title:
Theoretical Understanding of the Information Flow on Continual Learning
  Performance

abstract:
  Continual learning (CL) is a setting in which an agent has to learn from an
incoming stream of data sequentially. CL performance evaluates the model's
ability to continually learn and solve new problems with incremental available
information over time while retaining previous knowledge. Despite the numerous
previous solutions to bypass the catastrophic forgetting (CF) of previously
seen tasks during the learning process, most of them still suffer significant
forgetting, expensive memory cost, or lack of theoretical understanding of
neural networks' conduct while learning new tasks. While the issue that CL
performance degrades under different training regimes has been extensively
studied empirically, insufficient attention has been paid from a theoretical
angle. In this paper, we establish a probabilistic framework to analyze
information flow through layers in networks for task sequences and its impact
on learning performance. Our objective is to optimize the information
preservation between layers while learning new tasks to manage task-specific
knowledge passing throughout the layers while maintaining model performance on
previous tasks. In particular, we study CL performance's relationship with
information flow in the network to answer the question "How can knowledge of
information flow between layers be used to alleviate CF?". Our analysis
provides novel insights of information adaptation within the layers during the
incremental task learning process. Through our experiments, we provide
empirical evidence and practically highlight the performance improvement across
multiple tasks.


title:
Representation-Aware Experimentation: Group Inequality Analysis for A/B
  Testing and Alerting

abstract:
  As companies adopt increasingly experimentation-driven cultures, it is
crucial to develop methods for understanding any potential unintended
consequences of those experiments. We might have specific questions about those
consequences (did a change increase or decrease gender representation equality
among content creators?); we might also wonder whether if we have not yet
considered the right question (that is, we don't know what we don't know).
Hence we address the problem of unintended consequences in experimentation from
two perspectives: namely, pre-specified vs. data-driven selection, of
dimensions of interest. For a specified dimension, we introduce a statistic to
measure deviation from equal representation (DER statistic), give its
asymptotic distribution, and evaluate finite-sample performance. We explain how
to use this statistic to search across large-scale experimentation systems to
alert us to any extreme unintended consequences on group representation. We
complement this methodology by discussing a search for heterogeneous treatment
effects along a set of dimensions with causal trees, modified slightly for
practicalities in our ecosystem, and used here as a way to dive deeper into
experiments flagged by the DER statistic alerts. We introduce a method for
simulating data that closely mimics observed data at LinkedIn, and evaluate the
performance of DER statistics in simulations. Last, we give a case study from
LinkedIn, and show how these methodologies empowered us to discover surprising
and important insights about group representation. Code for replication is
available in an appendix.


title: Balanced subdivisions of cliques in graphs

abstract:
  Given a graph $H$, a balanced subdivision of $H$ is a graph obtained from $H$
by subdividing every edge the same number of times. In 1984, Thomason
conjectured that for each integer $k\ge 1$, high average degree is sufficient
to guarantee a balanced subdivision of $K_k$. Recently, Liu and Montgomery
resolved this conjecture. We give an optimal estimate up to an absolute
constant factor by showing that there exists $c>0$ such that for sufficiently
large $d$, every graph with average degree at least $d$ contains a balanced
subdivision of a clique with at least $cd^{1/2}$ vertices. It also confirms a
conjecture from Verstra{\"e}te: every graph of average degree $cd^2$, for some
absolute constant $c>0$, contains a pair of disjoint isomorphic subdivisions of
the complete graph $K_d$. We also prove that there exists some absolute $c>0$
such that for sufficiently large $d$, every $C_4$-free graph with average
degree at least $d$ contains a balanced subdivision of the complete graph
$K_{cd}$, which extends a result of Balogh, Liu and Sharifzadeh.


title:
Bamboo: Making Preemptible Instances Resilient for Affordable Training
  of Large DNNs

abstract:
  DNN models across many domains continue to grow in size, resulting in high
resource requirements for effective training, and unpalatable (and often
unaffordable) costs for organizations and research labs across scales. This
paper aims to significantly reduce training costs with effective use of
preemptible instances, i.e., those that can be obtained at a much cheaper price
while idle, but may be preempted whenever requested by priority users. Doing
so, however, requires new forms of resiliency and efficiency to cope with the
possibility of frequent preemptions - a failure model that is drastically
different from the occasional failures in normal cluster settings that existing
checkpointing techniques target.
  We present Bamboo, a distributed system that tackles these challenges by
introducing redundant computations into the training pipeline, i.e., whereby
one node performs computations over not only its own layers but also over some
layers in its neighbor. Our key insight is that training large models often
requires pipeline parallelism where "pipeline bubbles" naturally exist. Bamboo
carefully fills redundant computations into these bubbles, providing resilience
at a low cost. Across a variety of widely used DNN models, Bamboo outperforms
traditional checkpointing by 3.7x in training throughput, and reduces costs by
2.4x compared to a setting where on-demand instances are used.


title: Polarization Identities

abstract:
  We prove a generalization of the polarization identity of linear algebra
expressing the inner product of a complex inner product space in terms of the
norm, where the field of scalars is extended to an associative algebra equipped
with an involution, and polarization is viewed as an averaging operation over a
compact multiplicative subgroup of the scalars. Using this we prove a general
form of the Jordan-von Neumann theorem on characterizing inner product spaces
among normed linear spaces, when the scalars are taken in an associative
algebra.


title: Reassessing the strength of a class of Wigner's friend no-go theorems

abstract:
  Two recent, prominent theorems--the "no-go theorem for observer-independent
facts" and the "Local Friendliness no-go theorem"--employ so-called extended
Wigner's friend scenarios to try to impose novel, non-trivial constraints on
the possible nature of physical reality. While the former is argued to entail
that there can be no theory in which the results of Wigner and his friend can
both be considered objective, the latter is said to place on reality stronger
constraints than the Bell and Kochen-Specker theorems. Here, I conduct a
thorough analysis of these theorems and show that they suffer from a list of
shortcomings that question their validity and limit their strength. I conclude
that the "no-go theorem for observer-independent facts" and the "Local
Friendliness no-go theorem" fail to impose significant constraints on the
nature of physical reality.


title:
Accelerated-gradient-based generalized Levenberg--Marquardt method with
  oracle complexity bound and local quadratic convergence

abstract:
  Many machine learning tasks are formulated as a nonconvex optimization
problem of minimizing the sum of a convex function and a composite function.
The generalized Levenberg--Marquardt (LM) method, also known as the prox-linear
method, has been developed for such problems. The method iteratively solves
strongly convex subproblems with a damping term.
  This study proposes a new generalized LM method for solving the problem with
a smooth composite function. The method enjoys three theoretical guarantees:
iteration complexity bound, oracle complexity bound, and local convergence
under a H\"olderian growth condition. The local convergence results include
local quadratic convergence under the quadratic growth condition; this is the
first to extend a classical result for least-squares problems to a general
smooth composite function. In addition, this is the first LM method with both
an oracle complexity bound and local quadratic convergence under standard
assumptions. These results are achieved by carefully controlling the damping
parameter and solving the subproblems by the accelerated proximal gradient
method equipped with a particular termination condition. Experimental results
show that the proposed method performs well in practice for several instances,
including classification with a neural network and collaborative filtering with
nonnegative matrix factorization.


title: A Review of Searches for Evidence of Tachyons

abstract:
  Here we review empirical evidence for the possible existence of tachyons,
superluminal particles having m^2 < 0: The review considers searches for new
particles that might be tachyons, as well as evidence that neutrinos are
tachyons from data which may have been gathered for other purposes.


title:
Is the new CDF $M_W$ measurement consistent with the two higgs doublet
  model?

abstract:
  Motivated by the new CDF measurement of the $W$ boson mass reported recently
which clearly illustrates a large deviation compared to the Standard Model (SM)
prediction. In the present paper, we study the Two-Higgs Doublet Model (2HDM)
contributions to $M_W$ and its phenomenological implications in the case where
the heavy CP-even $H$ is identified as the observed Higgs boson with a mass of
$125$ GeV. Taking into account theoretical and all the available experimental
constraints as well as the new CDF measurement, we demonstrate that the 2HDM
parameter space can provide a large correction which predicts the W mass close
to the new CDF $M_W$ measurement. It is found that $M_{H\pm}=M_A$ is excluded
and the splitting of the charged Higgs boson with all other states is positive.
We also discuss the consequence on the effective mixing angle
$\sin^2\theta_{\text{eff}}$ as well as the phenomenological implications on the
charged Higgs and CP-odd decays.


title: Mixed-Norm Herz Spaces and Their Applications in Related Hardy Spaces

abstract:
  In this article, the authors introduce a class of mixed-norm Herz spaces,
$\dot{E}^{\vec{\alpha},\vec{p}}_{\vec{q}}(\mathbb{R}^{n})$, which is a natural
generalization of mixed Lebesgue spaces and some special cases of which
naturally appear in the study of the summability of Fourier transforms on
mixed-norm Lebesgue spaces. The authors also give their dual spaces and obtain
the Riesz-Thorin interpolation theorem on
$\dot{E}^{\vec{\alpha},\vec{p}}_{\vec{q}}(\mathbb{R}^{n})$. Applying these
Riesz-Thorin interpolation theorem and using some ideas from the extrapolation
theorem, the authors establish both the boundedness of the Hardy-Littlewood
maximal operator and the Fefferman-Stein vector-valued maximal inequality on
$\dot{E}^{\vec{\alpha},\vec{p}}_{\vec{q}}(\mathbb{R}^{n})$. As applications,
the authors develop various real-variable theory of Hardy spaces associated
with $\dot{E}^{\vec{\alpha},\vec{p}}_{\vec{q}}(\mathbb{R}^{n})$ by using the
existing results of Hardy spaces associated with ball quasi-Banach function
spaces. These results strongly depend on the duality of
$\dot{E}^{\vec{\alpha},\vec{p}}_{\vec{q}}(\mathbb{R}^{n})$ and the non-trivial
constructions of auxiliary functions in the Riesz-Thorin interpolation theorem.


title: Balancing Age-Energy Tradeoff in Sleep-Wake Server Systems

abstract:
  The surging demand for fresh information from various Internet of Things
(IoT) applications requires oceans of data to be transmitted and processed
timely. How to guarantee information freshness while reducing energy
consumption thus becomes imperative. We consider a multi-source single-server
queueing system, where we aim to design the optimal sleep-wake strategy for the
server to reduce its energy consumption while guaranteeing users' information
freshness. We propose a sleep-wake strategy that relies on an idling scheme
called Conditional Sleep (CS) scheme. We show that the proposed CS scheme can
achieve a smaller Age of Information (AoI) than the widely-used Hysteresis Time
(HT) scheme and Bernoulli Sleep (BS) scheme, while retaining the same power
consumption and Peak Age of Information (PAoI). Moreover, we find that
increasing the sleep period length can always reduce energy consumption and
enlarge the PAoI, but it does not always increase AoI. We also find that using
PAoI as the information freshness metric in designing the optimal sleep-wake
strategies would make the server sleep infinitely long. Our analysis reveals
that this result is due to the PAoI being a first-order statistic. We further
extend our discussion to the scenario where data sources choose sampling rates
strategically based on the sleep-wake strategy of the server. We show that
increasing the sleeping period length for the server while guaranteeing users'
PAoI could lead to a minor reduction of the server's energy consumption but
significantly increase the data sources' sampling costs.


title: Integrated Geostationary Solar Energetic Particle Events Catalog: GSEP

abstract:
  We present a catalog of solar energetic particle (SEP) events covering solar
cycles 22, 23 and 24. We correlate and integrate three existing catalogs based
on Geostationary Operational Environmental Satellite (GOES) integral proton
flux data. We visually verified and labeled each event in the catalog to
provide a homogenized data set. We have identified a total of 342 SEP events of
which 246 cross the space weather prediction center (SWPC) threshold of a
significant proton event. The metadata consists of physical parameters and
observables concerning the possible source solar eruptions, namely flares and
coronal mass ejections for each event. The sliced time series data of each
event, along with intensity profiles of proton fluxes in several energy bands,
have been made publicly available. This data set enables researchers in machine
learning (ML) and statistical analysis to understand the SEPs and the source
eruption characteristics useful for space weather prediction.


title: Estimating the Resize Parameter in End-to-end Learned Image Compression

abstract:
  We describe a search-free resizing framework that can further improve the
rate-distortion tradeoff of recent learned image compression models. Our
approach is simple: compose a pair of differentiable downsampling/upsampling
layers that sandwich a neural compression model. To determine resize factors
for different inputs, we utilize another neural network jointly trained with
the compression model, with the end goal of minimizing the rate-distortion
objective. Our results suggest that "compression friendly" downsampled
representations can be quickly determined during encoding by using an auxiliary
network and differentiable image warping. By conducting extensive experimental
tests on existing deep image compression models, we show results that our new
resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate
(BD-rate) improvement of about 10% against leading perceptual quality engines.
We also carried out a subjective quality study, the results of which show that
our new approach yields favorable compressed images. To facilitate reproducible
research in this direction, the implementation used in this paper is being made
freely available online at: https://github.com/treammm/ResizeCompression.


title: A One-Covariate-at-a-Time Method for Nonparametric Additive Models

abstract:
  This paper proposes a one-covariate-at-a-time multiple testing (OCMT)
approach to choose significant variables in high-dimensional nonparametric
additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018),
we consider the statistical significance of individual nonparametric additive
components one at a time and take into account the multiple testing nature of
the problem. One-stage and multiple-stage procedures are both considered. The
former works well in terms of the true positive rate only if the marginal
effects of all signals are strong enough; the latter helps to pick up hidden
signals that have weak marginal effects. Simulations demonstrate the good
finite sample performance of the proposed procedures. As an empirical
application, we use the OCMT procedure on a dataset we extracted from the
Longitudinal Survey on Rural Urban Migration in China. We find that our
procedure works well in terms of the out-of-sample forecast root mean square
errors, compared with competing methods.


title:
Reprint: a randomized extrapolation based on principal components for
  data augmentation

abstract:
  Data scarcity and data imbalance have attracted a lot of attention in many
fields. Data augmentation, explored as an effective approach to tackle them,
can improve the robustness and efficiency of classification models by
generating new samples. This paper presents REPRINT, a simple and effective
hidden-space data augmentation method for imbalanced data classification. Given
hidden-space representations of samples in each class, REPRINT extrapolates, in
a randomized fashion, augmented examples for target class by using subspaces
spanned by principal components to summarize distribution structure of both
source and target class. Consequently, the examples generated would diversify
the target while maintaining the original geometry of target distribution.
Besides, this method involves a label refinement component which allows to
synthesize new soft labels for augmented examples. Compared with different NLP
data augmentation approaches under a range of data imbalanced scenarios on four
text classification benchmark, REPRINT shows prominent improvements. Moreover,
through comprehensive ablation studies, we show that label refinement is better
than label-preserving for augmented examples, and that our method suggests
stable and consistent improvements in terms of suitable choices of principal
components. Moreover, REPRINT is appealing for its easy-to-use since it
contains only one hyperparameter determining the dimension of subspace and
requires low computational resource.


title:
Possible critical behavior and field-induced magnetic transition in
  YbCo$_2$

abstract:
  The first study of electrical resistivity, magnetization and specific heat on
YbCo$_2$ is reported. The measurements on single-phased sample of YbCo$_2$
bring no-evidence of magnetic ordering down to 0.3 K in zero magnetic field.
The manifestations of low Kondo temperature are observed. It can explain
evidently the very large electronic specific heat which places YbCo$_2$ among
heavy fermion systems. Application of magnetic field revealed metamagnetic-like
anomaly and field induced transition-like behavior. Despite the
metamagnetic-like anomaly being weakly pronounced, the temperature-dependent
measurements show obvious field induced-like transition under magnetic field
above 2 T. Further, ferromagnetic character is exhibited as the transition
temperature increases with increasing field. The origin of the observed
properties is discussed in comparison and contrast with other RCo$_2$ and
Yb-based heavy fermion compounds. It indicates that in the unique case of
YbCo$_2$, the itinerant electron magnetism of Co 3d-electrons and the Kondo
effect within the vicinity of quantum criticality of Yb 4f-local moments can
both play a role.


title: BATS: Best Action Trajectory Stitching

abstract:
  The problem of offline reinforcement learning focuses on learning a good
policy from a log of environment interactions. Past efforts for developing
algorithms in this area have revolved around introducing constraints to online
reinforcement learning algorithms to ensure the actions of the learned policy
are constrained to the logged data. In this work, we explore an alternative
approach by planning on the fixed dataset directly. Specifically, we introduce
an algorithm which forms a tabular Markov Decision Process (MDP) over the
logged data by adding new transitions to the dataset. We do this by using
learned dynamics models to plan short trajectories between states. Since exact
value iteration can be performed on this constructed MDP, it becomes easy to
identify which trajectories are advantageous to add to the MDP. Crucially,
since most transitions in this MDP come from the logged data, trajectories from
the MDP can be rolled out for long periods with confidence. We prove that this
property allows one to make upper and lower bounds on the value function up to
appropriate distance metrics. Finally, we demonstrate empirically how
algorithms that uniformly constrain the learned policy to the entire dataset
can result in unwanted behavior, and we show an example in which simply
behavior cloning the optimal policy of the MDP created by our algorithm avoids
this problem.


title:
On Routing, Wavelength, Network Coding Assignment and Protection
  Configuration Problem in Optical-processing-enabled Networks

abstract:
  In optical-processing-enabled network, transitional lightpaths crossing the
same node could be optically encoded to each other to achieve greater spectral
efficiency. In this context, we present a new research problem, entitled,
routing, wavelength, network coding assignment and protection configuration
(RWNCA-PC) arisen in exploiting photonic network coding (NC) for dedicated path
protection in wavelength division multiplexing (WDM) networks with an extra
degree of freedom in the selection of protection triggering mechanism, that is,
network-side and client-side, tailoring to each connection. In order to
maximize the NC benefits, we thus provide a weighted multi-objective
optimization model for solving RWNCA-PC problem so as to minimize the
wavelength count as the strictly prioritized goal and the redundant resources
measured by the number of client-side connections as the secondary objective.
Numerical results on the realistic COST239 network reveal that a saving of up
to $25\%$ wavelength resources could be achieved thanks to the optimal use of
NC compared to the non-coding designs and among coding-aware designs, the use
of mixed protection configurations would be spectrally more efficient than the
design with only network-side protection scheme. Our proposal yields the
highest spectrum efficiency compared to all reference designs and moreover,
features an average saving of more than $40\%$ transponder count compared with
its single objective counterpart.


title: A topologically rigid set of quotients of the Davis complex

abstract:
  A class of topological spaces is topologically rigid if for any pair of
spaces in the class, an isomorphism on the level of fundamental groups induces
a homeomorphism. Topological rigidity, in addition to its intrinsic interest,
has been useful for solving abstract commensurability questions. In this paper,
we explore the topological rigidity of quotients of the Davis complex of
certain right angled Coxeter groups by providing conditions on the defining
graphs that obstruct topological rigidity. Furthermore, we explore why
topological rigidity is hard to achieve for quotients of the Davis complex and
introduce a topologically rigid subclass.


title:
The Fractional Lam\'e-Navier Operator: Appearances, Properties and
  Applications

abstract:
  We introduce and analyze an explicit formulation of fractional powers of the
Lam\'e-Navier system of partial differential operators. We show that this
fractional Lam\'e-Navier operator is a nonlocal integro-differential operator
that appears in several widely-used continuum mechanics models. We demonstrate
that the fractional Lam\'e-Navier operator can be obtained using compositions
of nonlocal gradient operators. Additionally, the effective form of the
fractional Lam\'e-Navier operator is the same as the operator obtained as a
particular choice of parameters in state-based peridynamics. We further show
that the Dirichlet-to-Neumann map associated to the local classical
Lam\'e-Navier system posed in a half-space coincides with the square root power
of the Lam\'e-Navier operator for a particular choice of elastic coefficients.
  We establish basic analysis results for the fractional Lam\'e-Navier
operator, including the calculus of positive and negative powers, and explore
its interaction with the H\"older and Bessel classes of functions. We also
derive the fractional Lam\'e-Navier as the Dirichlet-to-Neumann map of a local
degenerate elliptic system of equations in the upper half-space. We use an
explicit formula for a Poisson kernel for the extension system to establish the
well-posedness in weighted Sobolev spaces. As an application, we derive the
equivalence of two fractional seminorms using a purely local argument in the
extension system, and then use this equivalence to obtain well-posedness for a
variational Dirichlet problem associated to the fractional Lam\'e-Navier
operator.


title: Wavelet analysis of MAXI J1535-571 with Insight-HXMT

abstract:
  In this paper, wavelet analysis is used to study spectral-timing properties
of MAXI J1535-571 observed by Insight-HXMT. The low-frequency quasi-periodic
oscillations (QPOs) are detected in nine observations. Based on wavelet
analysis, the time intervals with QPO and non-QPO are isolated separately, and
the corresponding spectra with QPO and non-QPO are analyzed. We find that the
spectra with QPO (hereafter QPO spectra) are softer than those without QPO
(hereafter non-QPO spectra) in the hard intermediate state (HIMS). While in the
soft intermediate state (SIMS), the QPO spectra are slightly harder. The disk
temperature of QPO regime is slightly lower during HIMS, but becomes higher
during SIMS. The cutoff energies of QPO spectra and non-QPO spectra do not show
significant differences. The flux ratio of the disk to total flux is higher for
the time intervals with non-QPO than that of QPO regime. We propose that these
differences in the spectral properties between QPO and non-QPO regimes could be
explained in the scenario of Lense-Thirring precession, and the reversal of the
QPO/non-QPO behavior between HIMS and SIMS may be associated with
appearance/disappearance of a type-B QPO which might origin from the precession
of the jet.


title: Boundary Smoothing for Named Entity Recognition

abstract:
  Neural named entity recognition (NER) models may easily encounter the
over-confidence issue, which degrades the performance and calibration. Inspired
by label smoothing and driven by the ambiguity of boundary annotation in NER
engineering, we propose boundary smoothing as a regularization technique for
span-based neural NER models. It re-assigns entity probabilities from annotated
spans to the surrounding ones. Built on a simple but strong baseline, our model
achieves results better than or competitive with previous state-of-the-art
systems on eight well-known NER benchmarks. Further empirical analysis suggests
that boundary smoothing effectively mitigates over-confidence, improves model
calibration, and brings flatter neural minima and more smoothed loss
landscapes.


title: Remnants of nonrelativistic Casimir effect on the lattice

abstract:
  The Casimir effect is a fundamental quantum phenomenon induced by the
zero-point energy for a particle. It is well-known for relativistic particles
with a linear dispersion relation, while its existence or absence for
nonrelativistic particles with a quadratic dispersion is an unsettled question.
Here, we investigate the Casimir effects for various dispersion relations on
the lattice. We find that Casimir effects for dispersions proportional to an
even power of momentum are absent in a long distance but a remnant of the
Casimir effect survives in a short distance. Such a remnant Casimir effect will
be experimentally observed in materials with lattice degrees of freedom, such
as thin films, narrow nanoribbons, and short nanowires. In terms of this
effect, we also give a reinterpretation of the Casimir effect for massive
particles.

categories: quant-ph cond-mat.mes-hall hep-lat hep-th


title: Pinning control of social fairness in the Ultimatum game

abstract:
  Decent social fairness is highly desired for both socio-economic activities
and individuals, as it is one of the cornerstones of the social welfare and
sustainability. How to effectively promote the level of fairness thus becomes a
significant issue to be addressed. Here, by adopting a pinning control
procedure, we find that when a very small fraction of individuals ($\sim3\%$)
are pinned to be fair players in the Ultimatum Game, the whole population
unexpectedly evolves into the full fairness level. This leverage effect is
robust to the underlying structures of the population, the process is generally
in the form of first-order phase transition for homogeneous networks ($1d$
lattice and Erd\H{o}s-R\'enyi random networks) and the well-mixed population as
the number of pinning individuals increases. For heterogeneous networks (e.g.
scale-free networks), however, one hub node is sufficient for the aim, and even
the on-off control procedure can be applied to further reduce the control cost.
Intermittent failures are seen in the latter case, our statistical analysis
indicates the evolution shows some sort of criticality. Our work suggests that
the pinning control procedure could potentially be a good strategy to promote
the social fairness for some real scenarios when necessary.


title: A construction of optimal locally recoverable codes

abstract:
  Locally recoverable codes are widely used in distributed and cloud storage
systems. The objective of this paper is to present a construction of near MDS
codes with oval polynomials and then determine the locality of the codes. It
turns out that the near MDS codes and their duals are both distance-optimal and
dimension-optimal locally recoverable codes. The lengths of the locally
recoverable codes are different from known ones in the literature.


title: Information Fusion: Scaling Subspace-Driven Approaches

abstract:
  In this work, we seek to exploit the deep structure of multi-modal data to
robustly exploit the group subspace distribution of the information using the
Convolutional Neural Network (CNN) formalism. Upon unfolding the set of
subspaces constituting each data modality, and learning their corresponding
encoders, an optimized integration of the generated inherent information is
carried out to yield a characterization of various classes. Referred to as deep
Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is
compared against the independently developed state-of-the-art approach named
Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal
datasets show that our approach is competitive and more robust in the presence
of noise.


title: Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

abstract:
  Temporal knowledge prediction is a crucial task for the event early warning
that has gained increasing attention in recent years, which aims to predict the
future facts by using relevant historical facts on the temporal knowledge
graphs. There are two main difficulties in this prediction task. First, from
the historical facts point of view, how to model the evolutionary patterns of
the facts to predict the query accurately. Second, from the query perspective,
how to handle the two cases where the query contains seen and unseen entities
in a unified framework. Driven by the two problems, we propose a novel adaptive
pseudo-siamese policy network for temporal knowledge prediction based on
reinforcement learning. Specifically, we design the policy network in our model
as a pseudo-siamese policy network that consists of two sub-policy networks. In
sub-policy network I, the agent searches for the answer for the query along the
entity-relation paths to capture the static evolutionary patterns. And in
sub-policy network II, the agent searches for the answer for the query along
the relation-time paths to deal with unseen entities. Moreover, we develop a
temporal relation encoder to capture the temporal evolutionary patterns.
Finally, we design a gating mechanism to adaptively integrate the results of
the two sub-policy networks to help the agent focus on the destination answer.
To assess our model performance, we conduct link prediction on four benchmark
datasets, the experimental results demonstrate that our method obtains
considerable performance compared with existing methods.


title:
Causal Reasoning with Spatial-temporal Representation Learning: A
  Prospective Study

abstract:
  Spatial-temporal representation learning is ubiquitous in various real-world
applications, including visual comprehension, video understanding, multi-modal
analysis, human-computer interaction, and urban computing. Due to the emergence
of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal
data in big data era, the lack of interpretability, robustness, and
out-of-distribution generalization are becoming the challenges of the existing
visual models. The majority of the existing methods tend to fit the original
data/variable distributions and ignore the essential causal relations behind
the multi-modal knowledge, which lacks an unified guidance and analysis about
why modern spatial-temporal representation learning methods are easily collapse
into data bias and have limited generalization and cognitive abilities.
Inspired by the strong inference ability of human-level agents, recent years
have therefore witnessed great effort in developing causal reasoning paradigms
to realize robust representation and model learning with good cognitive
ability. In this paper, we conduct a comprehensive review of existing causal
reasoning methods for spatial-temporal representation learning, covering
fundamental theories, models, and datasets. The limitations of current methods
and datasets are also discussed. Moreover, we propose some primary challenges,
opportunities, and future research directions for benchmarking causal reasoning
algorithms in spatial-temporal representation learning. This paper aims to
provide a comprehensive overview of this emerging field, attract attention,
encourage discussions, bring to the forefront the urgency of developing novel
causal reasoning methods, publicly available benchmarks, and consensus-building
standards for reliable spatial-temporal representation learning and related
real-world applications more efficiently.


title: Confidence Band Estimation for Survival Random Forests

abstract:
  Survival random forest is a popular machine learning tool for modeling
censored survival data. However, there is currently no statistically valid and
computationally feasible approach for estimating its confidence band. This
paper proposes an unbiased confidence band estimation by extending recent
developments in infinite-order incomplete U-statistics. The idea is to estimate
the variance-covariance matrix of the cumulative hazard function prediction on
a grid of time points. We then generate the confidence band by viewing the
cumulative hazard function estimation as a Gaussian process whose distribution
can be approximated through simulation. This approach is computationally easy
to implement when the subsampling size of a tree is no larger than half of the
total training sample size. Numerical studies show that our proposed method
accurately estimates the confidence band and achieves desired coverage rate. We
apply this method to veterans' administration lung cancer data.


title: Learning Weighting Map for Bit-Depth Expansion within a Rational Range

abstract:
  Bit-depth expansion (BDE) is one of the emerging technologies to display high
bit-depth (HBD) image from low bit-depth (LBD) source. Existing BDE methods
have no unified solution for various BDE situations, and directly learn a
mapping for each pixel from LBD image to the desired value in HBD image, which
may change the given high-order bits and lead to a huge deviation from the
ground truth. In this paper, we design a bit restoration network (BRNet) to
learn a weight for each pixel, which indicates the ratio of the replenished
value within a rational range, invoking an accurate solution without modifying
the given high-order bit information. To make the network adaptive for any
bit-depth degradation, we investigate the issue in an optimization perspective
and train the network under progressive training strategy for better
performance. Moreover, we employ Wasserstein distance as a visual quality
indicator to evaluate the difference of color distribution between restored
image and the ground truth. Experimental results show our method can restore
colorful images with fewer artifacts and false contours, and outperforms
state-of-the-art methods with higher PSNR/SSIM results and lower Wasserstein
distance. The source code will be made available at
https://github.com/yuqing-liu-dut/bit-depth-expansion


title: Deformations of Algebras with 2-Extensions

abstract:
  We apply the notion of 2-extensions of algebras to the deformation theory of
algebras. After standard results on butterflies between 2-extensions, we use
this (2, 0)-category to give three perspectives on the deformation theory of
algebras. We conclude by fixing an error in the literature.


title:
Modeling coupled electrochemical and mechanical behavior of soft ionic
  materials and ionotronic devices

abstract:
  Recently there has been an increase in demand for soft and biocompatible
electronic devices capable of withstanding large stretch. Ionically conductive
polymers present a promising class of soft materials for these emerging
applications due to their ability to realize charge transport across the
polymer network, while preserving the desired mechanical and chemical features.
As opposed to electron transfer in traditional electrical conductors, the
charge transport across these polymers is achieved through ion migration. When
such materials are used in combination with electrical systems, they are known
as ionotronic devices. The ability to simulate device performance based on its
material composition and geometry would accelerate and improve ionotronic
device design. The main challenge in developing reliable simulation
capabilities for ionically conductive polymers is the complex and coupled
electro-chemo-mechanical behavior. In this work we address this challenge by
introducing a multiphysics framework incorporating the coupled effects of ion
transport, electric fields and large deformation. The utility of the developed
multiphysics model is showcased by simulating representative ion transport
problems and the operation of soft ionotronic devices.


title:
The Effects of Disk Induced Apsidal Precession on Planets Captured into
  Mean Motion Resonance

abstract:
  The process of migration into resonance capture has been well studied for
planetary systems where the gravitational potential is generated exclusively by
the star and planets. However, massive protoplanetary disks add a significant
perturbation to these models. In this paper we consider two limiting cases of
disk-induced precession on migrating planets and find that small amounts of
precession significantly affect the equilibrium reached by migrating planets.
We investigate these effects with a combination of semi-analytic models of the
resonance and numerical integrations. We also consider the case of the disk's
dispersal, which can excite significant libration amplitude and can cause
ejection from resonance for large enough precession rates. Both of these
effects have implications for interpreting the known exoplanet population and
may prove to be important considerations as the population of
well-characterized exoplanet systems continues to grow.


title: An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search

abstract:
  We consider the popular tree-based search strategy within the framework of
reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of
finite-horizon Markov decision process. We propose a dynamic sampling tree
policy that efficiently allocates limited computational budget to maximize the
probability of correct selection of the best action at the root node of the
tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed
tree policy is more efficient than other competing methods.


title: ISTRBoost: Importance Sampling Transfer Regression using Boosting

abstract:
  Current Instance Transfer Learning (ITL) methodologies use domain adaptation
and sub-space transformation to achieve successful transfer learning. However,
these methodologies, in their processes, sometimes overfit on the target
dataset or suffer from negative transfer if the test dataset has a high
variance. Boosting methodologies have been shown to reduce the risk of
overfitting by iteratively re-weighing instances with high-residual. However,
this balance is usually achieved with parameter optimization, as well as
reducing the skewness in weights produced due to the size of the source
dataset. While the former can be achieved, the latter is more challenging and
can lead to negative transfer. We introduce a simpler and more robust fix to
this problem by building upon the popular boosting ITL regression methodology,
two-stage TrAdaBoost.R2. Our methodology,~\us{}, is a boosting and
random-forest based ensemble methodology that utilizes importance sampling to
reduce the skewness due to the source dataset. We show that~\us{}~performs
better than competitive transfer learning methodologies $63\%$ of the time. It
also displays consistency in its performance over diverse datasets with varying
complexities, as opposed to the sporadic results observed for other transfer
learning methodologies.


title: Further numerical radius inequalities

abstract:
  In this article, we present some new inequalities for the numerical radius of
products of Hilbert space operators and the generalized Aluthge transform. In
particular, we show some upper bounds for $\omega(ABC+DEF)$ using the
celebrated Buzano inequality, then some consequences that generalize some
results from the literature are discussed. After that, inequalities that
involve the generalized Aluthge transform are shown using some known bounds for
the numerical radius of the product of two operators.


title: Experience Report: Standards-Based Grading at Scale in Algorithms

abstract:
  We report our experiences implementing standards-based grading at scale in an
Algorithms course, which serves as the terminal required CS Theory course in
our department's undergraduate curriculum. The course had 200-400 students,
taught by two instructors, eight graduate teaching assistants, and supported by
two additional graders and several undergraduate course assistants. We
highlight the role of standards-based grading in supporting our students during
the COVID-19 pandemic. We conclude by detailing the successes and adjustments
we would make to the course structure.


title: Heavy-Ion Collisions toward High-Density Nuclear Matter

abstract:
  In the present paper, the current efforts in heavy-ion collisions toward
high-density nuclear matter will be discussed. First, the essential points
learned from RHIC and LHC will be reviewed. Then, the present data from the
STAR Beam Energy Scan are discussed. Finally, the current efforts, NICA, FAIR,
HIAF, and J-PARC-HI (heavy ion) are described. In particular, the efforts of
the J-PARC-HI project are described in detail.


title: Thompson Sampling for Bandit Learning in Matching Markets

abstract:
  The problem of two-sided matching markets has a wide range of real-world
applications and has been extensively studied in the literature. A line of
recent works have focused on the problem setting where the preferences of
one-side market participants are unknown \emph{a priori} and are learned by
iteratively interacting with the other side of participants. All these works
are based on explore-then-commit (ETC) and upper confidence bound (UCB)
algorithms, two common strategies in multi-armed bandits (MAB). Thompson
sampling (TS) is another popular approach, which attracts lots of attention due
to its easier implementation and better empirical performances. In many
problems, even when UCB and ETC-type algorithms have already been analyzed,
researchers are still trying to study TS for its benefits. However, the
convergence analysis of TS is much more challenging and remains open in many
problem settings. In this paper, we provide the first regret analysis for TS in
the new setting of iterative matching markets. Extensive experiments
demonstrate the practical advantages of the TS-type algorithm over the ETC and
UCB-type baselines.


title:
Exponential Entropy dissipation for weakly self-consistent
  Vlasov-Fokker-Planck equations

abstract:
  We study long-time dynamical behaviors of weakly self-consistent
Vlasov-Fokker-Planck equations. We introduce Hessian matrix conditions on
mean-field kernel functions, which characterizes the exponential convergence of
solutions in $L^1$ distances. The matrix condition is derived from the
dissipation of a selected Lyapunov functional, namely auxiliary Fisher
information functional. We verify proposed matrix conditions in examples.


title:
Self-recoverable Adversarial Examples: A New Effective Protection
  Mechanism in Social Networks

abstract:
  Malicious intelligent algorithms greatly threaten the security of social
users' privacy by detecting and analyzing the uploaded photos to social network
platforms. The destruction to DNNs brought by the adversarial attack sparks the
potential that adversarial examples serve as a new protection mechanism for
privacy security in social networks. However, the existing adversarial example
does not have recoverability for serving as an effective protection mechanism.
To address this issue, we propose a recoverable generative adversarial network
to generate self-recoverable adversarial examples. By modeling the adversarial
attack and recovery as a united task, our method can minimize the error of the
recovered examples while maximizing the attack ability, resulting in better
recoverability of adversarial examples. To further boost the recoverability of
these examples, we exploit a dimension reducer to optimize the distribution of
adversarial perturbation. The experimental results prove that the adversarial
examples generated by the proposed method present superior recoverability,
attack ability, and robustness on different datasets and network architectures,
which ensure its effectiveness as a protection mechanism in social networks.


title: Complexity of quantum circuits via sensitivity, magic, and coherence

abstract:
  Quantum circuit complexity-a measure of the minimum number of gates needed to
implement a given unitary transformation-is a fundamental concept in quantum
computation, with widespread applications ranging from determining the running
time of quantum algorithms to understanding the physics of black holes. In this
work, we study the complexity of quantum circuits using the notions of
sensitivity, average sensitivity (also called influence), magic, and coherence.
We characterize the set of unitaries with vanishing sensitivity and show that
it coincides with the family of matchgates. Since matchgates are tractable
quantum circuits, we have proved that sensitivity is necessary for a quantum
speedup. As magic is another measure to quantify quantum advantage, it is
interesting to understand the relation between magic and sensitivity. We do
this by introducing a quantum version of the Fourier entropy-influence
relation. Our results are pivotal for understanding the role of sensitivity,
magic, and coherence in quantum computation.


title:
Pretraining Chinese BERT for Detecting Word Insertion and Deletion
  Errors

abstract:
  Chinese BERT models achieve remarkable progress in dealing with grammatical
errors of word substitution. However, they fail to handle word insertion and
deletion because BERT assumes the existence of a word at each position. To
address this, we present a simple and effective Chinese pretrained model. The
basic idea is to enable the model to determine whether a word exists at a
particular position. We achieve this by introducing a special token
\texttt{[null]}, the prediction of which stands for the non-existence of a
word. In the training stage, we design pretraining tasks such that the model
learns to predict \texttt{[null]} and real words jointly given the surrounding
context. In the inference stage, the model readily detects whether a word
should be inserted or deleted with the standard masked language modeling
function. We further create an evaluation dataset to foster research on word
insertion and deletion. It includes human-annotated corrections for 7,726
erroneous sentences. Results show that existing Chinese BERT performs poorly on
detecting insertion and deletion errors. Our approach significantly improves
the F1 scores from 24.1\% to 78.1\% for word insertion and from 26.5\% to
68.5\% for word deletion, respectively.


title:
GeV Gamma-ray Emission and Molecular Clouds towards Supernova Remnant
  G35.6$-$0.4 and the TeV Source HESS J1858+020

abstract:
  It is difficult to distinguish hadronic process from the leptonic one in
$\gamma$-ray observation, which is however crucial in revealing the origin of
cosmic rays. As an endeavor in the regard, we focus in this work on the complex
$\gamma$-ray emitting region, which partially overlaps with the unidentified
TeV source HESS~J1858+020 and includes supernova remnant (SNR) G35.6$-$0.4 and
HII region G35.6$-$0.5. We reanalyze CO-line, HI, and Fermi-LAT GeV
$\gamma$-ray emission data of this region. The analysis of the molecular and HI
data suggests that SNR G35.6$-$0.4 and HII region G35.6$-$0.5 are located at
different distances. The analysis the GeV $\gamma$-rays shows that GeV emission
arises from two point sources: one (SrcA) coincident with the SNR, and the
other (SrcB) coincident with both HESS J1858+020 and HII region G35.6$-$0.5.
The GeV emission of SrcA can be explained by the hadronic process in the SNR-MC
association scenario. The GeV-band spectrum of SrcB and the TeV-band spectrum
of HESS J1858+020 can be smoothly connected by a power-law function, with an
index of $\sim$2.2. The connected spectrum is well explained with a hadronic
emission, with the cutoff energy of protons above 1 PeV. It thus indicates that
there is a potential PeVatron in the HII region and should be further verified
with ultra-high energy observations with, e.g., LHAASO.


title:
Detection of gamma-ray emission from the Sagittarius Dwarf Spheroidal
  galaxy

abstract:
  The Fermi Bubbles are giant, gamma-ray emitting lobes emanating from the
nucleus of the Milky Way discovered in 1-100 GeV data collected by the Large
Area Telescope on board the Fermi Gamma-Ray Space Telescope. Previous work has
revealed substructure within the Fermi Bubbles that has been interpreted as a
signature of collimated outflows from the Galaxy's super-massive black hole.
Here we show that much of the gamma-ray emission associated to the brightest
region of substructure - the so-called cocoon - is actually due to the
Sagittarius dwarf spheroidal (Sgr dSph) galaxy. This large Milky Way satellite
is viewed through the Fermi Bubbles from the position of the Solar System. As a
tidally and ram-pressure stripped remnant, the Sgr dSph has no on-going star
formation, but we demonstrate that its gamma-ray signal is naturally explained
by inverse Compton scattering of cosmic microwave background photons by
high-energy electron-positron pairs injected by the dwarf's millisecond pulsar
(MSP) population, combined with these objects' magnetospheric emission. This
finding suggests that MSPs likely produce significant gamma-ray emission
amongst old stellar populations, potentially confounding indirect dark matter
searches in regions such as the Galactic Centre, the Andromeda galaxy, and
other massive Milky Way dwarf spheroidals.


title: Faster Fundamental Graph Algorithms via Learned Predictions

abstract:
  We consider the question of speeding up classic graph algorithms with
machine-learned predictions. In this model, algorithms are furnished with extra
advice learned from past or similar instances. Given the additional
information, we aim to improve upon the traditional worst-case run-time
guarantees. Our contributions are the following:
  (i) We give a faster algorithm for minimum-weight bipartite matching via
learned duals, improving the recent result by Dinitz, Im, Lavastida, Moseley
and Vassilvitskii (NeurIPS, 2021);
  (ii) We extend the learned dual approach to the single-source shortest path
problem (with negative edge lengths), achieving an almost linear runtime given
sufficiently accurate predictions which improves upon the classic fastest
algorithm due to Goldberg (SIAM J. Comput., 1995);
  (iii) We provide a general reduction-based framework for learning-based graph
algorithms, leading to new algorithms for degree-constrained subgraph and
minimum-cost $0$-$1$ flow, based on reductions to bipartite matching and the
shortest path problem.
  Finally, we give a set of general learnability theorems, showing that the
predictions required by our algorithms can be efficiently learned in a PAC
fashion.


title: Z Cam star PY Per in SU UMa state?

abstract:
  PY Per has been known as a Z Cam star. Using VSOLJ, VSNET, AAVSO, ASAS-SN and
ZTF observations, I found that this object experienced faint states and
classified it to be a Z Cam + VY Scl star. Furthermore, the object changed the
outburst behavior since 2020 and it has been showing regularly recurring long,
bright outbursts with cycles of 110-160 d and short, faint outbursts between
them. The 2021 December--2022 January outburst particularly resembled a
superoutburst with a gradually fading plateau phase having a duration more than
25 d and two rebrightenings in the fading part. This object has a long orbital
period of 0.15468(5) d based on the TESS data and the previous radial-velocity
study. The existence of a superoutburst in a system with a long orbital period
would be unusual, but not unprecedented. Since the phenomenon may still be
ongoing, I draw attention to this object when it emerges from in the morning
sky after the solar conjunction.


title: Privacy-Utility Trade-Off

abstract:
  In this paper, we investigate the privacy-utility trade-off (PUT) problem,
which considers the minimal privacy loss at a fixed expense of utility. Several
different kinds of privacy in the PUT problem are studied, including
differential privacy, approximate differential privacy, maximal information,
maximal leakage, Renyi differential privacy, Sibson mutual information and
mutual information. The average Hamming distance is used to measure the
distortion caused by the privacy mechanism. We consider two scenarios: global
privacy and local privacy. In the framework of global privacy framework, the
privacy-distortion function is upper-bounded by the privacy loss of a special
mechanism, and lower-bounded by the optimal privacy loss with any possible
prior input distribution. In the framework of local privacy, we generalize a
coloring method for the PUT problem.


title:
Ensemble of Deep Convolutional Neural Networks for real-time
  gravitational wave signal recognition

abstract:
  With the rapid development of deep learning technology, more and more
researchers apply it to gravitational wave (GW) data analysis. Previous studies
focused on a single deep learning model. In this paper we design an ensemble
algorithm combining a set of convolutional neural networks (CNN) for GW signal
recognition. The whole ensemble model consists of two sub-ensemble models. Each
sub-ensemble model is also an ensemble model of deep learning. The two
sub-ensemble models treat data of Hanford and Livinston detectors respectively.
Proper voting scheme is adopted to combine the two sub-ensemble models to form
the whole ensemble model. We apply this ensemble model to all reported GW
events in the first observation and second observation runs (O1/O2) by
LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can
clearly identify all binary black hole merger events except GW170818. We also
apply the ensemble model to one month (August 2017) data of O2. There is no
false trigger happens although only O1 data are used for training. Our test
results indicate that the ensemble learning algorithms can be used in real-time
GW data analysis.


title:
Symmetry breaking and anomalous conductivity in a double moir\'e
  superlattice

abstract:
  A double moir\'e superlattice can be realized by stacking three layers of
atomically thin two-dimensional materials with designer interlayer twisting or
lattice mismatches. In this novel structure, atomic reconstruction of
constituent layers could introduce significant modifications to the lattice
symmetry and electronic structure at small twist angles. Here, we employ
conductive atomic force microscopy (cAFM) to investigate symmetry breaking and
local electrical properties in twisted trilayer graphene. We observe clear
double moir\'e superlattices with two distinct moire periods all over the
sample. At neighboring domains of the large moir\'e, the current exhibit either
two- or six-fold rotational symmetry, indicating delicate symmetry breaking
beyond the rigid model. Moreover, an anomalous current appears at the 'A-A'
stacking site of the larger moir\'e, contradictory to previous observations on
twisted bilayer graphene. Both behaviors can be understood by atomic
reconstruction, and we also show that the cAFM signal of twisted graphene
samples is dominated by the tip-graphene contact resistance that maps the local
work function of twisted graphene and the metallic tip qualitatively. Our
results unveil cAFM is an effective probe for visualizing atomic reconstruction
and symmetry breaking in novel moir\'e superlattices, which could provide new
insights for exploring and manipulating more exotic quantum states based on
twisted van der Waals heterostructures.


title:
Connecting Infrared Surface Brightness Fluctuation Distances to Type Ia
  Supernova Hosts: Testing the Top Rung of the Distance Ladder

abstract:
  We compare infrared surface brightness fluctuation (IR SBF) distances
measured in galaxies that have hosted type Ia supernovae (SNIa) to distances
estimated from SNIa light curve fits. We show that the properties of SNIa found
in IR SBF hosts are very different from those exploding in Cepheid calibrators,
therefore, this is a direct test of systematic uncertainties on estimation of
the Hubble constant (Ho) using supernovae. The IR SBF results from Jensen et
al. (2021; arXiv:2105.08299) provide a large and uniformly measured sample of
IR SBF distances which we directly compare with distances to 25 SNIa host
galaxies. We divide the Hubble flow SNIa into sub-samples that best match the
divergent supernova properties seen in the IR SBF hosts and Cepheid hosts. We
further divide the SNIa into a sample with light curve widths and host masses
that are congruent to those found in the SBF-calibrated hosts. We refit the
light curve stretch and color correlations with luminosity, and use these
revised parameters to calibrate the Hubble flow supernovae with IR SBF
calibrators. Relative to the Hubble flow, the average calibrator distance
moduli vary by 0.03mag depending on the SNIa subsamples examined and this adds
a 1.8% systematic uncertainty to our Hubble constant estimate. Based on the IR
SBF calibrators, Ho=74.6$\pm$0.9(stat)$\pm$ 2.7(syst) km/s/Mpc, which is
consistent with the Hubble constant derived from supernovae calibrated from
Cepheid variables. We conclude that IR SBF provides reliable calibration of
SNIa with a precision comparable to Cepheid calibrators, and with a significant
saving in telescope time.


title: PLOD: An Abbreviation Detection Dataset for Scientific Documents

abstract:
  The detection and extraction of abbreviations from unstructured texts can
help to improve the performance of Natural Language Processing tasks, such as
machine translation and information retrieval. However, in terms of publicly
available datasets, there is not enough data for training
deep-neural-networks-based models to the point of generalising well over data.
This paper presents PLOD, a large-scale dataset for abbreviation detection and
extraction that contains 160k+ segments automatically annotated with
abbreviations and their long forms. We performed manual validation over a set
of instances and a complete automatic validation for this dataset. We then used
it to generate several baseline models for detecting abbreviations and long
forms. The best models achieved an F1-score of 0.92 for abbreviations and 0.89
for detecting their corresponding long forms. We release this dataset along
with our code and all the models publicly in
https://github.com/surrey-nlp/PLOD-AbbreviationDetection


title:
Scheduling Virtual Conferences Fairly: Achieving Equitable Participant
  and Speaker Satisfaction

abstract:
  Recently, almost all conferences have moved to virtual mode due to the
pandemic-induced restrictions on travel and social gathering. Contrary to
in-person conferences, virtual conferences face the challenge of efficiently
scheduling talks, accounting for the availability of participants from
different timezones and their interests in attending different talks. A natural
objective for conference organizers is to maximize efficiency, e.g., total
expected audience participation across all talks. However, we show that
optimizing for efficiency alone can result in an unfair virtual conference
schedule, where individual utilities for participants and speakers can be
highly unequal. To address this, we formally define fairness notions for
participants and speakers, and derive suitable objectives to account for them.
As the efficiency and fairness objectives can be in conflict with each other,
we propose a joint optimization framework that allows conference organizers to
design schedules that balance (i.e., allow trade-offs) among efficiency,
participant fairness and speaker fairness objectives. While the optimization
problem can be solved using integer programming to schedule smaller
conferences, we provide two scalable techniques to cater to bigger conferences.
Extensive evaluations over multiple real-world datasets show the efficacy and
flexibility of our proposed approaches.


title: A Review-aware Graph Contrastive Learning Framework for Recommendation

abstract:
  Most modern recommender systems predict users preferences with two
components: user and item embedding learning, followed by the user-item
interaction modeling. By utilizing the auxiliary review information accompanied
with user ratings, many of the existing review-based recommendation models
enriched user/item embedding learning ability with historical reviews or better
modeled user-item interactions with the help of available user-item target
reviews. Though significant progress has been made, we argue that current
solutions for review-based recommendation suffer from two drawbacks. First, as
review-based recommendation can be naturally formed as a user-item bipartite
graph with edge features from corresponding user-item reviews, how to better
exploit this unique graph structure for recommendation? Second, while most
current models suffer from limited user behaviors, can we exploit the unique
self-supervised signals in the review-aware graph to guide two recommendation
components better? To this end, in this paper, we propose a novel Review-aware
Graph Contrastive Learning (RGCL) framework for review-based recommendation.
Specifically, we first construct a review-aware user-item graph with
feature-enhanced edges from reviews, where each edge feature is composed of
both the user-item rating and the corresponding review semantics. This graph
with feature-enhanced edges can help attentively learn each neighbor node
weight for user and item representation learning. After that, we design two
additional contrastive learning tasks (i.e., Node Discrimination and Edge
Discrimination) to provide self-supervised signals for the two components in
recommendation process. Finally, extensive experiments over five benchmark
datasets demonstrate the superiority of our proposed RGCL compared to the
state-of-the-art baselines.


title:
PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence
  in Communication

abstract:
  Artificial intelligence (AI) has been introduced in communication networks
and services to improve efficiency via self-optimization. Cooperative
intelligence (CI), also known as collective intelligence and collaborative
intelligence, is expected to become an integral element in next-generation
networks because it can aggregate the capabilities and intelligence of multiple
devices. However, privacy issues may intimidate, obstruct, and hinder the
deployment of CI in practice because collaboration heavily relies on data and
information sharing. Additional practical constraints in communication (e.g.,
limited bandwidth) further limit the performance of CI. To overcome these
challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme
based on multi-agent reinforcement learning (MARL). We apply and evaluate our
scheme in two communication-related use cases: mobility management in
drone-assisted communication and network control with edge intelligence.
Simulation results reveal that the proposed scheme can achieve efficient and
reliable collaboration with 1.1-6 times better privacy protection and lower
overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art
approaches.


title:
Search for supernova neutrinos and constraint on the galactic star
  formation rate with the KamLAND data

abstract:
  We present the results of a search for core-collapse supernova neutrinos,
using long-term KamLAND data from 2002 March 9th to 2020 April 25th. We focus
on the electron anti-neutrinos emitted from supernovae in the energy range of
1.8--111 MeV. Supernovae will make a neutrino event cluster with the length of
$\sim$10 s in the KamLAND data. We find no neutrino clusters and give the upper
limit on the supernova rate as to be 0.15 yr$^{-1}$ with a 90% confidence
level. The detectable range, which corresponds to a >95% detection probability,
is 40--59 kpc and 65--81 kpc for core-collapse supernovae and failed
core-collapse supernovae, respectively. This paper proposes to convert the
supernova rate obtained by the neutrino observation to the galactic star
formation rate. Assuming a modified Salpter-type initial mass function, the
upper limit on the galactic star formation rate is <17.5--22.7 (8.1--10.5)
$M_{\odot} \mathrm{yr}^{-1}$ with a 90% (68.3%) confidence level.


title: New conformal field theories by gauging electric and magnetic currents

abstract:
  Old folklore says that there is no non-trivial renormalization group fixed
point with $U(1)$ gauge symmetry in four dimensions, but it can be circumvented
by the existence of magnetic monopoles. We propose to construct (potentially
infinitely many) novel types of field theories with $U(1)$ gauge symmetry by
gauging $U(1) \times U(1)$ global symmetry electrically and magnetically. If
the construction is consistent, the resulting theory will most likely possess a
non-trivial renormalization group fixed point for the $U(1)$ gauge coupling
constant and it will be a non-trivial conformal field theory without the
$U(1)\times U(1)$ global symmetry in the infrared upon tunings of other
relevant deformations. If the construction is CP-violating, a renormalization
of the $\theta$-parameter is accompanied.


title: An Overview of Recent Work in Media Forensics: Methods and Threats

abstract:
  In this paper, we review recent work in media forensics for digital images,
video, audio (specifically speech), and documents. For each data modality, we
discuss synthesis and manipulation techniques that can be used to create and
modify digital media. We then review technological advancements for detecting
and quantifying such manipulations. Finally, we consider open issues and
suggest directions for future research.


title:
Improved AI-generated Solar Farside Magnetograms by STEREO and SDO Data
  Sets and Their Release

abstract:
  Here we greatly improve Artificial Intelligence (AI)-generated solar farside
magnetograms using data sets of Solar Terrestrial Relations Observatory
(STEREO) and Solar Dynamics Observatory (SDO). We modify our previous deep
learning model and configuration of input data sets to generate more realistic
magnetograms than before. First, our model, which is called Pix2PixCC, uses
updated objective functions which include correlation coefficients (CCs)
between the real and generated data. Second, we construct input data sets of
our model: solar farside STEREO extreme ultraviolet (EUV) observations together
with nearest frontside SDO data pairs of EUV observations and magnetograms. We
expect that the frontside data pairs provide the historic information of
magnetic field polarity distributions. We demonstrate that magnetic field
distributions generated by our model are more consistent with the real ones
than before in view of several metrics. The averaged pixel-to-pixel CC for full
disk, active regions, and quiet regions between real and AI-generated
magnetograms with 8 by 8 binning are 0.88, 0.91, and 0.70, respectively. Total
unsigned magnetic flux and net magnetic flux of the AI-generated magnetograms
are consistent with those of real ones for test data sets. It is interesting to
note that our farside magnetograms produce consistent polar field strengths and
magnetic field polarities with those of nearby frontside ones for solar cycle
24 and 25. Now we can monitor the temporal evolution of active regions using
solar farside magnetograms by the model together with the frontside ones. Our
AI-generated Solar Farside Magnetograms (AISFMs) are now publicly available at
Korean Data Center (KDC) for SDO.


title:
Suggesting Relevant Questions for a Query Using Statistical Natural
  Language Processing Technique

abstract:
  Suggesting similar questions for a user query has many applications ranging
from reducing search time of users on e-commerce websites, training of
employees in companies to holistic learning for students. The use of Natural
Language Processing techniques for suggesting similar questions is prevalent
over the existing architecture. Mainly two approaches are studied for finding
text similarity namely syntactic and semantic, however each has its draw-backs
and fail to provide the desired outcome. In this article, a self-learning
combined approach is proposed for determining textual similarity that
introduces a robust weighted syntactic and semantic similarity index for
determining similar questions from a predetermined database, this approach
learns the optimal combination of the mentioned approaches for a database under
consideration. Comprehensive analysis has been carried out to justify the
efficiency and efficacy of the proposed approach over the existing literature.


title: Symlink: A New Dataset for Scientific Symbol-Description Linking

abstract:
  Mathematical symbols and descriptions appear in various forms across document
section boundaries without explicit markup. In this paper, we present a new
large-scale dataset that emphasizes extracting symbols and descriptions in
scientific documents. Symlink annotates scientific papers of 5 different
domains (i.e., computer science, biology, physics, mathematics, and economics).
Our experiments on Symlink demonstrate the challenges of the symbol-description
linking task for existing models and call for further research effort in this
area. We will publicly release Symlink to facilitate future research.


title:
Modeling the Noticeability of User-Avatar Movement Inconsistency for
  Sense of Body Ownership Intervention

abstract:
  An avatar mirroring the user's movement is commonly adopted in Virtual
Reality(VR). Maintaining the user-avatar movement consistency provides the user
a sense of body ownership and thus an immersive experience. However, breaking
this consistency can enable new interaction functionalities, such as pseudo
haptic feedback or input augmentation, at the expense of immersion. We propose
to quantify the probability of users noticing the movement inconsistency while
the inconsistency amplitude is being enlarged, which aims to guide the
intervention of the users' sense of body ownership in VR. We applied angular
offsets to the avatar's shoulder and elbow joints and recorded whether the user
identified the inconsistency through a series of three user studies and built a
statistical model based on the results. Results show that the noticeability of
movement inconsistency increases roughly quadratically with the enlargement of
offsets and the offsets at two joints negatively affect the probability
distributions of each other. Leveraging the model, we implemented a technique
that amplifies the user's arm movements with unnoticeable offsets and then
evaluated implementations with different parameters(offset strength, offset
distribution). Results show that the technique with medium-level and
balanced-distributed offsets achieves the best overall performance. Finally, we
demonstrated our model's extendability in interventions in the sense of body
ownership with three VR applications including stroke rehabilitation, action
game and widget arrangement.


title: Know Thy Student: Interactive Learning with Gaussian Processes

abstract:
  Learning often involves interaction between multiple agents. Human
teacher-student settings best illustrate how interactions result in efficient
knowledge passing where the teacher constructs a curriculum based on their
students' abilities. Prior work in machine teaching studies how the teacher
should construct optimal teaching datasets assuming the teacher knows
everything about the student. However, in the real world, the teacher doesn't
have complete information about the student. The teacher must interact and
diagnose the student, before teaching. Our work proposes a simple diagnosis
algorithm which uses Gaussian processes for inferring student-related
information, before constructing a teaching dataset. We apply this to two
settings. One is where the student learns from scratch and the teacher must
figure out the student's learning algorithm parameters, eg. the regularization
parameters in ridge regression or support vector machines. Two is where the
student has partially explored the environment and the teacher must figure out
the important areas the student has not explored; we study this in the offline
reinforcement learning setting where the teacher must provide demonstrations to
the student and avoid sending redundant trajectories. Our experiments highlight
the importance of diagosing before teaching and demonstrate how students can
learn more efficiently with the help of an interactive teacher. We conclude by
outlining where diagnosing combined with teaching would be more desirable than
passive learning.


title:
One-pass additive-error subset selection for $\ell_{p}$ subspace
  approximation

abstract:
  We consider the problem of subset selection for $\ell_{p}$ subspace
approximation, that is, to efficiently find a \emph{small} subset of data
points such that solving the problem optimally for this subset gives a good
approximation to solving the problem optimally for the original input.
Previously known subset selection algorithms based on volume sampling and
adaptive sampling \cite{DeshpandeV07}, for the general case of $p \in [1,
\infty)$, require multiple passes over the data. In this paper, we give a
one-pass subset selection with an additive approximation guarantee for
$\ell_{p}$ subspace approximation, for any $p \in [1, \infty)$. Earlier subset
selection algorithms that give a one-pass multiplicative $(1+\epsilon)$
approximation work under the special cases. Cohen \textit{et al.}
\cite{CohenMM17} gives a one-pass subset section that offers multiplicative
$(1+\epsilon)$ approximation guarantee for the special case of $\ell_{2}$
subspace approximation. Mahabadi \textit{et al.} \cite{MahabadiRWZ20} gives a
one-pass \emph{noisy} subset selection with $(1+\epsilon)$ approximation
guarantee for $\ell_{p}$ subspace approximation when $p \in \{1, 2\}$. Our
subset selection algorithm gives a weaker, additive approximation guarantee,
but it works for any $p \in [1, \infty)$.


title: A structural optimization algorithm with stochastic forces and stresses

abstract:
  We propose an algorithm for optimizations in which the gradients contain
stochastic noise. This arises, for example, in structural optimizations when
computations of forces and stresses rely on methods involving Monte Carlo
sampling, such as quantum Monte Carlo or neural network states, or are
performed on quantum devices which have intrinsic noise. Our proposed algorithm
is based on the combination of two key ingredients: an update rule derived from
the steepest descent method, and a staged scheduling of the targeted
statistical error and step-size, with position averaging. We compare it with
commonly applied algorithms, including some of the latest machine learning
optimization methods, and show that the algorithm consistently performs
efficiently and robustly under realistic conditions. Applying this algorithm,
we achieve a full-degree optimizations in solids using ab initio many-body
computations, by auxiliary-field quantum Monte Carlo with planewaves and
pseudopotentials. A new metastable structure in Si was discovered in a mixed
geometry and lattice relaxing simulation. In addition to structural
optimization in materials, our algorithm can potentially be useful in other
problems in various fields where optimization with noisy gradients is needed.


title:
Thermally expanded graphite polyetherimide composite with superior
  electrical and thermal conductivity

abstract:
  Thermally expanded graphite (EG) polymer composite has high potential in
modern thermoelectronic industry due to cost effectiveness, excellent
electrical and thermal properties. In this study, we report that expanded
graphite polyetherimide(EG/PEID) composite has been fabricated by simple
solution mixing technique, reinforcing a low cost EG filler into host polymer
matrix, polyetherimide (PEID). This technique enables the inclusion of 3D
graphitic filler into polymer matrix retaining the worm structure of expanded
graphite, thus developing continuous network throughout the composite. In
result, ~19 orders of magnitude rise of the polymers electrical conductivity up
to 969 S/m has been achieved upon the inclusion of the 10wt% of EG filler.
Theoretical prediction using effective medium approach reveals the impact of an
imperfectly conducting interface and interfacial tunneling on electrical
conductivity of the composites. We also report that 10wt% EG filler composition
composite exhibits the thermal conductivity (k) value of 7.3W/mK, implying
significant enhancement of 3070% compared to pure PEID. Scanning Electron
Microscopy (SEM) represents the morphology of fillers and composites, revealing
the preserved structure of EG to develop the conductive pathway between polymer
and filler. Investigation on the structural properties before and after the
thermal expansion of EG fillers has been performed using Raman, X-ray
diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) analysis. In
summary, a simple fabrication route of solution mixing has been demonstrated to
achieve an outstanding electrically and thermally conductive EG/PEID composite
without compromising the efficiency of EG filler structure. The expanded
graphite-based polymer composites can retain their outstanding properties in
wide range of thermal and electronic industry.


title: ATST: Audio Representation Learning with Teacher-Student Transformer

abstract:
  Self-supervised learning (SSL) learns knowledge from a large amount of
unlabeled data, and then transfers the knowledge to a specific problem with a
limited number of labeled data. SSL has achieved promising results in various
domains. This work addresses the problem of segment-level general audio SSL,
and proposes a new transformer-based teacher-student SSL model, named ATST. A
transformer encoder is developed on a recently emerged teacher-student baseline
scheme, which largely improves the modeling capability of pre-training. In
addition, a new strategy for positive pair creation is designed to fully
leverage the capability of transformer. Extensive experiments have been
conducted, and the proposed model achieves the new state-of-the-art results on
almost all of the downstream tasks.


title:
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in
  Ultrasound Images

abstract:
  Various deep learning methods have been proposed to segment breast lesion
from ultrasound images. However, similar intensity distributions, variable
tumor morphology and blurred boundaries present challenges for breast lesions
segmentation, especially for malignant tumors with irregular shapes.
Considering the complexity of ultrasound images, we develop an adaptive
attention U-net (AAU-net) to segment breast lesions automatically and stably
from ultrasound images. Specifically, we introduce a hybrid adaptive attention
module, which mainly consists of a channel self-attention block and a spatial
self-attention block, to replace the traditional convolution operation.
Compared with the conventional convolution operation, the design of the hybrid
adaptive attention module can help us capture more features under different
receptive fields. Different from existing attention mechanisms, the hybrid
adaptive attention module can guide the network to adaptively select more
robust representation in channel and space dimensions to cope with more complex
breast lesions segmentation. Extensive experiments with several
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets show that our method has better performance on breast
lesion segmentation. Furthermore, robustness analysis and external experiments
demonstrate that our proposed AAU-net has better generalization performance on
the segmentation of breast lesions. Moreover, the hybrid adaptive attention
module can be flexibly applied to existing network frameworks.


title: Integrated Pockels Laser

abstract:
  The development of integrated semiconductor lasers has miniaturized
traditional bulky laser systems, enabling a wide range of photonic
applications. A progression from pure III-V based lasers to III-V/external
cavity structures has harnessed low-loss waveguides in different material
systems, leading to significant improvements in laser coherence and stability.
Despite these successes, however, key functions remain absent. In this work, we
address a critical missing function by integrating the Pockels effect into a
semiconductor laser. Using a hybrid integrated III-V/Lithium Niobate structure,
we demonstrate several essential capabilities that have not existed in previous
integrated lasers. These include a record-high frequency modulation speed of 2
exahertz/s (2.0$\times$10$^{18}$ Hz/s) and fast switching at 50 MHz, both of
which are made possible by integration of the electro-optic effect. Moreover,
the device co-lases at infrared and visible frequencies via the second-harmonic
frequency conversion process, the first such integrated multi-color laser.
Combined with its narrow linewidth and wide tunability, this new type of
integrated laser holds promise for many applications including LiDAR, microwave
photonics, atomic physics, and AR/VR.


title:
Exact Wirelength of Embedding 3-Ary n-Cubes into certain Cylinders and
  Trees

abstract:
  Graph embeddings play a significant role in the design and analysis of
parallel algorithms. It is a mapping of the topological structure of a guest
graph G into a host graph H, which is represented as a one-to-one mapping from
the vertex set of the guest graph to the vertex set of the host graph. In
multiprocessing systems the interconnection networks enhance the efficient
communication between the components in the system. Obtaining minimum
wirelength in embedding problems is significant in the designing of network and
simulating one architecture by another. In this paper, we determine the
wirelength of embedding 3-ary n-cubes into cylinders and certain trees.


title:
Observational Probes of Holography with Quantum Coherence on Causal
  Horizons

abstract:
  There is much recent development towards interferometric measurements of
holographic quantum uncertainties in an emergent background space-time. Despite
increasing promise for the target detection regime of Planckian strain spectral
density, the latest motivating theoretical ideas have not been connected to a
phenomenological model of observables measured in a realistic experiment. This
manuscript proposes a candidate model, based on the central hypothesis that all
horizons are universal boundaries of coherent quantum information -- where the
decoherence of space-time happens for the observer. The prediction is motivated
by 't Hooft's algebra for black hole information that gives coherent states on
horizons, whose spatial correlations were shown by Verlinde and Zurek to also
appear on holographic fluctuations of causal boundaries in flat space-time
(conformal Killing horizons). Time-domain correlations are modeled from
Planckian jitters whose coherence scales match causal diamonds, motivated by
Banks' framework for the emergence of space-time and locality. The universality
of this coherence on causal horizons compels a multimodal research program
probing concordant signatures: An analysis of cosmological data to probe
primordial correlations, motivated by Hogan's interpretation of well-known CMB
anomalies as coherent fluctuations on the inflationary horizon, and upcoming 3D
interferometers to probe causal diamonds in flat space-time. Candidate
interferometer geometries are presented, with a modeled frequency spectrum for
each design.


title:
Cyber-Physical Vulnerability Assessment of P2P Energy Exchanges in
  Active Distribution Networks

abstract:
  Owing to the decreasing costs of distributed energy resources (DERs) as well
as decarbonization policies, power systems are undergoing a modernization
process. The large deployment of DERs together with internet of things (IoT)
devices provide a platform for peer-to-peer (P2P) energy trading in active
distribution networks. However, P2P energy trading with IoT devices have driven
the grid more vulnerable to cyber-physical threats. To this end, in this paper,
a resilience-oriented P2P energy exchange model is developed considering three
phase unbalanced distribution systems. In addition, various scenarios for
vulnerability assessment of P2P energy exchanges considering adverse prosumers
and consumers, who provide false information regarding the price and quantity
with the goal of maximum financial benefit and system operation disruption, are
considered. Techno-economic survivability analysis against these attacks are
investigated on a IEEE 13-node unbalanced distribution test system. Simulation
results demonstrate that adverse peers can affect the physical operation of
grid, maximize their benefits, and cause financial loss of other agents.


title: Diagonalizable Thue Equations -- revisited

abstract:
  Let $r,h\in\mathbb{N}$ with $r\geq 7$ and let $F(x,y)\in \mathbb{Z}[x ,y]$ be
a binary form such that
  \[
  F(x , y) =(\alpha x + \beta y)^r -(\gamma x + \delta y)^r,
  \] where $\alpha$, $\beta$, $\gamma$ and $\delta$ are algebraic constants
with $\alpha\delta-\beta\gamma \neq 0$. We establish upper bounds for the
number of primitive solutions to the Thue inequality $0<|F(x, y)| \leq h$,
improving an earlier result of Siegel and of Akhtari, Saradha & Sharma.


title: Quintom fields from chiral K-essence cosmology

abstract:
  In this paper we present an analysis of a chiral cosmological scenario from
the perspective of the K-essence formalism. In this setup, several scalar
fields interact within the kinetic and potential sectors. However, we only
consider a flat Friedmann-Robertson-Lama$\hat{\i}$tre-Walker (FRLW) universe
coupled minimally to two quintom fields: one quintessence and one phantom. We
examine a classical cosmological framework, where analytical solutions are
obtained. Indeed, we present an explanation of the big-bang singularity by
means of a big-bounce. Moreover, having a barotropic fluid description and for
a particular set of parameters the phantom line is in fact crossed. On the
other hand, for the quantum counterpart, the Wheeler-DeWitt equation is
analytically solved for various instances, including the factor ordering
problem with a constant Q. Hence, this approach allows us to compute the
probability density, which behavior is in effect damped in the two subcases
solves classically, observing that the probability density is opens in the
direction of the evolution in the phantom field when the factor ordering
constant is more negative. In other subcase the universe is quantum forever and
the classical universe never takes place.


title: U-Net with ResNet Backbone for Garment Landmarking Purpose

abstract:
  We build a heatmap-based landmark detection model to locate important
landmarks on 2D RGB garment images. The main goal is to detect edges, corners
and suitable interior region of the garments. This let us re-create 3D garments
in modern 3D editing software by incorporate landmark detection model and
texture unwrapping. We use a U-net architecture with ResNet backbone to build
the model. With an appropriate loss function, we are able to train a moderately
robust model.


title:
Time Series Prediction by Multi-task GPR with Spatiotemporal Information
  Transformation

abstract:
  Making an accurate prediction of an unknown system only from a short-term
time series is difficult due to the lack of sufficient information, especially
in a multi-step-ahead manner. However, a high-dimensional short-term time
series contains rich dynamical information, and also becomes increasingly
available in many fields. In this work, by exploiting spatiotemporal
information (STI) transformation scheme that transforms such
high-dimensional/spatial information to temporal information, we developed a
new method called MT-GPRMachine to achieve accurate prediction from a
short-term time series. Specifically, we first construct a specific multi-task
GPR which is multiple linked STI mappings to transform high dimensional/spatial
information into temporal/dynamical information of any given target variable,
and then makes multi step-ahead prediction of the target variable by solving
those STI mappings. The multi-step-ahead prediction results on various
synthetic and real-world datasets clearly validated that MT-GPRMachine
outperformed other existing approaches.


title:
Electronic and structural properties of RbCeX$_2$ (X$_2$: O$_2$, S$_2$,
  SeS, Se$_2$, TeSe, Te$_2$)

abstract:
  Triangular lattice delafossite compounds built from magnetic lanthanide ions
are a topic of recent interest due to their frustrated magnetism and
realization of quantum disordered magnetic ground states. Here we report the
evolution of the structure and electronic ground states of RbCe$X_2$ compounds,
built from a triangular lattice of Ce$^{3+}$ ions, upon varying their anion
character ($X_2$= O$_2$, S$_2$, SeS, Se$_2$, TeSe, Te$_2$). This includes the
discovery of a new member of this series, RbCeO$_2$, that seemingly realizes a
quantum disordered ground state. Magnetization and susceptibility measurements
reveal that all compounds manifest mean-field antiferromagnetic interactions
and, with the exception of the oxide, possess a magnetic ordering transition
below 1 K. The crystalline electric field level scheme is explored via neutron
scattering and ab initio calculations to model the intramultiplet splitting of
the $J=5/2$ component. In addition to the two excited doublets expected from
the $J=5/2$ multiplet, we observe one extra, local mode present across the
sample series. This added mode shifts downward in energy with increasing anion
mass, suggesting a long-lived, vibronic state endemic to anion vibrations about
the Ce$^{3+}$ moments.


title: Optimal Network Membership Estimation Under Severe Degree Heterogeneity

abstract:
  Real networks often have severe degree heterogeneity. We are interested in
studying the effect of degree heterogeneity on estimation of the underlying
community structure. We consider the degree-corrected mixed membership model
(DCMM) for a symmetric network with $n$ nodes and $K$ communities, where each
node $i$ has a degree parameter $\theta_i$ and a mixed membership vector
$\pi_i$. The level of degree heterogeneity is captured by $F_n(\cdot)$ -- the
empirical distribution associated with $n$ (scaled) degree parameters. We first
show that the optimal rate of convergence for the $\ell^1$-loss of estimating
$\pi_i$'s depends on an integral with respect to $F_n(\cdot)$. We call a method
$\textit{optimally adaptive to degree heterogeneity}$ (in short, optimally
adaptive) if it attains the optimal rate for arbitrary $F_n(\cdot)$.
Unfortunately, none of the existing methods satisfy this requirement. We
propose a new spectral method that is optimally adaptive, the core idea behind
which is using a pre-PCA normalization to yield the optimal signal-to-noise
ratio simultaneously at all entries of each leading empirical eigenvector. As
one technical contribution, we derive a new row-wise large-deviation bound for
eigenvectors of the regularized graph Laplacian.


title:
A physics-informed deep neural network for surrogate modeling in
  classical elasto-plasticity

abstract:
  In this work, we present a deep neural network architecture that can
efficiently approximate classical elasto-plastic constitutive relations. The
network is enriched with crucial physics aspects of classical
elasto-plasticity, including additive decomposition of strains into elastic and
plastic parts, and nonlinear incremental elasticity. This leads to a
Physics-Informed Neural Network (PINN) surrogate model named here as
Elasto-Plastic Neural Network (EPNN). Detailed analyses show that embedding
these physics into the architecture of the neural network facilitates a more
efficient training of the network with less training data, while also enhancing
the extrapolation capability for loading regimes outside the training data. The
architecture of EPNN is model and material-independent, i.e. it can be adapted
to a wide range of elasto-plastic material types, including geomaterials and
metals; and experimental data can potentially be directly used in training the
network. To demonstrate the robustness of the proposed architecture, we adapt
its general framework to the elasto-plastic behavior of sands. We use synthetic
data generated from material point simulations based on a relatively advanced
dilatancy-based constitutive model for granular materials to train the neural
network. The superiority of EPNN over regular neural network architectures is
explored through predicting unseen strain-controlled loading paths for sands
with different initial densities.


title: Acquiring a Dynamic Light Field through a Single-Shot Coded Image

abstract:
  We propose a method for compressively acquiring a dynamic light field (a 5-D
volume) through a single-shot coded image (a 2-D measurement). We designed an
imaging model that synchronously applies aperture coding and pixel-wise
exposure coding within a single exposure time. This coding scheme enables us to
effectively embed the original information into a single observed image. The
observed image is then fed to a convolutional neural network (CNN) for
light-field reconstruction, which is jointly trained with the camera-side
coding patterns. We also developed a hardware prototype to capture a real 3-D
scene moving over time. We succeeded in acquiring a dynamic light field with
5x5 viewpoints over 4 temporal sub-frames (100 views in total) from a single
observed image. Repeating capture and reconstruction processes over time, we
can acquire a dynamic light field at 4x the frame rate of the camera. To our
knowledge, our method is the first to achieve a finer temporal resolution than
the camera itself in compressive light-field acquisition. Our software is
available from our project webpage


title:
Frequency Hopping Joint Radar-Communications with Hybrid Sub-pulse
  Frequency and Duration

abstract:
  Frequency-hopping (FH) joint radar-communications (JRC) can offer excellent
security for integrated sensing and communication systems. However, existing
JRC schemes mainly embed information using only the sub-pulse frequencies and
hence the data rate is limited. In this paper, we propose to use both sub-pulse
frequencies and durations for information modulation, leading to higher
communication data rates. For information demodulation, we propose a novel
scheme by using the time-frequency analysis (TFA) technique and a "you only
look once" (YOLO)-based detection system. As such, our system does not require
channel estimation, simplifying the transmission signal frame design.
Simulation results demonstrate the effectiveness of our scheme, and show that
it is robust against the Doppler shift and timing offset between the
transceiver and the communication receiver.


title:
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm
  Minimization with Efficiency

abstract:
  Synthetic aperture radar (SAR) tomography (TomoSAR) enables the
reconstruction and three-dimensional (3D) localization of targets based on
multiple two-dimensional (2D) observations of the same scene. The resolving
along the elevation direction can be treated as a line spectrum estimation
problem. However, traditional super-resolution spectrum estimation algorithms
require multiple snapshots and uncorrelated targets. Meanwhile, as the most
popular TomoSAR imaging method in modern years, compressed sensing (CS) based
methods suffer from the gridding mismatch effect which markedly degrades the
imaging performance. As a gridless CS approach, atomic norm minimization can
avoid the gridding effect but requires enormous computing resources. Addressing
the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR
elevation focusing by introducing the IVDST-ANM algorithm, which reduces the
huge computational complexity of the conventional time-consuming semi-positive
definite programming (SDP) by the iterative Vandermonde decomposition and
shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in
terms of gridless imaging and single snapshot recovery. We conducted
experiments using simulated data to evaluate the performance of the proposed
method, and reconstruction results of an urban area from the SARMV3D-Imaging
1.0 dataset are also presented.