title:
Analyzing and Simulating User Utterance Reformulation in Conversational
  Recommender Systems

abstract:
  User simulation has been a cost-effective technique for evaluating
conversational recommender systems. However, building a human-like simulator is
still an open challenge. In this work, we focus on how users reformulate their
utterances when a conversational agent fails to understand them. First, we
perform a user study, involving five conversational agents across different
domains, to identify common reformulation types and their transition
relationships. A common pattern that emerges is that persistent users would
first try to rephrase, then simplify, before giving up. Next, to incorporate
the observed reformulation behavior in a user simulator, we introduce the task
of reformulation sequence generation: to generate a sequence of reformulated
utterances with a given intent (rephrase or simplify). We develop methods by
extending transformer models guided by the reformulation type and perform
further filtering based on estimated reading difficulty. We demonstrate the
effectiveness of our approach using both automatic and human evaluation.


title:
Temperature-induced valence-state transition in double perovskite
  Ba2-xSrxTbIrO6

abstract:
  In this work, a temperature-induced valence-state transition is studied in a
narrow composition range of Ba$_{2-x}$Sr$_x$TbIrO$_6$. The valence-state
transition involves an electron transfer between Tb and Ir leading to the
valence-state change between Tb$^{3+}$/Ir$^{5+}$ and Tb$^{4+}$/Ir$^{4+}$
phases. This first-order transition has a dramatic effect on the lattice,
transport properties, and the long-range magnetic order at low temperatures for
both Tb and Ir ions. Ir$^{5+}$ ion has an electronic configuration of 5$d^4$
($J\rm_{eff}$ = 0) which is expected to be nonmagnetic. In contrast, Ir$^{4+}$
ion with a configuration of 5$d^5$($J\rm_{eff}$ = 1/2) favors a long-range
magnetic order. For $x$ = 0.1 with Tb$^{3+}$/Ir$^{5+}$ configuration to the
lowest temperature (2 K) investigated in this work, a spin-glass behavior is
observed around 5 K indicating Ir$^{5+}$ ($J\rm_{eff}$ = 0) ions act as a
spacer reducing the magnetic interactions between Tb$^{3+}$ ions. For $x$ = 0.5
with Tb$^{4+}$/Ir$^{4+}$ configuration below the highest temperature 400 K of
this work, a long-range antiferromagnetic order at $T\rm_N$ = 40 K is observed
highlighting the importance of Ir$^{4+}$ ($J\rm_{eff}$ = 1/2) ions in promoting
the long-range magnetic order of both Tb and Ir ions. For 0.2 $\leqslant x
\leqslant$ 0.375, a temperature-induced valence-state transition from
high-temperature Tb$^{3+}$/Ir$^{5+}$ phase to low-temperature
Tb$^{4+}$/Ir$^{4+}$ phase occurs in the temperature range 180 K $\leqslant T
\leqslant$ 325 K and the transition temperature increases with $x$. The
compositional dependence demonstrates the ability to tune the the valence state
for a critical region of $x$ that leads to a concurrent change in magnetism and
structure. This tuning ability could be employed with suitable strain in thin
films to act as a switch as the magnetism is manipulated.


title: Superspaces for heterotic pure spinor string compactifications

abstract:
  We study supersymmetry conditions for the heterotic pure superstring
preserving and N=1 supersymmetry in four dimensions directly from the curved
superspace defined by the Berkovits-Howe constraints.


title:
A publicly available multi-observatory data set of an enhanced network
  patch from the Photosphere to Corona

abstract:
  New instruments sensitive to chromospheric radiation at X-ray, UV, Visible,
IR, and sub-mm wavelengths have become available that significantly enhance our
ability to understand the bi-directional flow of energy through the
chromosphere. We describe the calibration, co-alignment, initial results, and
public release of a new data set combining a large number of these instruments
to obtain multi-wavelength photospheric, chromospheric, and coronal
observations capable of improving our understanding of the connectivity between
the photosphere and the corona via transient brightenings and wave signatures.
The observations center on a bipolar region of enhanced network magnetic flux
near disk center on SOL2017-03-17T14:00-17:00. The comprehensive data set
provides one of the most complete views of chromospheric activity related to
small scale brightenings in the corona and chromosphere to date. Our initial
analysis shows strong spatial correspondence between the areas of broadest
width of the Hydrogen-$\alpha$ spectral line and the hottest temperatures
observed in ALMA Band 3 radio data, with a linear coefficient of $6.12\times
10^{-5}$\AA{}/K. The correspondence persists for the duration of co-temporal
observations ($\approx 60$ minutes). Numerous transient brightenings were
observed in multiple data series. We highlight a single, well observed
transient brightening along a set of thin filamentary features with a duration
of 20 minutes. The timing of the peak intensity transitions from the cooler
(ALMA, 7000 K) to hotter (XRT, 3 MK) data series.


title: Optimal thermal shortcuts for harmonically confined Brownian particles

abstract:
  The overdamped Brownian dynamics of a harmonic oscillator is a paradigmatic
system in non-equilibrium statistical mechanics, which reliably models relevant
stochastic systems such as colloidal particles submitted to optical
confinement. In this work, optimal thermal protocols are tailored to minimise
the connection time between equilibrium states of overdamped $d$-dimensional
oscillators. Application of control theory reveals that optimal protocols are
of bang-bang type, that is, the temperature of the bath has to take
alternatively the minimum and maximum values allowed. Minimum connection times
increase with the considered dimension $d$. Remarkably, this is the case even
for symmetric oscillators, for example, with spherical symmetry -- in which the
degeneracy of the elastic constant along the $d$ possible directions seems to
imply a minimum connection time equal to that for the one-dimensional case.
This surprising unavoidable price to pay when increasing dimension is
thoroughly investigated and understood on a physical basis. Moreover,
information theory tools such as the thermodynamic length and its divergence
are analysed over the optimal protocol.


title:
Traversing Supervisor Problem: An Approximately Optimal Approach to
  Multi-Robot Assistance

abstract:
  The number of multi-robot systems deployed in field applications has
increased dramatically over the years. Despite the recent advancement of
navigation algorithms, autonomous robots often encounter challenging situations
where the control policy fails and the human assistance is required to resume
robot tasks. Human-robot collaboration can help achieve high-levels of
autonomy, but monitoring and managing multiple robots at once by a single human
supervisor remains a challenging problem. Our goal is to help a supervisor
decide which robots to assist in which order such that the team performance can
be maximized. We formulate the one-to-many supervision problem in uncertain
environments as a dynamic graph traversal problem. An approximation algorithm
based on the profitable tour problem on a static graph is developed to solve
the original problem, and the approximation error is bounded and analyzed. Our
case study on a simulated autonomous farm demonstrates superior team
performance than baseline methods in task completion time and human working
time, and that our method can be deployed in real-time for robot fleets with
moderate size.


title:
On the asymptotic distribution of the symmetrized Chatterjee's
  correlation coefficient

abstract:
  Chatterjee (2021) introduced an asymmetric correlation measure that has
attracted much attention over the past year. In this paper, we derive the
asymptotic distribution of the symmetric version of Chatterjee's correlation,
and suggest a finite sample test for independence.


title:
Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient
  Dynamic MR Image Reconstruction

abstract:
  Non-Cartesian sampling with subspace-constrained image reconstruction is a
popular approach to dynamic MRI, but slow iterative reconstruction limits its
clinical application. Data-consistent (DC) deep learning can accelerate
reconstruction with good image quality, but has not been formulated for
non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian
deep subspace learning framework for fast, accurate dynamic MR image
reconstruction. Four novel DC formulations are developed and evaluated: two
gradient decent approaches, a directly solved approach, and a conjugate
gradient approach. We applied a U-Net model with and without DC layers to
reconstruct T1-weighted images for cardiac MR Multitasking (an advanced
multidimensional imaging method), comparing our results to the iteratively
reconstructed reference. Experimental results show that the proposed framework
significantly improves reconstruction accuracy over the U-Net model without DC,
while significantly accelerating the reconstruction over conventional iterative
reconstruction.


title:
Topical Review: Greybody Factors and Quasinormal Modes for Black Holes
  in Various Theories -- Fingerprints of Invisibles

abstract:
  We give a pedagogical introduction to black holes (BHs) greybody factors
(GFs) and quasinormal modes (QNMs) and share the recent developments on those
subjects. In this study, our primary focus will be on the bosonic and fermionic
GFs and QNMs of various BH and brane geometries and reveal the fingerprints of
the invisibles with the radiation spectra to be obtained by the WKB
approximation and bounding the Bogoliubov coefficients (together with the
Miller-Good transformation) methods. (*Due to the notification of arXiv "The
Abstract field cannot be longer than 1,920 characters", the appeared Abstract
is shortened. For the full Abstract, please download the Article.)


title: Os Dados dos Brasileiros sob Risco na Era da Intelig\^encia Artificial?

abstract:
  Advances in image processing and analysis as well as machine learning
techniques have contributed to the use of biometric recognition systems in
daily people tasks. These tasks range from simple access to mobile devices to
tagging friends in photos shared on social networks and complex financial
operations on self-service devices for banking transactions. In China, the use
of these systems goes beyond personal use becoming a country's government
policy with the objective of monitoring the behavior of its population. On July
05th 2021, the Brazilian government announced acquisition of a biometric
recognition system to be used nationwide. In the opposite direction to China,
Europe and some American cities have already started the discussion about the
legality of using biometric systems in public places, even banning this
practice in their territory. In order to open a deeper discussion about the
risks and legality of using these systems, this work exposes the
vulnerabilities of biometric recognition systems, focusing its efforts on the
face modality. Furthermore, it shows how it is possible to fool a biometric
system through a well-known presentation attack approach in the literature
called morphing. Finally, a list of ten concerns was created to start the
discussion about the security of citizen data and data privacy law in the Age
of Artificial Intelligence (AI).


title: Optimal minimization of the covariance loss

abstract:
  Let $X$ be a random vector valued in $\mathbb{R}^{m}$ such that $\|X\|_{2}
\le 1$ almost surely. For every $k\ge 3$, we show that there exists a sigma
algebra $\mathcal{F}$ generated by a partition of $\mathbb{R}^{m}$ into $k$
sets such that
  \[\|\operatorname{Cov}(X) - \operatorname{Cov}(\mathbb{E}[X\mid\mathcal{F}])
  \|_{\mathrm{F}} \lesssim \frac{1}{\sqrt{\log{k}}}.\] This is optimal up to
the implicit constant and improves on a previous bound due to Boedihardjo,
Strohmer, and Vershynin.
  Our proof provides an efficient algorithm for constructing $\mathcal{F}$ and
leads to improved accuracy guarantees for $k$-anonymous or differentially
private synthetic data. We also establish a connection between the above
problem of minimizing the covariance loss and the pinning lemma from
statistical physics, providing an alternate (and much simpler) algorithmic
proof in the important case when $X \in \{\pm 1\}^m/\sqrt{m}$ almost surely.


title:
Efficient Algorithms for Minimizing Compositions of Convex Functions and
  Random Functions and Its Applications in Network Revenue Management

abstract:
  In this paper, we study a class of nonconvex stochastic optimization in the
form of $\min_{x\in\mathcal{X}} F(x):=\mathbb{E}_\xi [f(\phi(x,\xi))]$, where
the objective function $F$ is a composition of a convex function $f$ and a
random function $\phi$. Leveraging an (implicit) convex reformulation via a
variable transformation $u=\mathbb{E}[\phi(x,\xi)]$, we develop stochastic
gradient-based algorithms and establish their sample and gradient complexities
for achieving an $\epsilon$-global optimal solution. Interestingly, our
proposed Mirror Stochastic Gradient (MSG) method operates only in the original
$x$-space using gradient estimators of the original nonconvex objective $F$ and
achieves $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample and gradient complexities,
which matches the lower bounds for solving stochastic convex optimization
problems. Under booking limits control, we formulate the air-cargo network
revenue management (NRM) problem with random two-dimensional capacity, random
consumption, and routing flexibility as a special case of the stochastic
nonconvex optimization, where the random function $\phi(x,\xi)=x\wedge\xi$,
i.e., the random demand $\xi$ truncates the booking limit decision $x$.
Extensive numerical experiments demonstrate the superior performance of our
proposed MSG algorithm for booking limit control with higher revenue and lower
computation cost than state-of-the-art bid-price-based control policies,
especially when the variance of random capacity is large.
  KEYWORDS: stochastic nonconvex optimization, hidden convexity, air-cargo
network revenue management, gradient-based algorithms


title:
Proximal stabilized Interior Point Methods for quadratic programming and
  low-frequency-updates preconditioning techniques

abstract:
  In this work, in the context of Linear and Quadratic Programming, we
interpret Primal Dual Regularized Interior Point Methods (PDR-IPMs) in the
framework of the Proximal Point Method. The resulting Proximal Stabilized IPM
(PS-IPM) is strongly supported by theoretical results concerning convergence
and the rate of convergence, and can handle degenerate problems. Moreover, in
the second part of this work, we analyse the interactions between the
regularization parameters and the computational foot-print of the linear
algebra routines used to solve the Newton linear systems. In particular, when
these systems are solved using an iterative Krylov method, we propose general
purpose preconditioners which, exploiting the regularization and a new
rearrangement of the Schur complement, remain attractive for a series of
subsequent IPM iterations. Therefore they need to be recomputed only in a
fraction of the total IPM iterations. The resulting regularized second order
methods, for which low-frequency-updates of the preconditioners are allowed,
pave the path for an alternative third way in-between first and second order
methods.


title:
CO Line Emission Surfaces and Vertical Structure in Mid-Inclination
  Protoplanetary Disks

abstract:
  High spatial resolution CO observations of mid-inclination (30-75{\deg})
protoplanetary disks offer an opportunity to study the vertical distribution of
CO emission and temperature. The asymmetry of line emission relative to the
disk major axis allows for a direct mapping of the emission height above the
midplane, and for optically-thick, spatially-resolved emission in LTE, the
intensity is a measure of the local gas temperature. Our analysis of ALMA
archival data yields CO emission surfaces, dynamically-constrained stellar host
masses, and disk atmosphere gas temperatures for the disks around: HD 142666,
MY Lup, V4046 Sgr, HD 100546, GW Lup, WaOph 6, DoAr 25, Sz 91, CI Tau, and DM
Tau. These sources span a wide range in stellar masses (0.50-2.10 M$_{\odot}$),
ages (${\sim}$0.3-23 Myr), and CO gas radial emission extents
(${\approx}$200-1000 au). This sample nearly triples the number of disks with
mapped emission surfaces and confirms the wide diversity in line emitting
heights ($z/r\approx0.1$ to ${\gtrsim}0.5$) hinted at in previous studies. We
compute radial and vertical CO gas temperature distributions for each disk. A
few disks show local temperature dips or enhancements, some of which correspond
to dust substructures or the proposed locations of embedded planets. Several
emission surfaces also show vertical substructures, which all align with rings
and gaps in the millimeter dust. Combining our sample with literature sources,
we find that CO line emitting heights weakly decline with stellar mass and gas
temperature, which, despite large scatter, is consistent with simple scaling
relations. We also observe a correlation between CO emission height and disk
size, which is due to the flared structure of disks. Overall, CO emission
surfaces trace ${\approx}2$-$5\times$ gas pressure scale heights (H$_{\rm{g}}$)
and could potentially be calibrated as empirical tracers of H$_{\rm{g}}$.


title:
Deep Multi-Scale U-Net Architecture and Noise-Robust Training Strategies
  for Histopathological Image Segmentation

abstract:
  Although the U-Net architecture has been extensively used for segmentation of
medical images, we address two of its shortcomings in this work. Firstly, the
accuracy of vanilla U-Net degrades when the target regions for segmentation
exhibit significant variations in shape and size. Even though the U-Net already
possesses some capability to analyze features at various scales, we propose to
explicitly add multi-scale feature maps in each convolutional module of the
U-Net encoder to improve segmentation of histology images. Secondly, the
accuracy of a U-Net model also suffers when the annotations for supervised
learning are noisy or incomplete. This can happen due to the inherent
difficulty for a human expert to identify and delineate all instances of
specific pathology very precisely and accurately. We address this challenge by
introducing auxiliary confidence maps that emphasize less on the boundaries of
the given target regions. Further, we utilize the bootstrapping properties of
the deep network to address the missing annotation problem intelligently. In
our experiments on a private dataset of breast cancer lymph nodes, where the
primary task was to segment germinal centres and sinus histiocytosis, we
observed substantial improvement over a U-Net baseline based on the two
proposed augmentations.


title:
Remarks on cyclic inner functions in growth classes and applications to
  approximation problems

abstract:
  It is well-known that for any inner function $\theta$ defined in the unit
disk $D$ the following two conditons: $(i)$ there exists a sequence of
polynomials $\{p_n\}_n$ such that $\lim_{n \to \infty} \theta(z) p_n(z) = 1$
for all $z \in D$, and $(ii)$ $\sup_n \| \theta p_n \|_\infty < \infty$, are
incompatible, i.e., cannot be satisfied simultaneously. In this note we discuss
and apply a consequence of a result by Thomas Ransford, which shows that if we
relax the second condition to allow for arbitrarily slow growth of the sequence
$\{ \theta(z) p_n(z)\}_n$ as $|z| \to 1$, then condition $(i)$ can be met. In
other words, every growth class of analytic functions contains cyclic singular
inner functions. We apply this observation to properties of decay of Taylor
coefficients and moduli of continuity of functions in model spaces $K_\theta$.
In particular, we establish a variant of a result of Khavinson and Dyakonov on
non-existence of functions with certain smoothness properties in $K_\theta$,
and we show that the classical Aleksandrov theorem on density of continuous
functions in $K_\theta$, and its generalization to de Branges-Rovnyak spaces
$\mathcal{H}(b)$, is essentially sharp.


title: Lifting globally $F$-split surfaces to characteristic zero

abstract:
  We prove that every globally F-split surface admits an equisingular lifting
over the ring of Witt vectors.


title:
The ICML 2022 Expressive Vocalizations Workshop and Competition:
  Recognizing, Generating, and Personalizing Vocal Bursts

abstract:
  The ICML Expressive Vocalization (ExVo) Competition is focused on
understanding and generating vocal bursts: laughs, gasps, cries, and other
non-verbal vocalizations that are central to emotional expression and
communication. ExVo 2022, includes three competition tracks using a large-scale
dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-MultiTask,
requires participants to train a multi-task model to recognize expressed
emotions and demographic traits from vocal bursts. The second, ExVo-Generate,
requires participants to train a generative model that produces vocal bursts
conveying ten different emotions. The third, ExVo-FewShot, requires
participants to leverage few-shot learning incorporating speaker identity to
train a model for the recognition of 10 emotions conveyed by vocal bursts. This
paper describes the three tracks and provides performance measures for baseline
models using state-of-the-art machine learning strategies. The baseline for
each track is as follows, for ExVo-MultiTask, a combined score, computing the
harmonic mean of Concordance Correlation Coefficient (CCC), Unweighted Average
Recall (UAR), and inverted Mean Absolute Error (MAE) ($S_{MTL}$) is at best,
0.335 $S_{MTL}$; for ExVo-Generate, we report Fr\'echet inception distance
(FID) scores ranging from 4.81 to 8.27 (depending on the emotion) between the
training set and generated samples. We then combine the inverted FID with
perceptual ratings of the generated samples ($S_{Gen}$) and obtain 0.174
$S_{Gen}$; and for ExVo-FewShot, a mean CCC of 0.444 is obtained.


title: The time-dependent harmonic oscillator revisited

abstract:
  We re-examine the time-dependent harmonic oscillator $\ddot q=-\omega^2 q$
under various regularity assumptions. Where $\omega(t)$ is continuously
differentiable we reduce its integration to that of a single first order
equation, i.e. the Hamilton equation for the angle variable $\psi$ alone (the
action variable ${\cal I}$ does not appear). Reformulating the generic Cauchy
problem for $\psi(t)$ as a Volterra-type integral equation and applying the
fixed point theorem we obtain a sequence $\{\psi^{(h)}\}_{h\in\mathbb{N}_0}$
converging uniformly to $\psi$ in every compact time interval; if $\omega$
varies slowly or little, already $\psi^{(0)}$ approximates $\psi$ well for
rather long time lapses. The discontinuities of $\omega$, if any, determine
those of $\psi,{\cal I}$. The zeroes of $q,\dot q$ are investigated with the
help of two Riccati equations. As a demo of the potential of our approach, we
briefly argue that it yields good approximations of the solutions (as well as
exact bounds on them) and thereby may simplify the study of: the stability of
the trivial one; the occurrence of parametric resonance when $\omega(t)$ is
periodic; the adiabatic invariance of ${\cal I}$; the asymptotic expansions in
a slow time parameter $\varepsilon$; etc.


title:
Learning Multi-dimensional Edge Feature-based AU Relation Graph for
  Facial Action Unit Recognition

abstract:
  The activations of Facial Action Units (AUs) mutually influence one another.
While the relationship between a pair of AUs can be complex and unique,
existing approaches fail to specifically and explicitly represent such cues for
each pair of AUs in each facial display. This paper proposes an AU relationship
modelling approach that deep learns a unique graph to explicitly describe the
relationship between each pair of AUs of the target facial display. Our
approach first encodes each AU's activation status and its association with
other AUs into a node feature. Then, it learns a pair of multi-dimensional edge
features to describe multiple task-specific relationship cues between each pair
of AUs. During both node and edge feature learning, our approach also considers
the influence of the unique facial display on AUs' relationship by taking the
full face representation as an input. Experimental results on BP4D and DISFA
datasets show that both node and edge feature learning modules provide large
performance improvements for CNN and transformer-based backbones, with our best
systems achieving the state-of-the-art AU recognition results. Our approach not
only has a strong capability in modelling relationship cues for AU recognition
but also can be easily incorporated into various backbones. Our PyTorch code is
made available.


title:
A Pre-study on Data Processing Pipelines for Roadside Object Detection
  Systems Towards Safer Road Infrastructure

abstract:
  Single-vehicle accidents are the most common type of fatal accidents in
Sweden, where a car drives off the road and runs into hazardous roadside
objects. Proper installation and maintenance of protective objects, such as
crash cushions and guard rails, may reduce the chance and severity of such
accidents. Moreover, efficient detection and management of hazardous roadside
objects also plays an important role in improving road safety. To better
understand the state-of-the-art and system requirements, in this pre-study, we
investigate the feasibility, implementation, limitations and scaling up of data
processing pipelines for roadside object detection. In particular, we divide
our investigation into three parts: the target of interest, the sensors of
choice and the algorithm design. The data sources we consider in this study
cover two common setups: 1) road surveying fleet - annual scans conducted by
Trafikverket, the Swedish Transport Administration, and 2) consumer vehicle -
data collected using a research vehicle from the laboratory of Resource for
vehicle research at Chalmers (REVERE). The goal of this report is to
investigate how to implement a scalable roadside object detection system
towards safe road infrastructure and Sweden's Vision Zero.


title: Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration

abstract:
  Laser-driven ion beams have gained considerable attention for their potential
use in multidisciplinary research and technology. Pre-clinical studies into
their radiobiological effectiveness have established the prospect of using
laser-driven ion beams for radiotherapy. In particular, research into the
beneficial effects of ultra-high instantaneous dose rates is enabled by the
high ion bunch charge and uniquely short bunch lengths present for laser-driven
ion beams. Such studies require reliable, online dosimetry methods to monitor
the bunch charge for every laser shot to ensure that the prescribed dose is
accurately applied to the biological sample. In this paper we present the first
successful use of an Integrating Current Transformer (ICT) for laser-driven ion
accelerators. This is a non-invasive diagnostic to measure the charge of the
accelerated ion bunch. It enables online dose measurements in radiobiological
experiments and facilitates ion beam tuning, in particular, optimization of the
laser ion source and alignment of the proton transport beamline. We present the
ICT implementation and the correlation with other diagnostics such as
radiochromic films, a Thomson parabola spectrometer and a scintillator.


title: The limitations of the theoretical analysis of applied algorithms

abstract:
  The theoretical analysis of performance has been an important tool in the
engineering of algorithms in many application domains. Its goals are to predict
the empirical performance of an algorithm and to be a yardstick that drives the
design of novel algorithms that perform well in practice. While these goals
have been achieved in many instances, they have not been achieved in some other
crucial application domains. In this paper, I focus on the example of
sequencing bioinformatics, an inter-disciplinary field that uses algorithms to
extract biological meaning from genome sequencing data. I will demonstrate two
concrete examples of how theoretical analysis has failed to achieve its goals
but also give one encouraging example of success. I will then catalog some of
the challenges of applying theoretical analysis to sequencing bioinformatics,
argue why empirical analysis is not enough, and give a vision for improving the
relevance of theoretical analysis to sequencing bioinformatics and other
application domains. By recognizing the problem, understanding its roots, and
providing potential solutions, this work can hopefully be a crucial first step
towards making theoretical analysis more relevant in modern application
domains.


title: On Perturbation theory for the Fokker--Planck equation

abstract:
  We discuss a recent application of the Modified Homotopy Perturbation Method
to the Fokker--Planck equation and show that the selected examples do not have
any connection with actual physical problems.


title: The Landscape of Large Cardinals

abstract:
  The purpose of this paper is to provide an introductory overview of the large
cardinal hierarchy in set theory. By a large cardinal, we mean any cardinal
$\kappa$ whose existence is strong enough of an assumption to prove the
consistency of ZFC. We assume basic familiarity with set theory, model theory,
and the ZFC axioms, though certain concepts will be reviewed as necessary. We
attempt to clarify the vast landscape of the higher infinite, motivating the
definitions of some (but certainly not all) of the known large cardinals. We
also discuss connections with the Continuum Hypothesis and provide some
philosophical reflections on belief in the consistency of large cardinals.


title:
Logical metatheorems for accretive and (generalized) monotone set-valued
  operators

abstract:
  Accretive and monotone operator theory are central branches of nonlinear
functional analysis and constitute the abstract study of set-valued mappings
between function spaces. This paper deals with the computational properties of
certain large classes of operators, namely accretive and (generalized) monotone
set-valued ones. In particular, we develop (and extend) for this field the
theoretical framework of proof mining, a program in mathematical logic that
seeks to extract computational information from prima facie `non-computational'
proofs from the mainstream literature. To this end, we establish logical
metatheorems that guarantee and quantify the computational content of theorems
pertaining to accretive and (generalized) monotone set-valued operators. On one
hand, our results unify a number of recent case studies, while they also
provide characterizations of central analytical notions in terms of proof
theoretic ones which provides a crucial perspective on needed quantitative
assumptions in future applications of proof mining to these branches.


title: Do More Negative Samples Necessarily Hurt in Contrastive Learning?

abstract:
  Recent investigations in noise contrastive estimation suggest, both
empirically as well as theoretically, that while having more "negative samples"
in the contrastive loss improves downstream classification performance
initially, beyond a threshold, it hurts downstream performance due to a
"collision-coverage" trade-off. But is such a phenomenon inherent in
contrastive learning? We show in a simple theoretical setting, where positive
pairs are generated by sampling from the underlying latent class (introduced by
Saunshi et al. (ICML 2019)), that the downstream performance of the
representation optimizing the (population) contrastive loss in fact does not
degrade with the number of negative samples. Along the way, we give a
structural characterization of the optimal representation in our framework, for
noise contrastive estimation. We also provide empirical support for our
theoretical results on CIFAR-10 and CIFAR-100 datasets.


title: Complex dynamics of a heterogeneous network of Hindmarsh-Rose neurons

abstract:
  In this contribution, we have considered the collective behavior of the two
as well as the network of heterogeneous coupled Hindmarsh Rose (HR) neurons.
The heterogeneous models were made of a memristive 2D (HR) and the traditional
3D HR neurons. Investigating a model of two coupled neurons through an
electrical synapse reveals dissipative properties. When control parameters are
varied, the coupled neuron model exhibits rich dynamics, such as the periodic,
quasi-periodic, and chaotic dynamics involving either bursting or spiking
oscillations. For weak electrical coupling strength, non-synchronized motion is
observed. But in the case of higher coupling strength, synchronized cluster
states are observed. Besides, ring-star networks of up to 100 under three
different heterogeneous topologies are being investigated, and various
spatiotemporal patterns are explored. It is found that the spatiotemporal
patterns depend on the topology of the heterogeneous network considered. A new
clustered chimera state is revealed qualitatively via the recurrence plot. The
cluster states are indicated in the ring and star configurations of the
heterogeneous network. Single and double-well chimera states have been revealed
in the ring and ring-star structures. Finally, an equivalent electronic circuit
for the two coupled heterogeneous is designed and investigated in the PSIM
simulation environment. A perfect match is observed between the results
obtained from the designed analog circuit and the mathematical model of the two
coupled neurons, which supports the fact that our obtained results are not
related to an artifact.


title: TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

abstract:
  We present TartanDrive, a large scale dataset for learning dynamics models
for off-road driving. We collected a dataset of roughly 200,000 off-road
driving interactions on a modified Yamaha Viking ATV with seven unique sensing
modalities in diverse terrains. To the authors' knowledge, this is the largest
real-world multi-modal off-road driving dataset, both in terms of number of
interactions and sensing modalities. We also benchmark several state-of-the-art
methods for model-based reinforcement learning from high-dimensional
observations on this dataset. We find that extending these models to
multi-modality leads to significant performance on off-road dynamics
prediction, especially in more challenging terrains. We also identify some
shortcomings with current neural network architectures for the off-road driving
task. Our dataset is available at https://github.com/castacks/tartan_drive.


title: Growth of groups with linear Schreier graphs

abstract:
  We introduce a new method of proving upper estimates of growth of finitely
generated groups and constructing groups of intermediate growth using graphs of
their actions. These estimates are of the form $\exp(n^\alpha)$ for some
$\alpha<1$, and provide the first examples of such bounds for simple groups of
intermediate growth.


title:
Robustness of binary stochastic neurons implemented with low barrier
  nanomagnets made of dilute magnetic semiconductors

abstract:
  Binary stochastic neurons (BSNs) are excellent hardware accelerators for
machine learning. A popular platform for implementing them are low- or
zero-energy barrier nanomagnets possessing in-plane magnetic anisotropy (e.g.
circular disks or quasi-elliptical disks with very small eccentricity).
Unfortunately, small geometric variations in the lateral shapes of such
nanomagnets can produce large changes in the BSN response times if the
nanomagnets are made of common metallic ferromagnets (Co, Ni, Fe) with large
saturation magnetization. Additionally, the response times are also very
sensitive to initial conditions. Here, we show that if the nanomagnets are made
of dilute magnetic semiconductors with much smaller saturation magnetization,
then the variability in their response times (due to shape variations and
variation in the initial condition) is drastically suppressed. This
significantly reduces the device-to-device variation, which is a serious
challenge for large scale neuromorphic systems.


title:
Meta-Cognition. An Inverse-Inverse Reinforcement Learning Approach for
  Cognitive Radars

abstract:
  This paper considers meta-cognitive radars in an adversarial setting. A
cognitive radar optimally adapts its waveform (response) in response to
maneuvers (probes) of a possibly adversarial moving target. A meta-cognitive
radar is aware of the adversarial nature of the target and seeks to mitigate
the adversarial target. How should the meta-cognitive radar choose its
responses to sufficiently confuse the adversary trying to estimate the radar's
utility function? This paper abstracts the radar's meta-cognition problem in
terms of the spectra (eigenvalues) of the state and observation noise
covariance matrices, and embeds the algebraic Riccati equation into an
economics-based utility maximization setup. This adversarial target is an
inverse reinforcement learner. By observing a noisy sequence of radar's
responses (waveforms), the adversarial target uses a statistical hypothesis
test to detect if the radar is a utility maximizer. In turn, the meta-cognitive
radar deliberately chooses sub-optimal responses that increasing its Type-I
error probability of the adversary's detector. We call this counter-adversarial
step taken by the meta-cognitive radar as inverse inverse reinforcement
learning (I-IRL). We illustrate the meta-cognition results of this paper via
simple numerical examples. Our approach for meta-cognition in this paper is
based on revealed preference theory in micro-economics and inspired by results
in differential privacy and adversarial obfuscation in machine learning.


title: Bayesian index models for heterogeneous treatment effects

abstract:
  The general idea of this article is to develop a Bayesian model with a
flexible link function connecting an exponential family treatment response to a
linear combination of covariates and a treatment indicator and the interaction
between the two. Generalized linear models allowing data-driven link functions
are often called "single-index models," and among popular semi-parametric
modeling methods. In this article, we will focus on modeling heterogeneous
treatment effects, with the goal of developing a treatment benefit index (TBI)
incorporating prior information from historical data. This treatment benefit
index can be useful for stratifying patients according to their predicted
treatment benefit levels and can be especially useful for precision health
applications. The proposed method is applied to a COVID-19 treatment study.


title: Iterated Jump Graphs

abstract:
  The jump graph $J(G)$ of a simple graph $G$ has vertices which represent
edges in $G$ where two vertices in $J(G)$ are adjacent if and only if the
corresponding edges in $G$ do not share an endpoint. In this paper, we examine
sequences of graphs generated by iterating the jump graph operation and
characterize the behavior of this sequence for all initial graphs. We build on
work by Chartrand et al. who showed that a handful of jump graph sequences
terminate and two sequences converge. We extend these results by showing that
there are no non-trivial repeating sequences of jump graphs. All diverging jump
graph sequences grow without bound while accumulating certain subgraphs.


title: Coded Transaction Broadcasting for High-throughput Blockchains

abstract:
  High-throughput blockchains require efficient transaction broadcast
mechanisms that can deliver transactions to most network nodes with low
bandwidth overhead and latency. Existing schemes coordinate transmissions
across peers to avoid sending redundant data, but they either incur a high
latency or are not robust against adversarial network nodes. We present
Strokkur, a new transaction broadcasting mechanism that provides both low
bandwidth overhead and low latency. The core idea behind Strokkur is to avoid
explicit coordination through randomized transaction coding. Rather than
forward individual transactions. Strokkur nodes send out codewords -- XOR sums
of multiple transactions selected at random. Since almost every codeword is
useful for the receiver to decode new transactions, Strokkur nodes do not
require coordination, for example, to determine which transactions the receiver
is missing. Strokkur's coding strategy builds on LT codes, a popular class of
rateless erasure codes, and extends them to support multiple uncoordinated
senders with partially-overlapping continual streams of transaction data.
Strokkur introduces mechanisms to cope with adversarial senders that may send
corrupt codewords, and a simple rate control algorithm that enables each node
to independently determine an appropriate sending rate of codewords for each
peer. Our implementation of Strokkur in Golang supports 647k transactions per
second using only one CPU core. Our evaluation across a 19-node Internet
deployment and large-scale simulation show that Strokkur consumes 2--7.6x less
bandwidth than the existing scheme in Bitcoin, and 9x lower latency that Shrec
when only 4% of nodes are adversarial.


title:
SNR G292.0+1.8: A Remnant of a Low-Mass Progenitor Stripped-Envelope
  Supernova

abstract:
  We present a study of the Galactic supernova remnant (SNR) G292.0+1.8, a
classic example of a core-collapse SNR that contains oxygen-rich ejecta,
circumstellar material, a rapidly moving pulsar, and a pulsar wind nebula
(PWN). We use hydrodynamic simulations of the remnant evolution to show that
the SNR reverse shock is interacting with the PWN and has most likely shocked
the majority of supernova ejecta. In our models, such a scenario requires a
total ejecta mass of $\lesssim 3\: \rm M_{\odot}$ and implies that there is no
significant quantity of cold ejecta in the interior of the reverse shock. In
light of these results, we compare the estimated elemental masses and abundance
ratios in the reverse-shocked ejecta to nucleosynthesis models and find that
they are consistent with a progenitor star with an initial mass of 12-16 $\:
\rm M_{\odot}$. We conclude that the progenitor of G292.0+1.8 was likely a
relatively low mass star that experienced significant mass loss through a
binary interaction and would have produced a stripped-envelope supernova
explosion. We also argue that the region known as the "spur" in G292.0+1.8
arises as a result of the pulsar's motion through the supernova ejecta and that
its dynamical properties may suggest a line-of-sight component to the pulsar's
velocity, leading to a total space velocity of $\sim 600\: \rm km\:s^{-1}$ and
implying a significant natal kick. Finally, we discuss binary mass loss
scenarios relevant to G292.0+1.8 and their implications for the binary
companion properties and future searches.


title: Snowmass 2021 White Paper: Observational Signatures of Quantum Gravity

abstract:
  This short review is intended as a colloquium-level summary, for the Snowmass
2021 process, on recent theoretical results on infrared observables in quantum
gravity. We rely on simple physical arguments, most notably a random walk
intuition, to show how effects of quantum gravity in the ultraviolet (at the
Planck length $\ell_p \approx 10^{-35} \mbox{ m}$) may integrate into the
infrared when the large measurement length scale $L$ enters into the
observable. A quantum uncertainty at lightsheet horizons would give rise to an
accumulated effect of size $\delta L^2 \simeq \ell_p L/4 \pi$. We discuss how
the random walk intuition falls out from more formal calculations, such as from
AdS/CFT, from the dimensional reduction of the Einstein-Hilbert action to
dilaton gravity, from multiple gravitational shockwaves generated by vacuum
energy fluctuations, as well as from an effective description of gravity as a
fluid. We overview experimental prospects for measuring this effect with a
simple Michelson interferometer utilizing many of the tools developed for
gravitational wave observatories.


title:
Synthesized Speech Detection Using Convolutional Transformer-Based
  Spectrogram Analysis

abstract:
  Synthesized speech is common today due to the prevalence of virtual
assistants, easy-to-use tools for generating and modifying speech signals, and
remote work practices. Synthesized speech can also be used for nefarious
purposes, including creating a purported speech signal and attributing it to
someone who did not speak the content of the signal. We need methods to detect
if a speech signal is synthesized. In this paper, we analyze speech signals in
the form of spectrograms with a Compact Convolutional Transformer (CCT) for
synthesized speech detection. A CCT utilizes a convolutional layer that
introduces inductive biases and shared weights into a network, allowing a
transformer architecture to perform well with fewer data samples used for
training. The CCT uses an attention mechanism to incorporate information from
all parts of a signal under analysis. Trained on both genuine human voice
signals and synthesized human voice signals, we demonstrate that our CCT
approach successfully differentiates between genuine and synthesized speech
signals.


title:
Quantitative results on algorithms for zeros of differences of monotone
  operators in Hilbert space

abstract:
  We provide quantitative information in the form of a rate of metastability in
the sense of T. Tao and (under a metric regularity assumption) a rate of
convergence for an algorithm approximating zeros of differences of maximally
monotone operators due to A. Moudafi by using techniques from `proof mining', a
subdiscipline of mathematical logic. For the rate of convergence, we provide an
abstract and general result on the construction of rates of convergence for
quasi-Fej\'er monotone sequences with metric regularity assumptions,
generalizing previous results for Fej\'er monotone sequences due to U.
Kohlenbach, G. L\'opez-Acedo and A. Nicolae.


title:
A Review on Pushing the Limits of Baseline Recommendation Systems with
  the integration of Opinion Mining & Information Retrieval Techniques

abstract:
  Recommendations Systems allow users to identify trending items among a
community while being timely and relevant to the user's expectations. When the
purpose of various Recommendation Systems differs, the required type of
recommendations also differs for each use case. While one Recommendation System
may focus on recommending popular items, another may focus on recommending
items that are comparable to the user's interests. Content-based filtering,
user-to-user & item-to-item Collaborative filtering, and more recently; Deep
Learning methods have been brought forward by the researchers to achieve better
quality recommendations.
  Even though each of these methods has proven to perform well individually,
there have been attempts to push the boundaries of their limitations. Following
a wide range of methods, researchers have tried to expand on the capabilities
of standard recommendation systems to provide the most effective
recommendations to users while being more profitable from a business's
perspective. This has been achieved by taking a hybrid approach when building
models and architectures for Recommendation Systems.
  This paper is a review of the novel models & architectures of hybrid
Recommendation Systems. The author identifies possibilities of expanding the
capabilities of baseline models & the advantages and drawbacks of each model
with selected use cases in this review.


title:
Identification of weakly- to strongly-turbulent three-wave processes in
  a micro-scale system

abstract:
  We study capillary wave turbulence (WT) inherently spanning multiple
dynamical regimes and geometries-from weakly to strongly nonlinear WT (SWT) and
from shallow to deep domains-all within a 40 uL volume. These conditions
violate idealizations used in modern WT theories. This study is now viable with
recent advances in ultra-high-speed digital holographic microscopy, providing
10 us time and 10 nm spatial resolutions for images across the entire field of
view, encompassing a complete wave system. We derive tractable parameters
permitting direct identification of four fundamental WT regimes, all present in
this system. A proposed nonlinearity measure permits comparative analysis over
varied inputs. Results indicate SWT precedes ultrasonic atomization. Our
findings promote further SWT study toward improving associated applications.
This work augments current understanding of WT regimes and behaviors, and
directly applies to many fields beyond fluid mechanics. Curated data (300 GB)
are freely provided for download.


title: Data fusion for predicting long-term program impacts

abstract:
  Policymakers often require information on programs' long-term impacts that is
not available when decisions are made. We demonstrate how data fusion methods
may be used address the problem of missing final outcomes and predict long-run
impacts of interventions before the requisite data are available. We implement
this method by concatenating data on an intervention with auxiliary long-term
data and then imputing missing long-term outcomes using short-term surrogate
outcomes while approximating uncertainty with replication methods. We use
simulations to examine the performance of the methodology and apply the method
in a case study. Specifically, we fuse data on the Oregon Health Insurance
Experiment with data from the National Longitudinal Mortality Study and
estimate that being eligible to apply for subsidized health insurance will lead
to a statistically significant improvement in long-term mortality.


title:
Splicing Detection and Localization In Satellite Imagery Using
  Conditional GANs

abstract:
  The widespread availability of image editing tools and improvements in image
processing techniques allow image manipulation to be very easy. Oftentimes,
easy-to-use yet sophisticated image manipulation tools yields
distortions/changes imperceptible to the human observer. Distribution of forged
images can have drastic ramifications, especially when coupled with the speed
and vastness of the Internet. Therefore, verifying image integrity poses an
immense and important challenge to the digital forensic community. Satellite
images specifically can be modified in a number of ways, including the
insertion of objects to hide existing scenes and structures. In this paper, we
describe the use of a Conditional Generative Adversarial Network (cGAN) to
identify the presence of such spliced forgeries within satellite images.
Additionally, we identify their locations and shapes. Trained on pristine and
falsified images, our method achieves high success on these detection and
localization objectives.


title: Frequency Domain-Based Detection of Generated Audio

abstract:
  Attackers may manipulate audio with the intent of presenting falsified
reports, changing an opinion of a public figure, and winning influence and
power. The prevalence of inauthentic multimedia continues to rise, so it is
imperative to develop a set of tools that determines the legitimacy of media.
We present a method that analyzes audio signals to determine whether they
contain real human voices or fake human voices (i.e., voices generated by
neural acoustic and waveform models). Instead of analyzing the audio signals
directly, the proposed approach converts the audio signals into spectrogram
images displaying frequency, intensity, and temporal content and evaluates them
with a Convolutional Neural Network (CNN). Trained on both genuine human voice
signals and synthesized voice signals, we show our approach achieves high
accuracy on this classification task.


title: Learning Dynamic Bipedal Walking Across Stepping Stones

abstract:
  In this work, we propose a learning approach for 3D dynamic bipedal walking
when footsteps are constrained to stepping stones. While recent work has shown
progress on this problem, real-world demonstrations have been limited to
relatively simple open-loop, perception-free scenarios. Our main contribution
is a more advanced learning approach that enables real-world demonstrations,
using the Cassie robot, of closed-loop dynamic walking over moderately
difficult stepping-stone patterns. Our approach first uses reinforcement
learning (RL) in simulation to train a controller that maps footstep commands
onto joint actions without any reference motion information. We then learn a
model of that controller's capabilities, which enables prediction of feasible
footsteps given the robot's current dynamic state. The resulting controller and
model are then integrated with a real-time overhead camera system for detecting
stepping stone locations. For evaluation, we develop a benchmark set of
stepping stone patterns, which are used to test performance in both simulation
and the real world. Overall, we demonstrate that sim-to-real learning is
extremely promising for enabling dynamic locomotion over stepping stones. We
also identify challenges remaining that motivate important future research
directions.


title:
Control sets of linear control systems on $\mathbb{R}^2$. The complex
  case

abstract:
  This paper explicitly computes the unique control set $D$ with non-empty
interior of a linear control system on $\mathbb{R}^2$, when the associated
matrix has complex eigenvalues. It turns out that the closure of $D$ coincides
with the the region delimited by a computable periodic orbit $\mathcal{O}$ of
the system.


title:
Scientific Explanation and Natural Language: A Unified
  Epistemological-Linguistic Perspective for Explainable AI

abstract:
  A fundamental research goal for Explainable AI (XAI) is to build models that
are capable of reasoning through the generation of natural language
explanations. However, the methodologies to design and evaluate
explanation-based inference models are still poorly informed by theoretical
accounts on the nature of explanation. As an attempt to provide an
epistemologically grounded characterisation for XAI, this paper focuses on the
scientific domain, aiming to bridge the gap between theory and practice on the
notion of a scientific explanation. Specifically, the paper combines a detailed
survey of the modern accounts of scientific explanation in Philosophy of
Science with a systematic analysis of corpora of natural language explanations,
clarifying the nature and function of explanatory arguments from both a
top-down (categorical) and a bottom-up (corpus-based) perspective. Through a
mixture of quantitative and qualitative methodologies, the presented study
allows deriving the following main conclusions: (1) Explanations cannot be
entirely characterised in terms of inductive or deductive arguments as their
main function is to perform unification; (2) An explanation must cite causes
and mechanisms that are responsible for the occurrence of the event to be
explained; (3) While natural language explanations possess an intrinsic
causal-mechanistic nature, they are not limited to causes and mechanisms, also
accounting for pragmatic elements such as definitions, properties and taxonomic
relations; (4) Patterns of unification naturally emerge in corpora of
explanations even if not intentionally modelled; (5) Unification is realised
through a process of abstraction, whose function is to provide the inference
substrate for subsuming the event to be explained under recurring patterns and
high-level regularities.


title:
Applications of the Isolating Fusion Algorithm to Table Algebras and
  Association Schemes

abstract:
  Let $\mathbf{B}$ be a basis for an $r$-dimensional algebra $A$ over a field
or commutative ring with unity. The semifusions of $\mathbf{B}$ are the
partitions of $\mathbf{B}$ whose characteristic functions form the basis of a
subalgebra of $A$, and fusions are semifusions that respect a given involution
on $A$.
  In this paper, we give an algorithm for computing a minimal semifusion (or
fusion) of $\mathbf{B}$ that isolates a prescribed list of disjoint sums of
basis elements of $\mathbf{B}$, when such a semifusion (or fusion) exists. We
apply this algorithm to three problems: (1) computing the fusion lattices for
small association schemes of a given order; (2) producing explicit realizations
of association schemes with transitive automorphism groups; and (3) producing
examples of non-Schurian fusions of Schurian association schemes whose
adjacency matrices have noncyclotomic eigenvalues. The latter is of interest to
the open question asking whether association schemes with transitive
automorphism groups can have noncyclotomic character values.


title: Assessing Dataset Bias in Computer Vision

abstract:
  A biased dataset is a dataset that generally has attributes with an uneven
class distribution. These biases have the tendency to propagate to the models
that train on them, often leading to a poor performance in the minority class.
In this project, we will explore the extent to which various data augmentation
methods alleviate intrinsic biases within the dataset. We will apply several
augmentation techniques on a sample of the UTKFace dataset, such as
undersampling, geometric transformations, variational autoencoders (VAEs), and
generative adversarial networks (GANs). We then trained a classifier for each
of the augmented datasets and evaluated their performance on the native test
set and on external facial recognition datasets. We have also compared their
performance to the state-of-the-art attribute classifier trained on the
FairFace dataset. Through experimentation, we were able to find that training
the model on StarGAN-generated images led to the best overall performance. We
also found that training on geometrically transformed images lead to a similar
performance with a much quicker training time. Additionally, the best
performing models also exhibit a uniform performance across the classes within
each attribute. This signifies that the model was also able to mitigate the
biases present in the baseline model that was trained on the original training
set. Finally, we were able to show that our model has a better overall
performance and consistency on age and ethnicity classification on multiple
datasets when compared with the FairFace model. Our final model has an accuracy
on the UTKFace test set of 91.75%, 91.30%, and 87.20% for the gender, age, and
ethnicity attribute respectively, with a standard deviation of less than 0.1
between the accuracies of the classes of each attribute.


title: Non-local interactions and supersolidity of moir\'e excitons

abstract:
  Heterobilayer transition metal dichalcogenide (TMDC) moir\'e systems provide
an ideal framework to investigate strongly correlated physics. Here we
theoretically study bosonic many-body phases of excitons in moir\'e TMDCs. By
using two moir\'e models and cluster mean-field theory, we reveal that, due to
non-local Coulomb interactions, moir\'e excitons can feature exotic supersolid
phases, i.e. superfluids of broken translational invariance, and correlated
insulating states. The correlated phases exist at experimentally accessible
temperatures and are tunable via the twist angle and exciton density.


title: Diverse Image Captioning with Grounded Style

abstract:
  Stylized image captioning as presented in prior work aims to generate
captions that reflect characteristics beyond a factual description of the scene
composition, such as sentiments. Such prior work relies on given sentiment
identifiers, which are used to express a certain global style in the caption,
e.g. positive or negative, however without taking into account the stylistic
content of the visual scene. To address this shortcoming, we first analyze the
limitations of current stylized captioning datasets and propose COCO
attribute-based augmentations to obtain varied stylized captions from COCO
annotations. Furthermore, we encode the stylized information in the latent
space of a Variational Autoencoder; specifically, we leverage extracted image
attributes to explicitly structure its sequential latent space according to
different localized style characteristics. Our experiments on the Senticap and
COCO datasets show the ability of our approach to generate accurate captions
with diversity in styles that are grounded in the image.


title: Enhanced moments of inertia for rotation in neutron-rich nuclei

abstract:
  Ground-state moments of inertia (MoI) are investigated for about 1700
even-even nuclei from the proton drip line to the neutron drip line up to
$Z=120$ and $N=184$. The cranked Skyrme-Hartree-Fock-Bogoliubov equation is
solved in the coordinate space. This model describes well the available
experimental data of more than 300 nuclides possessing an appreciable
deformation. I find that the MoI greatly increase near the drip line whereas
the deformation is not as strong as estimated by the empirical relation.
Systematic measurements of the excitation energy and the transition probability
to the first $I^\pi=2^+$ state in neutron-rich nuclei not only reveal the
evolution of deformation but can also constrain an effective pair interaction.


title: A kinetic interpretation of the classical Rayleigh-Taylor instability

abstract:
  Rayleigh-Taylor (RT) instabilities are prevalent in many physical regimes
ranging from astrophysical to laboratory plasmas and have primarily been
studied using fluid models, the majority of which have been ideal fluid models.
This work is the first of its kind to present a 5-dimensional (2 spatial
dimensions, 3 velocity space dimensions) simulation using the continuum-kinetic
model to study the effect of the collisional mean-free-path and transport on
the instability growth. The continuum-kinetic model provides noise-free access
to the full particle distribution function permitting a detailed investigation
of the role of kinetic physics in hydrodynamic phenomena such as the RT
instability. For long mean-free-path, there is no RT instability growth, but as
collisionality increases, particles relax towards the Maxwellian velocity
distribution, and the kinetic simulations reproduce the fluid simulation
results. An important and novel contribution of this work is in the
intermediate collisional cases that are not accessible with traditional fluid
models and require kinetic modeling. Simulations of intermediate collisional
cases show that the RT instability evolution is significantly altered compared
to the highly collisional fluid-like cases. Specifically, the growth rate of
the intermediate collisionality RT instability is lower than the high
collisionality case while also producing a significantly more diffused
interface. The higher moments of the distribution function play a more
significant role relative to inertial terms for intermediate collisionality
during the evolution of the RT instability interface. Particle energy-flux is
calculated from moments of the distribution and shows that transport is
significantly altered in the intermediate collisional case and deviates much
more so from the high collisionality limit of the fluid regime.


title:
First measurement of the characteristic depletion radius of dark matter
  haloes from weak lensing

abstract:
  We use weak lensing observations to make the first measurement of the
characteristic depletion radius, one of the three radii that characterize the
region where matter is being depleted by growing haloes. The lenses are taken
from the halo catalog produced by the extended halo-based group/cluster finder
applied to DESI Legacy Imaging Surveys DR9, while the sources are extracted
from the DECaLS DR8 imaging data with the Fourier_Quad pipeline. We study halo
masses $12 < \log ( M_{\rm grp} ~[{\rm M_{\odot}}/h] ) \leq 15.3$ within
redshifts $0.2 \leq z \leq 0.3$. The virial and splashback radii are also
measured and used to test the original findings on the depletion region. When
binning haloes by mass, we find consistency between most of our measurements
and predictions from the CosmicGrowth simulation, with exceptions to the lowest
mass bins. The characteristic depletion radius is found to be roughly $2.5$
times the virial radius and $1.7 - 3$ times the splashback radius, in line with
an approximately universal outer density profile, and the average enclosed
density within the characteristic depletion radius is found to be roughly $29$
times the mean matter density of the Universe in our sample. When binning
haloes by both mass and a proxy for halo concentration, we do not detect a
significant variation of the depletion radius with concentration, on which the
simulation prediction is also sensitive to the choice of concentration proxy.
We also confirm that the measured splashback radius varies with concentration
differently from simulation predictions.


title: A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

abstract:
  The Covid-19 pandemic has led to infodemic of low quality information leading
to poor health decisions. Combating the outcomes of this infodemic is not only
a question of identifying false claims, but also reasoning about the decisions
individuals make. In this work we propose a holistic analysis framework
connecting stance and reason analysis, and fine-grained entity level moral
sentiment analysis. We study how to model the dependencies between the
different level of analysis and incorporate human insights into the learning
process. Experiments show that our framework provides reliable predictions even
in the low-supervision settings.


title: i-Code: An Integrative and Composable Multimodal Learning Framework

abstract:
  Human intelligence is multimodal; we integrate visual, linguistic, and
acoustic signals to maintain a holistic worldview. Most current pretraining
methods, however, are limited to one or two modalities. We present i-Code, a
self-supervised pretraining framework where users may flexibly combine the
modalities of vision, speech, and language into unified and general-purpose
vector representations. In this framework, data from each modality are first
given to pretrained single-modality encoders. The encoder outputs are then
integrated with a multimodal fusion network, which uses novel attention
mechanisms and other architectural innovations to effectively combine
information from the different modalities. The entire system is pretrained
end-to-end with new objectives including masked modality unit modeling and
cross-modality contrastive learning. Unlike previous research using only video
for pretraining, the i-Code framework can dynamically process single, dual, and
triple-modality data during training and inference, flexibly projecting
different combinations of modalities into a single representation space.
Experimental results demonstrate how i-Code can outperform state-of-the-art
techniques on five video understanding tasks and the GLUE NLP benchmark,
improving by as much as 11% and demonstrating the power of integrative
multimodal pretraining.


title:
Lessons for instructor-student interactions in physics from the world of
  improvisational theater

abstract:
  A considerable share of the literature on physics education and on education
more broadly focuses on the principles which should guide the design of courses
and of classroom activities. In this short article I wish to place more
attention on the unplanned aspects of teaching: specifically, the spontaneous
interactions that occur between instructors and students in settings like
office hours, recitations, and when students ask questions during lecture.
Because by their nature these interactions require thinking on one's feet, and
depend upon the interplay between instructor and student, they share many
characteristics with improvisational theater (improv). I document three
foundational principles from improv literature (active listening, "yes-and,"
and the "button") and describe how they relate to established principles from
physics education research. I provide examples of how each principle can be
used to bring solid pedagogical practices to the unstructured space of
instructor-student interactions.


title: Simulation of flow induced vibration of a cylinder in expansion tube

abstract:
  In this study, a series of simulations are carried out to understand the
motion of a small cylinder in the expansion tube. The simulations are fixed at
two-dimensional. These simulations are performed on the FLUENT platform
employing the Overset function. The collision between the cylinder and the tube
wall is considered a positive rigid body collision without losing energy. Two
key parameters, the dimensionless gravity (Mg*) and the Reynolds number (Re),
are focused. Mg* and Re is taken into account for 4.9-79.4 and 1-300,
respectively. Two types of inflow are considered: the regular inflow or
superimposed a sinusoidal inflow of periodical fluctuation. For regular inflow,
three patterns of motion are found in the phase diagrams of (Mg*, Re), which
can be divided into four rigimes. At low Mg* and low Re, such as Mg* =4.9, Re
=1, the cylinder may drain at high thrust. At higher Mg* and Re, such as Mg*
=4.9, Re =4 or Mg* =19.6, Re =3, the cylinder may eventually be fixed at a
certain position for the balance of gravity and thrust. At higher Mg* and Re,
such as Mg* =9.8, Re =5, the cylinder may vibrate in the dilation tube for the
wake vortex shedding. At elevated Mg* and Re, such as Mg* =78.4, Re =300, a
strange phenomenon is discovered that the cylinder may stop at a point where
deviate from the central axis. The high amplitude vibrations of the cylinder is
proved widespread within the range of Reynolds numbers from 5 to 40 for regular
inflow. Re =300, Mg* =39.25 with sinusoidal periodic fluctuation incoming flow
is also considered. Unlike the regular inflow, the cylinder can also vibrate
violently that way. The two-dimensional cylinder may be considered as a
simplification of a sphere if the three-dimensional motion of the sphere is
disregarded. Our research may help to understand this ancient flow problem.


title:
Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics
  Features

abstract:
  Poetry generation, and creative language generation in general, usually
suffers from the lack of large training data. In this paper, we present a novel
framework to generate sonnets that does not require training on poems. We
design a hierarchical framework which plans the poem sketch before decoding.
Specifically, a content planning module is trained on non-poetic texts to
obtain discourse-level coherence; then a rhyme module generates rhyme words and
a polishing module introduces imagery and similes for aesthetics purposes.
Finally, we design a constrained decoding algorithm to impose the
meter-and-rhyme constraint of the generated sonnets. Automatic and human
evaluation show that our multi-stage approach without training on poem corpora
generates more coherent, poetic, and creative sonnets than several strong
baselines.


title:
Study of Hybrid MicroPattern Gas Detector with CsI Photocathode For
  Super Tau-Charm Factory RICH

abstract:
  Super tau-Charm facility(STCF) is a future electron-position collider
operating at tau-Charm energy region aimed to study hadron structure and
spectroscopy. The baseline design of the STCF barrel particle
identification(PID) detector in the momentum range up to 2 GeV/c is provided by
a Ring Imaging Cherenkov Counter(RICH). The architecture of the RICH is an
approximately focusing design with liquid perfluorohexane sealed in the quartz
container as radiator and a hybrid combination of a CsI coated layers of THGEMs
and a Micromegas as the photo-electron detector. A 16*16 cm^2 prototype with
quartz radiator has been built and tested at DESY. It was stably operated with
10^5 effective gain. In this paper, the design, the performance, the
reconstruction algorithm and the systematic error for single photon electron
angular resolution in the aspect of RICH detectors are discusses.


title:
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose
  Estimation

abstract:
  We propose a keypoint-based object-level SLAM framework that can provide
globally consistent 6DoF pose estimates for symmetric and asymmetric objects
alike. To the best of our knowledge, our system is among the first to utilize
the camera pose information from SLAM to provide prior knowledge for tracking
keypoints on symmetric objects -- ensuring that new measurements are consistent
with the current 3D scene. Moreover, our semantic keypoint network is trained
to predict the Gaussian covariance for the keypoints that captures the true
error of the prediction, and thus is not only useful as a weight for the
residuals in the system's optimization problems, but also as a means to detect
harmful statistical outliers without choosing a manual threshold. Experiments
show that our method provides competitive performance to the state of the art
in 6DoF object pose estimation, and at a real-time speed. Our code, pre-trained
models, and keypoint labels are available https://github.com/rpng/suo_slam.


title:
Non-vanishing of quadratic twists of modular $L$-functions of
  prime-related moduli

abstract:
  In this paper, we study central values of the family of quadratic twists of
modular $L$-functions of moduli $8p$, with $p$ ranging over odd primes.
Assuming the truth of the generalized Riemann hypothesis, we establish a
positive proportion non-vanishing result for the corresponding $L$-values.


title: AmbiPun: Generating Humorous Puns with Ambiguous Context

abstract:
  In this paper, we propose a simple yet effective way to generate pun
sentences that does not require any training on existing puns. Our approach is
inspired by humor theories that ambiguity comes from the context rather than
the pun word itself. Given a pair of definitions of a pun word, our model first
produces a list of related concepts through a reverse dictionary. We then
utilize one-shot GPT3 to generate context words and then generate puns
incorporating context words from both concepts. Human evaluation shows that our
method successfully generates pun 52\% of the time, outperforming well-crafted
baselines and the state-of-the-art models by a large margin.


title: Unified Semantic Typing with Meaningful Label Inference

abstract:
  Semantic typing aims at classifying tokens or spans of interest in a textual
context into semantic categories such as relations, entity types, and event
types. The inferred labels of semantic categories meaningfully interpret how
machines understand components of text. In this paper, we present UniST, a
unified framework for semantic typing that captures label semantics by
projecting both inputs and labels into a joint semantic embedding space. To
formulate different lexical and relational semantic typing tasks as a unified
task, we incorporate task descriptions to be jointly encoded with the input,
allowing UniST to be adapted to different tasks without introducing
task-specific model components. UniST optimizes a margin ranking loss such that
the semantic relatedness of the input and labels is reflected from their
embedding similarity. Our experiments demonstrate that UniST achieves strong
performance across three semantic typing tasks: entity typing, relation
classification and event typing. Meanwhile, UniST effectively transfers
semantic knowledge of labels and substantially improves generalizability on
inferring rarely seen and unseen types. In addition, multiple semantic typing
tasks can be jointly trained within the unified framework, leading to a single
compact multi-tasking model that performs comparably to dedicated single-task
models, while offering even better transferability.


title: Spray characteristics from nasal spray atomization

abstract:
  In addition to topical delivery, nasal sprays offer an alternative drug
administration route to the systemic circulation, therefore avoiding the need
for painful, invasive delivery techniques. Studies on the efficacy of nasal
drug delivery to date have been conflicting despite its potential. In
particular computational studies, e.g. Computational Fluid Dynamics (CFD), of
nasal spray deposition require realistic initial spray conditions, which are
lacking in the literature. This study aims to provide benchmark experimental
data for CFD inputs by characterising four different types of commercially
available nasal sprays used in an ENT (Ear Nose Throat) clinic. Spray
characteristics were obtained via high-speed videography, while droplet size
distribution (DSD) measurements were obtained with laser diffraction. Three
actuation forces were evaluated, representing the use-case of an average adult,
average child and the maximum force applied to the spray device. The results
demonstrated that actuation force influenced spray velocity, spray duration,
DSD, and breakup length. Quantitative data of Rosin-Rammler diameter
distributions, spray cone angles, dispersion angles and breakup lengths were
found, assisting computational models with realistic values, thereby improving
future CFD studies of nasal spray drug delivery in the nasal cavity.


title: Turaev-Viro invariants and cabling operations

abstract:
  In this paper, we study the variation of the Turaev-Viro invariants for
$3$-manifolds with toroidal boundary under the operation of attaching a
$(p,q)$-cable space. We apply our results to a conjecture of Chen and Yang
which relates the asymptotics of the Turaev-Viro invariants to the simplicial
volume of a compact oriented $3$-manifold. For $p$ and $q$ coprime, we show
that the Chen-Yang volume conjecture is preserved under
$\left(p,q\right)$-cabling.


title: A note on the BMO and Calder\'{o}n-Zygmund estimate

abstract:
  In this note, we give a simple proof of the pointwise BMO estimate for
Poisson's equation. Then the Calder\'{o}n-Zygmund estimate follows by the
interpolation and duality.


title: MacWilliams extending conditions and quasi-Frobenius rings

abstract:
  MacWilliams proved that every finite field has the extension property for
Hamming weight which was later extended in a seminal work by Wood who
characterized finite Frobenius rings as precisely those rings which satisfy the
MacWilliams extension property. In this paper, the question of when is a
MacWilliams ring quasi-Frobenius is addressed. It is proved that a right or
left noetherian left 1-MacWilliams ring is quasi-Frobenius thus answering the
different questions asked in [M. C. Iovanov, On infinite MacWilliams rings and
minimal injectivity conditions, Proc. Amer. Math. Soc., DOI:
10.1090/proc/15929] and [F. M. Schneider, J. Zumbr\"{a}gel, MacWilliams'
extension theorem for infinite rings, Proc. Amer. Math. Soc. 147, 3 (2019),
947-961]. We also prove that a right perfect, left automorphism-invariant ring
is left self-injective. In particular, this yields that if $R$ is a right (or
left) artinian, left automorphism-invariant ring, then $R$ is quasi-Frobenius,
thus answering a question asked in [M. C. Iovanov, On infinite MacWilliams
rings and minimal injectivity conditions, Proc. Amer. Math. Soc., DOI:
10.1090/proc/15929].


title:
Optimization and stability analysis of the cascaded EEHG-HGHG
  free-electron laser

abstract:
  X-ray free-electron lasers (XFELs) are powerful tools to explore and study
nature for achieving remarkable advances. Generally, seeded FELs are ideal
sources for supplying full coherent soft x-ray pulses. Benefiting from the
high-frequency up-conversion efficiency, the cascading configuration with
echo-enabled harmonic generation (EEHG) and high-gain harmonic generation
(HGHG) holds promising prospects for generating full coherent radiation at 1 nm
wavelength. In this paper, we design and optimize EEHG-HGHG configuration using
parameters of Shanghai High-Repetition-Rate XFEL and Extreme Light Facility. In
addition, we systematically analyze the effect of relative timing jitter on the
output FEL performance based on various start-to-end electron beams. The
intensive numerical simulations show that the cascaded EEHG-HGHG scheme can
achieve 1 nm FEL pulses with peak power up to 15 GW. Further sensitivity
analysis indicates that the relative timing jitter between the electron beam
and seed laser has a significant impact on the FEL performance. The RMS timing
jitter of 3 fs can lead to the final output pulse energy fluctuations of
29.16%.


title:
Underdamped harmonic oscillator driven by a train of short pulses:
  Analytical analysis

abstract:
  A theoretical model of an underdamped harmonic oscillator (UHO) driven by
periodic short pulses may find plenty of applications in classical,
semiclassical, and quantum physics. We present here two different forms of
analytical solutions: {\it time-periodic solutions} and {\it harmonic
solutions} for one-dimensional classical UHO driven by three different trains
of short pulses. They are a Dirac comb, a train of square pulses, and a train
of Gaussian pulses with the same pulse-to-pulse time interval $T$ and pulse
width $2\tau$. Two solutions for square and Gaussian pulses approach to that of
the Dirac comb when the pulse width $2\tau \rightarrow 0$ as expected. In
particular, the harmonic solutions for Dirac comb and Gaussian pulses could be
expressed approximately with harmonic terms of the repetition frequency
$\omega_{\rm R} = 2\pi/T$ up to the second order. The presented analytical
solutions would provide a practical way to determine experimentally the system
parameters such as the underdamped oscillation frequency $\omega =
\sqrt{\omega_0^2-\gamma^2}$, the natural frequency $\omega_0$, and the damping
rate $\gamma$, by nonlinear curve fitting procedures for different driving
force parameters of $T$ and $2\tau$.


title:
OpenAlex: A fully-open index of scholarly works, authors, venues,
  institutions, and concepts

abstract:
  OpenAlex is a new, fully-open scientific knowledge graph (SKG), launched to
replace the discontinued Microsoft Academic Graph (MAG). It contains metadata
for 209M works (journal articles, books, etc); 2013M disambiguated authors;
124k venues (places that host works, such as journals and online repositories);
109k institutions; and 65k Wikidata concepts (linked to works via an automated
hierarchical multi-tag classifier). The dataset is fully and freely available
via a web-based GUI, a full data dump, and high-volume REST API. The resource
is under active development and future work will improve accuracy and coverage
of citation information and author/institution parsing and deduplication.


title: A Crystal Analysis of $P$-Arrays

abstract:
  Gasharov introduced the combinatorial objects known as $P$-arrays to prove
$s$-positivity for the chromatic symmetric functions of incomparability graphs
of (3+1)-free posets. We define a crystal, a directed colored graph with some
additional axioms, on the set of $P$-arrays. The components of the crystal have
$s$-positive characters, thereby refining the $s$-positivity theorems of
Gasharov, as well as Shareshian and Wachs. The crystal hints at a possible
generalization of the Robinson-Schensted correspondence applied to $P$-arrays.


title:
Search for narrow resonances in the b-tagged dijet mass spectrum in
  proton-proton collisions at $\sqrt{s}$ = 13 TeV

abstract:
  A search is performed for narrow resonances decaying to final states of two
jets, with at least one jet originating from a b quark, in proton-proton
collisions at $\sqrt{s}$ = 13 TeV. The data set corresponds to an integrated
luminosity of 138 fb$^{-1}$ collected with the CMS detector at the LHC. Jets
originating from energetic b hadrons are identified through a b tagging
algorithm that utilizes a deep neural network or the presence of a muon inside
a jet. The invariant mass spectrum of jet pairs is well described by a smooth
parametrization and no evidence for the production of new particles is
observed. Upper limits on the production cross section are set for excited b
quarks and other resonances decaying to dijet final states containing b quarks.
These limits exclude at 95% confidence level models of Z' bosons with masses
from 1.8 to 2.4 TeV and of excited b quarks with masses from 1.8 to 4.0 TeV.
This is the most stringent exclusion of excited b quarks to date.


title:
Explainable Knowledge Graph Embedding: Inference Reconciliation for
  Knowledge Inferences Supporting Robot Actions

abstract:
  Learned knowledge graph representations supporting robots contain a wealth of
domain knowledge that drives robot behavior. However, there does not exist an
inference reconciliation framework that expresses how a knowledge graph
representation affects a robot's sequential decision making. We use a
pedagogical approach to explain the inferences of a learned, black-box
knowledge graph representation, a knowledge graph embedding. Our interpretable
model, uses a decision tree classifier to locally approximate the predictions
of the black-box model, and provides natural language explanations
interpretable by non-experts. Results from our algorithmic evaluation affirm
our model design choices, and the results of our user studies with non-experts
support the need for the proposed inference reconciliation framework.
Critically, results from our simulated robot evaluation indicate that our
explanations enable non-experts to correct erratic robot behaviors due to
nonsensical beliefs within the black-box.


title:
Acoustic, phononic, Brillouin light scattering and Faraday wave based
  frequency combs: physical foundations and applications

abstract:
  Frequency combs (FCs) -- spectra containing equidistant coherent peaks --
have enabled researchers and engineers to measure the frequencies of complex
signals with high precision thereby revolutionising the areas of sensing,
metrology and communications and also benefiting the fundamental science.
Although mostly optical FCs have found widespread applications thus far, in
general FCs can be generated using waves other than light. Here, we review and
summarise recent achievements in the emergent field of acoustic frequency combs
(AFCs) including phononic FCs and relevant acousto-optical, Brillouin light
scattering and Faraday wave-based techniques that have enabled the development
of phonon lasers, quantum computers and advanced vibration sensors. In
particular, our discussion is centred around potential applications of AFCs in
precision measurements in various physical, chemical and biological systems in
conditions, where using light, and hence optical FCs, faces technical and
fundamental limitations, which is, for example, the case in underwater distance
measurements and biomedical imaging applications. This review article will also
be of interest to readers seeking a discussion of specific theoretical aspects
of different classes of AFCs. To that end, we support the mainstream discussion
by the results of our original analysis and numerical simulations that can be
used to design the spectra of AFCs generated using oscillations of gas bubbles
in liquids, vibrations of liquid drops and plasmonic enhancement of Brillouin
light scattering in metal nanostructures. We also discuss the application of
non-toxic room-temperature liquid-metal alloys in the field of AFC generation.

categories: physics.optics cond-mat.soft nlin.CD physics.class-ph physics.flu-dyn


title:
A model for malaria treatment evaluation in the presence of multiple
  species

abstract:
  Plasmodium (P.) falciparum and P. vivax are the two most common causes of
malaria. While the majority of deaths and severe morbidity are due to P.
falciparum, P. vivax poses a greater challenge to eliminating malaria outside
of Africa due to its ability to form latent liver stage parasites
(hypnozoites), which can cause relapsing episodes within an individual patient.
In areas where P. falciparum and P. vivax are co-endemic, individuals can carry
parasites of both species simultaneously. These mixed infections complicate
dynamics in several ways; treatment of mixed infections will simultaneously
affect both species, P. falciparum can mask the detection of P. vivax, and it
has been hypothesised that clearing P. falciparum may trigger a relapse of
dormant P. vivax. When mixed infections are treated for only blood-stage
parasites, patients are at risk of relapse infections due to P. vivax
hypnozoites.
  We present a stochastic mathematical model that captures interactions between
P. falciparum and P. vivax, and incorporates both standard schizontocidal
treatment (which targets blood-stage parasites) and radical treatment (which
additionally targets liver-stage parasites). We apply this model to assess the
implications of different treatment coverage of radical cure for mixed and P.
vivax infections and a so-called "unified radical cure" treatment strategy for
P. falciparum, P. vivax and mixed infections. We find that a unified radical
cure strategy, with G6PD screening, leads to a substantially lower incidence of
malaria cases and deaths overall. We perform a one-way sensitivity analysis to
highlight important model parameters.


title: Ramsey, expanders, and Borel chromatic numbers

abstract:
  We construct bounded degree acyclic Borel graphs with large Borel chromatic
number using a graph arising from Ramsey theory and limits of expander
sequences.


title:
FedMix: Mixed Supervised Federated Learning for Medical Image
  Segmentation

abstract:
  The purpose of federated learning is to enable multiple clients to jointly
train a machine learning model without sharing data. However, the existing
methods for training an image segmentation model have been based on an
unrealistic assumption that the training set for each local client is annotated
in a similar fashion and thus follows the same image supervision level. To
relax this assumption, in this work, we propose a label-agnostic unified
federated learning framework, named FedMix, for medical image segmentation
based on mixed image labels. In FedMix, each client updates the federated model
by integrating and effectively making use of all available labeled data ranging
from strong pixel-level labels, weak bounding box labels, to weakest
image-level class labels. Based on these local models, we further propose an
adaptive weight assignment procedure across local clients, where each client
learns an aggregation weight during the global model update. Compared to the
existing methods, FedMix not only breaks through the constraint of a single
level of image supervision, but also can dynamically adjust the aggregation
weight of each local client, achieving rich yet discriminative feature
representations. To evaluate its effectiveness, experiments have been carried
out on two challenging medical image segmentation tasks, i.e., breast tumor
segmentation and skin lesion segmentation. The results validate that our
proposed FedMix outperforms the state-of-the-art method by a large margin.


title:
Great Truths are Always Simple: A Rather Simple Knowledge Encoder for
  Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models

abstract:
  Commonsense reasoning in natural language is a desired ability of artificial
intelligent systems. For solving complex commonsense reasoning tasks, a typical
solution is to enhance pre-trained language models~(PTMs) with a
knowledge-aware graph neural network~(GNN) encoder that models a commonsense
knowledge graph~(CSKG). Despite the effectiveness, these approaches are built
on heavy architectures, and can't clearly explain how external knowledge
resources improve the reasoning capacity of PTMs. Considering this issue, we
conduct a deep empirical analysis, and find that it is indeed relation features
from CSKGs (but not node features) that mainly contribute to the performance
improvement of PTMs. Based on this finding, we design a simple MLP-based
knowledge encoder that utilizes statistical relation paths as features.
Extensive experiments conducted on five benchmarks demonstrate the
effectiveness of our approach, which also largely reduces the parameters for
encoding CSKGs. Our codes and data are publicly available at
https://github.com/RUCAIBox/SAFE.


title: An Empirical Study on Maintainable Method Size in Java

abstract:
  Code metrics have been widely used to estimate software maintenance effort.
Metrics have generally been used to guide developer effort to reduce or avoid
future maintenance burdens. Size is the simplest and most widely deployed
metric. The size metric is pervasive because size correlates with many other
common metrics (e.g., McCabe complexity, readability, etc.). Given the ease of
computing a method's size, and the ubiquity of these metrics in industrial
settings, it is surprising that no systematic study has been performed to
provide developers with meaningful method size guidelines with respect to
future maintenance effort. In this paper we examine the evolution of around
785K Java methods and show that developers should strive to keep their Java
methods under 24 lines in length. Additionally, we show that decomposing larger
methods to smaller methods also decreases overall maintenance efforts. Taken
together, these findings provide empirical guidelines to help developers design
their systems in a way that can reduce future maintenance.


title:
Direct observation of nodeless superconductivity and phonon modes in
  electron-doped copper oxide Sr$_{1-x}$Nd$_x$CuO$_2$

abstract:
  The microscopic understanding of high-temperature superconductivity in
cuprates has been hindered by the apparent complexity of crystal structures in
these materials. We used scanning tunneling microscopy and spectroscopy to
study an electron-doped copper oxide compound Sr$_{1-x}$Nd$_x$CuO$_2$ that has
only bare cations separating the CuO$_2$ planes and thus the simplest
infinite-layer structure among all cuprate superconductors. Tunneling
conductance spectra of the major CuO$_2$ planes in the superconducting state
revealed direct evidence for a nodeless pairing gap, regardless of variation of
its magnitude with the local doping of trivalent neodymium. Furthermore, three
distinct bosonic modes are observed as multiple peak-dip-hump features outside
the superconducting gaps and their respective energies depend little on the
spatially varying gaps. Along with the bosonic modes with energies identical to
those of the external, bending and stretching phonons of copper oxides, our
findings indicate their origin from lattice vibrations rather than spin
excitations.


title:
Percolative Superconductivity in Electron-Doped
  Sr$_{1-x}$Eu$_{x}$CuO$_{2+y}$ Films

abstract:
  Electron-doped infinite-layer Sr$_{1-x}$Eu$_{x}$CuO$_{2+y}$ films over a wide
doping range have been prepared epitaxially on SrTiO$_3$(001) using reactive
molecular beam epitaxy. In-plane transport measurements of the single
crystalline samples reveal a dome-shaped nodeless superconducting phase
centered at $x$ $\sim$ 0.15, a Fermi-liquid behavior and pronounced upturn in
low temperature resistivity. We show that the resistivity upturn follows
square-root temperature dependence, suggesting the emergence of
superconductivity via a three-dimensional percolation process. The percolative
superconductivity is corroborated spectroscopically by imaging the electronic
phase separation between superconducting and metallic phases with
low-temperature scanning tunneling microscopy. Furthermore, we visualize
interstitial and apical oxygen anions that rapidly increase in number as $x>$
0.12, and elucidate their impacts on the superconductivity and normal-state
resistivity.


title: Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

abstract:
  Discovering latent topics from text corpora has been studied for decades.
Many existing topic models adopt a fully unsupervised setting, and their
discovered topics may not cater to users' particular interests due to their
inability of leveraging user guidance. Although there exist seed-guided topic
discovery approaches that leverage user-provided seeds to discover
topic-representative terms, they are less concerned with two factors: (1) the
existence of out-of-vocabulary seeds and (2) the power of pre-trained language
models (PLMs). In this paper, we generalize the task of seed-guided topic
discovery to allow out-of-vocabulary seeds. We propose a novel framework, named
SeeTopic, wherein the general knowledge of PLMs and the local semantics learned
from the input corpus can mutually benefit each other. Experiments on three
real datasets from different domains demonstrate the effectiveness of SeeTopic
in terms of topic coherence, accuracy, and diversity.


title:
Interpreting the statistical properties of high-z extragalactic sources
  detected by the South Pole Telescope survey

abstract:
  The results of the recently published spectroscopically complete survey of
dusty star-forming galaxies detected by the South Pole Telescope (SPT) over
2500 deg^2 proved to be challenging for galaxy formation models that generally
underpredict the observed abundance of high-z galaxies. In this paper we
interpret these results in the light of a physically grounded model for the
evolution of spheroidal galaxies. The model accurately reproduces the measured
redshift distribution of galaxies without any adjustment of the parameters. The
data do not support the indications of an excess of z > 4 dusty galaxies
reported by some analyses of Herschel surveys.


title: Rates of estimation for high-dimensional multi-reference alignment

abstract:
  We study the continuous multi-reference alignment model of estimating a
periodic function on the circle from noisy and circularly-rotated observations.
Motivated by analogous high-dimensional problems that arise in cryo-electron
microscopy, we establish minimax rates for estimating generic signals that are
explicit in the dimension $K$. In a high-noise regime with noise variance
$\sigma^2 \gtrsim K$, the rate scales as $\sigma^6$ and has no further
dependence on the dimension. This rate is achieved by a bispectrum inversion
procedure, and our analyses provide new stability bounds for bispectrum
inversion that may be of independent interest. In a low-noise regime where
$\sigma^2 \lesssim K/\log K$, the rate scales instead as $K\sigma^2$, and we
establish this rate by a sharp analysis of the maximum likelihood estimator
that marginalizes over latent rotations. A complementary lower bound that
interpolates between these two regimes is obtained using Assouad's hypercube
lemma.