title:
SoK: Preventing Transaction Reordering Manipulations in Decentralized
  Finance

abstract:
  User transactions on Ethereum's peer-to-peer network are at risk of being
attacked. The smart contracts building decentralized finance (DeFi) have
introduced a new transaction ordering dependency to the Ethereum blockchain. As
a result, attackers can profit from front- and back-running transactions.
Multiple approaches to mitigate transaction reordering manipulations have
surfaced recently. However, the success of individual approaches in mitigating
such attacks and their impact on the entire blockchain remains largely
unstudied. In this systematization of knowledge (SoK), we categorize and
analyze state-of-the-art transaction reordering manipulation mitigation
schemes. Instead of restricting our analysis to a scheme's success at
preventing transaction reordering attacks, we evaluate its full impact on the
blockchain. Therefore, we are able to provide a complete picture of the
strengths and weaknesses of current mitigation schemes. We find that currently
no scheme fully meets all the demands of the blockchain ecosystem. In fact, all
approaches demonstrate unsatisfactory performance in at least one area relevant
to the blockchain ecosystem.


title: The neighbour sum distinguishing edge-weighting with local constraints

abstract:
  A $k$-edge-weighting of $G$ is a mapping $\omega:E(G)\longrightarrow
\{1,\ldots,k\}$. The edge-weighting naturally induces a vertex colouring
$\sigma_\omega:V(G)\longrightarrow \mathbb{N}$ given by
$\sigma_{\omega}(v)=\sum_{u\in N_G(v)}\omega(vu)$ for every $v\in V(G)$. The
edge-weighting $\omega$ is neighbour sum distinguishing if it yields a proper
vertex colouring $\sigma_{\omega}$, i.e., $\sigma_{\omega}(u)\neq
\sigma_{\omega}(v)$ for every edge $uv$ of $G$.We investigate a neighbour sum
distinguishing edge-weighting with local constraints, namely, we assume that
the set of edges incident to a vertex of large degree is not monochromatic. The
graph is nice if it has no components isomorphic to $K_2$. We prove that every
nice graph with maximum degree at most~5 admits a neighbour sum distinguishing
$(\Delta(G)+2)$-edge-weighting such that all the vertices of degree at least~2
are incident with at least two edges of different weights. Furthermore, we
prove that every nice graph admits a neighbour sum distinguishing
$7$-edge-weighting such that all the vertices of degree at least~6 are incident
with at least two edges of different weights. Finally, we show that nice
bipartite graphs admit a neighbour sum distinguishing $6$-edge-weighting such
that all the vertices of degree at least~2 are incident with at least two edges
of different weights.


title:
Early Adapting to Trends: Self-Stabilizing Information Spread using
  Passive Communication

abstract:
  How to efficiently and reliably spread information in a system is one of the
most fundamental problems in distributed computing. Recently, inspired by
biological scenarios, several works focused on identifying the minimal
communication resources necessary to spread information under faulty
conditions. Here we study the self-stabilizing bit-dissemination problem,
introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem
considers a fully-connected network of n agents, with a binary world of
opinions, one of which is called correct. At any given time, each agent holds
an opinion bit as its public output. The population contains a source agent
which knows which opinion is correct. This agent adopts the correct opinion and
remains with it throughout the execution. We consider the basic PULL model of
communication, in which each agent observes relatively few randomly chosen
agents in each round. The goal of the non-source agents is to quickly converge
on the correct opinion, despite having an arbitrary initial configuration,
i.e., in a self-stabilizing manner. Once the population converges on the
correct opinion, it should remain with it forever. Motivated by biological
scenarios in which animals observe and react to the behavior of others, we
focus on the extremely constrained model of passive communication, which
assumes that when observing another agent the only information that can be
extracted is the opinion bit of that agent. We prove that this problem can be
solved in a poly-logarithmic in n number of rounds with high probability, while
sampling a logarithmic number of agents at each round. Previous works solved
this problem faster and using fewer samples, but they did that by decoupling
the messages sent by agents from their output opinion, and hence do not fit the
framework of passive communication. Moreover, these works use complex recursive
algorithms with refined clocks that are unlikely to be used by biological
entities. In contrast, our proposed algorithm has a natural appeal as it is
based on letting agents estimate the current tendency direction of the
dynamics, and then adapt to the emerging trend.


title:
How to Read the Snowmass White Papers on Power Dynamics in Physics,
  Informal Socialization in Physics Training, and Policing and Gatekeeping in
  STEM

abstract:
  The Community Engagement Frontier presents this set of three white papers, as
part of Snowmass 2021. These papers address critical issues -- Power Dynamics
in Physics, Informal Socialization in Physics Training, and Policing and
Gatekeeping in STEM -- that make significant impacts on the experiences of the
people who work in and learn particle physics. In this introductory document,
we present crosscutting concepts that appear in each paper, and some advice on
how to manage readers' responses to the contents. We expect that you will learn
something new here. We hope that whatever you encounter, you will be energized
to increase justice in this discipline we all love.


title:
Correlating atom probe tomography with X-Ray and electron spectroscopies
  to understand microstructure-activity relationships in electrocatalysts

abstract:
  The search for a new energy paradigm with net-zero carbon emissions requires
new technologies for energy generation and storage that are at the crossroad
between engineering, chemistry, physics, surface and materials sciences. To
keep pushing the inherent boundaries of device performance and lifetime, we
need to step away from a cook-and-look approach and aim to establish the
scientific ground to guide the design of new materials. This requires strong
efforts in establishing bridges between microscopy and spectroscopy techniques,
across multiple scales. Here, we discuss how the complementarities of X-ray-
and electron-based spectroscopies and atom probe tomography can be exploited in
the study of surfaces and sub-surfaces to understand structure-property
relationships in electrocatalysts.


title:
CMS Phase-1 Pixel detector refurbishment during LS2 and readiness
  towards the LHC Run-3

abstract:
  The CMS Phase-1 pixel detector was extracted from the underground cavern
after the end of the LHC Run-2 in 2019 and has been kept cold to protect the
silicon sensors during the long shutdown period (LS2) in 2019-2021. The LHC is
now preparing for the next installment of the data taking beginning 2022. The
Phase-1 pixel detector is scheduled to be installed this year and is going
through a series of refurbishment and repairs to improve the quality of the
collected data and enhance the operational experience. The innermost barrel
layer has been replaced with new modules and features improved readout chips
(PROC600v4), front-end ASICs (TBM10d), and circuit boards to rectify the issues
discovered during the previous data taking. The forward pixel detector has been
equipped with new cooling inlets for safe handling and features a revised
high-voltage power distribution scheme to better match the low-voltage
granularity. All the DC-DC converters have been replaced with a new production,
consisting of an improved ASIC (FEAST2.3) to prevent them from breaking during
operation. Overall, this talk will summarize the refurbishment work of the
pixel detector during LS2, and highlight the readiness towards the LHC Run-3
after installation and commissioning.


title:
Action Candidate Driven Clipped Double Q-learning for Discrete and
  Continuous Action Tasks

abstract:
  Double Q-learning is a popular reinforcement learning algorithm in Markov
decision process (MDP) problems. Clipped Double Q-learning, as an effective
variant of Double Q-learning, employs the clipped double estimator to
approximate the maximum expected action value. Due to the underestimation bias
of the clipped double estimator, the performance of clipped Double Q-learning
may be degraded in some stochastic environments. In this paper, in order to
reduce the underestimation bias, we propose an action candidate-based clipped
double estimator for Double Q-learning. Specifically, we first select a set of
elite action candidates with high action values from one set of estimators.
Then, among these candidates, we choose the highest valued action from the
other set of estimators. Finally, we use the maximum value in the second set of
estimators to clip the action value of the chosen action in the first set of
estimators and the clipped value is used for approximating the maximum expected
action value. Theoretically, the underestimation bias in our clipped Double
Q-learning decays monotonically as the number of action candidates decreases.
Moreover, the number of action candidates controls the trade-off between the
overestimation and underestimation biases. In addition, we also extend our
clipped Double Q-learning to continuous action tasks via approximating the
elite continuous action candidates. We empirically verify that our algorithm
can more accurately estimate the maximum expected action value on some toy
environments and yield good performance on several benchmark problems.


title: Rotating quantum wave turbulence

abstract:
  Rotating turbulence is ubiquitous in nature. Previous works suggest that such
turbulence could be described as an ensemble of interacting inertial waves
across a wide range of length scales. For turbulence in macroscopic quantum
condensates, the nature of the transition between the quasiclassical dynamics
at large scales and the corresponding dynamics at small scales, where the
quantization of vorticity is essential, remains an outstanding unresolved
question. Here we expand the paradigm of wave-driven turbulence to rotating
quantum fluids where the spectrum of waves extends to microscopic scales as
Kelvin waves on quantized vortices. We excite inertial waves at the largest
scale by periodic modulation of the angular velocity and observe
dissipation-independent transfer of energy to smaller scales and the eventual
onset of the elusive Kelvin-wave cascade at the lowest temperatures. We further
find that energy is pumped to the system through a boundary layer distinct from
the classical Ekman layer and support our observations with numerical
simulations. Our experiments demonstrate a new regime of turbulent motion in
quantum fluids where the role of vortex reconnections can be neglected, thus
stripping the transition between the classical and the quantum regimes of
turbulence down to its bare bones.


title: Out-of-distribution Generalization with Causal Invariant Transformations

abstract:
  In real-world applications, it is important and desirable to learn a model
that performs well on out-of-distribution (OOD) data. Recently, causality has
become a powerful tool to tackle the OOD generalization problem, with the idea
resting on the causal mechanism that is invariant across domains of interest.
To leverage the generally unknown causal mechanism, existing works assume a
linear form of causal feature or require sufficiently many and diverse training
domains, which are usually restrictive in practice. In this work, we obviate
these assumptions and tackle the OOD problem without explicitly recovering the
causal feature. Our approach is based on transformations that modify the
non-causal feature but leave the causal part unchanged, which can be either
obtained from prior knowledge or learned from the training data in the
multi-domain scenario. Under the setting of invariant causal mechanism, we
theoretically show that if all such transformations are available, then we can
learn a minimax optimal model across the domains using only single domain data.
Noticing that knowing a complete set of these causal invariant transformations
may be impractical, we further show that it suffices to know only a subset of
these transformations. Based on the theoretical findings, a regularized
training procedure is proposed to improve the OOD generalization capability.
Extensive experimental results on both synthetic and real datasets verify the
effectiveness of the proposed algorithm, even with only a few causal invariant
transformations.


title:
Four-hundred Very Metal-Poor Stars Studied with LAMOST and Subaru. II.
  Elemental abundances

abstract:
  We present homogeneous abundance analysis of over 20 elements for 385 very
metal-poor (VMP) stars based on the LAMOST survey and follow-up observations
with the Subaru Telescope. It is the largest high-resolution VMP sample
(including 363 new objects) studied by a single program, and the first attempt
to accurately determine evolutionary stages for such a large sample based on
Gaia parallaxes. The sample covers a wide metallicity range from [Fe/H]=-1.7
down to [Fe/H]=-4.3, including over 110 objects with [Fe/H]<-3.0. The expanded
coverage in evolutionary status makes it possible to define abundance trends
respectively for giants and turn-off stars. The newly obtained abundance data
confirm most abundance trends found by previous studies, but also provide
useful update and new sample of outliers. The Li plateau is seen in -2.5 <
[Fe/H] <-1.7 in our sample, whereas the average Li abundance is clearly lower
at lower metallicity. Mg, Si, and Ca are overabundant with respect to Fe,
showing decreasing trend with increasing metallicity. Comparisons with chemical
evolution models indicate that the over-abundance of Ti, Sc, and Co are not
well reproduced by current theoretical predictions. Correlations are seen
between Sc and alpha-elements, while Zn shows a detectable correlation only
with Ti but not with other alpha-elements. The fraction of carbon-enhanced
stars ([C/Fe]> 0.7) is in the range of 20-30% for turn-off stars depending on
the treatment of objects for which C abundance is not determined, which is much
higher than that in giants (~8%). Twelve Mg-poor stars ([Mg/Fe] < 0.0) have
been identified in a wide metallicity range from [Fe/H] =-3.8 through -1.7.
Twelve Eu-rich stars ([Eu/Fe]> 1.0) have been discovered in -3.4 <[Fe/H]< -2.0,
enlarging the sample of r-process-enhanced stars with relatively high
metallicity.


title:
Quantum-jump vs stochastic Schr\"{o}dinger dynamics for Gaussian states
  with quadratic Hamiltonians and linear Lindbladians

abstract:
  The dynamics of Gaussian states for open quantum systems described by
Lindblad equations can be solved analytically for systems with quadratic
Hamiltonians and linear Lindbladians, showing the familiar phenomena of
dissipation and decoherence. It is well known that the Lindblad dynamics can be
expressed as an ensemble average over stochastic pure-state dynamics, which can
be interpreted as individual experimental implementations, where the form of
the stochastic dynamics depends on the measurement setup. Here we consider
quantum-jump and stochastic Schr\"odinger dynamics for initially Gaussian
states. While both unravellings converge to the same Lindblad dynamics when
averaged, the individual dynamics can differ qualitatively. For the stochastic
Schr\"odinger equation, Gaussian states remain Gaussian during the evolution,
with stochastic differential equations governing the evolution of the
phase-space centre and a deterministic evolution of the covariance matrix. In
contrast to this, individual pure-state dynamics arising from the quantum-jump
evolution do not remain Gaussian in general. Applying results developed in the
non-Hermitian context for Hagedorn wavepackets, we formulate a method to
generate quantum-jump trajectories that is described entirely in terms of the
evolution of an underlying Gaussian state. To illustrate the behaviours of the
different unravellings in comparison to the Lindblad dynamics, we consider two
examples in detail, which can be largely treated analytically, a harmonic
oscillator subject to position measurement and a damped harmonic oscillator. In
both cases, we highlight the differences as well as the similarities of the
stochastic Schr\"odinger and the quantum-jump dynamics.

categories: quant-ph math-ph math.MP physics.comp-ph


title:
SRGz: building an optical cross-match model for the X-ray SRG/eROSITA
  sources using the Lockman Hole data

abstract:
  We present a probabilistic model built for the optical cross-match between
the SRG/eROSITA X-ray sources and photometric data from the DESI Legacy Imaging
Surveys. The model relies both on positional and photometric information on
optical objects nearby X-ray sources and allows performing selection with
precision and recall $\approx94$% (for $F_{\rm X,0.5-2}>10^{-14}$
erg/s/cm$^2$). With this model, we calibrated positional error of the
SRG/eROSITA sources detected in the Lockman Hole: $\sigma_{\rm corr} =
0.87\sqrt{ \sigma_{\rm det}^{2.53} + 1.12^2}$. The model will become a part of
the SRGz system for data analysis of the X-ray data obtained from the all-sky
SRG/eROSITA survey.


title: Quickstrom: Property Based Acceptance Testing with LTL Specifications

abstract:
  We present Quickstrom, a property-based testing system for acceptance testing
of interactive applications. Using Quickstrom, programmers can specify the
behaviour of web applications as properties in our testing-oriented dialect of
Linear Temporal Logic (LTL) called QuickLTL, and then automatically test their
application against the given specification with hundreds of automatically
generated interactions. QuickLTL extends existing finite variants of LTL for
the testing use-case, determining likely outcomes from partial traces whose
minimum length is itself determined by the LTL formula. This temporal logic is
embedded in our specification language, Specstrom, which is designed to be
approachable to web programmers, expressive for writing specifications, and
easy to analyse. Because Quickstrom tests only user-facing behaviour, it is
agnostic to the implementation language of the system under test. We therefore
formally specify and test many implementations of the popular TodoMVC
benchmark, used for evaluation and comparison across various web frontend
frameworks and languages. Our tests uncovered bugs in almost half of the
available implementations.


title: The OmegaWhite Survey for Short Period Variable Stars VI. Open Clusters

abstract:
  Using light curves with $\sim$3 min cadence and a duration of 2 hrs made
using the OmegaWhite survey, we present the results of a search for
short-period variable stars in the field of 20 open clusters. We identified 92
variable stars in these fields. Using a range of cluster member catalogues and
Gaia EDR3 data, we have determined that 10 are cluster members and 2 more are
probable members. Based on their position on the Gaia HRD and their photometric
periods, we find that most of these are $\delta$ Sct stars. We obtained
low-resolution optical spectroscopy of some of these cluster members and field
stars. We discuss the cluster variable stars in the context of $\delta$ Sct
stars in other open clusters.


title: Multiresolution-analysis for stochastic hyperbolic conservation laws

abstract:
  A multiresolution analysis for solving stochastic conservation laws is
proposed. Using a novel adaptation strategy and a higher dimensional
deterministic problem, a discontinuous Galerkin (DG) solver is derived. A
multiresolution analysis of the DG spaces for the proposed adaptation strategy
is presented. Numerical results show that in the case of general stochastic
distributions the performance of the DG solver is significantly improved by the
novel adaptive strategy. The gain in efficiency is validated in computational
experiments.


title: Unlabeled sample compression schemes for oriented matroids

abstract:
  A long standing sample compression conjecture asks to linearly bound the size
of the optimal sample compression schemes by the Vapnik-Chervonenkis (VC)
dimension of an arbitrary class. In this paper we explore the rich metric and
combinatorial structure of oriented matroids (OMs) to construct proper
unlabeled sample compression schemes for the classes of topes of OMs bounded by
their VC-dimension. The result extends to the topes of affine OMs, as well as
to the topes of the complexes of OMs that posses a corner peeling. The main
tool that we use are the solutions of certain linear programs in OMs.


title:
Port-Hamiltonian inspired gradient diffusion model for
  magnetohydrodynamic turbulence

abstract:
  As a reduced representation of the nonlinear spectral fluxes of ideal
invariants in incompressible magnetohydrodynamics, we construct a
gradient-diffusion network model that combines phenomenological considerations
and geometrical analysis of the exact nonlinear energy transfer function. The
reduced-order representation of the conservative spectral transport of energy
and cross-helicity is of port-Hamiltonian form, which highlights the
flexibility and modularity of this approach. Numerical experiments with
Reynolds numbers up to $~10^6$ yield clear power-law signatures of
inertial-range energy spectra. Depending on the dominant timescale of energy
transfer, Kolmogorov (-5/3), weak-turbulence (-2), or Iroshnikov-Kraichnan-like
(-3/2) scaling exponents are observed. Anisotropic turbulence in a mean
magnetic field is successfully modelled as well. The characteristic exponents
of turbulence decay and the observed influence of cross-helicity on the energy
transfer are consistent with the literature and in agreement with theoretical
results.


title:
Convolutional Neural Network-based Efficient Dense Point Cloud
  Generation using Unsigned Distance Fields

abstract:
  Dense point cloud generation from a sparse or incomplete point cloud is a
crucial and challenging problem in 3D computer vision and computer graphics. So
far, the existing methods are either computationally too expensive, suffer from
limited resolution, or both. In addition, some methods are strictly limited to
watertight surfaces -- another major obstacle for a number of applications. To
address these issues, we propose a lightweight Convolutional Neural Network
that learns and predicts the unsigned distance field for arbitrary 3D shapes
for dense point cloud generation using the recently emerged concept of implicit
function learning. Experiments demonstrate that the proposed architecture
achieves slightly better quality results than the state of the art with 87%
less model parameters and 40% less GPU memory usage.


title:
Isoparametric singularity extraction technique for 3D potential problems
  in BEM

abstract:
  To solve boundary integral equations for potential problems using collocation
Boundary Element Method (BEM) on smooth curved 3D geometries, an analytical
singularity extraction technique is employed. By adopting the isoparametric
approach, curved geometries that are represented by mapped rectangles or
triangles from the parametric domain are considered. The singularity extraction
on the governing singular integrals can be performed either as an operation of
subtraction or division, each having some advantages. A particular series
expansion of a singular kernel about a source point is investigated. The series
in the intrinsic coordinates consists of functions of a type $R^p x^q y^r$,
where $R$ is a square root of a quadratic bivariate homogeneous polynomial,
corresponding to the first fundamental form of a smooth surface, and $p,q,r$
are integers, satisfying $p\leq -1$ and $q,r \geq 0$. By extracting more terms
from the series expansion of the singular kernel, the smoothness of the
regularized kernel at the source point can be increased. Analytical formulae
for integrals of such terms are obtained from antiderivatives of $R^p x^q y^r$,
using recurrence formulae, and by evaluating them at the edges of rectangular
or triangular parametric domains. Numerical tests demonstrate that the
singularity extraction technique can be a useful prerequisite for a numerical
quadrature scheme to obtain accurate evaluations of the governing singular
integrals in 3D collocation BEM.


title: Measures of Fluctuations for a Liquid Near Critical Drying

abstract:
  We investigate density fluctuations in a liquid close to a solvophobic
substrate at which a surface critical drying transition occurs. Using classical
density functional theory, we determine three measures of the spatial extent
and strength of the fluctuations, i.e., the local compressibility
$\chi_{\mu}(z)$, the local thermal susceptibility $\chi_T(z)$ and the reduced
density $\chi_*(z)$; $z$ is the distance from the substrate. Whilst the first
measure is frequently used, the second and third were introduced very recently
by Eckert et.al., Phys. Rev. Lett. 125, 268004 (2020). For state points in the
critical drying regime, all three measures, each scaled by its bulk value,
exhibit very similar forms and the ratio of $\chi_T(z)$ to $\chi_{\mu}(z)$, for
fixed $z$ in the vapour-liquid transition region, is constant. Using a scaling
treatment of surface thermodynamics we show that such behaviour is to be
expected on general grounds.


title: Scale-out Systolic Arrays

abstract:
  Multi-pod systolic arrays are emerging as the architecture of choice in DNN
inference accelerators. Despite their potential, designing multi-pod systolic
arrays to maximize effective throughput/Watt (i.e., throughput/Watt adjusted
when accounting for array utilization) poses a unique set of challenges. In
this work, we study three key pillars in multi-pod systolic array designs,
namely array granularity, interconnect, and tiling. We identify optimal array
granularity across workloads and show that state-of-the-art commercial
accelerators use suboptimal array sizes for single-tenancy workloads. We, then
evaluate the bandwidth/latency trade-offs in interconnects and show that
Butterfly networks offer a scalable topology for accelerators with a large
number of pods. Finally, we introduce a novel data tiling scheme with custom
partition size to maximize utilization in optimally sized pods. We propose
Scale-out Systolic Arrays, a multi-pod inference accelerator for both single-
and multi-tenancy based on these three pillars. We show that SOSA exhibits
scaling of up to 600 TeraOps/s in effective throughput for state-of-the-art DNN
inference workloads, and outperforms state-of-the-art multi-pod accelerators by
a factor of 1.5x.


title:
$L^p(\mathbb{R}^d)$ boundedness for the Calder\'on commutator with rough
  kernel

abstract:
  Let $k\in\mathbb{N}$, $\Omega$ be homogeneous of degree zero, integrable on
$S^{d-1}$ and have vanishing moment of order $k$, $a$ be a function on
$\mathbb{R}^d$ such that $\nabla a\in L^{\infty}(\mathbb{R}^d)$, and
$T_{\Omega,\,a;k}$ be the $d$-dimensional Calder\'on commutator defined by
$$T_{\Omega,\,a;k}f(x)={\rm
p.\,v.}\int_{\mathbb{R}^d}\frac{\Omega(x-y)}{|x-y|^{d+k}}\big(a(x)-a(y)\big)^kf(y){d}y.$$
In this paper, the authors prove that if $$\sup_{\zeta\in
S^{d-1}}\int_{S^{d-1}}|\Omega(\theta)|\log ^{\beta}
\big(\frac{1}{|\theta\cdot\zeta|}\big)d\theta<\infty,$$ with
$\beta\in(1,\,\infty]$, then for $\frac{2\beta}{2\beta-1}<p<2\beta$,
$T_{\Omega,\,a;\,k}$ is bounded on $L^p(\mathbb{R}^d)$.


title:
Mask Usage Recognition using Vision Transformer with Transfer Learning
  and Data Augmentation

abstract:
  The COVID-19 pandemic has disrupted various levels of society. The use of
masks is essential in preventing the spread of COVID-19 by identifying an image
of a person using a mask. Although only 23.1% of people use masks correctly,
Artificial Neural Networks (ANN) can help classify the use of good masks to
help slow the spread of the Covid-19 virus. However, it requires a large
dataset to train an ANN that can classify the use of masks correctly.
MaskedFace-Net is a suitable dataset consisting of 137016 digital images with 4
class labels, namely Mask, Mask Chin, Mask Mouth Chin, and Mask Nose Mouth.
Mask classification training utilizes Vision Transformers (ViT) architecture
with transfer learning method using pre-trained weights on ImageNet-21k, with
random augmentation. In addition, the hyper-parameters of training of 20
epochs, an Stochastic Gradient Descent (SGD) optimizer with a learning rate of
0.03, a batch size of 64, a Gaussian Cumulative Distribution (GeLU) activation
function, and a Cross-Entropy loss function are used to be applied on the
training of three architectures of ViT, namely Base-16, Large-16, and Huge-14.
Furthermore, comparisons of with and without augmentation and transfer learning
are conducted. This study found that the best classification is transfer
learning and augmentation using ViT Huge-14. Using this method on
MaskedFace-Net dataset, the research reaches an accuracy of 0.9601 on training
data, 0.9412 on validation data, and 0.9534 on test data. This research shows
that training the ViT model with data augmentation and transfer learning
improves classification of the mask usage, even better than convolutional-based
Residual Network (ResNet).


title:
Light emission properties of mechanical exfoliation induced extended
  defects in hexagonal boron nitride flakes

abstract:
  Recently hBN has become an interesting platform for quantum optics due to the
peculiar defect-related luminescence properties. Concomitantly, hBN was
established as the ideal insulating support for realizing 2D materials device,
where, on the contrary, defects can affect the device performance. In this
work, we study the light emission properties of hBN flakes obtained by
mechanical exfoliation with particular focus on extended defects generated in
the process. In particular, we tackle different issues as the light emission in
hBN flakes of different thicknesses in the range of hundreds of nm, revealing a
higher concentration of deep level emission in thinner area of the flake. We
recognize the effect of crystal deformation in some areas of the flake with an
important blue-shift (130 meV) of the room temperature near band edge emission
of hBN and the concurrent presence of a novel emission at 2.36 eV related to
the formation of array of dislocations. We studied the light emission
properties by means of cathodoluminescence and sub-bandgap excitation
photoluminescence of thickness steps with different crystallographic
orientations, revealing the presence of different concentration of radiative
centers. CL mapping allows to detect buried thickness steps, invisible to the
SEM and AFM morphological analysis.


title: Visuo-Haptic Object Perception for Robots: An Overview

abstract:
  This article summarizes the current state of multimodal object perception for
robotic applications. It covers aspects of biological inspiration, sensor
technologies, data sets, and sensory data processing for object recognition and
grasping. Firstly, the biological basis of multimodal object perception is
outlined. Then the sensing technologies and data collection strategies are
discussed. Next, an introduction to the main computational aspects is
presented, highlighting a few representative articles for each main application
area, including object recognition, object manipulation and grasping, texture
recognition, and transfer learning. Finally, informed by the current
advancements in each area, this article outlines promising new research
directions.


title:
Investigation on the self-starting Mamyshev oscillator with low
  threshold

abstract:
  We have demonstrated a mode-locked Mamyshev oscillator based on a
single-mode-fibre configuration with low threshould. The influences of spectral
filters and the pump power on the output laser characteristics have been
investigated. In the case with spectral-filter central wavelength far away from
the peak of gain spectrum, the laser pulses have deviated slightly from
similariton evolution with gain-shaping under high pump power. Moreover,
self-starting mode-locking operation has been realized when the pump power was
only 520 mW, which might benefit from the appropriate polarization conditions
and small spectral filter separation. The laser can generate 1.89-nJ
ultra-short pulses with 20-dB spectral width of 54.6 nm. The pulse duration can
be compressed externally to 64.69 fs with the peak power of 21.3 kW.


title:
Rapidly encoding generalizable dynamics in a Euclidean symmetric neural
  network: a Slinky case study

abstract:
  Slinky, a helical elastic rod, is a seemingly simple structure with unusual
mechanical behavior; for example, it can walk down a flight of stairs under its
own weight. Taking the Slinky as a test-case, we propose a physics-informed
deep learning approach for building reduced-order models of physical systems.
The approach introduces a Euclidean symmetric neural network (ESNN)
architecture that is trained under the neural ordinary differential equation
framework to learn the 2D latent dynamics from the motion trajectory of a
reduced-order representation of the 3D Slinky. The ESNN implements a
physics-guided architecture that simultaneously preserves energy invariance and
force equivariance on Euclidean transformations of the input, including
translation, rotation, and reflection. The embedded Euclidean symmetry provides
physics-guided interpretability and generalizability, while preserving the full
expressive power of the neural network. We demonstrate that the ESNN approach
is able to accelerate simulation by one to two orders of magnitude compared to
traditional numerical methods and achieve a superior generalization
performance, i.e., the neural network, trained on a single demonstration case,
predicts accurately on unseen cases with different Slinky configurations and
boundary conditions.


title: Explainability in reinforcement learning: perspective and position

abstract:
  Artificial intelligence (AI) has been embedded into many aspects of people's
daily lives and it has become normal for people to have AI make decisions for
them. Reinforcement learning (RL) models increase the space of solvable
problems with respect to other machine learning paradigms. Some of the most
interesting applications are in situations with non-differentiable expected
reward function, operating in unknown or underdefined environment, as well as
for algorithmic discovery that surpasses performance of any teacher, whereby
agent learns from experimental experience through simple feedback. The range of
applications and their social impact is vast, just to name a few: genomics,
game-playing (chess, Go, etc.), general optimization, financial investment,
governmental policies, self-driving cars, recommendation systems, etc. It is
therefore essential to improve the trust and transparency of RL-based systems
through explanations. Most articles dealing with explainability in artificial
intelligence provide methods that concern supervised learning and there are
very few articles dealing with this in the area of RL. The reasons for this are
the credit assignment problem, delayed rewards, and the inability to assume
that data is independently and identically distributed (i.i.d.). This position
paper attempts to give a systematic overview of existing methods in the
explainable RL area and propose a novel unified taxonomy, building and
expanding on the existing ones. The position section describes pragmatic
aspects of how explainability can be observed. The gap between the parties
receiving and generating the explanation is especially emphasized. To reduce
the gap and achieve honesty and truthfulness of explanations, we set up three
pillars: proactivity, risk attitudes, and epistemological constraints. To this
end, we illustrate our proposal on simple variants of the shortest path
problem.


title:
Unitarity Problems for an Effective Field Theory Description of Early
  Universe Cosmology

abstract:
  In the context of Effective Field Theory, the Hilbert space of states
increases in an expanding universe. Hence, the time evolution cannot be
unitary. The formation of structure is usually studied using effective field
theory techniques. We study the constraints on effective field theory analyses
of early universe models which come from demanding that the factor of the space
of states corresponding to length scales where the primordial fluctuations are
manifest does not suffer from the unitarity problem. For bouncing and emergent
cosmologies, no constraints arise provided that the energy scale of the bounce
or emergent phases is smaller than the ultraviolet (UV) cutoff scale. On the
other hand, in the case of the inflationary scenario, non-trivial upper bounds
on the energy scale of inflation arise.


title:
Multicomponent relativistic dissipative fluid dynamics from the
  Boltzmann equation

abstract:
  We derive multicomponent relativistic second-order dissipative fluid dynamics
from the Boltzmann equations for a reactive mixture of $N_{\text{spec}}$
particle species with $N_q$ intrinsic quantum numbers (e.g. electric charge,
baryon number, and strangeness) using the method of moments. We obtain the
continuity equations for multiple conserved charges as well as the conservation
equations for the total energy and momentum in the single-fluid approximation.
These $4+N_q$ conservation laws are closed by deriving the second-order
equations of motion for the dissipative quantities in the $(10+4N_q)$-moment
approximation. The resulting fluid-dynamical equations are formally similar to
those of a single-component system, but feature different thermodynamic
relations and transport coefficients. We derive general relations for all
transport coefficients and compute them explicitly in the ultrarelativistic
limit.


title:
Running Time Analysis of the Non-dominated Sorting Genetic Algorithm II
  (NSGA-II) using Binary or Stochastic Tournament Selection

abstract:
  Evolutionary algorithms (EAs) have been widely used to solve multi-objective
optimization problems, and have become the most popular tool. However, the
theoretical foundation of multi-objective EAs (MOEAs), especially the essential
theoretical aspect, i.e., running time analysis, has been still largely
underdeveloped. The few existing theoretical works mainly considered simple
MOEAs, while the non-dominated sorting genetic algorithm II (NSGA-II), probably
the most influential MOEA, has not been analyzed except for a very recent work
considering a simplified variant without crossover. In this paper, we present a
running time analysis of the standard NSGA-II for solving LOTZ, OneMinMax and
COCZ, the three commonly used bi-objective optimization problems. Specifically,
we prove that the expected running time (i.e., number of fitness evaluations)
is $O(n^3)$ for LOTZ, and $O(n^2\log n)$ for OneMinMax and COCZ, which is
surprisingly as same as that of the previously analyzed simple MOEAs, GSEMO and
SEMO. Next, we introduce a new parent selection strategy, stochastic tournament
selection (i.e., $k$ tournament selection where $k$ is uniformly sampled at
random), to replace the binary tournament selection strategy of NSGA-II,
decreasing the required expected running time to $O(n^2)$ for all the three
problems. Experiments are also conducted, suggesting that the derived running
time upper bounds are tight for LOTZ, and almost tight for OneMinMax and COCZ.


title:
Probing hairy black holes caused by gravitational decoupling using
  quasinormal modes, and greybody bounds

abstract:
  Extended gravitational decoupling can add hair to the black holes in general
relativity by adding extra sources. The quasinormal modes of hairy black holes
caused by gravitational decoupling for the massless scalar field,
electromagnetic field, and axial gravitational perturbation are investigated.
The equation of effective potential for three perturbations is derived in hairy
black holes spacetime. We also study the time evolution corresponding to the
three perturbations, and the quasinormal mode frequencies are calculated using
the Prony method through the time-domain profiles. Particularly, we find that
the response of hairy black hole spacetime to axial gravitational perturbations
is completely different from scalar field and electromagnetic field
perturbations, which may be due to the fact that the gravitational radiation
produced by the perturbations of the hairy black hole metric itself is much
stronger than that of the external field. Furthermore, we have calculated the
bounds on this greybody factor and high-energy absorption cross section with
the Sinc approximation. The study reveals that the charges ($\alpha$ and $l_0$)
generating primary hair contributes positively to the greybody bounds and
absorption cross section, whereas the tidal charge $Q$ from the extra sources
does the opposite.


title: Factual Consistency of Multilingual Pretrained Language Models

abstract:
  Pretrained language models can be queried for factual knowledge, with
potential applications in knowledge base acquisition and tasks that require
inference. However, for that, we need to know how reliable this knowledge is,
and recent work has shown that monolingual English language models lack
consistency when predicting factual knowledge, that is, they fill-in-the-blank
differently for paraphrases describing the same fact. In this paper, we extend
the analysis of consistency to a multilingual setting. We introduce a resource,
mParaRel, and investigate (i) whether multilingual language models such as
mBERT and XLM-R are more consistent than their monolingual counterparts; and
(ii) if such models are equally consistent across languages. We find that mBERT
is as inconsistent as English BERT in English paraphrases, but that both mBERT
and XLM-R exhibit a high degree of inconsistency in English and even more so
for all the other 45 languages.


title: Wide range linear magnetometer based on a sub-microsized K vapor cell

abstract:
  $^{39}$K atoms have the smallest ground state ($^2S_{1/2}$) hyperfine
splitting of all the most naturally abundent alkali isotopes and, consequently,
the smallest characteristic magnetic field value $B_0 = A_{^2S_{1/2}}/\mu_B
\approx 170$ G, where $A_{^2S_{1/2}}$ is the ground state's magnetic dipole
interaction constant. In the hyperfine Paschen-Back regime ($B \gg B_0$, where
$B$ is the magnitude of the external magnetic field applied on the atoms), only
8 Zeeman transitions are visible in the absorption spectrum of the $D_1$ line
of $^{39}$K, while the probabilities of the remaining 16 Zeeman transitions
tend to zero. In the case of $^{39}$K, this behavior is reached already at
relatively low magnetic field $B > B_0$. For each circular polarization
($\sigma^-,\sigma^+$), 4 spectrally resolved atomic transitions having a
sub-Doppler width are recorded using a sub-microsized vapor cell of thickness
$L = 120 - 390$ nm. We present a method that allows to measure the magnetic
field in the range $0.1 - 10$ kG with micrometer spatial resolution, which is
relevant in particular for the determination of magnetic fields with a large
gradient (up to 3 G$/\mu$m). The theoretical model describes well the
experimental results.


title: The Decomposition of UV Divergence

abstract:
  We present an efficient algorithm to decompose the ultraviolet (UV)
divergences of Feynman integrals to local divergences and various types of
sub-divergences. We show that with some reasonable assumptions the local
divergences of Feynman integrals can be uniquely defined in dimensional
regularization scheme. By an asymptotic expansion in the hard momenta, the
computation of local and sub-divergences is reduced to the computation of local
divergences of massless vacuum integrals. In theories with spin
$\le\frac{1}{2}$, the beta functions and anomalous dimensions can be extracted
directly from the local divergence of integrals.
  We also propose two methods to reduce the tensor structures which can be used
in the computation of local divergence. The first method is based on
dimensional shift and is extremely powerful for integrals with loop number
$L\le3$. The second method is based on a PV reduction in a $d_{\infty}$
dimension subspace, and it is more suited in four and more loops.


title:
Gradient flows and randomised thresholding: sparse inversion and
  classification

abstract:
  Sparse inversion and classification problems are ubiquitous in modern data
science and imaging. They are often formulated as non-smooth minimisation
problems. In sparse inversion, we minimise, e.g., the sum of a data fidelity
term and an L1/LASSO regulariser. In classification, we consider, e.g., the sum
of a data fidelity term and a non-smooth Ginzburg--Landau energy. Standard
(sub)gradient descent methods have shown to be inefficient when approaching
such problems. Splitting techniques are much more useful: here, the target
function is partitioned into a sum of two subtarget functions -- each of which
can be efficiently optimised. Splitting proceeds by performing optimisation
steps alternately with respect to each of the two subtarget functions.
  In this work, we study splitting from a stochastic continuous-time
perspective. Indeed, we define a differential inclusion that follows one of the
two subtarget function's negative subgradient at each point in time. The choice
of the subtarget function is controlled by a binary continuous-time Markov
process. The resulting dynamical system is a stochastic approximation of the
underlying subgradient flow. We investigate this stochastic approximation for
an L1-regularised sparse inversion flow and for a discrete Allen-Cahn equation
minimising a Ginzburg--Landau energy. In both cases, we study the longtime
behaviour of the stochastic dynamical system and its ability to approximate the
underlying subgradient flow at any accuracy. We illustrate our theoretical
findings in a simple sparse estimation problem and also in a low-dimensional
classification problem.


title: VQ-Flows: Vector Quantized Local Normalizing Flows

abstract:
  Normalizing flows provide an elegant approach to generative modeling that
allows for efficient sampling and exact density evaluation of unknown data
distributions. However, current techniques have significant limitations in
their expressivity when the data distribution is supported on a low-dimensional
manifold or has a non-trivial topology. We introduce a novel statistical
framework for learning a mixture of local normalizing flows as "chart maps"
over the data manifold. Our framework augments the expressivity of recent
approaches while preserving the signature property of normalizing flows, that
they admit exact density evaluation. We learn a suitable atlas of charts for
the data manifold via a vector quantized auto-encoder (VQ-AE) and the
distributions over them using a conditional flow. We validate experimentally
that our probabilistic framework enables existing approaches to better model
data distributions over complex manifolds.


title:
The wave function of the universe in the presence of trans-Planckian
  censorship

abstract:
  The wave function for a closed de Sitter universe has been computed,
demanding consistency with the recently proposed Trans-Planckian Censorship
Conjecture (TCC). We extend the Einstein-Hilbert action to contain a
complex-valued term which provides an exponentially decaying weight for the
geometries violating TCC in the Lorentzian path integral sum while working in
the minisuperspace approach to quantum cosmology. This \textit{postulated}
modification suppresses the probability of evolution of the universe into
configurations that violate TCC. We show that due to the presence of this
suppression factor, the Hubble rate of the universe at the end of the inflation
gets subdued and assumes a value less than what is expected classically.
Moreover, the consequences of this quantum gravity motivated correction in the
primordial power spectrum are discussed as well.


title:
Electron transfer under the Floquet modulation in donor-bridge-acceptor
  systems

abstract:
  Electron transfer (ET) processes are of broad interest in modern chemistry.
With the advancements of experimental techniques, one may modulate the ET via
such as the light-matter interactions. In this work, we study the ET under a
Floquet modulation occurring in the donor-bridge-acceptor systems, with the
rate kernels projected out from the exact disspaton equation of motion
formalism. This together with the Floquet theorem enables us to investigate the
interplay between the intrinsic non-Markovianity and the driving periodicity.
The observed rate kernel exhibits a Herzberg-Teller-like mechanism induced by
the bridge fluctuation subject to effective modulation.


title:
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image
  Change Detection

abstract:
  In this paper, we devise a novel interactive satellite image change detection
algorithm based on active learning. The proposed framework is iterative and
relies on a question and answer model which asks the oracle (user) questions
about the most informative display (subset of critical images), and according
to the user's responses, updates change detections. The contribution of our
framework resides in a novel display model which selects the most
representative and diverse virtual exemplars that adversely challenge the
learned change detection functions, thereby leading to highly discriminating
functions in the subsequent iterations of active learning. Extensive
experiments, conducted on the challenging task of interactive satellite image
change detection, show the superiority of the proposed virtual display model
against the related work.


title:
Modelling continual learning in humans with Hebbian context gating and
  exponentially decaying task signals

abstract:
  Humans can learn several tasks in succession with minimal mutual interference
but perform more poorly when trained on multiple tasks at once. The opposite is
true for standard deep neural networks. Here, we propose novel computational
constraints for artificial neural networks, inspired by earlier work on gating
in the primate prefrontal cortex, that capture the cost of interleaved training
and allow the network to learn two tasks in sequence without forgetting. We
augment standard stochastic gradient descent with two algorithmic motifs,
so-called "sluggish" task units and a Hebbian training step that strengthens
connections between task units and hidden units that encode task-relevant
information. We found that the "sluggish" units introduce a switch-cost during
training, which biases representations under interleaved training towards a
joint representation that ignores the contextual cue, while the Hebbian step
promotes the formation of a gating scheme from task units to the hidden layer
that produces orthogonal representations which are perfectly guarded against
interference. Validating the model on previously published human behavioural
data revealed that it matches performance of participants who had been trained
on blocked or interleaved curricula, and that these performance differences
were driven by misestimation of the true category boundary.


title: Improved Differentially Private Euclidean Distance Approximation

abstract:
  This work shows how to privately and more accurately estimate Euclidean
distance between pairs of vectors. Input vectors $x$ and $y$ are mapped to
differentially private sketches $x'$ and $y'$, from which one can estimate the
distance between $x$ and $y$. Our estimator relies on the Sparser
Johnson-Lindenstrauss constructions by Kane \& Nelson (Journal of the ACM
2014), which for any $0<\alpha,\beta<1/2$ have optimal output dimension
$k=\Theta(\alpha^{-2}\log(1/\beta))$ and sparsity
$s=O(\alpha^{-1}\log(1/\beta))$. We combine the constructions of Kane \& Nelson
with either the Laplace or the Gaussian mechanism from the differential privacy
literature, depending on the privacy parameters $\varepsilon$ and $\delta$. We
also suggest a differentially private version of Fast Johnson-Lindenstrauss
Transform (FJLT) by Ailon \& Chazelle (SIAM Journal of Computing 2009) which
offers a tradeoff in speed for variance for certain parameters. We answer an
open question by Kenthapadi et al.~(Journal of Privacy and Confidentiality
2013) by analyzing the privacy and utility guarantees of an estimator for
Euclidean distance, relying on Laplacian rather than Gaussian noise. We prove
that the Laplace mechanism yields lower variance than the Gaussian mechanism
whenever $\delta<\beta^{O(1/\alpha)}$. Thus, our work poses an improvement over
the work of Kenthapadi et al.~by giving a more efficient estimator with lower
variance for sufficiently small $\delta$. Our sketch also achieves \emph{pure}
differential privacy as a neat side-effect of the Laplace mechanism rather than
the \emph{approximate} differential privacy guarantee of the Gaussian
mechanism, which may not be sufficiently strong for some settings.


title:
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial
  Fine-Tuning Results for Child Speech Synthesis

abstract:
  Speech synthesis has come a long way as current text-to-speech (TTS) models
can now generate natural human-sounding speech. However, most of the TTS
research focuses on using adult speech data and there has been very limited
work done on child speech synthesis. This study developed and validated a
training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models
using child speech datasets. This approach adopts a multi-speaker TTS retuning
workflow to provide a transfer-learning pipeline. A publicly available child
speech dataset was cleaned to provide a smaller subset of approximately 19
hours, which formed the basis of our fine-tuning experiments. Both subjective
and objective evaluations were performed using a pretrained MOSNet for
objective evaluation and a novel subjective framework for mean opinion score
(MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech
intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency.
Objective evaluation using a pretrained MOSNet showed a strong correlation
between real and synthetic child voices. Speaker similarity was also verified
by calculating the cosine similarity between the embeddings of utterances. An
automatic speech recognition (ASR) model is also used to provide a word error
rate (WER) comparison between the real and synthetic child voices. The final
trained TTS model was able to synthesize child-like speech from reference audio
samples as short as 5 seconds.


title:
Gentle algebras arising from surfaces with orbifold points of order 3,
  Part I: scattering diagrams

abstract:
  To each triangulation of any surface with marked points on the boundary and
orbifold points of order three, we associate a quiver (with loops) with
potential whose Jacobian algebra is finite dimensional and gentle. We study the
stability scattering diagrams of such gentle algebras and use them to prove
that the Caldero--Chapoton map defines a bijection between reachable
$\tau$-rigid pairs and cluster monomials of the generalized cluster algebra
associated to the surface by Chekhov and Shapiro.


title: Reinforcement-based frugal learning for satellite image change detection

abstract:
  In this paper, we introduce a novel interactive satellite image change
detection algorithm based on active learning. The proposed approach is
iterative and asks the user (oracle) questions about the targeted changes and
according to the oracle's responses updates change detections. We consider a
probabilistic framework which assigns to each unlabeled sample a relevance
measure modeling how critical is that sample when training change detection
functions. These relevance measures are obtained by minimizing an objective
function mixing diversity, representativity and uncertainty. These criteria
when combined allow exploring different data modes and also refining change
detections. To further explore the potential of this objective function, we
consider a reinforcement learning approach that finds the best combination of
diversity, representativity and uncertainty, through active learning
iterations, leading to better generalization as corroborated through
experiments in interactive satellite image change detection.


title:
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose
  CT Image Reconstruction

abstract:
  The recently proposed sparsifying transform models incur low computational
cost and have been applied to medical imaging. Meanwhile, deep models with
nested network structure reveal great potential for learning features in
different layers. In this study, we propose a network-structured sparsifying
transform learning approach for X-ray computed tomography (CT), which we refer
to as multi-layer clustering-based residual sparsifying transform (MCST)
learning. The proposed MCST scheme learns multiple different unitary transforms
in each layer by dividing each layer's input into several classes. We apply the
MCST model to low-dose CT (LDCT) reconstruction by deploying the learned MCST
model into the regularizer in penalized weighted least squares (PWLS)
reconstruction. We conducted LDCT reconstruction experiments on XCAT phantom
data and Mayo Clinic data and trained the MCST model with 2 (or 3) layers and
with 5 clusters in each layer. The learned transforms in the same layer showed
rich features while additional information is extracted from representation
residuals. Our simulation results demonstrate that PWLS-MCST achieves better
image reconstruction quality than the conventional FBP method and PWLS with
edge-preserving (EP) regularizer. It also outperformed recent advanced methods
like PWLS with a learned multi-layer residual sparsifying transform prior
(MARS) and PWLS with a union of learned transforms (ULTRA), especially for
displaying clear edges and preserving subtle details.


title:
Lattice Anharmonicity in BiS2-Based Layered Superconductor RE(O,F)BiS2
  (RE = La, Ce, Pr, Nd)

abstract:
  We studied Gr\"uneisen parameter ({\gamma}G) to investigate lattice
anharmonicity in a layered BiS2-based superconductor system REO1-xFxBiS2 (RE =
La, Ce, Pr, Nd), where in-plane chemical pressure was tuned by substituting the
RE elements. With increasing in-plane chemical pressure, bulk modulus
remarkably increases, and a high {\gamma}G is observed for RE = Nd. The
dependence of {\gamma}G on in-plane chemical pressure exhibits a good
correlation with Tc, and a higher Tc is achieved when {\gamma}G is large for RE
= Nd. In addition, {\gamma}G shows a slight decrease by a decrease of F
concentration (x) in REO1-xFxBiS2. Our results show that the anharmonic
vibration of Bi along the c-axis is present in REO1-xFxBiS2, and the
enhancement of the anharmonicity is positively linked to superconducting Tc and
pairing mechanisms.


title:
The b-symbol weight distribution of irreducible cyclic codes and related
  consequences

abstract:
  The $b$-symbol read channel is motivated by the limitations of the reading
process in high density data storage systems. The corresponding new metric is a
generalization of the Hamming metric known as the $b$-symbol weight metric and
has become an important object in coding theory. In this paper, the general
$b$-symbol weight enumerator formula for irreducible cyclic codes is presented
by using the Gaussian period and a new invariant $\#U(b,j,N_1)$. The related
$b$-symbol weight hierarchies $\{d_1(\C),d_2(\C),\ldots,d_K(\C)\}$
($K=\dim(\C)$) are given for some cases. The shortened codes which are optimal
from some classes of irreducible cyclic codes are given, where the shorten set
$\mathcal{T}$ is the complementary set of $b$-symbol support of some codeword
with the minimal $b$-symbol weight.


title:
High-field magnetoresistance of microcrystalline and nanocrystalline Ni
  metal at 3 K and 300 K

abstract:
  The magnetoresistance (MR) and the magnetization isotherms were studied up to
high magnetic fields at T = 3 K and 300 K for a microcrystalline ($\mu$c) Ni
foil corresponding to bulk Ni and for a nanocrystalline (nc) Ni foil. At T = 3
K, for the $\mu$c-Ni sample with a residual resistivity ratio (RRR) of 331, the
field dependence of the resistivity was similar to what was reported previously
for high-purity ferromagnets whereas the MR(H) behavior for the nc-Ni sample
with RRR = 9 resembled that what was observed at low temperatures for Ni-based
alloys with low impurity concentration. In the magnetically saturated state,
the resistivity increased with magnetic field for both samples at T = 3 K and
the field dependence was dominated by the ordinary MR due to the Lorentz force
acting on the electron trajectories. However, the MR(H) curves were found to be
saturating for $\mu$c-Ni and non-saturating for nc-Ni, the difference arising
from their very different electron mean free paths. At T = 300 K, the MR(H)
curves of both Ni samples were very similar to those known for bulk Ni. After
magnetic saturation, the resistivity decreased nearly linearly with magnetic
field which behavior is due to the suppression of thermally-induced magnetic
disorder with increasing magnetic field. The MR(H) data were analyzed at both
temperatures with the help of Kohler plots from which the resistivity
anisotropy splitting ($\Delta\rho_{AMR}$) and the anisotropic magnetoresistance
(AMR) ratio were derived. It was demonstrated that at T = 300 K,
$\rho(H\rightarrow 0)=\rho(B\rightarrow 0)$ due to the negligible contribution
of the ordinary MR. The data for the two Ni samples at 3 K and 300 K were found
to indicate an approximately linear scaling of $\Delta\rho_{AMR}$ with the
zero-field resistivity. This implies that the AMR ratio does not vary
significantly with temperature in either microstructural state of Ni.


title:
Towards lattice-gas description of low-temperature properties above the
  Haldane and cluster-based Haldane ground states of a mixed spin-(1,1/2)
  Heisenberg octahedral chain

abstract:
  The rich ground-state phase diagram of the mixed spin-(1,1/2) Heisenberg
octahedral chain was previously elaborated from effective mixed-spin Heisenberg
chains, which were derived by employing a local conservation of a total spin on
square plaquettes of an octahedral chain. Here we present a comprehensive
analysis of the thermodynamic properties of this model. In the highly
frustrated parameter region the lowest-energy eigenstates of the mixed-spin
Heisenberg octahedral chain belong to flat bands, which allow a precise
description of low-temperature magnetic properties within the localized-magnon
approach exploiting a classical lattice-gas model of hard-core monomers. The
present article provides a more comprehensive version of the localized-magnon
approach, which extends the range of its validity down to a less frustrated
parameter region involving the Haldane and cluster-based Haldane ground states.
A comparison between results of the developed localized-magnon theory and
accurate numerical methods such as full exact diagonalization and
finite-temperature Lanczos technique convincingly evidence that the
low-temperature magnetic properties above the Haldane and the cluster-based
Haldane ground states can be extracted from a classical lattice-gas model of
hard-core monomers and dimers, which is additionally supplemented by a
hard-core particle spanned over the whole lattice representing the gapped
Haldane phase.


title: Conditional Generative Data Augmentation for Clinical Audio Datasets

abstract:
  In this work, we propose a novel data augmentation method for clinical audio
datasets based on a conditional Wasserstein Generative Adversarial Network with
Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our
method, we created a clinical audio dataset which was recorded in a real-world
operating room during Total Hip Arthroplasty (THA) procedures and contains
typical sounds which resemble the different phases of the intervention. We
demonstrate the capability of the proposed method to generate realistic
class-conditioned samples from the dataset distribution and show that training
with the generated augmented samples outperforms classical audio augmentation
methods in terms of classification accuracy. The performance was evaluated
using a ResNet-18 classifier which shows a mean per-class accuracy improvement
of 1.51% in a 5-fold cross validation experiment using the proposed
augmentation method. Because clinical data is often expensive to acquire, the
development of realistic and high-quality data augmentation methods is crucial
to improve the robustness and generalization capabilities of learning-based
algorithms which is especially important for safety-critical medical
applications. Therefore, the proposed data augmentation method is an important
step towards improving the data bottleneck for clinical audio-based machine
learning systems. The code and dataset will be published upon acceptance.


title: When all holes have the same length

abstract:
  For every integer $\ell \geq 7$, we give a structural description of the
class of graphs whose chordless cycles of length at least 4 all have length
$\ell$.


title:
Fast Multi-view Clustering via Ensembles: Towards Scalability,
  Superiority, and Simplicity

abstract:
  Despite significant progress, there remain three limitations to the previous
multi-view clustering algorithms. First, they often suffer from high
computational complexity, restricting their feasibility for large-scale
datasets. Second, they typically fuse multi-view information via one-stage
fusion, neglecting the possibilities in multi-stage fusions. Third,
dataset-specific hyperparameter-tuning is frequently required, further
undermining their practicability. In light of this, we propose a fast
multi-view clustering via ensembles (FastMICE) approach. Particularly, the
concept of random view groups is presented to capture the versatile view-wise
relationships, through which the hybrid early-late fusion strategy is designed
to enable efficient multi-stage fusions. With multiple views extended to many
view groups, three levels of diversity (w.r.t. features, anchors, and
neighbors, respectively) are jointly leveraged for constructing the
view-sharing bipartite graphs in the early-stage fusion. Then, a set of
diversified base clusterings for different view groups are obtained via fast
graph partitioning, which are further formulated into a unified bipartite graph
for final clustering in the late-stage fusion. Remarkably, FastMICE has almost
linear time and space complexity, and is free of dataset-specific tuning.
Experiments on twenty multi-view datasets demonstrate its advantages in
scalability (for extremely large datasets), superiority (in clustering
performance), and simplicity (to be applied) over the state-of-the-art.


title: CT-SAT: Contextual Transformer for Sequential Audio Tagging

abstract:
  Sequential audio event tagging can provide not only the type information of
audio events, but also the order information between events and the number of
events that occur in an audio clip. Most previous works on audio event sequence
analysis rely on connectionist temporal classification (CTC). However, CTC's
conditional independence assumption prevents it from effectively learning
correlations between diverse audio events. This paper first attempts to
introduce Transformer into sequential audio tagging, since Transformers perform
well in sequence-related tasks. To better utilize contextual information of
audio event sequences, we draw on the idea of bidirectional recurrent neural
networks, and propose a contextual Transformer (cTransformer) with a
bidirectional decoder that could exploit the forward and backward information
of event sequences. Experiments on the real-life polyphonic audio dataset show
that, compared to CTC-based methods, the cTransformer can effectively combine
the fine-grained acoustic representations from the encoder and coarse-grained
audio event cues to exploit contextual information to successfully recognize
and predict audio event sequences.


title: Higher order dynamic mode decomposition to model reacting flows

abstract:
  In this work, the application of the multi-dimensional higher order dynamic
mode decomposition (HODMD) is proposed for the first time to analyse combustion
databases. In particular, HODMD has been adapted and combined with other
pre-processing techniques (generally used in machine learning), in light of the
multivariate nature of the data. A truncation step separate the main dynamics
driving the flow from less relevant non-linear dynamics. The method is applied
to analyse a database obtained from a Computational Fluid Dynamics (CFD)
simulation of an axisymmetric, time varying, non-premixed, co-flow methane
flame carried out by means of a detailed kinetic mechanism. Results show that
HODMD can reconstruct the main jet dynamics with a reduced number of relevant
modes, able to reproduce the system dynamics. These modes are found to be
representative for the main flow physics with two main advantages: (i) they
provide for the possibility to achieve a strong simplification with respect to
the high-dimensional input data, and at the same time (ii) a small
reconstruction error with respect to the original dataset is observed. In
addition, the method was also validated considering a reduced matrix obtained
using Principal Component Analysis (PCA) based feature selection and the
Varimax rotation. This validation also reveals that it is not important to have
all the variables in the dataset, just a group of them is necessary to obtain
the main dynamics of the system. This has an impact on feature selection and on
the cost these methodologies for very massive data.


title:
iMaNGA: mock MaNGA galaxies based on IllustrisTNG and MaStar SSPs. I.
  Construction and analysis of the mock data cubes

abstract:
  Galaxy formation and evolution simulations are essential tools to probe
poorly known astrophysics processes, but particular care is needed to compare
simulations with galaxy observations, as observed data need to be modelled as
well. We present a method to generate mock galaxies from the hydro-dynamical
IllustrisTNG simulations which are suited to compare with integral field
spectroscopic observation of galaxies from the SDSS-IV/MaNGA survey. Firstly,
we include the same instrumental effects and procedures as adopted in the
acquisition and analysis of real data. Furthermore, we generate the galaxy
spectra from the simulations using new stellar population models based on the
MaNGA stellar library (MaStar). In this way, our mock data cubes have the same
spatial sampling, cover the same wavelength range (3600-10300 \r{A}), and share
the same spectral resolution ($R\approx1800$) and flux calibration of real
MaNGA galaxy spectra. In this first paper, we demonstrate the method over an
early-type and a late-type simulated galaxy from TNG50. We analyse the
correspondent mock MaNGA-like data cubes with the same full spectral fitting
code, \textsc{FIREFLY}, which was used for the observed spectra. We find that
the intrinsic and recovered age and metallicity gradients are consistent within
1${\sigma}$, with residuals over all tassels consistent with $0$ at the 68$\%$
confidence level. We also perform the challenging test at comparing intrinsic
and recovered star formation histories, finding a close resemblance between
input and output. In follow-up papers, we will present a full simulated
MaNGA-like catalogue ($\approx10,000$ galaxies) with a comprehensive comparison
of TNG50 simulations to MaNGA observational results.


title: Sparse Synthetic Controls

abstract:
  This paper introduces a new penalized synthetic control method for policy
evaluation. The proposed sparse synthetic control penalizes the number of
predictors used in generating the counterfactual to improve pre-treatment fit
and select the most important predictors. To motivate the method theoretically
I derive, in a linear factor model framework, a model selection consistency
result and a mean squared error convergence rate result. Through a simulation
study, I then show that the sparse synthetic control achieves lower bias and
has better post-treatment fit than the unpenalized synthetic control. Finally,
I apply the method to study the effects of the passage of Proposition 99 in
California in a setting with a large number of predictors.


title: Polish topologies on endomorphism monoids of relational structures

abstract:
  In this paper we present general techniques for characterising minimal and
maximal semigroup topologies on the endomorphism monoid
$\operatorname{End}(\mathbb{A})$ of a countable relational structure
$\mathbb{A}$. As applications, we show that the endomorphism monoids of several
well-known relational structures, including the random graph, the random
directed graph, and the random partial order, possess a unique Polish semigroup
topology. In every case this unique topology is the subspace topology induced
by the usual topology on the Baire space $\mathbb{N} ^ \mathbb{N}$. We also
show that many of these structures have the property that every homomorphism
from their endomorphism monoid to a second countable topological semigroup is
continuous; referred to as automatic continuity. Many of the results about
endomorphism monoids are extended to clones of polymorphisms on the same
structures.


title: Mathematical modeling of glioma invasion and therapy approaches

abstract:
  A multiscale model for glioma spread in brain tissue under the influence of
vascularization and vascular endothelial growth factors is proposed. It
accounts for the interplay between the different components of the neoplasm and
the healthy tissue and it investigates and compares various therapy approaches.
Precisely, these involve radio- and chemotherapy in a concurrent or adjuvant
manner together with anti-angiogenic therapy affecting the vascular component
of the system. We assess tumor growth and spread on the basis of DTI data,
which allows us to reconstruct a realistic brain geometry and tissue structure,
and we apply our model to real glioma patient data. In this latter case, a
space-dependent radiotherapy description is considered using data about the
corresponding isodose curves.


title:
Local Stochastic Factored Gradient Descent for Distributed Quantum State
  Tomography

abstract:
  We propose a distributed Quantum State Tomography (QST) protocol, named Local
Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor
of a density matrix over a set of local machines. QST is the canonical
procedure to characterize the state of a quantum system, which we formulate as
a stochastic nonconvex smooth optimization problem. Physically, the estimation
of a low-rank density matrix helps characterizing the amount of noise
introduced by quantum computation. Theoretically, we prove the local
convergence of Local SFGD for a general class of restricted strongly
convex/smooth loss functions, i.e., Local SFGD converges locally to a small
neighborhood of the global optimum at a linear rate with a constant step size,
while it locally converges exactly at a sub-linear rate with diminishing step
sizes. With a proper initialization, local convergence results imply global
convergence. We validate our theoretical findings with numerical simulations of
QST on the Greenberger-Horne-Zeilinger (GHZ) state.


title:
The second cohomology of regular semisimple Hessenberg varieties from
  GKM theory

abstract:
  We describe the second cohomology of a regular semisimple Hessenberg variety
by generators and relations explicitly in terms of GKM theory. The cohomology
of a regular semisimple Hessenberg variety becomes a module of a symmetric
group $\mathfrak{S}_n$ by the dot action introduced by Tymoczko. As an
application of our explicit description, we give a formula describing the
isomorphism class of the second cohomology as an $\mathfrak{S}_n$-module. Our
formula is not exactly the same as the known formula by Chow or Cho-Hong-Lee
but they are equivalent. We also discuss its higher degree generalization.


title:
Theoretical study on the interfacial instability of a spherical droplet
  subject to vertical vibration

abstract:
  Interfacial instability would be aroused on a spherical liquid droplet when
it is subject to external vertical vibration. In this paper, a linear analysis
was conducted on this instability problem. The polar-angle dependent
acceleration in the spherical coordinate is strongly coupled with the temporal
and spatial component of the surface deformation displacement, which gives a
recursion equation that implicitly expresses the dispersion relation between
the growth rate and spherical mode numbers. The unstable regions (or unstable
tongues) for the inviscid fluids considering latitudinal mode (longitudinal
mode number m = 0) were derived and presented in the parameter plane. Compared
with the solution of the spherical Faraday instability under radial vibration
acceleration, the regions of harmonic unstable tongues for the mono-directional
vibration case is much narrowed and the subharmonic unstable tongues almost
become straight lines. The analysis shows that the latitudinal waves emerging
on the spherical droplet surface ought to oscillate harmonically instead of
subharmonically, which is opposite to the results for the case under radial
vibration acceleration. A corresponding experiment of a liquid droplet lying on
a vertically vibrating plate was conducted and the observations substantiate
our theoretical predictions.


title:
Unconventional spin frustration due to two competing ferromagnetic
  interactions of a spin-1/2 Ising-Heisenberg model on martini and
  martini-diced lattice

abstract:
  The spin-1/2 Ising-Heisenberg model on martini and martini-diced lattice is
exactly solved using a star-triangle transformation, which affords an exact
mapping correspondence to an effective spin-1/2 Ising model on a triangular
lattice. The ground-state phase diagram of both investigated quantum spin
models display two spontaneously ordered ferromagnetic phases and one
macroscopically degenerate disordered phase. In contrast to a classical
ferromagnetic phase where the spontaneous magnetization of the Ising as well as
Heisenberg spins acquire fully saturated values the spontaneous magnetization
of the Heisenberg spins is subject to a quantum reduction to one-third of its
saturated value within a quantum ferromagnetic phase. The spontaneous
magnetization and logarithmic divergence of the specific heat as the most
essential features of both ferromagnetic phases disappear whenever the
investigated quantum spin model is driven to the highly degenerate disordered
phase. The disordered phase with nonzero residual entropy originates either
from a geometric spin frustration caused by antiferromagnetic interactions or
more strikingly it may also alternatively arise from a competition of the
ferromagnetic Ising and Heisenberg interactions of easy-axis and easy-plane
type, respectively. All three available ground states coexist together at a
single triple point, around which anomalous magnetic and thermodynamic behavior
can be detected.


title:
Fluctuation Conductivity and Vortex State in Superconductor with Strong
  Paramagnetic Pair Breaking

abstract:
  The fluctuation conductivity of a moderately clean type II superconductor
with strong Pauli paramagnetic pair-breaking (PPB) is studied by focusing on
the quantum regime at low temperatures and in high magnetic fields. First, it
is pointed out that, as the PPB effect becomes stronger, the quantum
superconducting fluctuation is generally enhanced so that the Aslamasov-Larkin
(AL) fluctuation conductivity tends to vanish upon cooling. Further, by
examining other (the DOS and the Maki-Thompson (MT)) terms of the fluctuation
conductivity, the field dependence of the resulting total conductivity is found
to depend significantly on the type of the vortex lattice (or, glass) ordered
state at low temperatures where the strong PPB plays important roles. By
comparing the present theoretical results with the fluctuation-induced negative
magnetoresistance behavior upon entering a PPB-induced novel SC phase of Iron
selenide (FeSe), it is argued that the vortex matter states of the
superconducting order parameter in the second lowest ($n=1$) Landau level are
realized in FeSe in the parallel field configuration in high fields and at low
temperatures


title: Linear superposition in the general heavenly equation

abstract:
  Evidently, the linear superposition principle can not be exactly established
as a general principle in the presence of nonlinearity, and, at the first
glance, there is no expectation for it to hold even approximately. In this
letter, it is shown that the balance of different nonlinear effects describes
what linear superpositions may occur in nonlinear systems. The heavenly
equations are of significance in several scientific fields, especially in
relativity, gravity, field theory, and fluid dynamics. A special type of
implicit shock wave solution with three two-dimensional arbitrary functions of
the general heavenly equation is revealed. Restrict the two-dimensional
arbitrary functions to some types of one-dimensional arbitrary functions, it is
found that the nonlinear effects can be balanced such that the "impossible"
linear superposition solutions can be nontrivially constituted to new solutions
of the general heavenly equation.


title:
Environment induced emergence of collective behaviour in evolving swarms
  with limited sensing

abstract:
  Designing controllers for robot swarms is challenging, because human
developers have typically no good understanding of the link between the details
of a controller that governs individual robots and the swarm behavior that is
an indirect result of the interactions between swarm members and the
environment. In this paper we investigate whether an evolutionary approach can
mitigate this problem. We consider a very challenging task where robots with
limited sensing and communication abilities must follow the gradient of an
environmental feature and use Differential Evolution to evolve a neural network
controller for simulated robots. We conduct a systematic study to measure the
flexibility and scalability of the method by varying the size of the arena and
number of robots in the swarm. The experiments confirm the feasibility of our
approach, the evolved robot controllers induced swarm behavior that solved the
task. We found that solutions evolved under the harshest conditions (where the
environmental clues were the weakest) were the most flexible and that there is
a sweet spot regarding the swarm size. Furthermore, we observed collective
motion of the swarm, showcasing truly emergent behavior that was not
represented in- and selected for during evolution.


title: Privacy: An axiomatic approach

abstract:
  The increasing prevalence of large-scale data collection in modern society
represents a potential threat to individual privacy. Addressing this threat,
for example through privacy-enhancing technologies (PETs), requires a rigorous
definition of what exactly is being protected, that is, of privacy itself. In
this work, we formulate an axiomatic definition of privacy based on
quantifiable and irreducible information flows. Our definition synthesizes
prior work from the domain of social science with a contemporary understanding
of PETs such as differential privacy (DP). Our work highlights the fact that
the inevitable difficulties of protecting privacy in practice are fundamentally
information-theoretic. Moreover, it enables quantitative reasoning about PETs
based on what they are protecting, thus fostering objective policy discourse
about their societal implementation.


title: Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue

abstract:
  Context modeling plays a significant role in building multi-turn dialogue
systems. In order to make full use of context information, systems can use
Incomplete Utterance Rewriting(IUR) methods to simplify the multi-turn dialogue
into single-turn by merging current utterance and context information into a
self-contained utterance. However, previous approaches ignore the intent
consistency between the original query and rewritten query. The detection of
omitted or coreferred locations in the original query can be further improved.
In this paper, we introduce contrastive learning and multi-task learning to
jointly model the problem. Our method benefits from carefully designed
self-supervised objectives, which act as auxiliary tasks to capture semantics
at both sentence-level and token-level. The experiments show that our proposed
model achieves state-of-the-art performance on several public datasets.


title: The Lie coalgebra of multiple polylogarithms

abstract:
  We use Goncharov's coproduct of multiple polylogarithms to define a Lie
coalgebra over an arbitrary field. It is generated by symbols subject to
inductively defined relations, which we think of as functional relations for
multiple polylogarithms. In particular, we have inversion relations and shuffle
relations. We relate our definition to Goncharov's Bloch groups, and to the
concrete model in weight less than 5 by Goncharov and Rudenko.


title: Adaptive Patch Exiting for Scalable Single Image Super-Resolution

abstract:
  Since the future of computing is heterogeneous, scalability is a crucial
problem for single image super-resolution. Recent works try to train one
network, which can be deployed on platforms with different capacities. However,
they rely on the pixel-wise sparse convolution, which is not hardware-friendly
and achieves limited practical speedup. As image can be divided into patches,
which have various restoration difficulties, we present a scalable method based
on Adaptive Patch Exiting (APE) to achieve more practical speedup.
Specifically, we propose to train a regressor to predict the incremental
capacity of each layer for the patch. Once the incremental capacity is below
the threshold, the patch can exit at the specific layer. Our method can easily
adjust the trade-off between performance and efficiency by changing the
threshold of incremental capacity. Furthermore, we propose a novel strategy to
enable the network training of our method. We conduct extensive experiments
across various backbones, datasets and scaling factors to demonstrate the
advantages of our method. Code will be released.


title:
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding
  Alignment

abstract:
  This paper investigates the problem of temporally interpolating dynamic 3D
point clouds with large non-rigid deformation. We formulate the problem as
estimation of point-wise trajectories (i.e., smooth curves) and further reason
that temporal irregularity and under-sampling are two major challenges. To
tackle the challenges, we propose IDEA-Net, an end-to-end deep learning
framework, which disentangles the problem under the assistance of the
explicitly learned temporal consistency. Specifically, we propose a temporal
consistency learning module to align two consecutive point cloud frames
point-wisely, based on which we can employ linear interpolation to obtain
coarse trajectories/in-between frames. To compensate the high-order nonlinear
components of trajectories, we apply aligned feature embeddings that encode
local geometry properties to regress point-wise increments, which are combined
with the coarse estimations. We demonstrate the effectiveness of our method on
various point cloud sequences and observe large improvement over
state-of-the-art methods both quantitatively and visually. Our framework can
bring benefits to 3D motion data acquisition. The source code is publicly
available at https://github.com/ZENGYIMING-EAMON/IDEA-Net.git.


title:
HOP: History-and-Order Aware Pre-training for Vision-and-Language
  Navigation

abstract:
  Pre-training has been adopted in a few of recent works for
Vision-and-Language Navigation (VLN). However, previous pre-training methods
for VLN either lack the ability to predict future actions or ignore the
trajectory contexts, which are essential for a greedy navigation process. In
this work, to promote the learning of spatio-temporal visual-textual
correspondence as well as the agent's capability of decision making, we propose
a novel history-and-order aware pre-training paradigm (HOP) with VLN-specific
objectives that exploit the past observations and support future action
prediction. Specifically, in addition to the commonly used Masked Language
Modeling (MLM) and Trajectory-Instruction Matching (TIM), we design two proxy
tasks to model temporal order information: Trajectory Order Modeling (TOM) and
Group Order Modeling (GOM). Moreover, our navigation action prediction is also
enhanced by introducing the task of Action Prediction with History (APH), which
takes into account the history visual perceptions. Extensive experimental
results on four downstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the
effectiveness of our proposed method compared against several state-of-the-art
agents.


title:
Channel Hardening of IRS-Aided Multi-Antenna Systems: How Should IRSs
  Scale?

abstract:
  Unlike active array antennas, intelligent reflecting surfaces (IRSs) are
efficiently implemented at large dimensions. This allows for traceable
realizations of large-scale IRS-aided MIMO systems in which not necessarily the
array antennas, but the passive IRSs are large. It is widely believed that
large IRS-aided MIMO settings maintain the fundamental features of massive MIMO
systems, and hence they are the implementationally feasible technology for
establishing the performance of large-scale MIMO settings. This work gives a
rigorous proof to this belief. We show that using a large passive IRS, the
end-to-end MIMO channel between the transmitter and the receiver always
hardens, even if the IRS elements are strongly correlated.
  For the fading direct and reflection links between the transmitter and the
receiver, our derivations demonstrate that as the number of IRS elements grows
large, the capacity of end-to-end channel converges in distribution to a
real-valued Gaussian random variable whose variance goes to zero. The order of
this drop depends on how the physical dimensions of the IRS grow. We derive
this order explicitly. Numerical experiments depict that the analytical
asymptotic distribution almost perfectly matches the histogram of the capacity,
even in practical scenarios.
  As a sample application of the results, we use the asymptotic
characterization to study the dimensional trade-off between the transmitter and
the IRS. The result is intuitive: For a given target performance, the larger
the IRS is, the less transmit antennas are required to achieve the target. For
an arbitrary ergodic and outage performance, we characterize this trade-off
analytically. Our investigations demonstrate that using a practical IRS size,
the target performance can be achieved with significantly small end-to-end MIMO
dimensions.


title:
Unified Negative Pair Generation toward Well-discriminative Feature
  Space for Face Recognition

abstract:
  The goal of face recognition (FR) can be viewed as a pair similarity
optimization problem, maximizing a similarity set $\mathcal{S}^p$ over positive
pairs, while minimizing similarity set $\mathcal{S}^n$ over negative pairs.
Ideally, it is expected that FR models form a well-discriminative feature space
(WDFS) that satisfies $\inf{\mathcal{S}^p} > \sup{\mathcal{S}^n}$. With regard
to WDFS, the existing deep feature learning paradigms (i.e., metric and
classification losses) can be expressed as a unified perspective on different
pair generation (PG) strategies. Unfortunately, in the metric loss (ML), it is
infeasible to generate negative pairs taking all classes into account in each
iteration because of the limited mini-batch size. In contrast, in
classification loss (CL), it is difficult to generate extremely hard negative
pairs owing to the convergence of the class weight vectors to their center.
This leads to a mismatch between the two similarity distributions of the
sampled pairs and all negative pairs. Thus, this paper proposes a unified
negative pair generation (UNPG) by combining two PG strategies (i.e., MLPG and
CLPG) from a unified perspective to alleviate the mismatch. UNPG introduces
useful information about negative pairs using MLPG to overcome the CLPG
deficiency. Moreover, it includes filtering the similarities of noisy negative
pairs to guarantee reliable convergence and improved performance. Exhaustive
experiments show the superiority of UNPG by achieving state-of-the-art
performance across recent loss functions on public benchmark datasets. Our code
and pretrained models are publicly available.


title:
Budgeted Influence Maximization via Boost Simulated Annealing in Social
  Networks

abstract:
  Due to much closer to real application scenarios,the budgeted influence
maximization (BIM) problem has attracted great attention among researchers. As
a variant of the influence maximization (IM) problem, the BIM problem aims at
mining several nodes with different costs as seeds with limited budget to
maximize the influence as possible. By first activating these seed nodes and
spreading influence under the given propagation model, the maximized spread of
influence can be reached in the network.
  Several approaches have been proposed for BIM. Most of them are modified
versions of the greedy algorithm, which work well on the IM but seems
inefficient for the BIM because huge time consuming is inevitable. Recently,
some intelligence algorithms are proposed in order to reduce the running time,
but analysis shows that they cannot fully utilize the relationships between
nodes in networks, which will result in influence loss.
  Inspired by this, we propose an efficient method based on boosted simulated
annealing (SA) algorithm in this paper. Three heuristic strategies are proposed
to improve the performance and speed up the proposed algorithm. Experimental
results on both real world and synthetic networks demonstrate that the proposed
boosted SA performs much better than existed algorithms on performance with
almost equal or less running time.


title: Filling curves for $\mathbb{P}^1 \times \mathbb{P}^1$

abstract:
  We determine the minimal bi-degree(s) of an irreducible filling curve over
$\mathbb{F}_q$ for $\mathbb{P}^1\times \mathbb{P}^1$. It is $(q+1, q+1)$ if
$q\neq 2$, and they are $(4,3)$ and $(3,4)$ if $q=2$.


title: Application of Pythagorean means and Differential Subordination

abstract:
  For $0\leq\alpha\leq 1,$ let $H_{\alpha}(x,y)$ be the convex weighted
harmonic mean of $x$ and $y.$ We establish differential subordination
implications of the form \begin{equation*}
  H_{\alpha}(p(z),p(z)\Theta(z)+zp'(z)\Phi(z))\prec h(z)\Rightarrow p(z)\prec
h(z), \end{equation*} where $\Phi,\;\Theta$ are analytic functions and $h$ is a
univalent function satisfying some special properties. Further, we prove
differential subordination implications involving a combination of three
classical means. As an application, we generalize many existing results and
obtain sufficient conditions for starlikeness and univalence.


title: Navigating through the O(N) archipelago

abstract:
  A novel method for finding allowed regions in the space of CFT-data, coined
navigator method, was recently proposed in arXiv:2104.09518. Its efficacy was
demonstrated in the simplest example possible, i.e. that of the
mixed-correlator study of the 3D Ising Model. In this paper, we would like to
show that the navigator method may also be applied to the study of the family
of $d$-dimensional $O(N)$ models. We will aim to follow these models in the
$(d,N)$ plane. We will see that the "sailing" from island to island can be
understood in the context of the navigator as a parametric optimization
problem, and we will exploit this fact to implement a simple and effective
path-following algorithm. By sailing with the navigator through the $(d,N)$
plane, we will provide estimates of the scaling dimensions
$(\Delta_{\phi},\Delta_{s},\Delta_{t})$ in the entire range $(d,N) \in [3,4]
\times [1,3]$. We will show that to our level of precision, we cannot see the
non-unitary nature of the $O(N)$ models due to the fractional values of $d$ or
$N$ in this range. We will also study the limit $N \xrightarrow[]{} 1$, and see
that we cannot find any solution to the unitary mixed-correlator crossing
equations below $N=1$.


title:
Demonstration of sub-GV/m Accelerating Field in a Photoemission Electron
  Gun Powered by Nanosecond $X$-Band Radiofrequency Pulses

abstract:
  Radiofrequency (RF) electron guns operating at high accelerating gradients
offer a pathway to producing bright electron bunches. Such beams are expected
to revolutionize many areas of science: they could form the backbone of
next-generation compact x-ray free-electron lasers or provide coherent
ultrafast quantum electron probes. We report on the experimental demonstration
of an RF photoemission electron source supporting an accelerating field close
to 400~MV/m at the photocathode surface. The gun was operated in an RF
transient mode driven by short $\sim 9$~ns X-band (\SI{11.7}{\giga\hertz}) RF
pulses. We did not observe any major RF breakdown or significant dark current
over a three-week experimental run at high accelerating fields. The
demonstrated paradigm provides a viable path to forming relativistic electron
beams with unprecedented brightness.


title: Sufficient conditions and radius problems for the Silverman class

abstract:
  For $0<\alpha\leq1$ and $\lambda>0,$ let \begin{equation}\label{1}
  G_{\lambda,\alpha}=\left\{f\in\mathcal{A}:\left|\dfrac{1-\alpha+\alpha
zf''(z)/f'(z)}{zf'(z)/f(z)}-(1-\alpha)\right|<\lambda, z\in\mathbb{D}\right\},
\end{equation} the general form of Silverman class introduced by Tuneski and
Irnak. For this class we derive some sufficient conditions in the form of
differential inequalities. Further, we consider the class $\Omega,$ given by
\begin{equation}\label{omega}
  \Omega=\left\{f\in\mathcal{A}:|zf'(z)-f(z)|<\dfrac{1}{2},\;z\in\mathbb{D}\right\}.
\end{equation} For the above two classes, we establish inclusion relations
involving some other well known subclasses of $\mathcal{S}^*$ and find radius
estimates for different pairs involving these classes.