title:
Data driven soliton solution of the nonlinear Schr\"odinger equation
  with certain $\mathcal{PT}$-symmetric potentials via deep learning

abstract:
  We investigate the physics informed neural network method, a deep learning
approach, to approximate soliton solution of the nonlinear Schr\"odinger
equation with parity time symmetric potentials. We consider three different
parity time symmetric potentials, namely Gaussian, periodic and Rosen-Morse
potentials. We use physics informed neural network to solve the considered
nonlinear partial differential equation with the above three potentials. We
compare the predicted result with actual result and analyze the ability of deep
learning in solving the considered partial differential equation. We check the
ability of deep learning in approximating the soliton solution by taking
squared error between real and predicted values. {Further, we examine the
factors that affect the performance of the considered deep learning method with
different activation functions, namely ReLU, sigmoid and tanh. We also use a
new activation function, namely sech which is not used in the field of deep
learning and analyze whether this new activation function is suitable for the
prediction of soliton solution of nonlinear Schr\"odinger equation for the
aforementioned parity time symmetric potentials. In addition to the above, we
present how the network's structure and the size of the training data influence
the performance of the physics informed neural network. Our results show that
the constructed deep learning model successfully approximates the soliton
solution of the considered equation with high accuracy.


title:
Uniform spectral gap and orthogeodesic counting for strong convergence
  of Kleinian groups

abstract:
  We show convergence of small eigenvalues for geometrically finite hyperbolic
$n$-manifolds under strong limits. We then use the spectral gap of the limit
manifold and exponentially mixing geodesic flow to count uniformly along the
sequence the number of orthogeodesics between converging Margulis tubes.


title:
Solar calibration of $\alpha_{\rm MLT}$ using {\AE}SOPUS opacities in
  MESA

abstract:
  The simplistic but ubiquitous Mixing Length Theory (MLT) formalism is used to
model convective energy transport within 1D stellar evolution calculations. The
formalism relies on the free parameter $\alpha_{\rm MLT}$, which must be
independently calibrated within each stellar evolution program and for any
given set of physical assumptions. We present a solar calibration of
$\alpha_{\text{MLT}}$ appropriate for use with the AESOPUS opacities, which
have recently been made available for use with the MESA stellar evolution
software. We report a calibrated value of $\alpha_{\rm MLT}=1.931$ and
demonstrate the impact of using an appropriately calibrated value in
simulations of a $3 M_{\odot}$ asymptotic giant branch star.


title:
Extremely Correlated Fermi Liquid theory for $U=\infty$, $d=\infty$
  Hubbard model to ${\cal O}(\lambda^3)$

abstract:
  We present the ${\cal O}(\lambda^3)$ results from the $\lambda$ expansion in
the extremely correlated Fermi liquid theory applied to the
infinite-dimensional $t$-$J$ model (with $J=0$), and compare the results with
the earlier ${\cal O}(\lambda^2)$ results as well as the results from the
dynamical mean field theory. We focus attention on the $T$ dependence of the
resistivity $\rho(T)$, the Dyson self energy, and the quasiparticle weight $Z$
at various densities. The comparison shows that all the methods display
quadratic in T resistivity followed by a quasi-linear in T resistivity
characterizing a strange metal, and gives an estimate of the different scales
of these variables relative to the exact results.


title: Vanishing lines in chromatic homotopy theory

abstract:
  We show that at the prime 2, for any height $h$ and $G \subset \mathbb{G}_h$
a finite subgroup of the Morava stabilizer group, the $RO(G)$-graded homotopy
fixed point spectral sequence for $E_h$ has a strong horizontal vanishing line
of filtration $N_{h, G}$, a specific number depending on $h$ and $G$. It is a
consequence of the nilpotence theorem that such homotopy fixed point spectral
sequences all admit strong horizontal vanishing lines at some finite
filtration. Here, we establish specific bounds for them. Our bounds are sharp
for all the known computations of $E_h^{hG}$ when $G$ is cyclic.
  Our proof is by investigating the effect of the norm functor on the
Hill-Hopkins-Ravenel differentials in the Tate spectral sequence. As a
consequence, we will also show that the $RO(G)$-graded slice spectral sequence
of $(N_{C_2}^{G}\bar{v}_h)^{-1}BP^{((G))}$ has a horizontal vanishing line at
the same filtration $N_{h, G}$. As an immediate application, we establish a
bound for the orientation order $\Theta(h, G)$, the smallest number such that
the $\Theta(h, G)$-fold direct sum of any real vector bundle is
$E_h^{hG}$-orientable.


title: A Tour of Visualization Techniques for Computer Vision Datasets

abstract:
  We survey a number of data visualization techniques for analyzing Computer
Vision (CV) datasets. These techniques help us understand properties and latent
patterns in such data, by applying dataset-level analysis. We present various
examples of how such analysis helps predict the potential impact of the dataset
properties on CV models and informs appropriate mitigation of their
shortcomings. Finally, we explore avenues for further visualization techniques
of different modalities of CV datasets as well as ones that are tailored to
support specific CV tasks and analysis needs.


title: On De la Pe\~{n}a Type Inequalities for Point Processes

abstract:
  There has been a renewed interest in exponential concentration inequalities
for stochastic processes in probability and statistics over the last three
decades. De la Pe\~{n}a \cite{d} establishes a nice exponential inequality for
discrete time locally square integrable martingale . In this paper, we obtain
de la Pe\~{n}a's inequalities for stochastic integral of multivariate point
processes. The proof is primarily based on Dol\'{e}ans-Dade exponential formula
and the optional stopping theorem. As application, we obtain an exponential
inequality for block counting process in $\Lambda-$coalescents.


title:
Minimizing Fleet Size and Improving Bike Allocation of Bike Sharing
  under Future Uncertainty

abstract:
  As a rapidly expanding service, bike sharing is facing severe problems of
bike over-supply and demand fluctuation in many Chinese cities. This study
develops a large-scale method to determine the minimum fleet size under
uncertainty, based on the bike sharing data of millions of trips in Nanjing. It
is found that the algorithm of minimizing fleet size under the
incomplete-information scenario is effective in handling future uncertainty.
For a dockless bike sharing system, supplying 14.5% of the original fleet could
meet 96.8% of trip demands. Meanwhile, the results suggest that providing a
integrated service platform that integrates multiple companies can
significantly reduce the total fleet size by 44.6%. Moreover, in view of the
COVID-19 pandemic, this study proposes a social distancing policy that
maintains a suitable usage interval. These findings provide useful insights for
improving the resource efficiency and operational service of bike sharing and
shared mobility.


title:
Molecular beam homoepitaxy of N-polar AlN: enabling role of Al-assisted
  surface cleaning

abstract:
  N-polar aluminum nitride (AlN) is an important building block for
next-generation high-power RF electronics. We report successful homoepitaxial
growth of N-polar AlN by molecular beam epitaxy (MBE) on large-area
cost-effective N-polar AlN templates. Direct growth without any in-situ surface
cleaning leads to films with inverted Al-polarity. It is found that Al-assisted
cleaning before growth enables the epitaxial film to maintain N-polarity. The
grown N-polar AlN epilayer with its smooth, pit-free surface duplicates the
structural quality of the substrate as evidenced by a clean and smooth growth
interface with no noticeable extended defects generation. Near band-edge
photoluminescence peaks are observed at room temperature on samples with
MBE-grown layers but not on the bare AlN substrates, implying the suppression
of non-radiative recombination centers in the epitaxial N-polar AlN. These
results are pivotal steps towards future high-power RF electronics and deep
ultraviolet photonics based on the N-polar AlN platform.


title: Quantum computing hardware for HEP algorithms and sensing

abstract:
  Quantum information science harnesses the principles of quantum mechanics to
realize computational algorithms with complexities vastly intractable by
current computer platforms. Typical applications range from quantum chemistry
to optimization problems and also include simulations for high energy physics.
The recent maturing of quantum hardware has triggered preliminary explorations
by several institutions (including Fermilab) of quantum hardware capable of
demonstrating quantum advantage in multiple domains, from quantum computing to
communications, to sensing. The Superconducting Quantum Materials and Systems
(SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in
quantum computing and sensing, mediating quantum engineering and HEP based
material science. The main goal of the Center is to deploy quantum systems with
superior performance tailored to the algorithms used in high energy physics. In
this Snowmass paper, we discuss the two most promising superconducting quantum
architectures for HEP algorithms, i.e. three-level systems (qutrits) supported
by transmon devices coupled to planar devices and multi-level systems (qudits
with arbitrary N energy levels) supported by superconducting 3D cavities. For
each architecture, we demonstrate exemplary HEP algorithms and identify the
current challenges, ongoing work and future opportunities. Furthermore, we
discuss the prospects and complexities of interconnecting the different
architectures and individual computational nodes. Finally, we review several
different strategies of error protection and correction and discuss their
potential to improve the performance of the two architectures. This whitepaper
seeks to reach out to the HEP community and drive progress in both HEP research
and QIS hardware.


title: The Covariogram and an extension of Siegel's formula

abstract:
  We extend a formula of C. L. Siegel in the geometry of numbers, allowing the
body to contain an arbitrary number of interior lattice points. Our extension
involves a lattice sum of the covariogram of any compact set in Euclidean
space. The Fourier methods herein also allow for more general admissible sets,
due to the Poisson summation formula. As one of the consequences of these
results, we also obtain a new characterization of multi-tilings of Euclidean
space by translations, using the lower bound on lattice sums of such
covariograms.


title:
Conditions of general $Z_{2}$ symmetry in an arbitrary basis and
  TM$_{1,2}$ mixing for the minimal type-I seesaw mechanism

abstract:
  In this paper, using a formula for the minimal type-I seesaw mechanism by
$LDL^{T}$ decomposition, conditions of general $Z_{2}$-invariance of the
neutrino mass matrix $m$ is obtained for Lagrangian parameters in an arbitrary
basis. The conditions are found to be $(M_{22} a_{i}^{+} - M_{12} b_{i}^{+}) \,
( M_{22} a_{j}^{-} - M_{12} b_{j}^{-}) = - \det M \, b_{i}^{+} \, b_{j}^{-}$
for the $Z_{2}$-symmetric and -antisymmetric part of a Yukawa matrix
$Y_{ij}^{\pm} \equiv (Y \pm T Y )_{ij} /2 \equiv (a_{j}^{\pm}, b_{j}^{\pm})$
and the right-handed neutrino mass matrix $M_{ij}$. In other words, the
symmetric and antisymmetric part of $b_{i}$ must be proportional to those of
the quantity $(M_{22} a_{i} - M_{12} b_{i})$.
  Since the eigenvectors of the generator $T$ are orthogonal, we can analyze
the eigenvectors and mass eigenvalues of $m m^{\dagger}$ for a
$Z_{2}$-symmetric $m$. Two eigenvectors $u_{1,2}$ of $m m^{\dagger}$ coincide
with any of those of $T$, and the remaining one is a vector $(u_{1} \times
u_{2})^{*}$ orthogonal to them. Furthermore, if the Yukawa matrix does not have
the $Z_{2}$ symmetry, two nonzero neutrino masses are concisely represented.
  These results are applied to three $Z_{2}$ symmetries, the $\mu-\tau$
symmetry, the TM$_{1}$ mixing, and the magic symmetry which predicts the
TM$_{2}$ mixing. In particular, for the TM$_{2}$ mixing, the magic
(anti-)symmetric Yukawa matrix with $S_{2} Y = \pm Y$ is phenomenologically
rejected because it predicts $m_{2}=0$ or $m_{1}, m_{3} = 0$.


title:
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural
  Network and Self-Training

abstract:
  Retrosynthesis prediction is one of the fundamental challenges in organic
chemistry and related fields. The goal is to find reactants molecules that can
synthesize product molecules. To solve this task, we propose a new
graph-to-graph transformation model, G2GT, in which the graph encoder and graph
decoder are built upon the standard transformer structure. We also show that
self-training, a powerful data augmentation method that utilizes unlabeled
molecule data, can significantly improve the model's performance. Inspired by
the reaction type label and ensemble learning, we proposed a novel weak
ensemble method to enhance diversity. We combined beam search, nucleus, and
top-k sampling methods to further improve inference diversity and proposed a
simple ranking algorithm to retrieve the final top-10 results. We achieved new
state-of-the-art results on both the USPTO-50K dataset, with top1 accuracy of
54%, and the larger data set USPTO-full, with top1 accuracy of 50%, and
competitive top-10 results.


title:
"Flux+Mutability": A Conditional Generative Approach to One-Class
  Classification and Anomaly Detection

abstract:
  Anomaly Detection is becoming increasingly popular within the experimental
physics community. At experiments such as the Large Hadron Collider, anomaly
detection is at the forefront of finding new physics beyond the Standard Model.
This paper details the implementation of a novel Machine Learning architecture,
called Flux+Mutability, which combines cutting-edge conditional generative
models with clustering algorithms. In the `flux' stage we learn the
distribution of a reference class. The `mutability' stage at inference
addresses if data significantly deviates from the reference class. We
demonstrate the validity of our approach and its connection to multiple
problems spanning from one-class classification to anomaly detection. In
particular, we apply our method to the isolation of neutral showers in an
electromagnetic calorimeter and show its performance in detecting anomalous
dijets events from standard QCD background. This approach limits assumptions on
the reference sample and remains agnostic to the complementary class of objects
of a given problem. We describe the possibility of dynamically generating a
reference population and defining selection criteria via quantile cuts.
Remarkably this flexible architecture can be deployed for a wide range of
problems, and applications like multi-class classification or data quality
control are left for further exploration.


title: Image Data Augmentation for Deep Learning: A Survey

abstract:
  Deep learning has achieved remarkable results in many computer vision tasks.
Deep neural networks typically rely on large amounts of training data to avoid
overfitting. However, labeled data for real-world applications may be limited.
By improving the quantity and diversity of training data, data augmentation has
become an inevitable part of deep learning model training with image data.
  As an effective way to improve the sufficiency and diversity of training
data, data augmentation has become a necessary part of successful application
of deep learning models on image data. In this paper, we systematically review
different image data augmentation methods. We propose a taxonomy of reviewed
methods and present the strengths and limitations of these methods. We also
conduct extensive experiments with various data augmentation methods on three
typical computer vision tasks, including semantic segmentation, image
classification and object detection. Finally, we discuss current challenges
faced by data augmentation and future research directions to put forward some
useful research guidance.


title:
Pure Interchange Oscillations of Thin Filaments in an Average
  Magnetosphere

abstract:
  This paper describes magnetospheric waves of very long wavelength in thin
magnetic filaments. We consider an average magnetospheric configuration with
zero ionospheric conductance and calculate waves using two different
formulations: classic interchange theory and ideal MHD. Classic interchange
theory, which is developed in detail in this paper, is basically analytic and
is relatively straightforward to determine computationally, but it cannot offer
very high accuracy. The two formalisms agree well for the plasma sheet and also
for the inner magnetosphere. The eigenfrequencies range over about a factor of
seven, but the formulations generally agree with a root-mean-square difference
between the logarithms of interchange and MHD frequencies to be $\sim 0.054$.
The pressure perturbations in the classic interchange theory are assumed
constant along each field line, but the MHD computed pressure perturbations
along the field line vary in a range $\sim 30 \%$ in the plasma sheet but are
larger in the inner magnetosphere. The parallel and perpendicular
displacements, which are very different in the plasma sheet and inner
magnetosphere, show good qualitative agreement between the two approaches. In
the plasma sheet, the perpendicular displacements are strongly concentrated in
the equatorial plane, whereas the parallel displacements are spread through
most of the plasma sheet away from the equatorial plane; and can be regarded as
buoyancy waves. In the inner magnetosphere, the displacements are more
sinusoidal and are more like conventional slow modes. The different forms of
the waves are best characterized by the flux tube entropy $PV^\gamma$.


title: Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

abstract:
  Deepfakes utilise Artificial Intelligence (AI) techniques to create synthetic
media where the likeness of one person is replaced with another. There are
growing concerns that deepfakes can be maliciously used to create misleading
and harmful digital contents. As deepfakes become more common, there is a dire
need for deepfake detection technology to help spot deepfake media. Present
deepfake detection models are able to achieve outstanding accuracy (>90%).
However, most of them are limited to within-dataset scenario, where the same
dataset is used for training and testing. Most models do not generalise well
enough in cross-dataset scenario, where models are tested on unseen datasets
from another source. Furthermore, state-of-the-art deepfake detection models
rely on neural network-based classification models that are known to be
vulnerable to adversarial attacks. Motivated by the need for a robust deepfake
detection model, this study adapts metamorphic testing (MT) principles to help
identify potential factors that could influence the robustness of the examined
model, while overcoming the test oracle problem in this domain. Metamorphic
testing is specifically chosen as the testing technique as it fits our demand
to address learning-based system testing with probabilistic outcomes from
largely black-box components, based on potentially large input domains. We
performed our evaluations on MesoInception-4 and TwoStreamNet models, which are
the state-of-the-art deepfake detection models. This study identified makeup
application as an adversarial attack that could fool deepfake detectors. Our
experimental results demonstrate that both the MesoInception-4 and TwoStreamNet
models degrade in their performance by up to 30\% when the input data is
perturbed with makeup.


title: Self-Supervised Equivariant Learning for Oriented Keypoint Detection

abstract:
  Detecting robust keypoints from an image is an integral part of many computer
vision problems, and the characteristic orientation and scale of keypoints play
an important role for keypoint description and matching. Existing
learning-based methods for keypoint detection rely on standard
translation-equivariant CNNs but often fail to detect reliable keypoints
against geometric variations. To learn to detect robust oriented keypoints, we
introduce a self-supervised learning framework using rotation-equivariant CNNs.
We propose a dense orientation alignment loss by an image pair generated by
synthetic transformations for training a histogram-based orientation map. Our
method outperforms the previous methods on an image matching benchmark and a
camera pose estimation benchmark.


title: Higher-order modulations in the skyrmion-lattice phase of Cu$_2$OSeO$_3$

abstract:
  Using small angle neutron scattering, we have investigated higher-order peaks
in the skyrmion-lattice phase of Cu$_2$OSeO$_3$, in which two different
skyrmion lattices, SkX1 and SkX2, are known to form. For each skyrmion-lattice
phase, we observed two sets of symmetrically inequivalent peaks at the
higher-order-reflection positions with the indices $(110)$ and $(200)$. Under
the condition where the SkX1 and SkX2 coexist, we confirmed the absence of the
scattering at $\mathbf{Q}$ positions combining reflections from the two phases,
indicating a significantly weak double-scattering component. Detailed analysis
of the peak profile, as well as the temperature and magnetic-field dependence
of the peak intensity, also supports the intrinsic higher-order modulation
rather than the parasitic double scattering. The two higher-order modulations
show contrasting magnetic-field dependence; the former $(110)$ increases as the
field is increased, whereas the latter $(200)$ decreases. This indicates that,
in Cu$_2$OSeO$_3$, skyrmions are weakly distorted, and the distortion is
field-dependent in a way that the dominant higher-order modulation switches
from $(110)$ to $(200)$ under field. Monte Carlo simulations under sweeping
external magnetic field qualitatively reproduce the observed magnetic-field
dependence, and suggests that the higher-order modulations correspond to the
superlattices of weak swirlings appearing in the middle of the original
triangular-latticed skyrmions.


title: Poisons that are learned faster are more effective

abstract:
  Imperceptible poisoning attacks on entire datasets have recently been touted
as methods for protecting data privacy. However, among a number of defenses
preventing the practical use of these techniques, early-stopping stands out as
a simple, yet effective defense. To gauge poisons' vulnerability to
early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic
poisons in terms of peak test accuracy over 100 epochs and make a number of
surprising observations. First, we find that poisons that reach a low training
loss faster have lower peak test accuracy. Second, we find that a current
state-of-the-art error-maximizing poison is 7 times less effective when poison
training is stopped at epoch 8. Third, we find that stronger, more transferable
adversarial attacks do not make stronger poisons. We advocate for evaluating
poisons in terms of peak test accuracy.


title:
A Barzilai-Borwein Descent Method for Multiobjective Optimization
  Problems

abstract:
  The steepest descent method proposed by Fliege et al. motivates the research
on descent methods for multiobjective optimization, which has received
increasing attention in recent years. However, empirical results show that the
Armijo line search often gives a very small stepsize along the steepest
direction, which decelerates the convergence seriously. This paper points out
that the issue is mainly due to the imbalances among objective functions. To
address this issue, we propose a Barzilai-Borwein descent method for
multiobjective optimization (BBDMO) that dynamically tunes gradient magnitudes
using Barzilai-Borwein's rule in direction-finding subproblem. With monotone
and nonmonotone line search techniques, it is proved that accumulation points
generated by BBDMO are Pareto critical points, respectively. Furthermore,
theoretical results indicate the Armijo line search can achieve a better
stepsize in BBDMO. Finally, comparative results of numerical experiments are
reported to illustrate the efficiency of BBDMO and verify the theoretical
results.


title: Size Distribution of Small Jupiter Trojans in the L5 Swarm

abstract:
  We present an analysis of survey observations of the trailing L5 Jupiter
Trojan swarm using the wide-field Hyper Suprime-Cam CCD camera on the 8.2 m
Subaru Telescope. We detected 189 L5 Trojans from our survey that covered about
15 deg^2 of sky with a detection limit of m_r = 24.1 mag, and selected an
unbiased sample consisting of 87 objects with absolute magnitude 14 < H_r < 17
corresponding to diameter 2 km < D < 10 km for analysis of size distribution.
We fit their differential magnitude distribution to a single-slope power-law
with an index \alpha = 0.37 +- 0.01, which corresponds to a cumulative size
distribution with an index of b = 1.85 +- 0.05. Combining our results with data
for known asteroids, we obtained the size distribution of L5 Jupiter Trojans
over the entire size range for 9 < H_V < 17, and found that the size
distributions of the L4 and L5 swarms agree well with each other for a wide
range of sizes. This is consistent with the scenario that asteroids in the two
swarms originated from the same primordial population. Based on the above
results, the ratio of the total number of asteroids with D > 2 km in the two
swarms is estimated to be N_L4/N_L5=1.40 +- 0.15, and the total number of L_5
Jupiter Trojans with D > 1 km is estimated to be 1.1 x 10^5 by extrapolating
the obtained distribution.


title:
Scalability of gadolinium-doped-water Cherenkov detectors for nuclear
  nonproliferation

abstract:
  Antineutrinos are an unavoidable byproduct of the fission process. The
kton-scale KamLAND experiment has demonstrated the capability to detect reactor
antineutrinos at few-hundred-km range. But to detect or rule out the existence
of a single small reactor over many km requires a large detector. So large in
fact that the optical opacity of the detection medium itself becomes an
important factor. If the detector is so large that photons cannot traverse
across the detector medium to an optical detector, then it becomes impractical.
Gadolinium-doped water-Cherenkov detectors have been proposed for very large
reactor monitoring detectors. Even though Cherenkov emission does not produce
many photons and the energy resolution is poor, there may be a place for
Gd-doped water detectors for the largest of far-field detectors.
  In this paper, we focus on the reactor discovery potential of large-volume
Gd-doped water-Cherenkov detectors for nuclear nonproliferation applications.
Realistic background models for the worldwide reactor flux, geo-neutrinos,
cosmogenic fast neutrons, and detector-associated backgrounds are included. We
calculate the detector run time required to detect a small 50-MWt reactor at a
variety of stand-off distances as a function of detector size. We highlight
that at present, PMT dark rate and event reconstruction algorithms are the
limiting factors to extending beyond ~50-kt fiducial mass.


title: Partial Relaxed Optimal Transport for Denoised Recommendation

abstract:
  The interaction data used by recommender systems (RSs) inevitably include
noises resulting from mistaken or exploratory clicks, especially under implicit
feedbacks. Without proper denoising, RS models cannot effectively capture
users' intrinsic preferences and the true interactions between users and items.
To address such noises, existing methods mostly rely on auxiliary data which
are not always available. In this work, we ground on Optimal Transport (OT) to
globally match a user embedding space and an item embedding space, allowing
both non-deep and deep RS models to discriminate intrinsic and noisy
interactions without supervision. Specifically, we firstly leverage the OT
framework via Sinkhorn distance to compute the continuous many-to-many
user-item matching scores. Then, we relax the regularization in Sinkhorn
distance to achieve a closed-form solution with a reduced time complexity.
Finally, to consider individual user behaviors for denoising, we develop a
partial OT framework to adaptively relabel user-item interactions through a
personalized thresholding mechanism. Extensive experiments show that our
framework can significantly boost the performances of existing RS models.


title:
Equity in Resident Crowdsourcing: Measuring Under-reporting without
  Ground Truth Data

abstract:
  Modern city governance relies heavily on crowdsourcing (or "co-production")
to identify problems such as downed trees and power-lines. A major concern in
these systems is that residents do not report problems at the same rates,
leading to an inequitable allocation of government resources. However,
measuring such under-reporting is a difficult statistical task, as, almost by
definition, we do not observe incidents that are not reported. Thus,
distinguishing between low reporting rates and low ground-truth incident rates
is challenging. We develop a method to identify (heterogeneous) reporting
rates, without using external (proxy) ground truth data. Our insight is that
rates on $\textit{duplicate}$ reports about the same incident can be leveraged,
to turn the question into a standard Poisson rate estimation task -- even
though the full incident reporting interval is also unobserved. We apply our
method to over 100,000 resident reports made to the New York City Department of
Parks and Recreation, finding that there are substantial spatial and
socio-economic disparities in reporting rates, even after controlling for
incident characteristics.


title: Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs

abstract:
  Learning neural ODEs often requires solving very stiff ODE systems, primarily
using explicit adaptive step size ODE solvers. These solvers are
computationally expensive, requiring the use of tiny step sizes for numerical
stability and accuracy guarantees. This paper considers learning neural ODEs
using implicit ODE solvers of different orders leveraging proximal operators.
The proximal implicit solver consists of inner-outer iterations: the inner
iterations approximate each implicit update step using a fast optimization
algorithm, and the outer iterations solve the ODE system over time. The
proximal implicit ODE solver guarantees superiority over explicit solvers in
numerical stability and computational efficiency. We validate the advantages of
proximal implicit solvers over existing popular neural ODE solvers on various
challenging benchmark tasks, including learning continuous-depth graph neural
networks and continuous normalizing flows.


title:
The young embedded disk L1527 IRS: constraints on the water snowline and
  cosmic ray ionization rate from HCO+ observations

abstract:
  The water snowline in circumstellar disks is a crucial component in planet
formation, but direct observational constraints on its location remain sparse
due to the difficulty of observing water in both young embedded and mature
protoplanetary disks. Chemical imaging provides an alternative route to locate
the snowline, and HCO$^+$ isotopologues have been shown to be good tracers in
protostellar envelopes and Herbig disks. Here we present
$\sim$0.5$^{\prime\prime}$ resolution ($\sim$35 au radius) Atacama Large
Millimeter/submillimeter Array (ALMA) observations of HCO$^+$ $J=4-3$ and
H$^{13}$CO$^+$ $J=3-2$ toward the young (Class 0/I) disk L1527 IRS. Using a
source-specific physical model with the midplane snowline at 3.4 au and a small
chemical network, we are able to reproduce the HCO$^+$ and H$^{13}$CO$^+$
emission, but for HCO$^+$ only when the cosmic ray ionization rate is lowered
to $10^{-18}$ s$^{-1}$. Even though the observations are not sensitive to the
expected HCO$^+$ abundance drop across the snowline, the reduction in HCO$^+$
above the snow surface and the global temperature structure allow us to
constrain a snowline location between 1.8 and 4.1 au. Deep observations are
required to eliminate the envelope contribution to the emission and to derive
more stringent constraints on the snowline location. Locating the snowline in
young disks directly with observations of H$_2$O isotopologues may therefore
still be an alternative option. With a direct snowline measurement, HCO$^+$
will be able to provide constraints on the ionization rate.


title: CorrGAN: Input Transformation Technique Against Natural Corruptions

abstract:
  Because of the increasing accuracy of Deep Neural Networks (DNNs) on
different tasks, a lot of real times systems are utilizing DNNs. These DNNs are
vulnerable to adversarial perturbations and corruptions. Specifically, natural
corruptions like fog, blur, contrast etc can affect the prediction of DNN in an
autonomous vehicle. In real time, these corruptions are needed to be detected
and also the corrupted inputs are needed to be de-noised to be predicted
correctly. In this work, we propose CorrGAN approach, which can generate benign
input when a corrupted input is provided. In this framework, we train
Generative Adversarial Network (GAN) with novel intermediate output-based loss
function. The GAN can denoise the corrupted input and generate benign input.
Through experimentation, we show that up to 75.2% of the corrupted
misclassified inputs can be classified correctly by DNN using CorrGAN.


title: Topology and geometry of data manifold in deep learning

abstract:
  Despite significant advances in the field of deep learning in applications to
various fields, explaining the inner processes of deep learning models remains
an important and open question. The purpose of this article is to describe and
substantiate the geometric and topological view of the learning process of
neural networks. Our attention is focused on the internal representation of
neural networks and on the dynamics of changes in the topology and geometry of
the data manifold on different layers. We also propose a method for assessing
the generalizing ability of neural networks based on topological descriptors.
In this paper, we use the concepts of topological data analysis and intrinsic
dimension, and we present a wide range of experiments on different datasets and
different configurations of convolutional neural network architectures. In
addition, we consider the issue of the geometry of adversarial attacks in the
classification task and spoofing attacks on face recognition systems. Our work
is a contribution to the development of an important area of explainable and
interpretable AI through the example of computer vision.


title:
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and
  Cross-Language Speech Emotion Recognition

abstract:
  Despite the recent advancement in speech emotion recognition (SER) within a
single corpus setting, the performance of these SER systems degrades
significantly for cross-corpus and cross-language scenarios. The key reason is
the lack of generalisation in SER systems towards unseen conditions, which
causes them to perform poorly in cross-corpus and cross-language settings.
Recent studies focus on utilising adversarial methods to learn domain
generalised representation for improving cross-corpus and cross-language SER to
address this issue. However, many of these methods only focus on cross-corpus
SER without addressing the cross-language SER performance degradation due to a
larger domain gap between source and target language data. This contribution
proposes an adversarial dual discriminator (ADDi) network that uses the
three-players adversarial game to learn generalised representations without
requiring any target data labels. We also introduce a self-supervised ADDi
(sADDi) network that utilises self-supervised pre-training with unlabelled
data. We propose synthetic data generation as a pretext task in sADDi, enabling
the network to produce emotionally discriminative and domain invariant
representations and providing complementary synthetic data to augment the
system. The proposed model is rigorously evaluated using five publicly
available datasets in three languages and compared with multiple studies on
cross-corpus and cross-language SER. Experimental results demonstrate that the
proposed model achieves improved performance compared to the state-of-the-art
methods.


title:
A Subject-Independent Brain-Computer Interface Framework Based on
  Supervised Autoencoder

abstract:
  A calibration procedure is required in motor imagery-based brain-computer
interface (MI-BCI) to tune the system for new users. This procedure is
time-consuming and prevents na\"ive users from using the system immediately.
Developing a subject-independent MI-BCI system to reduce the calibration phase
is still challenging due to the subject-dependent characteristics of the MI
signals. Many algorithms based on machine learning and deep learning have been
developed to extract high-level features from the MI signals to improve the
subject-to-subject generalization of a BCI system. However, these methods are
based on supervised learning and extract features useful for discriminating
various MI signals. Hence, these approaches cannot find the common underlying
patterns in the MI signals and their generalization level is limited. This
paper proposes a subject-independent MI-BCI based on a supervised autoencoder
(SAE) to circumvent the calibration phase. The suggested framework is validated
on dataset 2a from BCI competition IV. The simulation results show that our
SISAE model outperforms the conventional and widely used BCI algorithms, common
spatial and filter bank common spatial patterns, in terms of the mean Kappa
value, in eight out of nine subjects.


title: Self-organized critical dynamics of RNA virus evolution

abstract:
  RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus
evolution is vital for understanding molecular evolution and medicine
development. Scientists lack, however, general frameworks to characterize the
dynamics of RNA virus evolution directly from empirical data and identify
potential physical laws. To fill this gap, we present a theory to characterize
the RNA virus evolution as a physical system with absorbing states and
avalanche behaviors. This approach maps accessible biological data (e.g.,
phylogenetic tree and infection) to a general stochastic process of RNA virus
infection and evolution, enabling researchers to verify potential
self-organized criticality underlying RNA virus evolution. We apply our
framework to SARS-CoV-2, the virus accounting for the global epidemic of
COVID-19. We find that SARS-CoV-2 exhibits scale-invariant avalanches as
mean-field theory predictions. The observed scaling relation, universal
collapse, and slowly decaying auto-correlation suggest a self-organized
critical dynamics of SARS-CoV-2 evolution. Interestingly, the lineages that
emerge from critical evolution processes coincidentally match with threatening
lineages of SARS-CoV-2 (e.g., the Delta virus). We anticipate our approach to
be a general formalism to portray RNA virus evolution and help identify
potential virus lineages to be concerned.


title:
Asymptotic Independence of the Quadratic form and Maximum of Independent
  Random Variables with Applications to High-Dimensional Tests

abstract:
  This paper establishes the asymptotic independence between the quadratic form
and maximum of a sequence of independent random variables. Based on this
theoretical result, we find the asymptotic joint distribution for the quadratic
form and maximum, which can be applied into the high-dimensional testing
problems. By combining the sum-type test and the max-type test, we propose the
Fisher's combination tests for the one-sample mean test and two-sample mean
test. Under this novel general framework, several strong assumptions in
existing literature have been relaxed. Monte Carlo simulation has been done
which shows that our proposed tests are strongly robust to both sparse and
dense data.


title:
Quaternion Optimized Model with Sparse Regularization for Color Image
  Recovery

abstract:
  This paper addresses the color image completion problem in accordance with
low-rank quatenrion matrix optimization that is characterized by sparse
regularization in a transformed domain. This research was inspired by an
appreciation of the fact that different signal types, including audio formats
and images, possess structures that are inherently sparse in respect of their
respective bases. Since color images can be processed as a whole in the
quaternion domain, we depicted the sparsity of the color image in the
quaternion discrete cosine transform (QDCT) domain. In addition, the
representation of a low-rank structure that is intrinsic to the color image is
a vital issue in the quaternion matrix completion problem. To achieve a more
superior low-rank approximation, the quatenrion-based truncated nuclear norm
(QTNN) is employed in the proposed model. Moreover, this model is facilitated
by a competent alternating direction method of multipliers (ADMM) based on the
algorithm. Extensive experimental results demonstrate that the proposed method
can yield vastly superior completion performance in comparison with the
state-of-the-art low-rank matrix/quaternion matrix approximation methods tested
on color image recovery.


title:
Immediate blowup of classical solutions to the vacuum free boundary
  problem of non-isentropic compressible Navier--Stokes equations

abstract:
  This paper considers the immediate blowup of classical solutions to the
vacuum free boundary problem of non-isentropic compressible Navier--Stokes
equations, where the viscosities and the heat conductivity could be constants,
or more physically, the degenerate, temperature-dependent functions which
vanish on the vacuum boundary (i.e., $\mu=\bar{\mu} \theta^{\alpha}, ~
\lambda=\bar{\lambda} \theta^{\alpha},~ \kappa=\bar{\kappa} \theta^{\alpha}$,
for constants $0\leq \alpha\leq 1/(\gamma-1)$,
$\bar{\mu}>0,~2\bar{\mu}+n\bar{\lambda}\geq0,~\bar{\kappa}\geq 0$, and
adiabatic exponent $\gamma>1$).
  In our previous study (Liu and Yuan, Math. Models Methods Appl. Sci. (9) 12,
2019), with three-dimensional spherical symmetry and constant shear viscosity,
vanishing bulk viscosity and heat conductivity, we established a class of
global-in-time large solutions, with bounded entropy and entropy derivatives,
under the condition the decaying rate of the initial density to the vacuum
boundary is of any positive power of the distance function to the boundary. In
this paper we prove that such classical solutions do not exist for any small
time for non-vanishing bulk viscosity, provided the initial velocity is
expanding near the boundary.
  When the heat conductivity does not vanish, it is automatically satisfied
that the normal derivative of the temperature of the classical solution across
the free boundary does not degenerate; meanwhile, the entropy of the classical
solution immediately blowups if the decaying rate of the initial density is not
of $1/(\gamma-1)$ power of the distance function to the boundary.
  The proofs are obtained by the analyzing the boundary behaviors of the
velocity, entropy and temperature, and investigating the maximum principles for
parabolic equations with degenerate coefficients.


title: Coloring ($P_5$, kite)-free graphs

abstract:
  Let $P_n$ and $K_n$ denote the induced path and complete graph on $n$
vertices, respectively. The {\em kite} is the graph obtained from a $P_4$ by
adding a vertex and making it adjacent to all vertices in the $P_4$ except one
vertex with degree 1. A graph is ($P_5$, kite)-free if it has no induced
subgraph isomorphic to a $P_5$ or a kite. For a graph $G$, the chromatic number
of $G$ (denoted by $\chi(G)$) is the minimum number of colors needed to color
the vertices of $G$ such that no two adjacent vertices receive the same color,
and the clique number of $G$ is the size of a largest clique in $G$. Here, we
are interested in the class of ($P_5$, kite)-free graphs with small clique
number. It is known that every ($P_5$,~kite, $K_3$)-free graph $G$ satisfies
$\chi(G)\leq 3$, every ($P_5$,~kite, $K_4$)-free graph $G$ satisfies
$\chi(G)\leq 4$, and that every ($P_5$,~kite, $K_5$)-free graph $G$ satisfies
$\chi(G)\leq 6$. In this paper, we showed the following:
  $\bullet$ Every ($P_5$, kite, $K_6$)-free graph $G$ satisfies $\chi(G)\leq
7$.
  $\bullet$ Every ($P_5$, kite, $K_7$)-free graph $G$ satisfies $\chi(G)\leq
9$.
  We also give examples to show that the above bounds are tight.


title:
The Electromagnetic Characteristics of the Tianlai Cylindrical
  Pathfinder Array

abstract:
  A great challenge for 21 cm intensity mapping experiments is the strong
foreground radiation which is orders of magnitude brighter than the 21cm
signal. Removal of the foreground takes advantage of the fact that its
frequency spectrum is smooth while the redshifted 21cm signal spectrum is
stochastic. However, a complication is the non-smoothness of the instrument
response. This paper describes the electromagnetic simulation of the Tianlai
cylinder array, a pathfinder for 21 cm intensity mapping experiments. Due to
the vast scales involved, a direct simulation requires large amount of
computing resources. We have made the simulation practical by using a
combination of methods: first simulate a single feed, then an array of feed
units, finally with the feed array and a cylindrical reflector together, to
obtain the response for a single cylinder. We studied its radiation pattern,
bandpass response and the effects of mutual coupling between feed units, and
compared the results with observation. Many features seen in the measurement
result are well reproduced in the simulation, especially the oscillatory
features which are associated with the standing waves on the reflector. The
mutual coupling between feed units is quantified with S-parameters, which
decrease as the distance between the two feeds increases. Based on the
simulated S-parameters, we estimate the correlated noise which has been seen in
the visibility data, the results show very good agreement with the data in both
magnitude and frequency structures. These results provide useful insights on
the problem of 21cm signal extraction for real instruments.


title:
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention
  Mechanism

abstract:
  Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG)
signals can enhance the performance of the brain-computer interface (BCI)
system and reduce the computational burden required for processing lengthy
MI-EEG signals. In this paper, we propose an unsupervised method based on the
self-attention mechanism to detect the salient intervals of MI-EEG signals
automatically. Our suggested method can be used as a preprocessing step within
any BCI algorithm to enhance its performance. The effectiveness of the
suggested method is evaluated on the most widely used BCI algorithm, the common
spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The
results indicate that the proposed method can effectively prune MI-EEG signals
and significantly enhance the performance of the CSP algorithm in terms of
classification accuracy.


title: Quantum Capacity and Vacuum Compressibility of Spacetime: Thermal Fields

abstract:
  An important yet perplexing result from work in the 90s and 00s is the
near-unity value of the ratio of fluctuations in the vacuum energy density of
quantum fields to the mean in a collection of generic spacetimes. This was done
by way of calculating the noise kernels which are the correlators of the
stress-energy tensor of quantum fields. In this paper we revisit this issue via
a quantum thermodynamics approach, by calculating two quintessential
thermodynamic quantities: the heat capacity and the quantum compressibility of
some model geometries filled with a quantum field at high and low temperatures.
This is because heat capacity at constant volume gives a measure of the
fluctuations of the energy density to the mean. When this ratio approaches or
exceeds unity, the validity of the canonical distribution is called into
question. Likewise, a system's compressibility at constant pressure is a
criterion for the validity of grand canonical ensemble. We derive the free
energy density and, from it, obtain the expressions for these two thermodynamic
quantities for thermal and quantum fields in 2d Casimir space, 2d Einstein
cylinder and 4d ($S^1 \times S^3$ ) Einstein universe. To examine the
dependence on the dimensionality of space, for completeness, we have also
derived these thermodynamic quantities for the Einstein universes with
even-spatial dimensions: $S^1 \times S^2$ and $S^1 \times S^4$. With this array
of spacetimes we can investigate the thermodynamic stability of quantum matter
fields in them and make some qualitative observations on the compatibility
condition for the co-existence between quantum fields and spacetimes, a
fundamental issue in the quantum and gravitation conundrum.

categories: gr-qc cond-mat.stat-mech hep-th quant-ph


title: Herz-slice spaces and applications

abstract:
  Let $\alpha\in\mathbb R^n$, $t\in(0,\infty)$, $p\in(0,\infty]$,
$r\in(1,\infty)$ and $q\in[1,\infty]$. We introduce the homogeneous Herz-slice
space $(\dot KE_{q,r}^{\alpha,p})_t(\mathbb R^n)$, the non-homogeneous
Herz-slice space $(KE_{q,r}^{\alpha,p})_t(\mathbb R^n)$ and show some
properties of them. As an application, the bounds for the Hardy--Littlewood
maximal operator on these spaces is considered.


title:
Detection Interval for Diffusion Molecular Communication: How Long is
  Enough?

abstract:
  Molecular communication has a key role to play in future medical
applications, including detecting, analyzing, and addressing infectious disease
outbreaks. Overcoming inter-symbol interference (ISI) is one of the key
challenges in the design of molecular communication systems. In this paper, we
propose to optimize the detection interval to minimize the impact of ISI while
ensuring the accurate detection of the transmitted information symbol, which is
suitable for the absorbing and passive receivers. For tractability, based on
the signal-to-interference difference (SID) and
signal-to-interference-and-noise amplitude ratio (SINAR), we propose a
modified-SINAR (mSINAR) to measure the bit error rate (BER) performance for the
molecular communication system with a variable detection interval. Besides, we
derive the optimal detection interval in closed form. Using simulation results,
we show that the BER performance of our proposed mSINAR scheme is superior to
the competing schemes, and achieves similar performance to optimal intervals
found by the exhaustive search.


title: A seven-equation diffused interface method for resolved multiphase flows

abstract:
  The seven-equation model is a compressible multiphase formulation that allows
for phasic velocity and pressure disequilibrium. These equations are solved
using a diffused interface method that models resolved multiphase flows. Novel
extensions are proposed for including the effects of surface tension,
viscosity, multi-species, and reactions. The allowed non-equilibrium of
pressure in the seven-equation model provides numerical stability in strong
shocks and allows for arbitrary and independent equations of states. A discrete
equations method (DEM) models the fluxes. We show that even though stiff
pressure- and velocity-relaxation solvers have been used, they are not needed
for the DEM because the non-conservative fluxes are accurately modeled. An
interface compression scheme controls the numerical diffusion of the interface,
and its effects on the solution are discussed. Test cases are used to validate
the computational method and demonstrate its applicability. They include
multiphase shock tubes, shock propagation through a material interface, a
surface-tension-driven oscillating droplet, an accelerating droplet in a
viscous medium, and shock-detonation interacting with a deforming droplet.
Simulation results are compared against exact solutions and experiments when
possible.


title:
Possible quadrupole-order-driven commensurate-incommensurate phase
  transition in B20 CoGe

abstract:
  The B20-type cobalt germanide CoGe was investigated by measuring the specific
heat, resistivity, and $^{59}$Co nuclear magnetic resonance (NMR). We observed
a phase transition at $T_Q=13.7$ K, evidenced by a very narrow peak of the
specific heat and sharp changes of the nuclear spin-spin ($T_2^{-1}$) and
spin-lattice ($T_1^{-1}$) relaxation rates. The fact that the entropy release
is extremely small and the Knight shift is almost independent of temperature
down to low temperatures as anticipated in a paramagnetic metal indicates that
the $T_Q$ transition is of non-magnetic origin. In addition, we detected a
crossover scale $T_0\sim30$ K below which the resistivity and the NMR linewidth
increase, and $T_1^{-1}$ is progressively distributed in space, that is, a
static and dynamical spatial inhomogeneity develops. While the order parameter
for the $T_Q$ transition remains an open question, a group-theoretical analysis
suggests that the finite electric quadrupole density arising from the low local
site symmetry at cobalt sites could drive the crystal symmetry lowering from
the P2$_1$3 symmetry that is commensurate to the R3 symmetry with an
incommensurate wavevector, which fairly well accounts for the $T_Q$ transition.
The quadrupole-order-driven commensurate-incommensurate phase transition may be
another remarkable phenomenon arising from the structural chirality inherent in
the noncentrosymmetric B20 family.


title:
Congruences of Galois representations attached to effective $A$-motives
  over global function fields

abstract:
  This article investigates congruences of $\mathfrak{p}$-adic representations
arising from effective $A$-motives defined over a global function field $K$. We
give a criterion for two congruent $\mathfrak{p}$-adic representations coming
from strongly semi-stable effective $A$-motives to be isomorphic up to
semi-simplification when restricted to decomposition groups of suitable places
of $K$. This is a function field analog of Ozeki-Taguchi's criterion for
$\ell$-adic representations of number fields. Motivated by a non-existence
conjecture on abelian varieties over number fields stated by Rasmussen and
Tamagawa, we also show that there exist no strongly semi-stable effective
$A$-motives with some constrained.


title: An alternative framework for quantifying coherence of quantum channels

abstract:
  We present an alternative framework for quantifying the coherence of quantum
channels, which contains three conditions: the faithfulness, nonincreasing
under sets of all the incoherent superchannels and the additivity. Based on the
characteristics of the coherence of quantum channels and the additivity of
independent channels, our framework provides an easier way to certify whether a
function is a bona fide coherence measure of quantum channels. Detailed example
is given to illustrate the effectiveness and advantages of our framework.


title:
Averaged transverse momentum correlations of hadrons in relativistic
  heavy-ion collisions

abstract:
  We compile experimental data for the averaged transverse momentum
($\left\langle p_{T}\right\rangle $) of proton, $\Lambda$, $\Xi^{-}$,
$\Omega^{-}$ and $\phi$ at mid-rapidity in Au+Au collisions at $\sqrt{s_{NN}}=$
200, 39, 27, 19.6, 11.5, 7.7 GeV and in Pb+Pb collisions at $\sqrt{s_{NN}}=$
2.76 TeV, and find that experimental data of these hadrons exhibit systematic
correlations. We apply a quark combination model with equal-velocity
combination approximation to derive analytic formulas of hadronic $\left\langle
p_{T}\right\rangle $ in the case of exponential form of quark $p_{T}$ spectra
at hadronization. We use them to successfully explain the systematic
correlations exhibited in $\left\langle p_{T}\right\rangle $ data of
$p\Lambda$, $\Lambda\Xi^{-}$, $\Xi^{-}\Omega^{-}$ and $\Xi^{-}\phi$ pairs. We
also use them to successfully explain the regularity observed in $\left\langle
p_{T}\right\rangle $ of these hadrons as the function of
$(dN_{ch}/dy)/(N_{part}/2)$ at mid-rapidity in central heavy-ion collisions at
both RHIC and LHC energies. Our results suggest that the constituent quark
degrees of freedom and the equal-velocity combination of these constituent
quarks at hadronization play important role in understanding the production of
baryons and $\phi$ meson at these RHIC and LHC energies.


title: Symplectic Effective Field Theory for Nuclear Structure Studies

abstract:
  A Symplectic Effective Field Theory that unveils the observed emergence of
symplectic symmetry in atomic nuclei is advanced. Specifically, starting from a
simple extension of the harmonic-oscillator Lagrangian, an effective field
theory applied against symplectic basis states is shown to yield a Hamiltonian
system with one fitted parameter. The scale of the system can be determined
self consistently as the ratio of the average volume of a nucleus assumed to be
spherical to its volume as determined by the average number of oscillator
quanta, which is stretched by the fact that the plane-wave solution satisfies
the equations of motion at every order without the need for perturbative
corrections. As an application of the theory, results for 20Ne, 22Ne and 22Mg
are presented that yield energy spectra, B(E2) values, and matter radii in good
agreement with experimentally measured results.


title: Example-based Synthesis of Static Analysis Rules

abstract:
  Static Analysis tools have rules for several code quality issues and these
rules are created by experts manually. In this paper, we address the problem of
automatic synthesis of code quality rules from examples. We formulate the rule
synthesis problem as synthesizing first order logic formulas over graph
representations of code. We present a new synthesis algorithm RhoSynth that is
based on Integer Linear Programming-based graph alignment for identifying code
elements of interest to the rule. We bootstrap RhoSynth by leveraging code
changes made by developers as the source of positive and negative examples. We
also address rule refinement in which the rules are incrementally improved with
additional user-provided examples. We validate RhoSynth by synthesizing more
than 30 Java code quality rules. These rules have been deployed as part of a
code review system in a company and their precision exceeds 75% based on
developer feedback collected during live code-reviews. Through comparisons with
recent baselines, we show that current state-of-the-art program synthesis
approaches are unable to synthesize most of these rules.


title:
Multilingual Syntax-aware Language Modeling through Dependency Tree
  Conversion

abstract:
  Incorporating stronger syntactic biases into neural language models (LMs) is
a long-standing goal, but research in this area often focuses on modeling
English text, where constituent treebanks are readily available. Extending
constituent tree-based LMs to the multilingual setting, where dependency
treebanks are more common, is possible via dependency-to-constituency
conversion methods. However, this raises the question of which tree formats are
best for learning the model, and for which languages. We investigate this
question by training recurrent neural network grammars (RNNGs) using various
conversion methods, and evaluating them empirically in a multilingual setting.
We examine the effect on LM performance across nine conversion methods and five
languages through seven types of syntactic tests. On average, the performance
of our best model represents a 19 \% increase in accuracy over the worst choice
across all languages. Our best model shows the advantage over
sequential/overparameterized LMs, suggesting the positive effect of syntax
injection in a multilingual setting. Our experiments highlight the importance
of choosing the right tree formalism, and provide insights into making an
informed decision.


title: Artificial Intelligence for Imaging Cherenkov Detectors at the EIC

abstract:
  Imaging Cherenkov detectors form the backbone of particle identification
(PID) at the future Electron Ion Collider (EIC). Currently all the designs for
the first EIC detector proposal use a dual Ring Imaging CHerenkov (dRICH)
detector in the hadron endcap, a Detector for Internally Reflected Cherenkov
(DIRC) light in the barrel, and a modular RICH (mRICH) in the electron endcap.
These detectors involve optical processes with many photons that need to be
tracked through complex surfaces at the simulation level, while for
reconstruction they rely on pattern recognition of ring images. This proceeding
summarizes ongoing efforts and possible applications of AI for imaging
Cherenkov detectors at EIC. In particular we will provide the example of the
dRICH for the AI-assisted design and of the DIRC for simulation and particle
identification from complex patterns and discuss possible advantages of using
AI.


title: GraphHop++: New Insights into GraphHop and Its Enhancement

abstract:
  An enhanced label propagation (LP) method called GraphHop has been proposed
recently. It outperforms graph convolutional networks (GCNs) in the
semi-supervised node classification task on various networks. Although the
performance of GraphHop was explained intuitively with joint node attributes
and labels smoothening, its rigorous mathematical treatment is lacking. In this
paper, new insights into GraphHop are provided by analyzing it from a
constrained optimization viewpoint. We show that GraphHop offers an alternate
optimization to a certain regularization problem defined on graphs. Based on
this interpretation, we propose two ideas to improve GraphHop furthermore,
which leads to GraphHop++. We conduct extensive experiments to demonstrate the
effectiveness and efficiency of GraphHop++. It is observed that GraphHop++
outperforms all other benchmarking methods, including GraphHop, consistently on
five test datasets as well as an object recognition task at extremely low label
rates (i.e., 1, 2, 4, 8, 16, and 20 labeled samples per class).


title:
Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe
  Quadruped Navigation

abstract:
  For autonomous quadruped robot navigation in various complex environments, a
typical SOTA system is composed of four main modules -- mapper, global planner,
local planner, and command-tracking controller -- in a hierarchical manner. In
this paper, we build a robust and safe local planner which is designed to
generate a velocity plan to track a coarsely planned path from the global
planner. Previous works used waypoint-based methods (e.g.
Proportional-Differential control and pure pursuit) which simplify the path
tracking problem to local point-goal navigation. However, they suffer from
frequent collisions in geometrically complex and narrow environments because of
two reasons; the global planner uses a coarse and inaccurate model and the
local planner is unable to track the global plan sufficiently well. Currently,
deep learning methods are an appealing alternative because they can learn
safety and path feasibility from experience more accurately. However, existing
deep learning methods are not capable of planning for a long horizon. In this
work, we propose a learning-based fully autonomous navigation framework
composed of three innovative elements: a learned forward dynamics model (FDM),
an online sampling-based model-predictive controller, and an informed
trajectory sampler (ITS). Using our framework, a quadruped robot can
autonomously navigate in various complex environments without a collision and
generate a smoother command plan compared to the baseline method. Furthermore,
our method can reactively handle unexpected obstacles on the planned path and
avoid them. Project page
https://awesomericky.github.io/projects/FDM_ITS_navigation/.


title: Phase diagram of hard core bosons with anisotropic interactions

abstract:
  The phase diagram of lattice hard core bosons with nearest-neighbor
interactions allowed to vary independently, from repulsive to attractive, along
different crystallographic directions, is studied by Quantum Monte Carlo
simulations. We observe a superfluid phase, as well as two crystalline phases
at half filling, either checkerboard or striped. Just like in the case of
isotropic interactions, no supersolid phase is observed.


title:
LitMC-BERT: transformer-based multi-label classification of biomedical
  literature with an application on COVID-19 literature curation

abstract:
  The rapid growth of biomedical literature poses a significant challenge for
curation and interpretation. This has become more evident during the COVID-19
pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed,
has accumulated over 180,000 articles with millions of accesses. Approximately
10,000 new articles are added to LitCovid every month. A main curation task in
LitCovid is topic annotation where an article is assigned with up to eight
topics, e.g., Treatment and Diagnosis. The annotated topics have been widely
used both in LitCovid (e.g., accounting for ~18% of total uses) and downstream
studies such as network generation. However, it has been a primary curation
bottleneck due to the nature of the task and the rapid literature growth. This
study proposes LITMC-BERT, a transformer-based multi-label classification
method in biomedical literature. It uses a shared transformer backbone for all
the labels while also captures label-specific features and the correlations
between label pairs. We compare LITMC-BERT with three baseline models on two
datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the
current best results, respectively, and only requires ~18% of the inference
time than the Binary BERT baseline. The related datasets and models are
available via https://github.com/ncbi/ml-transformer.


title:
Crossovers and quantum phase transitions in two-band superfluids: The
  evolution from BCS to Bose pairing by tuning interactions and band offset

abstract:
  We show that in two-band $s$-wave superfluids it is possible to induce
quantum phase transitions (QPTs) by tuning intraband and interband $s$-wave
interactions, in sharp contrast to single-band $s$-wave superfluids, where only
a crossover between Bardeen-Cooper-Schrieffer (BCS) and Bose-Einstein
condensation (BEC) superfluidity occurs. For non-zero interband and attractive
intraband interactions, we demonstrate that the ground state has always two
interpenetrating superfluids possessing three spectroscopically distinct
regions where pairing is qualitatively different: (I) BCS pairing in both bands
(BCS-BCS), (II) BCS pairing in one band and BEC pairing in the other (BCS-BEC),
and (III) Bose pairing in both bands (BEC-BEC). Furthermore, we show that by
fine tuning the interband interactions to zero one can induce QPTs in the
ground state between three distinct superfluid phases. There are two phases
where only one band is superfluid ($S_1$ or $S_2$), and one phase where both
bands are superfluid $(S_1 + S_2)$, a situation which is absent in one-band
$s$-wave systems. Lastly, we suggest that these crossovers and QPTs may be
observed in $^{173}$Yb and $^{87}$Sr.


title: Evolving Programmable Computational Metamaterials

abstract:
  Granular metamaterials are a promising choice for the realization of
mechanical computing devices. As preliminary evidence of this, we demonstrate
here how to embed Boolean logic gates (AND and XOR) into a granular
metamaterial by evolving where particular grains are placed in the material.
Our results confirm the existence of gradients of increasing "AND-ness" and
"XOR-ness" within the space of possible materials that can be followed by
evolutionary search. We measure the computational functionality of a material
by probing how it transforms bits encoded as vibrations with zero or non-zero
amplitude. We compared the evolution of materials built from mass-contrasting
particles and materials built from stiffness-contrasting particles, and found
that the latter were more evolvable. We believe this work may pave the way
toward evolutionary design of increasingly sophisticated, programmable, and
computationally dense metamaterials with certain advantages over more
traditional computational substrates.


title:
Effect of Oxygen Vacancy Defects on Electronic and Optical Properties of
  $MgO$ Monolayers: First Principles Study

abstract:
  The optoelectronic properties induced by oxygen vacancy defects in MgO(111)
monolayers have been studied using hybrid level of DFT method. HSE calculations
shows significant reduction in electronic band gap of MgO monolayer as a result
of introduction of oxygen vacancies. The pristine monolayer has a wide band gap
(4.84 eV, indirect) semiconducting behaviour, which changes gap to 2.97 eV
(indirect) and 2.28 eV (direct) with increment in oxygen vacancy defect
concentration of 6.25% and 12.5%,respectively. Consequently, presence of oxygen
vacancies leads to energy red shift of the observed optical phenomena with
reference to the pristine monolayer. Most importantly, the divacancy system
with two consecutive vacancy sites displays the strongest optical absorption
and also becomes optically responsive over the spectral range from visible to
ultraviolet region in the electromagnetic spectrum. Thus creation of oxygen
vancancies in MgO monolayers may be a fruitful technique for achieving a
suitable solar energy material.


title:
On The Cross-Modal Transfer from Natural Language to Code through
  Adapter Modules

abstract:
  Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently
used in software engineering as models pre-trained on large source code
corpora. Their knowledge is transferred to downstream tasks (e.g. code clone
detection) via fine-tuning. In natural language processing (NLP), other
alternatives for transferring the knowledge of PTLMs are explored through using
adapters, compact, parameter efficient modules inserted in the layers of the
PTLM. Although adapters are known to facilitate adapting to many downstream
tasks compared to fine-tuning the model that require retraining all of the
models' parameters -- which owes to the adapters' plug and play nature and
being parameter efficient -- their usage in software engineering is not
explored.
  Here, we explore the knowledge transfer using adapters and based on the
Naturalness Hypothesis proposed by Hindle et. al \cite{hindle2016naturalness}.
Thus, studying the bimodality of adapters for two tasks of cloze test and code
clone detection, compared to their benchmarks from the CodeXGLUE platform.
These adapters are trained using programming languages and are inserted in a
PTLM that is pre-trained on English corpora (N-PTLM). Three programming
languages, C/C++, Python, and Java, are studied along with extensive
experiments on the best setup used for adapters. Improving the results of the
N-PTLM confirms the success of the adapters in knowledge transfer to software
engineering, which sometimes are in par with or exceed the results of a PTLM
trained on source code; while being more efficient in terms of the number of
parameters, memory usage, and inference time. Our results can open new
directions to build smaller models for more software engineering tasks. We open
source all the scripts and the trained adapters.


title:
Thermoelectric properties of Fe$_{2}$VAl at high temperature region: A
  combined experimental and theoretical study

abstract:
  Heusler type compounds have long been recognized as potential thermoelectric
(TE) materials. Here, the experimentally observed TE properties of Fe$_{2}$VAl
are understood through electronic structure calculations in the temperature
range of $300-800$ K. The observed value of $S$ is $\sim-$138 $\mu$V/K at 300
K. Then, the $|S|$ decreases with increase in temperature up to the highest
temperature with the value of $\sim-$18 $\mu$V/K at 800 K. The negative sign of
$S$ in the full temperature window signifies the dominating $n$-type character
of the compound. The temperature dependent of electrical conductivity, $\sigma$
(thermal conductivity, $\kappa$) exhibits the increasing (decreasing) trend
with the values of $\sim$1.2 $\times$ 10$^{5}$ $\Omega^{-1}$m$^{-1}$
($\sim$23.7 W/m-K) and $\sim$2.2 $\times$ 10$^{5}$ $\Omega^{-1}$m$^{-1}$
($\sim$15.3 W/m-K) at 300 K and 800 K, respectively. In order to understand
these transport properties, the DFT based semi-classical Boltzmann theory is
used. The contributions of multi-band electron and hole pockets are found to be
mainly responsible for the temperature dependent trend of these properties. The
decrement of $|S|$ and increment of $\sigma/\tau$ $\&$ $\kappa_{e}/\tau$
($\tau$ is relaxation time) with temperature is directly related with the
contribution of multiple hole pockets. Present study suggests that DFT based
electronic calculations provide reasonably good explanations of experimental TE
properties of Fe$_{2}$VAl in the high temperature range of $300-800$ K.


title: Interaction-Aware Labeled Multi-Bernoulli Filter

abstract:
  Tracking multiple objects through time is an important part of an intelligent
transportation system. Random finite set (RFS)-based filters are one of the
emerging techniques for tracking multiple objects. In multi-object tracking
(MOT), a common assumption is that each object is moving independent of its
surroundings. But in many real-world applications, target objects interact with
one another and the environment. Such interactions, when considered for
tracking, are usually modeled by an interactive motion model which is
application specific. In this paper, we present a novel approach to incorporate
target interactions within the prediction step of an RFS-based multi-target
filter, i.e. labeled multi-Bernoulli (LMB) filter. The method has been
developed for two practical applications of tracking a coordinated swarm and
vehicles. The method has been tested for a complex vehicle tracking dataset and
compared with the LMB filter through the OSPA and OSPA$^{(2)}$ metrics. The
results demonstrate that the proposed interaction-aware method depicts
considerable performance enhancement over the LMB filter in terms of the
selected metrics.


title:
Network Bandwidth Variation-Adapted State Transfer for Geo-Replicated
  State Machines and its Application to Dynamic Replica Replacement

abstract:
  This paper proposes a new state transfer method for geographic state machine
replication (SMR) that dynamically allocates the state to be transferred among
replicas according to changes in communication bandwidths. SMR improves fault
tolerance by replicating a service to multiple replicas. When a replica is
newly added or recovered from a failure, the other replicas transfer the
current state of the service to it. However, in geographic SMR, the
communication bandwidths of replicas are different and constantly changing.
Therefore, existing state transfer methods cannot fully utilize the available
bandwidth, and their state transfer time increases. To overcome this problem,
our method divides the state into multiple chunks and assigns them to replicas
based on each replica's bandwidth so that the broader a replica's bandwidth is,
the more chunks it transfers. The proposed method also updates the chunk
assignment of each replica dynamically based on the currently estimated
bandwidth. The performance evaluation on Amazon EC2 shows that the proposed
method reduces the state transfer time by up to 47% compared to the existing
one. In addition, we apply the proposed method to dynamic replacement of
replicas, which can mitigate latency degradation caused by network trouble, and
evaluate how fast the method can relocate a replica.


title: Modeling Cosmological Perturbations of Thermal Inflation

abstract:
  We consider a simple system consisting of matter, radiation and vacuum
components to model the impact of thermal inflation on the evolution of
primordial perturbations. The vacuum energy magnifies the modes entering the
horizon before its domination, making them potentially observable, and the
resulting transfer function reflects the phase changes and energy contents. To
determine the transfer function, we follow the curvature perturbation from well
outside the horizon during radiation domination to well outside the horizon
during vacuum domination and evaluate it on a constant radiation density
hypersurface, as is appropriate for the case of thermal inflation. The shape of
the transfer function is determined by the ratio of vacuum energy to radiation
at matter-radiation equality, which we denote by $\upsilon$, and has two
characteristic scales, $k_{\rm a}$ and $k_{\rm b}$, corresponding to the
horizon sizes at matter radiation equality and the beginning of the inflation,
respectively. If $\upsilon \ll 1$, the universe experiences radiation, matter
and vacuum domination eras and the transfer function is flat for $k \ll k_{\rm
b}$, oscillates with amplitude $1/5$ for $ k_{\rm b} \ll k \ll k_{\rm a}$ and
oscillates with amplitude $1$ for $k \gg k_{\rm a}$. For $\upsilon \gg 1$, the
matter domination era disappears, and the transfer function reduces to being
flat for $k \ll k_{\rm b}$ and oscillating with amplitude $1$ for $k \gg k_{\rm
b}$.


title: Superconductivity in the nodal-line compound La$_3$Pt$_3$Bi$_4$

abstract:
  Owing to the specific topological states in nodal-line semimetals, novel
topological superconductivity is expected to emerge in these systems. In this
letter, by combination of the first-principles calculations and resistivity,
susceptibility and specific heat measurements, we demonstrate that
La$_3$Pt$_3$Bi$_4$ is a topologically nontrivial nodal-ring semimetal protected
by the gliding-mirror symmetry even in the presence of spin-orbit coupling.
Meanwhile, we discover bulk superconductivity with a transition temperature of
$\sim$1.1 K, and an upper critical field of $\sim$0.41 T. These findings
demonstrate that La$_3$Pt$_3$Bi$_4$ provides a material platform for studying
novel superconductivity in the nodal-ring system.


title: Markovian Persuasion with Stochastic Revelations

abstract:
  In the classical Bayesian persuasion model an informed player and an
uninformed one engage in a static interaction. The informed player, the sender,
knows the state of nature, while the uninformed one, the receiver, does not.
The informed player partially shares his private information with the receiver
and the latter then, based on her belief about the state, takes an action. This
action, together with the state of nature, determines the utility of both
players. This paper analyzes a dynamic Bayesian persuasion model where the
state of nature evolves according to a Markovian law. Here, the sender always
knows the realized state, while the receiver randomly gets to know it. We
discuss the value of the sender when he becomes more and more patient and its
relation to the revelation rate, namely the probability at which the true state
is revealed to the receiver at any stage.


title:
Smoothness of integrated density of states of the Anderson model on
  Bethe lattice in high disorder

abstract:
  In this work we consider the Anderson model on Bethe lattice and prove that
the integrated density of states (IDS) is as smooth as the single site
distribution (SSD), in high disorder


title:
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on
  Signal Strength represented by Antenna Gain Array Manifold

abstract:
  Inspection robots are widely used in the field of smart grid monitoring in
substations, and partial discharge (PD) is an important sign of the insulation
state of equipments. PD direction of arrival (DOA) algorithms using
conventional beamforming and time difference of arrival (TDOA) require
large-scale antenna arrays and high computational complexity, which make them
difficult to implement on inspection robots. To address this problem, a novel
directional multiple signal classification (Dir-MUSIC) algorithm for PD
direction finding based on signal strength is proposed, and a miniaturized
directional spiral antenna circular array is designed in this paper. First, the
Dir-MUSIC algorithm is derived based on the array manifold characteristics.
This method uses strength intensity information rather than the TDOA
information, which could reduce the computational difficulty and the
requirement of array size. Second, the effects of signal-to-noise ratio (SNR)
and array manifold error on the performance of the algorithm are discussed
through simulations in detail. Then according to the positioning requirements,
the antenna array and its arrangement are developed, optimized, and simulation
results suggested that the algorithm has reliable direction-finding performance
in the form of 6 elements. Finally, the effectiveness of the algorithm is
tested by using the designed spiral circular array in real scenarios. The
experimental results show that the PD direction-finding error is 3.39{\deg},
which can meet the need for Partial discharge DOA estimation using inspection
robots in substations.


title:
Indecomposable doubling for representations of the type I Lie
  superalgebras sl(m/n) and osp(2/2n)

abstract:
  We establish that for the type I Lie superalgebras $sl(m/n)$ and $osp(2/2n)$,
each Kac module admits a 1 parameter family of indecomposable double
extensions. The result follows from the explicit evaluation of the $H^1$
cohomology valued in the tensor product of the module and its dual.


title:
Pre-training of Deep Protein Models with Molecular Dynamics Simulations
  for Drug Binding

abstract:
  The latest biological findings discover that the motionless 'lock-and-key'
theory is no longer applicable and the flexibility of both the receptor and
ligand plays a significant role in helping understand the principles of the
binding affinity prediction. Based on this mechanism, molecular dynamics (MD)
simulations have been invented as a useful tool to investigate the dynamical
properties of this molecular system. However, the computational expenditure
prohibits the growth of reported protein trajectories. To address this
insufficiency, we present a novel spatial-temporal pre-training protocol,
PretrainMD, to grant the protein encoder the capacity to capture the
time-dependent geometric mobility along MD trajectories. Specifically, we
introduce two sorts of self-supervised learning tasks: an atom-level denoising
generative task and a protein-level snapshot ordering task. We validate the
effectiveness of PretrainMD through the PDBbind dataset for both linear-probing
and fine-tuning. Extensive experiments show that our PretrainMD exceeds most
state-of-the-art methods and achieves comparable performance. More importantly,
through visualization we discover that the learned representations by
pre-training on MD trajectories without any label from the downstream task
follow similar patterns of the magnitude of binding affinities. This strongly
aligns with the fact that the motion of the interactions of protein and ligand
maintains the key information of their binding. Our work provides a promising
perspective of self-supervised pre-training for protein representations with
very fine temporal resolutions and hopes to shed light on the further usage of
MD simulations for the biomedical deep learning community.


title:
Toward Understanding the Use of Centralized Exchanges for Decentralized
  Cryptocurrency

abstract:
  Cryptocurrency has been extensively studied as a decentralized financial
technology built on blockchain. However, there is a lack of understanding of
user experience with cryptocurrency exchanges, the main means for novice users
to interact with cryptocurrency. We conduct a qualitative study to provide a
panoramic view of user experience and security perception of exchanges. All 15
Chinese participants mainly use centralized exchanges (CEX) instead of
decentralized exchanges (DEX) to trade decentralized cryptocurrency, which is
paradoxical. A closer examination reveals that CEXes provide better usability
and charge lower transaction fee than DEXes. Country-specific security
perceptions are observed. Though DEXes provide better anonymity and privacy
protection, and are free of governmental regulation, these are not necessary
features for many participants. Based on the findings, we propose design
implications to make cryptocurrency trading more decentralized.


title:
Interventional Behavior Prediction: Avoiding Overly Confident
  Anticipation in Interactive Prediction

abstract:
  Conditional behavior prediction (CBP) builds up the foundation for a coherent
interactive prediction and planning framework that can enable more efficient
and less conservative maneuvers in interactive scenarios. In CBP task, we train
a prediction model approximating the posterior distribution of target agents'
future trajectories conditioned on the future trajectory of an assigned ego
agent. However, we argue that CBP may provide overly confident anticipation on
how the autonomous agent may influence the target agents' behavior.
Consequently, it is risky for the planner to query a CBP model. Instead, we
should treat the planned trajectory as an intervention and let the model learn
the trajectory distribution under intervention. We refer to it as the
interventional behavior prediction (IBP) task. Moreover, to properly evaluate
an IBP model with offline datasets, we propose a Shapley-value-based metric to
testify if the prediction model satisfies the inherent temporal independence of
an interventional distribution. We show that the proposed metric can
effectively identify a CBP model violating the temporal independence, which
plays an important role when establishing IBP benchmarks.


title: Consensus of networked double integrator systems under sensor bias

abstract:
  A novel distributed control law for consensus of networked double integrator
systems with biased measurements is developed in this article. The agents
measure relative positions over a time-varying, undirected graph with an
unknown and constant sensor bias corrupting the measurements. An adaptive
control law is derived using Lyapunov methods to estimate the individual sensor
biases accurately. The proposed algorithm ensures that position consensus is
achieved exponentially in addition to bias estimation. The results leverage
recent advances in collective initial excitation based results in adaptive
estimation. Conditions connecting bipartite graphs and collective initial
excitation are also developed. The algorithms are illustrated via simulation
studies on a network of double integrators with local communication and biased
measurements.


title:
Enhanced proton-boron nuclear fusion cross sections in intense
  high-frequency laser

abstract:
  We investigate the proton-boron nuclear fusion cross sections under the
influence of the intense linearly polarized monochromatic laser fields with
high frequency. First, we rewrite the time-dependent Schr\"{o}dinger equation
using Kramers-Henneberger (KH) transformation which allows for shifting all
time dependence of the problem into the potential function. Then, for the
intense laser fields that satisfy the high frequency limit, the time-averaged
scheme in the KH framework should be valid. We can use WKB approximation to
evaluate Coulomb barrier penetrability and then calculate proton-boron nuclear
fusion cross sections by a phenomenological Gamow form. We show that the
corresponding Coulomb barrier penetrability increases significantly due to the
depression of the time-averaged potential barrier. As a result, we find that
proton-boron nuclear fusion cross sections can be enhanced effectively
depending on a dimensionless quantity $n_{\mathrm{d}}$, which equals the ratio
of the quiver oscillation amplitude to the geometrical touching radius of the
proton and boron nucleus. For $n_{\mathrm{d}}=9$, we predict that the resonance
peak of the fusion cross-section is enhanced by about $26$ times at the
incident energy of $\varepsilon=148$ keV. And for another incident energy of
$\varepsilon=586$ keV, the resonance peak of fusion cross-section is not only
enhanced but also shifted to lower energy of $\varepsilon=392$ keV due to the
mechanism of over-barrier fusion.


title:
Neutrino Flavor Model Building and the Origins of Flavor and CP
  Violation: A Snowmass White Paper

abstract:
  The neutrino sector offers one of the most sensitive probes of new physics
beyond the Standard Model of Particle Physics. The mechanism of neutrino mass
generation is still unknown. The observed suppression of neutrino masses hints
at a large scale, conceivably of the order of the scale of a Grand Unified
Theory (GUT), a unique feature of neutrinos that is not shared by the charged
fermions. The origin of neutrino masses and mixing is part of the outstanding
puzzle of fermion masses and mixings, which is not explained in the SM. Flavor
model building for both quark and lepton sectors is important in order to gain
a better understanding of the origin of the structure of mass hierarchy and
flavor mixing, which constitute the dominant fraction of the SM parameters.
Recent activities in neutrino flavor model building based on non-Abelian
discrete flavor symmetries and modular flavor symmetries have been shown to be
a promising direction to explore. The emerging models provide a framework that
has a significantly reduced number of undetermined parameters in the flavor
sector. Model building based on non-Abelian discrete flavor symmetries and
their modular variants enables the particle physics community to interpret the
current and anticipated upcoming data from neutrino experiments. Pursuit of
flavor model building based on such frameworks can also provide connections to
possible UV completions, in particular to string theory. We emphasize the
importance of constructing models in which the uncertainties of theoretical
predictions are smaller than, or at most compatible with, the error bars of
measurements in neutrino experiments.


title:
Mono vs Multilingual BERT for Hate Speech Detection and Text
  Classification: A Case Study in Marathi

abstract:
  Transformers are the most eminent architectures used for a vast range of
Natural Language Processing tasks. These models are pre-trained over a large
text corpus and are meant to serve state-of-the-art results over tasks like
text classification. In this work, we conduct a comparative study between
monolingual and multilingual BERT models. We focus on the Marathi language and
evaluate the models on the datasets for hate speech detection, sentiment
analysis and simple text classification in Marathi. We use standard
multilingual models such as mBERT, indicBERT and xlm-RoBERTa and compare with
MahaBERT, MahaALBERT and MahaRoBERTa, the monolingual models for Marathi. We
further show that Marathi monolingual models outperform the multilingual BERT
variants on five different downstream fine-tuning experiments. We also evaluate
sentence embeddings from these models by freezing the BERT encoder layers. We
show that monolingual MahaBERT based models provide rich representations as
compared to sentence embeddings from multi-lingual counterparts. However, we
observe that these embeddings are not generic enough and do not work well on
out of domain social media datasets. We consider two Marathi hate speech
datasets L3Cube-MahaHate, HASOC-2021, a Marathi sentiment classification
dataset L3Cube-MahaSent, and Marathi Headline, Articles classification
datasets.


title:
Basilic: Resilient Optimal Consensus Protocols With Benign and Deceitful
  Faults

abstract:
  The problem of Byzantine consensus has been key to designing secure
distributed systems. However, it is particularly difficult, mainly due to the
presence of Byzantine processes that act arbitrarily and the unknown message
delays in general networks.
  Although it is well known that both safety and liveness are at risk as soon
as $n/3$ Byzantine processes fail, very few works attempted to characterize
precisely the faults that produce safety violations from the faults that
produce termination violations.
  In this paper, we present a new lower bound on the solvability of the
consensus problem by distinguishing deceitful faults violating safety and
benign faults violating termination from the more general Byzantine faults, in
what we call the Byzantine-deceitful-benign fault model. We show that one
cannot solve consensus if $n\leq 3t+d+2q$ with $t$ Byzantine processes, $d$
deceitful processes, and $q$ benign processes.
  In addition, we show that this bound is tight by presenting the Basilic class
of consensus protocols that solve consensus when $n > 3t+d+2q$. These protocols
differ in the number of processes from which they wait to receive messages
before progressing. Each of these protocols is thus better suited for some
applications depending on the predominance of benign or deceitful faults.
  Finally, we study the fault tolerance of the Basilic class of consensus
protocols in the context of blockchains that need to solve the weaker problem
of eventual consensus. We demonstrate that Basilic solves this problem with
only $n > 2t+d+q$, hence demonstrating how it can strengthen blockchain
security.


title: ActAR: Actor-Driven Pose Embeddings for Video Action Recognition

abstract:
  Human action recognition (HAR) in videos is one of the core tasks of video
understanding. Based on video sequences, the goal is to recognize actions
performed by humans. While HAR has received much attention in the visible
spectrum, action recognition in infrared videos is little studied. Accurate
recognition of human actions in the infrared domain is a highly challenging
task because of the redundant and indistinguishable texture features present in
the sequence. Furthermore, in some cases, challenges arise from the irrelevant
information induced by the presence of multiple active persons not contributing
to the actual action of interest. Therefore, most existing methods consider a
standard paradigm that does not take into account these challenges, which is in
some part due to the ambiguous definition of the recognition task in some
cases. In this paper, we propose a new method that simultaneously learns to
recognize efficiently human actions in the infrared spectrum, while
automatically identifying the key-actors performing the action without using
any prior knowledge or explicit annotations. Our method is composed of three
stages. In the first stage, optical flow-based key-actor identification is
performed. Then for each key-actor, we estimate key-poses that will guide the
frame selection process. A scale-invariant encoding process along with embedded
pose filtering are performed in order to enhance the quality of action
representations. Experimental results on InfAR dataset show that our proposed
model achieves promising recognition performance and learns useful action
representations.


title: A Score-based Geometric Model for Molecular Dynamics Simulations

abstract:
  Molecular dynamics (MD) has long been the \emph{de facto} choice for modeling
complex atomistic systems from first principles, and recently deep learning
become a popular way to accelerate it. Notwithstanding, preceding approaches
depend on intermediate variables such as the potential energy or force fields
to update atomic positions, which requires additional computations to perform
back-propagation. To waive this requirement, we propose a novel model called
ScoreMD by directly estimating the gradient of the log density of molecular
conformations. Moreover, we analyze that diffusion processes highly accord with
the principle of enhanced sampling in MD simulations, and is therefore a
perfect match to our sequential conformation generation task. That is, ScoreMD
perturbs the molecular structure with a conditional noise depending on atomic
accelerations and employs conformations at previous timeframes as the prior
distribution for sampling. Another challenge of modeling such a conformation
generation process is that the molecule is kinetic instead of static, which no
prior studies strictly consider. To solve this challenge, we introduce a
equivariant geometric Transformer as a score function in the diffusion process
to calculate the corresponding gradient. It incorporates the directions and
velocities of atomic motions via 3D spherical Fourier-Bessel representations.
With multiple architectural improvements, we outperforms state-of-the-art
baselines on MD17 and isomers of C7O2H10. This research provides new insights
into the acceleration of new material and drug discovery.


title:
Quantum-Secured Space-Air-Ground Integrated Networks: Concept,
  Framework, and Case Study

abstract:
  In the upcoming 6G era, existing terrestrial networks have evolved toward
space-air-ground integrated networks (SAGIN), providing ultra-high data rates,
seamless network coverage, and ubiquitous intelligence for communications of
applications and services. However, conventional communications in SAGIN still
face data confidentiality issues. Fortunately, the concept of Quantum Key
Distribution (QKD) over SAGIN is able to provide information-theoretic security
for secure communications in SAGIN with quantum cryptography. Therefore, in
this paper, we propose the quantum-secured SAGIN which is feasible to achieve
proven secure communications using quantum mechanics to protect data channels
between space, air, and ground nodes. Moreover, we propose a universal QKD
service provisioning framework to minimize the cost of QKD services under the
uncertainty and dynamics of communications in quantum-secured SAGIN. In this
framework, fiber-based QKD services are deployed in passive optical networks
with the advantages of low loss and high stability. Moreover, the widely
covered and flexible satellite- and UAV-based QKD services are provisioned as a
supplement during the real-time data transmission phase. Finally, to examine
the effectiveness of the proposed concept and framework, a case study of
quantum-secured SAGIN in the Metaverse is conducted where uncertain and dynamic
factors of the secure communications in Metaverse applications are effectively
resolved in the proposed framework.