title: Computing eigenvalues of semi-infinite quasi-Toeplitz matrices

abstract:
  A quasi-Toeplitz (QT) matrix is a semi-infinite matrix of the form $A=T(a)+E$
where $T(a)$ is the Toeplitz matrix with entries $(T(a))_{i,j}=a_{j-i}$, for
$a_{j-i}\in\mathbb C$, $i,j\ge 1$, while $E$ is a matrix representing a compact
operator in $\ell^2$. The matrix $A$ is finitely representable if $a_k=0$ for
$k<-m$ and for $k>n$, given $m,n>0$, and if $E$ has a finite number of nonzero
entries. The problem of numerically computing eigenpairs of a finitely
representable QT matrix is investigated, i.e., pairs $(\lambda,{\bf v})$ such
that $A{\bf v}=\lambda {\bf v}$, with $\lambda\in\mathbb C$, ${\bf
v}=(v_j)_{j\in\mathbb Z^+}$, ${\bf v}\ne 0$, and $\sum_{j=1}^\infty
|v_j|^2<\infty$. It is shown that the problem is reduced to a finite nonlinear
eigenvalue problem of the kind $ WU(\lambda){\pmb \beta}=0$, where $W$ is a
constant matrix and $U$ depends on $\lambda$ and can be given in terms of
either a Vandermonde matrix or a companion matrix. Algorithms relying on
Newton's method applied to the equation $\det WU(\lambda)=0$ are analyzed.
Numerical experiments show the effectiveness of this approach. The algorithms
have been included in the CQT-Toolbox [Numer. Algorithms 81 (2019), no. 2,
741--769].


title:
Bayesian Shape Reconstruction and Optimal Guidance for Autonomous
  Landing on Asteroids

abstract:
  Construction of the precise shape of an asteroid is critical for spacecraft
operations as the gravitational potential is determined by spatial mass
distribution. The typical approach to shape determination requires a prolonged
mapping phase of the mission over which extensive measurements are collected
and transmitted for Earth-based processing. This paper presents a set of
approaches to explore an unknown asteroid with onboard calculations, and to
land on its surface area selected in an optimal fashion. The main motivation is
to avoid the extended period of mapping or preliminary ground observations that
are commonly required in spacecraft missions around asteroids. First, range
measurements from the spacecraft to the surface are used to incrementally
correct an initial shape estimate according to the Bayesian framework. Then, an
optimal guidance scheme is proposed to control the vantage point of the range
sensor to construct a complete 3D model of the asteroid shape. This shape model
is then used in a nonlinear controller to track a desired trajectory about the
asteroid. Finally, a multi resolution approach is presented to construct a
higher fidelity shape representation in a specified location while avoiding the
inherent burdens of a uniformly high resolution mesh. This approach enables for
an accurate shape determination around a potential landing site. We demonstrate
this approach using several radar shape models of asteroids and provide a full
dynamical simulation about asteroid 4769 Castalia.


title: Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

abstract:
  Charts are commonly used for exploring data and communicating insights.
Generating natural language summaries from charts can be very helpful for
people in inferring key insights that would otherwise require a lot of
cognitive and perceptual efforts. We present Chart-to-text, a large-scale
benchmark with two datasets and a total of 44,096 charts covering a wide range
of topics and chart types. We explain the dataset construction process and
analyze the datasets. We also introduce a number of state-of-the-art neural
models as baselines that utilize image captioning and data-to-text generation
techniques to tackle two problem variations: one assumes the underlying data
table of the chart is available while the other needs to extract data from
chart images. Our analysis with automatic and human evaluation shows that while
our best models usually generate fluent summaries and yield reasonable BLEU
scores, they also suffer from hallucinations and factual errors as well as
difficulties in correctly explaining complex patterns and trends in charts.


title:
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can
  Existing Algorithms Fulfill Clinical Requirements?

abstract:
  Being able to explain the prediction to clinical end-users is a necessity to
leverage the power of artificial intelligence (AI) models for clinical decision
support. For medical images, a feature attribution map, or heatmap, is the most
common form of explanation that highlights important features for AI models'
prediction. However, it is unknown how well heatmaps perform on explaining
decisions on multi-modal medical images, where each image modality or channel
visualizes distinct clinical information of the same underlying biomedical
phenomenon. Understanding such modality-dependent features is essential for
clinical users' interpretation of AI decisions. To tackle this clinically
important but technically ignored problem, we propose the modality-specific
feature importance (MSFI) metric. It encodes clinical image and explanation
interpretation patterns of modality prioritization and modality-specific
feature localization. We conduct a clinical requirement-grounded, systematic
evaluation using computational methods and a clinician user study. Results show
that the examined 16 heatmap algorithms failed to fulfill clinical requirements
to correctly indicate AI model decision process or decision quality. The
evaluation and MSFI metric can guide the design and selection of XAI algorithms
to meet clinical requirements on multi-modal explanation.


title:
TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded
  Boundaries

abstract:
  The jigsaw puzzle problem (JPP) is a well-known research problem, which has
been studied for many years. Solving this problem typically involves a
two-stage scheme, consisting of the computation of a pairwise piece
compatibility measure (CM), coupled with a subsequent puzzle reconstruction
algorithm. Many effective CMs, which apply a simple distance measure, based
merely on the information along the piece edges, have been proposed. However,
the practicality of these classical methods is rather doubtful for problem
instances harder than pure synthetic images. Specifically, these methods tend
to break down in more realistic scenarios involving, e.g., monochromatic
puzzles, eroded boundaries due to piece degradation over long time periods,
missing pieces, etc. To overcome this significant deficiency, a few deep
convolutional neural network (CNN)-based CMs have been recently introduced.
Despite their promising accuracy, these models are very computationally
intensive. Twin Embedding Networks (TEN), to represent a piece with respect to
its boundary in a latent embedding space. Combining this latent representation
with a simple distance measure, we then demonstrate a superior performance, in
terms of accuracy, of our newly proposed pairwise CM, compared to that of
various classical methods, for the problem domain of eroded tile boundaries, a
testbed for a number of real-world JPP variants. Furthermore, we also
demonstrate that TEN is faster by a few orders of magnitude, on average, than
the recent NN models, i.e., it is as fast as the classical methods. In this
regard, the paper makes a significant first attempt at bridging the gap between
the relatively low accuracy (of classical methods) and the intensive
computational complexity (of NN models), for practical, real-world puzzle-like
problems.


title: Large-scale five- and seven-junction epitaxial graphene devices

abstract:
  The utilization of multiple current terminals on millimeter-scale graphene
p-n junction devices has enabled the measurement of many atypical, fractional
multiples of the quantized Hall resistance at the i=2 plateau. These fractions
take the form a/b R_H and can be determined both analytically and by
simulations. These experiments validate the use of either the LTspice circuit
simulator or the analytical framework recently presented in similar work.
Furthermore, the production of several devices with large-scale junctions
substantiates the approach of using simple ultraviolet lithography to obtain
junctions of sufficient sharpness.


title: On the stability of a growing annular hyperelastic plate

abstract:
  Growth-induced instabilities are ubiquitous in biological systems and lead to
diverse morphologies in the form of wrinkling, folding, and creasing. The
current work focuses on the mechanics behind growth-induced wrinkling
instabilities in an incompressible annular hyperelastic plate subjected to two
types of boundary constraints. Governing differential equations for a
two-dimensional plate system are derived using a variational principle and
series expansion along the thickness direction. The incremental differential
equations for studying the bifurcation behaviour of the growing hyperelastic
annular plate are derived by considering both axisymmetric and asymmetric
perturbations. The resulting equations are then solved numerically using the
compound matrix method to evaluate the critical growth stretch that causes
bifurcation. The effect of boundary constraints, thickness, and radius ratio of
the annular plate on the critical growth factor is studied. For most of the
considered cases, an asymmetric bifurcation is the preferred mode of
instability for an annular plate. The obtained results are relevant to
modelling wrinkling phenomena in planar soft tissues, pattern transition in
two-dimensional films growing on an elastic substrate, and manufacturing smart
materials.


title:
Can Scale-free Network Growth with Triad Formation Capture Simplicial
  Complex Distributions in Real Communication Networks?

abstract:
  In recent years, there has been a growing recognition that higher-order
structures are important features in real-world networks. A particular class of
structures that has gained prominence is known as a simplicial complex. Despite
their application to complex processes such as social contagion and novel
measures of centrality, not much is currently understood about the
distributional properties of these complexes in communication networks.
Furthermore, it is also an open question as to whether an established growth
model, such as scale-free network growth with triad formation, is sophisticated
enough to capture the distributional properties of simplicial complexes. In
this paper, we use empirical data on five real-world communication networks to
propose a functional form for the distributions of two important simplicial
complex structures. We also show that, while the scale-free network growth
model with triad formation captures the form of these distributions in networks
evolved using the model, the best-fit parameters are significantly different
between the real network and its simulated equivalent. An auxiliary
contribution is an empirical profile of the two simplicial complexes in these
five real-world networks.


title: Adaptive Information Bottleneck Guided Joint Source-Channel Coding

abstract:
  Joint source channel coding (JSCC) has attracted increasing attentions due to
its robustness and high efficiency. However, the existing research on JSCC
mainly focuses on minimizing the distortion between the transmitted and
received information, while limiting the required data rate. Therefore, even
though the transmitted information is well recovered, the transmitted bits may
be far more than the minimal threshold according to the rate-distortion (RD)
theory. In this paper, we propose an adaptive Information Bottleneck (IB)
guided JSCC (AIB-JSCC), which aims at achieving the theoretically maximal
compression ratio for a given reconstruction quality. In particular, we first
derive a mathematically tractable form of loss function for AIB-JSCC. To keep a
better tradeoff between compression and reconstruction quality, we further
propose an adaptive algorithm that adjusts hyperparameter beta of the proposed
loss function dynamically according to the distortion during training.
Experiment results show that AIB-JSCC can significantly reduce the required
amount of the transmitted data and improve the reconstruction quality and
downstream artificial-intelligent task performance.


title:
Stochastic completeness and $L^1$-Liouville property for second-order
  elliptic operators

abstract:
  Let $P$ be a linear, second-order, elliptic operator with real coefficients
defined on a noncompact Riemannian manifold $M$ and satisfies $P1=0$ in $M$.
Assume further that $P$ admits a minimal positive Green function in $M$. We
prove that there exists a smooth positive function $\rho$ defined on $M$ such
that $M$ is stochastically incomplete with respect to the operator $ P_{\rho}
:= \rho \, P $, that is,
  \[
  \int_{M} k_{P_{\rho}}^{M}(x, y, t) \ {\rm d}y < 1 \qquad \forall (x, t) \in M
\times (0, \infty),
  \]
  where $k_{P_{\rho}}^{M}$ denotes the minimal positive heat kernel associated
with $P_{\rho}$.
  Moreover, $M$ is $L^1$-Liouville with respect to $P_{\rho}$ if and only if
$M$ is $L^1$-Liouville with respect to $P$. In addition, we study the interplay
between stochastic completeness and the $L^1$-Liouville property of the skew
product of two second-order elliptic operators.


title:
Simulating Transient Noise Bursts in LIGO with Generative Adversarial
  Networks

abstract:
  The noise of gravitational-wave (GW) interferometers limits their sensitivity
and impacts the data quality, hindering the detection of GW signals from
astrophysical sources. For transient searches, the most problematic are
transient noise artifacts, known as glitches, that happen at a rate around $ 1
\text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need
for better modeling and inclusion of glitches in large-scale studies, such as
stress testing the pipelines. In this proof-of concept work we employ
Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning
algorithm inspired by Game Theory, to learn the underlying distribution of blip
glitches and to generate artificial populations. We reconstruct the glitch in
the time-domain, providing a smooth input that the GAN can learn. With this
methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford
and Livingston detectors in less than one second. Furthermore, we employ
several metrics to measure the performance of our methodology and the quality
of its generations. This investigation will be extended in the future to
different glitch classes with the final goal of creating an open-source
interface for mock data generation.


title: Phase behaviour of active particles in block copolymer melts

abstract:
  Block copolymer melts offer a unique template to control the position and
alignment of nanoparticles due to their ability to self-assemble into periodic
ordered structures. Active Brownian particles are shown to co-assemble with
block copolymers leading to emergent organised structures. The block copolymer
acts as a soft confinement that can control the self-propulsion of the active
Brownian particles, both for interface-segregated and selective nanoparticles.
At moderate activity regimes, active Brownian particles can form organised
structures such as polarised trains or rotating vortexes. At high activity, the
contrast in the polymeric and colloidal time scales can lead to particle swarms
with distorted block copolymer morphology, due to the competition between the
polymeric self-assembly and the active Brownian self-propulsion.


title: Maxway CRT: Improving the Robustness of Model-X Inference

abstract:
  The model-X conditional randomization test (CRT) proposed by Cand\`es et al.
(2018) is known as a flexible and powerful testing procedure for the
conditional independence hypothesis: X is independent of Y conditional on Z.
Though having many attractive properties, the model-X CRT relies on the model-X
assumption that we have access to perfect knowledge of the distribution of X
conditional on Z. If there is a specification error in modeling the
distribution of X conditional on Z, this approach may lose its validity. This
problem is even more severe when the adjustment covariates Z are of high
dimensionality, in which situation precise modeling of X against Z can be hard.
In response to this, we propose the Maxway (Model and Adjust X With the
Assistance of Y) CRT, a more robust inference approach for conditional
independence when the conditional distribution of X is unknown and needs to be
estimated from the data. Besides the distribution of X | Z, the Maxway CRT also
learns the distribution of Y | Z, using it to calibrate the resampling
distribution of X to gain robustness to the error in modeling X. We show that
the type-I error inflation of the Maxway CRT can be controlled by the learning
error for the low-dimensional adjusting model plus the product of learning
errors for the distribution of X | Z and the distribution of Y | Z. This result
can be interpreted as an "almost doubly robust" property of the Maxway CRT.
Through extensive simulation studies, we demonstrate that the Maxway CRT
achieves significantly better type-I error control than existing model-X
inference approaches while having similar power. Finally, we apply our
methodology to the UK biobank dataset with the goal of studying the
relationship between the functional SNP of statins and the risk for type II
diabetes mellitus.


title: Cyclone Preparedness, Rescue Operations and Damage Assessment using UAVs

abstract:
  UAV's capability to access remote and inaccessible areas within a quick time
can be utilized for effective cyclone management. This paper presents the
possible application of UAVs at different stages of cyclone mitigation. The
overall system architecture necessary for preparedness, rescue operation,
resource allocation, and damage assessment using UAVs during cyclones is
described. Although general commercial UAVs are reported to be used in cyclone
operations, UAV systems should be planned specifically for cyclone operations
to improve efficiency. Here, the specification required for effective and safe
UAV operations in the post-cyclone scenario is presented. Mission planning
required for various rescue, relief, and damage assessment missions related to
cyclone management is discussed. A case study of deploying UAV in Amphan
cyclone operation in West Bengal is also presented. This paper can help
disaster management authorities to develop UAV systems specifically to cater to
cyclone operations.


title:
The worst of both worlds: A comparative analysis of errors in learning
  from data in psychology and machine learning

abstract:
  Recent concerns that machine learning (ML) may be facing a reproducibility
and replication crisis suggest that some published claims in ML research cannot
be taken at face value. These concerns inspire analogies to the replication
crisis affecting the social and medical sciences, as well as calls for greater
integration of statistical approaches to causal inference and predictive
modeling. A deeper understanding of what reproducibility concerns in research
in supervised ML have in common with the replication crisis in experimental
science can put the new concerns in perspective, and help researchers avoid
"the worst of both worlds" that can emerge when ML researchers begin borrowing
methodologies from explanatory modeling without understanding their
limitations, and vice versa. We contribute a comparative analysis of concerns
about inductive learning that arise in different stages of the modeling
pipeline in causal attribution as exemplified in psychology versus predictive
modeling as exemplified by ML. We identify themes that re-occur in reform
discussions like overreliance on asymptotic theory and non-credible beliefs
about real-world data generating processes. We argue that in both fields,
claims from learning are implied to generalize outside the specific environment
studied (e.g., the input dataset or subject sample, modeling implementation,
etc.) but are often impossible to refute due to forms of underspecification. In
particular, many errors being acknowledged in ML expose cracks in long-held
beliefs that optimizing predictive accuracy using huge datasets absolves one
from having to make assumptions about the underlying data generating process.
We conclude by discussing rhetorical risks like error misdiagnosis that arise
in times of methodological uncertainty.


title:
Statistical assessment of spatio-temporal impact of lockdown on air
  pollution using different modelling approaches in India

abstract:
  One of the main contributors to air pollution is particulate matter (PMxy),
which causes several COVID-19 related diseases such as respiratory problems and
cardiovascular disorders. Therefore, the spatial and temporal trend analysis of
particulate matter and the mass concentration of all aerosol particles less
than 2.5 m in diameter (PM2.5) has become critical to control the risk factors
of co-morbidity of a patient. Lockdown plays a significant role in maintaining
COVID-19 cases as well as air pollution, including particulate matter. This
study aims to analyse the effect of the lockdown on controlling air pollution
in metropolitan cities in India through various statistical modelling
approaches. Most research articles in the literature assume a linear
relationship between responses and covariates and take independent and
identically distributed error terms in the model, which may not be appropriate
for analysing such air pollution data. In this study, we performed a pattern
analysis of daily PM2.5 emissions in various major activity zones during 2019
and 2020. By measuring the lockdown effect, we also considered seasonal
influence.


title:
Effect of paramagnetic impurities on superconductivity in polyhydrides:
  $\textit{s}$-wave order parameter in Nd-doped LaH$_{10}$

abstract:
  Polyhydrides are a novel class of superconducting materials with extremely
high critical parameters, which is very promising for applications. On the
other hand, complete experimental study of the magnetic phase diagram for the
best so far known superconductor, lanthanum decahydride LaH$_{10}$, encounters
a serious complication because of the large upper critical magnetic field
$\textit{H}$$_{C2}$(0), exceeding 120-160 T. Partial replacement of La atoms by
magnetic Nd atoms results in a decrease of the upper critical field, which
makes it attainable for existing pulse magnets. We found that addition of
neodymium leads to significant suppression of superconductivity in LaH$_{10}$:
each atomic % of Nd causes decrease in $\textit{T}$$_{C}$ by 10-11 K. Using
strong pulsed magnetic fields up to 68 T, we constructed the magnetic phase
diagram of the ternary (La,Nd)H$_{10}$ superhydride, which appears to be
surprisingly linear with $\textit{H}$$_{C2}$ $\propto$ |$\textit{T}$ -
$\textit{T}$$_C$|. The pronounced suppression of superconductivity in
LaH$_{10}$ by magnetic Nd atoms and the robustness of $\textit{T}$$_C$ with
respect to nonmagnetic impurities (e.g., Y, Al, C) under Anderson's theorem
indicate the isotropic ($\textit{s}$-wave) character of conventional
electron-phonon pairing in the synthesized superhydrides.


title: Wasserstein Adversarial Transformer for Cloud Workload Prediction

abstract:
  Predictive Virtual Machine (VM) auto-scaling is a promising technique to
optimize cloud applications operating costs and performance. Understanding the
job arrival rate is crucial for accurately predicting future changes in cloud
workloads and proactively provisioning and de-provisioning VMs for hosting the
applications. However, developing a model that accurately predicts cloud
workload changes is extremely challenging due to the dynamic nature of cloud
workloads. Long-Short-Term-Memory (LSTM) models have been developed for cloud
workload prediction. Unfortunately, the state-of-the-art LSTM model leverages
recurrences to predict, which naturally adds complexity and increases the
inference overhead as input sequences grow longer. To develop a cloud workload
prediction model with high accuracy and low inference overhead, this work
presents a novel time-series forecasting model called WGAN-gp Transformer,
inspired by the Transformer network and improved Wasserstein-GANs. The proposed
method adopts a Transformer network as a generator and a multi-layer perceptron
as a critic. The extensive evaluations with real-world workload traces show
WGAN-gp Transformer achieves 5 times faster inference time with up to 5.1
percent higher prediction accuracy against the state-of-the-art approach. We
also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud
platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms
the LSTM-based mechanism by significantly reducing VM over-provisioning and
under-provisioning rates.


title:
Characterizing and Understanding Software Security Vulnerabilities in
  Machine Learning Libraries

abstract:
  The application of machine learning (ML) libraries has been tremendously
increased in many domains, including autonomous driving systems, medical, and
critical industries. Vulnerabilities of such libraries result in irreparable
consequences. However, the characteristics of software security vulnerabilities
have not been well studied. In this paper, to bridge this gap, we take the
first step towards characterizing and understanding the security
vulnerabilities of five well-known ML libraries, including Tensorflow, PyTorch,
Sickit-learn, Pandas, and Numpy. To do so, in total, we collected 596
security-related commits to exploring five major factors: 1) vulnerability
types, 2) root causes, 3) symptoms, 4) fixing patterns, and 5) fixing efforts
of security vulnerabilities in ML libraries. The findings of this study can
assist developers in having a better understanding of software security
vulnerabilities across different ML libraries and gain a better insight into
their weaknesses of them. To make our finding actionable, we further developed
DeepMut, an automated mutation testing tool, as a proof-of-concept application
of our findings. DeepMut is designed to assess the adequacy of existing test
suites of ML libraries against security-aware mutation operators extracted from
the vulnerabilities studied in this work. We applied DeepMut on the Tensorflow
kernel module and found more than 1k alive mutants not considered by the
existing test suits. The results demonstrate the usefulness of our findings.


title:
Convolutional neural networks enable high-fidelity prediction of
  path-dependent diffusion barrier spectra in multi-principal element alloys

abstract:
  The emergent multi-principal element alloys (MPEAs) provide a vast
compositional space to search for novel materials for technological advances.
However, how to efficiently identify optimal compositions from such a large
design space for targeted properties is a grand challenge in material science.
Here we developed a convolutional neural network (CNN) model that can
accurately and efficiently predict path-dependent vacancy migration energy
barriers, which are critical to diffusion behaviors and many high-temperature
properties, of MPEAs at any compositions and with different chemical
short-range orders within a given alloy system. The success of the CNN model
makes it promising for developing a database of diffusion barriers for
different MPEA systems, which would accelerate alloy screening for the
discovery of new compositions with desirable properties. Besides, the length
scale of local configurations relevant to migration energy barriers is
uncovered, and the implications of this success to other aspects of materials
science are discussed.


title:
A Mixed Quantization Network for Computationally Efficient Mobile
  Inverse Tone Mapping

abstract:
  Recovering a high dynamic range (HDR) image from a single low dynamic range
(LDR) image, namely inverse tone mapping (ITM), is challenging due to the lack
of information in over- and under-exposed regions. Current methods focus
exclusively on training high-performing but computationally inefficient ITM
models, which in turn hinder deployment of the ITM models in
resource-constrained environments with limited computing power such as edge and
mobile device applications.
  To this end, we propose combining efficient operations of deep neural
networks with a novel mixed quantization scheme to construct a well-performing
but computationally efficient mixed quantization network (MQN) which can
perform single image ITM on mobile platforms. In the ablation studies, we
explore the effect of using different attention mechanisms, quantization
schemes, and loss functions on the performance of MQN in ITM tasks. In the
comparative analyses, ITM models trained using MQN perform on par with the
state-of-the-art methods on benchmark datasets. MQN models provide up to 10
times improvement on latency and 25 times improvement on memory consumption.


title:
Using digital traces to build prospective and real-time county-level
  early warning systems to anticipate COVID-19 outbreaks in the United States

abstract:
  The ongoing COVID-19 pandemic continues to affect communities around the
world. To date, almost 6 million people have died as a consequence of COVID-19,
and more than one-quarter of a billion people are estimated to have been
infected worldwide. The design of appropriate and timely mitigation strategies
to curb the effects of this and future disease outbreaks requires close
monitoring of their spatio-temporal trajectories. We present machine learning
methods to anticipate sharp increases in COVID-19 activity in US counties in
real-time. Our methods leverage Internet-based digital traces -- e.g.,
disease-related Internet search activity from the general population and
clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories
from neighboring locations -- to monitor potential changes in population-level
health trends. Motivated by the need for finer spatial-resolution
epidemiological insights to improve local decision-making, we build upon
previous retrospective research efforts originally conceived at the state level
and in the early months of the pandemic. Our methods -- tested in real-time and
in an out-of-sample manner on a subset of 97 counties distributed across the US
-- frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before
the onset of local outbreaks (defined as the time when the effective
reproduction number $R_t$ becomes larger than 1 consistently). Given the
continued emergence of COVID-19 variants of concern -- such as the most recent
one, Omicron -- and the fact that multiple countries have not had full access
to vaccines, the framework we present, while conceived for the county-level in
the US, could be helpful in countries where similar data sources are available.


title:
The Dilaton Black Hole on a Conformal Invariant Five Dimensional Warped
  Spacetime: Paradoxes Possibly Resolved?

abstract:
  A thorough investigation is presented of the exact black hole solution on a
warped five-dimensional spacetime in conformal dilaton gravity (CDG), found in
earlier work. Summarized, we will prove: The black hole solution in the CDG
model on a warped 5D spacetime: 1. It is an exact solution for the metric
components as well as for the dilaton field. 2.The quintic polynomial
describing the zero's of the model, has no essential singularities. 3.If we
write $^{(5)}g_{\mu\nu}=\omega^{4/3}{^{(5)}}{\tilde g_{\mu\nu}}, ^{(5)}\tilde
g_{\mu\nu}=^{(4)}\tilde g_{\mu\nu}+n_\mu n_\nu, ^{(4)}\tilde
g_{\mu\nu}=\bar\omega^2 {^{(4)}}\bar g{\mu\nu}$ then ${^{(4)}}\bar g{\mu\nu}$
is conformally flat and with $n_\mu$ the normal to the brane. 4. It fits the
antipodal boundary condition, i.e., antipodal points in the projected space are
identified using the embedding of a Klein surface in $\mathbb{C}^4$. 5. One can
apply 't Hooft's back reaction method in constructing the unitary S-matrix and
there is no "inside" of the black hole. 6. The contribution from the bulk
determines the poles on the effective 4D spacetime. 7. The zeros of the quintic
resolvent can analytically described by the icosahedral equation, i.e., in
terms of hypergeometric functions and elliptic modular functions. 8. The Hopf
fibration of the Klein bottle can be applied.


title:
Angular Dependence of Coherent Radio Emission from Magnetars with
  Multipolar Magnetic Fields

abstract:
  The recent detection of a Fast Radio Burst (FRB) from a Galactic magnetar
secured the fact that neutron stars (NSs) with super-strong magnetic fields are
capable of producing these extremely bright coherent radio bursts. One of the
leading mechanisms to explain the origin of such coherent radio emission is the
curvature radiation process within the dipolar magnetic field structure. It
has, however, already been demonstrated that magnetars likely have a more
complex magnetic field topology. Here we critically investigate curvature radio
emission in the presence of inclined dipolar and quadrupolar ("quadrudipolar")
magnetic fields and show that such field structures differ in their angular
characteristics from a purely dipolar case. We analytically show that the shape
of open field lines can be modified significantly depending on both the ratio
of quadrupole to dipole field strength and their inclination angle at the NS
surface. This creates multiple points along each magnetic field line that
coincides with the observer's line of sight and may explain the complex
spectral and temporal structure of the observed FRBs. We also find that in
quadrudipole, the radio beam can take a wider angular range and the beam width
can be wider than in pure dipole. This may explain why the pulse width of the
transient radio pulsation from magnetars is as large as that of ordinary radio
pulsars.


title:
Snowmass2021 Cosmic Frontier White Paper: Ultraheavy particle dark
  matter

abstract:
  We outline the unique opportunities and challenges in the search for
"ultraheavy" dark matter candidates with masses between roughly $10~{\rm TeV}$
and the Planck scale $m_{\rm pl} \approx 10^{16}~{\rm TeV}$. This mass range
presents a wide and relatively unexplored dark matter parameter space, with a
rich space of possible models and cosmic histories. We emphasize that both
current detectors and new, targeted search techniques, via both direct and
indirect detection, are poised to contribute to searches for ultraheavy
particle dark matter in the coming decade. We highlight the need for new
developments in this space, including new analyses of current and imminent
direct and indirect experiments targeting ultraheavy dark matter and
development of new, ultra-sensitive detector technologies like next-generation
liquid noble detectors, neutrino experiments, and specialized quantum sensing
techniques.


title: Distributed Community Detection in Large Networks

abstract:
  Community detection for large networks is a challenging task due to the high
computational cost as well as the heterogeneous community structure. Stochastic
block model (SBM) is a popular model to analyze community structure where nodes
belonging to the same communities are connected with equal probability.
Modularity optimization methods provide a fast and effective way for community
detection under SBM with assortative community structure, where nodes within
communities are densely connected and nodes across communities are relatively
loosely connected. However, the non-assortative community structure, where
nodes across communities are densely connected, widely exists in real-world
networks, thus leading to the inconsistency of modularity optimization methods
in applications. In this paper, we consider networks with "grouped communities"
(or "the group structure"), where nodes within grouped communities are densely
connected and nodes across grouped communities are relatively loosely
connected, while nodes belonging to the same group but different communities
can be either densely or loosely connected. We incorporate the group structure
in the SBM and propose a novel divide-and-conquer algorithm to detect the
community structure. We show that the proposed method can recover both the
group structure and the community structure asymptotically. Numerical studies
demonstrate that the proposed method can reduce the computational cost
significantly while still achieving competitive performance. Further, we extend
the proposed framework from the SBM to the degree-corrected SBM for more
general applications.


title:
Torsional Periodic Lattice Distortions and Diffraction of Twisted 2D
  Materials

abstract:
  Twisted 2D materials form complex moir\'e structures that spontaneously
reduce symmetry through picoscale deformation within a mesoscale lattice. We
show twisted 2D materials contain a torsional displacement field comprised of
three transverse periodic lattice distortions (PLD). The torsional PLD
amplitude provides a single order parameter that concisely describes the
structural complexity of twisted bilayer moir\'es. Moreover, the structure and
amplitude of a torsional periodic lattice distortion is quantifiable using
rudimentary electron diffraction methods sensitive to reciprocal space. In
twisted bilayer graphene, the torsional PLD begins to form at angles below
3.89{\deg} and the amplitude reaches 8 pm around the magic angle of 1.1{\deg}.
At extremely low twist angles (e.g. below 0.25{\deg}) the amplitude increases
and additional PLD harmonics arise to expand Bernal stacked domains separated
by well defined solitonic boundaries. The torsional distortion field in twisted
bilayer graphene is analytically described and has an upper bound of 22.6 pm.
Similar torsional distortions are observed in twisted WS$_2$, CrI$_3$, and
WS$_2$ / MoSe$_2$.


title: Semiclassical 3D gravity as an average of large-c CFTs

abstract:
  A two-dimensional CFT dual to a semiclassical theory of gravity in three
dimensions must have a large central charge $c$ and a sparse low energy
spectrum. This constrains the OPE coefficients and density of states of the CFT
via the conformal bootstrap. We define an ensemble of CFT data by averaging
over OPE coefficients subject to these bootstrap constraints, and show that
calculations in this ensemble reproduce semiclassical 3D gravity. We analyze a
wide variety of gravitational solutions, both in pure Einstein gravity and
gravity coupled to massive point particles, including Euclidean wormholes with
multiple boundaries and higher topology spacetimes with a single boundary. In
all cases we find that the on-shell action of gravity agrees with the
ensemble-averaged CFT at large $c$. The one-loop corrections also match in the
cases where they have been computed. We also show that the bulk effective
theory has random couplings induced by wormholes, providing a controlled,
semiclassical realization of the mechanism of Coleman, Giddings, and
Strominger.


title: On Information Hiding in Natural Language Systems

abstract:
  With data privacy becoming more of a necessity than a luxury in today's
digital world, research on more robust models of privacy preservation and
information security is on the rise. In this paper, we take a look at Natural
Language Steganography (NLS) methods, which perform information hiding in
natural language systems, as a means to achieve data security as well as
confidentiality. We summarize primary challenges regarding the secrecy and
imperceptibility requirements of these systems and propose potential directions
of improvement, specifically targeting steganographic text quality. We believe
that this study will act as an appropriate framework to build more resilient
models of Natural Language Steganography, working towards instilling security
within natural language-based neural models.


title:
Energy conserving particle-in-cell methods for relativistic
  Vlasov--Maxwell equations of laser-plasma interaction

abstract:
  Energy conserving particle-in-cell schemes are constructed for a class of
reduced relativistic Vlasov--Maxwell equations of laser-plasma interaction.
Discrete Poisson equation is also satisfied by the numerical solution.
Specifically, distribution function is discretized using particle-in-cell
method, discretization of electromagnetic fields is done using compatible
finite element method in the framework of finite element of exterior calculus,
and time discretization used is based on discrete gradient method combined with
Poisson splitting. Numerical experiments of parametric instability are done to
validate the conservation properties and good long time behavior of the
numerical methods constructed.


title:
Sparsity and Heterogeneous Dropout for Continual Learning in the Null
  Space of Neural Activations

abstract:
  Continual/lifelong learning from a non-stationary input data stream is a
cornerstone of intelligence. Despite their phenomenal performance in a wide
variety of applications, deep neural networks are prone to forgetting their
previously learned information upon learning new ones. This phenomenon is
called "catastrophic forgetting" and is deeply rooted in the
stability-plasticity dilemma. Overcoming catastrophic forgetting in deep neural
networks has become an active field of research in recent years. In particular,
gradient projection-based methods have recently shown exceptional performance
at overcoming catastrophic forgetting. This paper proposes two
biologically-inspired mechanisms based on sparsity and heterogeneous dropout
that significantly increase a continual learner's performance over a long
sequence of tasks. Our proposed approach builds on the Gradient Projection
Memory (GPM) framework. We leverage K-winner activations in each layer of a
neural network to enforce layer-wise sparse activations for each task, together
with a between-task heterogeneous dropout that encourages the network to use
non-overlapping activation patterns between different tasks. In addition, we
introduce Continual Swiss Roll as a lightweight and interpretable -- yet
challenging -- synthetic benchmark for continual learning. Lastly, we provide
an in-depth analysis of our proposed method and demonstrate a significant
performance boost on various benchmark continual learning problems.


title:
Photovoltaic-ferroelectric materials for the realization of all-optical
  devices

abstract:
  Following how the electrical transistor revolutionized the field of
electronics,the realization of an optical transistor in which the flow of light
is controlled optically should open the long-sought era of optical computing
and new data processing possibilities. However, such function requires photons
to influence each other, an effect which is unnatural in free space. Here it is
shown that a ferroelectric and photovoltaic crystal gated optically at the
onset of its bandgap energy can act as a photonic transistor. The light-induced
charge generation and distribution processes alter the internal electric field
and therefore impact the optical transmission with a memory effect and
pronounced nonlinearity. The latter results in an optical computing
possibility, which does not need to operate coherently. These findings advance
efficient room temperature optical transistors, memristors, modulators and
all-optical logic circuits.


title: Topological fluids and FQH edge dynamics

abstract:
  In this letter, we map the Chern-Simons-Ginzburg-Landau theory into a
hydrodynamic action with topological terms, written in terms of the auxiliary
Clebsch variables. The hydrodynamic equations we obtain are first order in
gradients with an additional constitutive equation known as Hall constraint. In
the absence of external fields the first order hydrodynamics allows for two
possible tangential boundary conditions at the hard wall interface: no-slip or
no-stress. The no-slip condition means that the fluid sticks to the wall, i.e.,
the tangential velocity must vanish at the boundary. The no-stress condition,
on the other hand, allows the fluid to slip at the wall, as long as the flow
does not generate tangential forces at the boundary. We show that the anomaly
inflow mechanism is fundamentally incompatible with the no-slip condition. In
order to obtain the tangential no-stress condition within variational
principle, we are required to add a chiral boson action at the boundary which
is coupled non-linearly to the edge density. This non-linear edge coupling is
unusual from the Chern-Simons perspective; nevertheless it arises naturally
from the requirement of vanishing tangential stress at the boundary. Upon
coupling to the external gauge fields, this edge action leads to the correct
anomaly equation. In fact, this action provides a natural starting point to
study fluid aspects of the FQH state beyond topological quantum field theories
and, equivalently, the edge dynamics beyond the chiral Luttinger liquid theory.


title:
SA-SASV: An End-to-End Spoof-Aggregated Spoofing-Aware Speaker
  Verification System

abstract:
  Research in the past several years has boosted the performance of automatic
speaker verification systems and countermeasure systems to deliver low Equal
Error Rates (EERs) on each system. However, research on joint optimization of
both systems is still limited. The Spoofing-Aware Speaker Verification (SASV)
2022 challenge was proposed to encourage the development of integrated SASV
systems with new metrics to evaluate joint model performance. This paper
proposes an ensemble-free end-to-end solution, known as Spoof-Aggregated-SASV
(SA-SASV) to build a SASV system with multi-task classifiers, which are
optimized by multiple losses and has more flexible requirements in training
set. The proposed system is trained on the ASVSpoof 2019 LA dataset, a spoof
verification dataset with small number of bonafide speakers. Results of
SASV-EER indicate that the model performance can be further improved by
training in complete automatic speaker verification and countermeasure
datasets.


title:
Transition to the viscoelastic regime in the thinning of polymer
  solutions

abstract:
  In this study, we investigate the transition between the Newtonian and the
viscoelastic regimes during the pinch-off of droplets of dilute polymer
solutions and discuss its link to the coil-stretch transition. The detachment
of a drop from a nozzle is associated with the formation of a liquid neck that
causes the divergence of the local stress in a vanishingly small region. If the
liquid is a polymer solution, this increasing stress progressively unwinds the
polymer chains, up to a point where the resulting increase in the viscosity
slows down drastically the thinning. This threshold to a viscoelastic behavior
corresponds to a macroscopic strain rate $\dot{\varepsilon}_{\rm c}$. In the
present study, we characterize the variations of $\dot{\varepsilon}_{\rm c}$
with respect to the polymer concentration and molar weight, to the solvent
viscosity, and to the nozzle size, i.e., the weight of the drop. We provide
empirical scaling laws for these variations. We also analyze the thinning
dynamics at the transition and show that it follows a self-similar dynamics
controlled by the time scale ${\dot{\varepsilon}_{\rm c}}^{-1}$. This
characteristic time is different and always shorter than the relaxation time of
the polymer.


title: A hyperbolic Kac-Moody Calogero model

abstract:
  A new kind of quantum Calogero model is proposed, based on a hyperbolic
Kac-Moody algebra. We formulate nonrelativistic quantum mechanics on the
Minkowskian root space of the simplest rank-3 hyperbolic Lie algebra $AE_3$
with an inverse-square potential given by its real roots and reduce it to the
unit future hyperboloid. By stereographic projection this defines a quantum
mechanics on the Poincar\'e disk with a unique potential. Since the Weyl group
of $AE_3$ is a $\mathbb{Z}_2$ extension of the modular group
PSL(2,$\mathbb{Z}$), the model is naturally formulated on the complex upper
half plane, and its potential is a real modular function. We present and
illustrate the relevant features of $AE_3$, give some approximations to the
potential and rewrite it as an (almost everywhere convergent) Poincar\'e
series. The corresponding Dunkl operators are constructed and investigated. We
find that their commutativity is obstructed by rank-2 subgroups of hyperbolic
type (the simplest one given by the Fibonacci sequence), casting doubt on the
integrability of the model. We close with some remarks about the energy
spectrum, which is a deformation of the discrete parity-odd part of the
spectrum of the hyperbolic Laplacian on automorphic functions. An appendix with
Don Zagier investigates the computability of the potential. We foresee
applications to cosmological billards and to quantum chaos.

categories: math-ph hep-th math.AG math.DS math.MP math.NT


title: The Future Circular Collider: a Summary for the US 2021 Snowmass Process

abstract:
  In this white paper for the 2021 Snowmass process, we give a description of
the proposed Future Circular Collider (FCC) project and its physics program.
The paper summarizes and updates the discussion submitted to the European
Strategy on Particle Physics. After construction of an approximately 90 km
tunnel, an electron-positron collider based on established technologies allows
world-record instantaneous luminosities at center-of-mass energies from the Z
resonance up to tt thresholds, enabling a rich set of fundamental measurements
including Higgs couplings determinations at the sub percent level, precision
tests of the weak and strong forces, and searches for new particles, including
dark matter, both directly and via virtual corrections or mixing. Among other
possibilities, the FCC-ee will be able to (i) indirectly discover new particles
coupling to the Higgs and/or electroweak bosons up to scales around 7 and 50
TeV, respectively; (ii) perform competitive SUSY tests at the loop level in
regions not accessible at the LHC; (iii) study heavy-flavor and tau physics in
ultra-rare decays beyond the LHC reach, and (iv) achieve the best potential in
direct collider searches for dark matter, sterile neutrinos, and axion-like
particles with masses up to around 90 GeV. The tunnel can then be reused for a
proton-proton collider, establishing record center-of-mass collision energy,
allowing unprecedented reach for direct searches for new particles up to the
around 50 TeV scale, and a diverse program of measurements of the Standard
Model and Higgs boson, including a precision measurement of the Higgs
self-coupling, and conclusively testing weakly-interacting massive particle
scenarios of thermal relic dark matter.


title:
Connecting Structure, Conformation and Energetics of Human Telomere
  G-quadruplex Multimers

abstract:
  G-quadruplexes (G4s) are helical four-stranded structures forming from
guanine-rich nucleic acid sequences, which are thought to play a role in cancer
development and malignant transformation. Most current studies focus on G4
monomers, yet under suitable and biologically relevant conditions G4s undergo
multimerization. Here, we address the structural, conformational and energetic
features of G4 multimers formed in solutions by the human telomere sequence. A
novel multi-technique approach is used combining Small Angle X-ray Scattering
(SAXS) and circular dichroism experiments with coarse-grained simulations, to
provide quantitative information about large-scale structural features and the
stability of G4 multimers. The latter show a significant polydispersity with an
exponential distribution of contour lengths, suggesting a step-growth
polymerization. On increasing DNA concentration, the strength of G4 stacking
interaction increases, as well as the number of the units in the aggregates,
with dimers and trimers as the most probable forms. At the same time, a
variation of G4 conformation is observed. Our findings provide a quantitative
picture of human telomere G4 multimers, which must be accounted for to achieve
a rational design of anticancer drugs targeting DNA structures.


title: Triangular Prism Equations and Categorification

abstract:
  We introduce the triangular prism equations for fusion categories, which turn
out to be equivalent to the pentagon equations in the spherical case (up to a
change of basis), but provide insight to manage the complexity by localization.
We also prove a conjecture of Z. Wang about the second Frobenius-Schur
indicator on pivotal fusion categories. As applications, we provide new
categorification criteria and complete the classification of the unitary simple
integral fusion categories of Frobenius type up to rank 8 and FPdim 4080.

categories: math.QA math-ph math.CT math.MP math.RA math.RT


title:
A time-fractional dual-phase-lag framework to investigate transistors
  with TMTC channels (TiS3, In4Se3) and size-dependent properties

abstract:
  In this study, a time fractional dual-phase-lag model with temperature jump
boundary condition as a choice for the Fourier's law replacement in thermal
modeling of transistors, is utilized. In more details, the numerical simulation
of heat transfer in newly proposed TMTC field effect transistors using
fractional DPL equation has been investigated. Moreover, the Caputo fractional
derivative is employed to formulate the finite difference scheme for
discretization of the fractional DPL model. In order to obtain more precise
results for the peak temperature rise, the temperature and heat flux profiles,
the size-dependent thermal properties are taken into account. Also, the
temperature jump boundary condition has been also applied by means of a
mixed-type boundary condition. It is obtained that considering size-dependent
thermal characteristics for transistors under study, results in increase of the
peak temperature rise up to 250 percent. Furthermore, considering constant bulk
thermal properties for the silicon MOSFET, certain oscillations are observed in
the time-variation of the peak temperature rise for alpha= 0.7, 0.9 and 1. This
presents the so-called negative bias temperature instability appearing in
electronic nano-semiconductor devices. Finally, the hotspot temperature has
been researched in transistors containing two-dimensional materials with quasi
one-dimensional band structure channels. It is obtained that among the studied
FETs, titanium trisulfide with maximum temperature increase of 19.63 K exhibits
the least peak temperature rise. This presents that TiS3 may be an acceptable
silicon channel replacement as far as the thermal issues are concerned.


title:
The role of low-lying resonances for $^{10}$Be(p,$\alpha$) reaction rate
  and implications for the formation of the Solar System

abstract:
  Evidence for the presence of short-lived radioactive isotopes when the Solar
System formed is preserved in meteorites, providing insights into the
conditions at the birth of our Sun. A low-mass core-collapse supernova had been
postulated as a candidate for the origin of $^{10}$Be, reinforcing the idea
that a supernova triggered the formation of the Solar System. We present a
detailed study of the production of $^{10}$Be by the $\nu$ process in
supernovae, which is very sensitive to the reaction rate of the major
destruction channel, $^{10}$Be(p,$\alpha$)$^{7}$Li. With data from recent
nuclear experiments that show the presence of a resonant state in $^{11}$B at
$\approx$~193 keV, we derive new values for the $^{10}$Be(p,$\alpha$)$^{7}$Li
reaction rate which are significantly higher than previous estimates. We show
that, with the new $^{10}$Be(p,$\alpha$)$^{7}$Li reaction rate, a low mass CCSN
is unlikely to produce enough $^{10}$Be to explain the observed $^{10}$Be/
$^{9}$Be ratio in meteorites, even for a wide range of neutrino spectra
considered in our models. These findings point towards spallation reactions
induced by solar energetic particles in the early Solar System as the origin of
$^{10}$Be.


title:
MPGDs for tracking and Muon detection at future high energy physics
  colliders

abstract:
  In the next years, the energy and intensity frontiers of the experimental
Particle Physics will be pushed forward with the upgrade of existing
accelerators (LHC at CERN) and the envisaged construction of new machines at
energy scales up to hundreds TeV or with unprecedented intensity (FCC-hh,
FCC-ee, ILC, Muon Collider). Large size, cost-effective, high-efficiency
detection systems in high background environments are required in order to
accomplish the physics program. MPGDs offer a diversity of technologies that
allow them to meet the required performance challenges at future facilities
thanks to the specific advantages that each technology provides. MPGDs allow
stable operation, with environmentally friendly gas mixtures, at very high
background particle flux with high detection efficiency and excellent spatial
resolution. These features make MPGD one of the primary choices as precise muon
tracking and trigger system in general-purpose detectors at future HEP
colliders. In addition, the low material budget and the flexibility of the base
material make MPGDs suitable for the development of very light, full
cylindrical fine tracking inner trackers at lepton colliders. On-going R&Ds aim
at pushing the detector performance at the limits of each technology. We are
working in continuing to consolidate the construction and stable operation of
large-size detectors, able to cope with large particle fluxes. In this white
paper, we describe some of the most prominent MPGD technologies, their
performance measurements, the challenges faced in the most recent applications,
and the areas of improvement towards efficient tracking and Muon detection at
future high energy physics colliders.


title:
Towards parallel time-stepping for the numerical simulation of
  atherosclerotic plaque growth

abstract:
  The numerical simulation of atherosclerotic plaque growth is computationally
prohibitive, since it involves a complex cardiovascular fluid-structure
interaction (FSI) problem with a characteristic time scale of milliseconds to
seconds, as well as a plaque growth process governed by reaction-diffusion
equations, which takes place over several months. In this work we combine a
temporal homogenization approach, which separates the problem in
computationally expensive FSI problems on a micro scale and a
reaction-diffusion problem on the macro scale, with parallel time-stepping
algorithms. It has been found in the literature that parallel time-stepping
algorithms do not perform well when applied directly to the FSI problem. To
circumvent this problem, a parareal algorithm is applied on the macro-scale
reaction-diffusion problem instead of the micro-scale FSI problem. We
investigate modifications in the coarse propagator of the parareal algorithm,
in order to further reduce the number of costly micro problems to be solved.
The approaches are tested in detailed numerical investigations based on serial
simulations.


title:
Whats Missing? Learning Hidden Markov Models When the Locations of
  Missing Observations are Unknown

abstract:
  The Hidden Markov Model (HMM) is one of the most widely used statistical
models for sequential data analysis, and it has been successfully applied in a
large variety of domains. One of the key reasons for this versatility is the
ability of HMMs to deal with missing data. However, standard HMM learning
algorithms rely crucially on the assumption that the positions of the missing
observations within the observation sequence are known. In some situations
where such assumptions are not feasible, a number of special algorithms have
been developed. Currently, these algorithms rely strongly on specific
structural assumptions of the underlying chain, such as acyclicity, and are not
applicable in the general case. In particular, there are numerous domains
within medicine and computational biology, where the missing observation
locations are unknown and acyclicity assumptions do not hold, thus presenting a
barrier for the application of HMMs in those fields. In this paper we consider
a general problem of learning HMMs from data with unknown missing observation
locations (i.e., only the order of the non-missing observations are known). We
introduce a generative model of the location omissions and propose two learning
methods for this model, a (semi) analytic approach, and a Gibbs sampler. We
evaluate and compare the algorithms in a variety of scenarios, measuring their
reconstruction precision and robustness under model misspecification.


title: Core packing of well-defined x-ray and NMR structures is the same

abstract:
  Numerous studies have investigated the differences and similarities between
protein structures determined by solution NMR spectroscopy and those determined
by x-ray crystallography. A fundamental question is whether any observed
differences are due to differing methodologies, or to differences in the
behavior of proteins in solution versus in the crystalline state. Here, we
compare the properties of the hydrophobic cores of high-resolution protein
crystal structures and those in NMR structures, determined using increasing
numbers and types of restraints. Prior studies have reported that many NMR
structures have denser cores compared to those of high-resolution x-ray crystal
structures. Our current work investigates this result in more detail, and finds
that these NMR structures tend to violate basic features of protein
stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran
and side chain dihedral angle outliers. We find that NMR structures solved with
more restraints, and which do not significantly violate stereochemistry, have
hydrophobic cores that have a similar size and packing fraction as their
counterparts determined by x-ray crystallography at high-resolution. These
results lead us to conclude that, at least regarding the core packing
properties, high-quality structures determined by NMR and x-ray crystallography
are the same, and the differences reported earlier are most likely a
consequence of methodology, rather than fundamental differences between the
protein in the two different environments.


title:
Quantum transport evidence of the boundary states and Lifshitz
  transition in Bi$_4$Br$_4$

abstract:
  The quasi-one-dimensional van der Waals compound Bi$_4$Br$_4$ was recently
found to be a promising high-order topological insulator with exotic electronic
states. In this paper, we study the electrical transport properties of
Bi$_4$Br$_4$ bulk crystals. Two electron-type samples with different electron
concentrations are investigated. Both samples have saturation resistivity
behavior in low temperature. In the low-concentration sample, two-dimensional
quantum oscillations are clearly observed in the magnetoresistance
measurements, which are attributed to the band-bending-induced surface state on
the (001) facet. In the high-concentration sample, the angular
magnetoresistance exhibits two pairs of symmetrical sharp valleys with an
angular difference close to the angle between the crystal planes (001) and
(100). The additional valley can be explained by the contribution of the
boundary states on the (100) facet. Besides, Hall measurements at low
temperatures reveal an anomalous decrease of electron concentration with
increasing temperature, which can be explained by the temperature-induced
Lifshitz transition. These results shed light on the abundant surface and
boundary state transport signals and the temperature-induced Lifshitz
transition in Bi$_4$Br$_4$.


title:
Forced symmetry breaking as a mechanism for rogue bursts in a
  dissipative nonlinear dynamical lattice

abstract:
  We propose an alternative to the standard mechanisms for the formation of
rogue waves in a non-conservative, nonlinear lattice dynamical system. We
consider an ODE system that features regular periodic bursting arising from
forced symmetry breaking. We then connect such potentially exploding units via
a diffusive lattice coupling and investigate the resulting spatio-temporal
dynamics for different types of initial conditions (localized or extended). We
find that in both cases, particular oscillators undergo extremely fast and
large amplitude excursions, resembling a rogue wave burst. Furthermore, the
probability distribution of different amplitudes exhibits bimodality, with
peaks at both vanishing and very large amplitude. While this phenomenology
arises over a range of coupling strengths, for large values thereof the system
eventually synchronizes and the above phenomenology is suppressed. We use both
distributed (such as a synchronization order parameter) and individual
oscillator diagnostics to monitor the dynamics and identify potential
precursors to large amplitude excursions. We also examine similar behavior with
amplitude-dependent diffusive coupling.


title: Adaptive movement strategy in rock-paper-scissors models

abstract:
  Organisms may respond to local stimuli that benefit or threaten their
fitness. The adaptive movement behaviour may allow individuals to adjust their
speed to maximise the chances of being in comfort zones, where death risk is
minimal. We investigate spatial cyclic models where the rock-paper-scissors
game rules describe the nonhierarchical dominance. We assume that organisms of
one out of the species can control the mobility rate in response to the
information obtained from scanning the environment. Running a series of
stochastic simulations, we quantify the effects of the movement strategy on the
spatial patterns and population dynamics. Our findings show that the ability to
change mobility to adapt to environmental clues is not reflected in an
advantage in cyclic spatial games. The adaptive movement provokes a delay in
the spatial domains occupied by the species in the spiral waves, making the
group more vulnerable to the advance of the dominant species and less efficient
in taking territory from the dominated species. Our outcomes also show that the
effects of adaptive movement behaviour accentuate whether most individuals have
a long-range neighbourhood perception. Our results may be helpful for
biologists and data scientists to comprehend the dynamics of ecosystems where
adaptive processes are fundamental.

categories: q-bio.PE nlin.AO nlin.PS physics.bio-ph physics.data-an


title:
Self-consistent theory of current injection into $d$ and $d + is$
  superconductors

abstract:
  We present results for the steady-state nonlinear response of a $d_{x^2-y^2}$
superconducting film connected to normal-metal reservoirs under voltage bias,
allowing for a subdominant $s$-wave component appearing near the interfaces.
Our investigation is based on a current-conserving theory that
self-consistently includes the non-equilibrium distribution functions, charge
imbalance, and the voltage-dependencies of order parameters and scalar impurity
self-energies. For a pure $d$-wave superconductor with [110] orientation of the
interfaces to the contacts, the conductance contains a zero-bias peak
reflecting the large density of zero-energy interface Andreev bound states.
Including a subdominant $s$-wave pairing channel, it is in equilibrium
energetically favorable for an $s$-wave order parameter component
$\Delta_\mathrm{s}$ to appear near the interfaces in the time-reversal symmetry
breaking combination $d+is$. The Andreev states then shift to finite energies
in the density of states. Under voltage bias, we find that the non-equilibrium
distribution in the contact area causes a rapid suppression of the $s$-wave
component to zero as the voltage $eV\rightarrow\Delta_\mathrm{s}$. The
resulting spectral rearrangements and voltage-dependent scattering amplitudes
lead to a pronounced non-thermally broadened split of the zero-bias conductance
peak that is not seen in a non-selfconsistent Landauer-B\"uttiker scattering
approach.


title:
A thermodynamically consistent constitutive equation describing polymer
  disentanglement under flow

abstract:
  We derive a thermodynamically consistent framework for incorporating
entanglement dynamics into constitutive equations for flowing polymer melts. We
use this to combine the convected constraint release (CCR) dynamics of
Ianniruberto-Marriccui into a finitely-extensible version of the Rolie-Poly
model, and also include an anisotropic mobility as in the Giesekus model. The
reversible dynamics are obtained from a free energy that describes both a
finitely-extensible conformation tensor and an ideal gas of entanglements along
the chain. The dissipative dynamics give rise to coupled kinetic equations for
the conformation tensor and entanglements, whose coupling terms describe
shear-induced disentanglement. The relaxation dynamics of the conformation
tensor follow the GLaMM and Rolie-Poly models, and account for reptation,
retraction and CCR. We propose that the relaxation time $\tau_\nu$ for
entanglement recovery is proportional to the Rouse time $\tau_R$ which governs
polymer stretch within the tube. This which matches recent molecular dynamics
simulations, and corresponds to relaxing the entanglement number before the
entire polymer anisotropy has relaxed on the longer reptation time $\tau_d$.


title:
Contracting and Expanding Binary Black Holes in 3D Low-Mass AGN Disks:
  The Importance of Separation

abstract:
  LIGO/Virgo has detected several binary black hole (BBH) merger events that
may have originated in the accretion disks of Active Galactic Nuclei (AGN).
These events require individual black hole masses that fall within the pair
instability supernova mass gap, and therefore these black holes may have been
grown from hierarchical mergers. AGN disks are a prime environment for
hierarchical mergers, and thus a potential location for the progenitors of BBH
gravitational wave events. Understanding how a BBH embedded in an AGN disk
interacts with the surrounding environment is thus crucial for determining if
this interaction can lead to its merger. However, there are few high fidelity
simulations of this process, and almost all are two-dimensional. We present the
results from 3D, high-resolution, local shearing-box simulations of an embedded
BBH interacting with an AGN disk. In these first simulations of their kind, we
focus on determining the mass accretion rate and the orbital evolution rate at
different BBH separations. We find that circular, equal-mass BBHs with
separations greater than 10% of their Hill radius contract while accreting at a
super-Eddington rate. At smaller separations, however, our 3D simulations find
that BBHs expand their orbits. This result suggests that it may be difficult
for an AGN disk to push a BBH to merger, but we discuss several mechanisms,
including MHD turbulence and radiative and mechanical feedback, that could
alleviate this difficulty.


title: Well quasi-orders arising from finite ordered semigroups

abstract:
  In 1985, Bucher, Ehrenfeucht and Haussler studied derivation relations
associated with a given set of context-free rules. Their research motivated a
question regarding homomorphisms from the semigroup of all words onto a finite
ordered semigroup. The question is which of these homomorphisms induce a well
quasi-order on the set of all words. We show that this problem is decidable and
the answer does not depend on the homomorphism, but it is a property of the
ordered semigroup.


title: Hybridized Frequency Combs in Multimode Cavity Electromechanical System

abstract:
  The cavity electromechanical devices with radiation-pressure-interaction
induced Kerr-like nonlinearity are promising candidates to generate microwave
frequency combs. We construct a silicon-nitride-membrane-based superconducting
cavity electromechanical device and study two mechanical modes mediated
synergistic frequency combs. Around the threshold of intracavity field
instability, we firstly show independent frequency combs with tooth spacing
equalling to each mechanical mode frequency. At the overlap boundaries of these
two individual mechanical mode mediated instability thresholds, we observe
hybridization of frequency combs based on the cavity field mediated indirect
coupling between these two mechanical modes. The spectrum lines turn to be
unequally spaced but can be recognized into combinations of the coexisting
frequency combs. Beyond the boundary, the comb reverts to the single-mode case,
and which mechanical mode frequency will the tooth spacing depend on the mode
competition. Our work demonstrates mechanical mode competition enabled
switchability of frequency comb tooth spacing and can be extended to other
devices with multiple nonlinearities.

categories: quant-ph nlin.CD physics.atom-ph physics.optics


title:
Fast Simulation-Based Bayesian Estimation of Heterogeneous and
  Representative Agent Models using Normalizing Flow Neural Networks

abstract:
  This paper proposes a simulation-based deep learning Bayesian procedure for
the estimation of macroeconomic models. This approach is able to derive
posteriors even when the likelihood function is not tractable. Because the
likelihood is not needed for Bayesian estimation, filtering is also not needed.
This allows Bayesian estimation of HANK models with upwards of 800 latent
states as well as estimation of representative agent models that are solved
with methods that don't yield a likelihood--for example, projection and value
function iteration approaches. I demonstrate the validity of the approach by
estimating a 10 parameter HANK model solved via the Reiter method that
generates 812 covariates per time step, where 810 are latent variables, showing
this can handle a large latent space without model reduction. I also estimate
the algorithm with an 11-parameter model solved via value function iteration,
which cannot be estimated with Metropolis-Hastings or even conventional maximum
likelihood estimators. In addition, I show the posteriors estimated on
Smets-Wouters 2007 are higher quality and faster using simulation-based
inference compared to Metropolis-Hastings. This approach helps address the
computational expense of Metropolis-Hastings and allows solution methods which
don't yield a tractable likelihood to be estimated.


title:
Emergent gauge field from self-referencing phase factor on many-body
  wave functions and superconductivity

abstract:
  Superconductivity is a phenomenon where electrical current flows without
friction. The current standard theory for it is the BCS
(Bardeen-Cooper-Schrieffer) theory, which explains it as due to the energy gap
formation by the electron-pairing, and the key ingredient for the supercurrent
generation is the gauge symmetry breaking brought about by it. It was thought
that superconductivity was fully understood by this standard theory; however,
the discovery of superconductivity in cuprates in 1986 changed this situation,
showing a number of experimental results that contradict the standard theory.
It is also notable that the standard theory contradicts in the supecurrent
carrier mass in the London moment; the predicted mass is an effective mass,
while the experimental value is the free electron mass. The above
contradictions suggest the necessity for a fundamental revision for the theory
of superconductivity. Here we show that the required revision may be achieved
by using the Berry phase formalism. It was developed after the establishment of
the BCS theory, and provides a way to detect emergent gauge fields. A
self-referencing phase factor on the wave function detected by the Berry phase
formalism explains the supercurrent generation in the conventional and cuprate
superconductors. It gives rise to a gauge field that enables the gauge symmetry
breaking in the standard theory interpretation.


title: Regression Monte Carlo for Impulse Control

abstract:
  I develop a numerical algorithm for stochastic impulse control in the spirit
of Regression Monte Carlo for optimal stopping. The approach consists in
generating statistical surrogates (aka functional approximators) for the
continuation function. The surrogates are recursively trained by empirical
regression over simulated state trajectories. In parallel, the same surrogates
are used to learn the intervention function characterizing the optimal impulse
amounts. I discuss appropriate surrogate types for this task, as well as the
choice of training sets. Case studies from forest rotation and irreversible
investment illustrate the numerical scheme and highlight its flexibility and
extensibility. Implementation in \texttt{R} is provided as a publicly available
package posted on GitHub.


title:
Improving Macroeconomic Model Validity and Forecasting Performance with
  Pooled Country Data using Structural, Reduced Form, and Neural Network Model

abstract:
  We show that pooling countries across a panel dimension to macroeconomic data
can improve by a statistically significant margin the generalization ability of
structural, reduced form, and machine learning (ML) methods to produce
state-of-the-art results. Using GDP forecasts evaluated on an out-of-sample
test set, this procedure reduces root mean squared error by 12\% across
horizons and models for certain reduced-form models and by 24\% across horizons
for dynamic structural general equilibrium models. Removing US data from the
training set and forecasting out-of-sample country-wise, we show that
reduced-form and structural models are more policy-invariant when trained on
pooled data, and outperform a baseline that uses US data only. Given the
comparative advantage of ML models in a data-rich regime, we demonstrate that
our recurrent neural network model and automated ML approach outperform all
tested baseline economic models. Robustness checks indicate that our
outperformance is reproducible, numerically stable, and generalizable across
models.


title:
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks
  Inherent Relation Learning

abstract:
  Heatmap regression methods have dominated face alignment area in recent years
while they ignore the inherent relation between different landmarks. In this
paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the
inherent relation. The SLPT generates the representation of each single
landmark from a local patch and aggregates them by an adaptive inherent
relation based on the attention mechanism. The subpixel coordinate of each
landmark is predicted independently based on the aggregated feature. Moreover,
a coarse-to-fine framework is further introduced to incorporate with the SLPT,
which enables the initial landmarks to gradually converge to the target facial
landmarks using fine-grained features from dynamically resized local patches.
Extensive experiments carried out on three popular benchmarks, including WFLW,
300W and COFW, demonstrate that the proposed method works at the
state-of-the-art level with much less computational complexity by learning the
inherent relation between facial landmarks. The code is available at the
project website.


title:
Ca II Triplet Spectroscopy of Small Magellanic Cloud Red Giants. V.
  Abundances and Velocities for 12 Massive Clusters

abstract:
  We aim to analyze the chemical evolution of the Small Magellanic Cloud adding
12 additional clusters to our existing sample having accurate and homogeneously
derived metallicities. We are particularly interested in seeing if there is any
correlation between age and metallicity for the different structural components
to which the clusters belong. Spectroscopic metallicities of red giant stars
are derived from the measurement of the equivalent width of the near-IR calcium
triplet lines. Cluster membership analysis was carried out using criteria that
include radial velocities, metallicities, proper motions and distance from the
cluster center. The mean cluster radial velocity and metallicity were
determined with a typical error of 2.1 km/s and 0.03 dex, respectively. We
added this information to that available in the literature for other clusters
studied with the same method, compiling a final sample of 48 clusters with
metallicities homogeneously determined. Clusters of the final sample are
distributed in an area of ~ 70 deg^2 and cover an age range from 0.4 Gyr to
10.5 Gyr. The metallicity distribution of our new cluster sample shows a lower
probability of being bimodal than suggested in previous studies. The separate
chemical analysis of clusters in the six components (Main Body, Counter-Bridge,
West Halo, Wing/Bridge, Northern Bridge and Southern Bridge) shows that only
clusters belonging to the Northern Bridge appear to trace a V-Shape, showing a
clear inversion of the metallicity gradient in the outer regions. There is a
suggestion of a metallicity gradient in the West Halo, similar to that
previously found for field stars. It presents, however, a very large
uncertainty. Also, clusters belonging to the West Halo, Wing/Bridge and
Southern Bridge exhibit a well-defined age-metallicity relation with relatively
little scatter in abundance at fixed age compared to other regions.


title:
Change Detection from Synthetic Aperture Radar Images via Dual Path
  Denoising Network

abstract:
  Benefited from the rapid and sustainable development of synthetic aperture
radar (SAR) sensors, change detection from SAR images has received increasing
attentions over the past few years. Existing unsupervised deep learning-based
methods have made great efforts to exploit robust feature representations, but
they consume much time to optimize parameters. Besides, these methods use
clustering to obtain pseudo-labels for training, and the pseudo-labeled samples
often involve errors, which can be considered as "label noise". To address
these issues, we propose a Dual Path Denoising Network (DPDNet) for SAR image
change detection. In particular, we introduce the random label propagation to
clean the label noise involved in preclassification. We also propose the
distinctive patch convolution for feature representation learning to reduce the
time consumption. Specifically, the attention mechanism is used to select
distinctive pixels in the feature maps, and patches around these pixels are
selected as convolution kernels. Consequently, the DPDNet does not require a
great number of training samples for parameter optimization, and its
computational efficiency is greatly enhanced. Extensive experiments have been
conducted on five SAR datasets to verify the proposed DPDNet. The experimental
results demonstrate that our method outperforms several state-of-the-art
methods in change detection results.


title:
Solubility of organic salts in solvent-antisolvent mixtures: A combined
  experimental and molecular dynamics simulations approach

abstract:
  We combine molecular dynamics simulations with experiments to estimate
solubilities of organic salts in complex growth environments. We predict the
solubility by simulations of the growth and dissolution of ions at the crystal
surface kink sites at different solution concentrations. Thereby, the
solubility is identified as the solution's salt concentration, where the energy
of the ion pair dissolved in solution equals the energy of the ion pair
crystallized at the kink sites. The simulation methodology is demonstrated for
the case of anhydrous sodium acetate crystallized from various
solvent-antisolvent mixtures. To validate the predicted solubilities, we have
measured the solubilities of sodium acetate in-house, using an experimental
setup and measurement protocol that guarantees moisture-free conditions, which
is key for a hygroscopic compound like sodium acetate. We observe excellent
agreement between the experimental and the computationally evaluated
solubilities for sodium acetate in different solvent-antisolvent mixtures.
Given the agreement and the rich data the simulations produce, we can use them
to complement experimental tasks which in turn will reduce time and capital in
the design of complicated industrial crystallization processes of organic
salts.


title: Possible in situ formation of Uranus and Neptune via Pebble Accretion

abstract:
  The origin of Uranus and Neptune is still unknown. In particular, it has been
challenging for planet formation models to form the planets in their current
radial distances within the expected lifetime of the solar nebula. In this
paper, we simulate the in-situ formation of Uranus and Neptune via pebble
accretion and show that both planets can form within ~ 3 Myr at their current
locations, and have final compositions that are consistent with the
heavy-element to H-He ratios predicted by structure models. We find that Uranus
and Neptune could have been formed at their current locations. In several cases
a few earth masses of heavy elements are missing, suggesting that Uranus and/or
Neptune may have accreted $\sim$1 -- 3 M Earth's mass of heavy elements after
their formation via planetesimal accretion and/or giant impacts.


title:
On the Computational Power of Energy-Constrained Mobile Robots:
  Algorithms and Cross-Model Analysis

abstract:
  We consider distributed systems of identical autonomous computational
entities, called robots, moving and operating in the plane in synchronous
Look-Compute-Move (LCM) cycles. The algorithmic capabilities of these systems
have been extensively investigated in the literature under four distinct models
(OBLOT, FSTA, FCOM, LUMI), each identifying different levels of memory
persistence and communication capabilities of the robots. Despite their
differences, they all always assume that robots have unlimited amounts of
energy.
  In this paper, we remove this assumption and start the study of the
computational capabilities of robots whose energy is limited, albeit renewable.
We first study the impact that memory persistence and communication
capabilities have on the computational power of such energy-constrained systems
of robots; we do so by analyzing the computational relationship between the
four models under this energy constraint. We provide a complete
characterization of this relationship.
  We then study the difference in computational power caused by the energy
restriction and provide a complete characterization of the relationship between
energy-constrained and unrestricted robots in each model. We prove that within
LUMI there is no difference; an integral part of the proof is the design and
analysis of an algorithm that in LUMI allows energy-constrained robots to
execute correctly any protocol for robots with unlimited energy. We then show
the (apparently counterintuitive) result that in all other models, the energy
constraint actually provides the robots with a computational advantage.


title:
Model-free Value Iteration Algorithm for Continuous-time Stochastic
  Linear Quadratic Optimal Control Problems

abstract:
  This paper presents a novel value iteration (VI) algorithm for finding the
optimal control for a kind of infinite-horizon stochastic linear quadratic
(SLQ) problem with unknown systems. First, an off-line algorithm is
estabilished to obtain the optimal feedback control of our problem. Then, based
on the off-line algorithm, the VI-based model-free algorithm and its
convergence proof is provided. The main feature of the model-free algorithm is
that a stabilizing control is not needed to initiate the algorithm. Finally, we
validate our results with a simulation example.


title: Impact of sensor placement in soil water estimation: A real-case study

abstract:
  One of the essential elements in implementing a closed-loop irrigation system
is soil moisture estimation based on a limited number of available sensors. One
associated problem is the determination of the optimal locations to install the
sensors such that good soil moisture estimation can be obtained. In our
previous work, the modal degree of observability was employed to address the
problem of optimal sensor placement for soil moisture estimation of
agro-hydrological systems. It was demonstrated that the optimally placed
sensors can improve the soil moisture estimation performance. However, it is
unclear whether the optimal sensor placement can significantly improve the soil
moisture estimation performance in actual applications. In this work, we
investigate the impact of sensor placement in soil moisture estimation for an
actual agricultural field in Lethbridge, Alberta, Canada. In an experiment on
the studied field, 42 soil moisture sensors were installed at different depths
to collect the soil moisture measurements for one growing season. A
three-dimensional agro-hydrological model with heterogeneous soil parameters of
the studied field is developed. The modal degree of observability is applied to
the three-dimensional system to determine the optimal sensor locations. The
extended Kalman filter (EKF) is chosen as the data assimilation tool to
estimate the soil moisture content of the studied field. Soil moisture
estimation results for different scenarios are obtained and analyzed to
investigate the effects of sensor placement on the performance of soil moisture
estimation in the actual applications.


title:
Entanglement-interference complementarity and experimental demonstration
  in a superconducting circuit

abstract:
  Quantum entanglement between an interfering particle and a detector for
acquiring the which-path information plays a central role for enforcing Bohr's
complementary principle, but the quantitative relation between this
entanglement and the fringe visibility remains untouched upon. Here we find an
equality for quantifying this relation. Our equality characterizes how well the
interference pattern can be preserved when an interfering particle, initially
carrying a definite amount of coherence, is entangled with a which-path
detector to a certain degree. This equality provides a connection between
entanglement and interference in the unified framework of coherence, revealing
the quantitative entanglement-interference complementarity for the first time.
We experimentally demonstrate this relation with a superconducting circuit,
where a resonator serves as a which-path detector for an interfering qubit. The
results demonstrate quantum entanglement is the mechanism for prohibiting any
detector from acquiring which-path information without deteriorating the
interference pattern, which was not confirmed previously.


title: Reinforced Imitative Graph Learning for Mobile User Profiling

abstract:
  Mobile user profiling refers to the efforts of extracting users'
characteristics from mobile activities. In order to capture the dynamic varying
of user characteristics for generating effective user profiling, we propose an
imitation-based mobile user profiling framework. Considering the objective of
teaching an autonomous agent to imitate user mobility based on the user's
profile, the user profile is the most accurate when the agent can perfectly
mimic the user behavior patterns. The profiling framework is formulated into a
reinforcement learning task, where an agent is a next-visit planner, an action
is a POI that a user will visit next, and the state of the environment is a
fused representation of a user and spatial entities. An event in which a user
visits a POI will construct a new state, which helps the agent predict users'
mobility more accurately. In the framework, we introduce a spatial Knowledge
Graph (KG) to characterize the semantics of user visits over connected spatial
entities. Additionally, we develop a mutual-updating strategy to quantify the
state that evolves over time. Along these lines, we develop a reinforcement
imitative graph learning framework for mobile user profiling. Finally, we
conduct extensive experiments to demonstrate the superiority of our approach.


title:
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained
  Data

abstract:
  Data augmentation has been proved effective in training deep models. Existing
data augmentation methods tackle the fine-grained problem by blending image
pairs and fusing corresponding labels according to the statistics of mixed
pixels, which produces additional noise harmful to the performance of networks.
Motivated by this, we present a simple yet effective cross ensemble knowledge
distillation (CEKD) model for fine-grained feature learning. We innovatively
propose a cross distillation module to provide additional supervision to
alleviate the noise problem, and propose a collaborative ensemble module to
overcome the target conflict problem. The proposed model can be trained in an
end-to-end manner, and only requires image-level label supervision. Extensive
experiments on widely used fine-grained benchmarks demonstrate the
effectiveness of our proposed model. Specifically, with the backbone of
ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three
datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06%
and 1.16%.


title:
Mathematically Quantifying Gerrymandering and the Non-responsiveness of
  the 2021 Georgia Congressional Districting Plan

abstract:
  While gerrymandering has been widely suspected in Georgia for years, it has
been difficult to quantify. Using historic voting data, we compare the Georgia
congressional redistricting plan enacted in 2021 with a large collection of
randomly generated non-partisan maps. We find that the 2021 plan will likely be
highly non-responsive to changing opinions of the electorate. The enacted map
is structured so that it will reliably elect nine Republicans and five
Democrats over a wide range of studied voting patterns. In contrast, for the
non-partisan maps in our ensemble, only 0.12% of the plans behave similarly to
the enacted plan over the seventeen elections considered between 2016-2020. We
find the major cause of the non-responsive nature of the 2021 map is that it
polarizes voters across the more competitive districts. Specifically, there are
five districts that would be significantly more competitive under maps with our
non-partisan considerations but have been shifted to become more Republican;
similarly, there are three districts that have more Democratic voters than is
typical.
  Utilizing a combination of our ensemble statistics and spatial analysis, we
find evidence that the heavily Democratic 4th, 5th and 13th Congressional
Districts and the heavily Republican 9th District contain an unrepresentatively
large number of Democrats. The consolidation or packing of Democratic voters in
the 4th, 5th, and 13th Districts creates significant numbers of wasted votes
and dilutes their voting power. Furthermore, based on comparisons with the
previously enacted 2011 map and the ensemble of plans, we find that a
significant number of Democratic voters have been added to the solidly
Republican 9th District, replacing Republican voters who have been moved to the
6th and 10th Districts. This redrawing substantially weakens, or cracks, the
potential influence of Democratic votes in these districts.


title:
Contrastive Learning for Automotive mmWave Radar Detection Points Based
  Instance Segmentation

abstract:
  The automotive mmWave radar plays a key role in advanced driver assistance
systems (ADAS) and autonomous driving. Deep learning-based instance
segmentation enables real-time object identification from the radar detection
points. In the conventional training process, accurate annotation is the key.
However, high-quality annotations of radar detection points are challenging to
achieve due to their ambiguity and sparsity. To address this issue, we propose
a contrastive learning approach for implementing radar detection points-based
instance segmentation. We define the positive and negative samples according to
the ground-truth label, apply the contrastive loss to train the model first,
and then perform training for the following downstream task. In addition, these
two steps can be merged into one, and pseudo labels can be generated for the
unlabeled data to improve the performance further. Thus, there are four
different training settings for our method. Experiments show that when the
ground-truth information is only available for 5% of the training data, our
method still achieves a comparable performance to the approach trained in a
supervised manner with 100% ground-truth information.


title:
Finite-Key Analysis of Quantum Key Distribution with Characterized
  Devices Using Entropy Accumulation

abstract:
  The Entropy Accumulation Theorem (EAT) was introduced to significantly
improve the finite-size rates for device-independent quantum information
processing tasks such as device-independent quantum key distribution (QKD). A
natural question would be whether it also improves the rates for
device-dependent QKD. In this work, we provide an affirmative answer to this
question. We present new tools for applying the EAT in the device-dependent
setting. We present sufficient conditions for the Markov chain conditions to
hold as well as general algorithms for constructing the needed min-tradeoff
function. Utilizing Dupuis' recent privacy amplification without smoothing
result, we improve the key rate by optimizing the sandwiched R\'{e}nyi entropy
directly rather than considering the traditional smooth min-entropy. We
exemplify these new tools by considering several examples including the BB84
protocol with the qubit-based version and with a realistic parametric
downconversion source, the six-state four-state protocol and a high-dimensional
analog of the BB84 protocol.


title:
Label-only Model Inversion Attack: The Attack that Requires the Least
  Information

abstract:
  In a model inversion attack, an adversary attempts to reconstruct the data
records, used to train a target model, using only the model's output. In
launching a contemporary model inversion attack, the strategies discussed are
generally based on either predicted confidence score vectors, i.e., black-box
attacks, or the parameters of a target model, i.e., white-box attacks. However,
in the real world, model owners usually only give out the predicted labels; the
confidence score vectors and model parameters are hidden as a defense mechanism
to prevent such attacks. Unfortunately, we have found a model inversion method
that can reconstruct the input data records based only on the output labels. We
believe this is the attack that requires the least information to succeed and,
therefore, has the best applicability. The key idea is to exploit the error
rate of the target model to compute the median distance from a set of data
records to the decision boundary of the target model. The distance, then, is
used to generate confidence score vectors which are adopted to train an attack
model to reconstruct the data records. The experimental results show that
highly recognizable data records can be reconstructed with far less information
than existing methods.