title: Axion-Like Particle Interpretation of Dark Matter and Muon $(g-2)$

abstract:
  We investigate the dark matter (DM) phenomenology of axion-like particles
(ALP) along with the ALP interpretation of muon $(g-2)$ in a minimal extension
of the Standard Model (SM). Here we have considered flavor dependent ALP
couplings with SM leptons to study the allowed region of parameter space from
the muon $(g-2)$ measurement, taking into account all possible one and two loop
diagrams. We have found that in a certain region of parameter space ALP can act
as a DM, satisfying the constraint from muon $(g-2)$ and other experimental
bounds. From our relic density calculation, we can infer that the ALP can act
as a DM candidate in the mass range between ${55\rm MeV}$ to $100 {\rm MeV}$
while satisfying muon $(g-2)$ observation and other experimental constraints.


title: Fast Multi-grid Methods for Minimizing Curvature Energy

abstract:
  The geometric high-order regularization methods such as mean curvature and
Gaussian curvature, have been intensively studied during the last decades due
to their abilities in preserving geometric properties including image edges,
corners, and image contrast. However, the dilemma between restoration quality
and computational efficiency is an essential roadblock for high-order methods.
In this paper, we propose fast multi-grid algorithms for minimizing both mean
curvature and Gaussian curvature energy functionals without sacrificing the
accuracy for efficiency. Unlike the existing approaches based on operator
splitting and the Augmented Lagrangian method (ALM), no artificial parameters
are introduced in our formulation, which guarantees the robustness of the
proposed algorithm. Meanwhile, we adopt the domain decomposition method to
promote parallel computing and use the fine-to-coarse structure to accelerate
the convergence. Numerical experiments are presented on both image denoising
and CT reconstruction problem to demonstrate the ability to recover image
texture and the efficiency of the proposed method.


title:
A Survey on Efficient Processing of Similarity Queries over Neural
  Embeddings

abstract:
  Similarity query is the family of queries based on some similarity metrics.
Unlike the traditional database queries which are mostly based on value
equality, similarity queries aim to find targets "similar enough to" the given
data objects, depending on some similarity metric, e.g., Euclidean distance,
cosine similarity and so on. To measure the similarity between data objects,
traditional methods normally work on low level or syntax features(e.g., basic
visual features on images or bag-of-word features of text), which makes them
weak to compute the semantic similarities between objects. So for measuring
data similarities semantically, neural embedding is applied. Embedding
techniques work by representing the raw data objects as vectors (so called
"embeddings" or "neural embeddings" since they are mostly generated by neural
network models) that expose the hidden semantics of the raw data, based on
which embeddings do show outstanding effectiveness on capturing data
similarities, making it one of the most widely used and studied techniques in
the state-of-the-art similarity query processing research. But there are still
many open challenges on the efficiency of embedding based similarity query
processing, which are not so well-studied as the effectiveness. In this survey,
we first provide an overview of the "similarity query" and "similarity query
processing" problems. Then we talk about recent approaches on designing the
indexes and operators for highly efficient similarity query processing on top
of embeddings (or more generally, high dimensional data). Finally, we
investigate the specific solutions with and without using embeddings in
selected application domains of similarity queries, including entity resolution
and information retrieval. By comparing the solutions, we show how neural
embeddings benefit those applications.


title: Accelerated MRI With Deep Linear Convolutional Transform Learning

abstract:
  Recent studies show that deep learning (DL) based MRI reconstruction
outperforms conventional methods, such as parallel imaging and compressed
sensing (CS), in multiple applications. Unlike CS that is typically implemented
with pre-determined linear representations for regularization, DL inherently
uses a non-linear representation learned from a large database. Another line of
work uses transform learning (TL) to bridge the gap between these two
approaches by learning linear representations from data. In this work, we
combine ideas from CS, TL and DL reconstructions to learn deep linear
convolutional transforms as part of an algorithm unrolling approach. Using
end-to-end training, our results show that the proposed technique can
reconstruct MR images to a level comparable to DL methods, while supporting
uniform undersampling patterns unlike conventional CS methods. Our proposed
method relies on convex sparse image reconstruction with linear representation
at inference time, which may be beneficial for characterizing robustness,
stability and generalizability.

categories: eess.IV cs.CV cs.LG eess.SP physics.med-ph


title:
StyleT2F: Generating Human Faces from Textual Description Using
  StyleGAN2

abstract:
  AI-driven image generation has improved significantly in recent years.
Generative adversarial networks (GANs), like StyleGAN, are able to generate
high-quality realistic data and have artistic control over the output, as well.
In this work, we present StyleT2F, a method of controlling the output of
StyleGAN2 using text, in order to be able to generate a detailed human face
from textual description. We utilize StyleGAN's latent space to manipulate
different facial features and conditionally sample the required latent code,
which embeds the facial features mentioned in the input text. Our method proves
to capture the required features correctly and shows consistency between the
input text and the output images. Moreover, our method guarantees
disentanglement on manipulating a wide range of facial features that
sufficiently describes a human face.


title:
Josephson-like oscillations in toroidal spinor Bose-Einstein
  condensates: a prospective symmetry probe

abstract:
  Josephson junctions are essential ingredients in the superconducting circuits
used in many existing quantum technologies. Additionally, ultracold atomic
quantum gases have also become essential platforms to study superfluidity.
Here, we explore the analogy between superconductivity and superfluidity to
present an intriguing effect caused by a thin finite barrier in a
quasi-one-dimensional toroidal spinor Bose--Einstein condensate (BEC). In this
system, the atomic current density flowing through the edges of the barrier
oscillates, such as the electrical current through a Josephson junction in a
superconductor, but in our case, there is no current circulation through the
barrier. We also show how the nontrivial broken-symmetry states of spinor BECs
change the structure of this Josephson-like current, creating the possibility
to probe the spinor symmetry, solely using measurements of this superfluid
current.


title: Nonlinear optical radiation of a lithium niobate microcavity

abstract:
  The nonlinear optical radiation of an integrated lithium niobate microcavity
is demonstrated, which has been neglected in previous studies of nonlinear
photonic devices. We find that the nonlinear coupling between confined optical
modes on the chip and continuum modes in free space can be greatly enhanced on
the platform of integrated microcavity, with feasible relaxation of the
phase-matching condition. With an infrared pump laser, we observe the vertical
radiation of second-harmonic wave at the visible band, which indicates a robust
phase-matching-free chip-to-free-space frequency converter and also unveils an
extra energy dissipation channel for integrated devices. Such an unexpected
coherent nonlinear interaction between the free-space beam and the confined
mode is also validated by the different frequency generation. Furthermore,
based on the phase-matching-free nature of the nonlinear radiation, we build an
integrated atomic gas sensor to characterize Rb isotopes with a single telecom
laser. The unveiled mechanism of nonlinear optical radiation is universal for
all dielectric photonic integrated devices, and provides a simple and robust
chip-to-free-space as well as visible-to-telecom interface.


title: In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking

abstract:
  The paper focuses on a classical tracking model, subspace learning, grounded
on the fact that the targets in successive frames are considered to reside in a
low-dimensional subspace or manifold due to the similarity in their
appearances. In recent years, a number of subspace trackers have been proposed
and obtained impressive results. Inspired by the most recent results that the
tracking performance is boosted by the subspace with discrimination capability
learned over the recently localized targets and their immediately surrounding
background, this work aims at solving such a problem: how to learn a robust
low-dimensional subspace to accurately and discriminatively represent these
target and background samples. To this end, a discriminative approach, which
reliably separates the target from its surrounding background, is injected into
the subspace learning by means of joint learning, achieving a
dimension-adaptive subspace with superior discrimination capability. The
proposed approach is extensively evaluated and compared with the
state-of-the-art trackers on four popular tracking benchmarks. The experimental
results demonstrate that the proposed tracker performs competitively against
its counterparts. In particular, it achieves more than 9% performance increase
compared with the state-of-the-art subspace trackers.


title: Optimally Reconfiguring List and Correspondence Colourings

abstract:
  The reconfiguration graph $\mathcal{C}_k(G)$ for the $k$-colourings of a
graph $G$ has a vertex for each proper $k$-colouring of $G$, and two vertices
of $\mathcal{C}_k(G)$ are adjacent precisely when those $k$-colourings differ
on a single vertex of $G$. Much work has focused on bounding the maximum value
of ${\rm{diam}}~\mathcal{C}_k(G)$ over all $n$-vertex graphs $G$. We consider
the analogous problems for list colourings and for correspondence colourings.
We conjecture that if $L$ is a list-assignment for a graph $G$ with $|L(v)|\ge
d(v)+2$ for all $v\in V(G)$, then ${\rm{diam}}~\mathcal{C}_L(G)\le
n(G)+\mu(G)$. We also conjecture that if $(L,H)$ is a correspondence cover for
a graph $G$ with $|L(v)|\ge d(v)+2$ for all $v\in V(G)$, then
${\rm{diam}}~\mathcal{C}_{(L,H)}(G)\le n(G)+\tau(G)$. (Here $\mu(G)$ and
$\tau(G)$ denote the matching number and vertex cover number of $G$.) For every
graph $G$, we give constructions showing that both conjectures are best
possible. Our first main result proves the upper bounds (for the list and
correspondence versions, respectively) ${\rm{diam}}~\mathcal{C}_L(G)\le
n(G)+2\mu(G)$ and ${\rm{diam}}~\mathcal{C}_{(L,H)}(G)\le n(G)+2\tau(G)$. Our
second main result proves that both conjectured bounds hold, whenever all $v$
satisfy $|L(v)|\ge 2d(v)+1$. We also prove more precise results when $G$ is a
tree. We conclude by proving one or both conjectures for various classes of
graphs such as complete bipartite graphs, subcubic graphs, cactuses, and graphs
with bounded maximum average degree.


title:
Kinetic and chemorheological modeling of thermosetting polyurethanes
  obtained from an epoxidized soybean oil polyol crosslinked with glycerin

abstract:
  Thermosetting polyurethanes were obtained using an aromatic isocyanate and a
hydrophobic polyol formulation obtained from epoxidized soybean oil (ESO)
crosslinked with glycerin. A systematic DSC analysis of the effect of catalyst
type, crosslinker concentration, isocyanate index and ESO crystallization on
the cure kinetics was conducted. The combination of a stannic catalyst at 0.2
wt.\% and glycerin at 20 wt.\% produced a cure kinetics governed by
autocatalytic heat flow where vitrification played a key role in the formation
of chemical bonds. The evolution of Tg as a function of conversion, which
followed Di-Benedetto{\'}s predictions, supported the hypothesis that
vitrification was a preponderant phenomenon during cure. The ESO
crystallization had a melting endotherm centered at 35{\deg}C, affecting
substantially the availability of reactive hydroxide groups. Dynamic Mechanical
Analysis (DMA) of a post-cured sample revealed a Tg centered at 220{\deg}C,
whereas quasi-static flexural mechanical tests revealed a flexural modulus of
2.14 GPa and a flexural strength of 99.4 MPa. Rheological experiments using a
rheometer at isothermal conditions supported the hypothesis that vitrification
played a key role in the evolution of apparent viscosity. A master model using
Kim-Macosko equations was obtained for the proposed formulation.


title:
A Modified Nonlinear Conjugate Gradient Algorithm for Functions with
  Non-Lipschitz Gradient

abstract:
  In this paper, we propose a modified nonlinear conjugate gradient (NCG)
method for functions with a non-Lipschitz continuous gradient. First, we
present a new formula for the conjugate coefficient \beta_k in NCG, conducting
a search direction that provides an adequate function decrease. We can derive
that our NCG algorithm guarantees strongly convergent for continuous
differential functions without Lipschitz continuous gradient. Second, we
present a simple interpolation approach that could automatically achieve
shrinkage, generating a step length satisfying the standard Wolfe conditions in
each step. Our framework considerably broadens the applicability of NCG and
preserves the superior numerical performance of the PRP-type methods.


title:
On the Origin of Hallucinations in Conversational Models: Is it the
  Datasets or the Models?

abstract:
  Knowledge-grounded conversational models are known to suffer from producing
factually invalid statements, a phenomenon commonly called hallucination. In
this work, we investigate the underlying causes of this phenomenon: is
hallucination due to the training data, or to the models? We conduct a
comprehensive human study on both existing knowledge-grounded conversational
benchmarks and several state-of-the-art models. Our study reveals that the
standard benchmarks consist of >60% hallucinated responses, leading to models
that not only hallucinate but even amplify hallucinations. Our findings raise
important questions on the quality of existing datasets and models trained
using them. We make our annotations publicly available for future research.


title:
Towards Comprehensive Testing on the Robustness of Cooperative
  Multi-agent Reinforcement Learning

abstract:
  While deep neural networks (DNNs) have strengthened the performance of
cooperative multi-agent reinforcement learning (c-MARL), the agent policy can
be easily perturbed by adversarial examples. Considering the safety critical
applications of c-MARL, such as traffic management, power management and
unmanned aerial vehicle control, it is crucial to test the robustness of c-MARL
algorithm before it was deployed in reality. Existing adversarial attacks for
MARL could be used for testing, but is limited to one robustness aspects (e.g.,
reward, state, action), while c-MARL model could be attacked from any aspect.
To overcome the challenge, we propose MARLSafe, the first robustness testing
framework for c-MARL algorithms. First, motivated by Markov Decision Process
(MDP), MARLSafe consider the robustness of c-MARL algorithms comprehensively
from three aspects, namely state robustness, action robustness and reward
robustness. Any c-MARL algorithm must simultaneously satisfy these robustness
aspects to be considered secure. Second, due to the scarceness of c-MARL
attack, we propose c-MARL attacks as robustness testing algorithms from
multiple aspects. Experiments on \textit{SMAC} environment reveals that many
state-of-the-art c-MARL algorithms are of low robustness in all aspect,
pointing out the urgent need to test and enhance robustness of c-MARL
algorithms.


title: Precise Indoor Positioning Based on UWB and Deep Learning

abstract:
  We examined UWB-based indoor location in conjunction with a fingerprint
technique in this work. We built a connection between the measured and real
distances for the UWB indoor positioning system. This connection is used to
produce a distance database that may be used to generate fringerprints. We
created a BP neural network to classify the target node to the relevant
fringerpint using the distance database. Our suggested deep learning technology
considerably enhances location accuracy when compared to existing trilateration
systems.


title:
Flowfield prediction of airfoil off-design conditions based on a
  modified variational autoencoder

abstract:
  Airfoil aerodynamic optimization based on single-point design may lead to
poor off-design behaviors. Multipoint optimization that considers the
off-design flow conditions is usually applied to improve the robustness and
expand the flight envelope. Many deep learning models have been utilized for
the rapid prediction or reconstruction of flowfields. However, the flowfield
reconstruction accuracy may be insufficient for cruise efficiency optimization,
and the model generalization ability is also questionable when facing airfoils
different from the airfoils with which the model has been trained. Because a
computational fluid dynamic evaluation of the cruise condition is usually
necessary and affordable in industrial design, a novel deep learning framework
is proposed to utilize the cruise flowfield as a prior reference for the
off-design condition prediction. A prior variational autoencoder is developed
to extract features from the cruise flowfield and to generate new flowfields
under other free stream conditions. Physical-based loss functions based on
aerodynamic force and conservation of mass are derived to minimize the
prediction error of the flowfield reconstruction. The results demonstrate that
the proposed model can reduce the prediction error on test airfoils by 30%
compared to traditional models. The physical-based loss function can further
reduce the prediction error by 4%. The proposed model illustrates a better
balance of the time cost and the fidelity requirements of evaluation for cruise
and off-design conditions, which makes the model more feasible for industrial
applications.


title:
Causal Intervention for Subject-Deconfounded Facial Action Unit
  Recognition

abstract:
  Subject-invariant facial action unit (AU) recognition remains challenging for
the reason that the data distribution varies among subjects. In this paper, we
propose a causal inference framework for subject-invariant facial action unit
recognition. To illustrate the causal effect existing in AU recognition task,
we formulate the causalities among facial images, subjects, latent AU semantic
relations, and estimated AU occurrence probabilities via a structural causal
model. By constructing such a causal diagram, we clarify the causal effect
among variables and propose a plug-in causal intervention module, CIS, to
deconfound the confounder \emph{Subject} in the causal diagram. Extensive
experiments conducted on two commonly used AU benchmark datasets, BP4D and
DISFA, show the effectiveness of our CIS, and the model with CIS inserted,
CISNet, has achieved state-of-the-art performance.


title: Robust Task Planning for Assembly Lines with Human-Robot Collaboration

abstract:
  Efficient and robust task planning for a human-robot collaboration (HRC)
system remains challenging. The human-aware task planner needs to assign jobs
to both robots and human workers so that they can work collaboratively to
achieve better time efficiency. However, the complexity of the tasks and the
stochastic nature of the human collaborators bring challenges to such task
planning. To reduce the complexity of the planning problem, we utilize the
hierarchical task model, which explicitly captures the sequential and parallel
relationships of the task. We model human movements with the sigma-lognormal
functions to account for human-induced uncertainties. A human action model
adaptation scheme is applied during run-time, and it provides a measure for
modeling the human-induced uncertainties. We propose a sampling-based method to
estimate human job completion time uncertainties. Next, we propose a robust
task planner, which formulates the planning problem as a robust optimization
problem by considering the task structure and the uncertainties. We conduct
simulations of a robot arm collaborating with a human worker in an electronics
assembly setting. The results show that our proposed planner can reduce task
completion time when human-induced uncertainties occur compared to the baseline
planner.


title: Unsupervised Cross-Task Generalization via Retrieval Augmentation

abstract:
  Humans can perform unseen tasks by recalling relevant skills that are
acquired previously and then generalizing them to the target tasks, even if
there is no supervision at all. In this paper, we aim to improve such
cross-task generalization ability of massive multi-task language models such as
T0 (Sanh et al., 2021) in an unsupervised setting. We propose a
retrieval-augmentation method named ReCross that takes a few unlabelled
examples as queries to retrieve a small subset of upstream data and uses them
to update the multi-task model for better generalization. Our empirical results
show that the proposed ReCross consistently outperforms non-retrieval baselines
by a significant margin.


title: Bott-Chern hypercohomology and bimeromorphic invariants

abstract:
  The aim of this article is to study the geometry of Bott-Chern
hypercohomology from the bimeromorphic point of view. We construct some new
bimeromorphic invariants involving the cohomology for the sheaf of germs of
pluriharmonic functions, the truncated holomorphic de Rham cohomology, and the
de Rham cohomology. To define these invariants, using a sheaf-theoretic
approach, we establish a blow-up formula together with a canonical morphism for
the Bott-Chern hypercohomology. In particular, we compute the invariants of
some compact complex threefolds, such as Iwasawa manifolds and quintic
threefolds.


title:
Long-Horizon Motion Planning via Sampling and Segmented Trajectory
  Optimization

abstract:
  This paper presents a hybrid robot motion planner that generates long-horizon
motion plans for robot navigation in environments with obstacles. We propose a
hybrid planner, RRT* with segmented trajectory optimization (RRT*-sOpt), which
combines the merits of sampling-based planning, optimization-based planning,
and trajectory splitting to quickly plan for a collision-free and
dynamically-feasible motion plan. When generating a plan, the RRT* layer
quickly samples a semi-optimal path and sets it as an initial reference path.
Then, the sOpt layer splits the reference path and performs optimization on
each segment. It then splits the new trajectory again and repeats the process
until the whole trajectory converges. We also propose to reduce the number of
segments before convergence with the aim of further reducing computation time.
Simulation results show that RRT*-sOpt benefits from the hybrid structure with
trajectory splitting and performs robustly in various robot platforms and
scenarios.


title:
WhyGen: Explaining ML-powered Code Generation by Referring to Training
  Examples

abstract:
  Deep learning has demonstrated great abilities in various code generation
tasks. However, despite the great convenience for some developers, many are
concerned that the code generators may recite or closely mimic copyrighted
training data without user awareness, leading to legal and ethical concerns. To
ease this problem, we introduce a tool, named WhyGen, to explain the generated
code by referring to training examples. Specifically, we first introduce a data
structure, named inference fingerprint, to represent the decision process of
the model when generating a prediction. The fingerprints of all training
examples are collected offline and saved to a database. When the model is used
at runtime for code generation, the most relevant training examples can be
retrieved by querying the fingerprint database. Our experiments have shown that
WhyGen is able to precisely notify the users about possible recitations and
highly similar imitations with a top-10 accuracy of 81.21%. The demo video can
be found at https://youtu.be/EtoQP6850To.


title: Spaces of Real Functions, Covers and Dense Subsets

abstract:
  In paper we study relationships between covering properties of a topological
space $X$ and the space $(USC^*(X),\tau_{\mathcal{B}})$ of bounded upper
semicontinuous functions on $X$ with the topology $\tau_{\mathcal{B}}$ defined
by the bornology $\mathcal{B}$ on $X$. We present characterizations of various
local properties of $(USC^*(X),\tau_{\mathcal{B}})$ in terms of selection
principles related to bornological covers of $X$. We also investigate
topological properties of sequences of upper dense and upper sequentially dense
subsets of $(USC^*(X),\tau_{\mathcal{B}})$.


title: Wound Severity Classification using Deep Neural Network

abstract:
  The classification of wound severity is a critical step in wound diagnosis.
An effective classifier can help wound professionals categorize wound
conditions more quickly and affordably, allowing them to choose the best
treatment option. This study used wound photos to construct a deep neural
network-based wound severity classifier that classified them into one of three
classes: green, yellow, or red. The green class denotes wounds still in the
early stages of healing and are most likely to recover with adequate care.
Wounds in the yellow category require more attention and treatment than those
in the green category. Finally, the red class denotes the most severe wounds
that require prompt attention and treatment. A dataset containing different
types of wound images is designed with the help of wound specialists. Nine deep
learning models are used with applying the concept of transfer learning.
Several stacked models are also developed by concatenating these transfer
learning models. The maximum accuracy achieved on multi-class classification is
68.49%. In addition, we achieved 78.79%, 81.40%, and 77.57% accuracies on green
vs. yellow, green vs. red, and yellow vs. red classifications for binary
classifications.


title:
Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing
  for Vehicle Re-Identification

abstract:
  In this paper, we propose a Global-Supervised Contrastive loss and a
view-aware-based post-processing (VABPP) method for the field of vehicle
re-identification. The traditional supervised contrastive loss calculates the
distances of features within the batch, so it has the local attribute. While
the proposed Global-Supervised Contrastive loss has new properties and has good
global attributes, the positive and negative features of each anchor in the
training process come from the entire training set. The proposed VABPP method
is the first time that the view-aware-based method is used as a post-processing
method in the field of vehicle re-identification. The advantages of VABPP are
that, first, it is only used during testing and does not affect the training
process. Second, as a post-processing method, it can be easily integrated into
other trained re-id models. We directly apply the view-pair distance scaling
coefficient matrix calculated by the model trained in this paper to another
trained re-id model, and the VABPP method greatly improves its performance,
which verifies the feasibility of the VABPP method.


title: Impact of lattice $s(x)-\bar{s}(x)$ data in the CTEQ-TEA global analysis

abstract:
  We study the impact of the lattice data on the determination of the
strangeness asymmetry distribution $s_-(x) \equiv s(x) - {\bar s}(x)$ in the
general CTEQ-TEA global analysis of parton distribution functions (PDFs) of the
proton. Firstly, we find that allowing a non-vanishing $s_-(x)$, at the initial
$Q_0=1.3$ GeV scale, in a global PDF analysis leads to the quality of the
CT18As fit similar to CT18A. Secondly, including the lattice data in the
CT18As\_Lat fit greatly reduces the $s_-$-PDF error band size in the large $x$
region. To further reduce its error would require more precise lattice data,
extended to smaller $x$ values.


title: DR-GAN: Distribution Regularization for Text-to-Image Generation

abstract:
  This paper presents a new Text-to-Image generation model, named Distribution
Regularization Generative Adversarial Network (DR-GAN), to generate images from
text descriptions from improved distribution learning. In DR-GAN, we introduce
two novel modules: a Semantic Disentangling Module (SDM) and a Distribution
Normalization Module (DNM). SDM combines the spatial self-attention mechanism
and a new Semantic Disentangling Loss (SDL) to help the generator distill key
semantic information for the image generation. DNM uses a Variational
Auto-Encoder (VAE) to normalize and denoise the image latent distribution,
which can help the discriminator better distinguish synthesized images from
real images. DNM also adopts a Distribution Adversarial Loss (DAL) to guide the
generator to align with normalized real image distributions in the latent
space. Extensive experiments on two public datasets demonstrated that our
DR-GAN achieved a competitive performance in the Text-to-Image task.


title:
Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation
  and Seat Belt Segmentation

abstract:
  Recently, along with interest in autonomous vehicles, the importance of
monitoring systems for both drivers and passengers inside vehicles has been
increasing. This paper proposes a novel in-vehicle monitoring system the
combines 3D pose estimation, seat-belt segmentation, and seat-belt status
classification networks. Our system outputs various information necessary for
monitoring by accurately considering the data characteristics of the in-vehicle
environment. Specifically, the proposed 3D pose estimation directly estimates
the absolute coordinates of keypoints for a driver and passengers, and the
proposed seat-belt segmentation is implemented by applying a structure based on
the feature pyramid. In addition, we propose a classification task to
distinguish between normal and abnormal states of wearing a seat belt using
results that combine 3D pose estimation with seat-belt segmentation. These
tasks can be learned simultaneously and operate in real-time. Our method was
evaluated on a private dataset we newly created and annotated. The experimental
results show that our method has significantly high performance that can be
applied directly to real in-vehicle monitoring systems.


title: Four-operator splitting algorithms for solving monotone inclusions

abstract:
  Monotone inclusions involving the sum of three maximally monotone operators
or more have received much attention in recent years. In this paper, we propose
three splitting algorithms for finding a zero of the sum of four monotone
operators, which are two maximally monotone operators, one monotone Lipschitz
operator, and one cocoercive operator. These three splitting algorithms are
based on the forward-reflected-Douglas-Rachford splitting algorithm,
backward-forward-reflected-backward splitting algorithm, and
backward-reflected-forward-backward splitting algorithm, respectively. As
applications, we apply the proposed algorithms to solve the monotone inclusions
problem involving a finite sum of maximally monotone operators. Numerical
results on the Projection on Minkowski sums of convex sets demonstrate the
effectiveness of the proposed algorithms.


title:
Massive Trajectory Matching and Construction from Aerial Videos based on
  Frame-by-Frame Vehicle Detections

abstract:
  Vehicle trajectory data provides critical information for traffic flow
modeling and analysis. Unmanned aerial vehicles (UAV) is an emerging technology
for traffic data collection because of its flexibility and diversity on spatial
and temporal coverage. Vehicle trajectories are constructed from frame-by-frame
detections. The increase of vehicle counts makes multiple-target matching more
challenging. Errors are caused by pixel jitter, vehicle shadows, road marks as
well as some missing detections. This research proposes a novel framework for
construction of massive vehicle trajectories from aerial videos by matching
vehicle detections based on traffic flow dynamic features. The You Look Only
Once (YOLO) v4 is used for vehicle detection in UAV videos based on Convolution
Neural Network (CNN). Trajectory construction is proposed in detected bounding
boxes with trajectory identification, integrity enhancement, and coordinate
transformation from image coordinates to the Frenet coordinates. The raw
trajectory obtained is then denoised by the ensemble empirical mode
decomposition (EEMD). Our framework is tested on two aerial videos taken by a
UAV on city expressway covering congested and free-flow traffic conditions. The
results show that the proposed framework achieves a Recall of 93.00% and
86.69%, and a Precision of 98.86% and 98.83% for vehicle trajectories in the
free-flow and congested traffic conditions.The trajectory processing speed is
about 30s per track.


title:
On best uniform approximation of finite sets by linear combinations of
  real valued functions using linear programming

abstract:
  We study the best approximation problem: \[ \displaystyle \min_{\alpha\in
\mathbb R^m}\max_{1\leq i\leq n}\left|y_i -\sum_{j=1}^m \alpha_j \Gamma_j ({\bf
x}_i) \right|. \] Here: $\Gamma:=\left\{\Gamma_1,...,\Gamma_m\right\}$ is a
list of functions where for each $1\leq j\leq m$, $\Gamma_j:\Delta \rightarrow
\mathbb R$ with $\Delta$ a set of evaluation points $\left\{{\bf x_1},...,{\bf
x_n}\right\}$. $\left\{y_1,...,y_n\right\}$ is a set of real values and
$\mathbb R^m:=\left\{(\alpha_1,...,\alpha_m),\, \alpha_j\in \mathbb R,\, 1\leq
j\leq m\right\}$.


title:
Li-Yorke and Devaney chaotic uniform dynamical systems amongst weighted
  shifts

abstract:
  In this paper, for finite discrete field $F$, nonempty set $\Gamma$, weight
vector $\mathfrak{w}=({\mathfrak w}_\alpha)_{\alpha\in\Gamma}\in F^\Gamma$ and
weighted generalized shift $\sigma_{\varphi,{\mathfrak w}}:F^\Gamma\to
F^\Gamma$, we find necessary and sufficient conditions for uniform dynamical
system $(F^\Gamma,\sigma_{\varphi,{\mathfrak w}})$ to be Li--Yorke chaotic.
Next we find necessary and sufficient conditions for
$(F^\Gamma,\sigma_{\varphi,{\mathfrak w}})$ to be Devaney chaotic.


title: Aggregation of self-propelled particles with sensitivity to local order

abstract:
  We study a system of self-propelled particles (SPPs) in which individual
particles are allowed to switch between a fast aligning and a slow nonaligning
state depending upon the degree of the alignment in the neighborhood. The
switching is modeled using a threshold for the local order parameter. This
additional attribute gives rise to a mixed phase, in contrast to the ordered
phases found in clean SPP systems. As the threshold is increased from zero, we
find the sudden appearance of clusters of nonaligners. Clusters of nonaligners
coexist with moving clusters of aligners with continual coalescence and
fragmentation. The behavior of the system with respect to the clustering of
nonaligners appears to be very different for values of low and high global
densities. In the low density regime, for an optimal value of the threshold,
the largest cluster of nonaligners grows in size up to a maximum that varies
logarithmically with the total number of particles. However, on further
increasing the threshold the size decreases. In contrast, for the high density
regime, an initial abrupt rise is followed by the appearance of a giant cluster
of nonaligners. The latter growth can be characterized as a continuous
percolation transition. In addition, we find that the speed differences between
aligners and nonaligners is necessary for the segregation of aligners and
nonaligners.


title:
Strong convergence of propagation of chaos for McKean-Vlasov SDEs with
  singular interactions

abstract:
  In this work we show the strong convergence of propagation of chaos for the
particle approximation of McKean-Vlasov SDEs with singular $L^p$-interactions
as well as the moderate interaction particle system in the level of particle
trajectories. One of the main obstacles is to establish the strong
well-posedness of particle system with singular interaction. In particular, we
develop the theory of strong well-posedness of Krylov and R\"ockner
\cite{Kr-Ro} in the case of mixed $L^\bbp$-drifts, where the heat kernel
estimates play a crucial role. Moreover, when the interaction kernel is bounded
measurable, we also obtain the optimal rate of strong convergence, which is
partially based on Jabin and Wang's entropy method \cite{JW16} and Zvonkin's
transformation.


title: Learning with Signatures

abstract:
  In this work we investigate the use of the Signature Transform in the context
of Learning. Under this assumption, we advance a supervised framework that
provides state-of-the-art classification accuracy with the use of very few
labels without the need of credit assignment and with minimal or no
overfitting. We leverage tools from harmonic analysis by the use of the
signature and log-signature, and use as a score function RMSE and MAE Signature
and log-signature. We develop a closed-form equation to compute probably good
optimal scale factors. Classification is performed at the CPU level orders of
magnitude faster than other methods. We report results on AFHQ, MNIST and
CIFAR10 achieving 100% accuracy on all tasks assuming we can determine at test
time which probably good optimal scale factor to use for each category.


title:
Recurrent neural networks that generalize from examples and optimize by
  dreaming

abstract:
  The gap between the huge volumes of data needed to train artificial neural
networks and the relatively small amount of data needed by their biological
counterparts is a central puzzle in machine learning. Here, inspired by
biological information-processing, we introduce a generalized Hopfield network
where pairwise couplings between neurons are built according to Hebb's
prescription for on-line learning and allow also for (suitably stylized)
off-line sleeping mechanisms. Moreover, in order to retain a learning
framework, here the patterns are not assumed to be available, instead, we let
the network experience solely a dataset made of a sample of noisy examples for
each pattern. We analyze the model by statistical-mechanics tools and we obtain
a quantitative picture of its capabilities as functions of its control
parameters: the resulting network is an associative memory for pattern
recognition that learns from examples on-line, generalizes and optimizes its
storage capacity by off-line sleeping. Remarkably, the sleeping mechanisms
always significantly reduce (up to $\approx 90\%$) the dataset size required to
correctly generalize, further, there are memory loads that are prohibitive to
Hebbian networks without sleeping (no matter the size and quality of the
provided examples), but that are easily handled by the present "rested" neural
networks.


title:
Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment
  Analysis

abstract:
  As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment
Analysis (MABSA) has attracted increasing attention in recent years. However,
previous approaches either (i) use separately pre-trained visual and textual
models, which ignore the crossmodal alignment or (ii) use vision-language
models pre-trained with general pre-training tasks, which are inadequate to
identify finegrained aspects, opinions, and their alignments across modalities.
To tackle these limitations, we propose a task-specific Vision-Language
Pre-training framework for MABSA (VLPMABSA), which is a unified multimodal
encoder-decoder architecture for all the pretraining and downstream tasks. We
further design three types of task-specific pre-training tasks from the
language, vision, and multimodal modalities, respectively. Experimental results
show that our approach generally outperforms the state-of-the-art approaches on
three MABSA subtasks. Further analysis demonstrates the effectiveness of each
pretraining task. The source code is publicly released at
https://github.com/NUSTM/VLP-MABSA.


title:
Incompatibility of observables, channels and instruments in information
  theories

abstract:
  Every theory of information, including classical and quantum, can be studied
in the framework of operational probabilistic theories--where the notion of
test generalizes that of quantum instrument, namely a collection of quantum
operations summing to a channel, and simple rules are given for the composition
of tests in parallel and in sequence. Here we study the notion of compatibility
for tests of an operational probabilistic theory. Following the quantum
literature, we first introduce the notion of strong compatibility, and then we
illustrate its ultimate relaxation, that we deem weak compatibility. It is
shown that the two notions coincide in the case of observation tests--which are
the counterpart of quantum POVMs--while there exist weakly compatible channels
that are not strongly compatible. We prove necessary and sufficient conditions
for a theory to exhibit incompatible tests. We show that a theory admits of
incompatible tests if and only if some information cannot be extracted without
disturbance.


title:
Feasibility study on ground-state cooling and single-phonon readout of
  trapped electrons using hybrid quantum systems

abstract:
  Qubits of long coherence time and fast quantum operations are long-sought
objectives towards the realization of high-fidelity quantum operations and
their applications to the quantum technologies. An electron levitated in a
vacuum by a Paul trap is expected to be a good candidate, for its light mass
and hence the high secular frequency which allows for the faster gate
operations than those in trapped ions. Controlling the motional state of the
trapped electron is a crucial issue, for it mediates an interaction between
electron spins, intrinsic qubits embedded in electrons, and its decoherence
results in degraded fidelity of two-qubit gates. In addition, an efficient
readout of the motional state is important, regarding the possibility of
detecting spin state by using it. Despite of such an importance, how to achieve
the motional ground state and how to efficiently detect it are not reported so
far. Here we propose methods addressing these issues by utilizing hybrid
quantum systems involving electron-superconducting circuit and electron-ion
coupled systems and analyze the feasibility of our schemes. In both systems, we
show that the ground-state cooling and the single-phonon readout of the
motional state of the trapped electron are possible. Our work shed light on the
way to precisely control the motional states of the trapped electrons, that
provides an interesting playground for the development of quantum technologies.


title:
On an interior-exterior nonoverlapping domain decomposition method for
  the Poisson--Boltzmann equation

abstract:
  A nonoverlapping domain decomposition method is studied for the linearized
Poisson--Boltzmann equation, which is essentially an interior-exterior
transmission problem with bounded interior and unbounded exterior. This problem
is different from the classical Schwarz alternating method for bounded
nonoverlapping subdomains well studied by Lions in 1990, and is challenging due
to the existence of unbounded subdomain. To obtain the convergence, a new
concept of interior-exterior Sobolev constant is introduced and a spectral
equivalence of related Dirichlet-to-Neumann operators is established
afterwards. We prove rigorously that the spectral equivalence results in the
convergence of interior-exterior iteration. Some numerical simulations are
provided to investigate the optimal stepping parameter of iteration and to
verify our convergence analysis.


title:
Exploring the potential of neural networks to predict statistics of
  solar wind turbulence

abstract:
  Time series datasets often have missing or corrupted entries, which need to
be ignored in subsequent data analysis. For example, in the context of space
physics, calibration issues, satellite telemetry issues, and unexpected events
can make parts of a time series unusable. Various approaches exist to tackle
this problem, including mean/median imputation, linear interpolation, and
autoregressive modeling. Here we study the utility of artificial neural
networks (ANNs) to predict statistics, particularly second-order structure
functions, of turbulent time series concerning the solar wind. Using a dataset
with artificial gaps, a neural network is trained to predict second-order
structure functions and then tested on an unseen dataset to quantify its
performance. A small feedforward ANN, with only 20 hidden neurons, can predict
the large-scale fluctuation amplitudes better than mean imputation or linear
interpolation when the percentage of missing data is high. Although, they
perform worse than the other methods when it comes to capturing both the shape
and fluctuation amplitude together, their performance is better in a
statistical sense for large fractions of missing data. Caveats regarding their
utility, the optimisation procedure, and potential future improvements are
discussed.


title:
A phase field model combined with genetic algorithm for polycrystalline
  hafnium zirconium oxide ferroelectrics

abstract:
  Ferroelectric hafnium zirconium oxide (HZO) thin films show significant
promise for applications in ferroelectric random-access memory, ferroelectric
field-effect transistors, and ferroelectric tunneling junctions. However, there
are shortcomings in understanding ferroelectric switching, which is crucial in
the operation of these devices. Here a computational model based on phase field
method is developed to simulate the switching behavior of polycrystalline HZO
thin films. Furthermore, we introduce a novel approach to optimize the
effective Landau coefficients describing the free energy of HZO by combining
the phase field model with a genetic algorithm. We validate the model by
accurately simulating switching curves for HZO thin films with different
ferroelectric phase fractions. The simulated domain dynamics during switching
also shows amazing similarity to the available experimental observations. The
present work also provides fundamental insights into enhancing the
ferroelectricity in HZO thin films by controlling grain morphology and
crystalline texture. It can potentially be extended to improve the
ferroelectric properties of other hafnia based thin films.


title:
Higher order log-concavity of the overpartition function and its
  consequences

abstract:
  Let $\bar{p}(n)$ denote the overpartition function. In this paper, we study
the asymptotic higher order $\log$-concavity property of the overpatition
function in a similar framework done by Hou and Zhang for the partition
function. This will enable us to move on further in order to prove
$\log$-concavity of overpartitions, explicitly by studying the asymptotic
expansion of the quotient $\bar{p}(n-1)\bar{p}(n+1)/\bar{p}(n)^2$ upto a
certain order so that one can finally ends up with the phenomena of
$2$-$\log$-concavity and higher order Tur\'{a}n property of $\bar{p}(n)$ by
following a sort of unified approach.


title: An Extendable, Efficient and Effective Transformer-based Object Detector

abstract:
  Transformers have been widely used in numerous vision problems especially for
visual recognition and detection. Detection transformers are the first fully
end-to-end learning systems for object detection, while vision transformers are
the first fully transformer-based architecture for image classification. In
this paper, we integrate Vision and Detection Transformers (ViDT) to construct
an effective and efficient object detector. ViDT introduces a reconfigured
attention module to extend the recent Swin Transformer to be a standalone
object detector, followed by a computationally efficient transformer decoder
that exploits multi-scale features and auxiliary techniques essential to boost
the detection performance without much increase in computational load. In
addition, we extend it to ViDT+ to support joint-task learning for object
detection and instance segmentation. Specifically, we attach an efficient
multi-scale feature fusion layer and utilize two more auxiliary training
losses, IoU-aware loss and token labeling loss. Extensive evaluation results on
the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP
and latency trade-off among existing fully transformer-based object detectors,
and its extended ViDT+ achieves 53.2AP owing to its high scalability for large
models. The source code and trained models are available at
https://github.com/naver-ai/vidt.


title: AFSC: Adaptive Fourier Space Compression for Anomaly Detection

abstract:
  Anomaly Detection (AD) on medical images enables a model to recognize any
type of anomaly pattern without lesion-specific supervised learning. Data
augmentation based methods construct pseudo-healthy images by "pasting" fake
lesions on real healthy ones, and a network is trained to predict healthy
images in a supervised manner. The lesion can be found by difference between
the unhealthy input and pseudo-healthy output. However, using only manually
designed fake lesions fail to approximate to irregular real lesions, hence
limiting the model generalization. We assume by exploring the intrinsic data
property within images, we can distinguish previously unseen lesions from
healthy regions in an unhealthy image. In this study, we propose an Adaptive
Fourier Space Compression (AFSC) module to distill healthy feature for AD. The
compression of both magnitude and phase in frequency domain addresses the hyper
intensity and diverse position of lesions. Experimental results on the BraTS
and MS-SEG datasets demonstrate an AFSC baseline is able to produce promising
detection results, and an AFSC module can be effectively embedded into existing
AD methods.


title:
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive
  Object Detection

abstract:
  Domain adaptive object detection (DAOD) is a promising way to alleviate
performance drop of detectors in new scenes. Albeit great effort made in single
source domain adaptation, a more generalized task with multiple source domains
remains not being well explored, due to knowledge degradation during their
combination. To address this issue, we propose a novel approach, namely
target-relevant knowledge preservation (TRKP), to unsupervised multi-source
DAOD. Specifically, TRKP adopts the teacher-student framework, where the
multi-head teacher network is built to extract knowledge from labeled source
domains and guide the student network to learn detectors in unlabeled target
domain. The teacher network is further equipped with an adversarial
multi-source disentanglement (AMSD) module to preserve source domain-specific
knowledge and simultaneously perform cross-domain alignment. Besides, a
holistic target-relevant mining (HTRM) scheme is developed to re-weight the
source images according to the source-target relevance. By this means, the
teacher network is enforced to capture target-relevant knowledge, thus
benefiting decreasing domain shift when mentoring object detection in the
target domain. Extensive experiments are conducted on various widely used
benchmarks with new state-of-the-art scores reported, highlighting the
effectiveness.


title:
Entropy-based Active Learning for Object Detection with Progressive
  Diversity Constraint

abstract:
  Active learning is a promising alternative to alleviate the issue of high
annotation cost in the computer vision tasks by consciously selecting more
informative samples to label. Active learning for object detection is more
challenging and existing efforts on it are relatively rare. In this paper, we
propose a novel hybrid approach to address this problem, where the
instance-level uncertainty and diversity are jointly considered in a bottom-up
manner. To balance the computational complexity, the proposed approach is
designed as a two-stage procedure. At the first stage, an Entropy-based
Non-Maximum Suppression (ENMS) is presented to estimate the uncertainty of
every image, which performs NMS according to the entropy in the feature space
to remove predictions with redundant information gains. At the second stage, a
diverse prototype (DivProto) strategy is explored to ensure the diversity
across images by progressively converting it into the intra-class and
inter-class diversities of the entropy-based class-specific prototypes.
Extensive experiments are conducted on MS COCO and Pascal VOC, and the proposed
approach achieves state of the art results and significantly outperforms the
other counterparts, highlighting its superiority.


title: MRXCAT-CDTI: A Numerical Cardiac Diffusion Tensor Imaging Phantom

abstract:
  Magnetic Resonance cardiac diffusion tensor imaging (cDTI) and cardiac
intravoxel incoherent motion imaging enables probing of in vivo myofiber
architecture and myocardial perfusion surrogates. To study the impact of
experimental parameters such as resolution, off-resonances and heart-rate
variations, we propose a numerical open-source framework called MRXCAT-CDTI. It
allows simulating diffusion and perfusion contrast for spin-echo (SE) and
stimulated echo acquisition mode (STEAM) cDTI sequences. The Fourier encoder
supports in-plane and/or through-slice off-resonance effects, as well as T2*
effects during single-shot image encoding. Optional lesions are included to
mimic ischemic and infarcted myocardial regions. MRXCAT-CDTI allows assessing
realistic influences on data acquisition, and how these affect the data
encoding process and subsequent data processing. As an example, heart-rate
variations lead to differences in partial saturation and relaxation of
magnetization that end up in errors of 9 to 30% for cDTI angle metrics if not
accounted for. For SE echo-planar cDTI, in-plane off-resonance effects more
adversely affect cDTI metrics compared to through-slice off-resonances. With
this work we propose an open-source MRXCAT-CDTI numerical simulation framework
that offers realistic image encoding effects found in cardiac diffusion and
perfusion data to systematically study influences of data encoding,
reconstruction, and post-processing to promote reproducible research.


title:
A Survey on Energy Optimization Techniques in UAV-Based Cellular
  Networks: From Conventional to Machine Learning Approaches

abstract:
  Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.


title:
Kinematic study of radio-loud CMEs associated with solar flares and DH
  type II radio emissions during solar cycles 23 and 24

abstract:
  We have statistically analyzed 379 radio-loud (RL) CMEs and their associated
flares during the period 1996 - 2019 covering both solar cycles (SC) 23 and 24.
We classified them into two sets of populations based on the observation
period: i) 235 events belong to SC 23 (August 1996 - December 2008) and ii) 144
events belong to SC 24 (January 2009 - December 2019). The average residual
acceleration of RL CMEs in SC 24 (--17.39 $\pm$ 43.51 m s$^{-2}$) is two times
lower than that of the RL CMEs in SC 23 (--8.29 $\pm$ 36.23 m s$^{-2}$), which
means that deceleration of RL CMEs in SC 24 is twice as fast as in SC 23. RL
CMEs of SC 23 (1443 $\pm$ 504 km s$^{-1}$; 13.82 $\pm$ 7.40
\emph{R}$_{\circledcirc}$) reach their peak speed at higher altitudes than RL
CMEs of SC 24 (1920 $\pm$ 649 km s$^{-1}$; 12.51 $\pm$ 7.41
\emph{R}$_{\circledcirc}$).We also observed that the mean apparent widths of RL
CMEs in SC 23 are less than in SC 24which is statistically significant. SC 23
has a lower average CME nose height (3.85 \emph{R}$_{\circledcirc}$) at the
start time of DH type II bursts than that of SC 24 (3.46
\emph{R}$_{\circledcirc}$). The starting frequencies of DH type II bursts
associated with RL CMEs for SC 24 are significantly larger (formed at lower
heights) than that of SC 23. We found that there is a good correlation between
the drift rates and the mid-frequencies of DH type II radio bursts for both the
solar cycles (\emph{R} = 0.80, $\epsilon$ = 1.53). Most of the RL CMEs
kinematics and their associated solar flare properties are found similar for SC
23 and SC 24. We concluded that the reduced total pressure in the heliosphere
for SC 24 enables RL CMEs to expand wider and decelerate faster, resulting in
DH type II radio emissions at lower heights than SC 23.


title:
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation
  of Agricultural Aerial Images

abstract:
  In this paper, we investigate the problem of Semantic Segmentation for
agricultural aerial imagery. We observe that the existing methods used for this
task are designed without considering two characteristics of the aerial data:
(i) the top-down perspective implies that the model cannot rely on a fixed
semantic structure of the scene, because the same scene may be experienced with
different rotations of the sensor; (ii) there can be a strong imbalance in the
distribution of semantic classes because the relevant objects of the scene may
appear at extremely different scales (e.g., a field of crops and a small
vehicle). We propose a solution to these problems based on two ideas: (i) we
use together a set of suitable augmentation and a consistency loss to guide the
model to learn semantic representations that are invariant to the photometric
and geometric shifts typical of the top-down perspective (Augmentation
Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the
training images based on a measure of pixel-wise distribution of classes and
actual network confidence. With an extensive set of experiments conducted on
the Agriculture-Vision dataset, we demonstrate that our proposed strategies
improve the performance of the current state-of-the-art method.


title: A toy model for the W/Z mass ratio

abstract:
  The recently reported $7\sigma$ anomaly in measurements of the W/Z mass
ratio, if confirmed, will demand an extension of some kind to the standard
model of particle physics. In this paper I consider whether some recently
proposed models are capable, in principle, of resolving this anomaly, subject
to experimental confirmation.


title: On strong avoiding games

abstract:
  Given an increasing graph property $\cal F$, the strong Avoider-Avoider $\cal
F$ game is played on the edge set of a complete graph. Two players, Red and
Blue, take turns in claiming previously unclaimed edges with Red going first,
and the player whose graph possesses $\cal F$ first loses the game. If the
property $\cal F$ is "containing a fixed graph $H$", we refer to the game as
the $H$ game.
  We prove that Blue has a winning strategy in two strong Avoider-Avoider
games, $P_4$ game and ${\cal CC}_{>3}$ game, where ${\cal CC}_{>3}$ is the
property of having at least one connected component on more than three
vertices.
  We also study a variant, the strong CAvoider-CAvoider games, with additional
requirement that the graph of each of the players must stay connected
throughout the game. We prove that Blue has a winning strategy in the strong
CAvoider-CAvoider games $S_3$ and $P_4$, as well as in the $Cycle$ game, where
the players aim at avoiding all cycles.


title:
An index upper bound for non-orientable minimal surfaces in the
  $n-$dimensional Euclidean space

abstract:
  In the present paper we obtain an upper bound on the Morse index of a
complete (possibly branched) immersed non-orientable minimal surface in the
$n-$dimensional Euclidean space. It is an analog of the upper bound of Ejiri
and Micallef for orientable surfaces. The obtained upper bound enables us to
compute the index of the Alarc\'on-Forstneri\v{c}-L\'opez M\"obius band in the
4-dimensional Euclidean space. According to our computation it is equal to one.
In Appendix we discuss inequalities on the index of a closed non-orientable
minimal surface in a general Riemannian manifold.


title:
Detection of frequency-dependent dispersion measure toward the
  millisecond pulsar PSR J2241-5236 from contemporaneous wide-band observations

abstract:
  Making precise measurements of pulsar dispersion measures (DMs) and applying
suitable corrections for them is amongst the major challenges in high-precision
timing programmes such as pulsar timing arrays (PTAs). While the advent of
wide-band pulsar instrumentation can enable more precise DM measurements and
thence improved timing precision, it also necessitates doing careful
assessments of frequency-dependent (chromatic) DMs that was theorised by Cordes
et al. (2016). Here we report the detection of such an effect in broadband
observations of the millisecond pulsar PSR J2241-5236, a high-priority target
for current and future PTAs. The observations were made contemporaneously using
the wide-band receivers and capabilities now available at the Murchison
Widefield Array (MWA), the upgraded Giant Metrewave Radio Telescope (uGMRT) and
the Parkes telescopes, thus providing an unprecedentedly large frequency
coverage from 80 MHz to 4 GHz. Our analysis shows the measurable changes in DM
that scale with the observing frequency ($\nu$) as $\rm \delta DM \sim \nu^{2.5
\pm 0.1}$. We discuss the potential implications of such a frequency dependence
in the measured DMs, and the likely impact on the timing noise budget, and
comment on the usefulness of low-frequency observations in advancing PTA
efforts.


title: Anomaly Detection in Autonomous Driving: A Survey

abstract:
  Nowadays, there are outstanding strides towards a future with autonomous
vehicles on our roads. While the perception of autonomous vehicles performs
well under closed-set conditions, they still struggle to handle the unexpected.
This survey provides an extensive overview of anomaly detection techniques
based on camera, lidar, radar, multimodal and abstract object level data. We
provide a systematization including detection approach, corner case level,
ability for an online application, and further attributes. We outline the
state-of-the-art and point out current research gaps.


title:
Field-angle dependence reveals odd-parity superconductivity in
  CeRh$_2$As$_2$

abstract:
  CeRh$_2$As$_2$ is an unconventional superconductor with multiple
superconducting phases and $T_\mathrm{c} = 0.26$ K. When $H\parallel c$, it
shows a field-induced transition at $\mu_0H^* = 4$ T from a low-field
superconducting state SC1 to a high-field state SC2 with a large critical field
of $\mu_0H_\mathrm{c2} = 14$ T. In contrast, for $H\perp c$, only the SC1 with
$\mu_0H_\mathrm{c2} = 2$ T is observed. A simple model based on the crystal
symmetry was able to reproduce the phase-diagrams and their anisotropy,
identifying SC1 and SC2 with even and odd parity superconducting states,
respectively. However, additional orders were observed in the normal state
which might have an influence on the change of the superconducting state at
$H^*$. Here, we present a comprehensive study of the angle dependence of the
upper critical fields using magnetic ac-susceptibility, specific heat and
torque on single crystals of CeRh$_2$As$_2$. The experiments show that the
state SC2 is strongly suppressed when rotating the magnetic field away from the
$c$ axis and it disappears for an angle of 35$^{\circ}$. This behavior agrees
perfectly with our extended model of a pseudospin triplet state with $\vec{d}$
vector in the plane and hence allows to nail down that SC2 is indeed the
suggested odd-parity state.


title:
The expected values, variances and limiting distributions of Gutman
  index, Schultz index, multiplicative degree-Kirchhoff index and additive
  degree-Kirchhoff index for a class of random chain networks

abstract:
  There has been an upsurge of research on complex networks in recent years.
The purpose of this paper is to study the mathematical properties of the random
chain networks PGn with the help of graph theory. We first solve the expected
value expressions of the Gutman index, Schultz index, multiplicative
degree-Kirchhoff index and additive degree-Kirchhoff index, and then we get the
explicit expression formulas of their variances. Finally, we find that their
limiting distributions all have the probabilistic and statistical significance
of normal distribution.


title: Multiplicities for Strongly Tempered Spherical Varieties

abstract:
  In this paper, we study the local multiplicity of 10 strongly tempered
spherical varieties. We will formulate a uniform epsilon dichotomy conjecture
for all these models regarding the unique distinguished element in tempered
$L$-packets. Then we will prove this conjecture in many cases, including all
the Archimedean cases.


title:
Quasi-static magnetic compression of field-reversed configuration
  plasma: Amended scalings and limits from two-dimensional MHD equilibrium

abstract:
  In this work, several key scaling laws of the quasi-static magnetic
compression of field reversed configuration (FRC) plasma [Spencer, Tuszewski,
and Linford, 1983] are amended from a series of 2D FRC MHD equilibriums
numerically obtained using the Grad-Shafranov equation solver NIMEQ. Based on
the new scaling for the elongation and the magnetic fields at the separatrix
and the wall, the empirically stable limits for the compression ratio, the
fusion gain, and the neutron yield are evaluated, which may serve as a more
accurate estimate for the upper ceiling of performance from the magnetic
compression of FRC plasma as a potential fusion energy as well as neutron
source devices.


title: Confinement-driven inverse domain scaling in polycrystalline ErMnO3

abstract:
  The research on topological phenomena in ferroelectric materials has
revolutionized the way we understand polar order. Intriguing examples are polar
skyrmions, vortex/anti-vortex structures and ferroelectric incommensurabilties,
which promote emergent physical properties ranging from
electric-field-controllable chirality to negative capacitance effects. Here, we
study the impact of topologically protected vortices on the domain formation in
improper ferroelectric ErMnO3 polycrystals, demonstrating inverted domain
scaling behavior compared to classical ferroelectrics. We observe that as the
grain size increases, smaller domains are formed, which we relate to the
interaction of the topological vortices with local strain fields. The inversion
of the domain scaling behavior has far-reaching implications, providing
fundamentally new opportunities for topology-based domain engineering and the
tuning of the electromechanical and dielectric performance of ferroelectrics in
general.


title:
Does Recommend-Revise Produce Reliable Annotations? An Analysis on
  Missing Instances in DocRED

abstract:
  DocRED is a widely used dataset for document-level relation extraction. In
the large-scale annotation, a \textit{recommend-revise} scheme is adopted to
reduce the workload. Within this scheme, annotators are provided with candidate
relation instances from distant supervision, and they then manually supplement
and remove relational facts based on the recommendations. However, when
comparing DocRED with a subset relabeled from scratch, we find that this scheme
results in a considerable amount of false negative samples and an obvious bias
towards popular entities and relations. Furthermore, we observe that the models
trained on DocRED have low recall on our relabeled dataset and inherit the same
bias in the training data. Through the analysis of annotators' behaviors, we
figure out the underlying reason for the problems above: the scheme actually
discourages annotators from supplementing adequate instances in the revision
phase. We appeal to future research to take into consideration the issues with
the recommend-revise scheme when designing new models and annotation schemes.
The relabeled dataset is released at
\url{https://github.com/AndrewZhe/Revisit-DocRED}, to serve as a more reliable
test set of document RE models.


title: Geometrical scaling of heavy-quark contributions in the low $x$ region

abstract:
  We describe the determination of the heavy quarks structure functions
$F_{2,L}^{\mathcal{Q}\bar{\mathcal{Q}}}$ with help of the scaling properties.
We observe that the structure functions for inclusive charm and bottom
production exhibits geometric scaling at low $x$. The geometrical scaling means
that the heavy quark structure function is a function of only one dimensionless
variable $\tau{\equiv}Q^{2}/Q^{2}_{sat}(x)$ including quark mass. These results
are valid for any value of $\delta$, being $x^{-\delta}$ the behavior of the
parton densities at low $x$. The determination of the heavy quark structure
function is presented as a parameterization of the proton structure function
$F_{2}(x,Q^{2})$ and its derivative. Analytical expressions for
$F_{2,L}^{\mathcal{Q}\bar{\mathcal{Q}}}$ in terms of the effective parameters
of the parameterization of $F_{2}(x,Q^{2})$ ,with respect to the BDH and ASW
models, are presented. To study the heavy quark production processes, we use
the collinear results in DAS approach. Numerical calculations and comparison
with HERA data demonstrate that the suggested method provides reliable
$F_{2}^{\mathcal{Q}\bar{\mathcal{Q}}}$, $R^{c\bar{c}}$ and
$\sigma^{\mathcal{Q}\bar{\mathcal{Q}}}$ at low $x$ in a wide range of the low
absolute four-momentum transfers squared
($4~\mathrm{GeV}^{2}<Q^{2}<2000~\mathrm{GeV}^{2}$). Also, in the HERA kinematic
range, the ratio of $F_{2}^{c\bar{c}}/F_{2}^{b\bar{b}}$,
$F_{2}^{c\bar{c}}/F_{2}^{BDHM}$ and $F_{2}^{b\bar{b}}/F_{2}^{BDHM}$ are
obtained. Expanding the method to low and ultra low values of $x$ can be
considered in the process analysis of the LHeC and FCC-eh colliders.


title: On the algebraic K-theory of Hecke algebras

abstract:
  Consider a totally disconnected group G, which is covirtually cyclic, i.e.,
contains a normal compact open subgroup L such that G/L is infinite cyclic. We
establish a Wang sequence, which computes the algebraic K-groups of the Hecke
algebra of G in terms of the one of L, and show that all negative K-groups
vanish. This confirms the K-theoretic Farrell-Jones Conjecture for the Hecke
algebra of G in this special case. Our ultimate long term goal is to prove it
for any closed subgroup of any reductive p-adic group. The results of this
paper will play a role in the final proof.


title: Chaos bound and its violation in charged Kiselev black hole

abstract:
  The chaos bound in the near-horizon regions has been studied through the
expansions of the metric functions on the horizon. In this paper, we
investigate the chaos bound in the the near-horizon region and at a certain
distance from the horizon of a charged Kiselev black hole. The value of the
Lyapunov exponent is accurately calculated by a Jacobian matrix. The angular
momentum of a charged particle around the black hole affects not only the
exponent, but also the position of the equilibrium orbit. This position
gradually moves away from the horizon with the increase of the angular
momentum. We find that the the bound is violated at a certain distance from the
horizon and there is no violation in the near-horizon region when the charge
mass ratio of the particle is fixed. The small value of the normalization
factor is more likely to cause the violation.


title:
Two-dimensional shaping of Solov'ev equilibrium with vacuum using
  external coils

abstract:
  In this work, we demonstrate a method for constructing the Solov'ev
equilibrium with any given 2D shape surrounded by a vacuum region using
external poloidal field coils. The computational domain consists of two parts:
the plasma region, where the solution is the same as the Solov'ev solution, and
the vacuum region, where the magnetic field generated by external coils as well
as plasma current is determined using the Green function method through a
matching condition near the separatrix. However, the method is not limited to
the Solov'ev equilibrium in particular. The accuracy, efficiency, and
robustness of such a scheme suggest that this method may be applied to the 2D
shaping of tokamak plasma with vacuum region using external coils in general.


title: Reflexive homology

abstract:
  Reflexive homology is the homology theory associated to the reflexive crossed
simplicial group. It is defined in terms of functor homology and is the most
general way one can build an involution into Hochschild homology. In this paper
we give a bicomplex for computing reflexive homology together with some
calculations. We show that reflexive homology satisfies Morita invariance. We
give a decomposition of the reflexive homology of a group algebra indexed by
conjugacy classes of group elements, where the summands are defined in terms of
a reflexive analogue of group homology. We prove that under nice conditions the
involutive Hochschild homology studied by Braun and by Fern\`andez-Val\`encia
and Giansiracusa coincides with reflexive homology.


title:
Penetration trajectory optimization for the hypersonic gliding vehicle
  encountering two interceptors

abstract:
  The penetration trajectory optimization problem for the hypersonic gliding
vehicle (HGV) encountering two interceptors is investigated. The HGV
penetration trajectory optimization problem considering the terminal target
area is formulated as a nonconvex optimal control problem. The nonconvex
optimal control problem is transformed into a second-order cone programming
(SOCP) problem, which can be solved by state-of-the-art interior-point methods.
In addition, a penetration strategy that only requires the initial
line-of-sight angle information of the interceptors is proposed. The convergent
trajectory obtained by the proposed method allows the HGV to evade two
interceptors and reach the target area successfully. Furthermore, a successive
SOCP method with a variable trust region is presented, which is critical to
balancing the trade-off between time consumption and optimality. Finally, the
effectiveness and performance of the proposed method are verified by numerical
simulations.


title:
Fair Classification under Covariate Shift and Missing Protected
  Attribute -- an Investigation using Related Features

abstract:
  This study investigated the problem of fair classification under Covariate
Shift and missing protected attribute using a simple approach based on the use
of importance-weights to handle covariate-shift and, Related Features
arXiv:2104.14537 to handle missing protected attribute.


title:
Automatic spinal curvature measurement on ultrasound spine images using
  Faster R-CNN

abstract:
  Ultrasound spine imaging technique has been applied to the assessment of
spine deformity. However, manual measurements of scoliotic angles on ultrasound
images are time-consuming and heavily rely on raters experience. The objectives
of this study are to construct a fully automatic framework based on Faster
R-CNN for detecting vertebral lamina and to measure the fitting spinal curves
from the detected lamina pairs. The framework consisted of two closely linked
modules: 1) the lamina detector for identifying and locating each lamina pairs
on ultrasound coronal images, and 2) the spinal curvature estimator for
calculating the scoliotic angles based on the chain of detected lamina. Two
hundred ultrasound images obtained from AIS patients were identified and used
for the training and evaluation of the proposed method. The experimental
results showed the 0.76 AP on the test set, and the Mean Absolute Difference
(MAD) between automatic and manual measurement which was within the clinical
acceptance error. Meanwhile the correlation between automatic measurement and
Cobb angle from radiographs was 0.79. The results revealed that our proposed
technique could provide accurate and reliable automatic curvature measurements
on ultrasound spine images for spine deformities.


title: Second-order accuracy metrics for scoring models and their practical use

abstract:
  The paper proposes new second-order accuracy metrics for scoring or rating
models, which show the target preference of the model, it is better to diagnose
good objects or better to diagnose bad ones for a constant generally accepted
predictive power determined by the first order metric that is known as the Gini
index. There are two metrics, they have both an integral representation and a
numerical one. The numerical representation of metrics is of two types, the
first of which is based on binary events to evaluate the model, the second on
the default probability given by the model. Comparison of the results of
calculating the metrics allows you to validate the calibration settings of the
scoring or rating model and reveals its distortions. The article provides
examples of calculating second-order accuracy metrics for ratings of several
rating agencies, as well as for the well known approach to calibration based on
van der Burg's ROC curves.


title:
The small index property of the Fra\"iss\'e limit of finite Heyting
  algebras

abstract:
  We show that if a subgroup of the automorphism group of the Fraisse limit of
finite Heyting algebras has a countable index, then it lies between the
pointwise and setwise stabilizer of some finite set.


title: Gibbs measures for hyperbolic attractors defined by densities

abstract:
  In this article we will describe a new construction for Gibbs measures for
hyperbolic attractors generalizing the original construction of Sinai, Bowen
and Ruelle of SRB measures. The classical construction of the SRB measure is
based on pushing forward the normalized volume on a piece of unstable manifold.
By modifying the density at each step appropriately we show that the resulting
measure is a prescribed Gibbs measure. This contrasts with, and complements,
the construction of Climenhaga-Pesin-Zelerowicz who replace the volume on the
unstable manifold by a fixed reference measure. Moreover, the simplicity of our
proof, which uses only explicit properties on the growth rate of unstable
manifold and entropy estimates, has the additional advantage that it applies in
more general settings.


title:
One RING to Rule Them All: Radon Sinogram for Place Recognition,
  Orientation and Translation Estimation

abstract:
  LiDAR-based global localization is a fundamental problem for mobile robots.
It consists of two stages, place recognition and pose estimation, and yields
the current orientation and translation, using only the current scan as query
and a database of map scans. Inspired by the definition of a recognized place,
we consider that a good global localization solution should keep the pose
estimation accuracy with a lower place density. Following this idea, we propose
a novel framework towards sparse place-based global localization, which
utilizes a unified and learning-free representation, Radon sinogram (RING), for
all sub-tasks. Based on the theoretical derivation, a translation invariant
descriptor and an orientation invariant metric are proposed for place
recognition, achieving certifiable robustness against arbitrary orientation and
large translation between query and map scan. In addition, we also utilize the
property of RING to propose a global convergent solver for both orientation and
translation estimation, arriving at global localization. Evaluation of the
proposed RING based framework validates the feasibility and demonstrates a
superior performance even under a lower place density.


title:
Elastic properties of a Sc-Zr-Nb-Ta-Rh-Pd high-entropy alloy
  superconductor

abstract:
  We report the elastic properties of the hexanary high-entropy alloy
superconductor (ScZrNbTa)$_{0.685}$[RhPd]$_{0.315}$ ($T_c\approx7$ K), by
Resonant Ultrasound Spectroscopy experiments. The derived elastic constants are
bulk modulus $K=132.7$ GPa, Young's modulus $E=121.0$ GPa, shear modulus
$G=44.9$ GPa, and Poisson's ratio $\nu$=0.348 for room temperature. The Young's
and shear moduli are $\sim 10\%$ larger than those in NbTi/Cu composite
superconductor with similar $T_c$, while the ductility is comparable. Moreover,
the mechanical performance is further enhanced at cryogenic temperature. Our
work confirms the advantageous mechanical properties of high-entropy alloy
superconductors and suggests the application prospects.

categories: cond-mat.supr-con cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.other cond-mat.str-el


title: On Effectively Learning of Knowledge in Continual Pre-training

abstract:
  Pre-trained language models (PLMs) like BERT have made significant progress
in various downstream NLP tasks. However, by asking models to do cloze-style
tests, recent work finds that PLMs are short in acquiring knowledge from
unstructured text. To understand the internal behaviour of PLMs in retrieving
knowledge, we first define knowledge-baring (K-B) tokens and knowledge-free
(K-F) tokens for unstructured text and ask professional annotators to label
some samples manually. Then, we find that PLMs are more likely to give wrong
predictions on K-B tokens and attend less attention to those tokens inside the
self-attention module. Based on these observations, we develop two solutions to
help the model learn more knowledge from unstructured text in a fully
self-supervised manner. Experiments on knowledge-intensive tasks show the
effectiveness of the proposed methods. To our best knowledge, we are the first
to explore fully self-supervised learning of knowledge in continual
pre-training.


title:
High-resolution spectroscopy and single-photon Rydberg excitation of
  reconfigurable ytterbium atom tweezer arrays utilizing a metastable state

abstract:
  We present an experimental system for Rydberg tweezer arrays with ytterbium
(Yb) atoms featuring internal state manipulation between the ground ${}^1$S$_0$
and the metastable ${}^3$P$_2$ states, and single-photon excitation from the
${}^3$P$_2$ to Rydberg states. In the experiments, single Yb atoms are trapped
in two-dimensional arrays of optical tweezers and are detected by fluorescence
imaging with the intercombination ${}^1$S$_0 \leftrightarrow {}^3$P$_1$
transition, and the defect-free single atom arrays are prepared by the
rearrangement with the feedaback. We successfully perform high-resolution
${}^1$S$_0\leftrightarrow {}^3$P$_2$ state spectroscopy for the single atoms,
demonstrating the utilities of this ultranarrow transition. We further perform
single-photon excitation from the ${}^3$P$_2$ to Rydberg states for the single
atoms, which is a key for the efficient Rydberg excitation. We also perform a
systematic measurement of a complex energy structure of a series of D states
including newly observed ${}^3$D$_3$ states. The developed system shows
feasibility of future experiments towards quantum simulations and computations
using single Yb atoms.


title:
Efficient Quantum Image Encryption Technique for Securing Multimedia
  Applications

abstract:
  Multimedia security is a vital sector due to its significant impact on the
development of industry 5.0. The current multimedia security systems depend on
complex mathematical calculations, proven theoretically and practically in
their inability to provide complete protection of information against internal
and external attacks and penetration attempts. Unfortunately, the advancement
of the quantum computer allowing the decryption of secured multimedia data by
classical cryptographic algorithms will influence smart industries since no one
can imagine the actual processing power of the quantum computer. Therefore, we
have developed an efficient quantum image encryption technique for multimedia
applications using generalized affine transform and logistic map. The designed
generalized quantum circuits for the developed approach are tested and
evaluated using the IBM cloud-based quantum computer. The proposed algorithms'
performance and computational complexity analysis are measured and proved its
efficiency against various criteria. Furthermore, a hybrid approach to reduce
the circuit complexity and quantum cost using the Espresso algorithm to
approximately 50\% for the cost of adding one more qubit to the circuit is
presented. Finally, the robustness and security analysis against various noise
attacks proved that the proposed quantum image encryption method forms a
secured and accurately measurable quantum image processing system.


title: How Fast are Domino Waves?

abstract:
  The paper is concerned with the problem of toppling propagation velocity in
domino-like mechanical systems. We build on the work of Efthimiou and Johnson,
who developed the theory of perfectly elastic collisions of thin rigid dominoes
on a frictional foundation. This theory nicely predicts important aspects of
domino wave propagation, however, it leads to infinite propagation velocity for
the limit of zero spacing between dominoes. In our work we account for finite
stiffness of the dominoes and obtain a refined theory of fast domino waves,
taking into account a limit velocity of the perturbation propagation in the
system of dominoes. Moreover, finite collision time allows to extract dynamic
quantities of collisions and establish upper and lower borders for domino
separations where the theory is still applicable. Within the established
bounds, our theory agrees with the results of real experiments and DEM
numerical modeling of domino waves.


title:
Critical Angle at a Moving Interface Formed by a Space-Time Modulation
  Step

abstract:
  This paper addresses the problem of wave scattering at a moving interface
formed by a space-time modulation step. Specifically, it derives, using the
technique of frame hopping with Lorentz transformation, the formula for the
corresponding critical angle beyond which the transmitted field is evanescent.
It shows that this angle is smaller (resp. larger) than $\pi/2$ (position of
the interface) for a modulation that is codirectional (resp. contradirectional)
to the direction of wave propagation, and that the critical angle versus the
modulation velocity function monotonically decreases to zero at the velocity
where the incident wave cannot catch up any more with the interface. The theory
is illustrated and validated by full-wave FDTD simulation.


title:
Introduction of Integrated Image Deep Learning Solution and how it
  brought laboratorial level heart rate and blood oxygen results to everyone

abstract:
  The general public and medical professionals recognized the importance of
accurately measuring and storing blood oxygen levels and heart rate during the
COVID-19 pandemic. The demand for accurate contact-less devices was motivated
by the need for cross-infection reduction and the shortage of conventional
oximeters, partially due to the global supply chain issue. This paper evaluated
a contact-less mini-program HealthyPai's heart rate (HR) and oxygen saturation
(SpO2) measurements compared with other wearable devices. In the HR study of
185 samples (81 in the laboratory environment, 104 in the real-life
environment), the mean absolute error (MAE) $\pm$ standard deviation was
$1.4827 \pm 1.7452$ in the lab, $6.9231 \pm 5.6426$ in the real-life setting.
In the SpO2 study of 24 samples, the mean absolute error (MAE) $\pm$ standard
deviation of the measurement was $1.0375 \pm 0.7745$. Our results validated
that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS)
framework can accurately measure HR and SpO2, providing the test quality at
least comparable to other FDA-approved wearable devices in the market and
surpassing the consumer-grade and research-grade wearable standards.


title:
LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource
  Heart Network

abstract:
  An electrocardiogram (ECG) monitors the electrical activity generated by the
heart and is used to detect fatal cardiovascular diseases (CVDs).
Conventionally, to capture the precise electrical activity, clinical experts
use multiple-lead ECGs (typically 12 leads). But in recent times, large-size
deep learning models have been used to detect these diseases. However, such
models require heavy compute resources like huge memory and long inference
time. To alleviate these shortcomings, we propose a low-parameter model, named
Low Resource Heart-Network (LRH-Net), which uses fewer leads to detect ECG
anomalies in a resource-constrained environment. A multi-level knowledge
distillation process is used on top of that to get better generalization
performance on our proposed model. The multi-level knowledge distillation
process distills the knowledge to LRH-Net trained on a reduced number of leads
from higher parameter (teacher) models trained on multiple leads to reduce the
performance gap. The proposed model is evaluated on the PhysioNet-2020
challenge dataset with constrained input. The parameters of the LRH-Net are
106x less than our teacher model for detecting CVDs. The performance of the
LRH-Net was scaled up to 3.2% and the inference time scaled down by 75%
compared to the teacher model. In contrast to the compute- and
parameter-intensive deep learning techniques, the proposed methodology uses a
subset of ECG leads using the low resource LRH-Net, making it eminently
suitable for deployment on edge devices.


title:
Regular parallelisms on PG(3,R) from generalized line stars: The
  oriented case

abstract:
  Using the Klein correspondence, regular parallelisms of PG(3,R) have been
described by Betten and Riesinger in terms of a dual object, called a
hyperflock determining (hfd) line set. In the special case where this set has a
span of dimension 3, a second dualization leads to a more convenient object,
called a generalized star of lines. Both constructions have later been
simplified by the author. Here we refine our simplified approach in order to
obtain similar results for regular parallelisms of oriented lines. As a
consequence, we can demonstrate that for oriented parallelisms, as we call
them, there are distinctly more possibilities than in the non-oriented case.
The proofs require a thorough analysis of orientation in projective spaces (as
manifolds and as lattices) and in projective planes and, in particular, in
translation planes. This is used in order to handle continuous families of
oriented regular spreads in terms of the Klein model of PG(3,R). This turns out
to be quite subtle. Even the definition of suitable classes of dual objects
modeling oriented parallelisms is not so obvious.


title:
Multiplication of freestanding semiconductor membranes from a single
  wafer by advanced remote epitaxy

abstract:
  Freestanding single-crystalline membranes are an important building block for
functional electronics. Especially, compounds semiconductor membranes such as
III-N and III-V offer great opportunities for optoelectronics, high-power
electronics, and high-speed computing. Despite huge efforts to produce such
membranes by detaching epitaxial layers from donor wafers, however, it is still
challenging to harvest epitaxial layers using practical processes. Here, we
demonstrate a method to grow and harvest multiple epitaxial membranes with
extremely high throughput at the wafer scale. For this, 2D materials are
directly formed on III-N and III-V substrates in epitaxy systems, which enables
an advanced remote epitaxy scheme comprised of multiple alternating layers of
2D materials and epitaxial layers that can be formed by a single epitaxy run.
Each epilayer in the multi-stack structure is then harvested by layer-by-layer
peeling, producing multiple freestanding membranes with unprecedented
throughput from a single wafer. Because 2D materials allow peeling at the
interface without damaging the epilayer or the substrate, wafers can be reused
for subsequent membrane production. Therefore, this work represents a
meaningful step toward high-throughput and low-cost production of
single-crystal membranes that can be heterointegrated.


title:
Efficient Circular Polarized Metamaterial RF Energy Harvester for Wi-Fi
  Applications

abstract:
  A new metamaterial based, circular polarized electromagnetic energy harvester
is presented in this work. The structure, which is multilayer and utilises the
well-known to the literature geometry of the spiral split ring resonator,
operates at the Wi-Fi frequency band. Based on full electromagnetic analysis,
the proposed harvester presents high efficiency (i.e., greater than 90%),
adequate angle insensitivity (i.e., 44 deg.) and it is capable of absorbing
circular polarised plane waves.


title:
Doubly resonant photoacoustic spectroscopy: ultra-high sensitivity meets
  ultra-wide dynamic range

abstract:
  Photoacoustic spectroscopy (PAS) based gas sensors with high sensitivity,
wide dynamic range, low cost, and small footprint are desirable across a broad
range of applications in energy, environment, safety, and public health.
However, most works have focused on either acoustic resonator to enhance
acoustic wave or optical resonator to enhance optical wave. Herein, we develop
a gas sensor based on doubly resonant PAS in which the acoustic and optical
waves are simultaneously enhanced using combined optical and acoustic
resonators in a centimeter-long configuration. Not only the lower detection
limit is enhanced by the double standing waves, but also the upper detection
limit is expanded due to the short resonators. As an example, we developed a
sensor by detecting acetylene (C2H2), achieving a noise equivalent absorption
of 5.7*10-13 cm-1 and a dynamic range of eight orders. Compared to the
state-of-the-art PAS gas sensors, the developed sensor increases the
sensitivity by two orders of magnitude and extends the dynamic range by three
orders of magnitude. Besides, a laser-cavity-molecule locking strategy is
proposed to provide additional flexibility of fast gas detection.


title: A Survey on Location-Driven Influence Maximization

abstract:
  Influence Maximization (IM), which aims to select a set of users from a
social network to maximize the expected number of influenced users, is an
evergreen hot research topic. Its research outcomes significantly impact
real-world applications such as business marketing. The booming location-based
network platforms of the last decade appeal to the researchers embedding the
location information into traditional IM research. In this survey, we provide a
comprehensive review of the existing location-driven IM studies from the
perspective of the following key aspects: (1) a review of the application
scenarios of these works, (2) the diffusion models to evaluate the influence
propagation, and (3) a comprehensive study of the approaches to deal with the
location-driven IM problems together with a particular focus on the
accelerating techniques. In the end, we draw prospects into the research
directions in future IM research.


title: Nested Named Entity Recognition as Holistic Structure Parsing

abstract:
  As a fundamental natural language processing task and one of core knowledge
extraction techniques, named entity recognition (NER) is widely used to extract
information from texts for downstream tasks. Nested NER is a branch of NER in
which the named entities (NEs) are nested with each other. However, most of the
previous studies on nested NER usually apply linear structure to model the
nested NEs which are actually accommodated in a hierarchical structure. Thus in
order to address this mismatch, this work models the full nested NEs in a
sentence as a holistic structure, then we propose a holistic structure parsing
algorithm to disclose the entire NEs once for all. Besides, there is no
research on applying corpus-level information to NER currently. To make up for
the loss of this information, we introduce Point-wise Mutual Information (PMI)
and other frequency features from corpus-aware statistics for even better
performance by holistic modeling from sentence-level to corpus-level.
Experiments show that our model yields promising results on widely-used
benchmarks which approach or even achieve state-of-the-art. Further empirical
studies show that our proposed corpus-aware features can substantially improve
NER domain adaptation, which demonstrates the surprising advantage of our
proposed corpus-level holistic structure modeling.


title: Renormalizable and unitary Lorentz invariant model of quantum gravity

abstract:
  We analyze the R + R2 model of quantum gravity where terms quadratic in the
curvature tensor are added to the General Relativity action. This model was
recently proved to be a self-consistent quantum theory of gravitation, being
both renormalizable and unitary. The model can be made practically
indistinguishable from General Relativity at astrophysical and cosmological
scales by the proper choice of parameters.


title:
A survey of known results on the m-step solvable anabelian geometry for
  hyperbolic curves

abstract:
  In this survey, we introduce the three theorems about the m-step solvable
Grothendieck conjecture in anabelian geometry of hyperbolic curves by H.
Nakamura, S. Mochizuki, and the author. We also give sketches of the proofs of
these theorems.