industry-scale
machine learning development if one is able to generate Beaver (multiplication)
triple offline efficiently. This paper studies secure yet efficient Beaver
triple generators leveraging privacy-preserving scalar product protocols which
in turn can be constructed from additive-only homomorphic encryptions(AHEs).
Different from the state-of-the-art solutions, where a party first splits her
private input into a shared vector and then invokes an AHE to compute scalar
product of the shared vectors managed by individual MPC server, we formalize
Beaver triple generators in the context of 2-party shared scalar product
protocol and then dispense the generated shares to MPC servers. As such, the
protocol presented in this paper can be viewed as a dual construction of the
state-of-the-art AHE based solutions. Furthermore, instead of applying the
Paillier encryption as a basis of our previous constructions or inheriting from
somewhat homomorphic encryptions, we propose an alternative construction of AHE
from polynomial ring learning with error (RLWE) which results in an efficient
implementation of Beaver triple generators.


title:
Supersonic-subsonic transition region in radiative heat flow via
  self-similar solutions

abstract:
  We study the radiative hydrodynamics flow of Marshak waves in hot dense
plasmas, using approximate self-similar solutions. Specifically, we have
focused on the intermediate regime between pure radiative supersonic flow and
the pure subsonic regime. These two regimes were investigated both using exact
self-similar solutions and numerical simulations, however, most of the study
used numerical simulations, mainly because the radiative heat wave and the
shock regions are not self-similar altogether. In a milestone work [J. Garnier
et al., Phys. Plas., 13, 092703 (2006)], it was found that for a specific power
law dependency temperature profile, a unique exact self-similar solution
exists, that is valid for all physical regimes. In this work we approximate
Garnier's exact solution for a general power-law temperature-dependency, using
simple analytical considerations. This approximate solution yields a good
agreement compared to numerical simulations for the different thermodynamic
profiles within the expected range of validity. In addition, we offer an
approximate solution for the energies absorbed in the matter, again, for a
general power-law temperature profile. Our approximate self-similar solution
for the energy yields very good results comparing to exact numerical
simulations for both gold and $\mathrm{Ta_2O_5}$. We also set a comparison of
our self-similar solutions with the results of an experiment for radiation
temperature measurement in a hohlraum in low-density foams that is addressed
directly, to the intermediate regime, yielding a good agreement and similar
trends. The different models as well as the numerical simulations are powerful
tools to analyze the supersonic-subsonic transition region.


title:
Casimir energy for N superconducting cavities: a model for the YBCO
  (BSCCO)

abstract:
  In this paper we study the Casimir energy of a sample made by N cavities,
with N >> 1, across the transition from the metallic to the superconducting
phase of the constituting plates. After having characterized the energy for the
configuration in which the layers constituting the cavities are made by
dielectric and for the configuration in which the layers are made by plasma
sheets, we concentrate our analysis on the latter. It represents the final step
towards the macroscopic characterization of a "multi cavity" (with N large)
necessary to fully understand the behaviour of the Casimir energy of a YBCO (or
a BSCCO) sample across the transition. Our analysis is especially useful to the
Archimedes experiment, aimed at measuring the interaction of the
electromagnetic vacuum energy with a gravitational field. To this purpose, we
aim at modulating the Casimir energy of a layered structure, the multi cavity,
by inducing a transition from the metallic to the superconducting phase. After
having characterized the Casimir energy of such a structure for both the
metallic and the superconducting phase, we give an estimate of the modulation
of the energy across the transition.


title: New scaling laws for pinning force density in superconductors

abstract:
  Since the report by Fietz and Webb (1968 Phys. Rev. 178 657), who considered
the pinning force density, $|F_p| = |J_c \times B|$ (where $J_c$ is the
critical current density and $B$ is applied magnetic field), in isotropic
superconductors as a unique function of the reduced applied field, $B/B_{c2}$
(where $B_c2$ is the upper critical field), $|F_p|$ has been scaled based on
B/B_c2 ratio, for which there is widely used scaling law of
$|F_p(B)|=F_{p,max}((B/B_{c2})^p)((1-B/B_{c2})^q)$, where $F_{p,max}$,
$B_{c2}$, $p$, and $q$ are free-fitting parameters, proposed by Kramer (1973 J.
Appl. Phys. 44 1360) and Dew-Hughes (1974 Phil. Mag. 30 293). To describe
$|F_p(B)|$ in high-temperature superconductors, Kramer-Dew-Hughes scaling law
was modified by (a) an assumption of the angular dependence of all free-fitting
parameters on the rotation angle and (b) by the replacement of the upper
critical field, $B_{c2}$, by the irreversibility field, $B_{irr}$. Here we note
that the pinning force density is also a function of critical current density
and, thus, $|F_p(J_c)|$ scaling law should exist. In an attempt to reveal this
law, we considered the full $|F_p(B,J_c)|$ function and reported that there are
three distinctive characteristic ranges of $(B/B_{c2}, J_c/(J_c(sf)))$ (where
$J_c(sf)$ is the self-field critical current density) on which $|F_p(B,J_c)|$
can be splatted. Several new scaling laws for $|F_p(J_c)|$ were proposed,
discussed, and applied to scale $|F_p(J_c)|$ in MgB2, NdFeAs(O,F), REBCO, and
near-room temperature superconducting super hydrides (La,Y)H10 and YH6. We
pointed out that the general scaling law for the pinning force density is on
the quest.


title:
Optimal lofted B-spline surface interpolation based on serial closed
  contours

abstract:
  Modern shape design and capture techniques often lead to the geometric data
presented in the form of serial rows of data points. In general, the number of
data points varies from row to row. Lofted or skinned B-spline surface
interpolation is a technique that generates a B-spline surface that passes
through these data points precisely. The traditional process often causes a
large increase in the number of control points of the resulting B-spline
surface. Much of the work to date in mitigating the effects of this increase
has been restricted to open section-curves. The lofting of sequential closed
contours using the interpolation technique has not been addressed in the
existing literature. In this paper, we present two novel conjectures relating
to closed B-spline curve interpolation. We derive the equivalent closed
B-spline interpolation condition of the well-established Schoenberg-Whitney
condition for open B-spline interpolation, a condition that the parameter
values and the domain knots should satisfy to guarantee the system matrix is
always invertible or full-rank. We then apply the interpolation condition to
the problem of lofted B-spline surface interpolation to serial closed contours.
The correctness of these conjectures is validated via numerical results and
several practical experiments. Github repository
https://github.com/ShutaoTang/LBSI-Project


title: Nanostars planarity modulates the elasticity of DNA hydrogels

abstract:
  In analogy with classic rigidity problems of networks and frames, the elastic
properties of hydrogels made of DNA nanostars (DNAns) are expected to strongly
depend on the precise geometry of their building blocks. However, it is
currently not possible to determine DNAns shape experimentally. Computational
coarse-grained models that can retain the correct geometry of DNA nanostars and
account for the bulk properties observed in recent experiments could provide
missing insights. In this study, we perform metadynamics simulations to obtain
the preferred configuration of three-armed DNA nanostars simulated with the
oxDNA model. Based on these results we introduce a coarse-grained computational
model of nanostars that can self assemble into complex three dimensional
percolating networks. We compare two systems with different designs, in which
either planar or non-planar nanostars are used. Structural and network analysis
reveal completely different features for the two cases, leading to two
contrasting elastic properties. The mobility of molecules is larger in the
non-planar case, which is consistent with a lower viscosity measured from
Green-Kubo simulations in equilibrium. To the best of our knowledge, this is
the first work connecting the geometry of DNAns with the bulk rheological
properties of DNA hydrogels and may inform the design of future DNA based
materials.


title:
A Local Area Quantum Teleportation Network Based on an Array of
  Electrically Activated Graphene Waveguide

abstract:
  We present a scheme to generate a continuous variable (CV) multipartite
entangled state using an array of plasmonic graphene waveguides that are
activated by nonclassical driving microwave modes. Within this scheme, we can
exploit the interaction of two light fields coupled to the same microwave mode
in each waveguide to produce any type of multipartite Gaussian entangled state.
A teleportation network is illustrated using the resultant CV multipartite
entangled state. In particular, the proposed setup enables coherent state
teleportation across remotely connected nodes with fidelity above a threshold
limit of 2/3, providing secure quantum teleportation networking even in the
presence of losses.


title:
On Generalisation of Isotropic Central Difference for Higher Order
  Approximation of Fractional Laplacian

abstract:
  The study of generalising the central difference for integer order Laplacian
to fractional order is discussed in this paper. Analysis shows that, in
contrary to the conclusion of a previous study, difference stencils evaluated
through fast Fourier transform prevents the convergence of the solution of
fractional Laplacian. We propose a composite quadrature rule in order to
efficiently evaluate the stencil coefficients with the required convergence
rate in order to guarantee convergence of the solution. Furthermore, we propose
the use of generalised higher order lattice Boltzmann method to generate
stencils which can approximate fractional Laplacian with higher order
convergence speed and error isotropy. We also review the formulation of the
lattice Boltzmann method and discuss the explicit sparse solution formulated
using Smolyak's algorithm, as well as the method for the evaluation of the
Hermite polynomials for efficient generation of the higher order stencils.
Numerical experiments are carried out to verify the error analysis and
formulations.


title:
Detection and parameter estimation challenges of Type-II lensed binary
  black hole signals

abstract:
  Strong lensing can produce three types of images, denoted as Type-I, Type-II
and Type-III corresponding to the minima, saddle and maxima of the time delay
of the lensed images. Type-II images, in particular, receive a non-trivial
phase shift of $\pi/2$. This phase shift can introduce additional distortions
in the strains produced by the Type-II image of the binary black hole signals
depending on the morphology of the signals, e.g., when they have contributions
from higher harmonics, precession, eccentricity etc. The optical depth to the
Type-II images is nearly the same as the strong lensing (from galaxies) and
thus are very likely to be observed in the near future. In this work, we
investigate the potential applicability of these distortions in helping
identify Type-II signals from a single detection, and also the systematic
biases that could arise in the inference of their parameters if they are
unknowingly recovered with unlensed gravitational-wave templates. We show that
at total network signal-to-noise ratio (SNR) $\rho=20(50)$, individual Type-II
images should be identifiable with log Bayes factor $\ln \mathcal{B} > 2$ for
inclinations $ \iota > 5 \pi/12 (\pi/3) $. Furthermore, based on the trends we
observe in these results we predict that, at high SNRs ($\gtrsim 100$),
individual Type-II images would be identifiable even when the inclination angle
is much lower ($\sim \pi/6$). We then show that neglecting physical effects
arising from these identifiable Type-II images can significantly bias estimates
of parameters (such as sky location, distance, inclination, etc.), and
sometimes lead to fully inaccurate measurement of these parameters. Thus, in
the future, using Type-II lensed template for such signals would be necessary.


title:
ET-BERT: A Contextualized Datagram Representation with Pre-training
  Transformers for Encrypted Traffic Classification

abstract:
  Encrypted traffic classification requires discriminative and robust traffic
representation captured from content-invisible and imbalanced traffic data for
accurate classification, which is challenging but indispensable to achieve
network security and network management. The major limitation of existing
solutions is that they highly rely on the deep features, which are overly
dependent on data size and hard to generalize on unseen data. How to leverage
the open-domain unlabeled traffic data to learn representation with strong
generalization ability remains a key challenge. In this paper,we propose a new
traffic representation model called Encrypted Traffic Bidirectional Encoder
Representations from Transformer (ET-BERT), which pre-trains deep
contextualized datagram-level representation from large-scale unlabeled data.
The pre-trained model can be fine-tuned on a small number of task-specific
labeled data and achieves state-of-the-art performance across five encrypted
traffic classification tasks, remarkably pushing the F1 of ISCX-Tor to 99.2%
(4.4% absolute improvement), ISCX-VPN-Service to 98.9% (5.2% absolute
improvement), Cross-Platform (Android) to 92.5% (5.4% absolute improvement),
CSTNET-TLS 1.3 to 97.4% (10.0% absolute improvement). Notably, we provide
explanation of the empirically powerful pre-training model by analyzing the
randomness of ciphers. It gives us insights in understanding the boundary of
classification ability over encrypted traffic. The code is available at:
https://github.com/linwhitehat/ET-BERT.