title: The Spherical Cap Discrepancy of HEALPix Points

abstract:
  In this paper we show that the spherical cap discrepancy of the point set
given by centers of pixels in the HEALPix tessellation (short for Hierarchical,
Equal Area and iso-Latitude Pixelation) of unit $2$-sphere is lower and upper
bounded by order square root of the number of points, and compute explicit
constants. This adds to the known collection of explicitly constructed sets
whose discrepancy converges with order $N^{-1/2}$, matching the asymptotic
order for i.i.d. random point sets. We describe the HEALPix framework in more
detail and give explicit formulas for the boundaries and pixel centers. We then
introduce the notion of an $n$-convex curve and prove an upper bound on how
many fundamental domains are intersected by such curves, and in particular we
show that boundaries of spherical caps have this property. Lastly, we mention
briefly that a jittered sampling technique works in the HEALPix framework as
well.


title:
VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single
  and Multi-view 3D Reconstruction

abstract:
  We introduce a unified single and multi-view neural implicit 3D
reconstruction framework VPFusion. VPFusion~attains high-quality reconstruction
using both - 3D feature volume to capture 3D-structure-aware context, and
pixel-aligned image features to capture fine local detail. Existing approaches
use RNN, feature pooling, or attention computed independently in each view for
multi-view fusion. RNNs suffer from long-term memory loss and permutation
variance, while feature pooling or independently computed attention leads to
representation in each view being unaware of other views before the final
pooling step. In contrast, we show improved multi-view feature fusion by
establishing transformer-based pairwise view association. In particular, we
propose a novel interleaved 3D reasoning and pairwise view association
architecture for feature volume fusion across different views. Using this
structure-aware and multi-view-aware feature volume, we show improved 3D
reconstruction performance compared to existing methods. VPFusion improves the
reconstruction quality further by also incorporating pixel-aligned local image
features to capture fine detail. We verify the effectiveness of VPFusion~on the
ShapeNet and ModelNet datasets, where we outperform or perform on-par the
state-of-the-art single and multi-view 3D shape reconstruction methods.


title: Agile Maneuvers in Legged Robots: a Predictive Control Approach

abstract:
  Achieving agile maneuvers through multiple contact phases has been a
longstanding challenge in legged robotics. It requires to derive motion plans
and local control feedback policies in real-time to handle the nonholonomy of
the kinetic momenta. While a few recent predictive control approaches based on
centroidal momentum have been able to generate dynamic motions, they assume
unlimited actuation capabilities. This assumption is quite restrictive and does
not hold for agile maneuvers on most robots. In this work, we present a
contact-phase predictive and state-feedback controllers that enables legged
robots to plan and perform agile locomotion skills. Our predictive controller
models the contact phases using a hybrid paradigm that considers the robot's
actuation limits and full dynamics. We demonstrate the benefits of our approach
on agile maneuvers on ANYmal robots in realistic scenarios. To the best of our
knowledge, our work is the first to show that predictive control can handle
actuation limits, generate agile locomotion maneuvers and execute locally
optimal feedback policies on hardware without the use of a separate whole-body
controller.


title: Resilience of Input Metering in Dynamic Flow Networks

abstract:
  In this paper, we study robustness of input metering policies in dynamic flow
networks in the presence of transient disturbances and attacks. We consider a
compartmental model for dynamic flow networks with a First-In-First-Out (FIFO)
routing rule as found in, e.g., transportation networks. We model the effect of
the transient disturbance as an abrupt change to the state of the network and
use the notion of the region of attraction to measure the resilience of the
network to these changes. For constant and periodic input metering, we
introduce the notion of monotone-invariant points to establish inner-estimates
for the regions of attraction of free-flow equilibrium points and free-flow
periodic orbits using monotone systems theory. These results are applicable to,
e.g., networks with cycles, which have not been considered in prior literature
on dynamic flow networks with FIFO routing. Finally, we propose two approaches
for finding suitable monotone-invariant points in the flow networks with FIFO
rules.


title:
From atomic physics, to upper-atmospheric chemistry, to cosmology: A
  "laser photometric ratio star" to calibrate telescopes at major observatories

abstract:
  The expansion of our Universe is accelerating, due to dark energy. But the
nature of dark energy has been a mystery since its discovery at the end of the
past century. In Research Highlight https://doi.org/10.1002/ntls.20220003 ,
Justin Albert, Dmitry Budker and Hossein Sadeghpour provide an overview of how
a laser photometric ratio star (a novel light source generated by laser
excitation of the Earth's upper-atmospheric sodium layer, which will radiate
equally brightly at wavelengths of 589 nm and 820 nm) can help us precisely
calibrate telescopes in order to understand the nature of dark energy.

categories: astro-ph.IM astro-ph.CO physics.atom-ph physics.chem-ph


title: Fast Regression for Structured Inputs

abstract:
  We study the $\ell_p$ regression problem, which requires finding
$\mathbf{x}\in\mathbb R^{d}$ that minimizes
$\|\mathbf{A}\mathbf{x}-\mathbf{b}\|_p$ for a matrix $\mathbf{A}\in\mathbb R^{n
\times d}$ and response vector $\mathbf{b}\in\mathbb R^{n}$. There has been
recent interest in developing subsampling methods for this problem that can
outperform standard techniques when $n$ is very large. However, all known
subsampling approaches have run time that depends exponentially on $p$,
typically, $d^{\mathcal{O}(p)}$, which can be prohibitively expensive. We
improve on this work by showing that for a large class of common
\emph{structured matrices}, such as combinations of low-rank matrices, sparse
matrices, and Vandermonde matrices, there are subsampling based methods for
$\ell_p$ regression that depend polynomially on $p$. For example, we give an
algorithm for $\ell_p$ regression on Vandermonde matrices that runs in time
$\mathcal{O}(n\log^3 n+(dp^2)^{0.5+\omega}\cdot\text{polylog}\,n)$, where
$\omega$ is the exponent of matrix multiplication. The polynomial dependence on
$p$ crucially allows our algorithms to extend naturally to efficient algorithms
for $\ell_\infty$ regression, via approximation of $\ell_\infty$ by
$\ell_{\mathcal{O}(\log n)}$. Of practical interest, we also develop a new
subsampling algorithm for $\ell_p$ regression for arbitrary matrices, which is
simpler than previous approaches for $p \ge 4$.


title: Learning for Robot Decision Making under Distribution Shift: A Survey

abstract:
  With the recent advances in the field of deep learning, learning-based
methods are widely being implemented in various robotic systems that help
robots understand their environment and make informed decisions to achieve a
wide variety of tasks or goals. However, learning-based methods have repeatedly
been shown to have poor generalization when they are presented with inputs that
are different from those during training leading to the problem of distribution
shift. Any robotic system that employs learning-based methods is prone to
distribution shift which might lead the agents to make decisions that lead to
degraded performance or even catastrophic failure. In this paper, we discuss
various techniques that have been proposed in the literature to aid or improve
decision making under distribution shift for robotic systems. We present a
taxonomy of existing literature and present a survey of existing approaches in
the area based on this taxonomy. Finally, we also identify a few open problems
in the area that could serve as future directions for research.


title:
On the Calibration of Pre-trained Language Models using Mixup Guided by
  Area Under the Margin and Saliency

abstract:
  A well-calibrated neural model produces confidence (probability outputs)
closely approximated by the expected accuracy. While prior studies have shown
that mixup training as a data augmentation technique can improve model
calibration on image classification tasks, little is known about using mixup
for model calibration on natural language understanding (NLU) tasks. In this
paper, we explore mixup for model calibration on several NLU tasks and propose
a novel mixup strategy for pre-trained language models that improves model
calibration further. Our proposed mixup is guided by both the Area Under the
Margin (AUM) statistic (Pleiss et al., 2020) and the saliency map of each
sample (Simonyan et al.,2013). Moreover, we combine our mixup strategy with
model miscalibration correction techniques (i.e., label smoothing and
temperature scaling) and provide detailed analyses of their impact on our
proposed mixup. We focus on systematically designing experiments on three NLU
tasks: natural language inference, paraphrase detection, and commonsense
reasoning. Our method achieves the lowest expected calibration error compared
to strong baselines on both in-domain and out-of-domain test samples while
maintaining competitive accuracy.


title: Quantifying Energetic and Entropic Pathways in Molecular Systems

abstract:
  When examining dynamics occurring at non-zero temperatures, both energy and
entropy must be taken into account while describing activated barrier crossing
events. Furthermore, good reaction coordinates need to be constructed to
describe different metastable states and the transition mechanisms between
them. Here we use a physics-based machine learning method called the State
Predictive Information Bottleneck (SPIB) to find non-linear reaction
coordinates for three systems of varying complexity. The SPIB is able to
predict correctly an entropic bottleneck for an analytical flat-energy
double-well system and identify the entropy- and energy-dominated pathways for
an analytical four-well system. Finally, for a simulation of benzoic acid
permeation through a lipid bilayer, SPIB is able to discover the entropic and
energetic barriers to the permeation process. Given these results, we thus
establish that SPIB is a reasonable and robust method for finding the important
entropy and energy/enthalpy barriers in physical systems, which can then be
used for enhanced understanding and sampling of different activated mechanisms.


title: Toward the Detection of Polyglot Files

abstract:
  Standardized file formats play a key role in the development and use of
computer software. However, it is possible to abuse standardized file formats
by creating a file that is valid in multiple file formats. The resulting
polyglot (many languages) file can confound file format identification,
allowing elements of the file to evade analysis.This is especially problematic
for malware detection systems that rely on file format identification for
feature extraction. File format identification processes that depend on file
signatures can be easily evaded thanks to flexibility in the format
specifications of certain file formats. Although work has been done to identify
file formats using more comprehensive methods than file signatures, accurate
identification of polyglot files remains an open problem. Since malware
detection systems routinely perform file format-specific feature extraction,
polyglot files need to be filtered out prior to ingestion by these systems.
Otherwise, malicious content could pass through undetected. To address the
problem of polyglot detection we assembled a data set using the mitra tool. We
then evaluated the performance of the most commonly used file identification
tool, file. Finally, we demonstrated the accuracy, precision, recall and F1
score of a range of machine and deep learning models. Malconv2 and Catboost
demonstrated the highest recall on our data set with 95.16% and 95.45%,
respectively. These models can be incorporated into a malware detector's file
processing pipeline to filter out potentially malicious polyglots before file
format-dependent feature extraction takes place.


title: Safe adaptation in multiagent competition

abstract:
  Achieving the capability of adapting to ever-changing environments is a
critical step towards building fully autonomous robots that operate safely in
complicated scenarios. In multiagent competitive scenarios, agents may have to
adapt to new opponents with previously unseen behaviors by learning from the
interaction experiences between the ego-agent and the opponent. However, this
adaptation is susceptible to opponent exploitation. As the ego-agent updates
its own behavior to exploit the opponent, its own behavior could become more
exploitable as a result of overfitting to this specific opponent's behavior. To
overcome this difficulty, we developed a safe adaptation approach in which the
ego-agent is trained against a regularized opponent model, which effectively
avoids overfitting and consequently improves the robustness of the ego-agent's
policy. We evaluated our approach in the Mujoco domain with two competing
agents. The experiment results suggest that our approach effectively achieves
both adaptation to the specific opponent that the ego-agent is interacting with
and maintaining low exploitability to other possible opponent exploitation.


title: Neutron size from high energy scattering data

abstract:
  Alvarado, Aranda, and Bonilla propose a sub-GeV U(1)R gauge boson model to
explain the proton charge radius discrepancy. Their model assumes opposite sign
U(1)R charge assignments for up and down quarks in the nucleon. I point out
that might cause a difference between neutron and proton mass radii that
contradicts existing p-p and n-p scattering data. Using existing neutron and
proton scattering data I show that the neutron and proton mass radii are equal
within about 2%. This constitutes a challenge to models that attempt to explain
the discrepancy by modifying the quark couplings.


title: Belle II grid-based user analysis

abstract:
  The Belle II experiment at the SuperKEKB accelerator is a next-generation
B-factory aiming to collect 50 ab$^{-1}$, about 50 times the data collected at
Belle, to study rare processes and make precision measurements that may expose
physics beyond the Standard Model. Corresponding to roughly 100 PB of storage
for raw data, plus dozens of PBs per year for Monte Carlo (MC) and analysis
data, these massive samples require careful planning for the storage,
processing, and analysis of data. This white paper notes some of the challenges
that await grid-based user-analysis at the intensity frontier and invites
further discussion and exploration to improve the tools and techniques
necessary to leverage the massive data samples that will be available at Belle
II as part of the Snowmass process.


title:
An asymptotically compatible coupling formulation for nonlocal interface
  problems with jumps

abstract:
  We introduce a mathematically rigorous formulation for a nonlocal interface
problem with jumps and propose an asymptotically compatible finite element
discretization for the weak form of the interface problem. After proving the
well-posedness of the weak form, we demonstrate that solutions to the nonlocal
interface problem converge to the corresponding local counterpart when the
nonlocal data are appropriately prescribed. Several numerical tests in one and
two dimensions show the applicability of our technique, its numerical
convergence to exact nonlocal solutions, its convergence to the local limit
when the horizons vanish, and its robustness with respect to the patch test.


title:
ETH-tight algorithms for finding surfaces in simplicial complexes of
  bounded treewidth

abstract:
  Given a simplicial complex with $n$ simplices, we consider the Connected
Subsurface Recognition (c-SR) problem of finding a subcomplex that is
homeomorphic to a given connected surface with a fixed boundary. We also study
the related Sum-of-Genus Subsurface Recognition (SoG) problem, where we instead
search for a surface whose boundary, number of connected components, and total
genus are given.
  For both of these problems, we give parameterized algorithms with respect to
the treewidth $k$ of the Hasse diagram that run in $2^{O(k \log k)}n^{O(1)}$
time. For the SoG problem, we also prove that our algorithm is optimal assuming
the exponential-time hypothesis. In fact, we prove the stronger result that our
algorithm is ETH-tight even without restriction on the total genus.


title: Testing a Drop of Liquid Using Smartphone LiDAR

abstract:
  We present the first system to determine fluid properties using the LiDAR
sensors present on modern smartphones. Traditional methods of measuring
properties like viscosity require expensive laboratory equipment or a
relatively large amount of fluid. In contrast, our smartphone-based method is
accessible, contactless and works with just a single drop of liquid. Our design
works by targeting a coherent LiDAR beam from the phone onto the liquid. Using
the phone's camera, we capture the characteristic laser speckle pattern that is
formed by the interference of light reflecting from light-scattering particles.
By correlating the fluctuations in speckle intensity over time, we can
characterize the Brownian motion within the liquid which is correlated with its
viscosity. The speckle pattern can be captured on a range of phone cameras and
does not require external magnifiers. Our results show that we can distinguish
between different fat contents as well as identify adulterated milk. Further,
algorithms can classify between ten different liquids using the smartphone
LiDAR speckle patterns. Finally, we conducted a clinical study with whole blood
samples across 30 patients showing that our approach can distinguish between
coagulated and uncoagulated blood using a single drop of blood.


title: The g-Drazin invertibility in a Banach algebra

abstract:
  We present necessary and sufficient conditions under which the
anti-triangular matrix $\left(
  \begin{array}{cc}
  a&b
  1&0
  \end{array} \right)$ over a Banach algebra has g-Drazin inverse. New additive
results for g-Drazin inverse are obtained. Then we apply our results to
$2\times 2$ operator matrices and generalize many known results,
e.g.,~\cite[Theorem 2.2]{D}, ~\cite[Theorem 2.1]{YL} and \cite[Theorem 4.1]{Y}.


title: Mu2e-II: Muon to electron conversion with PIP-II

abstract:
  An observation of Charged Lepton Flavor Violation (CLFV) would be unambiguous
evidence for physics beyond the Standard Model. The Mu2e and COMET experiments,
under construction, are designed to push the sensitivity to CLFV in the mu to e
conversion process to unprecedented levels. Whether conversion is observed or
not, there is a strong case to be made for further improving sensitivity, or
for examining the process on additional target materials. Mu2e-II is a proposed
upgrade to Mu2e, with at least an additional order of magnitude in sensitivity
to the conversion rate over Mu2e. The approach and challenges for this proposal
are summarized. Mu2e-II may be regarded as the next logical step in a continued
high-intensity muon program at FNAL.


title: A quasar shedding its dust cocoon at redshift 2

abstract:
  We present the first near-IR spectroscopy and joint analyses of
multi-wavelength observations for SDSS J082747.14+425241.1, a dust-reddened,
weak broad emission-line quasar (WLQ) undergoing a remarkable broad absorption
line (BAL) transformation. The systemic redshift is more precisely measured to
be $z=2.070\pm0.001$ using H$\beta$ compared to $z=2.040\pm0.003$ using \mgii\
from the literature, signifying an extreme \mgii\ blueshift of $2140\pm530$
\kms\ relative to H$\beta$. Using the H$\beta$-based single-epoch scaling
relation with a systematic uncertainty of 0.3 dex, its black hole (BH) mass and
Eddington ratio are estimated to be $M_{\rm BH}\sim6.1\times10^8M_\odot$ and
$\lambda_{\rm Edd}\sim0.71$, indicative of being in a rapidly accreting phase.
Our investigations confirm the WLQ nature and the LoBAL$\rightarrow$HiBAL
transformation, along with a factor of 2 increase in the \mgii+\feii\ emission
strength and a decrease of 0.1 in $E(B-V)$ over two decades. The kinetic power
of this LoBAL wind at $R\sim$15 pc from its BH is estimated to be $\sim$43\% of
the Eddington luminosity, sufficient for quasar feedback upon its host galaxy
albeit with an order-of-magnitude uncertainty. This quasar provides a clear
example of the long-sought scenario where LoBAL quasars are surrounded by dust
cocoons, and wide-angle nuclear winds play a key role in the transition for red
quasars evolving into the commonly seen blue quasars.


title:
Constraining the original composition of the gas forming
  first-generation stars in globular clusters

abstract:
  Disentangling distinct stellar populations along the red-giant branches
(RGBs) of Globular Clusters (GCs) is possible by using the pseudo two-color
diagram dubbed chromosome map (ChM). One of the most intriguing findings is
that the so-called first-generation (1G) stars, characterized by the same
chemical composition of their natal cloud, exhibit extended sequences in the
ChM. Unresolved binaries and internal variations in helium or metallicity have
been suggested to explain this phenomenon. Here, we derive high-precision
Hubble Space Telescope photometry of the GCs NGC6362 and NGC6838 and build
their ChMs. We find that both 1G RGB and main-sequence (MS) stars exhibit wider
ChM sequences than those of second-generation (2G). The evidence of this
feature even among unevolved 1G MS stars indicates that chemical
inhomogeneities are imprinted in the original gas. We introduce a pseudo
two-magnitude diagram to distinguish between helium and metallicity, and
demonstrate that star-to-star metallicity variations are responsible for the
extended 1G sequence. Conversely, binaries provide a minor contribution to the
phenomenon. We estimate that the metallicity variations within 1G stars of 55
GCs range from less than [Fe/H]~0.05 to ~0.30 and mildly correlate with cluster
mass. We exploit these findings to constrain the formation scenarios of
multiple populations showing that they are qualitatively consistent with the
occurrence of multiple generations. In contrast, the fact that 2G stars have
more homogeneous iron content than the 1G challenges the scenarios based on
accretion of material processed in massive 1G stars onto existing protostars.


title:
Measuring the electron neutrino mass using the electron capture decay of
  163Ho

abstract:
  While the mass differences between neutrino mass states are known, their
absolute masses and mass hierarchy have not yet been determined. Determining
the mass of neutrinos provides access to physics beyond the Standard Model and
the resulting value has implications for the growth of large-scale structure in
the universe over cosmic history. Because of the importance of the topic, a
number of efforts are already underway to determine the mass of neutrinos
including direct kinematic measurements and indirect measurements of
astrophysical phenomena that constrain the sum of the mass eigenstates through
models of cosmic evolution. Here, we advocate for a collaborative international
effort to perform a kinematic determination of the effective electron neutrino
mass using calorimetric measurements of the decay of 163Ho. This effort is
justified by the success of current experiments using the technique, its high
benefit-to-cost ratio, the value of approaches with different systematic
errors, and the value of measuring the electron neutrino mass rather than the
electron anti-neutrino mass.


title: Thermodynamic engine powered by anisotropic fluctuations

abstract:
  The purpose of this work is to present the concept of an autonomous
Stirling-like engine powered by anisotropy of thermodynamic fluctuations.
Specifically, simultaneous contact of a thermodynamic system with two heat
baths along coupled degrees of freedom generates torque and circulatory
currents -- an arrangement referred to as a Brownian gyrator. The embodiment
that constitutes the engine includes an inertial wheel to sustain rotary motion
and average out the generated fluctuating torque, ultimately delivering power
to an external load. We detail an electrical model for such an engine that
consists of two resistors in different temperatures and three reactive elements
in the form of variable capacitors. The resistors generate Johnson-Nyquist
current fluctuations that power the engine, while the capacitors generate
driving forces via a coupling of their dielectric material with the inertial
wheel. A proof-of-concept is established via stability analysis to ensure the
existence of a stable periodic orbit generating sustained power output. We
conclude by drawing a connection to the dynamics of a damped pendulum with
constant torque and to those of a macroscopic Stirling engine. The sought
insights aim at nano-engines and biological processes that are similarly
powered by anisotropy in temperature and chemical potentials.

categories: cond-mat.stat-mech cs.SY eess.SY math-ph math.MP


title:
Time-series image denoising of pressure-sensitive paint data by
  projected multivariate singular spectrum analysis

abstract:
  Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement
data, may contain a significant amount of random noise. Thus, in this study, we
investigated a noise-reduction method that combines multivariate singular
spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a
state-space reconstruction technique that utilizes time-delay embedding, and
the low-dimensional representation is achieved by projecting data onto the
singular value decomposition (SVD) basis. The noise-reduction performance of
the proposed method for unsteady PSP data, i.e., the projected MSSA, is
compared with that of the truncated SVD method, one of the most employed
noise-reduction methods. The result shows that the projected MSSA exhibits
better performance in reducing random noise than the truncated SVD method.
Additionally, in contrast to that of the truncated SVD method, the performance
of the projected MSSA is less sensitive to the truncation rank. Furthermore,
the projected MSSA achieves denoising effectively by extracting smooth
trajectories in a state space from noisy input data. Expectedly, the projected
MSSA will be effective for reducing random noise in not only PSP measurement
data, but also various high-dimensional time-series data.


title:
Electron-hole symmetry in quasiparticle spectral weight of cuprates
  observed via infrared and photoemission spectroscopy

abstract:
  We performed an optical spectroscopy study on single crystals of
Pr$_{0.85}$LaCe$_{0.15}$CuO$_{4-\delta}$ (PLCCO) to revisit the electron-hole
asymmetry, which has been understood as a fundamental property of cuprates.
Four differently annealed samples - as-grown, reduced, optimally oxygenated,
and over-oxygenated samples - were prepared, which have superconducting
transition temperatures, $T_c$ = 0, 15, 24, and 18 K, respectively. We observed
that low-energy quasiparticle spectral weights of all the PLCCO samples are
significantly small in comparison with those of other electron-doped cuprate
families. Instead, they are rather close to those of hole-doped counterpart
La$_{2-x}$Sr$_x$CuO$_4$ (LSCO). Accordingly, estimated effective carrier
numbers per Cu atom ($N_{\mathrm{eff}}$/Cu) of superconducting samples are also
considerably small, despite their relatively high critical temperatures.
Complementary photoemission study reveals that the low-energy quasiparticle
spectral weight of PLCCO is much smaller than that of
Nd$_{1.85}$Ce$_{0.15}$CuO$_{4-\delta}$ (NCCO), consistent with the optical
results. Our observations demonstrate that PLCCO provides the electron-hole
symmetry in quasiparticle spectral weight, and highlight the importance of
Cu3$d$-O2$p$ hybridization to understand the low-energy spectral weight
transfer in doped cuprates.


title: Some more results on relativized Chaitin's $\Omega$

abstract:
  We prove that, assuming $\mathrm{ZF}$, and restricted to any pointed set,
Chaitin's $\Omega_U:x\mapsto
\Omega_U^x=\sum_{U^x(\sigma)\downarrow}2^{-|\sigma|}$ is not injective for any
universal prefix-free Turing machine $U$, and that $\Omega_U^x$ fails to be
degree invariant in a very strong sense, answering several recent questions in
descriptive set theory.


title: Efficient and Optimal Fixed-Time Regret with Two Experts

abstract:
  Prediction with expert advice is a foundational problem in online learning.
In instances with $T$ rounds and $n$ experts, the classical Multiplicative
Weights Update method suffers at most $\sqrt{(T/2)\ln n}$ regret when $T$ is
known beforehand. Moreover, this is asymptotically optimal when both $T$ and
$n$ grow to infinity. However, when the number of experts $n$ is small/fixed,
algorithms with better regret guarantees exist. Cover showed in 1967 a dynamic
programming algorithm for the two-experts problem restricted to $\{0,1\}$ costs
that suffers at most $\sqrt{T/2\pi} + O(1)$ regret with $O(T^2)$ pre-processing
time. In this work, we propose an optimal algorithm for prediction with two
experts' advice that works even for costs in $[0,1]$ and with $O(1)$ processing
time per turn. Our algorithm builds up on recent work on the experts problem
based on techniques and tools from stochastic calculus.


title: Long-diagonal pentagram maps

abstract:
  The pentagram map on polygons in the projective plane was introduced by R.
Schwartz in 1992 and is by now one of the most popular and classical discrete
integrable systems. In the present paper we introduce and prove integrability
of long-diagonal pentagram maps on polygons in $\mathbb{R}\mathrm{P}^d$,
encompassing all known integrable cases. We also establish an equivalence of
long-diagonal and bi-diagonal maps and present a simple self-contained
construction of the Lax form for both. Finally, we prove the continuous limit
of all these maps is equivalent to the $ (2,d+1)$-KdV equation, generalizing
the Boussinesq equation for $d=2$.


title:
Radiation Burst by Axion Star Collision with Star in the Andromeda
  Galaxy

abstract:
  Axion is a promising candidate of dark matter in the universe. A fraction of
dark matter axion may forms axion star with radius $\sim 10^2$km. We show that
the axion star emits radiation burst by the collision with K and M types main
sequence star in the Andromeda Galaxy. The emission arises in the atmosphere of
the star, in which electrons coherently oscillate due to oscillating electric
field of the axion star. The electric field is produced under magnetic field
$B$ of the star. We estimate the flux density of the radiation $\sim 1.6\times
10^{-3}\mbox{Jy}
(10^{-12}M_{\odot}/M_a)^2(10^{-5}\mbox{eV}/m_a)^3(B/10^2\mbox{G})^2\sqrt{3\times10^3\mbox{K}/T}$
and the rate of the collision per hour $\sim
0.06/\mbox{hour}\,(10^{-12}M_{\odot}/M_a)$ in the galaxy, where $M_a$ ( $m_a$ )
denotes the mass of axion star ( axion ) and $T$ does temperature of the
electrons. We assume the number $10^{11}$ of the stars with $B\sim 10^{2}$G and
radius $\sim 3.5\times10^{5}$km in the galaxy. We also assume that a half of
the dark matter is composed of axion star. We show that the emission of the
radiation burst only arises in the atmosphere in which the plasma frequency
$m_p\simeq m_a$. The duration of the burst lasts for the period which it takes
the axion star to pass the region with $m_p\simeq m_a$. It would be longer than
$1$ second.


title:
TSM: Measuring the Enticement of Honeyfiles with Natural Language
  Processing

abstract:
  Honeyfile deployment is a useful breach detection method in cyber deception
that can also inform defenders about the intent and interests of intruders and
malicious insiders. A key property of a honeyfile, enticement, is the extent to
which the file can attract an intruder to interact with it. We introduce a
novel metric, Topic Semantic Matching (TSM), which uses topic modelling to
represent files in the repository and semantic matching in an embedding vector
space to compare honeyfile text and topic words robustly. We also present a
honeyfile corpus created with different Natural Language Processing (NLP)
methods. Experiments show that TSM is effective in inter-corpus comparisons and
is a promising tool to measure the enticement of honeyfiles. TSM is the first
measure to use NLP techniques to quantify the enticement of honeyfile content
that compares the essential topical content of local contexts to honeyfiles and
is robust to paraphrasing.


title: A Sampling Lemma for unbounded kernels

abstract:
  We present some preliminary results on a First Sampling lemma for unbounded
kernels, showing that with high probability, the cut norm of an $L^m$-kernel
$U$ ($4\leq m<\infty$) and the cut norm of a random $k$-sample have a
difference of order $O(k^{-1/4+\gamma/2}(\ln k)^{1/4})$ provided $m\gamma>2$.


title: Group invertibility of the sum in rings and its applications

abstract:
  We present new additive results for the group invertibility in a ring. Then
we apply our results to block operator matrices over Banach spaces and derive
the existence of group inverses of $2\times 2$ block operator matrices. These
generalize many known results, e.g., Benitez, Liu and Zhu(Linear Multilinear
Algebra, {\bf 59}(2011), 279--289) and Zhou, Chen and Zhu(Comm. Algebra, {\bf
48}(2020),676-690).


title: Distributed Coded Modulation Schemes for Multiple Access Relay Channels

abstract:
  In this paper, we investigate network nest coded modulation schemes for
multiple access relay channels. The performance of the distributed systems
which are based on distributed convolutional codes with network coded
modulation is presented. An analytical upper bound on bit error probability
performance for the studied distributed systems with Maximum Likelihood
Sequence Detection (MLSD) is derived. The constructured bounds for the
investigated systems are shown to be asymptotically tight for increasing
channel Signal-to-Noise Ratio (SNR) values


title: Chains, Koch Chains, and Point Sets with many Triangulations

abstract:
  We introduce the abstract notion of a chain, which is a sequence of $n$
points in the plane, ordered by $x$-coordinates, so that the edge between any
two consecutive points is unavoidable as far as triangulations are concerned. A
general theory of the structural properties of chains is developed, alongside a
general understanding of their number of triangulations.
  We also describe an intriguing new and concrete configuration, which we call
the Koch chain due to its similarities to the Koch curve. A specific
construction based on Koch chains is then shown to have $\Omega(9.08^n)$
triangulations. This is a significant improvement over the previous and
long-standing lower bound of $\Omega(8.65^n)$ for the maximum number of
triangulations of planar point sets.


title: Accelerating Stochastic Probabilistic Inference

abstract:
  Recently, Stochastic Variational Inference (SVI) has been increasingly
attractive thanks to its ability to find good posterior approximations of
probabilistic models. It optimizes the variational objective with stochastic
optimization, following noisy estimates of the natural gradient. However,
almost all the state-of-the-art SVI algorithms are based on first-order
optimization algorithm and often suffer from poor convergence rate. In this
paper, we bridge the gap between second-order methods and stochastic
variational inference by proposing a second-order based stochastic variational
inference approach. In particular, firstly we derive the Hessian matrix of the
variational objective. Then we devise two numerical schemes to implement
second-order SVI efficiently. Thorough empirical evaluations are investigated
on both synthetic and real dataset to backup both the effectiveness and
efficiency of the proposed approach.


title: Long Document Summarization with Top-down and Bottom-up Inference

abstract:
  Text summarization aims to condense long documents and retain key
information. Critical to the success of a summarization model is the faithful
inference of latent representations of words or tokens in the source documents.
Most recent models infer the latent representations with a transformer encoder,
which is purely bottom-up. Also, self-attention-based inference models face the
challenge of quadratic complexity with respect to sequence length. We propose a
principled inference framework to improve summarization models on these two
aspects. Our framework assumes a hierarchical latent structure of a document
where the top-level captures the long range dependency at a coarser time scale
and the bottom token level preserves the details. Critically, this hierarchical
structure enables token representations to be updated in both a bottom-up and
top-down manner. In the bottom-up pass, token representations are inferred with
local self-attention to leverage its efficiency. Top-down correction is then
applied to allow tokens to capture long-range dependency. We demonstrate the
effectiveness of the proposed framework on a diverse set of summarization
datasets, including narrative, conversational, scientific documents and news.
Our model achieves (1) competitive or better performance on short documents
with higher memory and compute efficiency, compared to full attention
transformers, and (2) state-of-the-art performance on a wide range of long
document summarization benchmarks, compared to recent efficient transformers.
We also show that our model can summarize an entire book and achieve
competitive performance using $0.27\%$ parameters (464M vs. 175B) and much less
training data, compared to a recent GPT-3-based model. These results indicate
the general applicability and benefits of the proposed framework.


title: Further swampland constraint on Dirac Neutrino

abstract:
  Recent studies on swampland conjectures (e.g. non-SUSY AdS conjecture or AdS
distance conjecture) predict that the (lightest) neutrino must be Dirac and the
mass must be cosmologically small $m < c \Lambda^{1/4}_{\text{cc}}\sim 10 \
\text{meV}$. The Dirac neutrino naturally accompanies a $U(1)$ symmetry that
can be embedded in the anomaly-free $U(1)_{B-L}$ global symmetry of the
standard model. We point out that the swampland conjectures applied to a circle
compactification with the $U(1)$ symmetry twisting lead to further constraints.
In particular, the $U(1)$ symmetry must be broken down to
$\mathbb{Z}_4$,$\mathbb{Z}_8$ or $\mathbb{Z}_{10}$ in the case of normal
hierarchy, and $\mathbb{Z}_4$ in the case of inverted hierarchy, providing
evidence for the absence of continuous global symmetry in quantum gravity. We
also predict a more constrained upper bound of the neutrino mass for each case.


title: When Cell-Free Massive MIMO Meets OTFS Modulation: The Downlink Case

abstract:
  We provide a performance evaluation of orthogonal time frequency space (OTFS)
modulation in cell-free massive MIMO (multiple-input multiple-output) systems.
By leveraging the inherent sparsity of the delay-Doppler (DD) representation of
time-varying channels, we apply the embedded pilot-aided channel estimation
method with reduced guard intervals and derive the minimum mean-square error
estimate of the channel gains from received uplink pilots at the access points
(APs). Each AP applies conjugate beamforming to transmit data to the users. We
derive a closed-form expression for the individual user downlink throughput as
a function of the numbers of APs, users and DD channel estimate parameters. We
compare the OTFS performance with that of orthogonal frequency division
multiplexing (OFDM) at high-mobility conditions. Our findings reveal that with
uncorrelated shadowing, cell-free massive MIMO with OTFS modulation achieves up
to 35% gain in 95%-likely per-user throughput, compared with the OFDM
counterpart. Finally, the increase in the per user throughput is more
pronounced at the median rates over the correlated shadowing scenarios.


title: Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking

abstract:
  Recently, work on reinforcement learning (RL) for bipedal robots has
successfully learned controllers for a variety of dynamic gaits with robust
sim-to-real demonstrations. In order to maintain balance, the learned
controllers have full freedom of where to place the feet, resulting in highly
robust gaits. In the real world however, the environment will often impose
constraints on the feasible footstep locations, typically identified by
perception systems. Unfortunately, most demonstrated RL controllers on bipedal
robots do not allow for specifying and responding to such constraints. This
missing control interface greatly limits the real-world application of current
RL controllers. In this paper, we aim to maintain the robust and dynamic nature
of learned gaits while also respecting footstep constraints imposed externally.
We develop an RL formulation for training dynamic gait controllers that can
respond to specified touchdown locations. We then successfully demonstrate
simulation and sim-to-real performance on the bipedal robot Cassie. In
addition, we use supervised learning to induce a transition model for
accurately predicting the next touchdown locations that the controller can
achieve given the robot's proprioceptive observations. This model paves the way
for integrating the learned controller into a full-order robot locomotion
planner that robustly satisfies both balance and environmental constraints.


title: Scaling limit for determinantal point processes on spheres

abstract:
  The unitary group with the Haar probability measure is called Circular
Unitary Ensemble. All the eigenvalues lie on the unit circle in the complex
plane and they can be regarded as a determinantal point process on
$\mathbb{S}^1$. It is also known that the scaled point processes converge
weakly to the determinantal point process associated with the so-called sine
kernel as the size of matrices tends to $\infty$. We extend this result to the
case of high-dimensional spheres and show that the scaling limit processes are
determinantal point processes associated with the kernels expressed by the
Bessel functions of the first kind.


title:
Spatiotemporal continuous estimates of daily 1-km PM2.5 from 2000 to
  present under the Tracking Air Pollution in China (TAP) framework

abstract:
  High spatial resolution PM2.5 data products covering a long time period are
urgently needed to support population exposure assessment and refined air
quality management. In this study, we provided complete-coverage PM2.5
predictions with a 1-km spatial resolution from 2000 to the present under the
Tracking Air Pollution in China (TAP) framework. To support model fitting at a
high spatial resolution, we collected PM2.5 measurements from both national and
local monitoring stations that better described the PM2.5 distributions at the
local scale. To correctly reflect the temporal variations in land cover
characteristics that significantly affected the local variations in PM2.5
levels, we constructed continuous annual geoinformation datasets, including the
annual road distribution maps and ensemble gridded population distribution maps
in China from 2000 to 2021. We also examined various model structures and
predictor combinations to balance the computational cost and model performance.
The final model fused 0.1-degree TAP PM2.5 predictions, multi-angle
implementation of atmospheric correction (MAIAC) aerosol optical depth
retrievals and land use parameters with a random forest model. The land use
parameters correctly reflected the infrastructure development and population
migration in China. Our annual model had an out-of-bag R2 ranging between 0.80
and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. The
high-resolution daily and annual pollution maps successfully revealed the
local-scale spatial variations in PM2.5 distributions that could not be
characterized by the previous 0.1-degree resolution PM2.5 data. This
open-access 1-km resolution PM2.5 data product with complete coverage could
benefit environmental studies and policy-making.


title:
A classification of finite $p$-groups with a unique
  $\mathcal{A}_2$-subgroup

abstract:
  Finite $p$-groups with a unique $\mathcal{A}_2$-subgroup are classified up to
isomorphism. A problem proposed by Berkovich and Janko is solved.


title: Distraction is All You Need for Fairness

abstract:
  With the recent growth in artificial intelligence models and its expanding
role in automated decision making, ensuring that these models are not biased is
of vital importance. There is an abundance of evidence suggesting that these
models could contain or even amplify the bias present in the data on which they
are trained, inherent to their objective function and learning algorithms. In
this paper, we propose a novel classification algorithm that improves fairness,
while maintaining accuracy of the predictions. Utilizing the embedding layer of
a pre-trained classifier for the protected attributes, the network uses an
attention layer to distract the classification from depending on the protected
attribute in its predictions. We compare our model with six state-of-the-art
methodologies proposed in fairness literature, and show that the model is
superior to those methods in terms of minimizing bias while maintaining
accuracy.


title:
Quasinormal Modes and Hawking Radiation Sparsity of GUP corrected Black
  Holes in Bumblebee Gravity with Topological Defects

abstract:
  We have obtained the Generalized Uncertainty Principle (GUP) corrected de
Sitter and anti-de Sitter black hole solutions in bumblebee gravity with a
topological defect. We have calculated the scalar, electromagnetic and
gravitational quasinormal modes for the both vanishing and non-vanishing
effective cosmological constant using Pad\'e averaged sixth order WKB
approximation method. Apart from this, the time evolutions for all three
perturbations are studied, and quasinormal modes are calculated using the time
domain profile. We found that the first order and second order GUP parameters
$\alpha$ and $\beta$, respectively have opposite impacts on the quasinormal
modes. The study also finds that the presence of a global monopole can decrease
the quasinormal frequencies and the decay rate significantly. On the other
hand, Lorentz symmetry violation has noticeable impacts on the quasinormal
frequencies and the decay rate. We have studied the greybody factors, power
spectrum and sparsity of the black hole with the vanishing effective
cosmological constant for all the three perturbations. The presence of Lorentz
symmetry breaking and the GUP parameter $\alpha$ decrease, while other GUP
parameter $\beta$ and the presence of global monopole increase the probability
of Hawking radiation to reach the spatial infinity. The presence of Lorentz
violation can make the black holes less sparse, while the presence of a global
monopole can increase the sparsity of the black holes. Moreover, we have seen
that the black hole area quantization rule is modified by the presence of
Lorentz symmetry breaking.


title:
Local universality of determinantal point processes on Riemannian
  manifolds

abstract:
  We consider the Laplace-Beltrami operator $\Delta_g$ on a smooth, compact
Riemannian manifold $(M,g)$ and the determinantal point process
$\mathcal{X}_{\lambda}$ on $M$ associated with the spectral projection of
$-\Delta_g$ onto the subspace corresponding to the eigenvalues up to
$\lambda^2$. We show that the pull-back of $\mathcal{X}_{\lambda}$ by the
exponential map $\exp_p : T_p^*M \to M$ under a suitable scaling converges
weakly to the universal determinantal point process on $T_p^* M$ as $\lambda
\to \infty$.

categories: math.PR cond-mat.stat-mech math-ph math.MP math.SP


title: Task-Agnostic Robust Representation Learning

abstract:
  It has been reported that deep learning models are extremely vulnerable to
small but intentionally chosen perturbations of its input. In particular, a
deep network, despite its near-optimal accuracy on the clean images, often
mis-classifies an image with a worst-case but humanly imperceptible
perturbation (so-called adversarial examples). To tackle this problem, a great
amount of research has been done to study the training procedure of a network
to improve its robustness. However, most of the research so far has focused on
the case of supervised learning. With the increasing popularity of
self-supervised learning methods, it is also important to study and improve the
robustness of their resulting representation on the downstream tasks. In this
paper, we study the problem of robust representation learning with unlabeled
data in a task-agnostic manner. Specifically, we first derive an upper bound on
the adversarial loss of a prediction model (which is based on the learned
representation) on any downstream task, using its loss on the clean data and a
robustness regularizer. Moreover, the regularizer is task-independent, thus we
propose to minimize it directly during the representation learning phase to
make the downstream prediction model more robust. Extensive experiments show
that our method achieves preferable adversarial performance compared to
relevant baselines.


title: Quantum Finite Automata and Quiver Algebras

abstract:
  We find an application in quantum finite automata for the ideas and results
of [JL21] and [JL22]. We reformulate quantum finite automata with multiple-time
measurements using the algebraic notion of near-ring. This gives a unified
understanding towards quantum computing and deep learning. When the near-ring
comes from a quiver, we have a nice moduli space of computing machines with
metric that can be optimized by gradient descent.


title:
Understanding of coincidence detection in Franson-type nonlocal
  correlations for second-order quantum superposition

abstract:
  Coincidence detection is a key technique used in nonlocal quantum-correlation
measurements to test Bell inequality violation between remotely separated local
detectors. With individual uniform intensity of local measurements, the
nonlocal correlation fringe is a mysterious quantum feature that cannot be
achieved classically. Here, the coincidence detection is coherently
investigated to understand the fundamental physics of the nonlocal correlation
fringe via second-order quantum superposition between selected nonlocal
measurement events. Because of the coherence feature of paired photons, the
coincidence technique modifies the measurement events for the rule of thumb of
indistinguishability between selected measurement bases of paired photons. This
indistinguishability is quantum superposition between nonlocally detected
events resulting from a selected time slot of coincidence, where coherence
between individually measured photons is an absolute condition.


title: Giant Chern number of a Weyl nodal surface without upper limit

abstract:
  Weyl nodes can be classified into zero-dimensional (0D) Weyl points (WPs), 1D
Weyl nodal lines (WNL) and 2D Weyl nodal surfaces (WNS), which possess finite
Chern numbers. Up to date, the largest Chern number of WPs identified in Weyl
semimetals is 4, which is thought to be a maximal value for linearly crossing
points in solids. On the other hand, whether the Chern numbers of
nonzero-dimensional linear crossing Weyl nodal objects have one upper limit is
still an open question. In this work, combining angle-resolved photoemission
spectroscopy with density functional theory calculations, we show that the
chiral crystal AlPt hosts a cube-shaped charged Weyl nodal surface which is
formed by the linear crossings of two singly-degenerate bands. Different to
conventional Weyl nodes, the cube-shaped nodal surface in AlPt is enforced by
nonsymmorphic chiral symmetries and time reversal symmetry rather than
accidental band crossings, and it possesses a giant Chern number |C| = 26.
Moreover, our results and analysis prove that there is no upper limit for the
Chern numbers of such kind 2D Weyl nodal object.


title: Procedural Text Understanding via Scene-Wise Evolution

abstract:
  Procedural text understanding requires machines to reason about entity states
within the dynamical narratives. Current procedural text understanding
approaches are commonly \textbf{entity-wise}, which separately track each
entity and independently predict different states of each entity. Such an
entity-wise paradigm does not consider the interaction between entities and
their states. In this paper, we propose a new \textbf{scene-wise} paradigm for
procedural text understanding, which jointly tracks states of all entities in a
scene-by-scene manner. Based on this paradigm, we propose \textbf{S}cene
\textbf{G}raph \textbf{R}easoner (\textbf{SGR}), which introduces a series of
dynamically evolving scene graphs to jointly formulate the evolution of
entities, states and their associations throughout the narrative. In this way,
the deep interactions between all entities and states can be jointly captured
and simultaneously derived from scene graphs. Experiments show that SGR not
only achieves the new state-of-the-art performance but also significantly
accelerates the speed of reasoning.


title: Automatic HFL(Z) Validity Checking for Program Verification

abstract:
  We propose an automated method for checking the validity of a formula of
HFL(Z), a higher-order logic with fixpoint operators and integers. Combined
with Kobayashi et al.'s reduction from higher-order program verification to
HFL(Z) validity checking, our method yields a fully automated verification
method for temporal properties of higher-order functional programs. We have
implemented our method and obtained promising experimental results.


title:
On-chip ultra-compact hexagonal boron nitride topological ring-resonator
  in visible region

abstract:
  Ultra-compact topological ring-resonators with chirality are important
devices for quantum optics. However, there are limited demonstrations of chiral
resonators, especially in the visible region. We proposed a topological
photonic ring-resonator based on hexagonal boron nitride (hBN) valley photonic
crystal (VPC). The spin-valley locking effect in VPC allows achieving robust
unidirectional transmission of edge states in the visible region (600 nm-650
nm). As a result, a high quality factor (679.3) with a free spectral range of
15.2 nm in the visible region can be achieved in a hBN all-pass filter with a
compact size. In addition, we investigated the transmission properties of hBN
ring-resonators with different shapes and combinations, confirming the
flexibility of designing topological ring-resonators based on this principle.
This design can be readily integrated with quantum photonic chips for broad
applications.


title:
SmartValidator: A Framework for Automatic Identification and
  Classification of Cyber Threat Data

abstract:
  A wide variety of Cyber Threat Information (CTI) is used by Security
Operation Centres (SOCs) to perform validation of security incidents and
alerts. Security experts manually define different types of rules and scripts
based on CTI to perform validation tasks. These rules and scripts need to be
updated continuously due to evolving threats, changing SOCs' requirements and
dynamic nature of CTI. The manual process of updating rules and scripts delays
the response to attacks. To reduce the burden of human experts and accelerate
response, we propose a novel Artificial Intelligence (AI) based framework,
SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to
enable automated validation of alerts. It consists of three layers to perform
the tasks of data collection, model building and alert validation. It projects
the validation task as a classification problem. Instead of building and saving
models for all possible requirements, we propose to automatically construct the
validation models based on SOC's requirements and CTI. We built a Proof of
Concept (PoC) system with eight ML algorithms, two feature engineering
techniques and 18 requirements to investigate the effectiveness and efficiency
of SmartValidator. The evaluation results showed that when prediction models
were built automatically for classifying cyber threat data, the F1-score of
75\% of the models were above 0.8, which indicates adequate performance of the
PoC for use in a real-world organization. The results further showed that
dynamic construction of prediction models required 99\% less models to be built
than pre-building models for all possible requirements. The framework can be
followed by various industries to accelerate and automate the validation of
alerts and incidents based on their CTI and SOC's preferences.


title:
Development of a versatile micro-focused angle-resolved photoemission
  spectroscopy system with Kirkpatrick-Baez mirror optics

abstract:
  Angle-resolved photoemission spectroscopy using a micro-focused beam spot
(micro-ARPES) is becoming a powerful tool to elucidate key electronic states of
exotic quantum materials. We have developed a versatile micro-ARPES system
based on synchrotron radiation beam focused with a Kirkpatrick-Baez mirror
optics. The mirrors are monolithically installed on a stage, which is driven
with five-axes motion, and are vibrationally separated from the ARPES
measurement system. Spatial mapping of the Auphotolithography pattern on Si
signifies the beam spot size of 10 $\mu$m (horizontal) x 12 $\mu$m (vertical)
at the sample position, which is well suited to resolve the fine structure in
local electronic states. Utilization of the micro beam and the high precision
sample motion system enables the accurate spatially resolved band-structure
mapping, as demonstrated by the observation of a small band anomaly associated
with tiny sample bending near the edge of a cleaved topological insulator
single crystal.


title: Online Task Assignment Problems with Reusable Resources

abstract:
  We study online task assignment problem with reusable resources, motivated by
practical applications such as ridesharing, crowdsourcing and job hiring. In
the problem, we are given a set of offline vertices (agents), and, at each
time, an online vertex (task) arrives randomly according to a known
time-dependent distribution. Upon arrival, we assign the task to agents
immediately and irrevocably. The goal of the problem is to maximize the
expected total profit produced by completed tasks. The key features of our
problem are (1) an agent is reusable, i.e., an agent comes back to the market
after completing the assigned task, (2) an agent may reject the assigned task
to stay the market, and (3) a task may accommodate multiple agents. The setting
generalizes that of existing work in which an online task is assigned to one
agent under (1).
  In this paper, we propose an online algorithm that is $1/2$-competitive for
the above setting, which is tight. Moreover, when each agent can reject
assigned tasks at most $\Delta$ times, the algorithm is shown to have the
competitive ratio $\Delta/(3\Delta-1)\geq 1/3$. We also evaluate our proposed
algorithm with numerical experiments.


title:
Distribution of toric periods of modular forms on definite quaternion
  algebras

abstract:
  Let $D$ be a definite quaternion algebra over $\mathbb{Q}$ and $\mathcal{O}$
an Eichler order in $D$ of square-free level. We study distribution of the
toric periods of algebraic modular forms of level $\mathcal{O}$. We focus on
two problems: non-vanishing and sign changes. Firstly, under certain conditions
on $\mathcal{O}$, we prove the non-vanishing of the toric periods for positive
proportion of imaginary quadratic fields. This improves the known lower bounds
toward Goldfeld's conjecture in some cases and provides evidence for similar
non-vanishing conjectures for central values of twisted automorphic
$L$-functions. Secondly, we show that the sequence of toric periods has
infinitely many sign changes. This proves the sign changes of the Fourier
coefficients $\{a(n)\}_n$ of weight 3/2 modular forms, where $n$ ranges over
fundamental discriminants. In the final section, we present numerical
experiments in some cases and formulate several conjectures based on them.


title:
Magnetically hidden state on the ground floor of the magnetic Devil's
  staircase

abstract:
  We investigated the low-temperature and high-field thermodynamic and
ultrasonic properties of SrCu2(BO3)2, which exhibits various plateaux in its
magnetization curve above 27~T, called a magnetic Devil's staircase. The
results of the present study confirm that magnetic crystallization, the first
step of the staircase, occurs above 27~T as a 1st-order transition accompanied
by a sharp singularity in heat capacity $C_p$ and a kink in the elastic
constant. In addition, we observe a thermodynamic anomaly at lower fields
around 26~T, which has not been previously detected by any magnetic probes. At
low temperatures, this magnetically hidden state has a large entropy and does
not exhibit Schottky-type gapped behavior, which suggests the existence of
low-energy collective excitations. Based on our observations and theoretical
predictions, we propose that magnetic quadrupoles form a spin-nematic state
around 26~T as a hidden state on the ground floor of the magnetic Devil's
staircase.


title: Model for collective motion

abstract:
  Collective motion is a manifestation of emergent phenomena in medium-heavy
and heavy nuclei. A relatively large number of constituent nucleons contribute
coherently to nuclear excitations (vibrations, rotations) that are
characterized by large electromagnetic moments and transition rates. Basic
features of collective excitations are reviewed, and a simple model introduced
that describes large-amplitude quadrupole and octupole shape dynamics, as well
as the dynamics of induced fission. Modern implementations of the collective
Hamiltonian model are based on the microscopic framework of energy density
functionals, that provide an accurate global description of nuclear ground
states and collective excitations. Results of illustrative calculations are
discussed in comparison with available data.


title: Periodic oscillations in a 2N-body problem

abstract:
  Hip-Hop solutions of the $2N$-body problem are solutions that satisfy at
every instance of time, that the $2N$ bodies with the same mass $m$, are at the
vertices of two regular $N$-gons, each one of these $N$-gons are at planes that
are equidistant from a fixed plane $\Pi_0$ forming an antiprism. In this paper,
we first prove that for every $N$ and every $m$ there exists a family of
periodic hip-hop solutions. For every solution in these families the oriented
distance to the plane $\Pi_0$, which we call $d(t)$, is an odd function that is
also even with respect to $t=T$ for some $T>0.$ For this reason we call
solutions in these families, double symmetric solutions. By exploring more
carefully our initial set of periodic solutions, we numerically show that some
of the branches stablished in our existence theorem have bifurcations that
produce branches of solutions with the property that the oriented distance
function $d(t)$ is not even with respect to any $T>0$, we call these solutions
single symmetry solutions. We prove that no single symmetry solution is a
choreography. We also display explicit double symmetric solutions that are
choreographies.


title:
Dressed-state control of effective dipolar interaction between
  strongly-coupled solid-state spins

abstract:
  Strong interactions between spins in many-body solid-state quantum system is
a crucial resource for exploring and applying non-classical states. In
particular, electronic spins associated with defects in diamond system are a
leading platform for the study of collective quantum phenomena and for quantum
technology applications. While such solid-state quantum defect systems have the
advantage of scalability and operation under ambient conditions, they face the
key challenge of controlling interactions between the defects spins, since the
defects are spatially fixed inside the host lattice with relative positions
that cannot be well controlled during fabrication. In this work, we present a
dressed-state approach to control the effective dipolar coupling between
solid-state spins; and then demonstrate this scheme experimentally using two
strongly-coupled nitrogen vacancy (NV) centers in diamond. Including Rabi
driving terms between the m$_s$ = 0 and $\pm$1 states in the NV spin
Hamiltonian allows us to turn on and off or tune the effective dipolar coupling
between two NV spins. Through Ramsey spectroscopy, we detect the change of the
effective dipolar field generated by the control NV spin prepared in different
dressed states. To observe the change of interaction dynamics, we then deploy
spin-lock-based polarization transfer measurements via a Hartmann-Hahn matching
condition between two NV spins in different dressed states. We perform
simulations that indicate the promise for this robust scheme to control the
distribution of interaction strengths in strongly-interacting spin systems,
including interaction strength homogenization in a spin ensemble, which can be
a valuable tool for studying non-equilibrium quantum phases and generating high
fidelity multi-spin correlated states for quantum-enhanced sensing.


title:
The Characterizations of Anisotropic Mixed-Norm Hardy Spaces on
  $\mathbb{R}^n$ by Atoms and Molecules

abstract:
  Let $\vec{p}\in(0,\,\infty)^n$, $A$ be an expansive dilation on
$\mathbb{R}^n$,and $H^{\vec{p}}_A({\mathbb {R}}^n)$ be the anisotropic
mixed-norm Hardy space defined via the non-tangential grand maximal function
studied by \cite{hlyy20}. In this paper, the authors establish new atomic and
molecular decompositions of $H^{\vec{p}}_A({\mathbb {R}}^n)$. As an
application, the authors obtain a boundedness criterion for a class of linear
operators from $H^{\vec{p}}_{A}(\mathbb{R}^n)$ to
$H^{\vec{p}}_{A}(\mathbb{R}^n)$. Part of results are still new even in the
classical isotropic setting (in the case $A:=2\mathrm I_{n\times n}$,
${\mathrm{I}}_{n\times n}$ denotes the $n\times n$ unit matrix).


title: Holographic entanglement density for spontaneous symmetry breaking

abstract:
  We investigate the properties of the holographic entanglement entropy of the
systems in which the $U(1)$ or the translational symmetry is broken
\textit{spontaneously}. For this purpose, we define the entanglement density of
the strip-subsystems and examine both the first law of entanglement entropy
(FLEE) and the area theorem. We classify the conditions that FLEE and/or the
area theorem obey and show that such a classification may be useful for
characterizing the systems. We also find universalities from both FLEE and the
area theorem. In the spontaneous symmetry breaking case, FLEE is always obeyed
regardless of the type of symmetry: $U(1)$ or translation. For the
translational symmetry, the area theorem is always violated when the symmetry
is weakly broken, independent of the symmetry breaking patterns (explicit or
spontaneous). We also argue that the $\log$ contribution of the entanglement
entropy from the Goldstone mode may not appear in the strongly coupled systems.


title: CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

abstract:
  We introduce CARETS, a systematic test suite to measure consistency and
robustness of modern VQA models through a series of six fine-grained capability
tests. In contrast to existing VQA test sets, CARETS features balanced question
generation to create pairs of instances to test models, with each pair focusing
on a specific capability such as rephrasing, logical symmetry or image
obfuscation. We evaluate six modern VQA systems on CARETS and identify several
actionable weaknesses in model comprehension, especially with concepts such as
negation, disjunction, or hypernym invariance. Interestingly, even the most
sophisticated models are sensitive to aspects such as swapping the order of
terms in a conjunction or varying the number of answer choices mentioned in the
question. We release CARETS to be used as an extensible tool for evaluating
multi-modal model robustness.


title:
Detector and Beamline Simulation for Next-Generation High Energy Physics
  Experiments

abstract:
  The success of high energy physics programs relies heavily on accurate
detector simulations and beam interaction modeling. The increasingly complex
detector geometries and beam dynamics require sophisticated techniques in order
to meet the demands of current and future experiments. Common software tools
used today are unable to fully utilize modern computational resources, while
data-recording rates are often orders of magnitude larger than what can be
produced via simulation. In this paper, we describe the state, current and
future needs of high energy physics detector and beamline simulations and
related challenges, and we propose a number of possible ways to address them.


title: Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

abstract:
  Recently few-shot segmentation (FSS) has been extensively developed. Most
previous works strive to achieve generalization through the meta-learning
framework derived from classification tasks; however, the trained models are
biased towards the seen classes instead of being ideally class-agnostic, thus
hindering the recognition of new concepts. This paper proposes a fresh and
straightforward insight to alleviate the problem. Specifically, we apply an
additional branch (base learner) to the conventional FSS model (meta learner)
to explicitly identify the targets of base classes, i.e., the regions that do
not need to be segmented. Then, the coarse results output by these two learners
in parallel are adaptively integrated to yield precise segmentation prediction.
Considering the sensitivity of meta learner, we further introduce an adjustment
factor to estimate the scene differences between the input image pairs for
facilitating the model ensemble forecasting. The substantial performance gains
on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our
versatile scheme sets a new state-of-the-art even with two plain learners.
Moreover, in light of the unique nature of the proposed approach, we also
extend it to a more realistic but challenging setting, i.e., generalized FSS,
where the pixels of both base and novel classes are required to be determined.
The source code is available at github.com/chunbolang/BAM.


title: Visualization in virtual reality: a systematic review

abstract:
  Rapidly growing virtual reality (VR) technologies and techniques have gained
importance over the past few years, and academics and practitioners have been
searching for efficient visualizations in VR. To date, emphasis has been on the
employment of game technologies. Despite the growing interest and discussion,
visualization studies have lacked a common baseline in the transition period of
2D visualizations to immersive ones. To this end, the presented study aims to
provide a systematic literature review that explains the state-of-the-art
research and future trends on visualization in virtual reality. The research
framework is grounded in empirical and theoretical works of visualization. We
characterize the reviewed literature based on three dimensions: (a) Connection
with visualization background and theory, (b) Evaluation and design
considerations for virtual reality visualization, and (c) Empirical studies.
The results from this systematic review suggest that: (1) There are only a few
studies that focus on creating standard guidelines for virtual reality, and
each study individually provides a framework or employs previous studies on
traditional 2D visualizations; (2) With the myriad of advantages provided for
visualization and virtual reality, most of the studies prefer to use game
engines; (3) Although game engines are extensively used, they are not
convenient for critical scientific studies; and (4) 3D versions of traditional
statistical visualization techniques, such as bar plots and scatter plots, are
still commonly used in the data visualization context. This systematic review
attempts to add to the literature a clear picture of the emerging contexts,
different elements, and their interdependencies.


title: Analogies of Jacobi's formula

abstract:
  By considering Schwarz's map for the hypergeometric differential equation
with parameters $(a,b,c)=(1/6,1/2,1)$ or $(1/12,5/12,1)$, we give some
analogies of Jacobi's formula $\vartheta_{00}(\tau)^2=
F(1/2,1/2,1;\lambda(\tau))$, where $\vartheta_{00}(\tau)$ and $\lambda(\tau)$
are the theta constant and the lambda function defined on the upper-half plane,
and $F(a,b,c;z)$ is the hypergeometric series defined on the unit disk. As
applications of our formulas, we give several functional equations for
$F(a,b,c;z)$.


title: Do Language Models Plagiarize?

abstract:
  Past literature has illustrated that language models do not fully understand
the context and sensitivity of text and can sometimes memorize phrases or
sentences present in their training sets. In this paper, we investigate whether
they not only memorize but also plagiarize training samples when generating
artificial texts. Our findings support that they, especially GPT-2, reuse
particular pieces of texts from the training corpus with or without
obfuscation. We have four main results: 1) language models with more capacity
plagiarize more; 2) fine-tuned language models demonstrate differing patterns
of plagiarism based on characteristics of auxiliary data; 3) sampling from
truncated language modeling distributions tends to heighten the degree of
plagiarism as opposed to temperature sampling, and 4) plagiarism in language
models can have serious privacy consequences. Overall, our work implies that
future research on neural language models should take precautions to avoid
models plagiarizing their training datasets.


title: Enumeration of $d$-combining Tree-Child Networks

abstract:
  Tree-child networks are one of the most prominent network classes for
modeling evolutionary processes which contain reticulation events. Several
recent studies have addressed counting questions for {\it bicombining
tree-child networks} which are tree-child networks with every reticulation node
having exactly two parents. In this paper, we extend these studies to {\it
$d$-combining tree-child networks} where every reticulation node has now $d\geq
2$ parents. Moreover, we also give results and conjectures on the
distributional behavior of the number of reticulation nodes of a network which
is drawn uniformly at random from the set of all tree-child networks with the
same number of leaves.


title:
Innovations in trigger and data acquisition systems for next-generation
  physics facilities

abstract:
  Data-intensive physics facilities are increasingly reliant on heterogeneous
and large-scale data processing and computational systems in order to collect,
distribute, process, filter, and analyze the ever increasing huge volumes of
data being collected. Moreover, these tasks are often performed in hard
real-time or quasi real-time processing pipelines that place extreme
constraints on various parameters and design choices for those systems.
Consequently, a large number and variety of challenges are faced to design,
construct, and operate such facilities. This is especially true at the energy
and intensity frontiers of particle physics where bandwidths of raw data can
exceed 100 TB/s of heterogeneous, high-dimensional data sourced from 300M+
individual sensors. Data filtering and compression algorithms deployed at these
facilities often operate at the level of 1 part in $10^5$, and once executed,
these algorithms drive the data curation process, further highlighting the
critical roles that these systems have in the physics impact of those
endeavors. This White Paper aims to highlight the challenges that these
facilities face in the design of the trigger and data acquisition
instrumentation and systems, as well as in their installation, commissioning,
integration and operation, and in building the domain knowledge and technical
expertise required to do so.

categories: hep-ex cs.LG cs.SY eess.SY physics.ins-det


title: Practical Detectability for Persistent Lock-Free Data Structures

abstract:
  Persistent memory (PM) is an emerging class of storage technology that
combines the benefits of DRAM and SSD. This characteristic inspires research on
persistent objects in PM with fine-grained concurrency control. Among such
objects, persistent lock-free data structures (DSs) are particularly
interesting thanks to their efficiency and scalability. One of the most widely
used correctness criteria for persistent lock-free DSs is durable
linearizability (Izraelevitz et. al., DISC 2016). However, durable
linearizability is insufficient to use persistent DSs for fault-tolerant
systems requiring exactly-once semantics for storage systems, because we may
not be able to detect whether an operation is performed when a crash occurs.
  We present a practical programming framework for persistent lock-free DSs
with detectability. In contrast to the prior work on such DSs, our framework
supports (1) primitive detectable operations such as space-efficient
compare-and-swap, insertion, and deletion; (2) systematic transformation of
lock-free DSs in DRAM into those in PM requiring modest efforts; (3) comparable
performance with non-detectable DSs by DRAM scratchpad optimization; and (4)
recovery from both full system and thread crashes. The key idea is memento
objects serving as a lightweight, precise, and per-thread checkpoints in PM. As
a case study, we implement lock-free and combining queues and hash tables with
detectability that outperform (and perform comparably) the state-of-the-art DSs
with (and without, respectively) detectability.


title: The International Linear Collider: Report to Snowmass 2021

abstract:
  The International Linear Collider (ILC) is on the table now as a new global
energy-frontier accelerator laboratory taking data in the 2030s. The ILC
addresses key questions for our current understanding of particle physics. It
is based on a proven accelerator technology. Its experiments will challenge the
Standard Model of particle physics and will provide a new window to look beyond
it. This document brings the story of the ILC up to date, emphasizing its
strong physics motivation, its readiness for construction, and the opportunity
it presents to the US and the global particle physics community.


title:
Snowmass2021 Cosmic Frontier White Paper: Calibrations and backgrounds
  for dark matter direct detection

abstract:
  Future dark matter direct detection experiments will reach unprecedented
levels of sensitivity. Achieving this sensitivity will require more precise
models of signal and background rates in future detectors. Improving the
precision of signal and background modeling goes hand-in-hand with novel
calibration techniques that can probe rare processes and lower threshold
detector response. The goal of this white paper is to outline community needs
to meet the background and calibration requirements of next-generation dark
matter direct detection experiments.


title:
Snowmass White Paper: Implications of Quantum Gravity for Particle
  Physics

abstract:
  Quantum gravity places important consistency conditions on low-energy
effective field theory, such as the absence of global symmetries. These may
have important consequences in the search for particle physics beyond the
Standard Model. We review some of these conditions and their phenomenological
implications for the strong CP problem, the weak scale, new gauge interactions,
and cosmology. We also offer some general comments on how these ideas can guide
model building.


title:
On non-monogenic number fields defined by trinomials of type $x^n
  +ax^m+b$

abstract:
  Let $K=\Q(\theta)$ be a number field generated by a complex root $\th$ of a
monic irreducible trinomial $F(x) = x^n+ax^{m}+b \in \Z[x]$. In this paper, we
deal with the problem of the non-monogenity of $K$. More precisely, we provide
some explicit conditions on $a$, $b$, $n$, and $m$ for which $K$ is not
monogenic. As application, we show that there are infinite families of
non-monogenic number fields defined by trinomials of degree $n=2^r\cdot3^k$
with $r$ and $k$ are positive integers. We also give two infinite families of
non-monogenic number fields defined by trinomials of degree $6$. Finally, we
illustrate our results by giving some examples.


title: Monolithic Active Pixel Sensors on CMOS technologies

abstract:
  Collider detectors have taken advantage of the resolution and accuracy of
silicon detectors for at least four decades. Future colliders will need large
areas of silicon sensors for low mass trackers and sampling calorimetry.
Monolithic Active Pixel Sensors (MAPS), in which Si diodes and readout
circuitry are combined in the same pixels, and can be fabricated in some of
standard CMOS processes, are a promising technology for high-granularity and
light detectors. In this paper we review 1) the requirements on MAPS for
trackers and electromagnetic calorimeters (ECal) at future colliders
experiments, 2) the ongoing efforts towards dedicated MAPS for the Electron-Ion
Collider (EIC) at BNL, for which the EIC Silicon Consortium was already
instantiated, and 3) space-born applications for MeV $\gamma$-ray experiments
with MAPS based trackers (AstroPix).


title:
Multilingual Mix: Example Interpolation Improves Multilingual Neural
  Machine Translation

abstract:
  Multilingual neural machine translation models are trained to maximize the
likelihood of a mix of examples drawn from multiple language pairs. The
dominant inductive bias applied to these models is a shared vocabulary and a
shared set of parameters across languages; the inputs and labels corresponding
to examples drawn from different language pairs might still reside in distinct
sub-spaces. In this paper, we introduce multilingual crossover encoder-decoder
(mXEncDec) to fuse language pairs at an instance level. Our approach
interpolates instances from different language pairs into joint `crossover
examples' in order to encourage sharing input and output spaces across
languages. To ensure better fusion of examples in multilingual settings, we
propose several techniques to improve example interpolation across dissimilar
languages under heavy data imbalance. Experiments on a large-scale WMT
multilingual dataset demonstrate that our approach significantly improves
quality on English-to-Many, Many-to-English and zero-shot translation tasks
(from +0.5 BLEU up to +5.5 BLEU points). Results on code-switching sets
demonstrate the capability of our approach to improve model generalization to
out-of-distribution multilingual examples. We also conduct qualitative and
quantitative representation comparisons to analyze the advantages of our
approach at the representation level.


title:
P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose
  Estimation

abstract:
  This paper introduces a novel Pre-trained Spatial Temporal Many-to-One
(P-STMO) model for 2D-to-3D human pose estimation task. To reduce the
difficulty of capturing spatial and temporal information, we divide this task
into two stages: pre-training (Stage I) and fine-tuning (Stage II). In Stage I,
a self-supervised pre-training sub-task, termed masked pose modeling, is
proposed. The human joints in the input sequence are randomly masked in both
spatial and temporal domains. A general form of denoising auto-encoder is
exploited to recover the original 2D poses and the encoder is capable of
capturing spatial and temporal dependencies in this way. In Stage II, the
pre-trained encoder is loaded to STMO model and fine-tuned. The encoder is
followed by a many-to-one frame aggregator to predict the 3D pose in the
current frame. Especially, an MLP block is utilized as the spatial feature
extractor in STMO, which yields better performance than other methods. In
addition, a temporal downsampling strategy is proposed to diminish data
redundancy. Extensive experiments on two benchmarks show that our method
outperforms state-of-the-art methods with fewer parameters and less
computational overhead. For example, our P-STMO model achieves 42.1mm MPJPE on
Human3.6M dataset when using 2D poses from CPN as inputs. Meanwhile, it brings
a 1.5-7.1 times speedup to state-of-the-art methods. Code is available at
https://github.com/paTRICK-swk/P-STMO.


title: Snowmass White Paper: Cosmology at the Theory Frontier

abstract:
  The precision cosmological model describing the origin and expansion history
of the universe, with observed structure seeded at the inflationary cosmic
horizon, demands completion in the ultraviolet and in the infrared. The
dynamics of the cosmic horizon also suggests an associated entropy, again
requiring a microphysical theory. Recent years have seen enormous progress in
understanding the structure of de Sitter space and inflation in string theory,
and of cosmological observables captured by quantum field theory and solvable
deformations thereof. The resulting models admit ongoing observational tests
through measurements of the cosmic microwave background and large-scale
structure, as well as through analyses of theoretical consistency by means of
thought experiments. This paper, prepared for the TF01 and TF09 conveners of
the Snowmass 2021 process, provides a synopsis of this important area, focusing
on ongoing developments and opportunities.


title: An approximate recipe of chromospheric radiative losses for solar flares

abstract:
  Radiative losses in the chromosphere are very important in the energy
balance. There have been efforts to make simple lookup tables for chromospheric
radiative losses in the quiet Sun. During solar flares, the atmospheric
conditions are quite different, and the currently available recipe of Gan &
Fang (1990) is constructed from semi-empirical models. It remains to be
evaluated how these recipes work in flare conditions. We aim to construct an
approximate recipe of chromospheric radiative losses for solar flares. We
follow the method of Carlsson & Leenaarts (2012) to tabulate the optically thin
radiative loss, escape probability, and ionization fraction, while using a grid
of flare models from radiative hydrodynamic simulations as our dataset. We
provide new lookup tables to calculate chromospheric radiative losses for
flares. Compared with previous recipes, our recipe provides a better
approximation to the detailed radiative losses for flares.


title: Coefficient of Performance for Thermoelectric Thomson Device

abstract:
  The Thomson effect induces heat release or absorption under the simultaneous
application of a charge current and a temperature gradient to conductors.
Although a thermoelectric Thomson device can be a simple temperature modulator
without constructing heterojunction structure, in contrast to the Seebeck and
Peltier effects, the thermoelectric performance of the Thomson effect driven by
an external temperature difference has not been formulated so far. Here, we
present an efficiency formula, i.e., coefficient of performance (COP) for the
Thomson device. Based on the formulation, we find that COP of the Thomson
device is characterized not only by the Thomson coefficient but also by the
Seebeck coefficient. We show that the Thomson device can exhibit large COP for
the cooling operation even if the conductor has a small Seebeck coefficient. We
also present a more realistic discussion on COP of the Thomson device with
material parameters. The proposed formula will provide a new guideline for
finding thermoelectric materials.


title:
Graph Representation Learning for Popularity Prediction Problem: A
  Survey

abstract:
  The online social platforms, like Twitter, Facebook, LinkedIn and WeChat,
have grown really fast in last decade and have been one of the most effective
platforms for people to communicate and share information with each other. Due
to the "word of mouth" effects, information usually can spread rapidly on these
social media platforms. Therefore, it is important to study the mechanisms
driving the information diffusion and quantify the consequence of information
spread. A lot of efforts have been focused on this problem to help us better
understand and achieve higher performance in viral marketing and advertising.
On the other hand, the development of neural networks has blossomed in the last
few years, leading to a large number of graph representation learning (GRL)
models. Compared to traditional models, GRL methods are often shown to be more
effective. In this paper, we present a comprehensive review for existing works
using GRL methods for popularity prediction problem, and categorize related
literatures into two big classes, according to their mainly used model and
techniques: embedding-based methods and deep learning methods. Deep learning
method is further classified into six small classes: convolutional neural
networks, graph convolutional networks, graph attention networks, graph neural
networks, recurrent neural networks, and reinforcement learning. We compare the
performance of these different models and discuss their strengths and
limitations. Finally, we outline the challenges and future chances for
popularity prediction problem.


title:
Improving Event Representation via Simultaneous Weakly Supervised
  Contrastive Learning and Clustering

abstract:
  Representations of events described in text are important for various tasks.
In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive
learning and Clustering framework for event representation learning. SWCC
learns event representations by making better use of co-occurrence information
of events. Specifically, we introduce a weakly supervised contrastive learning
method that allows us to consider multiple positives and multiple negatives,
and a prototype-based clustering method that avoids semantically related events
being pulled apart. For model training, SWCC learns representations by
simultaneously performing weakly supervised contrastive learning and
prototype-based clustering. Experimental results show that SWCC outperforms
other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In
addition, a thorough analysis of the prototype-based clustering method
demonstrates that the learned prototype vectors are able to implicitly capture
various relations between events.


title: The extended population associated with W40

abstract:
  W40 is a heavily obscured bipolar HII region projected in the direction of
the Aquila Rift and ionized by hot stars in a central, partly embedded cluster.
The study of the cluster and its surroundings has been greatly hampered thus
far by the strong extinction in the region. We use the Gaia eDR3 catalog to
establish astrometric membership criteria based on the population of the W40
central cluster, reassess the distance of the region, and identify in this way
new members, both inside and outside the cluster. We obtain visible
spectroscopy in the red spectral region to classify both known and new members,
complemented with Gaia and Spitzer photometry to assess the evolutionary status
of the stellar population. We derive a high-confidence geometric distance to
the W40 region of 502 pc $\pm$ 4 pc and confirm the presence of a comoving
extended population of stars at the same distance, spreading over the whole
projected area of the HII region and beyond. Spectral classifications are
presented for 21 members of the W40 region, 10 of them belonging to the central
cluster. One of the newly identified B stars in the extended population is
clearly interacting with the shell surrounding the HII region, giving rise to a
small arc-shaped nebula that traces a bow shock. The infrared excess properties
suggest that the extended population is significantly older ($\sim 3$ Myr) than
the W40 central cluster ($< 1$ Myr). The area currently occupied by the W40 HII
region and its surroundings has a history of star formation extending at least
several million years in the past, of which the formation of the W40 central
cluster and the subsequent HII region is one of the latest episodes. The newly
determined distance suggests that W40 is behind, and physically detached from,
a pervasive large dust layer which is some 60 pc foreground to it as determined
by previous studies.


title:
A theoretical framework and some promising findings of grey wolf
  optimizer, part I: analytical model of sampling distribution and stability
  analysis

abstract:
  This paper proposes a theoretical framework of the grey wolf optimizer (GWO)
based on several interesting theoretical findings, involving sampling
distribution, order-1 and order-2 stability, and global convergence analysis.
In the part I of the paper, the characteristics of the sampling distribution of
the new solution and the probabilistic stability of the GWO are carefully
discussed based on the well-known stagnation assumption for simplification
purposes. Firstly, the characteristics of the sampling distribution of the new
solution, mainly related to the shape of the joint probability density function
(PDF), are discussed under the assumption that the original solution before
updating is constant. Then, the assumption that the original solution is
constant is eliminated to perform the sampling distribution analysis, based on
which several characteristics of the new solution are provided, containing the
shape of the joint PDF and central moments of any positive integer order.
Finally, the order-1 and order-2 stability of the GWO under stagnation
assumption is introduced and proved as the inference of conclusions above,
which are all verified by numerical simulations.


title:
A theoretical framework and some promising findings of grey wolf
  optimizer, part II: global convergence analysis

abstract:
  This paper proposes a theoretical framework of the grey wolf optimizer (GWO)
based on several interesting theoretical findings, involving sampling
distribution, order-1 and order-2 stability, and global convergence analysis.
In the part II of the paper, the global convergence analysis is carried out
based on the well-known stagnation assumption for simplification purposes.
Firstly, the global convergence property of the GWO under stagnation assumption
is abstracted and modelled into two propositions, corresponding to global
searching ability analysis and probability-1 global convergence analysis. Then,
the global searching ability analysis is carried out. Next, based on a
characteristic of the central moments of the new solution of the GWO under
stagnation assumption, the probability-1 global convergence property of the GWO
under stagnation assumption is proved. Finally, all conclusions are verified by
numerical simulations, and the corresponding discussions that the global
convergence property can still be guaranteed in the original GWO without
stagnation assumption are given.


title: Lifelong Matrix Completion with Sparsity-Number

abstract:
  Matrix completion problem has been previously studied under various adaptive
and passive settings. Previously, researchers have proposed passive, two-phase
and single-phase algorithms using coherence parameter, and multi phase
algorithm using sparsity-number. It has been shown that the method using
sparsity-number reaching to theoretical lower bounds in many conditions.
However, the aforementioned method is running in many phases through the matrix
completion process, therefore it makes much more informative decision at each
stage. Hence, it is natural that the method outperforms previous algorithms. In
this paper, we are using the idea of sparsity-number and propose and
single-phase column space recovery algorithm which can be extended to two-phase
exact matrix completion algorithm. Moreover, we show that these methods are as
efficient as multi-phase matrix recovery algorithm. We provide experimental
evidence to illustrate the performance of our algorithm.


title:
Snowmass2021 Cosmic Frontier: Cosmic Microwave Background Measurements
  White Paper

abstract:
  This is a solicited whitepaper for the Snowmass 2021 community planning
exercise. The paper focuses on measurements and science with the Cosmic
Microwave Background (CMB). The CMB is foundational to our understanding of
modern physics and continues to be a powerful tool driving our understanding of
cosmology and particle physics. In this paper, we outline the broad and unique
impact of CMB science for the High Energy Cosmic Frontier in the upcoming
decade. We also describe the progression of ground-based CMB experiments, which
shows that the community is prepared to develop the key capabilities and
facilities needed to achieve these transformative CMB measurements.

categories: astro-ph.CO astro-ph.IM gr-qc hep-ex hep-ph


title:
Fast and Accurate Linear Fitting for Incompletely Sampled Gaussian
  Function With a Long Tail

abstract:
  Fitting experiment data onto a curve is a common signal processing technique
to extract data features and establish the relationship between variables.
Often, we expect the curve to comply with some analytical function and then
turn data fitting into estimating the unknown parameters of a function. Among
analytical functions for data fitting, Gaussian function is the most widely
used one due to its extensive applications in numerous science and engineering
fields. To name just a few, Gaussian function is highly popular in statistical
signal processing and analysis, thanks to the central limit theorem [1];
Gaussian function frequently appears in the quantum harmonic oscillator,
quantum field theory, optics, lasers, and many other theories and models in
Physics [2]; moreover, Gaussian function is widely applied in chemistry for
depicting molecular orbitals, in computer science for imaging processing and in
artificial intelligence for defining neural networks.