title: Size Distribution of Raindrops

abstract:
  For more than a century scientists have systematically studied the diameters
of raindrops and discussed which type of drop-size distribution function is
best able to describe natural rain. Currently, the size distributions of
raindrops are fitted to the observations using different approximations,
including the gamma, Weibull, lognormal, Marshall-Palmer's exponential
equation, and other mathematical functions. Here we show from first principles
of matter and energy conservation, without presuming a-priori any type of drop
size dispersion process as in the previous research, that in the stationary
state both the mass and number fractions of raindrop diameters follow a
lognormal distribution law. From our theory, which accounts for energy
fluctuations of drops caused by random disturbances from both the raindrops and
the surrounding atmospheric air flow, we derive analytical equations for the
mass and count median diameters and size distribution of raindrops.
Furthermore, we develop a map for the graphical determination of the range of
possible diameters and expected median diameters of raindrops at different rain
intensities. Our results are in good agreement with experimental data published
by different research groups at various ground locations around the globe for
convective and stratiform rains for the rain intensities 0.4-40 mm/h and
insignificant evaporation. We anticipate that, besides rain, our theoretical
approach can find applications in adjacent areas such as in liquid atomization
of sprays and aerosols, production of bubbles and particles, prediction of
drop-size distributions of non-water rainfalls on other planets and in
assessing Earth conditions in ancient times that might help to explain the
"Faint Young Sun" paradox.


title:
The FAST Ultra-Deep Survey (FUDS): observational strategy, calibration
  and data reduction

abstract:
  The FAST Ultra-Deep Survey (FUDS) is a blind survey that aims for the direct
detection of HI in galaxies at redshifts $z<0.42$. The survey uses the
multibeam receiver on the Five Hundred Meter Aperture Spherical Telescope
(FAST) to map six regions, each of size 0.72 deg$^2$ at high sensitivity ($\sim
50 \mu$Jy) and high frequency resolution (23 kHz). The survey will enable
studies of the evolution of galaxies and their HI content with an eventual
sample size of $\sim 1000$. We present the science goals, observing strategy,
the effects of radio frequency interference (RFI) at the FAST site, our
mitigation strategies and the methods for calibration, data reduction and
imaging as applied to initial data. The observations and reductions for the
first field, FUDS0, are completed, with around 128 HI galaxies detected in a
preliminary analysis. Example spectra are given in this paper, including a
comparison with data from the overlapping GAL2577 field of Arecibo Ultra-Deep
Survey (AUDS).


title: Spectrum of the Transposition graph

abstract:
  Transposition graph $T_n$ is defined as a Cayley graph over the symmetric
group generated by all transpositions. It is known that all eigenvalues of
$T_n$ are integers. However, an explicit description of the spectrum is
unknown. In this paper we prove that for any integer $k\geqslant 0$ there
exists $n_0$ such that for any $n\geqslant n_0$ and any $m \in \{0, \dots,
k\}$, $m$ is an eigenvalue of $T_n$. In particular, it is proved that zero is
an eigenvalue of $T_n$ for any $n\neq2$, and one is an eigenvalue of $T_n$ for
any odd $n\geqslant 7$ and for any even $n \geqslant 14$. We also present exact
values of the third and the fourth largest eigenvalues of $T_n$ with their
multiplicities.


title:
Optimization Models and Interpretations for Three Types of Adversarial
  Perturbations against Support Vector Machines

abstract:
  Adversarial perturbations have drawn great attentions in various deep neural
networks. Most of them are computed by iterations and cannot be interpreted
very well. In contrast, little attentions are paid to basic machine learning
models such as support vector machines. In this paper, we investigate the
optimization models and the interpretations for three types of adversarial
perturbations against support vector machines, including sample-adversarial
perturbations (sAP), class-universal adversarial perturbations (cuAP) as well
as universal adversarial perturbations (uAP). For linear binary/multi
classification support vector machines (SVMs), we derive the explicit solutions
for sAP, cuAP and uAP (binary case), and approximate solution for uAP of
multi-classification. We also obtain the upper bound of fooling rate for uAP.
Such results not only increase the interpretability of the three adversarial
perturbations, but also provide great convenience in computation since
iterative process can be avoided. Numerical results show that our method is
fast and effective in calculating three types of adversarial perturbations.


title: Just-Noticeable-Difference Based Edge Map Quality Measure

abstract:
  The performance of an edge detector can be improved when assisted with an
effective edge map quality measure. Several evaluation methods have been
proposed resulting in different performance score for the same candidate edge
map. However, an effective measure is the one that can be automated and which
correlates with human judgement perceived quality of the edge map.
Distance-based edge map measures are widely used for assessment of edge map
quality. These methods consider distance and statistical properties of edge
pixels to estimate a performance score. The existing methods can be automated;
however, they lack perceptual features. This paper presents edge map quality
measure based on Just-Noticeable-Difference (JND) feature of human visual
system, to compensate the shortcomings of distance-based edge measures. For
this purpose, we have designed constant stimulus experiment to measure the JND
value for two spatial alternative. Experimental results show that JND based
distance calculation outperforms existing distance-based measures according to
subjective evaluation.


title: Multi-twisted codes as free modules over principal ideal domains

abstract:
  We begin this chapter by introducing the simple algebraic structure of cyclic
codes over finite fields. This structure undergoes a series of generalizations
to present algebraic descriptions of constacyclic, quasi-cyclic (QC),
quasi-twisted (QT), generalized quasi-cyclic (GQC), and multi-twisted (MT)
codes. The correspondence between these codes and submodules of the free
$\mathbb{F}_q[x]$-module $\left(\mathbb{F}_q[x]\right)^\ell$ is established.
Thus, any of these codes corresponds to a free linear code over the principal
ideal domain (PID) $\mathbb{F}_q[x]$. A basis of this code exists and is used
to build a generator matrix with polynomial entries, called the generator
polynomial matrix (GPM). The Hermite normal form of matrices over PIDs is
exploited to achieve the reduced GPMs of MT codes. Some properties of the
reduced GPM are introduced, for example, the identical equation. A formula for
a GPM of the dual code $\mathcal{C}^\perp$ of a MT code is given. At this
point, special attention is paid to QC codes. For a QC code $\mathcal{C}$, we
define its reversed code $\mathcal{R}$. We call $\mathcal{C}$ reversible or
self-dual if $\mathcal{R}=\mathcal{C}$ or $\mathcal{C}^\perp=\mathcal{C}$,
respectively. A formula for a GPM of $\mathcal{R}$ is given. We characterize
GPMs for QC codes that combine reversibility and
self-duality/self-orthogonality. For the reader interested in running computer
search for optimal codes, we show the existence of binary self-orthogonal
reversible QC codes that have the best known parameters as linear codes. These
results can be obtained by brute-force search using GPMs that meet the above
characterization.


title:
Chornobyl radiation spikes are not due to military vehicles disturbing
  soil

abstract:
  On 25th February 2022, increased gamma radiation dose rates were reported
within the Chornobyl Exclusion Zone (CEZ). This coincided with Russian military
vehicles entering the Ukrainian part of the CEZ from neighbouring Belarus. It
was speculated that contaminated soil resuspension by vehicle movements or a
leak from the Chornobyl Nuclear Power Plant (ChNPP) complex may explain these
spikes in radiation dose rates. The gamma dose rate monitoring network in the
CEZ provides a crucial early warning system for releases of radioactivity to
the environment and is part of the international safeguards for nuclear
facilities. With the potential for further military action in the CEZ and
concerns over nuclear safety, it is essential that such anomalous readings are
investigated. We evaluate the hypotheses suggested to explain the apparent
gamma dose rate increases, demonstrating that neither military vehicle-induced
soil resuspension nor a leak from the ChNPP are plausible. However, disruption
of the Chornobyl base-station's reception of wireless signals from the CRMS
network may potentially explain the dose rate increases recorded.


title:
Spin sensitive transport in a spin liquid material: revealing a
  robustness of spin anisotropy

abstract:
  Alpha-phase (a-) RuCl_3 has emerged as a prime candidate for a quantum spin
liquid (QSL) that promises exotic quasiparticles relevant for fault-tolerant
quantum computation. Here, we report spin sensitive transport measurements to
probe spin correlation in a-RuCl_3 using a proximal spin Hall metal platinum
(Pt). Both transverse and longitudinal resistivities exhibit oscillations as
function of the angle between an in-plane magnetic field and the current, akin
to previously measured spin Hall magnetoresistance (SMR) in antiferromagnet/Pt
heterostructures. The oscillations are observed from 1.5 T to 18 T, both within
and beyond the magnetic field range where the antiferromagnetic order and QSL
state are reported in a-RuCl_3. The SMR oscillations show that spins in a-RuCl3
are largely locked to an in-plane quantization axis transverse to the magnetic
field, constituting a continuous-symmetry-broken state that does not
necessarily represent a long-range order. This robust anisotropy of spin axis
uncovers critical energy scales connected with reported QSL signatures in
a-RuCl_3. Simulations suggest a predominantly antiferromagnetic correlation to
moderately high magnetic-fields, that may support the SMR oscillations. The
coupling of the spin states within a-RuCl_3 and Pt demonstrated in our
experiment opens a transport route to exploring exotic spin phases and device
functionalities of QSL materials.


title:
Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid
  Robot via Ensemble Deep Reinforcement Learning

abstract:
  Control of wheeled humanoid locomotion is a challenging problem due to the
nonlinear dynamics and under-actuated characteristics of these robots.
Traditionally, feedback controllers have been utilized for stabilization and
locomotion. However, these methods are often limited by the fidelity of the
underlying model used, choice of controller, and environmental variables
considered (surface type, ground inclination, etc). Recent advances in
reinforcement learning (RL) offer promising methods to tackle some of these
conventional feedback controller issues, but require large amounts of
interaction data to learn. Here, we propose a hybrid learning and model-based
controller Hybrid LMC that combines the strengths of a classical linear
quadratic regulator (LQR) and ensemble deep reinforcement learning. Ensemble
deep reinforcement learning is composed of multiple Soft Actor-Critic (SAC) and
is utilized in reducing the variance of RL networks. By using a feedback
controller in tandem the network exhibits stable performance in the early
stages of training. As a preliminary step, we explore the viability of Hybrid
LMC in controlling wheeled locomotion of a humanoid robot over a set of
different physical parameters in MuJoCo simulator. Our results show that Hybrid
LMC achieves better performance compared to other existing techniques and has
increased sample efficiency


title:
Comparison of Sub-Grid Scale Models for Large-Eddy Simulation using a
  High-Order Spectral Element Approximation of the Compressible Navier-Stokes
  Equations at Low Mach Number

abstract:
  This study aims to identify the properties, advantages, and drawbacks of some
common (and some less common) sub-grid scale (SGS) models for large eddy
simulation of low Mach compressible flows using high order spectral elements.
The models investigated are the classical constant coefficient
Smagorinsky-Lilly, the model by Vreman and two variants of a dynamic SGS (DSGS)
model designed to stabilize finite and spectral elements for transport
dominated problems. In particular, we compare one variant of DSGS that is based
on a time-dependent residual version (R-DSGS) in contrast to a time-independent
residual based scheme (T-DSGS). The SGS models are compared against the
reference model by Smagorinsky and Lilly for their ability to: (i) stabilize
the numerical solution, (ii) minimize undershoots and overshoots, (iii)
capture/preserve discontinuities, and (iv) transfer energy across different
length scales. These abilities are investigated on problems for: (1) passively
advected tracers, (2) coupled, nonlinear system of equations exhibiting
discontinuities, (3) gravity-driven flows in a stratified atmosphere, and (4)
homogenous, isotropic turbulence. All models were able to preserve sharp
discontinuities. Vreman and the R-DSGS models also reduce the undershoots and
overshoots in the solution of linear and non-linear advection with sharp
gradients. Our analysis shows that the R-DSGS and T-DSGS models are more robust
than Vreman and Smagorinsky-Lilly for numerical stabilization of high-order
spectral methods. The Smagorinsky and Vreman models are better able to resolve
the finer flow structures in shear flows, while the nodal R-DSGS model shows
better energy conservation. Overall, the nodal implementation of R-DSGS (in
contrast to its element-based counterpart) is shown to outperform the other SGS
models in most metrics listed above, and on par with respect to the remaining
ones.


title: Flexible Sampling for Long-tailed Skin Lesion Classification

abstract:
  Most of the medical tasks naturally exhibit a long-tailed distribution due to
the complex patient-level conditions and the existence of rare diseases.
Existing long-tailed learning methods usually treat each class equally to
re-balance the long-tailed distribution. However, considering that some
challenging classes may present diverse intra-class distributions, re-balancing
all classes equally may lead to a significant performance drop. To address
this, in this paper, we propose a curriculum learning-based framework called
Flexible Sampling for the long-tailed skin lesion classification task.
Specifically, we initially sample a subset of training data as anchor points
based on the individual class prototypes. Then, these anchor points are used to
pre-train an inference model to evaluate the per-class learning difficulty.
Finally, we use a curriculum sampling module to dynamically query new samples
from the rest training samples with the learning difficulty-aware sampling
probability. We evaluated our model against several state-of-the-art methods on
the ISIC dataset. The results with two long-tailed settings have demonstrated
the superiority of our proposed training strategy, which achieves a new
benchmark for long-tailed skin lesion classification.


title:
Winoground: Probing Vision and Language Models for Visio-Linguistic
  Compositionality

abstract:
  We present a novel task and dataset for evaluating the ability of vision and
language models to conduct visio-linguistic compositional reasoning, which we
call Winoground. Given two images and two captions, the goal is to match them
correctly - but crucially, both captions contain a completely identical set of
words, only in a different order. The dataset was carefully hand-curated by
expert annotators and is labeled with a rich set of fine-grained tags to assist
in analyzing model performance. We probe a diverse range of state-of-the-art
vision and language models and find that, surprisingly, none of them do much
better than chance. Evidently, these models are not as skilled at
visio-linguistic compositional reasoning as we might have hoped. We perform an
extensive analysis to obtain insights into how future work might try to
mitigate these models' shortcomings. We aim for Winoground to serve as a useful
evaluation set for advancing the state of the art and driving further progress
in the field. The dataset is available at
https://huggingface.co/datasets/facebook/winoground.


title: Low-Dose CT Denoising via Sinogram Inner-Structure Transformer

abstract:
  Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation
harm to human bodies, is now attracting increasing interest in the medical
imaging field. As the image quality is degraded by low dose radiation, LDCT
exams require specialized reconstruction methods or denoising algorithms.
However, most of the recent effective methods overlook the inner-structure of
the original projection data (sinogram) which limits their denoising ability.
The inner-structure of the sinogram represents special characteristics of the
data in the sinogram domain. By maintaining this structure while denoising, the
noise can be obviously restrained. Therefore, we propose an LDCT denoising
network namely Sinogram Inner-Structure Transformer (SIST) to reduce the noise
by utilizing the inner-structure in the sinogram domain. Specifically, we study
the CT imaging mechanism and statistical characteristics of sinogram to design
the sinogram inner-structure loss including the global and local
inner-structure for restoring high-quality CT images. Besides, we propose a
sinogram transformer module to better extract sinogram features. The
transformer architecture using a self-attention mechanism can exploit
interrelations between projections of different view angles, which achieves an
outstanding performance in sinogram denoising. Furthermore, in order to improve
the performance in the image domain, we propose the image reconstruction module
to complementarily denoise both in the sinogram and image domain.


title:
A Coronagraph with a Sub-$\lambda/D$ IWA and a Moderate Spectral
  Bandwidth

abstract:
  Future high-contrast imaging spectroscopy with a large segmented telescope
will be able to detect atmospheric molecules of Earth-like planets around G- or
K-type main-sequence stars. Increasing the number of target planets will
require a coronagraph with a small inner working angle (IWA), and wide spectral
bandwidth is required if we enhance a variety of detectable atmospheric
molecules. To satisfy these requirements, in this paper, we present a
coronagraphic system that provides an IWA less than 1$\lambda_0 / D$ over a
moderate wavelength band, where $\lambda_0$ is the design-center wavelength and
$D$ denotes the full width of the rectangular aperture included in the
telescope aperture. A performance simulation shows that the proposed system
approximately achieves a contrast below $10^{-10}$ at 1$\lambda_0 / D$ over the
wavelengths of 650--750nm. In addition, this system has a core throughput
$\geq$ 10\% at input separation angles of $\sim$ 0.7--1.4$\lambda_0 / D$; to
reduce telescope time, we need prior information on the target's orbit by other
observational methods to a precision higher than the width of the field of
view. For some types of aberration including tilt aberration, the proposed
system has a sensitivity less than ever-proposed coronagraphs that have IWAs of
approximately $1\lambda_0/D$. In future observations of Earth-like planets, the
proposed coronagraphic system may serve as a supplementary coronagraphic system
dedicated to achieving an extremely small IWA.


title:
Root-N Krylov-space correction-vectors for spectral functions with the
  density matrix renormalization group

abstract:
  We propose a method to compute spectral functions of generic Hamiltonians
using the density matrix renormalization group (DMRG) algorithm directly in the
frequency domain, based on a modified Krylov space decomposition to compute the
correction-vectors. Our approach entails the calculation of the root-N (N=2 is
the standard square root) of the Hamiltonian propagator using Krylov space
decomposition, and repeating this procedure N times to obtain the actual
correction-vector. We show that our method greatly alleviates the burden of
keeping a large bond dimension at large target frequencies, a problem found
with conventional correction-vector DMRG, while achieving better computational
performance at large N. We apply our method to spin and charge spectral
functions of t-J and Hubbard models in the challenging two-leg ladder geometry,
and provide evidence that the root-N approach reaches a much improved
resolution compared to conventional correction-vector.


title: Musical Information Extraction from the Singing Voice

abstract:
  Music information retrieval is currently an active research area that
addresses the extraction of musically important information from audio signals,
and the applications of such information. The extracted information can be used
for search and retrieval of music in recommendation systems, or to aid
musicological studies or even in music learning. Sophisticated signal
processing techniques are applied to convert low-level acoustic signal
properties to musical attributes which are further embedded in a rule-based or
statistical classification framework to link with high-level descriptions such
as melody, genre, mood and artist type. Vocal music comprises a large and
interesting category of music where the lead instrument is the singing voice.
The singing voice is more versatile than many musical instruments and therefore
poses interesting challenges to information retrieval systems. In this paper,
we provide a brief overview of research in vocal music processing followed by a
description of related work at IIT Bombay leading to the development of an
interface for melody detection of singing voice in polyphony.


title:
Implications of the Non-Observation of ${}^{6}{\rm Li}$ in Halo Stars
  for the Primordial ${}^{7}{\rm Li}$ Problem

abstract:
  The primordial Lithium Problem is intimately connected to the assumption that
${}^{7}{\rm Li}$ observed in metal-poor halo stars retains its primordial
abundance, which lies significantly below the predictions of standard big-bang
nucleosynthesis. Two key lines of evidence have argued that these stars have
not significantly depleted their initial ${}^{7}{\rm Li}$: i) the lack of
dispersion in Li abundances measured at low metallicity; and ii) the detection
of the more fragile ${}^{6}{\rm Li}$ isotope in at least two halo stars. The
purported ${}^{6}{\rm Li}$ detections were in good agreement with predictions
from cosmic-ray nucleosynthesis which is responsible for the origin of
${}^{6}{\rm Li}$. This concordance left little room for depletion of
${}^{6}{\rm Li}$ depletion, and implied that the more robust ${}^{7}{\rm Li}$
largely evaded destruction. Recent (re)-observations of halo stars challenge
the evidence against ${}^{7}{\rm Li}$ depletion: i) lithium abundances now show
significant dispersion, and ii) sensitive ${}^{6}{\rm Li}$ searches now reveal
only firm upper limits to the ${}^{6}{\rm Li}/{}^{7}{\rm Li}$ ratio. The tight
new ${}^{6}{\rm Li}$ upper limits generally fall far below the predictions of
cosmic-ray nucleosynthesis, implying that substantial ${}^{6}{\rm Li}$
depletion has occurred--by factors up to 50. We show that in stars with
${}^{6}{\rm Li}$ limits and thus lower bounds on ${}^{6}{\rm Li}$ depletion, an
equal amount of ${}^{7}{\rm Li}$ depletion is more than sufficient to resolve
the primordial ${}^{7}{\rm Li}$ Problem. This picture is consistent with
stellar models in which ${}^{7}{\rm Li}$ is less depleted than ${}^{6}{\rm
Li}$, and strengthen the case that the Lithium Problem has an astrophysical
solution. We conclude by suggesting future observations that could test these
ideas. (abridged)

categories: astro-ph.GA astro-ph.CO astro-ph.SR hep-ph


title:
Towards Programming Adaptive Linear Neural Networks Through Chemical
  Reaction Networks

abstract:
  This paper is concerned with programming adaptive linear neural networks
(ALNNs) using chemical reaction networks (CRNs) equipped with mass-action
kinetics. Through individually programming the forward propagation and the
backpropagation of ALNNs, and also utilizing the permeation walls technique, we
construct a powerful CRN possessing the function of ALNNs, especially having
the function of automatic computation. We also provide theoretical analysis and
a case study to support our construction. The results will have potential
implications for the developments of synthetic biology, molecular computer and
artificial intelligence.


title:
Magnet-free nonreciprocal metasurface for on-demand bi-directional phase
  modulation

abstract:
  Unconstrained by Lorentz reciprocity, nonreciprocal metasurfaces are uniquely
capable of encoding distinctive optical functions on forward- and
backward-propagating waves. The nonreciprocal metasurfaces reported to date
require external electric or magnetic field biasing or rely on nonlinear
effects, both of which are challenging to practically implement. Here, we
propose and experimentally realize a magnet-free, linear, and passive
nonreciprocal metasurface based on self-biased magnetic meta-atoms. Record
transmittance up to 77% and operation angle reaching 64 degree are
experimentally demonstrated. Moreover, on-demand bidirectional phase modulation
in a "LEGO-like" manner is theoretically proposed and experimentally
demonstrated, enabling a cohort of nonreciprocal functionalities such as
microwave isolation, nonreciprocal beam steering, nonreciprocal focusing, and
nonreciprocal holography. The design can also be extended to MHz and optical
frequencies, taking advantage of the wide variety of self-biased gyrotropic
materials available. We foresee that the nonreciprocal metasurfaces
demonstrated in this work will have a significant practical impact for
applications ranging from nonreciprocal antennas and radomes to full-duplex
wireless communication and radar systems.


title:
Decay Rate of $\exp(A^{-1}t)A^{-1}$ on a Hilbert Space and the
  Crank-Nicolson Scheme with Smooth Initial Data

abstract:
  This paper is concerned with the decay rate of $e^{A^{-1}t}A^{-1}$ for the
generator $A$ of an exponentially stable $C_0$-semigroup $(e^{At})_{t\geq 0}$
on a Hilbert space. To estimate the decay rate of $e^{A^{-1}t}A^{-1}$, we apply
a bounded functional calculus. Using this estimate and Lyapunov equations, we
also study the quantified asymptotic behavior of the Crank-Nicolson scheme with
smooth initial data. Analogous results are obtained for polynomially stable
$C_0$-semigroups whose generator is normal.


title:
Representations and cohomologies of differential 3-Lie algebras with any
  weight

abstract:
  The purpose of the present paper is to study representations and cohomologies
of differential 3-Lie algebras with any weight. We introduce the representation
of a differential 3-Lie algebra. Moreover,we develop cohomology theory of a
differential 3-Lie algebra. We also depict the relationship between the
cohomologies of a differential 3-Lie algebra and its associated differential
Leibniz algebra with weight zero. The deformation and O-operator of
differential 3-Lie algebras are also investigated. Finally, we consider abelian
extensions of differential 3-Lie algebras.


title: Representations and cohomolgies of Rota-Baxter 3-Lie algebras

abstract:
  The goal of the present paper is to investigate representations and
cohomologies of Rota-Baxter 3-Lie algebras with any weight. We introduce
representations, matched pairs and Manin triples of Rota-Baxter 3-Lie algebras.
Furthermore, we discuss cohomology theory of Rota-Baxter 3-Lie algebras. The
deformations and central extensions of Rota-Baxter 3-Lie algebras are also
studied.


title:
Enhancement on Model Interpretability and Sleep Stage Scoring
  Performance with A Novel Pipeline Based on Deep Neural Network

abstract:
  Considering the natural frequency characteristics in sleep medicine, this
paper first proposes a time-frequency framework for the representation learning
of the electroencephalogram (EEG) following the definition of the American
Academy of Sleep Medicine. To meet the temporal-random and transient nature of
the defining characteristics of sleep stages, we further design a
context-sensitive flexible pipeline that automatically adapts to the attributes
of data itself. That is, the input EEG spectrogram is partitioned into a
sequence of patches in the time and frequency axes, and then input to a
delicate deep learning network for further representation learning to extract
the stage-dependent features, which are used in the classification step
finally. The proposed pipeline is validated against a large database, i.e., the
Sleep Heart Health Study (SHHS), and the results demonstrate that the
competitive performance for the wake, N2, and N3 stages outperforms the
state-of-art works, with the F1 scores being 0.93, 0.88, and 0.87,
respectively, and the proposed method has a high inter-rater reliability of
0.80 kappa. Importantly, we visualize the stage scoring process of the model
decision with the Layer-wise Relevance Propagation (LRP) method, which shows
that the proposed pipeline is more sensitive and perceivable in the
decision-making process than the baseline pipelines. Therefore, the pipeline
together with the LRP method can provide better model interpretability, which
is important for clinical support.


title:
FedCos: A Scene-adaptive Federated Optimization Enhancement for
  Performance Improvement

abstract:
  As an emerging technology, federated learning (FL) involves training machine
learning models over distributed edge devices, which attracts sustained
attention and has been extensively studied. However, the heterogeneity of
client data severely degrades the performance of FL compared with that in
centralized training. It causes the locally trained models of clients to move
in different directions. On the one hand, it slows down or even stalls the
global updates, leading to inefficient communication. On the other hand, it
enlarges the distances between local models, resulting in an aggregated global
model with poor performance. Fortunately, these shortcomings can be mitigated
by reducing the angle between the directions that local models move in. Based
on this fact, we propose FedCos, which reduces the directional inconsistency of
local models by introducing a cosine-similarity penalty. It promotes the local
model iterations towards an auxiliary global direction. Moreover, our approach
is auto-adapt to various non-IID settings without an elaborate selection of
hyperparameters. The experimental results show that FedCos outperforms the
well-known baselines and can enhance them under a variety of FL scenes,
including varying degrees of data heterogeneity, different number of
participants, and cross-silo and cross-device settings. Besides, FedCos
improves communication efficiency by 2 to 5 times. With the help of FedCos,
multiple FL methods require significantly fewer communication rounds than
before to obtain a model with comparable performance.


title: Topological Superconducting Vortex From Trivial Electronic Bands

abstract:
  Superconducting vortices are promising traps to confine non-Abelian Majorana
quasi-particles. It has been widely believed that bulk-state topology, of
either normal-state or Cooper-paired electron wavefunctions, is crucial for
enabling Majorana zero modes. This common belief has shaped two major search
directions for Majorana modes, in either intrinsic topological superconductors
or trivial superconductors with topological electronic band structures. Here we
show that Majorana-carrying vortex is not a privilege of bulk-state topology,
but can arise from superconducting trivial bands as well. Notably, even for
superconductors with topological band structures, the existence of ubiquitous
low-energy trivial bands can significantly change the conclusion of vortex
Majorana physics that is based on simplified topological-band-only models. We
predict that the trivial bands in superconducting HgTe-class materials are
responsible for inducing anomalous vortex topology that goes beyond any
existing theoretical paradigms. A feasible scheme of strain-controlled Majorana
engineering and a new experimental "smoking gun" are also discussed. Our work
provides new guidelines for Majorana search in general superconductors.

categories: cond-mat.supr-con cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el


title:
Global dynamics below excited solitons for the non-radial NLS with
  potential

abstract:
  We consider the global dynamics of solutions to the $3d$ cubic nonlinear
Schr\"odinger equation in the presence of an external potential, in the setting
in which the equation admits both ground state solitons and excited solitons at
small mass. We prove that small mass solutions with energy below that of the
excited solitons either scatter to the ground states or grow their $H^1$-norm
in time. In particular, we give an extension of the result of Nakanishi [19]
from the radial to the non-radial setting.


title:
Bayesian vector autoregressive analysis of macroeconomic and transport
  influences on urban traffic accidents

abstract:
  The macro influencing factors analysis of urban traffic safety is important
to guide the direction of urban development to reduce the frequency of traffic
accidents. In this study, a Bayesian vector autoregressive(BVAR) model was
developed to exploring the impact of six macro-level economic and transport
factors, including population, GDP, private vehicle ownership, bus ownership,
subway rail mileage and road average speed on traffic accidents with the small
sample size transport annual report data in Beijing. The results show that the
BVAR model was suitable for time series analysis of traffic accidents in small
sample situations. In macroeconomic factors, GDP growth was considered to
reduce the number of traffic accidents in the long term, while population
growth had a positive effect on traffic accidents in the short term. With the
respect to macro-transport factors, road average speed and private vehicle
ownership was perceived to increase traffic accidents in long duration, whereas
bus ownership and subway rail mileage had long-term negative effects, with the
greatest positive effect for road average speed and the greatest negative
effect for subway rail mileage. This study suggests that government departments
can reduce the number of traffic accidents by increasing investment in public
transportation infrastructures, limiting private vehicles and road speed.


title:
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech
  recognition

abstract:
  Recently, Conformer based CTC/AED model has become a mainstream architecture
for ASR. In this paper, based on our prior work, we identify and integrate
several approaches to achieve further improvements for ASR tasks, which we
denote as multi-loss, multi-path and multi-level, summarized as "3M" model.
Specifically, multi-loss refers to the joint CTC/AED loss and multi-path
denotes the Mixture-of-Experts(MoE) architecture which can effectively increase
the model capacity without remarkably increasing computation cost. Multi-level
means that we introduce auxiliary loss at multiple level of a deep model to
help training. We evaluate our proposed method on the public WenetSpeech
dataset and experimental results show that the proposed method provides
12.2%-17.6% relative CER improvement over the baseline model trained by Wenet
toolkit. On our large scale dataset of 150k hours corpus, the 3M model has also
shown obvious superiority over the baseline Conformer model. Code is publicly
available at https://github.com/tencent-ailab/3m-asr.


title: Cosmological nanolensing by dense gas clouds

abstract:
  We study the influence of a cosmological population of dense gas clouds on
distant sources, with emphasis on quasar optical variability. In addition to
gravitational lensing such clouds affect flux measurements via refraction in
the neutral gas and via dust extinction, leading to a variety of possible light
curves even in the low optical depth limit. We classify and illustrate the
types of light curves that can arise. For sources as large as quasars we show
that gravitational lensing and extinction are the dominant effects, with gas
refraction playing only a minor role. We find that clouds with mass
~10^{-4.5+/-0.5} M_\odot can reproduce the observed distribution of quasar
variation amplitudes, but only if such clouds make up a large fraction of the
closure density. In that case there may also be substantial extinction of
distant optical sources, which can in principle be constrained by data on
"standard candles" such as type Ia supernovae. Unfortunately that extinction is
essentially grey, even when the material opacity is strongly wavelength
dependent, making it difficult to distinguish from the influence of the
background geometry. We propose a novel statistical test of the origin of
quasar variability, based on the angular structure of the variation timescale
for a large number of quasars distributed all over the sky. If quasar
variability is primarily due to nanolensing that angular structure is expected
to include a quadrupole term of amplitude $\sim5\%$, which ought to be
measurable with future data from the Gaia mission.


title: Discovery of Nine Super Li-rich Unevolved Stars from the LAMOST Survey

abstract:
  Based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope
(LAMOST) medium-resolution spectroscopic survey (MRS), we report the discovery
of nine super Li-rich unevolved stars with A(Li) $>$ 3.8 dex. These objects
show unusually high levels of lithium abundances up to three times higher than
the meteoritic value of 3.3 dex, which indicates that they must have
experienced a history of lithium enrichment. It is found that seven of our
program stars are fast rotators with $vsini>9$ km\,s$^{-1}$, which suggests
that the accretion of circumstellar matter may be the main contributor to the
lithium enhancement of these unevolved stars, however, other sources cannot be
excluded.


title:
Reaching Consensus in the Byzantine Empire: A Comprehensive Review of
  BFT Consensus Algorithms

abstract:
  Byzantine fault-tolerant (BFT) consensus algorithms are at the core of
providing safety and liveness guarantees for distributed systems that must
operate in the presence of arbitrary failures. Recently, numerous new BFT
algorithms have been proposed, not least due to the traction blockchain
technologies have garnered in search for consensus solutions that offer high
throughput, low latency, and robust system designs. In this paper, we conduct a
systematic survey of selected and distinguished BFT algorithms that have
received attention in academia and industry alike. We perform a qualitative
comparison among all algorithms we review along the lines of messaging and time
complexities. Furthermore, we decompose each consensus algorithm into its
constituent subprotocols for replication and view change backed by intuitive
figures illustrating its message-passing pattern; we also elaborate on the
strengths and weaknesses of each algorithm as compared to the state of the art.


title:
Supertranslation invariance of angular momentum at null infinity in
  double null gauge

abstract:
  The supertranslation invariance of the Chen-Wang-Yau (CWY) angular momentum
in the Bondi-Sachs formalism/gauge was ascertained by the authors in
\cite{CKWWY_evol, CWWY_atmp}. In this article, we study the corresponding
problem in the double null gauge. In particular, supertranslation ambiguity of
this gauge is identified and the CWY angular momentum is proven to be free of
this ambiguity. A similar result is obtained for the CWY center of mass
integral.


title: Muon Electric Dipole Moment as a Probe of Flavor-Diagonal CP Violation

abstract:
  Electric dipole moments (EDMs) of elementary particles are powerful probes of
new physics with flavor-diagonal CP violation. The reported discrepancy in the
muon anomalous magnetic moment motivates us to explore to what extent new
physics with flavor-diagonal CP violation to address the discrepancy is probed
by searches for the muon EDM. As a benchmark, we focus on a CP-violating
two-Higgs-doublet model to explain the muon $g-2$ anomaly where the muon
exclusively couples to one Higgs doublet. Since contributions to flavor
violating processes as well as the electron EDM are suppressed, the muon EDM
becomes an essential probe of the model. Our result shows that some viable
parameter space leads to the muon EDM of around $d_{\mu} \simeq 6 \times
10^{-23} \, e \, \rm cm$ probed by the PSI experiment and most of the parameter
space is covered by the proposed J-PARC experiment.


title:
Graphene oxide 2D films integrated with nanowires and ring resonators
  for enhanced nonlinear optics

abstract:
  We report enhanced nonlinear optics in nanowires, waveguides, and ring
resonators by introducing layered two-dimensional (2D) graphene oxide (GO)
films through experimental demonstration. The GO films are integrated on
silicon-on-insulator nanowires (SOI), high index doped silica glass, and
silicon nitride (SiN) waveguides and microring resonators (MRRs), to
demonstrate an improved optical nonlinearity including Kerr nonlinearity and
four-wave mixing (FWM). By using a large-area, transfer-free, layer-by-layer GO
coating method with photolithography and lift-off processes, we integrate GO
films on these complementary metal-oxide-semiconductor (CMOS)-compatible
devices. For SOI nanowires, significant spectral broadening of optical pulses
in GO-coated SOI nanowires induced by self-phase modulation (SPM) is observed,
achieving a high spectral broadening factor of 4.34 for a device with a
patterned film including 10 layers of GO. A significant enhancement in the
nonlinear figure of merit (FOM) for silicon nanowires by a factor of 20 is also
achieved, resulting in a FOM > 5. For Hydex and SiN waveguides, enhanced FWM in
the GO-coated waveguides is achieved, where conversion efficiency (CE)
enhancements of up to 6.9 dB and 9.1 dB relative to the uncoated waveguides.
For MRRs, an increase of up to ~10.3 dB in the FWM CE is achieved due to the
resonant enhancement effect. These results reveal the strong potential of GO
films to improve the nonlinear optics of nanowires, waveguides, and ring
resonators.


title:
The Eel Pulsar Wind Nebula: a PeVatron-Candidate Origin for HAWC
  J1826-128 and HESS J1826-130

abstract:
  HAWC J1826-128 is one of the brightest Galactic TeV gamma-ray sources
detected by the High Altitude Water Cherenkov (HAWC) Observatory, with photon
energies extending up to nearly $\sim$100 TeV. This HAWC source spatially
coincides with the H.E.S.S. TeV source HESS J1826-130 and the "Eel" pulsar wind
nebula (PWN), which is associated with the GeV pulsar PSR J1826-1256. In the
X-ray band, Chandra and XMM-Newton revealed that the Eel PWN is composed of
both a compact nebula ($\sim$15") and diffuse X-ray emission
($\sim$6'$\times$2') extending away from the pulsar. Our NuSTAR observation
detected hard X-ray emission from the compact PWN up to $\sim$20 keV and
evidence of the synchrotron burn-off effect. In addition to the spatial
coincidence between HESS J1826-130 and the diffuse X-ray PWN, our
multi-wavelength spectral energy distribution (SED) analysis using X-ray and
gamma-ray data establishes a leptonic origin of the TeV emission associated
with the Eel PWN. Furthermore, our evolutionary PWN SED model suggests (1) a
low PWN B-field of $\sim$1 $\mu$G, (2) a significantly younger pulsar age ($t
\sim5.7$ kyr) than the characteristic age ($\tau= 14.4$ kyr) and (3) a maximum
electron energy of $E_{max} = 2$ PeV. The low B-field as well as the putative
supersonic motion of the pulsar may account for the asymmetric morphology of
the diffuse X-ray emission. Our results suggest that the Eel PWN may be a
leptonic PeVatron particle accelerator powered by the $\sim$6-kyr-old pulsar
PSR J1826-1256 with a spin-down power of $3.6 \times 10^{36}$ erg s$^{-1}$.


title:
Energy-sensitive scatter estimation and correction for spectral x-ray
  imaging with photon-counting detectors

abstract:
  As photon counting detectors are being explored for medical and industrial
imaging applications, there is a critical need to understand spectral
characteristics of scattered x-ray photons. Scattered radiation is detrimental
to x-ray imaging by reducing image quality and quantitative accuracy. While
various scatter correction techniques have been proposed for x-ray imaging with
conventional energy-integrating detectors, additional efforts are required to
develop approaches for spectral x-ray imaging with energy-resolving PCDs. We
show the benefits of accurate scatter estimation and correction for each energy
bin when using a photon counting detector. We propose a scatter estimation
model that accounts for the energy-dependent scatter characteristics in
projection x-ray imaging. This can then be used to restore quantitative
accuracy for spectral x-ray imaging with PCDs. Results are shown in the context
of contrast-enhanced spectral mammography using dual-energy subtraction to
digitally isolate iodine targets. In the presence of scatter, the projected
iodine densities are increasingly underestimated as the object thickness
increases. The energysensitive scatter correction improves the iodine density
estimation. These results suggest that our scatter estimation model can
accurately account for the energy-dependent scatter distribution, which can be
an effective tool for scatter compensation in spectral x-ray imaging.
Implementing this scatter estimation model does not require any modifications
to the acquisition parameters and is transferable to other x-ray imaging
applications such as tomosynthesis and CT.


title:
Distributed Statistical Min-Max Learning in the Presence of Byzantine
  Agents

abstract:
  Recent years have witnessed a growing interest in the topic of min-max
optimization, owing to its relevance in the context of generative adversarial
networks (GANs), robust control and optimization, and reinforcement learning.
Motivated by this line of work, we consider a multi-agent min-max learning
problem, and focus on the emerging challenge of contending with worst-case
Byzantine adversarial agents in such a setup. By drawing on recent results from
robust statistics, we design a robust distributed variant of the extra-gradient
algorithm - a popular algorithmic approach for min-max optimization. Our main
contribution is to provide a crisp analysis of the proposed robust
extra-gradient algorithm for smooth convex-concave and smooth strongly
convex-strongly concave functions. Specifically, we establish statistical rates
of convergence to approximate saddle points. Our rates are near-optimal, and
reveal both the effect of adversarial corruption and the benefit of
collaboration among the non-faulty agents. Notably, this is the first paper to
provide formal theoretical guarantees for large-scale distributed min-max
learning in the presence of adversarial agents.


title: Two flags in a semimodular lattice generate an antimatroid

abstract:
  A basic property in a modular lattice is that any two flags generate a
distributive sublattice. It is shown (Abels 1991, Herscovic 1998) that two
flags in a semimodular lattice no longer generate such a good sublattice,
whereas shortest galleries connecting them form a relatively good
join-sublattice. In this note, we sharpen this investigation to establish an
analogue of the two-flag generation theorem for a semimodular lattice. We
consider the notion of a modular convex subset, which is a subset closed under
the join and meet only for modular pairs, and show that the modular convex hull
of two flags in a semimodular lattice of rank $n$ is isomorphic to a
union-closed family on $[n]$. This family uniquely determines an antimatroid,
which coincides with the join-sublattice of shortest galleries of the two
flags.


title:
Separation of concerning things: a simpler basis for defining and
  programming with the C/C++ memory model (extended version)

abstract:
  The C/C++ memory model provides an interface and execution model for
programmers of concurrent (shared-variable) code. It provides a range of
mechanisms that abstract from underlying hardware memory models -- that govern
how multicore architectures handle concurrent accesses to main memory -- as
well as abstracting from compiler transformations. The C standard describes the
memory model in terms of cross-thread relationships between events, and has
been influenced by several research works that are similarly based. In this
paper we provide a thread-local definition of the fundamental principles of the
C memory model, which, for concise concurrent code, serves as a basis for
relatively straightforward reasoning about the effects of the C ordering
mechanisms. We argue that this definition is more practical from a programming
perspective and is amenable to analysis by already established techniques for
concurrent code. The key aspect is that the memory model definition is separate
to other considerations of a rich programming language such as C, in
particular, expression evaluation and optimisations, though we show how to
reason about those considerations in the presence of C concurrency. A major
simplification of our framework compared to the description in the C standard
and related work in the literature is separating out considerations around the
"lack of multicopy atomicity", a concept that is in any case irrelevant to
developers of code for x86, Arm, RISC-V or SPARC architectures. We show how the
framework is convenient for reasoning about well-structured code, and for
formally addressing unintuitive behaviours such as "out-of-thin-air" writes.


title:
Universal Treatment of Reduction for One-Loop Integrals in Projective
  Space

abstract:
  Recently a nice work about the understanding of one-loop integrals has been
done in [1] using the tricks of the projective space language associated to
their Feynman parametrization. We find this language is also very suitable to
deal with the reduction problem of one-loop integrals with general tensor
structures as well as propagators with arbitrary higher powers. In this paper,
we show that how to combine Feynman parametrization and embedding formalism to
give a universal treatment of reductions for general one-loop integrals, even
including the degenerated cases, such as the vanishing Gram determinant.
Results from this method can be written in a compact and symmetric form.


title:
Efficient Community Detection in Large-Scale Dynamic Networks Using
  Topological Data Analysis

abstract:
  In this paper, we propose a method that extends the persistence-based
topological data analysis (TDA) that is typically used for characterizing
shapes to general networks. We introduce the concept of the community tree, a
tree structure established based on clique communities from the clique
percolation method, to summarize the topological structures in a network from a
persistence perspective. Furthermore, we develop efficient algorithms to
construct and update community trees by maintaining a series of clique graphs
in the form of spanning forests, in which each spanning tree is built on an
underlying Euler Tour tree. With the information revealed by community trees
and the corresponding persistence diagrams, our proposed approach is able to
detect clique communities and keep track of the major structural changes during
their evolution given a stability threshold. The results demonstrate its
effectiveness in extracting useful structural insights for time-varying social
networks.


title:
Improved proof-by-contraction method and relative homologous entropy
  inequalities

abstract:
  The celebrated holographic entanglement entropy triggered investigations on
the connections between quantum information theory and quantum gravity. An
important achievement is that we have gained more insights into the quantum
states. It allows us to diagnose whether a given quantum state is a holographic
state, a state whose bulk dual admits semiclassical geometrical description.
The effective tool of this kind of diagnosis is holographic entropy cone (HEC),
an entropy space bounded by holographic entropy inequalities allowed by the
theory. In order to fix the HEC and to prove a given holographic entropy
inequality, a proof-by-contraction technique has been developed. This method
heavily depends on a contraction map $f$, which is very difficult to construct
especially for more-region ($n\geq 4$) cases. In this work we develop a general
and effective rule to rule out most of the cases such that $f$ can be obtained
in a relatively simple way. In addition, we extend the whole framework to
relative homologous entropy, a generalization of holographic entanglement
entropy that is suitable for characterizing the entanglement of mixed states.


title:
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for
  Nonlinear Dimension Reduction, Uncertainty Quantification and Operator
  Learning of Forward and Inverse Stochastic Problems

abstract:
  A new data-driven method for operator learning of stochastic differential
equations(SDE) is proposed in this paper. The central goal is to solve forward
and inverse stochastic problems more effectively using limited data. Deep
operator network(DeepONet) has been proposed recently for operator learning.
Compared to other neural networks to learn functions, it aims at the problem of
learning nonlinear operators. However, it can be challenging by using the
original model to learn nonlinear operators for high-dimensional stochastic
problems. We propose a new multi-resolution autoencoder DeepONet model referred
to as MultiAuto-DeepONet to deal with this difficulty with the aid of
convolutional autoencoder. The encoder part of the network is designed to
reduce the dimensionality as well as discover the hidden features of
high-dimensional stochastic inputs. The decoder is designed to have a special
structure, i.e. in the form of DeepONet. The first DeepONet in decoder is
designed to reconstruct the input function involving randomness while the
second one is used to approximate the solution of desired equations. Those two
DeepONets has a common branch net and two independent trunk nets. This
architecture enables us to deal with multi-resolution inputs naturally. By
adding $L_1$ regularization to our network, we found the outputs from the
branch net and two trunk nets all have sparse structures. This reduces the
number of trainable parameters in the neural network thus making the model more
efficient. Finally, we conduct several numerical experiments to illustrate the
effectiveness of our proposed MultiAuto-DeepONet model with uncertainty
quantification.


title:
Equidistribution of non-uniformly stretching translates of shrinking
  smooth curves and weighted Dirichlet approximation

abstract:
  We show that under the action of
$\mathrm{diag}(e^{nt},e^{-r_1(t)},\ldots,e^{-r_n(t)})\in\mathrm{SL}(n+1,\mathbb{R})$,
where $r_i(t)\to\infty$, on the space of unimodular lattices in
$\mathbb{R}^{n+1}$, the translates of any fixed-sized piece of a
`non-degenerate' smooth curve, or a shrinking piece of size $e^{-t}$ about
almost any point of the curve, get equidistributed in the space as
$t\to\infty$. From this, it follows that the weighted Dirichlet approximation
theorem cannot be improved for almost all points on any non-degenerate $C^{2n}$
curve in $\mathbb{R}^n$. This result extends the corresponding result for
analytic curves due to Shah (2009) and answers some questions inspired by the
work of Davenport and Schmidt (1969) and Kleinbock and Weiss (2008).


title:
3D Perception based Imitation Learning under Limited Demonstration for
  Laparoscope Control in Robotic Surgery

abstract:
  Automatic laparoscope motion control is fundamentally important for surgeons
to efficiently perform operations. However, its traditional control methods
based on tool tracking without considering information hidden in surgical
scenes are not intelligent enough, while the latest supervised imitation
learning (IL)-based methods require expensive sensor data and suffer from
distribution mismatch issues caused by limited demonstrations. In this paper,
we propose a novel Imitation Learning framework for Laparoscope Control (ILLC)
with reinforcement learning (RL), which can efficiently learn the control
policy from limited surgical video clips. Specially, we first extract surgical
laparoscope trajectories from unlabeled videos as the demonstrations and
reconstruct the corresponding surgical scenes. To fully learn from limited
motion trajectory demonstrations, we propose Shape Preserving Trajectory
Augmentation (SPTA) to augment these data, and build a simulation environment
that supports parallel RGB-D rendering to reinforce the RL policy for
interacting with the environment efficiently. With adversarial training for IL,
we obtain the laparoscope control policy based on the generated rollouts and
surgical demonstrations. Extensive experiments are conducted in unseen
reconstructed surgical scenes, and our method outperforms the previous IL
methods, which proves the feasibility of our unified learning-based framework
for laparoscope control.


title:
A Framework for Following Temporal Logic Instructions with Unknown
  Causal Dependencies

abstract:
  Teaching a deep reinforcement learning (RL) agent to follow instructions in
multi-task environments is a challenging problem. We consider that user defines
every task by a linear temporal logic (LTL) formula. However, some causal
dependencies in complex environments may be unknown to the user in advance.
Hence, when human user is specifying instructions, the robot cannot solve the
tasks by simply following the given instructions. In this work, we propose a
hierarchical reinforcement learning (HRL) framework in which a symbolic
transition model is learned to efficiently produce high-level plans that can
guide the agent efficiently solve different tasks. Specifically, the symbolic
transition model is learned by inductive logic programming (ILP) to capture
logic rules of state transitions. By planning over the product of the symbolic
transition model and the automaton derived from the LTL formula, the agent can
resolve causal dependencies and break a causally complex problem down into a
sequence of simpler low-level sub-tasks. We evaluate the proposed framework on
three environments in both discrete and continuous domains, showing advantages
over previous representative methods.


title:
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and
  Reconstruction for Complex Missing

abstract:
  The interpolation and reconstruction of missing traces is a crucial step in
seismic data processing, moreover it is also a highly ill-posed problem,
especially for complex cases such as high-ratio random discrete missing,
continuous missing and missing in fault-rich or salt body surveys. These
complex cases are rarely mentioned in current sparse or low-rank priorbased and
deep learning-based approaches. To cope with complex missing cases, we propose
Multi-Dimensional Adversarial GAN (MDA GAN), a novel 3-D GAN framework. It
employs three discriminators to ensure the consistency of the reconstructed
data with the original data distribution in each dimension. The feature
splicing module (FSM) is designed and embedded into the generator of this
framework, which automatically splices the features of the unmissing part with
those of the reconstructed part (missing part), thus fully preserving the
information of the unmissing part. To prevent pixel distortion in the seismic
data caused by the adversarial learning process, we propose a new
reconstruction loss Tanh Cross Entropy (TCE) loss to provide smoother
gradients. We experimentally verified the effectiveness of the individual
components of the study and then tested the method on multiple publicly
available data. The method achieves reasonable reconstructions for up to 95% of
random discrete missing, 100 traces of continuous missing and more complex
hybrid missing. In surveys of fault-rich and salt bodies, the method can
achieve promising reconstructions with up to 75% missing in each of the three
directions (98.2% in total).


title:
Two-dimensional partitioned square ice confined in graphene/graphite
  nanocapillaries

abstract:
  As one of the most fascinating confined water/ice phenomena, two-dimensional
square ice has been extensively studied and experimentally confirmed in recent
years. Apart from the unidirectional homogeneous square icing patterns
considered in previous studies, the multidirectional partitioned square icing
patterns are discovered in this study and characterized by molecular dynamics
(MD) simulations. Square icing parameters are proposed to quantitatively
distinguish the partitioned patterns from the homogeneous patterns and the
liquid water. The number of graphene monolayers n is varied in this study, and
the results show that it is more energetically favorable to form partitioned
square icing patterns when the water molecules are confined between graphite
sheets (n >= 2) compared to graphene (n = 1). This phenomenon is insensitive to
n as long as n >= 2, because of the short-range nature of the interaction
between water molecules and the carbon substrate. Moreover, it is energetically
unfavorable to form partitioned square icing patterns for a single layer of
water molecules even for n >= 2, verifying that the interaction between layers
of water molecules is another dominant factor in the formation of partitioned
structures. The conversion from partitioned structure to homogenous square
patterns is investigated by changing the pressure and the temperature. Based on
the comprehensive MD simulations, this study unveils the formation mechanism of
the partitioned square icing patterns.


title: Stability and instability of Kelvin waves

abstract:
  The $m$-waves of Kelvin are uniformly rotating patch solutions of the 2D
Euler equations with $m$-fold rotational symmetry for $m\geq 2$. For Kelvin
waves sufficiently close to the disc, we prove nonlinear stability results in
the $L^1$ norm of the vorticity, for $m$-fold symmetric perturbations. This is
obtained by proving that the Kelvin wave is a strict local maximizer of the
energy functional in some admissible class of patches. Based on the $L^1$
stability, we establish that long time filamentation, or formation of long
arms, occurs near the Kelvin waves, which have been observed in various
numerical simulations. Additionally, we discuss stability of annular patches in
the same variational framework.


title:
The Distressing Ads That Persist: Uncovering The Harms of Targeted
  Weight-Loss Ads Among Users with Histories of Disordered Eating

abstract:
  Targeted advertising can harm vulnerable groups when it targets individuals'
personal and psychological vulnerabilities. We focus on how targeted
weight-loss advertisements harm people with histories of disordered eating. We
identify three features of targeted advertising that cause harm: the
persistence of personal data that can expose vulnerabilities, over-simplifying
algorithmic relevancy models, and design patterns encouraging engagement that
can facilitate unhealthy behavior. Through a series of semi-structured
interviews with individuals with histories of unhealthy body stigma, dieting,
and disordered eating, we found that targeted weight-loss ads reinforced low
self-esteem and deepened pre-existing anxieties around food and exercise. At
the same time, we observed that targeted individuals demonstrated agency and
resistance against distressing ads. Drawing on scholarship in postcolonial
environmental studies, we use the concept of slow violence to articulate how
online targeted advertising inflicts harms that may not be immediately
identifiable. CAUTION: This paper includes media that could be triggering,
particularly to people with an eating disorder. Please use caution when
reading, printing, or disseminating this paper.


title:
A new multiphysics finite element method for a Biot model with secondary
  consolidation

abstract:
  In this paper, we propose a new multiphysics finite element method for a Biot
model with secondary consolidation in soil dynamics. To better describe the
processes of deformation and diffusion underlying in the original model, we
reformulate Biot model by a new multiphysics approach, which transforms the
fluid-solid coupled problem to a fluid coupled problem--a generalized Stokes
problem and a diffusion problem. Then, we give the energy law and prior error
estimate of the weak solution. And we design a fully discrete time-stepping
scheme to use mixed finite element method for $P_2-P_1-P_1$ element pairs to
approximate the space variables and backward Euler method for the time
variable, and we prove the discrete energy laws and the optimal convergence
order error estimates. Also, we show some numerical examples to verify the
theoretical results. Finally, we draw a conclusion to summarize the main
results of this paper.


title:
Supercurrent reversal in ferromagnetic hybrid nanowire Josephson
  junctions

abstract:
  We report supercurrent transport measurements in hybrid Josephson junctions
comprised of semiconducting InAs nanowires with epitaxial ferromagnetic
insulator EuS and superconducting Al coatings. The wires display a hysteretic
superconducting window close to the coercivity, away from zero external
magnetic field. Using a multi-interferometer setup, we measure the
current-phase relation of multiple magnetic junctions and find an abrupt switch
between $\pi$ and 0 phases within the superconducting window. We attribute the
0-$\pi$ transition to the discrete flipping of the EuS domains and provide a
qualitative theory showing that a sizable exchange field can polarize the
junction and lead to the supercurrent reversal. Both $0$ and $\pi$ phases can
be realized at zero external field by demagnetizing the wire.


title:
Convex optimization for non-equilibrium steady states on a hybrid
  quantum processor

abstract:
  Finding the transient and steady state properties of open quantum systems is
a central problem in various fields of quantum technologies. Here, we present a
quantum-assisted algorithm to determine the steady states of open system
dynamics. By reformulating the problem of finding the fixed point of Lindblad
dynamics as a feasibility semi-definite program, we bypass several well known
issues with variational quantum approaches to solving for steady states. We
demonstrate that our hybrid approach allows us to estimate the steady states of
higher dimensional open quantum systems and discuss how our method can find
multiple steady states for systems with symmetries.


title:
Convolutional Neural Network for Early Pulmonary Embolism Detection via
  Computed Tomography Pulmonary Angiography

abstract:
  This study was conducted to develop a computer-aided detection (CAD) system
for triaging patients with pulmonary embolism (PE). The purpose of the system
was to reduce the death rate during the waiting period. Computed tomography
pulmonary angiography (CTPA) is used for PE diagnosis. Because CTPA reports
require a radiologist to review the case and suggest further management, this
creates a waiting period during which patients may die. Our proposed CAD method
was thus designed to triage patients with PE from those without PE. In contrast
to related studies involving CAD systems that identify key PE lesion images to
expedite PE diagnosis, our system comprises a novel classification-model
ensemble for PE detection and a segmentation model for PE lesion labeling. The
models were trained using data from National Cheng Kung University Hospital and
open resources. The classification model yielded 0.73 for receiver operating
characteristic curve (accuracy = 0.85), while the mean intersection over union
was 0.689 for the segmentation model. The proposed CAD system can distinguish
between patients with and without PE and automatically label PE lesions to
expedite PE diagnosis


title:
A classical model of the Franson-type nonlocal correlation using
  coherent photon pairs

abstract:
  Nonlocal quantum correlation has been the main subject of quantum mechanics
over the last century. Franson-type nonlocal correlation has been widely
applied for quantum communications using an energy-time bin method of entangled
photon pairs, where coincidence detection selectively controls measurement
events for inseparable basis-product superposition. Here, a classical model of
the Franson-type nonlocal correlation is proposed and analyzed for the same
quantum features as in the entangled photon cases. Like conventional nonlocal
correlation based on the particle nature of quantum mechanics, the wave nature
of photons has also demonstrated the same quantum features using coherent
photon pairs, where inherent phase information of each photon clarifies the
coincidence detection-based quantum correlation. Owing to the rule of thumb in
quantum mechanics that a photon never interferes with others, the classical
model of Franson-type nonlocal correlation can be applied for a new realm of
macroscopic quantum information.


title:
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly
  Supervised Semantic Segmentation

abstract:
  Mining precise class-aware attention maps, a.k.a, class activation maps, is
essential for weakly supervised semantic segmentation. In this paper, we
present L2G, a simple online local-to-global knowledge transfer framework for
high-quality object attention mining. We observe that classification models can
discover object regions with more details when replacing the input image with
its local patches. Taking this into account, we first leverage a local
classification network to extract attentions from multiple local patches
randomly cropped from the input image. Then, we utilize a global network to
learn complementary attention knowledge across multiple local attention maps
online. Our framework conducts the global network to learn the captured rich
object detail knowledge from a global view and thereby produces high-quality
attention maps that can be directly used as pseudo annotations for semantic
segmentation networks. Experiments show that our method attains 72.1% and 44.2%
mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014,
respectively, setting new state-of-the-art records. Code is available at
https://github.com/PengtaoJiang/L2G.


title:
BIMxAR: BIM-Empowered Augmented Reality for Learning Architectural
  Representations

abstract:
  Literature review shows limited research investigating the utilization of
Augmented Reality (AR) to improve learning and understanding architectural
representations, specifically section views. In this study, we present an AR
system prototype (BIMxAR), its new and accurate building-scale registration
method, and its novel visualization features that facilitate the comprehension
of building construction systems, materials configuration, and 3D section views
of complex structures through the integration of AR, Building Information
Modeling (BIM), and physical buildings. A pilot user study found improvements
after students studied building section views in a physical building with AR,
though not statistically significant, in terms of scores of the Santa Barbara
Solids Test (SBST) and the Architectural Representations Test (ART). When
incorporating time as a performance factor, the ART timed scores show a
significant improvement in the posttest session. BIMxAR has the potential to
enhance the students spatial abilities, particularly in understanding buildings
and complex section views.


title: A Joint Learning Approach for Semi-supervised Neural Topic Modeling

abstract:
  Topic models are some of the most popular ways to represent textual data in
an interpret-able manner. Recently, advances in deep generative models,
specifically auto-encoding variational Bayes (AEVB), have led to the
introduction of unsupervised neural topic models, which leverage deep
generative models as opposed to traditional statistics-based topic models. We
extend upon these neural topic models by introducing the Label-Indexed Neural
Topic Model (LI-NTM), which is, to the extent of our knowledge, the first
effective upstream semi-supervised neural topic model. We find that LI-NTM
outperforms existing neural topic models in document reconstruction benchmarks,
with the most notable results in low labeled data regimes and for data-sets
with informative labels; furthermore, our jointly learned classifier
outperforms baseline classifiers in ablation studies.


title: Speeding Up Sparsification using Inner Product Search Data Structures

abstract:
  We present a general framework that utilizes different efficient data
structures to improve various sparsification problems involving an iterative
process. We also provide insights and characterization for different iterative
process, and answer that when should we use which data structures in what type
of problem. We obtain improved running time for the following problems.
  * For constructing linear-sized spectral sparsifier (Batson, Spielman and
Srivastava, 2012), all the existing deterministic algorithms require
$\Omega(d^4)$ time. In this work, we provide the first deterministic algorithm
that breaks that barrier which runs in $O(d^{\omega+1})$ time, where $\omega$
is the exponent of matrix multiplication.
  * For one-sided Kadison-Singer-typed discrepancy problem, we give fast
algorithms for both small and large number of iterations.
  * For experimental design problem, we speed up a key swapping process.
  In the heart of our work is the design of a variety of different inner
product search data structures that have efficient initialization, query and
update time, compatible to dimensionality reduction and robust against adaptive
adversary.


title:
Early Evolution of a Newborn Magnetar with Strong Precession Motion in
  GRB 180620A

abstract:
  The observed early X-ray plateau in the afterglow lightcurves of some
gamma-ray bursts (GRBs) is attributed to the dipole radiations (DRs) of a
newborn magnetar. A quasi-periodic oscillation (QPO) signal in the plateau
would be strong evidence of the magnetar precession motion. By making a
time-frequency domain analysis for the X-ray afterglow lightcurve of GRB
180620A, we find a QPO signal of $\sim650$ seconds in its early X-ray plateau.
We fit the lightcurve with a magnetar precession model by adopting the Markov
chain Monte Carlo algorithm. The observed lightcurve and the QPO signal are
well represented with our model. The derived magnetic field strength of the
magnetar is $B_{\rm p}= (1.02^{+0.59}_{-0.61})\times10^{15}$~G. It rapidly
spins down with angular velocity evolving as $\Omega_{s} \propto(1+t/\tau_{\rm
sd})^{-0.96}$, where $\tau_{\rm sd}=9430$~s. Its precession velocity evolution
is even faster than $\Omega_s$, i.e. $\Omega_{ p}\propto
(1+t/\tau_{p})^{-2.18\pm0.11}$, where $\tau_{p}=2239\pm206$~s. The inferred
braking index is $n=2.04$. We argue that the extra energy loss via the
magnetospheric processes results in its rapid spin-down, a low braking index of
the magnetar, and the strong precession motion.


title: Elastic Model Aggregation with Parameter Service

abstract:
  Model aggregation, the process that updates model parameters, is an important
step for model convergence in distributed deep learning (DDL). However, the
parameter server (PS), a popular paradigm of performing model aggregation,
causes CPU underutilization in deep learning (DL) clusters, due to the bursty
nature of aggregation and static resource allocation. To remedy this problem,
we propose Parameter Service, an elastic model aggregation framework for DDL
training, which decouples the function of model aggregation from individual
training jobs and provides a shared model aggregation service to all jobs in
the cluster. In Parameter Service, model aggregations are efficiently packed
and dynamically migrated to fit into the available CPUs with negligible time
overhead. Furthermore, Parameter Service can elastically manage its CPU
resources based on its load to enhance resource efficiency. We have implemented
Parameter Service in a prototype system called AutoPS and evaluated it via
testbed experimentation and trace-driven simulations. AutoPS reduces up to 75%
of CPU consumption with little or no performance impact on the training jobs.
The design of Parameter Service is transparent to the users and can be
incorporated in popular DL frameworks.


title: Red sizes of quivers

abstract:
  In this article, we will expand on the notions of maximal green and reddening
sequences for quivers associated to cluster algebras. The existence of these
sequences has been studied for a variety of applications related to Fomin and
Zelevinsky's cluster algebras. Ahmad and Li considered a numerical measure of
how close a quiver is to admitting a maximal green sequence called a red
number. In this paper we generalized this notion to what we call unrestricted
red numbers which are related to reddening sequences. In addition to
establishing this more general framework we completely determine the red
numbers and unrestricted red numbers for all finite mutation type quivers.
Furthermore, we give conjectures on the possible values of red numbers and
unrestricted red numbers in general.


title:
MC-UNet Multi-module Concatenation based on U-shape Network for Retinal
  Blood Vessels Segmentation

abstract:
  Accurate segmentation of the blood vessels of the retina is an important step
in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks
have come up for retinal blood vessels segmentation tasks. However, the complex
vascular structure and uncertain pathological features make the blood vessel
segmentation still very challenging. A novel U-shaped network named
Multi-module Concatenation which is based on Atrous convolution and
multi-kernel pooling is put forward to retinal vessels segmentation in this
paper. The proposed network structure retains three layers the essential
structure of U-Net, in which the atrous convolution combining the multi-kernel
pooling blocks are designed to obtain more contextual information. The spatial
attention module is concatenated with dense atrous convolution module and
multi-kernel pooling module to form a multi-module concatenation. And different
dilation rates are selected by cascading to acquire a larger receptive field in
atrous convolution. Adequate comparative experiments are conducted on these
public retinal datasets: DRIVE, STARE and CHASE_DB1. The results show that the
proposed method is effective, especially for microvessels. The code will be put
out at https://github.com/Rebeccala/MC-UNet


title:
Transformer-Based Language Models for Software Vulnerability Detection:
  Performance, Model's Security and Platforms

abstract:
  The large transformer-based language models demonstrate excellent performance
in natural language processing. By considering the closeness of natural
languages to the high-level programming language such as C/C++, this work
studies how good are the large transformer-based language models detecting
software vulnerabilities. Our results demonstrate the well performance of these
models on software vulnerability detection. The answer enables extending
transformer-based language models to vulnerability detection and leveraging
superior performance beyond the natural language processing domain. Besides, we
perform the model's security check using Microsoft's Counterfit, a command-line
tool to assess the model's security. Our results find that these models are
vulnerable to adversarial examples. In this regard, we present a simple
countermeasure and its result. Experimenting with large models is always a
challenge due to the requirement of computing resources and platforms/libraries
& dependencies. Based on the experiences and difficulties we faced during this
work, we present our recommendation while choosing the platforms to run these
large models. Moreover, the popular platforms are surveyed thoroughly in this
paper.


title: Robust Model-based Inference for Non-Probability Samples

abstract:
  With the ubiquitous availability of unstructured data, growing attention is
paid as how to adjust for selection bias in such non-probability samples. The
majority of the robust estimators proposed by prior literature are either fully
or partially design-based, which may lead to inefficient estimates if outlying
(pseudo-)weights are present. In addition, correctly reflecting the uncertainty
of the adjusted estimator remains a challenge when the available reference
survey is complex in the sample design. This article proposes a fully
model-based method for inference using non-probability samples where the goal
is to predict the outcome variable for the entire population units. We employ a
Bayesian bootstrap method with Rubin's combing rules to derive the adjusted
point and interval estimates. Using Gaussian process regression, our method
allows for kernel matching between the non-probability sample units and
population units based on the estimated selection propensities when the outcome
model is misspecified. The repeated sampling properties of our method are
evaluated through two Monte Carlo simulation studies. Finally, we examine it on
a real-world non-probability sample with the aim to estimate crash-attributed
injury rates in different body regions in the United States.


title:
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm
  of spatio-temporal data

abstract:
  High-dimensional spatio-temporal dynamics can often be encoded in a
low-dimensional subspace. Engineering applications for modeling,
characterization, design, and control of such large-scale systems often rely on
dimensionality reduction to make solutions computationally tractable in
real-time. Common existing paradigms for dimensionality reduction include
linear methods, such as the singular value decomposition (SVD), and nonlinear
methods, such as variants of convolutional autoencoders (CAE). However, these
encoding techniques lack the ability to efficiently represent the complexity
associated with spatio-temporal data, which often requires variable geometry,
non-uniform grid resolution, adaptive meshing, and/or parametric dependencies.
To resolve these practical engineering challenges, we propose a general
framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic,
low-rank representation of large-scale, parametric, spatial-temporal data. NIF
consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which
isolates and represents the spatial complexity, and (ii) ParameterNet, which
accounts for any other input complexity, including parametric dependencies,
time, and sensor measurements. We demonstrate the utility of NIF for parametric
surrogate modeling, enabling the interpretable representation and compression
of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and
improved generalization performance for sparse reconstruction.


title: Resiliency of Nonlinear Control Systems to Stealthy Sensor Attacks

abstract:
  In this work, we focus on analyzing vulnerability of nonlinear dynamical
control systems to stealthy sensor attacks. We start by defining the notion of
stealthy attacks in the most general form by leveraging Neyman-Pearson lemma;
specifically, an attack is considered to be stealthy if it is stealthy from
(i.e., undetected by) any intrusion detector -- i.e., the probability of the
detection is not better than a random guess. We then provide a sufficient
condition under which a nonlinear control system is vulnerable to stealthy
attacks, in terms of moving the system to an unsafe region due to the attacks.
In particular, we show that if the closed-loop system is incrementally
exponentially stable while the open-loop plant is incrementally unstable, then
the system is vulnerable to stealthy yet impactful attacks on sensors. Finally,
we illustrate our results on a case study.


title:
MTH5: an archive and exchangeable data format for magnetotelluric time
  series data

abstract:
  Magnetotellurics (MT) is a passive electromagnetic geophysical method that
measures variations in subsurface electrical resistivity. MT data are collected
in the time domain and processed in the frequency domain to produce estimates
of a transfer function representing the Earth's electrical structure.
Unfortunately, the MT community lacks metadata and data standards for time
series data. As the community grows and findability, accessibility,
interoperability, and reuse of digital assets (FAIR) data principles are
enforced by government and funding agencies, a standard is needed for time
series data. Presented here is a hierarchical data format (MTH5) that is
logically formatted to how MT data are collected. Open-source Python packages
are also described to read, write, and manipulate MTH5 files. These include a
package to deal with metadata (\texttt{mt\symbol{95}metadata}) based on
standards developed by the Working Group for Magnetotelluric Data Handling and
Software assembled by the Incorporated Research Institutions for Seismology
(IRIS), and \texttt{mth5}: a package to interact with MTH5 files that uses
\texttt{mt\symbol{95}metadata}. Example code and workflows are presented.


title:
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training
  and Distribution of Opinion Scores

abstract:
  Mean opinion score (MOS) is a typical subjective evaluation metric for speech
synthesis systems. Since collecting MOS is time-consuming, it would be
desirable if there are accurate MOS prediction models for automatic evaluation.
In this work, we propose DDOS, a novel MOS prediction model. DDOS utilizes
domain adaptive pre-training to further pre-train self-supervised learning
models on synthetic speech. And a proposed module is added to model the opinion
score distribution of each utterance. With the proposed components, DDOS
outperforms previous works on BVCC dataset. And the zero shot transfer result
on BC2019 dataset is significantly improved. DDOS also wins second place in
Interspeech 2022 VoiceMOS challenge in terms of system-level score.


title: Continuous Comodules

abstract:
  Let $R$ be a commutative ring with unity and $C$ be an $R$-coalgebra. The
ring $R$ is clean if every $ r\in R $ is the sum of a unit and an idempotent
element of $R$. An $R$-module $M$ is clean if the endomorphism ring of $M$ over
$R$ is clean. Moreover, every continuous module is clean. We modify this idea
to the comodule and coalgebra cases. A $C$-comodule $M$ is called a clean
comodule if the $C$-comodule endomorphisms of $M$ are clean. We introduced
continuous comodules and proved that every continuous comodules is a clean
comodule.


title: Distributionally Robust Observable Strategic Queues

abstract:
  This paper presents an extension of Naor's analysis on the join-or-balk
problem in observable M/M/1 queues. While all other Markovian assumptions still
hold, we explore this problem assuming uncertain arrival rates under the
distributionally robust settings. We first study the problem with the classical
moment ambiguity set, where the support, mean, and mean-absolute deviation of
the underlying distribution are known. Next, we extend the model to the
data-driven setting, where decision makers only have access to a finite set of
samples. We develop three optimal joining threshold strategies from the
perspective of an individual customer, a social optimizer, and a revenue
maximizer, such that their respective worst-case expected benefit rates are
maximized. Finally, we compare our findings with Naor's original results and
the traditional sample average approximation scheme.


title: Where to find lossless metals?

abstract:
  Hypothetical metals having optical absorption losses as low as those of the
transparent insulators, if found, could revolutionize optoelectronics. We
perform the first high-throughput search for lossless metals among all known
inorganic materials in the databases of over 100,000 entries. The 381
candidates are identified -- having well-isolated partially-filled bands -- and
are analyzed by defining the figures of merit and classifying their real-space
conductive connectivity. The existing experimental evidence of most candidates
being insulating, instead of conducting, is due to the limitation of current
density functional theory in predicting narrow-band metals that are unstable
against magnetism, structural distortion, or electron-electron interactions. We
propose future research directions including conductive oxides, intercalating
layered materials, and compressing these false-metal candidates under high
pressures into eventual lossless metals.


title:
Semantic-functional Communications for Multiuser Event Transmissions via
  Random Maps

abstract:
  This work introduces a new perspective for physical media sharing in
multiuser communication by jointly considering (i) the meaning of the
transmitted message and (ii) its function at the end user. Specifically, we
have defined a scenario where multiple users (sensors) are continuously
transmitting their own states concerning a predetermined event. On the receiver
side there is an alarm monitoring system, whose function is to decide whether
such a predetermined event has happened in a certain time period and, if yes,
in which user. The media access control protocol proposed constitutes an
alternative approach to the conventional physical layer methods, because the
receiver does not decode the received waveform directly; rather, the relative
position of the absence or presence of energy within a multidimensional
resource space carries the (semantic) information. The protocol introduced here
provides high efficiency in multiuser networks that operate with
event-triggered sampling by enabling a constructive reconstruction of
transmission collisions. We have demonstrated that the proposed method leads to
a better event transmission efficiency than conventional methods like TDMA and
slotted ALOHA. Remarkably, the proposed method achieves 100\% efficiency and
0\% error probability in almost all the studied cases, while consistently
outperforming TDMA and slotted ALOHA.


title:
Renormalization of $\langle\phi^2\rangle$ at the inner horizon of
  rotating, accreting black holes

abstract:
  Classically, the inner horizon of a perturbed, rotating black hole undergoes
an instability known as mass inflation, wherein the spacetime curvature
diverges as a result of hyper-relativistic crossing streams of ingoing and
outgoing radiation. The generic outcome of this instability is currently
believed to be a strong, spacelike singularity, potentially alongside a weak,
null singularity surviving at late times. However, the quantum back-reaction in
this regime has yet to be fully calculated for a realistic black hole
spacetime. Here we consider a massless quantized scalar field $\phi$ over the
inflationary Kasner spacetime, a recently developed model for the inner horizon
geometry of a rotating, accreting black hole. With this spacetime, we use
numerical adiabatic regularization to calculate
$\langle\phi^2\rangle_\text{ren}$, the renormalized coincidence limit of the
two-point correlation function, as a pointer to the behavior of the quantum
stress-energy tensor.


title: Explicit Feature Interaction-aware Graph Neural Networks

abstract:
  Graph neural networks are powerful methods to handle graph-structured data.
However, existing graph neural networks only learn higher-order feature
interactions implicitly. Thus, they cannot capture information that occurred in
low-order feature interactions. To overcome this problem, we propose Explicit
Feature Interaction-aware Graph Neural Network (EFI-GNN), which explicitly
learns arbitrary-order feature interactions. EFI-GNN can jointly learn with any
other graph neural network. We demonstrate that the joint learning method
always enhances performance on the various node classification tasks.
Furthermore, since EFI-GNN is inherently a linear model, we can interpret the
prediction result of EFI-GNN. With the computation rule, we can obtain an
any-order feature's effect on the decision. By that, we visualize the effects
of the first-order and second-order features as a form of a heatmap.


title:
On non-monogenity of certain number fields defined by trinomials
  $x^4+ax^2+b$

abstract:
  We consider number fields $K$ generated by a root of an irreducible trinomial
$x^4+ax^2+b\in \Bbb Z[x]$ and characterize when a prime $p$ is a common index
divisor of $K$. The existence of a common index divisor $p$ implies that $K$ is
not monogenic. We illustrate our statements with a series of examples.


title: Accelerating Attention through Gradient-Based Learned Runtime Pruning

abstract:
  Self-attention is a key enabler of state-of-art accuracy for various
transformer-based Natural Language Processing models. This attention mechanism
calculates a correlation score for each word with respect to the other words in
a sentence. Commonly, only a small subset of words highly correlates with the
word under attention, which is only determined at runtime. As such, a
significant amount of computation is inconsequential due to low attention
scores and can potentially be pruned. The main challenge is finding the
threshold for the scores below which subsequent computation will be
inconsequential. Although such a threshold is discrete, this paper formulates
its search through a soft differentiable regularizer integrated into the loss
function of the training. This formulation piggy backs on the back-propagation
training to analytically co-optimize the threshold and the weights
simultaneously, striking a formally optimal balance between accuracy and
computation pruning. To best utilize this mathematical innovation, we devise a
bit-serial architecture, dubbed LeOPArd, for transformer language models with
bit-level early termination microarchitectural mechanism. We evaluate our
design across 43 back-end tasks for MemN2N, BERT, ALBERT, GPT-2, and Vision
transformer models. Post-layout results show that, on average, LeOPArd yields
1.9x and 3.9x speedup and energy reduction, respectively, while keeping the
average accuracy virtually intact (<0.2% degradation)


title:
Pressure induced isostructural phase transition in biskyrmion host
  hexagonal MnNiGa

abstract:
  Magnetic skyrmions are vortex-like spin textures, which can be manipulated by
external stress or pressure via magnetoelastic effects. Here, we present the
observation of isostructural phase transition in a biskyrmion host hexagonal
MnNiGa at a pressure around 4 GPa using pressure-dependent synchrotron x-ray
powder diffraction (XRD) data analysis. Our XRD data reveals anisotropic
compression behavior with pressure with different compression rates of the
a-axis in the basal plane and the c-axis in the prismatic plane. However, the
hexagonal symmetry remains unchanged for pressure up to 14 GPa. Fitting of unit
cell volume with pressure using a second-order Birch-Murnagan equation of state
reveals that the data to fall into two distinct curves for those above and
below 4 GPa. The present study contributes to the understanding of crystal
structure with the application of hydrostatic pressure in the biskyrmion host
MnNiGa, wherein the skyrmion textures can be manipulated by pressure due to
their magnetoelastic character.


title: Very High Energy Flat Spectral Radio Quasar Candidates

abstract:
  The attenuation of very high energy (VHE) photons by the extragalactic
background light (EBL) prevents the observation of high redshift flat spectrum
radio quasars (FSRQs). Existing EBL models predict only a handful of FSRQs
which fall above the sensitivity of the forthcoming VHE telescopes. However,
recent detection of high redshift FSRQs by existing Cherenkov telescopes
question the inferred opacity of the universe to VHE gamma-rays. In order to
justify the VHE detection of these FSRQs, we introduce a redshift dependent
correction factor to the existing opacity estimate. Using this we identify the
plausible VHE FSRQ candidates by extrapolating the Fermi gamma-ray spectrum to
VHE regime. Our study suggests among 744 FSRQs reported in Fermi fourth
catalog-Data release 2 (4FGL-DR2), 34 FSRQs will be detectable under Alpha
configuration of Cherenkov Telescope Array Observatory (CTAO) while the number
will increase to 37 under Omega configuration. Considering the flux variability
of blazars, doubling the average Fermi gamma-ray flux will result in additional
44 FSRQs detectable by CTAO Omega configuration.


title:
What You See is What You Get: Distributional Generalization for
  Algorithm Design in Deep Learning

abstract:
  We investigate and leverage a connection between Differential Privacy (DP)
and the recently proposed notion of Distributional Generalization (DG).
Applying this connection, we introduce new conceptual tools for designing
deep-learning methods that bypass "pathologies" of standard stochastic gradient
descent (SGD). First, we prove that differentially private methods satisfy a
"What You See Is What You Get (WYSIWYG)" generalization guarantee: whatever a
model does on its train data is almost exactly what it will do at test time.
This guarantee is formally captured by distributional generalization. WYSIWYG
enables principled algorithm design in deep learning by reducing
$\textit{generalization}$ concerns to $\textit{optimization}$ ones: in order to
mitigate unwanted behavior at test time, it is provably sufficient to mitigate
this behavior on the train data. This is notably false for standard (non-DP)
methods, hence this observation has applications even when privacy is not
required. For example, importance sampling is known to fail for standard SGD,
but we show that it has exactly the intended effect for DP-trained models.
Thus, with DP-SGD, unlike with SGD, we can influence test-time behavior by
making principled train-time interventions. We use these insights to construct
simple algorithms which match or outperform SOTA in several distributional
robustness applications, and to significantly improve the privacy vs. disparate
impact trade-off of DP-SGD. Finally, we also improve on known theoretical
bounds relating differential privacy, stability, and distributional
generalization.