title:
A Dynamical Evolution Study of the Open Clusters: Berkeley 10, Berkeley
  81, Berkeley 89 and Ruprecht 135

abstract:
  By utilising Gaia EDR3 photometric / astrometric data, we studied the
dynamical evolution from the obtained astrophysical, structural and dynamical
parameters of the open clusters (OCs), Berkeley 10 (Be 10), Berkeley 81 (Be
81), Berkeley 89 (Be 89), and Ruprecht 135 (Ru 135). The Gaia EDR3 photometric
distances from the isochrone fitting method are smaller than the ones of Gaia
EDR2. The relaxation times of four OCs are smaller than their ages, in this
regard, they are dynamically relaxed. Their steep overall mass function slopes
mean that their low mass stars outnumber their massive ones. Their large $\tau$
/ relatively small $t_{rlx}$ values imply an advanced mass segregation.
Therefore, they seem to have lost their low-mass stars much to the field. Be
89's outer parts indicate an expansion with time. However, Be 10 and Be 81 show
the relatively shrinkage core/cluster radii due to dynamical evolution. Ru 135
(1.0 Gyr) may have a primordial origin, instead of shrinking in size and mass
with time. Be 89's tidal radius is less than its cluster radius. This means
that its member stars lie within its tidal radius, in the sense it is
gravitationally bound to the cluster. For the rest OCs, the cluster members
beyond their tidal radii are gravitationally unbound to the clusters, which are
more influenced by the potential of the Galaxy.


title:
Unsupervised Attention-based Sentence-Level Meta-Embeddings from
  Contextualised Language Models

abstract:
  A variety of contextualised language models have been proposed in the NLP
community, which are trained on diverse corpora to produce numerous Neural
Language Models (NLMs). However, different NLMs have reported different levels
of performances in downstream NLP applications when used as text
representations. We propose a sentence-level meta-embedding learning method
that takes independently trained contextualised word embedding models and
learns a sentence embedding that preserves the complementary strengths of the
input source NLMs. Our proposed method is unsupervised and is not tied to a
particular downstream task, which makes the learnt meta-embeddings in principle
applicable to different tasks that require sentence representations.
Specifically, we first project the token-level embeddings obtained by the
individual NLMs and learn attention weights that indicate the contributions of
source embeddings towards their token-level meta-embeddings. Next, we apply
mean and max pooling to produce sentence-level meta-embeddings from token-level
meta-embeddings. Experimental results on semantic textual similarity benchmarks
show that our proposed unsupervised sentence-level meta-embedding method
outperforms previously proposed sentence-level meta-embedding methods as well
as a supervised baseline.


title: A Variational Approach to Bayesian Phylogenetic Inference

abstract:
  Bayesian phylogenetic inference is currently done via Markov chain Monte
Carlo (MCMC) with simple proposal mechanisms. This hinders exploration
efficiency and often requires long runs to deliver accurate posterior
estimates. In this paper, we present an alternative approach: a variational
framework for Bayesian phylogenetic analysis. We propose combining subsplit
Bayesian networks, an expressive graphical model for tree topology
distributions, and a structured amortization of the branch lengths over tree
topologies for a suitable variational family of distributions. We train the
variational approximation via stochastic gradient ascent and adopt gradient
estimators for continuous and discrete variational parameters separately to
deal with the composite latent space of phylogenetic models. We show that our
variational approach provides competitive performance to MCMC, while requiring
much less computation due to a more efficient exploration mechanism enabled by
variational inference. Experiments on a benchmark of challenging real data
Bayesian phylogenetic inference problems demonstrate the effectiveness and
efficiency of our methods.


title:
The generality of uncooperative and cooperative effects in elementary
  hydrogen-bonded systems

abstract:
  The cooperative effect plays a significant role in understanding the
intermolecular donor-acceptor interactions of hydrogen bonds (H-bonds,
D-H...A). Herein, using the benchmark method of high-precision ab initio, the
well-known cooperative effect is reproduced in elementary H-bonded systems with
different D and A atoms. That is, with the decreasing of intermolecular
distance, the D-H bond length first increases and then decreases, while the
H...A bond length decreases. On the contrary, when D and A are the same, as the
intermolecular distance decreases, the D-H bond length decreases without
increasing, which is referred to as the uncooperative effect. Further analyses
conclude that compared to cooperative H-bonded systems, uncooperative systems
at their respective equilibrium position have a larger core-valence bifurcation
(CVB) index (>0.022) and lower binding energies (<0.25 eV), showing a clear
linear inverse relationship related to H-bond strength. Therefore, the
intermolecular non-H-bonding interactions are predicted to reflect the
uncooperative characteristics, which is confirmed by high-precision ab initio
calculations. These findings provide a direction for the comprehensive
understanding of H-bonds.


title:
From Parental Control to Joint Family Oversight: Can Parents and Teens
  Manage Mobile Online Safety and Privacy as Equals?

abstract:
  Our research aims to highlight and alleviate the complex tensions around
online safety, privacy, and smartphone usage in families so that parents and
teens can work together to better manage mobile privacy and security-related
risks. We developed a mobile application ("app") for Community Oversight of
Privacy and Security ("CO-oPS") and had parents and teens assess whether it
would be applicable for use with their families. CO-oPS is an Android app that
allows a group of users to co-monitor the apps installed on one another's
devices and the privacy permissions granted to those apps. We conducted a study
with 19 parent-teen (ages 13-17) pairs to understand how they currently managed
mobile safety and app privacy within their family and then had them install,
use, and evaluate the CO-oPS app. We found that both parents and teens gave
little consideration to online safety and privacy before installing new apps or
granting privacy permissions. When using CO-oPS, participants liked how the app
increased transparency into one another's devices in a way that facilitated
communication, but were less inclined to use features for in-app messaging or
to hide apps from one another. Key themes related to power imbalances between
parents and teens surfaced that made co-management challenging. Parents were
more open to collaborative oversight than teens, who felt that it was not their
place to monitor their parents, even though both often believed parents lacked
the technological expertise to monitor themselves. Our study sheds light on why
collaborative practices for managing online safety and privacy within families
may be beneficial but also quite difficult to implement in practice. We provide
recommendations for overcoming these challenges based on the insights gained
from our study.


title: Fusion systems realizing certain Todd modules

abstract:
  We study a certain family of simple fusion systems over finite $3$-groups,
ones that involve Todd modules of the Mathieu groups $2M_{12}$, $M_{11}$, and
$A_6=O^2(M_{10})$ over $\mathbb{F}_3$, and show that they are all isomorphic to
the $3$-fusion systems of almost simple groups. As one consequence, we give new
$3$-local characterizations of Conway's sporadic simple groups.


title:
A User Study to Evaluate a Web-based Prototype for Smart Home Internet
  of Things Device Management

abstract:
  With the growing advances in the Internet of Things (IoT) technology, IoT
device management platforms are becoming increasingly important. We conducted a
web-based survey and usability study with 43 participants who use IoT devices
frequently to: 1) examine their smart home IoT usage patterns and privacy
preferences, and 2) evaluate a web-based prototype for smart home IoT device
management. We found that participants perceived privacy as more important than
the convenience afforded by the IoT devices. Based on their average scores of
privacy vs. convenience importance, participants with low privacy and low
convenience significantly reported less privacy control and convenience
preferences than participants with high privacy and high convenience. Overall,
all participants were satisfied with the proposed website prototype and their
actual usability evaluation demonstrated a good understanding of the website
features. This paper provides an empirical examination of the privacy versus
convenience trade-offs smart home users make when managing their IoT devices.


title:
Homomorphic Encryption and Federated Learning based Privacy-Preserving
  CNN Training: COVID-19 Detection Use-Case

abstract:
  Medical data is often highly sensitive in terms of data privacy and security
concerns. Federated learning, one type of machine learning techniques, has been
started to use for the improvement of the privacy and security of medical data.
In the federated learning, the training data is distributed across multiple
machines, and the learning process is performed in a collaborative manner.
There are several privacy attacks on deep learning (DL) models to get the
sensitive information by attackers. Therefore, the DL model itself should be
protected from the adversarial attack, especially for applications using
medical data. One of the solutions for this problem is homomorphic
encryption-based model protection from the adversary collaborator. This paper
proposes a privacy-preserving federated learning algorithm for medical data
using homomorphic encryption. The proposed algorithm uses a secure multi-party
computation protocol to protect the deep learning model from the adversaries.
In this study, the proposed algorithm using a real-world medical dataset is
evaluated in terms of the model performance.


title: Probing quantum correlations in a hybrid optomechanical system

abstract:
  Refining coherence is a central challenge in quantum simulations and
experiments on optomechanical cavities. Here we suggest a scheme of two coupled
optomechanical cavities to enhance the intracavity entanglement. The couplings
are established between the optical modes through a photon hopping process and
between the mechanical resonators by the phonon tunneling process. Both
cavities are driven by classical light. The generated quantum correlations
inside each cavity are explored in terms of strength couplings with two
different quantum measures, namely, logarithmic negativity and quantum
steering. These will allow us to analyze the various aspects of these quantum
measures and see the interest in their applications. Also, the stability
conditions are examined in terms of the coupling strengths. Consequently, it is
shown that the intracavity-entanglement degree can be quantified, and it is
found that the generated entanglement can be improved with an appropriate
choice of the photon and phonon hopping strengths. The calculations were
performed within the recent experimentally accessible parameters.


title:
Nanogap-Enhanced Infrared Spectroscopy with Template-Stripped
  Wafer-Scale Arrays of Buried Plasmonic Cavities

abstract:
  We have combined atomic layer lithography and template stripping to produce a
new class of substrates for surface-enhanced infrared absorption (SEIRA)
spectroscopy. Our structure consists of a buried and U-shaped
metal-insulator-metal waveguide whose folded vertical arms efficiently couple
normally incident light. The insulator is formed by atomic layer deposition
(ALD) of Al2O3 and precisely defines the gap size. The buried nanocavities are
protected from contamination by a silicon template until ready for use and
exposed by template stripping on demand. The exposed nanocavity generates
strong infrared resonances, tightly confines infrared radiation into a gap that
is as small as 3 nm ({\lambda}/3300), and creates a dense array of
millimeter-long hotspots. After partial removal of the insulators, the gaps are
backfilled with benzenethiol molecules, generating distinct Fano resonances due
to strong coupling with gap plasmons, and a SEIRA enhancement factor of 10^5 is
observed for a 3 nm gap. Because of the wafer-scale manufacturability,
single-digit-nanometer control of the gap size via ALD, and long-term storage
enabled by template stripping, our buried plasmonic nanocavity substrates will
benefit broad applications in sensing and spectroscopy.


title:
Unique electronic state in ferromagnetic semiconductor FeCl$_{2}$
  monolayer

abstract:
  Two-dimensional (2D) van der Waals (vdW) magnetic materials could be an ideal
platform for ultracompact spintronic applications. Among them, FeCl$_{2}$
monolayer in the triangular lattice is subject to a strong debate. Thus, we
critically examine its spin-orbital state, electronic structure, and magnetic
properties, using a set of delicate first-principles calculations, crystal
field level analyses, and Monte Carlo simulations. Our work reveals that
FeCl$_{2}$ monolayer is a ferromagnetic (FM) semiconductor in which the
electron correlation of the narrow Fe $3d$ bands determines the band gap of
about 1.2 eV. Note that only when the spin-orbit coupling (SOC) is properly
handled, the unique $d$$^{5\uparrow}$$l$$^\downarrow_{z+}$ electronic ground
state is achieved. Then, both the orbital and spin contributions (0.59
$\mu_{\rm B}$ plus 3.56 $\mu_{\rm B}$) to the total magnetic moment well
account for, for the first time, the experimental perpendicular moment of 4.3
$\mu_{\rm B}$/Fe. Moreover, we find that a compressive strain further
stabilizes the $d$$^{5\uparrow}$$l$$^\downarrow_{z+}$ ground state, and that
the enhanced magnetic anisotropy and exchange coupling would boost the Curie
temperature ($T_{\rm C}$) from 25 K for the pristine FeCl$_{2}$ monolayer to
69-102 K under 3$\%$-5$\%$ compressive strain. Therefore, FeCl$_{2}$ monolayer
is indeed an appealing 2D FM semiconductor.


title: Visual Attention Methods in Deep Learning: An In-Depth Survey

abstract:
  Inspired by the human cognitive system, attention is a mechanism that
imitates the human cognitive awareness about specific information, amplifying
critical details to focus more on the essential aspects of data. Deep learning
has employed attention to boost performance for many applications.
Interestingly, the same attention design can suit processing different data
modalities and can easily be incorporated into large networks. Furthermore,
multiple complementary attention mechanisms can be incorporated in one network.
Hence, attention techniques have become extremely attractive. However, the
literature lacks a comprehensive survey specific to attention techniques to
guide researchers in employing attention in their deep models. Note that,
besides being demanding in terms of training data and computational resources,
transformers only cover a single category in self-attention out of the many
categories available. We fill this gap and provide an in-depth survey of 50
attention techniques categorizing them by their most prominent features. We
initiate our discussion by introducing the fundamental concepts behind the
success of attention mechanism. Next, we furnish some essentials such as the
strengths and limitations of each attention category, describe their
fundamental building blocks, basic formulations with primary usage, and
applications specifically for computer vision. We also discuss the challenges
and open questions related to attention mechanism in general. Finally, we
recommend possible future research directions for deep attention.


title: Fluctuation suppression and enhancement in interacting particle systems

abstract:
  We investigate in this work the effects of interaction on the fluctuation of
empirical measures. The systems with positive definite interaction potentials
tend to exhibit smaller fluctuation compared to the fluctuation in standard
Monte Carlo sampling while systems with negative definite potentials tend to
exhibit larger fluctuation. Moreover, if the temperature goes to zero, the
fluctuation for positive definite kernels in the long time tends to vanish to
zero, while the fluctuation for negative definite kernels in the long time
tends to blow up to infinity. This phenomenon may gain deeper understanding to
some physical systems like the Poisson-Boltzmann system, and may help to
understand the properties of some particle based variational inference sampling
methods.


title: On the anisotropy theorem of Papadakis and Petrotou

abstract:
  We study the anisotropy theorem for Stanley-Reisner rings of simplicial
spheres by Papadakis and Petrotou. Our first result is an explicit description
of the quadratic form, allowing a simplification of the proof. We prove a
further conjecture stated by Papadakis and Petrotou, and study anisotropy in
characteristic 0.


title:
Ideal class groups of number fields and Bloch-Kato's Tate-Shafarevich
  groups for symmetric powers of elliptic curves

abstract:
  For an elliptic curve $E$ over $\mathbb{Q}$, putting $K=\mathbb{Q}(E[p])$
which is the $p$-th division field of $E$ for an odd prime $p$, we study the
ideal class group $\mathrm{Cl}_K$ of $K$ as a
$\mathrm{Gal}(K/\mathbb{Q})$-module. More precisely, for any $j$ with
$1\leqslant j \leqslant p-2$, we give a condition that $\mathrm{Cl}_K\otimes
\mathbb{F}_p$ has the symmetric power $\mathrm{Sym}^j E[p]$ of $E[p]$ as its
quotient $\mathrm{Gal}(K/\mathbb{Q})$-module, in terms of Bloch-Kato's
Tate-Shafarevich group of $\mathrm{Sym}^j V_p E$. Here $V_p E$ denotes the
rational $p$-adic Tate module of $E$. This is a partial generalization of a
result of Prasad and Shekhar for the case $j=1$.


title:
Cannikin's Law in Tensor Modeling: A Rank Study for Entanglement and
  Separability in Tensor Complexity and Model Capacity

abstract:
  This study clarifies the proper criteria to assess the modeling capacity of a
general tensor model. The work analyze the problem based on the study of tensor
ranks, which is not a well-defined quantity for higher order tensors. To
process, the author introduces the separability issue to discuss the Cannikin's
law of tensor modeling. Interestingly, a connection between entanglement
studied in information theory and tensor analysis is established, shedding new
light on the theoretical understanding for modeling capacity problems.


title: Language-Grounded Indoor 3D Semantic Segmentation in the Wild

abstract:
  Recent advances in 3D semantic segmentation with deep neural networks have
shown remarkable success, with rapid performance increase on available
datasets. However, current 3D semantic segmentation benchmarks contain only a
small number of categories -- less than 30 for ScanNet and SemanticKITTI, for
instance, which are not enough to reflect the diversity of real environments
(e.g., semantic image understanding covers hundreds to thousands of classes).
Thus, we propose to study a larger vocabulary for 3D semantic segmentation with
a new extended benchmark on ScanNet data with 200 class categories, an order of
magnitude more than previously studied. This large number of class categories
also induces a large natural class imbalance, both of which are challenging for
existing 3D semantic segmentation methods. To learn more robust 3D features in
this context, we propose a language-driven pre-training method to encourage
learned 3D features that might have limited training examples to lie close to
their pre-trained text embeddings. Extensive experiments show that our approach
consistently outperforms state-of-the-art 3D pre-training for 3D semantic
segmentation on our proposed benchmark (+9% relative mIoU), including
limited-data scenarios with +25% relative mIoU using only 5% annotations.


title:
Three-dimensional Magnetic and Thermodynamic Structures of Solar
  Microflares

abstract:
  Microflares, one of small-scale solar activities, are believed to be caused
by magnetic reconnection. Nevertheless, their three-dimensional (3D) magnetic
structures, thermodynamic structures, and physical links to the reconnection
have been unclear. In this Letter, based on high-resolution 3D radiative
magnetohydrodynamic simulation of the quiet Sun spanning from the upper
convection zone to the corona, we investigate 3D magnetic and thermodynamic
structures of three homologous microflares. It is found that they originate
from localized hot plasma embedded in the chromospheric environment at the
height of 2--10 Mm above the photosphere and last for 3--10 minutes with
released magnetic energy in the range of $10^{27}-10^{28}$ erg. The heated
plasma is almost co-spatial with the regions where the heating rate per
particle is maximal. The 3D velocity field reveals a pair of converging flows
with velocities of tens of km s$^{-1}$ toward and outflows with velocities of
about 100 km s$^{-1}$ moving away from the hot plasma. These features support
that magnetic reconnection plays a critical role in heating the localized
chromospheric plasma to coronal temperature, giving rise to observed
microflares. The magnetic topology analysis further discloses that the
reconnection region is located near quasi-separators where both current density
and squashing factors are maximal although the specific topology may vary from
tether-cutting to fan-spine-like structure.


title:
UFRC: A Unified Framework for Reliable COVID-19 Detection on
  Crowdsourced Cough Audio

abstract:
  We suggested a unified system with core components of data augmentation,
ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and
uncertainty estimation to quickly and consistently detect COVID-19 using
acoustic evidence. To increase the model's capacity to identify a minority
class, data augmentation and cost-sensitive loss are incorporated (infected
samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50
has been found to be effective. The unified framework also integrates deep
ensemble learning and uncertainty estimation to integrate predictions from
various base classifiers for generalisation and reliability. We ran a series of
tests using the DiCOVA2021 challenge dataset to assess the efficacy of our
proposed method, and the results show that our method has an AUC-ROC of 85.43
percent, making it a promising method for COVID-19 detection. The unified
framework also demonstrates that audio may be used to quickly diagnose
different respiratory disorders.


title: Biometric verification of humans by means of hand geometry

abstract:
  This paper describes a hand geometry biometric identification system. We have
acquired a database of 22 people, 10 acquisitions per person, using a
conventional document scanner. We propose a feature extraction and classifier.
The experimental results reveal a maximum identification rate equal to 93.64%,
and a minimum value of the detection cost function equal to 2.92% using a multi
layer perceptron classifier.


title:
Experimental violation of the Leggett-Garg inequality with a single-spin
  system

abstract:
  Investigation the boundary between quantum mechanical description and
classical realistic view is of fundamental importance. The Leggett-Garg
inequality provides a criterion to distinguish between quantum systems and
classical systems, and can be used to prove the macroscopic superposition
state. A larger upper bound of the LG function can be obtained in a multi-level
system. Here, we present an experimental violation of the Leggett-Garg
inequality in a three-level system using nitrogen-vacancy center in diamond by
ideal negative result measurement. The experimental maximum value of
Leggett-Garg function is $K_{3}^{exp}=1.625\pm0.022$ which exceeds the L\"uders
bound with a $5\sigma$ level of confidence.


title: An Integrated Programmable CPG with Bounded Output

abstract:
  Cyclic motions are fundamental patterns in robotic applications including
industrial manipulation and legged robot locomotion. This paper proposes an
approach for the online modulation of cyclic motions in robotic applications.
For this purpose, we present an integrated programmable Central Pattern
Generator (CPG) for the online generation of the reference joint trajectory of
a robotic system out of a library of desired periodic motions. The reference
trajectory is then followed by the lower-level controller of the robot. The
proposed CPG generates a smooth reference joint trajectory convergence to the
desired one while preserving the position and velocity joint limits of the
robot. The integrated programmable CPG consists of one novel bounded output
programmable oscillator. We design the programmable oscillator for encoding the
desired multidimensional periodic trajectory as a stable limit cycle. We also
use the state transformation method to ensure that the oscillator's output and
its first-time derivative preserve the joint position and velocity limits of
the robot. With the help of Lyapunov-based arguments, We prove that the
proposed CPG provides the global stability and convergence of the desired
trajectory. The effectiveness of the proposed integrated CPG for trajectory
generation is shown in a passive rehabilitation scenario on the Kuka iiwa robot
arm, and also in a walking simulation on a seven-link bipedal robot.


title:
A Distributed and Elastic Aggregation Service for Scalable Federated
  Learning Systems

abstract:
  Federated Learning has promised a new approach to resolve the challenges in
machine learning by bringing computation to the data. The popularity of the
approach has led to rapid progress in the algorithmic aspects and the emergence
of systems capable of simulating Federated Learning. State of art systems in
Federated Learning support a single node aggregator that is insufficient to
train a large corpus of devices or train larger-sized models. As the model size
or the number of devices increase the single node aggregator incurs memory and
computation burden while performing fusion tasks. It also faces communication
bottlenecks when a large number of model updates are sent to a single node. We
classify the workload for the aggregator into categories and propose a new
aggregation service for handling each load. Our aggregation service is based on
a holistic approach that chooses the best solution depending on the model
update size and the number of clients. Our system provides a fault-tolerant,
robust and efficient aggregation solution utilizing existing parallel and
distributed frameworks. Through evaluation, we show the shortcomings of the
state of art approaches and how a single solution is not suitable for all
aggregation requirements. We also provide a comparison of current frameworks
with our system through extensive experiments.


title: Uniqueness for fractional parabolic and elliptic equations with drift

abstract:
  We investigate uniqueness, in suitable weighted Lebesgue spaces, of solutions
to a class of fractional parabolic and elliptic equations with a drift.


title:
Impact of impurities on drift wave instabilities in reversed-field pinch
  plasmas

abstract:
  The drift wave in the presence of impurity ions was investigated numerically
in reversed field pinch (RFP) plasmas, using the gyrokinetic integral eigenmode
equation. It was found that in RFP plasmas with hollow density profiles, an
increase in $k_\theta \rho_s$ increases the growth rate of the ion temperature
gradient (ITG). Comparing the results of regular and hollow plasma density
profile shows that the ITG mode under the hollow density profile is much harder
to be excited. For the impurities' effects, when the impurities' density
gradient is opposite to the primary ions, namely when $L_{ez}$ is negative,
impurities could enhance the instability. On the contrary, when $L_{ez}$ is
positive, the instability is stabilized. Regarding the trapped electron mode
(TEMs), the growth rate under the plasma with hollow density profile remained
minor than that for the standard density gradient. There exists a threshold of
$L_{ez}$. When $L_{ez}$ is less than this value, impurity destabilizes TEMs,
while as $L_{ez}$ is greater than this, impurity stabilizes TEMs. The effects
of $L_{ez}$ on TEM also depend on both the plasma density gradient and the
impurity species. In addition, the influence of collisionality on TEMs was also
studied.


title:
UniGDD: A Unified Generative Framework for Goal-Oriented
  Document-Grounded Dialogue

abstract:
  The goal-oriented document-grounded dialogue aims at responding to the user
query based on the dialogue context and supporting document. Existing studies
tackle this problem by decomposing it into two sub-tasks: knowledge
identification and response generation. However, such pipeline methods would
unavoidably suffer from the error propagation issue. This paper proposes to
unify these two sub-tasks via sequentially generating the grounding knowledge
and the response. We further develop a prompt-connected multi-task learning
strategy to model the characteristics and connections of different tasks and
introduce linear temperature scheduling to reduce the negative effect of
irrelevant document information. Experimental results demonstrate the
effectiveness of our framework.


title:
Global existence and blow-up of solutions to the porous medium equation
  with reaction and singular coefficients

abstract:
  We study global in time existence versus blow-up in finite time of solutions
to the Cauchy problem for the porous medium equation with a variable density
$\rho(x)$ and a power-like reaction term posed in the one dimensional interval
$(-R,R)$, $R>0$. Here the weight function is singular at the boundary of the
domain $(-R,R)$, indeed it is such that $\rho(x)\sim (R-|x|)^{-q}$ as $|x|\to
R$, with $q\ge0$. We show a different behavior of solutions depending on the
three cases when $q>2$, $q=2$ and $q<2$.


title:
SETTI: A Self-supervised Adversarial Malware Detection Architecture in
  an IoT Environment

abstract:
  In recent years, malware detection has become an active research topic in the
area of Internet of Things (IoT) security. The principle is to exploit
knowledge from large quantities of continuously generated malware. Existing
algorithms practice available malware features for IoT devices and lack
real-time prediction behaviors. More research is thus required on malware
detection to cope with real-time misclassification of the input IoT data.
Motivated by this, in this paper we propose an adversarial self-supervised
architecture for detecting malware in IoT networks, SETTI, considering samples
of IoT network traffic that may not be labeled. In the SETTI architecture, we
design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and
ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial
sample generation in real-time. The GSelf-MDS builds a generative adversarial
network model to generate adversarial samples in the self-supervised structure.
Finally, ASelf-MDS utilizes three well-known perturbation sample techniques to
develop adversarial malware and inject it over the self-supervised
architecture. Also, we apply a defence method to mitigate these attacks, namely
adversarial self-supervised training to protect the malware detection
architecture against injecting the malicious samples. To validate the attack
and defence algorithms, we conduct experiments on two recent IoT datasets:
IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the
Self-MDS method has the most damaging consequences from the attacker's point of
view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the
ASelf-MDS method is the most devastating algorithm that can plunge the accuracy
rate from 98% to 77%.


title:
FedCau: A Proactive Stop Policy for Communication and Computation
  Efficient Federated Learning

abstract:
  This paper investigates efficient distributed training of a Federated
Learning~(FL) model over a wireless network of wireless devices. The
communication iterations of the distributed training algorithm may be
substantially deteriorated or even blocked by the effects of the devices'
background traffic, packet losses, congestion, or latency. We abstract the
communication-computation impacts as an `iteration cost' and propose a
cost-aware causal FL algorithm~(FedCau) to tackle this problem. We propose an
iteration-termination method that trade-offs the training performance and
networking costs. We apply our approach when clients use the slotted-ALOHA, the
carrier-sense multiple access with collision avoidance~(CSMA/CA), and the
orthogonal frequency-division multiple access~(OFDMA) protocols. We show that,
given a total cost budget, the training performance degrades as either the
background communication traffic or the dimension of the training problem
increases. Our results demonstrate the importance of proactively designing
optimal cost-efficient stopping criteria to avoid unnecessary
communication-computation costs to achieve only a marginal FL training
improvement. We validate our method by training and testing FL over the MNIST
dataset. Finally, we apply our approach to existing communication efficient FL
methods from the literature, achieving further efficiency. We conclude that
cost-efficient stopping criteria are essential for the success of practical FL
over wireless networks.


title:
Transport through amorphous photonic materials with localization and
  bandgap-regimes

abstract:
  We propose a framework that unifies the description of light transport in
three-dimensional amorphous dielectric materials that exhibit both localization
and a photonic bandgap. To this end, we argue that coherent reflection near and
in the bandgap attenuates the generation of diffuse or localized photons. Using
the eminent self-consistent theory of localization and considering the density
of states of photons, we can quantitatively describe all transport regimes:
Transparency, light diffusion, localization, and bandgap. Comparing the model
with numerical data on optical transport in hyperuniform dielectric networks
confirms our findings.

categories: physics.optics cond-mat.dis-nn cond-mat.mes-hall cond-mat.soft


title: TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark

abstract:
  Food Computing is currently a fast-growing field of research. Natural
language processing (NLP) is also increasingly essential in this field,
especially for recognising food entities. However, there are still only a few
well-defined tasks that serve as benchmarks for solutions in this area. We
introduce a new dataset -- called \textit{TASTEset} -- to bridge this gap. In
this dataset, Named Entity Recognition (NER) models are expected to find or
infer various types of entities helpful in processing recipes, e.g.~food
products, quantities and their units, names of cooking processes, physical
quality of ingredients, their purpose, taste.
  The dataset consists of 700 recipes with more than 13,000 entities to
extract. We provide a few state-of-the-art baselines of named entity
recognition models, which show that our dataset poses a solid challenge to
existing models. The best model achieved, on average, 0.95 $F_1$ score,
depending on the entity type -- from 0.781 to 0.982. We share the dataset and
the task to encourage progress on more in-depth and complex information
extraction from recipes.


title:
Learning to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on
  Unseen Shapes in Real World

abstract:
  In the peg insertion task, human pays attention to the seam between the peg
and the hole and tries to fill it continuously with visual feedback. By
imitating the human behavior, we design architectures with position and
orientation estimators based on the seam representation for pose alignment,
which proves to be general to the unseen peg geometries. By putting the
estimators into the closed-loop control with reinforcement learning, we further
achieve a higher or comparable success rate, efficiency, and robustness
compared with the baseline methods. The policy is trained totally in simulation
without any manual intervention. To achieve sim-to-real, a learnable
segmentation module with automatic data collecting and labeling can be easily
trained to decouple the perception and the policy, which helps the model
trained in simulation quickly adapt to the real world with negligible effort.
Results are presented in simulation and on a physical robot. Code, videos, and
supplemental material are available at https://github.com/xieliang555/SFN.git


title: Exploiting Multiple EEG Data Domains with Adversarial Learning

abstract:
  Electroencephalography (EEG) is shown to be a valuable data source for
evaluating subjects' mental states. However, the interpretation of multi-modal
EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are
highly subject-dependent, and are bound to the equipment and experimental setup
used, (i.e. domain). This leads to machine learning models often suffer from
poor generalization ability, where they perform significantly worse on
real-world data than on the exploited training data. Recent research heavily
focuses on cross-subject and cross-session transfer learning frameworks to
reduce domain calibration efforts for EEG signals. We argue that multi-source
learning via learning domain-invariant representations from multiple
data-sources is a viable alternative, as the available data from different EEG
data-source domains (e.g., subjects, sessions, experimental setups) grow
massively. We propose an adversarial inference approach to learn data-source
invariant representations in this context, enabling multi-source learning for
EEG-based brain-computer interfaces. We unify EEG recordings from different
source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP,
DREAMER), and demonstrate the feasibility of our invariant representation
learning approach in suppressing data-source-relevant information leakage by
35% while still achieving stable EEG-based emotion classification performance.


title:
3D mapping of the Wolf-Rayet nebula M1-67: clues for post-common
  envelope evolution in massive stars

abstract:
  We present a 3D mapping of the Wolf-Rayet (WR) nebula M1-67 around WR124. We
obtained high-resolution San Pedro M\'{a}rtir (SPM) Manchester Echelle
Spectrograph (MES) observations along 17 long-slit positions covering all
morphological features in M1-67. We are able to unveil the true morphology of
M1-67 and its kinematics by interpreting the SPM MES observations by means of
the 3D modelling tool for Astrophysics SHAPE. Our SHAPE model that best
reproduces the SPM MES data includes three concentric bipolar structures
composed by a hollow ellipsoidal structure and a torus. In addition, the model
requires the presence of expanding jets and broken blisters in order to
reproduce specific spectral features. Our results are consistent with the idea
that M1-67 and its progenitor star WR124 have formed through a common envelope
scenario that occurred 11.8$^{+4.6}_{-0.8}$ kyr ago. Our bipolar model strongly
questions previous suggestions of the presence of a bow shock structure
surrounding M1-67. We interpret that the bright structures detected in the
spectra extracted from the central regions are produced by wind compression at
the receding region of the innermost structure in M1-67. Furthermore, WR124 is
moving through a low-density region above the Galactic plane that has
negligibly affected the formation history of M1-67.


title: SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words

abstract:
  Pre-trained models are widely used in the tasks of natural language
processing nowadays. However, in the specific field of text simplification, the
research on improving pre-trained models is still blank. In this work, we
propose a continued pre-training method for text simplification. Specifically,
we propose a new masked language modeling (MLM) mechanism, which does not
randomly mask words but only masks simple words. The new mechanism can make the
model learn to generate simple words. We use a small-scale simple text dataset
for continued pre-training and employ two methods to identify simple words from
the texts. We choose BERT, a representative pre-trained model, and continue
pre-training it using our proposed method. Finally, we obtain SimpleBERT, which
surpasses BERT in both lexical simplification and sentence simplification tasks
and has achieved state-of-the-art results on multiple datasets. What's more,
SimpleBERT can replace BERT in existing simplification models without
modification.


title:
Towards Lightweight Transformer via Group-wise Transformation for
  Vision-and-Language Tasks

abstract:
  Despite the exciting performance, Transformer is criticized for its excessive
parameters and computation cost. However, compressing Transformer remains as an
open problem due to its internal complexity of the layer designs, i.e.,
Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this
issue, we introduce Group-wise Transformation towards a universal yet
lightweight Transformer for vision-and-language tasks, termed as
LW-Transformer. LW-Transformer applies Group-wise Transformation to reduce both
the parameters and computations of Transformer, while also preserving its two
main properties, i.e., the efficient attention modeling on diverse subspaces of
MHA, and the expanding-scaling feature transformation of FFN. We apply
LW-Transformer to a set of Transformer-based networks, and quantitatively
measure them on three vision-and-language tasks and six benchmark datasets.
Experimental results show that while saving a large number of parameters and
computations, LW-Transformer achieves very competitive performance against the
original Transformer networks for vision-and-language tasks. To examine the
generalization ability, we also apply our optimization strategy to a recently
proposed image Transformer called Swin-Transformer for image classification,
where the effectiveness can be also confirmed


title: Towards Unification of Discourse Annotation Frameworks

abstract:
  Discourse information is difficult to represent and annotate. Among the major
frameworks for annotating discourse information, RST, PDTB and SDRT are widely
discussed and used, each having its own theoretical foundation and focus.
Corpora annotated under different frameworks vary considerably. To make better
use of the existing discourse corpora and achieve the possible synergy of
different frameworks, it is worthwhile to investigate the systematic relations
between different frameworks and devise methods of unifying the frameworks.
Although the issue of framework unification has been a topic of discussion for
a long time, there is currently no comprehensive approach which considers
unifying both discourse structure and discourse relations and evaluates the
unified framework intrinsically and extrinsically. We plan to use automatic
means for the unification task and evaluate the result with structural
complexity and downstream tasks. We will also explore the application of the
unified framework in multi-task learning and graphical models.


title:
Multihop Optical Wireless Communication Over ${\cal{F}}$-Turbulence
  Channels and Generalized Pointing Errors with Fog-Induced Fading

abstract:
  Multihop relaying is a potential technique to mitigate channel impairments in
optical wireless communications (OWC). In this paper, multiple fixed-gain
amplify-and-forward (AF) relays are employed to enhance the OWC performance
under the combined effect of atmospheric turbulence, pointing errors, and fog.
We consider a long-range OWC link by modeling the atmospheric turbulence by the
Fisher-Snedecor ${\cal{F}}$ distribution, pointing errors by the generalized
non-zero boresight model, and random path loss due to fog. We also consider a
short-range OWC system by ignoring the impact of atmospheric turbulence. We
derive novel upper bounds on the probability density function (PDF) and
cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio
(SNR) for both short and long-range multihop OWC systems by developing exact
statistical results for a single-hop OWC system under the combined effect of
${\cal{F}}$-turbulence channels, non-zero boresight pointing errors, and
fog-induced fading. Based on these expressions, we present analytical
expressions of outage probability (OP) and average bit-error-rate (ABER)
performance for the considered OWC systems involving single-variate Fox's H and
Meijer's G functions. Moreover, asymptotic expressions of the outage
probability in high SNR region are developed using simpler Gamma functions to
provide insights on the effect of channel and system parameters. The derived
analytical expressions are validated through Monte-Carlo simulations, and the
scaling of the OWC performance with the number of relay nodes is demonstrated
with a comparison to the single-hop transmission.


title: Cartan equivalence method on fifth-order differential operator

abstract:
  In this paper, we carry out the equivalence problem for fifth-order
differential operators on the line under general fiber-preserving
transformation using the Cartan method of equivalence. Two versions of
equivalence problems have been solved. We consider the direct equivalence
problem and an equivalence problem to determine the sufficient and necessary
conditions on fifth-order differential operators such that there exists a
fiber--preserving transformation mapping one to the other according to gauge
equivalence.


title: The Irreducibility and Monogenicity of Power-Compositional Trinomials

abstract:
  A polynomial $f(x)\in {\mathbb Z}[x]$ of degree $N$ is called
\emph{monogenic} if $f(x)$ is irreducible over ${\mathbb Q}$ and
$\{1,\theta,\theta^2,\ldots ,\theta^{N-1}\}$ is a basis for the ring of
integers of ${\mathbb Q}(\theta)$, where $f(\theta)=0$. Define ${\mathcal
F}(x):=x^m+Ax^{m-1}+B$. In this article, we determine sets of conditions on
$m$, $A$, and $B$, such that the power-compositional trinomial ${\mathcal
F}(x^{p^n})$ is monogenic for all integers $n\ge 0$ and a given prime $p$.
Furthermore, we prove the actual existence of infinite families of such
trinomials ${\mathcal F}(x)$.


abstract:
  We study the Ohno-Zagier type relation for multiple $t$-values and multiple
$t$-star values. We represent the generating function of sums of multiple
$t$-(star) values with fixed weight, depth and height in terms of the
generalized hypergeometric function $\,_3F_2$. As applications, we get a
formula for the generating function of sums of multiple $t$-(star) values of
maximal height and a weighted sum formula for sums of multiple $t$-(star)
values with fixed weight and depth.


title:
Approaching sales forecasting using recurrent neural networks and
  transformers

abstract:
  Accurate and fast demand forecast is one of the hot topics in supply chain
for enabling the precise execution of the corresponding downstream processes
(inbound and outbound planning, inventory placement, network planning, etc). We
develop three alternatives to tackle the problem of forecasting the customer
sales at day/store/item level using deep learning techniques and the
Corporaci\'on Favorita data set, published as part of a Kaggle competition. Our
empirical results show how good performance can be achieved by using a simple
sequence to sequence architecture with minimal data preprocessing effort.
Additionally, we describe a training trick for making the model more time
independent and hence improving generalization over time. The proposed solution
achieves a RMSLE of around 0.54, which is competitive with other more specific
solutions to the problem proposed in the Kaggle competition.


title: Noncommutative spaces and superspaces from Snyder and Yang type models

abstract:
  The relativistic $D=4$ Snyder model is formulated in terms of $D=4$ $dS$
algebra $o(4,1)$ generators, with noncommutative Lorentz-invariant Snyder
quantum space-time provided by $\frac{O(4,1)}{O(3,1)}$ coset generators.
Analogously, in relativistic $D=4$ Yang models the quantum-deformed
relativistic phase space is described by the algebras of coset generators
$\frac{O(5,1)}{O(3,1)}$ or $\frac{O(4,2)}{O(3,1)}$. We extend these algebraic
considerations by using respective $dS$ superalgebras, which provide
Lorentz-covariant quantum superspaces (SUSY Snyder model) as well as
relativistic quantum phase super spaces (SUSY Yang model).


title: A simple, passive design for large optical trap arrays for single atoms

abstract:
  We present an approach for trapping cold atoms in a 2D optical trap array
generated with a novel 4f filtering scheme and custom transmission mask without
any active device. The approach can be used to generate arrays of bright or
dark traps, or both simultaneously with a single wavelength for forming
two-species traps. We demonstrate the design by creating a 2D array of 1225
dark trap sites, where single Cs atoms are loaded into regions of near-zero
intensity in an approximately Gaussian profile trap. Moreover, we demonstrate a
simple solution to the problem of out-of-focus trapped atoms, which occurs due
to the Talbot effect in periodic optical lattices. Using a high power yet low
cost spectrally and spatially broadband laser, out-of-focus interference is
mitigated, leading to near perfect removal of Talbot plane traps.


title:
Contrastive Learning with Hard Negative Entities for Entity Set
  Expansion

abstract:
  Entity Set Expansion (ESE) is a promising task which aims to expand entities
of the target semantic class described by a small seed entity set. Various NLP
and IR applications will benefit from ESE due to its ability to discover
knowledge. Although previous ESE methods have achieved great progress, most of
them still lack the ability to handle hard negative entities (i.e., entities
that are difficult to distinguish from the target entities), since two entities
may or may not belong to the same semantic class based on different granularity
levels we analyze on. To address this challenge, we devise an entity-level
masked language model with contrastive learning to refine the representation of
entities. In addition, we propose the ProbExpan, a novel probabilistic ESE
framework utilizing the entity representation obtained by the aforementioned
language model to expand entities. Extensive experiments and detailed analyses
on three datasets show that our method outperforms previous state-of-the-art
methods.


title: Wireless Semantic Communications for Video Conferencing

abstract:
  Video conferencing has become a popular mode of meeting even if it consumes
considerable communication resources. Conventional video compression causes
resolution reduction under limited bandwidth. Semantic video conferencing
maintains high resolution by transmitting some keypoints to represent motions
because the background is almost static, and the speakers do not change often.
However, the study on the impact of the transmission errors on keypoints is
limited. In this paper, we initially establish a basal semantic video
conferencing (SVC) network, which dramatically reduces transmission resources
while only losing detailed expressions. The transmission errors in SVC only
lead to a changed expression, whereas those in the conventional methods destroy
pixels directly. However, the conventional error detector, such as the cyclic
redundancy check, cannot reflect the degree of expression changes. To overcome
this issue, we develop an incremental redundancy hybrid automatic
repeat-request (IR-HARQ) framework for the varying channels (SVC-HARQ)
incorporating a novel semantic error detector. The SVC-HARQ has flexibility in
bit consumption and achieves good performance. In addition, SVC-CSI is designed
for channel state information (CSI) feedback to allocate the keypoint
transmission and enhance the performance dramatically. Simulation shows that
the proposed wireless semantic communication system can significantly improve
the transmission efficiency.This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after which
this version may no longer be accessible


title:
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth
  Completion

abstract:
  Depth prediction is a critical problem in robotics applications especially
autonomous driving. Generally, depth prediction based on binocular stereo
matching and fusion of monocular image and laser point cloud are two mainstream
methods. However, the former usually suffers from overfitting while building
cost volume, and the latter has a limited generalization due to the lack of
geometric constraint. To solve these problems, we propose a novel multimodal
neural network, namely UAMD-Net, for dense depth completion based on fusion of
binocular stereo matching and the weak constrain from the sparse point clouds.
Specifically, the sparse point clouds are converted to sparse depth map and
sent to the multimodal feature encoder (MFE) with binocular image, constructing
a cross-modal cost volume. Then, it will be further processed by the multimodal
feature aggregator (MFA) and the depth regression layer. Furthermore, the
existing multimodal methods ignore the problem of modal dependence, that is,
the network will not work when a certain modal input has a problem. Therefore,
we propose a new training strategy called Modal-dropout which enables the
network to be adaptively trained with multiple modal inputs and inference with
specific modal inputs. Benefiting from the flexible network structure and
adaptive training method, our proposed network can realize unified training
under various modal input conditions. Comprehensive experiments conducted on
KITTI depth completion benchmark demonstrate that our method produces robust
results and outperforms other state-of-the-art methods.


title:
Boson sampling cannot be faithfully simulated by only the lower-order
  multi-boson interferences

abstract:
  To simulate noisy boson sampling approximating it by only the lower-order
multi-boson interferences (e.g., by a smaller number of interfering bosons and
classical particles) is very popular idea. I show that the output data from any
such classical simulations can be efficiently distinguished from that of the
quantum device they try to simulate, even with finite noise in the latter. The
distinguishing datasets can be the experimental estimates of some large
probabilities, a wide class of such is presented. This is a sequel of
\textit{Quantum} \textbf{5}, 423 (2021), where I present more accessible
account of the main result enhanced by additional insight on the contribution
from the higher-order multi-boson interferences in presence of noise.


title:
Compressive Sensing-Based Recovery of Molecular Mixtures with
  Cross-Reactive Receptor Arrays

abstract:
  In this paper, we propose a novel concept for engineered molecular
communication (MC) systems inspired by animal olfaction. We focus on a
multi-user scenario where transmitters employ unique mixtures of different
types of signaling molecules to convey their messages to a central receiver,
which is equipped with an array comprising $R$ different types of receptors to
detect the emitted molecule mixtures. The hardware complexity of an MC system
employing \textit{orthogonal} molecule-receptor pairs would linearly scale with
the number of signaling molecule types $Q$ (i.e., $R=Q$). Natural olfaction
systems avoid such high complexity by employing arrays of
\textit{cross-reactive} receptors, where each type of molecule activates
multiple types of receptors and each type of receptor is predominantly
activated by multiple types of molecules albeit with different activation
strengths. For instance, the human olfactory system is believed to discriminate
several thousands of chemicals using only a few hundred receptor types, i.e.,
$Q\gg R$. Motivated by this observation, we first develop an end-to-end MC
channel model that accounts for the key properties of olfaction. Subsequently,
we formulate the molecule mixture recovery as a convex compressive sensing (CS)
problem which can be efficiently solved via available numerical solvers. Our
simulation results confirm the efficiency of the proposed CS problem for the
recovery of the molecular mixture signal and quantify the system performance
for various system parameters.


title: Maximizing dimension for Bernoulli measures and the Gauss map

abstract:
  We give a short proof that there exists a countable state Bernoulli measure
maximizing the dimension of their images under the continued fraction
expansion.


title:
Nested smoothing algorithms for inference and tracking of heterogeneous
  multi-scale state-space systems

abstract:
  Multi-scale problems, where variables of interest evolve in different
time-scales and live in different state-spaces. can be found in many fields of
science. Here, we introduce a new recursive methodology for Bayesian inference
that aims at estimating the static parameters and tracking the dynamic
variables of these kind of systems. Although the proposed approach works in
rather general multi-scale systems, for clarity we analyze the case of a
heterogeneous multi-scale model with 3 time-scales (static parameters, slow
dynamic state variables and fast dynamic state variables). The proposed scheme,
based on nested filtering methodology of P\'erez-Vieites et al. (2018),
combines three intertwined layers of filtering techniques that approximate
recursively the joint posterior probability distribution of the parameters and
both sets of dynamic state variables given a sequence of partial and noisy
observations. We explore the use of sequential Monte Carlo schemes in the first
and second layers while we use an unscented Kalman filter to obtain a Gaussian
approximation of the posterior probability distribution of the fast variables
in the third layer. Some numerical results are presented for a stochastic
two-scale Lorenz 96 model with unknown parameters.


title:
Fuzzy-based Robust Precision Consensus Tracking for Uncertain Networked
  Systems with Cooperative-Antagonistic Interactions

abstract:
  In bipartite consensus tracking (BCT) tasks for nonlinear multiagent systems,
stochastic disturbances and actuator faults are regarded as essential factors
that hamper effective controller formulation and tracking precision
improvement. To address these difficulties, we design an improved finitetime
performance function (FTPF) for a fuzzy fault-tolerant distributed cooperative
control scheme to achieve finite-time robust precision BCT tasks for nonlinear
multiagent systems. The parameter selection range of the improved FTPF is
relaxed, which renders systems to achieve better transient performance.
Benefitting from stochastic Lyapunov stability theory, it is shown that all
signals of systems are semi-global uniformly ultimately bounded in probability,
and bipartite consensus errors can satisfy the arbitrary precision with
probability in the predefined time. Finally, to verify its effectiveness, the
proposed control scheme is applied to BCT tasks of a group of vehicles, which
manifests anticipated control performance under various uncertainties.


title: Analytic expressions for the moving infinite line source model

abstract:
  Groundwater flow can have a significant impact on the thermal response of
ground heat exchangers. The moving infinite line source model is thus widely
used in practice as it considers both conductive and advective heat transfert
processes. Solution of this model involves a relatively heavy numerical
quadrature. Contrarily to the infinite line source model, there is currently no
known first-order approximation that could be useful for many practical
applications. In this paper, known analytical expressions of the Hantush well
function and generalized incomplete gamma function are first revisited. A clear
link between these functions and the moving infinite line source model is then
established. Then, two new exact and integral-free analytical expressions are
proposed, along with two new first-order approximations. The new analytical
expressions proposed take the form of convergent power series involving no
recursive evaluations. It is shown that relative errors less than 1% can be
obtained with only a few summands. The convergence properties of the series,
their accuracy and the validity domain of the first-order approximations are
also presented and discussed.


title:
The characterizing properties of (signless) Laplacian permanental
  polynomials of bicyclic graphs

abstract:
  Let $G$ be a graph with $n$ vertices, and let $L(G)$ and $Q(G)$ be the
Laplacian matrix and signless Laplacian matrix of $G$, respectively. The
polynomial $\pi(L(G);x)={\rm per}(xI-L(G))$ (resp. $\pi(Q(G);x)={\rm
per}(xI-Q(G))$) is called {\em Laplacian permanental polynomial} (resp. {\em
signless Laplacian permanental polynomial}) of $G$. In this paper, we show that
two classes of bicyclic graphs are determined by their (signless) Laplacian
permanental polynomials.


title:
Scheduling Coflows for Minimizing the Total Weighted Completion Time in
  Heterogeneous Parallel Networks

abstract:
  Coflow is a network abstraction used to represent communication patterns in
data centers. The coflow scheduling problem in large data centers is one of the
most important $NP$-hard problems. Many previous studies on coflow scheduling
mainly focus on the single-core model. However, with the growth of data
centers, this single-core model is no longer sufficient. This paper considers
the coflow scheduling problem in heterogeneous parallel networks. The
heterogeneous parallel network is an architecture based on multiple network
cores running in parallel. In this paper, two polynomial-time approximation
algorithms are developed for scheduling divisible and indivisible coflows in
heterogeneous parallel networks, respectively. Both algorithms achieve an
approximation ratio of $O(\log m/ \log \log m)$ with arbitrary release times.


title:
Observation of the $Y(4230)$ and a new structure in $e^+e^- \rightarrow
  K^+K^-J/\psi$

abstract:
  The cross sections of $e^+e^- \rightarrow K^+K^-J/\psi$ at center-of-mass
energies from 4.127 to 4.600 GeV are measured based on 15.6 fb$^{-1}$ data
collected with the BESIII detector operating at the BEPCII storage ring. Two
resonant structures are observed in the line shape of the cross sections. The
mass and width of the first structure are measured to be
($4225.3~\pm~2.3~\pm~21.5$) MeV and ($72.9 \pm 6.1 \pm 30.8$) MeV,
respectively. They are consistent with those of the established $Y(4230)$. The
second structure is observed for the first time with a statistical significance
greater than 8$\sigma$, denoted as $Y(4500)$. Its mass and width are determined
to be ($4484.7~\pm~13.3~\pm~24.1$) MeV and ($111.1~\pm~30.1~\pm~15.2$) MeV,
respectively. The first presented uncertainties are statistical and the second
ones are systematic. The product of the electronic partial width with the decay
branching fraction $ \Gamma(Y(4230)\to e^+ e^-) \mathcal{B}(Y(4230) \to K^+ K^-
J/\psi)$ is reported.


title:
Magnetic properties of triangular lattice antiferromagnets Ba3RB9O18 (R
  = Yb, Er)

abstract:
  Frustration, spin correlations and interplay between competing degrees of
freedom are some of the key ingredients that underlie exotic states with
fractional excitations in quantum materials. Rare-earth based two dimensional
magnetic lattice wherein crystal electric field, spin-orbit coupling,
anisotropy and electron correlation between rare-earth moments offer a new
paradigm in this context. Herein, we present crystal structure, magnetic
susceptibility and specific heat accompanied by crystal electric field
calculations on the polycrystalline sample of Ba3RB9O18 (R = Yb, Er) in which
R3+ ions form a perfect triangular lattice without anti-site disorder. The
localized R3+ spins show neither long-range order nor spin-glass state down to
1.9 K in Ba3RB9O18. Magnetization data reveal a pseudospin Jeff = 1/2 ( Yb3+)
in the Kramers doublet state and a weak antiferromagnetic interaction between
Jeff = 1/2 moments in the Yb variant. On the other hand, the effective moment
{\mu}eff = 8.8 {\mu}B was obtained from the Curie-Weiss fit of the
low-temperature susceptibility data of Er variant suggests the admixture of
higher crystal electric field states with the ground state. The Curie-Weiss fit
of low-temperature susceptibility data for Er system unveils the presence of a
relatively strong antiferromagnetic interaction between Er3+ moments compared
to its Yb3+ analog. Ba3ErB9O18 does not show long-range magnetic ordering down
to 500 mK. Furthermore, our crystal electric field calculations based on
magnetization data of Ba3ErB9O18 suggest the presence of a small gap between
the ground and first excited Kramers doublets. The broad maximum around 4 K in
magnetic specific heat in zero-field is attributed to the thermal population of
the first CEF excited state in Ba3ErB9O18, which is consistent with our CEF
calculations.


title: Neutrino dipole portal at electron colliders

abstract:
  We propose to search for a heavy neutral lepton (HNL), that is also know as
sterile neutrino, in electron colliders running with the center-of-mass
energies at few GeV, including BESIII, Belle II, and the proposed Super Tau
Charm Factory (STCF). We consider the HNL interacting with Standard Model
neutrino and photon via a transition magnetic moment, the so-called dipole
portal.We use the monophoton signature at electron colliders to probe the
constraints on the active-sterile neutrino transition magnetic moments $d$ as
the function of the HNL's mass $m_N$.It is found that BESIII, Belle II and STCF
can probe the upper limits for $d$ down to 1.3 $\times 10^{-5}\ {\rm
GeV}^{-1}$, 8 $\times 10^{-6}\ {\rm GeV}^{-1}$, and 1.3 $\times 10^{-6}\ {\rm
GeV}^{-1}$ with $m_N$ around GeV scale, respectively, and have sensitivity to
the previously unexplored parameter space for electron- ($d_e$) and
tau-neutrino ($d_\tau$) dipole portal with $m_N$ from dozens to thounsands MeV.
On $d_\mu$ for HNL mixing with the {muon}-neutrino, Belle II and STCF can also
provide leading constraints.


title: Logical Inference for Counting on Semi-structured Tables

abstract:
  Recently, the Natural Language Inference (NLI) task has been studied for
semi-structured tables that do not have a strict format. Although neural
approaches have achieved high performance in various types of NLI, including
NLI between semi-structured tables and texts, they still have difficulty in
performing a numerical type of inference, such as counting. To handle a
numerical type of inference, we propose a logical inference system for
reasoning between semi-structured tables and texts. We use logical
representations as meaning representations for tables and texts and use model
checking to handle a numerical type of inference between texts and tables. To
evaluate the extent to which our system can perform inference with numerical
comparatives, we make an evaluation protocol that focuses on numerical
understanding between semi-structured tables and texts in English. We show that
our system can more robustly perform inference between tables and texts that
requires numerical understanding compared with current neural approaches.


title: Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

abstract:
  Open intent classification is a practical yet challenging task in dialogue
systems. Its objective is to accurately classify samples of known intents while
at the same time detecting those of open (unknown) intents. Existing methods
usually use outlier detection algorithms combined with K-class classifier to
detect open intents, where K represents the class number of known intents.
Different from them, in this paper, we consider another way without using
outlier detection algorithms. Specifically, we directly train a (K+1)-class
classifier for open intent classification, where the (K+1)-th class represents
open intents. To address the challenge that training a (K+1)-class classifier
with training samples of only K classes, we propose a deep model based on Soft
Labeling and Manifold Mixup (SLMM). In our method, soft labeling is used to
reshape the label distribution of the known intent samples, aiming at reducing
model's overconfident on known intents. Manifold mixup is used to generate
pseudo samples for open intents, aiming at well optimizing the decision
boundary of open intents. Experiments on four benchmark datasets demonstrate
that our method outperforms previous methods and achieves state-of-the-art
performance. All the code and data of this work can be obtained at
https://github.com/zifengcheng/SLMM.


title:
Universal Solution Manifold Networks (USM-Nets): non-intrusive mesh-free
  surrogate models for problems in variable domains

abstract:
  We introduce Universal Solution Manifold Network (USM-Net), a novel surrogate
model, based on Artificial Neural Networks (ANNs), which applies to
differential problems whose solution depends on physical and geometrical
parameters. Our method employs a mesh-less architecture, thus overcoming the
limitations associated with image segmentation and mesh generation required by
traditional discretization methods. Indeed, we encode geometrical variability
through scalar landmarks, such as coordinates of points of interest. In
biomedical applications, these landmarks can be inexpensively processed from
clinical images. Our approach is non-intrusive and modular, as we select a
data-driven loss function. The latter can also be modified by considering
additional constraints, thus leveraging available physical knowledge. Our
approach can also accommodate a universal coordinate system, which supports the
USM-Net in learning the correspondence between points belonging to different
geometries, boosting prediction accuracy on unobserved geometries. Finally, we
present two numerical test cases in computational fluid dynamics involving
variable Reynolds numbers as well as computational domains of variable shape.
The results show that our method allows for inexpensive but accurate
approximations of velocity and pressure, avoiding computationally expensive
image segmentation, mesh generation, or re-training for every new instance of
physical parameters and shape of the domain.


title:
Softened sp2-sp3 bonding network leads to strong anharmonicity and weak
  hydrodynamics in graphene+

abstract:
  Graphene+, a novel carbon monolayer with sp2-sp3 hybridization, is recently
reported to exhibit graphene-like Dirac properties and unprecedented
out-of-plane half-auxetic behavior [Yu et al, Cell Reports Physical Science, 3
100790 (2022)]. Herein, from comprehensively state-of-the-art first-principles
studies, we report the exceptional lattice thermal transport properties of
graphene+ driven by the unique sp2-sp3 crystal configuration. At room
temperature, the thermal conductivity of graphene+ is calculated to be ~170
W/mK, which is much lower than that of graphene (~3170 W/mK) Despite the
buckling structure, weak phonon scattering phase space is trapped in graphene+.
Thus, the reduction in thermal conductivity magnitude stems from soft bonding
due to the unique sp2-sp3 crystal configuration. Soft bonding suppresses the
vibrations of acoustic phonons, which leads to strong anharmonicity and weak
phonon hydrodynamics. Further, lower group velocity, relaxation time and
smaller phonon mean free path emerge in graphene+, and the significantly
decreased thermal conductivity is achieved. Our study provides fundamental
physical insights into the thermal transport properties of graphene+, and it
serves as an ideal model to study atomic bonding versus thermal transport
properties due to weak scattering phase space.


title:
Deformation of rigid Galois representations and cohomology of certain
  quaternionic unitary Shimura variety

abstract:
  In this article, we use deformation theory of Galois representations valued
in the symplectic group of degree four to prove a freeness result for the
cohomology of certain quaternionic unitary Shimura variety over the universal
deformation ring for certain type of residual representation satisfying a
property called rigidity. This result plays an important role in the proof of
the arithmetic level raising theorem for the symplectic similitude group of
degree four over the field of rational numbers by the author.


title:
A Multi-Faceted Study of Nematic Order Reconstruction in Microfluidic
  Channels

abstract:
  We study order reconstruction (OR) solutions in the Beris-Edwards framework
for nematodynamics, for both passive and active nematic flows in a microfluidic
channel. OR solutions exhibit polydomains and domain walls, and as such, are of
physical interest. We show that OR solutions exist for passive flows with
constant velocity and pressure, but only for specific boundary conditions. We
prove the existence of unique, symmetric and non-singular nematic profiles, for
boundary conditions that do not allow for OR solutions. We compute asymptotic
expansions for OR-type solutions for passive flows with non-constant velocity
and pressure, and active flows, which shed light into the internal structure of
domain walls. The asymptotics are complemented by extensive numerical studies
that demonstrate the universality of OR-type structures in static and dynamic
scenarios.


title:
Interbilayer lipid friction coefficient from equilibrium canonical
  simulations

abstract:
  A fundamental result in the theory of Brownian motion is the
Einstein-Sutherland relation between mobility and diffusion constant. Any
classical linear response transport coefficient obeys a similar
Einstein-Helfand relation. We show in this work how to derive the interleaflet
friction coefficient of lipid bilayer by means of an adequate generalisation of
the Einstein relation. Special attention must be paid in practical cases to the
constraints on the system center of mass position that must be enforced when
coupling the system to thermostat.


title:
An iterative data-driven turbulence modeling framework based on Reynolds
  stress representation

abstract:
  Data-driven turbulence modeling studies have reached such a stage that the
fundamental framework is basically settled, but several essential issues remain
that strongly affect the performance, including accuracy, smoothness, and
generalization capacity. Two problems are studied in the current research: (1)
the processing of the Reynolds stress tensor and (2) the coupling method
between the machine learning turbulence model and CFD solver. The first
determines the form of predicting targets and the resulting physical
completeness and interpretability. The second determines the training process
and intrinsic relevance between the mean flow features and Reynolds stress. For
the Reynolds stress processing issue, we perform the theoretical derivation to
extend the relevant tensor arguments of Reynolds stress in addition to the
strain rate and rotation rate. Then, the tensor representation theorem is
employed to give the complete irreducible invariants and integrity basis. In
addition, an adaptive regularization term is employed to enhance the
representation performance. For the CFD coupling issue, an iterative coupling
data-driven turbulence modeling framework with consistent convergence is
proposed. The training data preparation, predicting target selection, and
computation platform are illustrated. The framework is then applied to a
canonical separated flow for verification. The mean flow results obtained by
coupling computation of the trained machine learning model and CFD solver have
high consistency with the DNS true values, which proves the validity of the
current approach.


title:
Enhanced Prediction of Three-dimensional Finite Iced Wing Separated Flow
  Near Stall

abstract:
  Icing on three-dimensional wings causes severe flow separation near stall.
Standard improved delayed detached eddy simulation (IDDES) is unable to
correctly predict the separating reattaching flow due to its inability to
accurately resolve the Kelvin-Helmholtz instability. In this study, a shear
layer adapted subgrid length scale is applied to enhance the IDDES prediction
of the flow around a finite NACA (National Advisory Committee for Aeronautics)
0012 wing with leading edge horn ice. It is found that applying the new length
scale contributes to a more accurate prediction of the separated shear layer
(SSL). The reattachment occurs earlier as one moves towards either end of the
wing due to the downwash effect of the wing tip vortex or the influence of
end-wall flow. Consequently, the computed surface pressure distributions agree
well with the experimental measurements. In contrast, standard IDDES severely
elongates surface pressure plateaus. For instantaneous flow, the new length
scale helps correctly resolve the rollup and subsequent pairing of vortical
structures due to its small values in the initial SSL. The computed Strouhal
numbers of vortical motions are approximately 0.2 in the initial SSL based on
the vorticity thickness and 0.1 around the reattachment based on the separation
bubble length. Both frequencies increase when moving towards the wing tip due
to the downwash effect of the tip vortex. In comparison, the excessive eddy
viscosity levels from the standard IDDES severely delay the rollup of spanwise
structures and give rise to "overcoherent" structures.


title:
Two-path interference in the resonance-enhanced few-photon ionization of
  atoms

abstract:
  We investigate the resonance-enhanced few-photon ionization of atomic lithium
by linearly polarized light whose frequency is tuned near the 2s-2p transition.
Considering the direction of light polarization orthogonal to the quantization
axis, the process can be viewed as an atomic "double-slit experiment" where the
2p states with magnetic quantum numbers m_l=+-1 act as the slits. In our
experiment, we can virtually close one of the two slits by preparing lithium in
one of the two circularly polarized 2p states before subjecting it to the
ionizing radiation, thereby allowing us to extract interference term between
the two pathways and obtain complex phase information on the final state. The
analysis of our experimental results is supported by numerical calculations.


title: Homology of weighted path complexes and directed hypergraphs

abstract:
  We introduce the weighted path homology on the category of weigh\-ted
directed hypergraphs and describe conditions of homotopy invariance of weighted
path homology groups. We give several examples that explain the nontriviality
of the introduced notions.


title: Stable laws for random dynamical systems

abstract:
  In this paper we consider random dynamical systems formed by concatenating
maps acting on the unit interval $[0,1]$ in an iid fashion. Considered as a
stationary Markov process, the random dynamical system possesses a unique
stationary measure $\nu$. We consider a class of non square-integrable
observables $\phi$, mostly of form $\phi(x)=d(x,x_0)^{-\frac{1}{\alpha}}$ where
$x_0$ is non-periodic point satisfying some other genericity conditions, and
more generally regularly varying observables with index $\alpha \in (0,2)$. The
two types of maps we concatenate are a class of piecewise $C^2$ expanding maps,
and a class of intermittent maps possessing an indifferent fixed point at the
origin. Under conditions on the dynamics and $\alpha$ we establish Poisson
limit laws, convergence of scaled Birkhoff sums to a stable limit law and
functional stable limit laws, in both the annealed and quenched case. The
scaling constants for the limit laws for almost every quenched realization are
the same as those of the annealed case and determined by $\nu$. This is in
contrast to the scalings in quenched central limit theorems where the centering
constants depend in a critical way upon the realization and are not the same
for almost every realization.


title:
Physically Interpretable Feature Learning and Inverse Design of
  Supercritical Airfoils

abstract:
  Machine-learning models have demonstrated a great ability to learn complex
patterns and make predictions. In high-dimensional nonlinear problems of fluid
dynamics, data representation often greatly affects the performance and
interpretability of machine learning algorithms. With the increasing
application of machine learning in fluid dynamics studies, the need for
physically explainable models continues to grow. This paper proposes a feature
learning algorithm based on variational autoencoders, which is able to assign
physical features to some latent variables of the variational autoencoder. In
addition, it is theoretically proved that the remaining latent variables are
independent of the physical features. The proposed algorithm is trained to
include shock wave features in its latent variables for the reconstruction of
supercritical pressure distributions. The reconstruction accuracy and physical
interpretability are also compared with those of other variational
autoencoders. Then, the proposed algorithm is used for the inverse design of
supercritical airfoils, which enables the generation of airfoil geometries
based on physical features rather than the complete pressure distributions. It
also demonstrates the ability to manipulate certain pressure distribution
features of the airfoil without changing the others.


title:
$2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via
  Scalable Deep Learning Method

abstract:
  For decades, people are developing efficient numerical methods for solving
the challenging quantum many-body problem, whose Hilbert space grows
exponentially with the size of the problem. However, this journey is far from
over, as previous methods all have serious limitations. The recently developed
deep learning methods provide a very promising new route to solve the
long-standing quantum many-body problems. We report that a deep learning based
simulation protocol can achieve the solution with state-of-the-art precision in
the Hilbert space as large as $2^{1296}$ for spin system and $3^{144}$ for
fermion system , using a HPC-AI hybrid framework on the new Sunway
supercomputer. With highly scalability up to 40 million heterogeneous cores,
our applications have measured 94% weak scaling efficiency and 72% strong
scaling efficiency. The accomplishment of this work opens the door to simulate
spin models and Fermion models on unprecedented lattice size with extreme high
precision.

categories: quant-ph cond-mat.dis-nn cond-mat.str-el physics.comp-ph


title: A topological construction of families of Galois covers of the line

abstract:
  We describe a new construction of families of Galois coverings of the line
using basic properties of configuration spaces, covering theory, and the
Grauert-Remmert Extension Theorem. Our construction provides an alternative to
a previous construction due to Gonz\'alez-D\'{i}ez and Harvey (which uses
Teichm\"uller theory and Fuchsian groups) and, in the case the Galois group is
non-abelian, corrects an inaccuracy therein.


title:
Graph-incorporated Latent Factor Analysis for High-dimensional and
  Sparse Matrices

abstract:
  A High-dimensional and sparse (HiDS) matrix is frequently encountered in a
big data-related application like an e-commerce system or a social network
services system. To perform highly accurate representation learning on it is of
great significance owing to the great desire of extracting latent knowledge and
patterns from it. Latent factor analysis (LFA), which represents an HiDS matrix
by learning the low-rank embeddings based on its observed entries only, is one
of the most effective and efficient approaches to this issue. However, most
existing LFA-based models perform such embeddings on a HiDS matrix directly
without exploiting its hidden graph structures, thereby resulting in accuracy
loss. To address this issue, this paper proposes a graph-incorporated latent
factor analysis (GLFA) model. It adopts two-fold ideas: 1) a graph is
constructed for identifying the hidden high-order interaction (HOI) among nodes
described by an HiDS matrix, and 2) a recurrent LFA structure is carefully
designed with the incorporation of HOI, thereby improving the representa-tion
learning ability of a resultant model. Experimental results on three real-world
datasets demonstrate that GLFA outperforms six state-of-the-art models in
predicting the missing data of an HiDS matrix, which evidently supports its
strong representation learning ability to HiDS data.


title:
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and
  Sparse data

abstract:
  High-dimensional and sparse (HiDS) matrices are omnipresent in a variety of
big data-related applications. Latent factor analysis (LFA) is a typical
representation learning method that extracts useful yet latent knowledge from
HiDS matrices via low-rank approximation. Current LFA-based models mainly focus
on a single-metric representation, where the representation strategy designed
for the approximation Loss function, is fixed and exclusive. However,
real-world HiDS matrices are commonly heterogeneous and inclusive and have
diverse underlying patterns, such that a single-metric representation is most
likely to yield inferior performance. Motivated by this, we in this paper
propose a multi-metric latent factor (MMLF) model. Its main idea is two-fold:
1) two vector spaces and three Lp-norms are simultaneously employed to develop
six variants of LFA model, each of which resides in a unique metric
representation space, and 2) all the variants are ensembled with a tailored,
self-adaptive weighting strategy. As such, our proposed MMLF enjoys the merits
originated from a set of disparate metric spaces all at once, achieving the
comprehensive and unbiased representation of HiDS matrices. Theoretical study
guarantees that MMLF attains a performance gain. Extensive experiments on eight
real-world HiDS datasets, spanning a wide range of industrial and science
domains, verify that our MMLF significantly outperforms ten state-of-the-art,
shallow and deep counterparts.


title:
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind
  Image Deblurring

abstract:
  Blind image deblurring (BID) remains a challenging and significant task.
Benefiting from the strong fitting ability of deep learning, paired data-driven
supervised BID method has obtained great progress. However, paired data are
usually synthesized by hand, and the realistic blurs are more complex than
synthetic ones, which makes the supervised methods inept at modeling realistic
blurs and hinders their real-world applications. As such, unsupervised deep BID
method without paired data offers certain advantages, but current methods still
suffer from some drawbacks, e.g., bulky model size, long inference time, and
strict image resolution and domain requirements. In this paper, we propose a
lightweight and real-time unsupervised BID baseline, termed Frequency-domain
Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with
attractive properties, i.e., no image domain limitation, no image resolution
limitation, 25x lighter than SOTA, and 5x faster than SOTA. To guarantee the
lightweight property and performance superiority, two new collaboration units
called lightweight domain conversion unit(LDCU) and parameter-free
frequency-domain contrastive unit(PFCU) are designed. LDCU mainly implements
inter-domain conversion in lightweight manner. PFCU further explores the
similarity measure, external difference and internal connection between the
blurred domain and sharp domain images in frequency domain, without involving
extra parameters. Extensive experiments on several image datasets demonstrate
the effectiveness of our FCL-GAN in terms of performance, model size and
reference time.


title:
Detection of Small Holes by the Scale-Invariant Robust Density-Aware
  Distance (RDAD) Filtration

abstract:
  A novel topological-data-analytical (TDA) method is proposed to distinguish,
from noise, small holes surrounded by high-density regions of a probability
density function whose mass is concentrated near a manifold (or more generally,
a CW complex) embedded in a high-dimensional Euclidean space. The proposed
method is robust against additive noise and outliers. In particular, sample
points are allowed to be perturbed away from the manifold. Traditional TDA
tools, like those based on the distance filtration, often struggle to
distinguish small features from noise, because of their short persistence. An
alternative filtration, called Robust Density-Aware Distance (RDAD) filtration,
is proposed to prolong the persistence of small holes surrounded by
high-density regions. This is achieved by weighting the distance function by
the density in the sense of Bell et al. Distance-to-measure is incorporated to
enhance stability and mitigate noise due to the density estimation. The utility
of the proposed filtration in identifying small holes, as well as its
robustness against noise, are illustrated through an analytical example and
extensive numerical experiments. Basic mathematical properties of the proposed
filtration are proven.


title:
Construction of Exact Solutions to Nahm's Equations for the
  Multimonopole

abstract:
  We construct high rank solutions to Nahm's equations for boundary conditions
that correspond to the Dirac multimonopole. Here, the spectral curve is
explicitly known and we achieve the integration by constructing a basis of
polynomial tuples that forms a frame for the flow of the eigenline bundle over
the curve.


title: Non-Linear Continuous Semimartingales

abstract:
  In this paper we study a family of non-linear (conditional) expectations that
can be understood as a continuous semimartingale with uncertain local
characteristics. Here, the differential characteristics are prescribed by a
set-valued function that depends on time and path in a non-Markovian way. We
provide a dynamic programming principle for the non-linear expectation and we
link the corresponding value function to a variational form of a non-linear
path-dependent partial differential equation. Furthermore, we prove that the
non-linear expectation solves a non-linear martingale problem, which confirms
our interpretation as a non-linear semimartingale.


title:
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection
  using limited triplets

abstract:
  Deep learning approaches applied to medical imaging have reached near-human
or better-than-human performance on many diagnostic tasks. For instance, the
CheXpert competition on detecting pathologies in chest x-rays has shown
excellent multi-class classification performance. However, training and
validating deep learning models require extensive collections of images and
still produce false inferences, as identified by a human-in-the-loop. In this
paper, we introduce a practical approach to improve the predictions of a
pre-trained model through Few-Shot Learning (FSL). After training and
validating a model, a small number of false inference images are collected to
retrain the model using \textbf{\textit{Image Triplets}} - a false positive or
false negative, a true positive, and a true negative. The retrained FSL model
produces considerable gains in performance with only a few epochs and few
images. In addition, FSL opens rapid retraining opportunities for
human-in-the-loop systems, where a radiologist can relabel false inferences,
and the model can be quickly retrained. We compare our retrained model
performance with existing FSL approaches in medical imaging that train and
evaluate models at once.


title: Symmetry-breaking and bifurcation diagrams of fractional-order maps

abstract:
  In this paper two important aspects related to Caputo fractional-order
discrete variant of a class of maps defined on the complex plane, are
analytically and numerically revealed: attractors symmetry-broken induced by
the fractional-order and the sensible problem of determining the right
bifurcation diagram of discrete systems of fractional-order. It is proved that
maps of integer order with dihedral symmetry or cycle symmetry loose their
symmetry once they are transformed in fractional-order maps. Also, it is
conjectured that, contrarily to integer-order maps, determining the bifurcation
diagrams of fractional-order maps is far from being a clarified problem. Two
examples are considered: dihedral logistic map and cyclic logistic map.


title: Beyond L1: Faster and Better Sparse Models with skglm

abstract:
  We propose a new fast algorithm to estimate any sparse generalized linear
model with convex or non-convex separable penalties. Our algorithm is able to
solve problems with millions of samples and features in seconds, by relying on
coordinate descent, working sets and Anderson acceleration. It handles
previously unaddressed models, and is extensively shown to improve state-of-art
algorithms. We provide a flexible, scikit-learn compatible package, which
easily handles customized datafits and penalties.


title:
Local treewidth of random and noisy graphs with applications to stopping
  contagion in networks

abstract:
  We study the notion of local treewidth in sparse random graphs: the maximum
treewidth over all $k$-vertex subgraphs of an $n$-vertex graph. When $k$ is not
too large, we give nearly tight bounds for this local treewidth parameter; we
also derive tight bounds for the local treewidth of noisy trees, trees where
every non-edge is added independently with small probability. We apply our
upper bounds on the local treewidth to obtain fixed parameter tractable
algorithms (on random graphs and noisy trees) for edge-removal problems
centered around containing a contagious process evolving over a network. In
these problems, our main parameter of study is $k$, the number of "infected"
vertices in the network. For a certain range of parameters the running time of
our algorithms on $n$-vertex graphs is $2^{o(k)}\textrm{poly}(n)$, improving
upon the $2^{\Omega(k)}\textrm{poly}(n)$ performance of the best-known
algorithms designed for worst-case instances of these edge deletion problems.


title: Time kink: modeling change of metric signature

abstract:
  The model of a signature change of a metric from the Lorenztian to Euclidean
one with the use of a time dependent kink as $g_{00}$ component of the metric
is considered. The metric which describes the continuous change of the
signature of this type on a hypersurface is constructed and corresponding
Einstein equations are solved in both regions of the space-time. The
discontinuities of the Einstein tensor components on the hypersurface are
discussed as well as junction conditions for the parameters of the solutions.
Additionally, the properties of simple cosmological model are discussed in the
model, the presence of an inflation in the model is demonstrated as a
consequence of the signature change without any additional fields need. The
possible appearance of this type of solution for the metric in the form of the
scalar field in the framework of the Einstein-Cartan gravity is described. The
Lagrangian for that field and it's properties are obtained as well as a time
dependent potential of the field which separate the Lorentzian and Euclidean
regions of the space-time.


title: Efficient and ultra-stable perovskite light-emitting diodes

abstract:
  Perovskite light-emitting diodes (PeLEDs) have emerged as a strong contender
for next-generation display and information technologies. However, similar to
perovskite solar cells, the poor operational stability remains the main
obstacle toward commercial applications. Here we demonstrate ultra-stable and
efficient PeLEDs with extraordinary operational lifetimes (T50) of 1.0x10^4 h,
2.8x10^4 h, 5.4x10^5 h, and 1.9x10^6 h at initial radiance (or current
densities) of 3.7 W/sr/m2 (~5 mA/cm2), 2.1 W/sr/m2 (~3.2 mA/cm2), 0.42 W/sr/m2
(~1.1 mA/cm2), and 0.21 W/sr/m2 (~0.7 mA/cm2) respectively, and external
quantum efficiencies of up to 22.8%. Key to this breakthrough is the
introduction of a dipolar molecular stabilizer, which serves two critical roles
simultaneously. First, it prevents the detrimental transformation and
decomposition of the alpha-phase FAPbI3 perovskite, by inhibiting the formation
of lead and iodide intermediates. Secondly, hysteresis-free device operation
and microscopic luminescence imaging experiments reveal substantially
suppressed ion migration in the emissive perovskite. The record-long PeLED
lifespans are encouraging, as they now satisfy the stability requirement for
commercial organic LEDs (OLEDs). These results remove the critical concern that
halide perovskite devices may be intrinsically unstable, paving the path toward
industrial applications.

categories: cond-mat.mtrl-sci physics.app-ph physics.chem-ph physics.optics


title:
Riemannian optimization using three different metrics for Hermitian PSD
  fixed-rank constraints: an extended version

abstract:
  We consider smooth optimization problems with a Hermitian positive
semi-definite fixed-rank constraint, where a quotient geometry with three
Riemannian metrics $g^i(\cdot, \cdot)$ $(i=1,2,3)$ is used to represent this
constraint. By taking the nonlinear conjugate gradient method (CG) as an
example, we show that CG on the quotient geometry with metric $g^1$ is
equivalent to CG on the factor-based optimization framework, which is often
called the Burer--Monteiro approach. We also show that CG on the quotient
geometry with metric $g^3$ is equivalent to CG on the commonly-used embedded
geometry. We call two CG methods equivalent if they produce an identical
sequence of iterates $\{X_k\}$. In addition, we show that if the limit point of
the sequence $\{X_k\}$ generated by an algorithm has lower rank, that is
$X_k\in \mathbb C^{n\times n}, k = 1, 2, \ldots$ has rank $p$ and the limit
point $X_*$ has rank $r < p$, then the condition number of the Riemannian
Hessian with metric $g^1$ can be unbounded, but those of the other two metrics
stay bounded. Numerical experiments show that the Burer--Monteiro CG method has
slower local convergence rate if the limit point has a reduced rank, compared
to CG on the quotient geometry under the other two metrics. This slower
convergence rate can thus be attributed to the large condition number of the
Hessian near a minimizer.