title:
Introducing the ICBe Dataset: Very High Recall and Precision Event
  Extraction from Narratives about International Crises

abstract:
  How do international crises unfold? We conceive of international affairs as a
strategic chess game between adversaries, necessitating a systematic way to
measure pieces, moves, and gambits accurately and consistently over different
contexts and periods. We develop such a measurement strategy with an ontology
of crisis actions and interactions and apply it to a high-quality corpus of
crisis narratives recorded by the International Crisis Behavior (ICB) Project.
We demonstrate that the ontology has high coverage over most of the thoughts,
speech, and actions contained in these narratives and produces high inter-coder
agreement when applied by human coders. We introduce a new crisis event dataset
ICB Events (ICBe). We find that ICBe captures the process of a crisis with
greater accuracy and granularity than other well-regarded events or crisis
datasets. We make the data, replication material, and additional visualizations
available at a companion website www.crisisevents.org.


title: Graph Neural Networks for Graphs with Heterophily: A Survey

abstract:
  Recent years have witnessed fast developments of graph neural networks (GNNs)
that have benefited myriads of graph analytic tasks and applications. In
general, most GNNs depend on the homophily assumption that nodes belonging to
the same class are more likely to be connected. However, as a ubiquitous graph
property in numerous real-world scenarios, heterophily, i.e., nodes with
different labels tend to be linked, significantly limits the performance of
tailor-made homophilic GNNs. Hence, \textit{GNNs for heterophilic graphs} are
gaining increasing attention in this community. To the best of our knowledge,
in this paper, we provide a comprehensive review of GNNs for heterophilic
graphs for the first time. Specifically, we propose a systematic taxonomy that
essentially governs existing heterophilic GNN models, along with a general
summary and detailed analysis. Furthermore, we summarize the mainstream
heterophilic graph benchmarks to facilitate robust and fair evaluations. In the
end, we point out the potential directions to advance and stimulate future
research and applications on heterophilic graphs.


title:
Role of lithium intercalation in fluorine-doped tin oxide thin films:
  Ab-initio calculations and experiment

abstract:
  Using a combination of experimental techniques and density functional theory
(DFT) calculations, the influence of lithium insertion on the electronic and
electrochemical properties of fluourine-doped SnO$_2$ (FTO) is assessed. For
this purpose, we investigate the electrochromic behavior of commercial FTO
electrode embedded in a solution of lithium perclorate (LiClO$_4$). The
electrochromic properties are evaluated by UV-VIS spectroscopy, cyclic
voltammetry, and chronoamperometry. These tests show that FTO exhibits
electrochromism with a respectable coloration efficiency ($\eta=47.9$ cm$^2$/C
@ 637 nm). DFT study indicates that lithium remains ionized in the lattice,
raising the Fermi level about 0.7 eV deep into the conduction band. X-ray
photoelectron spectroscopy (XPS) is used to study chemical bonding and
oxidation states. XPS analysis of the Sn 3d core levels reveals that lithium
insertion in FTO induces a shift of 350 meV in the Sn 3d states suggesting that
lithium is incorporated into the SnO$_2$ lattice.


title:
Coalescent process point process of a branching process in varying
  environment

abstract:
  Consider a doubly infinite branching tree in varying environment. The
genealogy of the current generation backwards in time is uniquely determined by
the coalescent point process $(A_i, i\geq 1)$, where $A_i$ is the coalescent
time between individuals $i$ and $i+1$. In general, this process is not
Markovian. In constant environment, Lambert and Popovic (2013) proposed a
Markov process of point measures $(B_i, i\geq 1)$ to reconstruct the coalescent
point process. We present a counterexample where we show that their process has
not the Markov property.
  We define a vector valued Markov process with the minimal amount of
information to reconstruct the genealogy of the standing population. We
highlight that this process is the Markovian correction of $(B_i, i\geq 1)$.
Finally, when the offspring distributions are lineal fractional, we show that
the variables $(A_i, i\geq 1)$ are independent and identically distributed.


title: How to De-Escalate a Cyber Conflict

abstract:
  De-escalation of a cyber conflict can be substantially more difficult than
de-escalation of a conventional military conflict. This paper will first
explain the reasons why de-escalation of a cyber conflict can be so difficult,
and then present a list of suggestions about how to overcome these specific
difficulties.


title: Price Cycles in Ridesharing Platforms

abstract:
  In ridesharing platforms such as Uber and Lyft, it is observed that drivers
sometimes collaboratively go offline when the price is low, and then return
after the price has risen due to the perceived lack of supply. This collective
strategy leads to cyclic fluctuations in prices and available drivers,
resulting in poor reliability and social welfare. We study a continuous time,
non-atomic model and prove that such online/offline strategies may form a Nash
equilibrium among drivers, but lead to a lower total driver payoff if the
market is sufficiently dense. Further, we show how to set price floors that
effectively mitigate the emergence and impact of price cycles.


title:
Modeling Stellar Jitter for the Detection of Earth-Mass Exoplanets via
  Precision Radial Velocity Measurements

abstract:
  The detection of Earth-size exoplanets is a technological and data analysis
challenge. Future progress in Earth-mass exoplanet detection is expected from
the development of extreme precision radial velocity measurements. Increasing
radial velocity precision requires developing a new physics-based data analysis
methodology to discriminate planetary signals from host-star-related effects,
taking stellar variability and instrumental uncertainties into account. In this
work, we investigate and quantify stellar disturbances of the planet-hosting
solar-type star HD121504 from 3D radiative modeling obtained with the
StellarBox code. The model has been used for determining statistical properties
of the turbulent plasma and obtaining synthetic spectroscopic observations for
several Fe I lines at different locations on the stellar disk to mimic
high-resolution spectroscopic observations.


title:
Scaling up Ranking under Constraints for Live Recommendations by
  Replacing Optimization with Prediction

abstract:
  Many important multiple-objective decision problems can be cast within the
framework of ranking under constraints and solved via a weighted bipartite
matching linear program. Some of these optimization problems, such as
personalized content recommendations, may need to be solved in real time and
thus must comply with strict time requirements to prevent the perception of
latency by consumers. Classical linear programming is too computationally
inefficient for such settings. We propose a novel approach to scale up ranking
under constraints by replacing the weighted bipartite matching optimization
with a prediction problem in the algorithm deployment stage. We show
empirically that the proposed approximate solution to the ranking problem leads
to a major reduction in required computing resources without much sacrifice in
constraint compliance and achieved utility, allowing us to solve larger
constrained ranking problems real-time, within the required 50 milliseconds,
than previously reported.


title: Reconstruction of the stacky approach to de Rham cohomology

abstract:
  In this short paper, we use Tannakian reconstruction techniques to prove a
result that explains how to reconstruct the stacky approach to de Rham
cohomology from the classical theory algebraic de Rham cohomology via an
application of the adjoint functor theorem.


title: Representation varieties of twisted Hopf links

abstract:
  We study the representation theory of the fundamental group of the complement
of a Hopf link with n twists. A general framework is described to analyze the
$SL_r(C)$-representation varieties of these twisted Hopf links as byproduct of
a combinatorial problem and equivariant Hodge theory. As application, close
formulas of their E-polynomials are provided for ranks 2 and 3, both for the
representation and character varieties.


title:
Student-created physics problems as an independent and equitable
  assessment tool

abstract:
  Traditional high-stakes summative assessments--timed, in-class exams
accounting for a large percentage of the term's overall grade--have often
received criticism from the educational community. Such assessments tend to
prize a particular "narrow bundle of skills", and have been shown in some
contexts to produce disparate outcomes between different demographic groups.
Alternative low-stakes assessments have shown potential to improve student
engagement and close demographic gaps. In this paper, we document our use of an
alternative form of assessment, in which students were asked to create and
solve a problem of their own design.


title:
A Reliability-aware Distributed Framework to Schedule Residential
  Charging of Electric Vehicles

abstract:
  Residential consumers have become active participants in the power
distribution network after being equipped with residential EV charging
provisions. This creates a challenge for the network operator tasked with
dispatching electric power to the residential consumers through the existing
distribution network infrastructure in a reliable manner. In this paper, we
address the problem of scheduling residential EV charging for multiple
consumers while maintaining network reliability. An additional challenge is the
restricted exchange of information: where the consumers do not have access to
network information and the network operator does not have access to consumer
load parameters. We propose a distributed framework which generates an optimal
EV charging schedule for individual residential consumers based on their
preferences and iteratively updates it until the network reliability
constraints set by the operator are satisfied. We validate the proposed
approach for different EV adoption levels in a synthetically created digital
twin of an actual power distribution network. The results demonstrate that the
new approach can achieve a higher level of network reliability compared to the
case where residential consumers charge EVs based solely on their individual
preferences, thus providing a solution for the existing grid to keep up with
increased adoption rates without significant investments in increasing grid
capacity.


title:
Cerebrovascular morphology in aging and disease -- imaging biomarkers
  for ischemic stroke and Alzheimers disease

abstract:
  Background and Purpose: Altered brain vasculature is a key phenomenon in
several neurologic disorders. This paper presents a quantitative assessment of
vascular morphology in healthy and diseased adults including changes during
aging and the anatomical variations in the Circle of Willis (CoW). Methods: We
used our automatic method to segment and extract novel geometric features of
the cerebral vasculature from MRA scans of 175 healthy subjects, 45 AIS, and 50
AD patients after spatial registration. This is followed by quantification and
statistical analysis of vascular alterations in acute ischemic stroke (AIS) and
Alzheimer's disease (AD), the biggest cerebrovascular and neurodegenerative
disorders. Results: We determined that the CoW is fully formed in only 35
percent of healthy adults and found significantly increased tortuosity and
fractality, with increasing age and with disease -- both AIS and AD. We also
found significantly decreased vessel length, volume and number of branches in
AIS patients. Lastly, we found that AD cerebral vessels exhibited significantly
smaller diameter and more complex branching patterns, compared to age-matched
healthy adults. These changes were significantly heightened with progression of
AD from early onset to moderate-severe dementia. Conclusion: Altered vessel
geometry in AIS patients shows that there is pathological morphology coupled
with stroke. In AD due to pathological alterations in the endothelium or
amyloid depositions leading to neuronal damage and hypoperfusion, vessel
geometry is significantly altered even in mild or early dementia. The specific
geometric features and quantitative comparisons demonstrate potential for using
vascular morphology as a non-invasive imaging biomarker for neurologic
disorders.


title: Matching Tweets With Applicable Fact-Checks Across Languages

abstract:
  An important challenge for news fact-checking is the effective dissemination
of existing fact-checks. This in turn brings the need for reliable methods to
detect previously fact-checked claims. In this paper, we focus on automatically
finding existing fact-checks for claims made in social media posts (tweets). We
conduct both classification and retrieval experiments, in monolingual (English
only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English)
settings using multilingual transformer models such as XLM-RoBERTa and
multilingual embeddings such as LaBSE and SBERT. We present promising results
for "match" classification (93% average accuracy) in four language pairs. We
also find that a BM25 baseline outperforms state-of-the-art multilingual
embedding models for the retrieval task during our monolingual experiments. We
highlight and discuss NLP challenges while addressing this problem in different
languages, and we introduce a novel curated dataset of fact-checks and
corresponding tweets for future research.


title: A Degree Formula for Equivariant Cohomology Rings

abstract:
  This paper generalizes a result of Lynn on the "degree" of an equivariant
cohomology ring $H^*_G(X)$. The degree of a graded module is a certain
coefficient of its Poincar\'{e} series, and is closely related to multiplicity.
In the present paper, we study these commutative algebraic invariants for
equivariant cohomology rings. The main theorem is an additivity formula for
degree: $$deg(H^*_G(X)) = \sum_{[A,c] \in
\mathcal{Q'}_{max}(G,X)}\frac{1}{|W_G(A,c)|} \deg(H^*_{C_G(A,c)}(c)).$$ We also
show how this formula relates to the additivity formula from commutative
algebra, demonstrating both the algebraic and geometric character of the degree
invariant.


title: Learning to Discover Medicines

abstract:
  Discovering new medicines is the hallmark of human endeavor to live a better
and longer life. Yet the pace of discovery has slowed down as we need to
venture into more wildly unexplored biomedical space to find one that matches
today's high standard. Modern AI-enabled by powerful computing, large
biomedical databases, and breakthroughs in deep learning-offers a new hope to
break this loop as AI is rapidly maturing, ready to make a huge impact in the
area. In this paper we review recent advances in AI methodologies that aim to
crack this challenge. We organize the vast and rapidly growing literature of AI
for drug discovery into three relatively stable sub-areas: (a) representation
learning over molecular sequences and geometric graphs; (b) data-driven
reasoning where we predict molecular properties and their binding, optimize
existing compounds, generate de novo molecules, and plan the synthesis of
target molecules; and (c) knowledge-based reasoning where we discuss the
construction and reasoning over biomedical knowledge graphs. We will also
identify open challenges and chart possible research directions for the years
to come.


title: Chemical enrichment in groups and clusters

abstract:
  As building blocks of dust, rocky planets, and even complex life, the
chemical elements heavier than hydrogen (H) and helium (He) - called "metals"
in astronomy - play an essential role in our Universe and its evolution. Up to
Fe and Ni, these metals are known to be created by stars and stellar remnants
via nuclear fusion and to be ejected into their immediate surroundings to
enrich new stellar generations. A spectacular finding, however, is that these
processed elements are found even outside stellar systems, in particular in the
hot, X-ray atmospheres surrounding early-type galaxies and pervading galaxy
clusters and groups. These large-scales structures are thus a remarkable fossil
record of the integrated history of the enrichment of our Universe. In this
Chapter, we briefly discuss the chemical properties of this intracluster - or
intragroup - medium (ICM). After introducing the concept of chemical
abundances, and recalling which stellar sources produce which elements, we
review the method to derive abundance measurements from observations of the ICM
and detail some chemical models implemented in numerical hydrodynamical
simulations of cosmic structures. In particular, we explore how synergies
between X-ray observations and numerical simulations help us to understand (i)
the cosmic epoch at which the bulk of the enrichment occurred, (ii) the physics
of these stellar sources responsible for this enrichment, and (iii) the main
mechanisms responsible for metal diffusion and mixing outside galaxies.


title:
Statistical Inference After Adaptive Sampling in Non-Markovian
  Environments

abstract:
  There is a great desire to use adaptive sampling methods, such as
reinforcement learning (RL) and bandit algorithms, for the real-time
personalization of interventions in digital applications like mobile health and
education. A major obstacle preventing more widespread use of such algorithms
in practice is the lack of assurance that the resulting adaptively collected
data can be used to reliably answer inferential questions, including questions
about time-varying causal effects. Current methods for statistical inference on
such data are insufficient because they (a) make strong assumptions regarding
the environment dynamics, e.g., assume a contextual bandit or Markovian
environment, or (b) require data to be collected with one adaptive sampling
algorithm per user, which excludes data collected by algorithms that learn to
select actions by pooling the data of multiple users. In this work, we make
initial progress by introducing the adaptive sandwich estimator to quantify
uncertainty; this estimator (a) is valid even when user rewards and contexts
are non-stationary and highly dependent over time, and (b) accommodates
settings in which an online adaptive sampling algorithm learns using the data
of all users. Furthermore, our inference method is robust to misspecification
of the reward models used by the adaptive sampling algorithm. This work is
motivated by our work designing experiments in which RL algorithms are used to
select actions, yet reliable statistical inference is essential for conducting
primary analyses after the trial is over.


title: Two Gaussian regularization methods for time-varying networks

abstract:
  We model time-varying network data as realizations from multivariate Gaussian
distributions with precision matrices that change over time. To facilitate
parameter estimation, we require not only that each precision matrix at any
given time point be sparse, but also that precision matrices at neighboring
time points be similar. We accomplish this with two different algorithms, by
generalizing the elastic net and the fused LASSO, respectively. Our main
focuses are efficient computational algorithms and convenient degree-of-freedom
formulae for choosing tuning parameters. We illustrate our methods with two
simulation studies. By applying them to an fMRI data set, we also detect some
interesting differences in brain connectivity between healthy individuals and
ADHD patients.


title:
Locally Finite Vertex-Rotary Maps and Coset Graphs with Finite Valency
  and Finite Edge Multiplicity

abstract:
  It is well-known that a simple $G$-arc-transitive graph can be represented as
a coset graph for the group $G$. This representation is extended to a
construction of $G$-arc-transitive coset graphs $\Cos(G,H,J)$ with finite
valency and finite edge-multiplicity, where $H, J$ are stabilisers in $G$ of a
vertex and incident edge, respectively. Given a group $G=\l a,z\r$ with $|z|=2$
and $|a|$ finite, the coset graph $\Cos(G,\l a\r,\l z\r)$ is shown, under
suitable finiteness assumptions, to have exactly two different arc-transitive
embeddings as a $G$-arc-transitive map $(V,E,F)$, namely, a {\it $G$-rotary}
map if $|az|$ is finite, and a {\it $G$-bi-rotary} map if $|zz^a|$ is finite.
The $G$-rotary map can be represented as a coset geometry for $G$, extending
the notion of a coset graph. However the $G$-bi-rotary map does not have such a
representation, and the face boundary cycles must be specified in addition to
incidences between faces and edges. We also give a coset geometry construction
of a flag-regular map $(V,E,F)$. In all of these constructions we prove that
the face boundary cycles are regular cycles which are simple cycles precisely
when the given group acts faithfully on $V\cup F$.


title: A Survey on Dynamic Neural Networks for Natural Language Processing

abstract:
  Effectively scaling large Transformer models is a main driver of recent
advances in natural language processing. Dynamic neural networks, as an
emerging research direction, are capable of scaling up neural networks with
sub-linear increases in computation and time by dynamically adjusting their
computational path based on the input. Dynamic neural networks could be a
promising solution to the growing parameter numbers of pretrained language
models, allowing both model pretraining with trillions of parameters and faster
inference on mobile devices. In this survey, we summarize progress of three
types of dynamic neural networks in NLP: skimming, mixture of experts, and
early exit. We also highlight current challenges in dynamic neural networks and
directions for future research.


title:
Investigation of the magnetoelastic coupling anisotropy in the Kitaev
  material $\alpha$-RuCl$_3$

abstract:
  The Kitaev material $\alpha$-RuCl$_3$ is among the most prominent candidates
to host a quantum spin-liquid state endowed with fractionalized excitations.
Recent experimental and theoretical investigations have separately revealed the
importance of both the magnetoelastic coupling and the magnetic anisotropy, in
dependence of the applied magnetic field direction. In this combined
theoretical and experimental research, we investigate the anisotropic magnetic
and magnetoelastic properties for magnetic fields applied along the main
crystallographic axes as well as for fields canted out of the honeycomb plane.
We found that the magnetostriction anisotropy is unusually large compared to
the anisotropy of the magnetization, which is related to the strong
magnetoelastic $\widetilde{\Gamma'}$-type coupling in our \textit{ab-initio}
derived model. We observed large, non-symmetric magnetic anisotropy for
magnetic fields canted out of the honeycomb $ab$-plane in opposite directions,
namely towards the $+c^*$ or $-c^*$ axes, respectively. The observed
directional anisotropy is explained by considering the relative orientation of
the magnetic field with respect to the co-aligned RuCl$_6$ octahedra.
Magnetostriction measurements in canted fields support this non-symmetric
magnetic anisotropy, however these experiments are affected by magnetic torque
effects. Comparison of theoretical predictions with experimental findings allow
us to recognize the significant contribution of torque effects in experimental
setups where $\alpha$-RuCl$_3$ is placed in canted magnetic fields.


title:
High-pressure synthesis of superconducting Sn$_{3}$S$_{4}$ using diamond
  anvil cell with boron-doped diamond heater

abstract:
  High-pressure techniques open exploration of functional materials in broad
research fields. An established diamond anvil cell with a boron-doped diamond
heater and transport measurement terminals has performed the high-pressure
synthesis of a cubic Sn$_{3}$S$_{4}$ superconductor. X-ray diffraction and
Raman spectroscopy reveal that the Sn$_{3}$S$_{4}$ phase is stable in the
pressure range of P>5 GPa in a decompression process. Transport measurement
terminals in the diamond anvil cell detect a metallic nature and
superconductivity in the synthesized Sn$_{3}$S$_{4}$ with a maximum onset
transition temperature of 13.3 K at 5.6 GPa. The observed pressure-T$_{c}$
relationship is consistent with that from the first-principles calculation. The
observation of superconductivity in Sn$_{3}$S$_{4}$ opens further materials
exploration under high temperature and pressure conditions.


title:
The Hitchhiker's Guide to Fused Twins -- A Conceptualization to Access
  Digital Twins in situ in Smart Cities

abstract:
  Smart Cities are happening everywhere around us and yet they are still
incomprehensibly far from directly impacting everyday life. What needs to
happen to make cities really smart? Digital Twins (DTs) represent their
Physical Twin (PT) in the real world through models, sensed data, context
awareness, and interactions. A Digital Twin of a city appears to offer the
right combination to make the Smart City accessible and thus usable. However,
without appropriate interfaces, the complexity of a city cannot be represented.
Ultimately, fully leveraging the potential of Smart Cities requires going
beyond the Digital Twin. Can this issue be addressed? I advance embedding the
Digital Twin into the Physical Twin, i.e. Fused Twins. Thus, this fusion allows
access to data where it is generated in a context that can make it easily
understandable. The Fused Twins paradigm is the formalization of this vision.
Prototypes of Fused Twins are appearing at an neck-break speed from different
domains but Smart Cities will be the context where Fused Twins will
predominantly be seen in the future. This paper reviews Digital Twins to
understand how Fused Twins can be constructed from Augmented Reality,
Geographic Information Systems, Building/City Information Models and Digital
Twins and provides an overview of current research and future directions.


title: A Survey on Model Compression for Natural Language Processing

abstract:
  With recent developments in new architectures like Transformer and
pretraining techniques, significant progress has been made in applications of
natural language processing (NLP). However, the high energy cost and long
inference delay of Transformer is preventing NLP from entering broader
scenarios including edge and mobile computing. Efficient NLP research aims to
comprehensively consider computation, time and carbon emission for the entire
life-cycle of NLP, including data preparation, model training and inference. In
this survey, we focus on the inference stage and review the current state of
model compression for NLP, including the benchmarks, metrics and methodology.
We outline the current obstacles and future research directions.


title: Learning to Mitigate AI Collusion on Economic Platforms

abstract:
  Algorithmic pricing on online e-commerce platforms raises the concern of
tacit collusion, where reinforcement learning algorithms learn to set collusive
prices in a decentralized manner and through nothing more than profit feedback.
This raises the question as to whether collusive pricing can be prevented
through the design of suitable "buy boxes," i.e., through the design of the
rules that govern the elements of e-commerce sites that promote particular
products and prices to consumers. In previous work, Johnson et al. (2020)
designed hand-crafted buy box rules that use demand-steering, based on the
history of pricing by sellers, to prevent collusive behavior. Although
effective against price collusion, these rules effect this by imposing severe
restrictions on consumer choice and consumer welfare. In this paper, we
demonstrate that reinforcement learning (RL) can also be used by platforms to
learn buy box rules that are effective in preventing collusion by RL sellers,
and to do so without reducing consumer choice. For this, we adopt the
methodology of Stackelberg MDPs, and demonstrate success in learning robust
rules that continue to provide high consumer welfare together with sellers
employing different behavior models or having out-of-distribution costs for
goods.


title:
Gaze-Guided Class Activation Mapping: Leveraging Human Attention for
  Network Attention in Chest X-rays Classification

abstract:
  The increased availability and accuracy of eye-gaze tracking technology has
sparked attention-related research in psychology, neuroscience, and, more
recently, computer vision and artificial intelligence. The attention mechanism
in artificial neural networks is known to improve learning tasks. However, no
previous research has combined the network attention and human attention. This
paper describes a gaze-guided class activation mapping (GG-CAM) method to
directly regulate the formation of network attention based on expert
radiologists' visual attention for the chest X-ray pathology classification
problem, which remains challenging due to the complex and often nuanced
differences among images. GG-CAM is a lightweight ($3$ additional trainable
parameters for regulating the learning process) and generic extension that can
be easily applied to most classification convolutional neural networks (CNN).
GG-CAM-modified CNNs do not require human attention as an input when fully
trained. Comparative experiments suggest that two standard CNNs with the GG-CAM
extension achieve significantly greater classification performance. The median
area under the curve (AUC) metrics for ResNet50 increases from $0.721$ to
$0.776$. For EfficientNetv2 (s), the median AUC increases from $0.723$ to
$0.801$. The GG-CAM also brings better interpretability of the network that
facilitates the weakly-supervised pathology localization and analysis.


title:
Dynamic optical contrast imaging for real-time delineation of tumor
  resection margins using head and neck cancer as a model

abstract:
  Complete surgical resection of the tumor for Head and neck squamous cell
carcinoma (HNSCC) remains challenging, given the devastating side effects of
aggressive surgery and the anatomic proximity to vital structures. To address
the clinical challenges, we introduce a wide-field, label-free imaging tool
that can assist surgeons delineate tumor margins real-time. We assume that
autofluorescence lifetime is a natural indicator of the health level of
tissues, and ratio-metric measurement of the emission-decay state to the
emission-peak state of excited fluorophores will enable rapid lifetime mapping
of tissues. Here, we describe the principle, instrumentation, characterization
of the imager and the intraoperative imaging of resected tissues from 13
patients undergoing head and neck cancer resection. 20 x 20 mm2 imaging takes 2
second/frame with a working distance of 50 mm, and characterization shows that
the spatial resolution reached 70 {\mu}m and the least distinguishable
fluorescence lifetime difference is 0.14 ns. Tissue imaging and
Hematoxylin-Eosin stain slides comparison reveals its capability of delineating
cancerous boundaries with submillimeter accuracy and a sensitivity of 91.86%
and specificity of 84.38%.

categories: eess.IV cs.SY eess.SY physics.bio-ph physics.med-ph


title:
Asymptotics of the quantization errors for Markov-type measures with
  complete overlaps

abstract:
  Let $\mathcal{G}$ be a directed graph with vertices $1,2,\ldots, 2N$. Let
$\mathcal{T}=(T_{i,j})_{(i,j)\in\mathcal{G}}$ be a family of contractive
similitudes. For every $1\leq i\leq N$, let $i^+:=i+N$. For $1\leq i,j\leq N$,
we define
$\mathcal{M}_{i,j}=\{(i,j),(i,j^+),(i^+,j),(i^+,j^+)\}\cap\mathcal{G}$. We
assume that $T_{\widetilde{i},\widetilde{j}}=T_{i,j}$ for every
$(\widetilde{i},\widetilde{j})\in \mathcal{M}_{i,j}$. Let $K$ denote the
Mauldin-Williams fractal determined by $\mathcal{T}$. Let
$\chi=(\chi_i)_{i=1}^{2N}$ be a positive probability vector and $P$ a
row-stochastic matrix which serves as an incidence matrix for $\mathcal{G}$. We
denote by $\nu$ the Markov-type measure associated with $\chi$ and $P$. Let
$\Omega=\{1,\ldots,2N\}$ and
$G_\infty=\{\sigma\in\Omega^{\mathbb{N}}:(\sigma_i,\sigma_{i+1})\in\mathcal{G},
\;i\geq 1\}$. Let $\pi$ be the natural projection from $G_\infty$ to $K$ and
$\mu=\nu\circ\pi^{-1}$. We consider the following two cases: 1. $\mathcal{G}$
has two strongly connected components consisting of $N$ vertices; 2.
$\mathcal{G}$ is strongly connected. With some assumptions for $\mathcal{G}$
and $\mathcal{T}$, for case 1, we determine the exact value $s_r$ of the
quantization dimension $D_r(\mu)$ for $\mu$ and prove that the
$s_r$-dimensional lower quantization coefficient is always positive, but the
upper one can be infinite; we establish a necessary and sufficient condition
for the upper quantization coefficient for $\mu$ to be finite; for case 2, we
determine $D_r(\mu)$ in terms of a pressure-like function and prove that
$D_r(\mu)$-dimensional upper and lower quantization coefficient are both
positive and finite.


title:
The Cauchy problem and continuation of periodic solutions for a
  generalized Camassa-Holm equation

abstract:
  We consider a three-parameter family of non-linear equations with
$(p+1)-$order non-linearities. Such family includes as a particular member the
well-known $b-$equation, which encloses the famous Camassa-Holm equation. For
certain choices of the parameters we establish a global existence result and
show a scenario that prevent the wave breaking of solutions. Also, we explore
unique continuation properties for some values of the parameters.


title: Wigner's Friend Depends on Self-Contradictory Quantum Amplification

abstract:
  In a recent paper, \.Zukowski and Markiewicz showed that Wigner's Friend
(and, by extension, Schr\"odinger's Cat) can be eliminated as physical
possibilities on purely logical grounds. I validate this result and demonstrate
the source of the contradiction in a simple experiment in which a scientist S
attempts to measure the position of object $|O\rangle = |A\rangle_S +
|B\rangle_S$ by using measuring device M chosen so that $|A\rangle_M \approx
|A\rangle_S$ and $|B\rangle_M \approx |B\rangle_S$. I assume that the
measurement occurs by quantum amplification without collapse, in which M can
entangle with O in a way that remains reversible by S for some nonzero time
period. This assumption implies that during this "reversible" time period,
$|A\rangle_M \neq |A\rangle_S$ and $|B\rangle_M \neq |B\rangle_S$ -- i.e., the
macroscopic pointer state to which M evolves is uncorrelated to the position of
O relative to S. When the scientist finally observes the measuring device, its
macroscopic pointer state is uncorrelated to the object in position
$|A\rangle_S$ or $|B\rangle_S$, rendering the notion of "reversible
measurement" a logical contradiction.


title:
Global solutions to a haptotaxis system with a potentially degenerate
  diffusion tensor in two and three dimensions

abstract:
  We consider the potentially degenerate haptotaxis system \begin{equation*}
\left\{
  \begin{aligned}
  u_t &= \nabla \cdot (\mathbb{D} \nabla u + u \nabla \cdot \mathbb{D}) - \chi
\nabla \cdot (u\mathbb{D}\nabla w) + \mu u(1-u^{r- 1}), \\
  w_t &= - uw
  \end{aligned} \right. \end{equation*} in a smooth bounded domain $\Omega
\subseteq \mathbb{R}^n$, $n \in \{2,3\}$, with a no-flux boundary condition,
positive initial data $u_0$, $w_0$ and parameters $\chi > 0$, $\mu > 0$, $r
\geq 2$ and $\mathbb{D}: \overline{\Omega} \rightarrow \mathbb{R}^{n\times n}$,
$\mathbb{D}$ positive semidefinite on $\overline{\Omega}$.
  Our main result regarding the above system is the construction of weak
solutions under fairly mild assumptions on $\mathbb{D}$ as well as the initial
data, encompassing scenarios of degenerate diffusion in the first equation. As
a step in this construction as well as a result of potential independent
interest, we further construct classical solutions for the same system under a
global positivity assumption for $\mathbb{D}$, which ensures the full
regularizing influence of its associated diffusion operator. In both
constructions, we naturally rely on the regularizing properties of a
sufficiently strong logistic source term in the first equation.


title:
Application of complex transition density to nuclear reaction and effect
  of phase factor

abstract:
  Complex transition density can be constructed by a nuclear structure model
with a complex basis and/or complex coefficient. In general, the complex
transition density is converted to the real one with phase factor. In this
study, we apply the complex transition density directly to the microscopic
reaction model. We compare with scattering cross sections calculated with the
real and complex transition densities in the frameworks of the optical model,
the distorted wave Born approximation, and the coupled-channel (CC)
calculation, respectively. In addition, we investigate the dependence of the
phase factor for the transition density in the elastic and inelastic cross
sections. The effect of the phase factor on the elastic and inelastic cross
sections can be seen in the CC calculation. Finally, we found an important role
of the phase factor in the nuclear elastic and inelastic scatterings.


title:
Recent Advances in Reliable Deep Graph Learning: Adversarial Attack,
  Inherent Noise, and Distribution Shift

abstract:
  Deep graph learning (DGL) has achieved remarkable progress in both business
and scientific areas ranging from finance and e-commerce to drug and advanced
material discovery. Despite the progress, applying DGL to real-world
applications faces a series of reliability threats including adversarial
attacks, inherent noise, and distribution shift. This survey aims to provide a
comprehensive review of recent advances for improving the reliability of DGL
algorithms against the above threats. In contrast to prior related surveys
which mainly focus on adversarial attacks and defense, our survey covers more
reliability-related aspects of DGL, i.e., inherent noise and distribution
shift. Additionally, we discuss the relationships among above aspects and
highlight some important issues to be explored in future research.


title:
Graph Neural Network-Based Scheduling for Multi-UAV-Enabled
  Communications in D2D Networks

abstract:
  In this paper, we jointly design the power control and position dispatch for
Multi-unmanned aerial vehicle (UAV)-enabled communication in device-to-device
(D2D) networks. Our objective is to maximize the total transmission rate of
downlink users (DUs). Meanwhile, the quality of service (QoS) of all D2D users
must be satisfied. We comprehensively considered the interference among D2D
communications and downlink transmissions. The original problem is strongly
non-convex, which requires high computational complexity for traditional
optimization methods. And to make matters worse, the results are not
necessarily globally optimal. In this paper, we propose a novel graph neural
networks (GNN) based approach that can map the considered system into a
specific graph structure and achieve the optimal solution in a low complexity
manner. Particularly, we first construct a GNN-based model for the proposed
network, in which the transmission links and interference links are formulated
as vertexes and edges, respectively. Then, by taking the channel state
information and the coordinates of ground users as the inputs, as well as the
location of UAVs and the transmission power of all transmitters as outputs, we
obtain the mapping from inputs to outputs through training the parameters of
GNN. Simulation results verified that the way to maximize the total
transmission rate of DUs can be extracted effectively via the training on
samples. Moreover, it also shows that the performance of proposed GNN-based
method is better than that of traditional means.


title: QCD Factorization of Quasi Generalized Quark Distributions

abstract:
  We study the factorization of quasi generalized quark distributions with
twist-2 generalized parton distributions. We use an approach which is different
than that used in literature. Using the approach we derive the factorization
relations of all quasi generalized quark distributions at one-loop. The
contributions from twist-2 generalized gluon distributions are included. Our
results apply not only to the quasi distributions of a spin-1/2 hadron but also
to those of a hadron with any spin.


title: Positive solutions to multi-critical Schr\"{o}dinger equations

abstract:
  In this paper, we investigate the existence of multiple positive solutions to
the following multi-critical Schr\"{o}dinger equation \begin{equation}
\label{p} \begin{cases}
  -\Delta u+\lambda V(x)u=\mu
|u|^{p-2}u+\sum\limits_{i=1}^{k}(|x|^{-(N-\alpha_i)}*
|u|^{2^*_i})|u|^{2^*_i-2}u\quad \text{in}\ \mathbb{R}^N,\\ \qquad\qquad\qquad
u\,\in H^1(\mathbb{R}^N), \end{cases} \end{equation} where $\lambda,\mu\in
\mathbb{R}^+, \, N\geqslant 4$, and $2^*_i=\frac{N+\alpha_i}{N-2}$ with
$N-4<\alpha_i<N,\,i=1,2,\cdots,k$ are critical exponents and
$2<p<2^*_{min}=\min\{2^*_i:i=1,2,\cdots,k\}$. Suppose that
$\Omega=int\,V^{-1}(0)\subset\mathbb{R}^N$ is a bounded domain, we show that
for $\lambda$ large, problem above possesses at least $cat_\Omega(\Omega)$
positive solutions.


title:
Multi-task UNet: Jointly Boosting Saliency Prediction and Disease
  Classification on Chest X-ray Images

abstract:
  Human visual attention has recently shown its distinct capability in boosting
machine learning models. However, studies that aim to facilitate medical tasks
with human visual attention are still scarce. To support the use of visual
attention, this paper describes a novel deep learning model for visual saliency
prediction on chest X-ray (CXR) images. To cope with data deficiency, we
exploit the multi-task learning method and tackles disease classification on
CXR simultaneously. For a more robust training process, we propose a further
optimized multi-task learning scheme to better handle model overfitting.
Experiments show our proposed deep learning model with our new learning scheme
can outperform existing methods dedicated either for saliency prediction or
image classification. The code used in this paper is available at
https://github.com/hz-zhu/MT-UNet.


title: Multiphoton Quantum van Cittert-Zernike Theorem

abstract:
  Recent progress on quantum state engineering has enabled the preparation of
quantum photonic systems comprising multiple interacting particles.
Interestingly, multiphoton quantum systems can host many complex forms of
interference and scattering processes that are essential to perform operations
that are intractable on classical systems. However, the control of quantum
multiphoton systems represents one of the most important challenges in the
field of quantum optics. Here, we unveil new mechanisms to control nonclassical
coherence of multiphoton systems, these interactions are described through the
quantum version of the van Cittert-Zernike theorem. More specifically, we
demonstrate that it is possible to exploit scattering and interference effects
in a propagating multiphoton system to control its quantum properties of
coherence. Furthermore, we show that the implementation of conditional
measurements enables the preparation of multiphoton systems with attenuated
quantum statistics below the shot-noise limit. We believe that the multiphoton
quantum van Cittert-Zernike theorem will have important implications for
describing the evolution of the properties of quantum coherence of many-body
bosonic systems.


title: Forced ignition and oscillating flame propagation in fine ethanol sprays

abstract:
  The present work investigates forced ignition and oscillating propagation of
spray flame in a mixture of fine ethanol droplets and air. Eulerian-Eulerian
method with two-way coupling is used and detailed chemical mechanism is
considered. Different droplet diameters and liquid fuel equivalence ratios (ER)
are studied. The evaporation completion front (ECF) is defined to study the
interactions between the evaporation zone and flame front. The gas composition
at the flame front is quantified through an effective ER. The results show that
the kernel trajectory is considerably affected by droplet size and liquid ER.
Generally, the flame ER reaches the maximum when the ECF start to move from the
spherical center. It gradually decreases and reaches a constant value when the
flame freely propagates. Quasi-stationary spherical flame is observed when the
liquid ER is low, whilst kernel extinction/re-ignition appears when liquid ER
is high. These flame behaviors are essentially affected by the heat conduction
and species diffusion timescales between the droplet evaporation zone and flame
front. The dependence of the minimum ignition energy (MIE) on liquid ER is
U-shaped and there is an optimal liquid equivalence ratio range (ERo) with the
smallest MIE. Long and short ignition failure modes are observed, respectively
for small and large liquid ERs. When liquid ER is less than ERo (long failure
mode), larger energy is required to initiate the kernel due to very lean
composition near the spark. For liquid ER larger than ERo (short mode),
ignition failure is caused by strong evaporative heat loss and rich gas
composition due to heavy droplet loading.


title:
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty
  Estimates Using Bayesian Neural Networks

abstract:
  We present results exploring the role that probabilistic deep learning models
can play in cosmology from large scale astronomical surveys through estimating
the distances to galaxies (redshifts) from photometry. Due to the massive scale
of data coming from these new and upcoming sky surveys, machine learning
techniques using galaxy photometry are increasingly adopted to predict galactic
redshifts which are important for inferring cosmological parameters such as the
nature of dark energy. Associated uncertainty estimates are also critical
measurements, however, common machine learning methods typically provide only
point estimates and lack uncertainty information as outputs. We turn to
Bayesian neural networks (BNNs) as a promising way to provide accurate
predictions of redshift values. We have compiled a new galaxy training dataset
from the Hyper Suprime-Cam Survey, designed to mimic large surveys, but over a
smaller portion of the sky. We evaluate the performance and accuracy of
photometric redshift (photo-z) predictions from photometry using machine
learning, astronomical and probabilistic metrics. We find that while the
Bayesian neural network did not perform as well as non-Bayesian neural networks
if evaluated solely by point estimate photo-z values, BNNs can provide
uncertainty estimates that are necessary for cosmology


title: Gigahertz Sub-Landauer Momentum Computing

abstract:
  We introduce a fast and highly-efficient physically-realizable bit swap.
Employing readily available and scalable Josephson junction microtechnology,
the design implements the recently introduced paradigm of momentum computing.
Its nanosecond speeds and sub-Landauer thermodynamic efficiency arise from
dynamically storing memory in momentum degrees of freedom. As such, during the
swap, the microstate distribution is never near equilibrium and the
memory-state dynamics fall far outside of stochastic thermodynamics that
assumes detailed-balanced Markovian dynamics. The device implements a bit-swap
operation -- a fundamental operation necessary to build reversible universal
computing. Extensive, physically-calibrated simulations demonstrate that device
performance is robust and that momentum computing can support
thermodynamically-efficient, high-speed, large-scale general-purpose computing.

categories: cond-mat.stat-mech cs.ET math.DS nlin.CD physics.comp-ph


title:
Rethinking Network Design and Local Geometry in Point Cloud: A Simple
  Residual MLP Framework

abstract:
  Point cloud analysis is challenging due to irregularity and unordered data
structure. To capture the 3D geometries, prior works mainly rely on exploring
sophisticated local geometric extractors using convolution, graph, or attention
mechanisms. These methods, however, incur unfavorable latency during inference,
and the performance saturates over the past few years. In this paper, we
present a novel perspective on this task. We notice that detailed local
geometrical information probably is not the key to point cloud analysis -- we
introduce a pure residual MLP network, called PointMLP, which integrates no
sophisticated local geometrical extractors but still performs very
competitively. Equipped with a proposed lightweight geometric affine module,
PointMLP delivers the new state-of-the-art on multiple datasets. On the
real-world ScanObjectNN dataset, our method even surpasses the prior best
method by 3.3% accuracy. We emphasize that PointMLP achieves this strong
performance without any sophisticated operations, hence leading to a superior
inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster,
tests 7x faster, and is more accurate on ModelNet40 benchmark. We hope our
PointMLP may help the community towards a better understanding of point cloud
analysis. The code is available at https://github.com/ma-xu/pointMLP-pytorch.


title:
A Measure Characterization of Embedding and Extension Domains for
  Sobolev, Triebel-Lizorkin, and Besov Spaces on Spaces of Homogeneous Type

abstract:
  In this article, for an optimal range of the smoothness parameter $s$ that
depends (quantitatively) on the geometric makeup of the underlying space, the
authors identify purely measure theoretic conditions that fully characterize
embedding and extension domains for the scale of Haj{\l}asz--Triebel--Lizorkin
spaces $M^s_{p,q}$ and Haj{\l}asz--Besov spaces $N^s_{p,q}$ in general spaces
of homogeneous type. Although stated in the context of quasi-metric spaces,
these characterizations improve related work even in the metric setting. In
particular, as a corollary of the main results in this article, the authors
obtain a new characterization for Sobolev embedding and extension domains in
the context of general doubling metric measure spaces.


title: Transformers in Time Series: A Survey

abstract:
  Transformers have achieved superior performances in many tasks in natural
language processing and computer vision, which also intrigues great interests
in the time series community. Among multiple advantages of transformers, the
ability to capture long-range dependencies and interactions is especially
attractive for time series modeling, leading to exciting progress in various
time series applications. In this paper, we systematically review transformer
schemes for time series modeling by highlighting their strengths as well as
limitations through a new taxonomy to summarize existing time series
transformers in two perspectives. From the perspective of network
modifications, we summarize the adaptations of module level and architecture
level of the time series transformers. From the perspective of applications, we
categorize time series transformers based on common tasks including
forecasting, anomaly detection, and classification. Empirically, we perform
robust analysis, model size analysis, and seasonal-trend decomposition analysis
to study how Transformers perform in time series. Finally, we discuss and
suggest future directions to provide useful research guidance. A corresponding
resource list that will be continuously updated can be found in the GitHub
repository. To the best of our knowledge, this paper is the first work to
comprehensively and systematically summarize the recent advances of
Transformers for modeling time series data. We hope this survey will ignite
further research interests in time series Transformers.


title:
Two-dimensional crystals on adhesive substrates subjected to uniform
  transverse pressure

abstract:
  In this work we consider bubbles that can form spontaneously when a
two-dimensional (2D) crystal is transferred to a substrate with gases or
liquids trapped at the crystal-substrate interface. The underlying mechanics
may be described by a thin sheet on an adhesive substrate with the trapped
fluid applying uniform transverse pressure. What makes this apparently simple
problem complex is the rich interplay among geometry, interface, elasticity and
instability. Particularly, extensive small-scale experiments have shown that
the 2D crystal surrounding a bubble can adhere to and, meanwhile, slide on the
substrate. The radially inward sliding causes hoop compression to the 2D
crystal which may exploit wrinkling instabilities to relax or partially relax
the compression. We present a theoretical model to understand the complex
behaviors of even a linearly elastic 2D crystal due to the combination of
nonlinear geometry, adhesion, sliding, and wrinkling in bubble systems. We show
that this understanding not only successfully predicts the geometry of a
spontaneous bubble but also reveals the strain-coupled physics of 2D crystals,
e.g., the pseudomagnetic fields in graphene bubbles.


title: Computing with Modular Robots

abstract:
  Propagating patterns are used to transfer and process information in chemical
and physical prototypes of unconventional computing devices. Logical values are
represented by fronts of traveling diffusive, trigger or phase waves. We apply
this concept of pattern based computation to develop experimental prototypes of
computing circuits implemented in small modular robots. In the experimental
prototypes the modular robots Cubelets are concatenated into channels and
junction. The structures developed by Cubelets propagate signals in parallel
and asynchronously. The approach is illustrated with a working circuit of a
one-bit full adder. Complementarily a formalization of these constructions are
developed across Sleptsov nets. Finally, a perspective to swarm dynamics is
discussed.


title: International Co-Branding and Firms Finance Performance

abstract:
  Co-branding has become a widely used marketing strategy, yet little attention
has been paid to its impact on a firm's stock value. Prior literature has shown
that using a co-branding strategy properly helps firms leverage the brand value
and equity. We discussed the theoretical foundations and main accomplishments
of prior studies. We developed a conceptual framework and hypothesis to close
the existing research gap in the topic of interest. We argued that co-branding
event announcement generates positive abnormal returns in the stock market.
Furthermore, we investigated the moderating impact of co-branding structure on
the relation between co-branding event announcements and abnormal returns. We
claimed that higher co-branding integration, greater co-branding exclusivity,
and longer co-branding duration generate a greater positive abnormal return for
the partnering firms.


title: Daylong sub-ambient radiative cooling with full color exterior

abstract:
  Terrestrial radiative cooling is an intriguing way to mitigate the
accelerating cooling demands in the residential and commercial sectors by
offering zero-energy cooling. However, the ultra-white or mirror-like
appearance of radiative coolers can be visually sterile and raise safety issues
when broadly applied to building facades and vehicles. To overcome the
fundamental trade-off between color diversity and cooling performance, we
propose a radiatively integrated, conductively insulated system that exploits
thermal non-equilibrium between colorants and thermal emitters. This allows
such radiative coolers to be cooled below the ambient temperature at all times
of the day while exhibiting any desired exterior color including black. We
experimentally demonstrate that even black coolers, absorbing 646 Wm-2 of solar
power under AM1.5 conditions, cools down to a maximum of 6.9 K (average of 3.5
K) below the ambient temperature during the daytime. These systems can
potentially be used in outdoor applications, especially in commercial buildings
and residential houses, where carbon-free thermal management is in high demand
but diversity of colors is also important for visual appeal and comfort.


title: STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation

abstract:
  The bilinear method is mainstream in Knowledge Graph Embedding (KGE), aiming
to learn low-dimensional representations for entities and relations in
Knowledge Graph (KG) and complete missing links. Most of the existing works are
to find patterns between relationships and effectively model them to accomplish
this task. Previous works have mainly discovered 6 important patterns like
non-commutativity. Although some bilinear methods succeed in modeling these
patterns, they neglect to handle 1-to-N, N-to-1, and N-to-N relations (or
complex relations) concurrently, which hurts their expressiveness. To this end,
we integrate scaling, the combination of translation and rotation that can
solve complex relations and patterns, respectively, where scaling is a
simplification of projection. Therefore, we propose a corresponding bilinear
model Scaling Translation and Rotation (STaR) consisting of the above two
parts. Besides, since translation cannot be incorporated into the bilinear
model directly, we introduce translation matrix as the equivalent. Theoretical
analysis proves that STaR is capable of modeling all patterns and handling
complex relations simultaneously, and experiments demonstrate its effectiveness
on commonly used benchmarks for link prediction.


title: Schwarzschild quasi-normal modes of non-minimally coupled vector fields

abstract:
  We study perturbations of massive and massless vector fields on a
Schwarzschild black-hole background, including a non-minimal coupling between
the vector field and the curvature. The coupling is given by the Horndeski
vector-tensor operator, which we show to be unique, also when the field is
massive, provided that the vector has a vanishing background value.
  We determine the quasi-normal mode spectrum of the vector field, focusing on
the fundamental mode of monopolar and dipolar perturbations of both even and
odd parity, as a function of the mass of the field and the coupling constant
controlling the non-minimal interaction. In the massless case, we also provide
results for the first two overtones, showing in particular that the
isospectrality between even and odd modes is broken by the non-minimal
gravitational coupling.
  We also consider solutions to the mode equations corresponding to quasi-bound
states and static configurations. Our results for quasi-bound states provide
strong evidence for the stability of the spectrum, indicating the impossibility
of a vectorization mechanism within our set-up. For static solutions, we
analytically and numerically derive results for the electromagnetic
susceptibilities (the spin-1 analogs of the tidal Love numbers), which we show
to be non-zero in the presence of the non-minimal coupling.


title: Memory via Temporal Delays in weightless Spiking Neural Network

abstract:
  A common view in the neuroscience community is that memory is encoded in the
connection strength between neurons. This perception led artificial neural
network models to focus on connection weights as the key variables to modulate
learning. In this paper, we present a prototype for weightless spiking neural
networks that can perform a simple classification task. The memory in this
network is stored in the timing between neurons, rather than the strength of
the connection, and is trained using a Hebbian Spike Timing Dependent
Plasticity (STDP), which modulates the delays of the connection.


title:
Sim-to-Real Domain Adaptation for Lane Detection and Classification in
  Autonomous Driving

abstract:
  While supervised detection and classification frameworks in autonomous
driving require large labelled datasets to converge, Unsupervised Domain
Adaptation (UDA) approaches, facilitated by synthetic data generated from
photo-real simulated environments, are considered low-cost and less
time-consuming solutions. In this paper, we propose UDA schemes using
adversarial discriminative and generative methods for lane detection and
classification applications in autonomous driving. We also present Simulanes
dataset generator to create a synthetic dataset that is naturalistic utilizing
CARLA's vast traffic scenarios and weather conditions. The proposed UDA
frameworks take the synthesized dataset with labels as the source domain,
whereas the target domain is the unlabelled real-world data. Using adversarial
generative and feature discriminators, the learnt models are tuned to predict
the lane location and class in the target domain. The proposed techniques are
evaluated using both real-world and our synthetic datasets. The results
manifest that the proposed methods have shown superiority over other baseline
schemes in terms of detection and classification accuracy and consistency. The
ablation study reveals that the size of the simulation dataset plays important
roles in the classification performance of the proposed methods. Our UDA
frameworks are available at https://github.com/anita-hu/sim2real-lane-detection
and our dataset generator is released at https://github.com/anita-hu/simulanes


title:
Deterministic and universal quantum squeezing gate in virtue of
  teleportation-like protocol

abstract:
  Squeezing transformation as an essential component, gives rise to the
possibility to perform various tasks of quantum information processing.
However, the reported squeezing gate with best performance so far is a
conditional realization at the expense of low success probability, while the
performance of the deterministic ones is currently circumscribed by the limited
squeezing degree of the non-classical ancilla. To address this issue, we
develop and demonstrate a new scheme of deterministic and universal quantum
squeezing gate with the property of non-local operation attributed to the
teleportation-like protocol. We demonstrate high fidelity squeezing operation,
even when the level of target squeezing being up to 10 dB, where a squeezed
state with non-classical noise reduction of 6.5 dB is directly observed.
Moreover, we perform a high-fidelity complex operation including a Fourier
transformation and a phase squeezing gate, exploring the potential of
implementing the complex task of quantum information processing with the
presented functional unit. Our method can be applied to distributed quantum
processor, the creation of exotic non-classical states, and quantum error
correction.


title: Compositional Scene Representation Learning via Reconstruction: A Survey

abstract:
  Visual scene representation learning is an important research problem in the
field of computer vision. The performance on vision tasks could be improved if
more suitable representations are learned for visual scenes. Complex visual
scenes are the composition of relatively simple visual concepts, and have the
property of combinatorial explosion. Compared with directly representing the
entire visual scene, extracting compositional scene representations can better
cope with the diverse combination of background and objects. Because
compositional scene representations abstract the concept of objects, performing
visual scene analysis and understanding based on these representations could be
easier and more interpretable. Moreover, learning compositional scene
representations via reconstruction can greatly reduce the need for training
data annotations. Therefore, compositional scene representation learning via
reconstruction has important research significance. In this survey, we first
discuss representative methods that either learn from a single viewpoint or
multiple viewpoints without object-level supervision, then the applications of
compositional scene representations, and finally the future directions on this
topic.


title: Debiased Pseudo Labeling in Self-Training

abstract:
  Deep neural networks achieve remarkable performances on a wide range of tasks
with the aid of large-scale labeled datasets. However, large-scale annotations
are time-consuming and labor-exhaustive to obtain on realistic tasks. To
mitigate the requirement for labeled data, self-training is widely used in both
academia and industry by pseudo labeling on readily-available unlabeled data.
Despite its popularity, pseudo labeling is well-believed to be unreliable and
often leads to training instability. Our experimental studies further reveal
that the performance of self-training is biased due to data sampling,
pre-trained models, and training strategies, especially the inappropriate
utilization of pseudo labels. To this end, we propose Debiased, in which the
generation and utilization of pseudo labels are decoupled by two independent
heads. To further improve the quality of pseudo labels, we introduce a
worst-case estimation of pseudo labeling and seamlessly optimize the
representations to avoid the worst-case. Extensive experiments justify that the
proposed Debiased not only yields an average improvement of $14.4$\% against
state-of-the-art algorithms on $11$ tasks (covering generic object recognition,
fine-grained object recognition, texture classification, and scene
classification) but also helps stabilize training and balance performance
across classes.


title:
Ultra Wide Band THz IRS Communications: Applications, Challenges, Key
  Techniques, and Research Opportunities

abstract:
  Terahertz (THz) communication is a promising technology for future wireless
networks due to its ultra-wide bandwidth. However, THz signals suffer from
severe attenuation and poor diffraction capability, making it vulnerable to
blocking obstacles. To compensate for these two shortcomings and improve the
system performance, an intelligent reflecting surface (IRS) can be exploited to
change the propagation direction and enhance the signal strength. In this
article, we investigate this promising ultra wide band (UWB) THz IRS
communication paradigm. We start by motivating our research and describing
several potential application scenarios. Then, we identify major challenges
faced by UWB THz IRS communications. To overcome these challenges, several
effective key techniques are developed, i.e., the time delayer-based sparse
radio frequency antenna structure, delay hybrid precoding and IRS deployment.
Simulation results are also presented to compare the system performance for
these proposed techniques, thus demonstrating their effectiveness. Finally, we
highlight several open issues and research opportunities for UWB THz IRS
communications.


title:
Integrating AI Planning with Natural Language Processing: A Combination
  of Explicit and Tacit Knowledge

abstract:
  Automated planning focuses on strategies, building domain models and
synthesizing plans to transit initial states to goals. Natural language
processing concerns with the interactions between agents and human language,
especially processing and analyzing large amounts of natural language data.
These two fields have abilities to generate explicit knowledge, e.g.,
preconditions and effects of action models, and learn from tacit knowledge,
e.g., neural models, respectively. Integrating AI planning and natural language
processing effectively improves the communication between human and intelligent
agents. This paper outlines the commons and relations between AI planning and
natural language processing, argues that each of them can effectively impact on
the other one by four areas: (1) planning-based text understanding, (2)
planning-based text generation, (3) text-based human-robot interaction, and (4)
text-based explainable planning. We also explore some potential future issues
between AI planning and natural language processing.


title: Strong quantum nonlocality in $N$-partite systems

abstract:
  A set of multipartite orthogonal quantum states is strongly nonlocal if it is
locally irreducible for every bipartition of the subsystems [Phys. Rev. Lett.
122, 040403 (2019)]. Although this property has been shown in three-, four- and
five-partite systems, the existence of strongly nonlocal sets in $N$-partite
systems remains unknown when $N\geq 6$. In this paper, we successfully show
that a strongly nonlocal set of orthogonal entangled states exists in
$(\mathbb{C}^d)^{\otimes N}$ for all $N\geq 3$ and $d\geq 2$, which for the
first time reveals the strong quantum nonlocality in general $N$-partite
systems. For $N=3$ or $4$ and $d\geq 3$, we present a strongly nonlocal set
consisting of genuinely entangled states, which has a smaller size than any
known strongly nonlocal orthogonal product set. Finally, we connect strong
quantum nonlocality with local hiding of information as an application.


title: Securing Reconfigurable Intelligent Surface-Aided Cell-Free Networks

abstract:
  In this paper, we investigate the physical layer security in the
reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum
weighted sum secrecy rate problem is formulated by jointly optimizing the
active beamforming (BF) at the base stations and passive BF at the RISs. To
handle this non-trivial problem, we adopt the alternating optimization to
decouple the original problem into two sub-ones, which are solved using the
semidefinite relaxation and continuous convex approximation theory. To decrease
the complexity for obtaining overall channel state information (CSI), we extend
the proposed framework to the case that only requires part of the RIS' CSI.
This is achieved via deliberately discarding the RIS that has a small
contribution to the user's secrecy rate. Based on this, we formulate a mixed
integer non-linear programming problem, and the linear conic relaxation is used
to obtained the solutions. Finally, the simulation results show that the
proposed schemes can obtain a higher secrecy rate than the existing ones.


title: Machine Learning in Aerodynamic Shape Optimization

abstract:
  Large volumes of experimental and simulation aerodynamic data have been
rapidly advancing aerodynamic shape optimization (ASO) via machine learning
(ML), whose effectiveness has been growing thanks to continued developments in
deep learning. In this review, we first introduce the state of the art and the
unsolved challenges in ASO. Next, we present a description of ML fundamentals
and detail the ML algorithms that have succeeded in ASO. Then we review ML
applications contributing to ASO from three fundamental perspectives: compact
geometric design space, fast aerodynamic analysis, and efficient optimization
architecture. In addition to providing a comprehensive summary of the research,
we comment on the practicality and effectiveness of the developed methods. We
show how cutting-edge ML approaches can benefit ASO and address challenging
demands like interactive design optimization. However, practical large-scale
design optimizations remain a challenge due to the costly ML training expense.
A deep coupling of ML model construction with ASO prior experience and
knowledge, such as taking physics into account, is recommended to train ML
models effectively.


title: Brauer-Manin obstruction for Markoff-type cubic surfaces

abstract:
  Following the works by Ghosh and Sarnak; Loughran and Mitankin;
Colliot-Th\'el\`ene, Wei, and Xu, we study the local-global principle and
Brauer--Manin obstruction for integral points on similar Markoff-type cubic
surfaces. Furthermore, we construct a family of counterexamples to the integral
Hasse principle which can be explained by the Brauer--Manin obstruction with a
greater frequency, compared to the results given by Loughran and Mitankin;
Colliot-Th\'el\`ene, Wei, and Xu.


title:
Nonlinear Hall effect with non-centrosymmetric topological phase in
  ZrTe$_5$

abstract:
  The non-centrosymmetric topological material has attracted intense attention
due to its superior characters as compared to the centrosymmetric one. On one
side, the topological phase coming from global geometric properties of the
quantum wave function remains unchanged, on the other side, abundant exotic
phenomena are predicted to be merely emerged in non-centrosymmetric ones, due
to the redistribution of local quantum geometry. Whereas, probing the local
quantum geometry in non-centrosymmetric topological material remains
challenging. Here, we report a non-centrosymmetric topological phase in
ZrTe$_5$, probed by the nonlinear Hall (NLH) effect. The angle-resolved and
temperature-dependent NLH measurement reveals the inversion and ab-plane mirror
symmetries breaking under 30 K, consistent with our theoretical calculation.
Our findings identify a new non-centrosymmetric phase of ZrTe$_5$ and provide a
platform to probe and control local quantum geometry via crystal symmetries.


title:
Agnes Mary Clerke and Mathematics in the Eleventh Encyclopedia
  Britannica

abstract:
  Agnes Mary Clerke (1842-1907) was an important contributor to the discussions
of Mathematics in the Eleventh Edition of the Encyclopedia Britannica. The
Eleventh Edition of the Encyclopedia Britannica had the audacity to "presume to
define and delineate the entire (and thus complete) opus of all human
knowledge." This is a survey of the Encyclopedia's treatment of Mathematics and
an appreciation of her writings.


title: GAN-generated Faces Detection: A Survey and New Perspectives (2022)

abstract:
  Generative Adversarial Networks (GAN) have led to the generation of very
realistic face images, which have been used in fake social media accounts and
other disinformation matters that can generate profound impacts. Therefore, the
corresponding GAN-face detection techniques are under active development that
can examine and expose such fake faces. In this work, we aim to provide a
comprehensive review of recent progress in GAN-face detection. We focus on
methods that can detect face images that are generated or synthesized from GAN
models. We classify the existing detection works into four categories: (1) deep
learning-based, (2) physical-based, (3) physiological-based methods, and (4)
evaluation and comparison against human visual performance. For each category,
we summarize the key ideas and connect them with method implementations. We
also discuss open problems and suggest future research directions.


title: NewsPod: Automatic and Interactive News Podcasts

abstract:
  News podcasts are a popular medium to stay informed and dive deep into news
topics. Today, most podcasts are handcrafted by professionals. In this work, we
advance the state-of-the-art in automatically generated podcasts, making use of
recent advances in natural language processing and text-to-speech technology.
We present NewsPod, an automatically generated, interactive news podcast. The
podcast is divided into segments, each centered on a news event, with each
segment structured as a Question and Answer conversation, whose goal is to
engage the listener. A key aspect of the design is the use of distinct voices
for each role (questioner, responder), to better simulate a conversation.
Another novel aspect of NewsPod allows listeners to interact with the podcast
by asking their own questions and receiving automatically generated answers. We
validate the soundness of this system design through two usability studies,
focused on evaluating the narrative style and interactions with the podcast,
respectively. We find that NewsPod is preferred over a baseline by
participants, with 80% claiming they would use the system in the future.


title:
Graph Meta-Reinforcement Learning for Transferable Autonomous
  Mobility-on-Demand

abstract:
  Autonomous Mobility-on-Demand (AMoD) systems represent an attractive
alternative to existing transportation paradigms, currently challenged by
urbanization and increasing travel needs. By centrally controlling a fleet of
self-driving vehicles, these systems provide mobility service to customers and
are currently starting to be deployed in a number of cities around the world.
Current learning-based approaches for controlling AMoD systems are limited to
the single-city scenario, whereby the service operator is allowed to take an
unlimited amount of operational decisions within the same transportation
system. However, real-world system operators can hardly afford to fully
re-train AMoD controllers for every city they operate in, as this could result
in a high number of poor-quality decisions during training, making the
single-city strategy a potentially impractical solution. To address these
limitations, we propose to formalize the multi-city AMoD problem through the
lens of meta-reinforcement learning (meta-RL) and devise an actor-critic
algorithm based on recurrent graph neural networks. In our approach, AMoD
controllers are explicitly trained such that a small amount of experience
within a new city will produce good system performance. Empirically, we show
how control policies learned through meta-RL are able to achieve near-optimal
performance on unseen cities by learning rapidly adaptable policies, thus
making them more robust not only to novel environments, but also to
distribution shifts common in real-world operations, such as special events,
unexpected congestion, and dynamic pricing schemes.


title: Estimating risks of option books using neural-SDE market models

abstract:
  In this paper, we examine the capacity of an arbitrage-free neural-SDE market
model to produce realistic scenarios for the joint dynamics of multiple
European options on a single underlying. We subsequently demonstrate its use as
a risk simulation engine for option portfolios. Through backtesting analysis,
we show that our models are more computationally efficient and accurate for
evaluating the Value-at-Risk (VaR) of option portfolios, with better coverage
performance and less procyclicality than standard filtered historical
simulation approaches.

categories: q-fin.CP math.PR q-fin.RM q-fin.ST stat.ML


title: Saturation for the $3$-uniform loose $3$-cycle

abstract:
  Let $F$ and $H$ be $k$-uniform hypergraphs. We say $H$ is $F$-saturated if
$H$ does not contain a subgraph isomorphic to $F$, but $H+e$ does for any
hyperedge $e\not\in E(H)$. The saturation number of $F$, denoted
$\mathrm{sat}_k(n,F)$, is the minimum number of edges in a $F$-saturated
$k$-uniform hypergraph $H$ on $n$ vertices. Let $C_3^{(3)}$ denote the
$3$-uniform loose cycle on $3$ edges. In this work, we prove that
  \[
  \left(\frac{4}3+o(1)\right)n\leq \mathrm{sat}_3(n,C_3^{(3)})\leq
\frac{3}2n+O(1).
  \]
  This is the first non-trivial result on the saturation number for a fixed
short hypergraph cycle.


title: Asymptotics of Cointegration Tests for High-Dimensional VAR($k$)

abstract:
  The paper studies non-stationary high-dimensional vector autoregressions of
order $k$, VAR($k$). Additional deterministic terms such as trend or
seasonality are allowed. The number of time periods, $T$, and number of
coordinates, $N$, are assumed to be large and of the same order. Under such
regime the first-order asymptotics of the Johansen likelihood ratio (LR),
Pillai-Barlett, and Hotelling-Lawley tests for cointegration is derived: Test
statistics converge to non-random integrals. For more refined analysis, the
paper proposes and analyzes a modification of the Johansen test. The new test
for the absence of cointegration converges to the partial sum of the Airy$_1$
point process. Supporting Monte Carlo simulations indicate that the same
behavior persists universally in many situations beyond our theorems.
  The paper presents an empirical implementation of the approach to the
analysis of stocks in S$\&$P$100$ and of cryptocurrencies. The latter example
has strong presence of multiple cointegrating relationships, while the former
is consistent with the null of no cointegration.


title: A compactness theorem for hyperkaehler 4-manifolds with boundary

abstract:
  In this paper, we study the compactness of a boundary value problem for
hyperkaehler 4-manifolds. We show that under certain topological conditions and
the positive mean curvature condition on the boundary, a sequence of
hyperkaehler triples converges smoothly up to diffeomorphisms if and only if
their restrictions to the boundary converge smoothly up to diffeomorphisms. We
also generalize this result to torsion-free hypersymplectic triples.


title:
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth
  Reinforcement Learning

abstract:
  This paper proposes a new regularization technique for reinforcement learning
(RL) towards making policy and value functions smooth and stable. RL is known
for the instability of the learning process and the sensitivity of the acquired
policy to noise. Several methods have been proposed to resolve these problems,
and in summary, the smoothness of policy and value functions learned mainly in
RL contributes to these problems. However, if these functions are extremely
smooth, their expressiveness would be lost, resulting in not obtaining the
global optimal solution. This paper therefore considers RL under local
Lipschitz continuity constraint, so-called L2C2. By designing the
spatio-temporal locally compact space for L2C2 from the state transition at
each time step, the moderate smoothness can be achieved without loss of
expressiveness. Numerical noisy simulations verified that the proposed L2C2
outperforms the task performance while smoothing out the robot action generated
from the learned policy.


title:
NICER observations of the evidence of Poynting-Robertson drag and disk
  reflection during type I X-ray bursts from 4U 1636$-$536

abstract:
  Type I X-ray bursts are the result of an unstable thermonuclear burning of
accreting matter on the neutron star (NS) surface. The quick release of
energetic X-ray photons during such bursts interacts with the surrounding
accretion disk, which raises the accretion rate due to Poynting-Robertson drag
and, thus, a fraction of the burst emission is reflected. We analyzed two
photospheric radius expansion bursts in the NS low-mass X-ray binary 4U
1636--536 that took place in 2017, using data from Neutron star Interior
Composition Explorer. The time-resolved burst spectra showed clear deviations
from a blackbody model. The spectral fitting can be significantly improved by
introducing either the enhanced persistent emission (the $f_a$ model) or the
reflection from the accretion disk (the \texttt{relxillNS} model). The $f_a$
model provides a higher blackbody temperature and higher burst flux compared
with the \texttt{relxillNS} model. The peak fluxes of two bursts from the $f_a$
model, $4.36\times10^{-8}~\mathrm{erg~cm^{-2}~s^{-1}}$ and
$9.10\times10^{-8}~\mathrm{erg~cm^{-2}~s^{-1}}$, are slightly higher than the
Eddington limits of mixed hydrogen-helium and pure helium bursts from previous
observations, respectively. When the disk reflections have been taken into
account simultaneously, the peak fluxes are lower to match the preferred
values. We find evidence to support the finding that both the
Poynting-Robertson drag and disk reflection have been appeared during these two
X-ray bursts. Moreover, the disk reflection may contribute $\sim20-30\%$ of the
total burst emissions.


title: Optimal range of Haar martingale transforms and its applications

abstract:
  Let $(\mathcal{F}_n)_{n\ge 0}$ be the standard dyadic filtration on $[0,1]$.
Let $\mathbb{E}_{\mathcal{F}_n}$ be the conditional expectation from $
L_1=L_1[0,1]$ onto $\mathcal{F} _n$, $n\ge 0$, and let $\mathbb{E}_{\mathcal{F}
_{-1}} =0$. We present the sharp estimate for the distribution function of the
martingale transform $T$ defined by \begin{align*} Tf=\sum_{m=0}^\infty \left(
\mathbb{E}_{\mathcal{F}_{2m}} f-\mathbb{E}_{\mathcal{F}_{2m-1}}f \right), ~f\in
L_1, \end{align*} in terms of the classical Calder\'{o}n operator. As an
application, for a given symmetric function space $E$ on $[0,1]$, we identify
the symmetric space $\mathcal{S}_E$, the optimal Banach symmetric range of
martingale transforms/Haar basis projections acting on $E$.


title:
New Penalized Stochastic Gradient Methods for Linearly Constrained
  Strongly Convex Optimization

abstract:
  For minimizing a strongly convex objective function subject to linear
inequality constraints, we consider a penalty approach that allows one to
utilize stochastic methods for problems with a large number of constraints
and/or objective function terms. We provide upper bounds on the distance
between the solutions to the original constrained problem and the penalty
reformulations, guaranteeing the convergence of the proposed approach. We give
a nested accelerated stochastic gradient method and propose a novel way for
updating the smoothness parameter of the penalty function and the step-size.
The proposed algorithm requires at most $\tilde O(1/\sqrt{\epsilon})$ expected
stochastic gradient iterations to produce a solution within an expected
distance of $\epsilon$ to the optimal solution of the original problem, which
is the best complexity for this problem class to the best of our knowledge. We
also show how to query an approximate dual solution after stochastically
solving the penalty reformulations, leading to results on the convergence of
the duality gap. Moreover, the nested structure of the algorithm and upper
bounds on the distance to the optimal solutions allows one to safely eliminate
constraints that are inactive at an optimal solution throughout the algorithm,
which leads to improved complexity results. Finally, we present computational
results that demonstrate the effectiveness and robustness of our algorithm.


title:
On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot
  Pools

abstract:
  Dialogue state tracking (DST) is a component of the task-oriented dialogue
system. It is responsible for extracting and managing slot values according to
dialogue utterances, where each slot represents an essential part of the
information to accomplish a task, and slot value is updated recurrently in each
dialogue turn. However, many DST models cannot update slot values
appropriately. These models may repeatedly inherit wrong slot values extracted
in previous turns, resulting in the fail of the entire DST task. They cannot
update indirectly mentioned slots well, either. This study designed a model
with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a
slot-specific memory that records all mentioned slot values that may be
inherited, and our model updates slot values according to the MSP and the
dialogue context. Our model rejects inheriting the previous slot value when it
predicates the value is wrong. Then, it re-extracts the slot value from the
current dialogue context. As the contextual information accumulates with the
dialogue progress, the new value is more likely to be correct. It also can
track the indirectly mentioned slot by picking a value from the MSP.
Experimental results showed our model reached state-of-the-art DST performance
on MultiWOZ 2.1 and 2.2 datasets.


title:
Phase-insensitive versus phase-sensitive ultrasound absorption
  tomography in the frequency domain

abstract:
  The sensitivity of phase-sensitive detectors, such as piezoelectric
detectors, becomes increasingly directional as the detector element size
increases. In contrast, pyroelectric sensors, which are phase-insensitive,
retain their omni-directionality even for large element sizes, although they
have significantly poorer temporal resolution. This study uses numerical models
to examine whether phase-insensitive detectors can be used advantageously in
ultrasound tomography, specifically absorption tomography, when the number of
detectors is sparse. We present measurement models for phase-sensitive and
phase-insensitive sensors and compare the quality of the absorption
reconstructions between these sensor types based on image contrast metrics. We
perform the inversion using synthetic data with a Jacobian-based linearised
matrix inversion approach.


title:
Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using
  Convolutional Neural Networks

abstract:
  Extreme mass ratio inspirals (EMRIs) are among the most interesting
gravitational wave (GW) sources for space-borne GW detectors. However,
successful GW data analysis remains challenging due to many issues, ranging
from the difficulty of modeling accurate waveforms, to the impractically large
template bank required by the traditional matched filtering search method. In
this work, we introduce a proof-of-principle approach for EMRI detection based
on convolutional neural networks (CNNs). We demonstrate the performance with
simulated EMRI signals buried in Gaussian noise. We show that over a wide range
of physical parameters, the network is effective for EMRI systems with a
signal-to-noise ratio larger than 50, and the performance is most strongly
related to the signal-to-noise ratio. The method also shows good generalization
ability towards different waveform models. Our study reveals the potential
applicability of machine learning technology like CNNs towards more realistic
EMRI data analysis.


title:
Disentangling the parameter space: The role of planet multiplicity in
  triggering dynamical instabilities on planetary systems around white dwarfs

abstract:
  Planets orbiting intermediate and low-mass stars are in jeopardy as their
stellar hosts evolve to white dwarfs (WDs) because the dynamics of the
planetary system changes due to the increase of the planet:star mass ratio
after stellar mass-loss. In order to understand how the planet multiplicity
affects the dynamical stability of post-main sequence (MS) systems, we perform
thousands of N-body simulations involving planetary multiplicity as the
variable and with a controlled physical and orbital parameter space: equal-mass
planets; the same orbital spacing between adjacent planet's pairs; and orbits
with small eccentricities and inclinations. We evolve the host star from the MS
to the WD phase following the system dynamics for 10 Gyr. We find that the
fraction of dynamically active simulations on the WD phase for two-planet
systems is $10.2^{+1.2}_{-1.0}$-$25.2^{+2.5}_{-2.2}$ $\%$ and increases to
$33.6^{+2.3}_{-2.2}$-$74.1^{+3.7}_{-4.6}$ $\%$ for the six-planet systems,
where the ranges cover different ranges of initial orbital separations. Our
simulations show that the more planets the system has, the more systems become
unstable when the star becomes a WD, regardless of the planet masses and range
of separations. Additional results evince that simulations with low-mass
planets (1, 10 $\mathrm{M_\oplus}$) lose at most two planets, have a large
fraction of systems undergoing orbit crossing without planet losses, and are
dynamically active for Gyr time-scales on the WD's cooling track. On the other
hand, systems with high-mass planets (100, 1000 $\mathrm{M_\oplus}$) lose up to
five planets, preferably by ejections, and become unstable in the first few
hundred Myr after the formation of the WD.


title:
Effective spin models of Kerr-nonlinear parametric oscillators for
  quantum annealing

abstract:
  A method of quantum annealing (QA) using Kerr-nonlinear parametric
oscillators (KPOs) was proposed. This method is described by bosonic operators
and has different characteristics from QA based on the transverse-field Ising
model. As the first step to describe this method in the conventional framework
of QA, we propose effective spin models of KPOs. The spin models are obtained
via a variant of the Holstein-Primakoff transformation and are described by
spin-$s$ operators. The terms for detuning, coherent driving, parametric
driving, and the Kerr effect are mapped to the transverse field, longitudinal
field, nonlinear terms for the z- and x-components of spins, respectively. By
analyzing the spin models corresponding to KPOs in several settings, we
demonstrate that the present spin models for a rather large $s$ and tuned
parameters qualitatively reproduce behavior of KPOs.


title:
Convergence analysis of the harmonic $B_z$ algorithm with single
  injection current in MREIT

abstract:
  Magnetic resonance electrical impedance tomography (MREIT) aims to recover
the electrical conductivity distribution of an object using partial information
of magnetic flux densities inside the tissue which can be measured using an MRI
scanner, with the advantage that a higher spatial resolution of conductivity
image can be provided than existing EIT techniques involving surface
measurements. Traditional MREIT reconstruction algorithms use two data sets
obtained with two linearly independent injected currents. However, injection of
two currents is often not possible in applications such as transcranial
electrical stimulation. Recently, we proposed an iterative conductivity
reconstruction algorithm called the single current harmonic $B_z$ algorithm
that demonstrated satisfactory performance in numerical and phantom tests. In
this paper, we provide a rigorous mathematical analysis of the convergence of
the iterative sequence for realizing this algorithm. We prove that, applying
some mild conditions on the exact conductivity, the iterative sequence
converges to the true solution within an explicit error bound. Such theoretical
results substantiate the reasonability and efficiency of the proposed
algorithm. We also provide more numerical evidence to validate these
theoretical results.


title:
On the robustness of the velocity anisotropy parameter in probing the
  stellar kinematics in Milky Way like galaxies: Take away from TNG50
  simulation

abstract:
  We analyze the velocity anisotropy of stars in real and energy space for a
sample of Milky Way-like galaxies in the TNG50 simulation. We employ different
selection criteria, including spatial, kinematic and metallicity cuts, and make
three halo classes ($\mathcal{A}$-$\mathcal{C}$) which show mild-to-strong
sensitivity to different selections. The above classes cover 48%, 16% and 36%
of halos, respectively. We analyze the $\beta$ radial profiles and divide them
into either monotonically increasing radial profiles or ones with peaks and
troughs. We demonstrate that halos with monotonically increasing $\beta$
profiles are mostly from class $\mathcal{A}$, whilst those with peaks/troughs
are part of classes $\mathcal{B}$-$\mathcal{C}$. This means that care must be
taken as the observationally reported peaks/troughs might be a consequence of
different selection criteria. We infer the anisotropy parameter $\beta$ energy
space and compare that against the $\beta$ radial profile. It is seen that 65%
of halos with very mild sensitivity to different selections in real space, are
those for which the $\beta$ radial and energy profiles are closely related.
Consequently, we propose that comparing the $\beta$ radial and energy profiles
might be a novel way to examine the sensitivity to different selection criteria
and thus examining the robustness of the anisotropy parameter in tracing
stellar kinematics. We compare simulated $\beta$ radial profiles against
various isolated and extended observations and demonstrate that, in most cases,
the model diversity is comparable with the error bars from different
observations, meaning that the TNG50 models are in good overall agreement with
observations.

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


title: Snowmass White Paper: Bootstrapping String Theory

abstract:
  We discuss progress and prospects in the application of bootstrap methods to
string theory.


title: Equatorial magnetoplasma waves

abstract:
  Due to its rotation, Earth traps a few equatorial ocean and atmospheric
waves, including Kelvin, Yanai, Rossby, and Poincare modes. It has been
recently demonstrated that the mathematical origin of equatorial waves is
intricately related to the nontrivial topology of hydrodynamic equations
describing oceans or the atmosphere. In the present work, we consider plasma
oscillations supported by a two-dimensional electron gas confined at the
surface of a sphere or a cylinder. We argue that in the presence of a uniform
magnetic field, these systems host a set of equatorial magnetoplasma waves that
are counterparts to the equatorial waves trapped by Earth. For a spherical
geometry, the equatorial modes are well developed only if their penetration
length is smaller than the radius of the sphere. For a cylindrical geometry,
the spectrum of equatorial modes is weakly dependent on the cylinder radius and
overcomes finite-size effects. We argue that this exceptional robustness can be
explained by destructive interference effects. We discuss possible experimental
setups, including grains and rods composed of topological insulators (e.g.,
Bi2Se3) or metal-coated dielectrics (e.g., Au2S).


title:
OLIVE: Oblivious and Differentially Private Federated Learning on
  Trusted Execution Environment

abstract:
  Differentially private federated learning (DP-FL) has received increasing
attention to mitigate the privacy risk in federated learning. Although
different schemes for DP-FL have been proposed, there is still a utility gap.
Employing central Differential Privacy in FL (CDP-FL) can provide a good
balance between the privacy and model utility, but requires a trusted server.
Using Local Differential Privacy for FL (LDP-FL) does not require a trusted
server, but suffers from lousy privacy-utility trade-off. Recently proposed
shuffle DP based FL has the potential to bridge the gap between CDP-FL and
LDP-FL without a trusted server; however, there is still a utility gap when the
number of model parameters is large. In this work, we propose OLIVE, a system
that combines the merits from CDP-FL and LDP-FL by leveraging Trusted Execution
Environment (TEE). Our main technical contributions are the analysis and
countermeasures against the vulnerability of TEE in OLIVE. Firstly, we
theoretically analyze the memory access pattern leakage of OLIVE and find that
there is a risk for sparsified gradients, which is common in FL. Secondly, we
design an inference attack to understand how the memory access pattern could be
linked to the training data. Thirdly, we propose oblivious yet efficient
algorithms to prevent the memory access pattern leakage in OLIVE. Our
experiments on real-world data demonstrate that OLIVE is efficient even when
training a model with hundreds of thousands of parameters and effective against
side-channel attacks on TEE.